7 Data cleaning
This part provides an introduction to data cleaning. When data is really messy, data cleaning can be a challenging task. We provide some approaches that however, will not include or solve all issues during data cleaning.
7.1 Why data cleaning?
The validity of results of statistical data analyses depends on the quality of the data which in turn depends on several factors such as:
- sampling (number of cases, representativeness, etc.)
- research design
- quality of the survey instrument
- operationalization
- …
- data cleaning & data preparation
7.2 What is data cleaning?
Data cleaning (or data cleansing) is the process of identifying and correcting errors, inconsistencies, and inaccuracies in the data. It is always the first step after data entry or data transfer from external sources.
The steps of data cleaning vary depending on the type and quality of data. The following steps can be seen as a general outline:
- Understand the structure of the data (set) → Codebook should help
-
Remove duplicate observations
-
Handling missing data
- Plausibility checks (aka data validation)
7.3 How to clean data?
Depending on how much data cleaning is needed, it is recommended to clean data in a sequential way (We like it neat!). This means after we succeed with one step of data cleaning, we save the respective R- or Quarto script and the data set with an appropriate description (see Figure 7.1).
The sequential procedure is also exemplified in the next section Remove duplicate observations.
Important
When cleaning a data set, you should NEVER(!) replace the raw data with processed data (i.e., overwrite the raw data file). The manipulated data should be assigned to a new object and thereafter MUST BE saved as a new file!
The raw data should also be a read-only file (i.e., right click on the file > Properties > Attributes > Read-only).
Because it is also crucial to document the data cleaning process (i.e., to reconstruct all steps), we also recommend to use Quarto documents because of the increased readability.
7.4 Remove duplicate observations
Duplicate observations are identified via the id and variable values (i.e., response patterns). In Table 7.1 there are 3 different scenarios depicted that are problematic.
| Scenario | Action | ||
|---|---|---|---|
Same id variable value, and same variable values (response pattern) |
→ | delete one observation | |
Same id variable value, but different variable values (response pattern) |
→ | check data (i.e., questionnaires) | |
Different id variable values, but same variable values (response pattern) |
→ | complicated, it is possible, but check data (i.e., questionnaires, especially open fields) |
Tip: Think carefully about the identification variables!
Identification (ID) variables are eminent when working on projects that contain several sources (e.g., different questionnaires), or span across multiple years (e.g., longitudinal studies) and must be planned before the data collection.
ID variables should be …
uniquely identifying → no duplicates
fully identifying → all observations have an ID variable value
constant throughout the duration of projects → observations do not have different IDs in a other datasets
anonymous
For more see here: https://dimewiki.worldbank.org/ID_Variable_Properties
The procedure is as follows:
Import data → we use the data set from below:
exDatIDFind duplicate observations
Exclude real (!) duplicates (but consult your supervisors!)
Save data set to a new file
Generate some duplicate observations.
!! Do not do this with real data !! !! Do not do this with real data !! !! Do not do this with real data !!
The example data set exDat does not contain any duplicate observations. Hence, we have to create them. This is done with the base::rbind function (i.e., adding the first five rows of the example data set (i.e., exDat[1:5]) to the same data set).
Show/hide code
exDatID <- rbind(exDat,exDat[1:5,])
exDatID[c(751, 752),"id"] <- c(751, 752)
exDatID[6,"id"] <- 7
set.seed(999)
exDatID$comment <- c("Hi, i like playing video games.",
"Hello, I am strong like Hulk.",
"i like apples!", "i like avocados!", "bye...",
sample(c("no", "nope", "nopeee", "no!", "no...", "..."), 745, replace = TRUE),
"Hi, i like playing video games.",
"Hello, I am strong like Hulk.",
"i like apples!", "i like avocados!", "bye...")
nrow(exDatID)[1] 755!! Do not do this with real data !! !! Do not do this with real data !! !! Do not do this with real data !!
For the exercise below, write the exDatID data set to your project folder.
Show/hide code
getwd()
write.csv2(x = exDatID,
file = "exDatID.csv",
row.names = FALSE)How to find and remove duplicate observations via the id and variable values? In the following there is a base R and a dplyr (Wickham, François, et al., 2023) solution shown. Both approaches use the duplicated() function which requires a vector, a data frame or an array as input x. The output is logical vector (TRUE/FALSE) of the same length as x.
Exercise: Remove the duplicate observations from the exDatId data set!
Open a new
R- orQuarto-script (i.e.,File > New File) and provide an meaningful name.Import the
exDatID.csvdata set (which was generated above)
Show/hide code
exDatID <- read.csv2(file = "exDatID.csv",
header = TRUE)Go through the following steps. Choose either between the base
Ror thedplyrapproach.Save the cleaned data set (i.e., without the duplicates) with a meaningful name that also matches the name of the script.
Show/hide code
getwd()
write.csv2(x = exDatIDclean,
file = "studyname-01-01-data-cleaned-remDup.csv",
row.names = FALSE)- Identify the duplicate
idvalues: Use theifelsefunction to transform the returned logical vector of theduplicatedfunction to acharactervariable and then it as a new variable1 (here:dupID) to the data set.
exDatID$dupID <- duplicated(exDatID$id)
table(exDatID$dupID)
FALSE TRUE
751 4 - Identify with the
whichfunction whichidvalues are duplicated.
whichID <- exDatID[which(exDatID$dupID == TRUE), "id"]
whichID[1] 7 3 4 5- Select only the the rows with duplicated
idvalues (unlistthewhichIDobject).
- Show the (ordered) data set.
dupIDs[order(dupIDs$id),]# A tibble: 8 × 11
msc1 msc2 msc3 msc4 age sex edu fLang id comment dupID
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <lgl>
1 2 2 3 3 12.8 0 0 german 3 i like apples! FALSE
2 2 2 3 3 12.8 0 0 german 3 i like apples! TRUE
3 2 2 3 2 10.6 0 0 german 4 i like avocados! FALSE
4 2 2 3 2 10.6 0 0 german 4 i like avocados! TRUE
5 3 2 2 2 9.89 1 0 german 5 bye... FALSE
6 3 2 2 2 9.89 1 0 german 5 bye... TRUE
7 3 3 3 2 10.4 0 0 german 7 nopeee FALSE
8 4 4 1 2 10.9 1 0 german 7 no! TRUE What do we see? 4 duplicated id values: 3, 4, 5, 7 were identified, but only 3, 4, and 5 are “real” duplicate observations. In a real data set, this would be probably a input error during data entry. Hence, it would be necessary to check the questionnaires. We know that it was a mistake, because we created the error (see above). Thus, we can reverse it without further ado. But be careful that you select the right observation.
exDatIDclean <- exDatID
exDatIDclean[exDatIDclean$id == 7 & exDatIDclean$comment == "nopeee","id"] <- 6
exDatIDclean[exDatIDclean$id == 7,"dupID"] <- " "The observations with the id values 3, 4, and 5 can be deleted. Therefore, we can use the subset function that requires the input x which can be matrices, data frames or vectors (including lists). In the subsetargument you can state logical expression indicating elements or rows to keep (here: != dup).
exDatIDclean msc1 msc2 msc3 msc4 age sex edu fLang id
1 2 3 2 2 9.815538 0 0 german 1
2 3 2 1 1 8.980194 1 0 german 2
3 2 2 3 3 12.758157 0 0 german 3
4 2 2 3 2 10.578846 0 0 german 4
5 3 2 2 2 9.894364 1 0 german 5
6 3 3 3 2 10.446850 0 0 german 6
7 4 4 1 2 10.897605 1 0 german 7
8 3 3 2 1 7.977382 1 0 german 8
9 3 3 2 1 10.688379 NA 0 german 9
10 3 3 2 2 9.105864 0 0 german 10
11 2 1 3 3 8.540117 0 0 german 11
12 2 2 3 3 11.841954 0 0 german 12
13 2 2 3 4 11.647655 0 0 german 13
14 2 3 3 3 11.745306 0 0 german 14
15 2 3 3 3 9.970630 0 0 german 15
16 3 3 2 1 8.186501 NA 0 german 16
17 2 2 2 2 7.773473 1 0 german 17
18 2 2 3 2 NA 1 0 german 18
19 2 2 3 3 9.339448 0 0 german 19
20 4 4 1 1 NA 1 0 german 20
21 4 3 2 2 9.831947 0 0 german 21
22 2 3 3 3 9.890266 1 0 german 22
23 2 3 2 2 11.501609 0 0 german 23
24 2 NA 3 2 9.341404 0 0 german 24
25 3 3 2 2 11.540030 1 0 german 25
26 2 3 3 3 10.098491 1 0 german 26
27 4 3 2 2 10.851320 1 0 german 27
28 3 2 2 2 6.735228 1 0 german 28
29 3 4 2 2 10.690059 1 0 german 29
30 3 2 2 3 9.746659 1 0 german 30
31 2 1 4 4 8.983450 0 0 german 31
32 2 NA 3 3 11.557894 0 0 german 32
33 2 2 3 2 8.057455 1 0 german 33
34 2 2 3 3 10.134752 0 0 german 34
35 2 3 3 2 8.096160 0 0 german 35
36 4 3 1 1 9.404151 NA 0 german 36
37 3 3 3 3 12.156118 0 0 german 37
38 2 2 3 2 9.600157 0 0 german 38
39 2 3 3 3 11.591400 0 0 german 39
40 3 4 2 2 10.691193 1 0 german 40
41 3 3 3 2 12.622380 0 0 german 41
42 3 2 2 2 10.043774 1 0 german 42
43 4 3 2 2 8.452508 0 0 german 43
44 4 3 2 2 12.029754 1 0 german 44
45 3 2 3 3 8.213722 1 0 german 45
46 2 3 2 3 11.654911 0 0 german 46
47 4 3 1 1 9.086209 1 0 german 47
48 3 NA 3 3 9.535470 1 0 german 48
49 2 3 3 3 9.617718 0 0 german 49
50 2 3 2 2 10.347768 1 0 german 50
51 2 1 3 3 10.735626 0 1 german 51
52 3 3 3 2 10.903281 1 1 german 52
53 2 NA 3 3 10.265831 1 1 german 53
54 2 3 4 3 10.824752 0 1 german 54
55 3 3 3 3 9.282584 0 1 german 55
56 2 3 2 2 NA 0 1 german 56
57 2 3 3 3 8.747505 1 1 german 57
58 2 2 3 4 8.992998 1 1 german 58
59 2 2 3 3 12.632563 1 1 german 59
60 2 2 3 3 11.015445 1 1 german 60
61 3 3 2 2 9.529734 1 1 german 61
62 4 3 1 1 11.569437 1 1 german 62
63 4 3 2 2 11.626011 1 1 german 63
64 2 NA 3 3 12.371110 0 1 german 64
65 2 2 2 3 8.712209 1 1 german 65
66 1 2 3 2 12.271368 0 1 german 66
67 3 3 2 2 11.514336 1 1 german 67
68 4 4 2 1 NA 0 1 german 68
69 2 2 2 2 10.858827 1 1 german 69
70 2 2 3 4 11.416986 0 1 german 70
71 2 NA 1 2 10.722411 0 1 german 71
72 2 3 3 3 9.417725 1 1 german 72
73 4 3 1 2 10.788379 1 1 german 73
74 2 3 2 2 7.527881 1 1 german 74
75 2 2 3 3 7.697320 0 1 german 75
76 3 4 3 2 9.723288 1 1 german 76
77 2 2 3 3 8.457035 NA 1 german 77
78 3 3 3 3 9.552697 1 1 german 78
79 3 3 3 3 10.766935 1 1 german 79
80 3 NA 2 2 8.086932 1 1 german 80
81 2 3 2 2 10.027268 0 1 german 81
82 4 3 2 2 11.108152 1 1 german 82
83 3 3 2 2 9.364853 1 1 german 83
84 2 NA 4 4 9.229549 0 1 german 84
85 4 3 1 1 8.791437 0 1 german 85
86 2 2 3 3 14.699514 1 1 german 86
87 1 2 4 3 9.310963 0 1 german 87
88 2 NA 2 2 7.862333 0 1 german 88
89 3 4 1 2 9.164070 0 1 german 89
90 3 2 3 2 9.904690 0 1 german 90
91 1 2 3 3 9.949049 0 1 german 91
92 1 2 3 2 10.874213 0 1 german 92
93 2 3 3 3 9.589440 0 1 german 93
94 2 2 3 3 8.396720 0 1 german 94
95 1 2 3 3 8.633062 1 1 german 95
96 3 4 2 2 10.174559 1 1 german 96
97 3 2 2 2 NA 1 1 german 97
98 4 3 2 2 6.022669 1 1 german 98
99 3 3 2 2 10.701638 1 1 german 99
100 3 NA 2 2 NA 0 1 german 100
101 2 3 3 3 11.536565 0 1 german 101
102 3 3 2 3 NA 1 1 german 102
103 2 2 3 3 7.082311 1 1 german 103
104 3 3 2 3 7.990254 1 1 german 104
105 2 2 3 3 13.223930 1 1 german 105
106 2 3 2 3 8.847114 0 1 german 106
107 2 NA 3 3 11.477532 0 1 german 107
108 3 4 1 1 8.809008 NA 1 german 108
109 2 2 3 2 8.917741 1 1 german 109
110 2 2 3 4 9.059646 0 1 german 110
111 2 2 3 3 NA 1 1 german 111
112 2 NA 2 2 7.801995 1 1 german 112
113 2 NA 3 3 NA 1 1 german 113
114 4 3 1 1 NA 1 1 german 114
115 2 2 3 3 8.336241 0 1 german 115
116 3 3 2 2 12.158201 1 1 german 116
117 2 2 2 3 NA 1 1 german 117
118 2 2 4 3 11.067872 1 1 german 118
119 3 NA 2 2 11.700517 0 1 german 119
120 3 2 3 2 9.562434 0 1 german 120
121 3 3 3 2 8.977396 0 1 german 121
122 3 2 3 2 10.162202 0 1 german 122
123 3 3 3 2 9.885336 0 1 german 123
124 3 2 3 3 12.560990 1 1 german 124
125 2 2 4 4 11.087431 1 1 german 125
126 3 3 4 3 10.371456 1 1 german 126
127 3 4 1 2 9.319275 1 1 german 127
128 2 2 2 3 11.008828 1 1 german 128
129 1 2 3 2 9.656535 0 1 german 129
130 1 2 3 4 10.032404 0 1 german 130
131 2 2 3 3 6.974716 1 1 german 131
132 3 2 2 3 9.168019 1 1 german 132
133 2 1 3 4 10.658201 1 1 german 133
134 3 2 2 2 10.723573 1 1 german 134
135 1 2 4 3 9.564175 1 1 german 135
136 2 3 3 2 9.440463 1 1 german 136
137 1 2 3 4 8.574188 1 1 german 137
138 2 2 3 3 8.336412 0 1 german 138
139 3 2 2 2 11.316498 0 1 german 139
140 4 3 2 2 10.089194 1 1 german 140
141 3 3 2 2 11.326115 0 1 german 141
142 2 3 2 2 10.544879 1 1 german 142
143 4 3 2 2 8.278763 0 1 german 143
144 3 3 2 2 9.165442 0 1 german 144
145 3 4 2 2 14.840083 0 1 german 145
146 4 4 1 1 9.177470 0 1 german 146
147 2 3 1 1 10.392509 1 1 german 147
148 3 3 2 3 10.074265 1 1 german 148
149 2 NA 2 2 10.394414 0 1 german 149
150 2 3 2 2 NA 0 1 german 150
151 2 2 3 3 11.303224 0 2 german 151
152 3 3 2 2 8.301101 0 2 german 152
153 3 4 2 2 11.552966 1 2 german 153
154 3 3 2 2 10.420355 1 2 german 154
155 3 4 2 2 12.648300 1 2 german 155
156 2 2 3 2 9.985757 1 2 german 156
157 2 3 2 2 10.504636 0 2 german 157
158 1 NA 4 4 NA 1 2 german 158
159 2 2 3 3 9.576623 0 2 german 159
160 3 3 2 2 12.855773 1 2 german 160
161 2 1 3 4 10.131071 0 2 german 161
162 2 3 3 3 11.018442 1 2 german 162
163 3 3 2 2 10.910317 1 2 german 163
164 1 NA 3 4 9.682431 0 2 german 164
165 3 2 3 3 10.402108 1 2 german 165
166 2 2 3 2 11.020302 0 2 german 166
167 2 2 3 3 10.862003 1 2 german 167
168 3 NA 2 1 12.021632 0 2 german 168
169 2 NA 2 3 11.145560 1 2 german 169
170 3 NA 1 2 11.157485 1 2 german 170
171 3 3 2 1 11.156488 1 2 german 171
172 1 2 3 3 11.774043 0 2 german 172
173 3 2 3 2 9.014673 0 2 german 173
174 4 4 1 1 9.755259 1 2 german 174
175 3 3 3 3 7.710862 0 2 german 175
176 1 1 4 4 11.418964 1 2 german 176
177 1 2 4 3 10.702477 0 2 german 177
178 3 2 3 2 11.237905 1 2 german 178
179 3 1 2 2 9.225546 0 2 german 179
180 3 3 2 2 9.673228 0 2 german 180
181 2 3 2 2 9.358897 0 2 german 181
182 3 2 3 3 9.252146 1 2 german 182
183 2 2 3 3 8.616693 0 2 german 183
184 3 4 2 2 10.721936 0 2 german 184
185 3 3 2 2 11.054393 0 2 german 185
186 1 1 4 4 8.094315 1 2 german 186
187 3 3 1 2 9.231779 0 2 german 187
188 3 3 2 2 8.547865 1 2 german 188
189 3 3 3 3 10.345779 0 2 german 189
190 1 2 3 3 13.226910 NA 2 german 190
191 2 2 3 4 10.411992 0 2 german 191
192 3 3 2 3 8.971620 1 2 german 192
193 1 2 4 4 10.898386 0 2 german 193
194 2 2 3 3 10.625394 1 2 german 194
195 2 2 3 4 6.357136 0 2 german 195
196 1 2 3 3 12.102026 1 2 german 196
197 2 3 3 2 NA 0 2 german 197
198 2 3 3 3 6.586317 1 2 german 198
199 3 3 3 2 9.050529 1 2 german 199
200 2 2 3 3 12.723541 1 2 german 200
201 2 2 3 3 9.753327 NA 2 german 201
202 2 2 2 2 13.303115 1 2 german 202
203 2 3 2 2 NA 0 2 german 203
204 3 3 2 2 11.445677 0 2 german 204
205 2 2 2 2 9.306572 0 2 german 205
206 3 1 3 3 8.328087 0 2 german 206
207 3 4 1 2 10.359319 0 2 german 207
208 4 3 1 1 NA 0 2 german 208
209 2 2 3 2 9.748836 1 2 german 209
210 2 2 3 3 11.357677 0 2 german 210
211 3 3 2 2 9.790493 1 2 german 211
212 3 3 3 3 8.325024 0 2 german 212
213 2 2 2 2 9.281008 1 2 german 213
214 3 2 2 2 13.164568 0 2 german 214
215 2 2 2 2 7.828867 0 2 german 215
216 3 3 3 3 10.665805 0 2 german 216
217 2 1 3 3 10.635206 1 2 german 217
218 2 NA 3 2 7.969089 1 2 german 218
219 2 2 2 3 8.923574 0 2 german 219
220 2 2 2 2 7.883175 NA 2 german 220
221 4 NA 2 1 8.577328 1 2 german 221
222 2 NA 4 4 8.257213 1 2 german 222
223 3 3 3 2 10.397873 1 2 german 223
224 4 3 2 2 10.241358 1 2 german 224
225 3 3 2 1 9.874614 1 2 german 225
226 2 3 2 2 9.251216 0 2 german 226
227 3 2 2 2 11.927287 0 2 german 227
228 4 4 2 2 10.327432 0 2 german 228
229 3 2 3 2 6.475004 0 2 german 229
230 2 3 3 3 11.338842 0 2 german 230
231 2 3 2 2 NA 1 2 german 231
232 3 3 2 2 7.933710 0 2 german 232
233 2 3 3 3 10.721250 1 2 german 233
234 3 2 3 3 8.086553 0 2 german 234
235 2 3 3 3 12.252961 NA 2 german 235
236 3 3 2 2 9.883740 NA 2 german 236
237 2 2 2 3 11.260712 0 2 german 237
238 4 3 2 1 NA 1 2 german 238
239 3 NA 2 2 13.991412 1 2 german 239
240 2 3 2 3 9.606693 1 2 german 240
241 2 2 2 2 9.965807 1 2 german 241
242 3 3 2 2 6.665730 NA 2 german 242
243 2 2 2 2 11.588374 1 2 german 243
244 2 1 3 3 12.054486 0 2 german 244
245 2 1 3 2 8.551229 1 2 german 245
246 1 1 4 4 14.758472 1 2 german 246
247 3 3 2 1 10.601619 1 2 german 247
248 2 3 2 3 9.608768 1 2 german 248
249 2 2 3 4 10.631563 0 2 german 249
250 3 4 1 1 9.195155 0 2 german 250
251 3 2 3 3 12.153778 1 2 german 251
252 2 1 4 4 11.594112 1 2 german 252
253 3 3 2 2 12.749052 0 2 german 253
254 2 1 3 3 10.904641 1 2 german 254
255 2 2 3 3 9.674235 NA 2 german 255
256 3 3 1 1 9.382583 1 2 german 256
257 2 2 4 3 10.075463 1 2 german 257
258 1 1 3 2 NA 0 2 german 258
259 3 NA 3 3 NA 1 2 german 259
260 3 3 2 2 10.003249 0 2 german 260
261 2 2 3 3 10.357645 0 2 german 261
262 1 2 3 3 11.427154 0 2 german 262
263 1 2 4 3 11.141758 1 2 german 263
264 2 2 3 3 11.717822 0 2 german 264
265 3 3 2 1 8.591719 1 2 german 265
266 4 3 2 2 7.199316 1 2 german 266
267 2 2 3 3 8.511259 NA 2 german 267
268 1 NA 4 4 13.183838 1 2 german 268
269 3 NA 2 2 9.723661 0 2 german 269
270 3 2 1 1 9.901684 1 2 german 270
271 2 2 2 3 11.543191 1 2 german 271
272 3 2 3 2 9.905296 0 2 german 272
273 3 3 2 2 10.804370 0 2 german 273
274 3 3 1 1 9.135004 1 2 german 274
275 3 NA 2 2 9.937281 1 2 german 275
276 2 2 4 4 8.449205 NA 2 german 276
277 4 4 2 2 10.110370 1 2 german 277
278 2 3 3 3 8.910521 1 2 german 278
279 3 3 2 2 11.335096 0 2 german 279
280 2 3 3 3 10.872968 1 2 german 280
281 3 3 2 2 9.550622 0 2 german 281
282 1 NA 4 4 NA 1 2 german 282
283 1 NA 3 4 12.176753 0 2 german 283
284 2 2 2 2 9.628909 0 2 german 284
285 2 2 3 3 13.948810 0 2 german 285
286 2 NA 3 3 12.154323 1 2 german 286
287 3 2 2 2 9.487855 1 2 german 287
288 3 3 3 2 10.250928 0 2 german 288
289 3 2 3 3 10.038493 0 2 german 289
290 3 2 2 2 9.110477 NA 2 german 290
291 3 3 2 3 11.294503 1 2 german 291
292 2 2 3 3 12.974758 0 2 german 292
293 3 3 2 2 10.285506 0 2 german 293
294 3 2 3 3 10.156128 1 2 german 294
295 3 4 3 3 10.477511 0 2 german 295
296 2 2 3 4 11.463436 0 2 german 296
297 2 3 2 2 10.958489 0 2 german 297
298 3 2 2 2 8.164545 0 2 german 298
299 2 3 3 3 10.140617 0 2 german 299
300 3 NA 2 2 9.653269 1 2 german 300
301 1 1 3 3 NA 0 3 german 301
302 2 3 2 3 9.384978 1 3 german 302
303 2 2 3 3 7.993749 1 3 german 303
304 2 3 3 3 6.955896 1 3 german 304
305 4 3 2 2 10.535078 0 3 german 305
306 4 3 1 2 NA 1 3 german 306
307 2 2 2 2 9.815517 NA 3 german 307
308 4 4 1 1 10.504347 1 3 german 308
309 2 3 2 2 9.939982 0 3 german 309
310 3 3 3 3 10.389400 1 3 german 310
311 2 2 3 3 9.448678 1 3 german 311
312 2 4 1 2 10.221263 1 3 german 312
313 2 NA 2 2 7.693476 0 3 german 313
314 3 3 2 1 11.177018 1 3 german 314
315 2 2 3 2 9.339305 0 3 german 315
316 3 2 2 2 6.542470 1 3 german 316
317 4 3 1 2 8.953333 1 3 german 317
318 3 2 3 3 NA 0 3 german 318
319 2 1 3 3 7.581036 0 3 german 319
320 3 3 2 1 9.966598 NA 3 german 320
321 3 2 3 3 10.881096 1 3 german 321
322 2 2 4 3 9.503126 1 3 german 322
323 3 3 2 2 10.542913 1 3 german 323
324 2 1 4 3 7.226064 1 3 german 324
325 2 2 3 3 NA 0 3 german 325
326 3 3 1 2 10.473550 1 3 german 326
327 3 2 2 2 NA 1 3 german 327
328 3 3 2 2 10.490264 0 3 german 328
329 3 3 2 1 11.740022 1 3 german 329
330 3 3 2 2 11.473238 0 3 german 330
331 3 3 2 3 12.689666 NA 3 german 331
332 3 3 2 2 NA 0 3 german 332
333 3 3 2 2 7.619042 0 3 german 333
334 3 2 2 2 9.525225 1 3 german 334
335 3 2 2 2 9.301695 1 3 german 335
336 1 1 4 4 10.235494 1 3 german 336
337 3 2 2 3 NA 0 3 german 337
338 3 2 3 3 10.078303 0 3 german 338
339 3 3 2 2 NA 1 3 german 339
340 2 3 2 2 9.613427 0 3 german 340
341 2 3 2 2 10.440469 1 3 german 341
342 1 2 4 3 NA 0 3 german 342
343 2 2 3 3 9.632019 0 3 german 343
344 3 NA 2 1 11.285079 0 3 german 344
345 3 4 2 2 12.780121 NA 3 german 345
346 3 3 2 3 10.358045 0 3 german 346
347 3 3 2 3 9.452587 0 3 german 347
348 2 2 3 3 10.616298 0 3 german 348
349 2 2 3 4 10.531881 1 3 german 349
350 3 2 3 3 10.917065 0 3 german 350
351 3 4 2 2 6.753558 1 3 german 351
352 3 3 1 1 9.081102 1 3 german 352
353 2 2 3 3 9.324282 0 3 german 353
354 3 4 2 2 9.556236 1 3 german 354
355 3 3 2 3 8.404846 1 3 german 355
356 3 3 2 2 9.835171 1 3 german 356
357 3 3 2 1 NA 1 3 german 357
358 3 3 2 2 9.347660 1 3 german 358
359 2 NA 3 3 7.015727 0 3 german 359
360 3 3 2 2 8.146446 0 3 german 360
361 3 3 1 2 7.774709 1 3 german 361
362 3 3 2 2 6.083013 1 3 german 362
363 2 3 3 3 8.618334 1 3 german 363
364 2 NA 3 2 10.029465 0 3 german 364
365 3 NA 2 1 10.539754 1 3 german 365
366 2 3 3 3 11.498595 0 3 german 366
367 2 2 4 4 NA 0 3 german 367
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comment dupID
1 Hi, i like playing video games. FALSE
2 Hello, I am strong like Hulk. FALSE
3 i like apples! FALSE
4 i like avocados! FALSE
5 bye... FALSE
6 nopeee FALSE
7 no!
8 no... FALSE
9 no FALSE
10 ... FALSE
11 no FALSE
12 nope FALSE
13 nope FALSE
14 ... FALSE
15 nopeee FALSE
16 no... FALSE
17 ... FALSE
18 ... FALSE
19 nopeee FALSE
20 nopeee FALSE
21 nope FALSE
22 no FALSE
23 no... FALSE
24 nopeee FALSE
25 nope FALSE
26 no! FALSE
27 no FALSE
28 no... FALSE
29 no... FALSE
30 no FALSE
31 nope FALSE
32 nope FALSE
33 no! FALSE
34 no FALSE
35 no... FALSE
36 nopeee FALSE
37 nope FALSE
38 no FALSE
39 no FALSE
40 nopeee FALSE
41 nope FALSE
42 nope FALSE
43 no! FALSE
44 no! FALSE
45 no! FALSE
46 no FALSE
47 nope FALSE
48 no... FALSE
49 no FALSE
50 nope FALSE
51 no... FALSE
52 ... FALSE
53 no FALSE
54 nope FALSE
55 no... FALSE
56 ... FALSE
57 no! FALSE
58 no... FALSE
59 no! FALSE
60 nope FALSE
61 nopeee FALSE
62 no FALSE
63 no! FALSE
64 no... FALSE
65 nopeee FALSE
66 no! FALSE
67 no FALSE
68 no! FALSE
69 no FALSE
70 no! FALSE
71 nopeee FALSE
72 no FALSE
73 no... FALSE
74 nope FALSE
75 nopeee FALSE
76 no FALSE
77 nopeee FALSE
78 nope FALSE
79 ... FALSE
80 no... FALSE
81 no FALSE
82 no... FALSE
83 no... FALSE
84 no! FALSE
85 ... FALSE
86 nope FALSE
87 ... FALSE
88 no! FALSE
89 ... FALSE
90 nopeee FALSE
91 no FALSE
92 no... FALSE
93 nope FALSE
94 no... FALSE
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97 nopeee FALSE
98 no! FALSE
99 ... FALSE
100 no! FALSE
101 nopeee FALSE
102 no FALSE
103 ... FALSE
104 no... FALSE
105 no FALSE
106 no! FALSE
107 nope FALSE
108 nopeee FALSE
109 ... FALSE
110 ... FALSE
111 nopeee FALSE
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115 no FALSE
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122 nopeee FALSE
123 nope FALSE
124 ... FALSE
125 no! FALSE
126 ... FALSE
127 nopeee FALSE
128 no FALSE
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130 nope FALSE
131 no FALSE
132 no! FALSE
133 nopeee FALSE
134 nope FALSE
135 no FALSE
136 nope FALSE
137 no FALSE
138 no... FALSE
139 ... FALSE
140 no... FALSE
141 nopeee FALSE
142 ... FALSE
143 no FALSE
144 no! FALSE
145 no! FALSE
146 nope FALSE
147 no! FALSE
148 ... FALSE
149 nopeee FALSE
150 nope FALSE
151 no... FALSE
152 nope FALSE
153 no! FALSE
154 ... FALSE
155 nopeee FALSE
156 nopeee FALSE
157 ... FALSE
158 no! FALSE
159 nope FALSE
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161 nopeee FALSE
162 no FALSE
163 no! FALSE
164 nope FALSE
165 nope FALSE
166 nope FALSE
167 no FALSE
168 ... FALSE
169 no! FALSE
170 ... FALSE
171 nopeee FALSE
172 ... FALSE
173 no FALSE
174 no FALSE
175 nope FALSE
176 nope FALSE
177 no! FALSE
178 ... FALSE
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180 nope FALSE
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182 nope FALSE
183 nopeee FALSE
184 no! FALSE
185 nope FALSE
186 no FALSE
187 no... FALSE
188 nope FALSE
189 ... FALSE
190 no! FALSE
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194 nope FALSE
195 nope FALSE
196 ... FALSE
197 nope FALSE
198 ... FALSE
199 no FALSE
200 no... FALSE
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202 ... FALSE
203 no FALSE
204 no FALSE
205 nope FALSE
206 nopeee FALSE
207 nope FALSE
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217 nope FALSE
218 no FALSE
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220 no FALSE
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222 nope FALSE
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225 nope FALSE
226 nopeee FALSE
227 no FALSE
228 ... FALSE
229 no FALSE
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234 no FALSE
235 nope FALSE
236 nope FALSE
237 no FALSE
238 no! FALSE
239 ... FALSE
240 nope FALSE
241 nopeee FALSE
242 nope FALSE
243 ... FALSE
244 nope FALSE
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249 nopeee FALSE
250 ... FALSE
251 no FALSE
252 ... FALSE
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255 nope FALSE
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262 nopeee FALSE
263 no! FALSE
264 ... FALSE
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275 nope FALSE
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283 nope FALSE
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296 no! FALSE
297 ... FALSE
298 no... FALSE
299 ... FALSE
300 nopeee FALSE
301 nopeee FALSE
302 ... FALSE
303 nope FALSE
304 nopeee FALSE
305 nopeee FALSE
306 no! FALSE
307 ... FALSE
308 nope FALSE
309 no... FALSE
310 ... FALSE
311 nopeee FALSE
312 nopeee FALSE
313 nope FALSE
314 nope FALSE
315 nope FALSE
316 nopeee FALSE
317 no! FALSE
318 no! FALSE
319 ... FALSE
320 no FALSE
321 no FALSE
322 nope FALSE
323 no FALSE
324 nopeee FALSE
325 no! FALSE
326 nopeee FALSE
327 no FALSE
328 no FALSE
329 nopeee FALSE
330 no! FALSE
331 nope FALSE
332 nopeee FALSE
333 nopeee FALSE
334 no... FALSE
335 nope FALSE
336 nope FALSE
337 no FALSE
338 no! FALSE
339 no! FALSE
340 nopeee FALSE
341 nope FALSE
342 no FALSE
343 nopeee FALSE
344 nopeee FALSE
345 nope FALSE
346 no FALSE
347 no! FALSE
348 ... FALSE
349 nope FALSE
350 no! FALSE
351 nopeee FALSE
352 no! FALSE
353 nopeee FALSE
354 no! FALSE
355 nope FALSE
356 no... FALSE
357 nope FALSE
358 ... FALSE
359 no... FALSE
360 nope FALSE
361 no... FALSE
362 no... FALSE
363 nope FALSE
364 no! FALSE
365 nope FALSE
366 no... FALSE
367 nope FALSE
368 nopeee FALSE
369 ... FALSE
370 ... FALSE
371 nopeee FALSE
372 no... FALSE
373 ... FALSE
374 nopeee FALSE
375 nopeee FALSE
376 no FALSE
377 no FALSE
378 no! FALSE
379 no! FALSE
380 no FALSE
381 no FALSE
382 no FALSE
383 nope FALSE
384 no! FALSE
385 no! FALSE
386 nopeee FALSE
387 no! FALSE
388 nopeee FALSE
389 no... FALSE
390 nopeee FALSE
391 no! FALSE
392 ... FALSE
393 nope FALSE
394 nope FALSE
395 no! FALSE
396 no! FALSE
397 no... FALSE
398 no! FALSE
399 no FALSE
400 nopeee FALSE
401 no FALSE
402 ... FALSE
403 nopeee FALSE
404 no! FALSE
405 ... FALSE
406 no FALSE
407 no... FALSE
408 no... FALSE
409 nopeee FALSE
410 no FALSE
411 no! FALSE
412 no FALSE
413 nopeee FALSE
414 ... FALSE
415 no! FALSE
416 nope FALSE
417 no FALSE
418 nopeee FALSE
419 no FALSE
420 nopeee FALSE
421 no... FALSE
422 no! FALSE
423 nope FALSE
424 no FALSE
425 no... FALSE
426 no FALSE
427 ... FALSE
428 no! FALSE
429 nopeee FALSE
430 no FALSE
431 nopeee FALSE
432 nopeee FALSE
433 ... FALSE
434 nope FALSE
435 no FALSE
436 nopeee FALSE
437 nope FALSE
438 no FALSE
439 no! FALSE
440 nopeee FALSE
441 ... FALSE
442 no FALSE
443 no! FALSE
444 nope FALSE
445 no! FALSE
446 no FALSE
447 no! FALSE
448 ... FALSE
449 ... FALSE
450 nopeee FALSE
451 nopeee FALSE
452 no FALSE
453 no... FALSE
454 no FALSE
455 ... FALSE
456 nope FALSE
457 no! FALSE
458 nopeee FALSE
459 no FALSE
460 no FALSE
461 nope FALSE
462 no FALSE
463 nope FALSE
464 nope FALSE
465 no FALSE
466 no FALSE
467 nopeee FALSE
468 ... FALSE
469 nope FALSE
470 nope FALSE
471 nope FALSE
472 ... FALSE
473 nopeee FALSE
474 no... FALSE
475 no FALSE
476 ... FALSE
477 nopeee FALSE
478 nope FALSE
479 no! FALSE
480 no... FALSE
481 ... FALSE
482 nope FALSE
483 nope FALSE
484 nope FALSE
485 no... FALSE
486 ... FALSE
487 no FALSE
488 nopeee FALSE
489 nopeee FALSE
490 no! FALSE
491 no... FALSE
492 ... FALSE
493 ... FALSE
494 no FALSE
495 no FALSE
496 no... FALSE
497 nopeee FALSE
498 ... FALSE
499 nopeee FALSE
500 no! FALSE
501 no! FALSE
502 nope FALSE
503 nope FALSE
504 no FALSE
505 nope FALSE
506 no FALSE
507 no FALSE
508 no FALSE
509 no... FALSE
510 ... FALSE
511 no! FALSE
512 no FALSE
513 nope FALSE
514 nopeee FALSE
515 ... FALSE
516 ... FALSE
517 no! FALSE
518 nopeee FALSE
519 nope FALSE
520 nope FALSE
521 no... FALSE
522 no FALSE
523 nope FALSE
524 no... FALSE
525 nopeee FALSE
526 no FALSE
527 nope FALSE
528 nopeee FALSE
529 ... FALSE
530 nopeee FALSE
531 ... FALSE
532 no... FALSE
533 ... FALSE
534 no! FALSE
535 no FALSE
536 no! FALSE
537 nope FALSE
538 nopeee FALSE
539 nopeee FALSE
540 no FALSE
541 no FALSE
542 no... FALSE
543 no! FALSE
544 no... FALSE
545 nopeee FALSE
546 nope FALSE
547 nopeee FALSE
548 no... FALSE
549 nopeee FALSE
550 nope FALSE
551 nope FALSE
552 no... FALSE
553 no! FALSE
554 nopeee FALSE
555 nopeee FALSE
556 no FALSE
557 no FALSE
558 nopeee FALSE
559 no... FALSE
560 nope FALSE
561 nopeee FALSE
562 nopeee FALSE
563 no FALSE
564 no FALSE
565 no FALSE
566 ... FALSE
567 ... FALSE
568 no! FALSE
569 no... FALSE
570 ... FALSE
571 no! FALSE
572 no FALSE
573 nopeee FALSE
574 nopeee FALSE
575 ... FALSE
576 no... FALSE
577 nopeee FALSE
578 ... FALSE
579 nope FALSE
580 no... FALSE
581 no! FALSE
582 ... FALSE
583 ... FALSE
584 no! FALSE
585 no FALSE
586 nope FALSE
587 ... FALSE
588 ... FALSE
589 ... FALSE
590 no... FALSE
591 nopeee FALSE
592 nopeee FALSE
593 ... FALSE
594 ... FALSE
595 ... FALSE
596 nope FALSE
597 ... FALSE
598 no! FALSE
599 ... FALSE
600 no! FALSE
601 nopeee FALSE
602 nopeee FALSE
603 no FALSE
604 ... FALSE
605 no! FALSE
606 no! FALSE
607 no! FALSE
608 no FALSE
609 no... FALSE
610 no FALSE
611 nope FALSE
612 nope FALSE
613 no FALSE
614 no! FALSE
615 no FALSE
616 no FALSE
617 no FALSE
618 nope FALSE
619 nopeee FALSE
620 no... FALSE
621 no FALSE
622 nopeee FALSE
623 nopeee FALSE
624 nopeee FALSE
625 no! FALSE
626 ... FALSE
627 no FALSE
628 nopeee FALSE
629 ... FALSE
630 no... FALSE
631 no... FALSE
632 no... FALSE
633 nopeee FALSE
634 nopeee FALSE
635 nope FALSE
636 no! FALSE
637 ... FALSE
638 no FALSE
639 nope FALSE
640 nope FALSE
641 no FALSE
642 nope FALSE
643 ... FALSE
644 ... FALSE
645 nope FALSE
646 nope FALSE
647 nopeee FALSE
648 nopeee FALSE
649 no FALSE
650 ... FALSE
651 ... FALSE
652 nopeee FALSE
653 no! FALSE
654 nopeee FALSE
655 no FALSE
656 no... FALSE
657 no FALSE
658 no! FALSE
659 no... FALSE
660 no! FALSE
661 no... FALSE
662 no FALSE
663 no... FALSE
664 ... FALSE
665 no! FALSE
666 no... FALSE
667 no! FALSE
668 nope FALSE
669 nopeee FALSE
670 ... FALSE
671 no FALSE
672 ... FALSE
673 no FALSE
674 no FALSE
675 ... FALSE
676 ... FALSE
677 nopeee FALSE
678 ... FALSE
679 nope FALSE
680 no... FALSE
681 nope FALSE
682 no... FALSE
683 nopeee FALSE
684 nope FALSE
685 nope FALSE
686 no... FALSE
687 nopeee FALSE
688 nope FALSE
689 no... FALSE
690 ... FALSE
691 no FALSE
692 no! FALSE
693 no FALSE
694 no FALSE
695 no! FALSE
696 no... FALSE
697 ... FALSE
698 nopeee FALSE
699 nope FALSE
700 ... FALSE
701 no FALSE
702 no! FALSE
703 no... FALSE
704 no... FALSE
705 nopeee FALSE
706 nopeee FALSE
707 nope FALSE
708 nopeee FALSE
709 nopeee FALSE
710 no FALSE
711 ... FALSE
712 ... FALSE
713 no! FALSE
714 nope FALSE
715 no... FALSE
716 ... FALSE
717 no! FALSE
718 nopeee FALSE
719 no... FALSE
720 nopeee FALSE
721 nopeee FALSE
722 nopeee FALSE
723 nope FALSE
724 no... FALSE
725 nopeee FALSE
726 nope FALSE
727 no... FALSE
728 no... FALSE
729 nope FALSE
730 no FALSE
731 no! FALSE
732 no FALSE
733 nope FALSE
734 no FALSE
735 no FALSE
736 no... FALSE
737 no... FALSE
738 nopeee FALSE
739 no FALSE
740 nopeee FALSE
741 no FALSE
742 no FALSE
743 nope FALSE
744 nopeee FALSE
745 no... FALSE
746 nope FALSE
747 no FALSE
748 no FALSE
749 no! FALSE
750 no... FALSE
751 Hi, i like playing video games. FALSE
752 Hello, I am strong like Hulk. FALSENote that there are still 2 duplicate observations left. Nevertheless, we can delete the variable dupID and clean our environment with the base::rm function:
exDatIDclean$dupID <- NULL
rm(whichID, dupIDs)What to do with the other 2 duplicate observation? We need to repeat the procedure. This time we do not examine the id values (i.e., actually we need to exclude this variable), but the response pattern. This is done as follows:
dupResp <- exDatIDclean[duplicated(exDatIDclean[, -which(names(exDatIDclean) == "id")]) |
duplicated(exDatIDclean[, -which(names(exDatIDclean) == "id")],
fromLast = TRUE), ]
dupResp msc1 msc2 msc3 msc4 age sex edu fLang id
1 2 3 2 2 9.815538 0 0 german 1
2 3 2 1 1 8.980194 1 0 german 2
381 3 3 2 2 NA 0 3 german 381
438 3 3 2 2 NA 0 3 german 438
466 2 2 3 3 NA 1 3 german 466
494 2 2 3 3 NA 1 3 german 494
751 2 3 2 2 9.815538 0 0 german 751
752 3 2 1 1 8.980194 1 0 german 752
comment
1 Hi, i like playing video games.
2 Hello, I am strong like Hulk.
381 no
438 no
466 no
494 no
751 Hi, i like playing video games.
752 Hello, I am strong like Hulk.Recall, the real duplicates are the observations with the id values 751 and 752, but we identified 4 more observations with non-unique patters…this needs careful investigation.
You may also want to check the base::unique function.
msc1 msc2 msc3 msc4 age sex edu fLang
1 2 3 2 2 9.815538 0 0 german
2 3 2 1 1 8.980194 1 0 german
3 2 2 3 3 12.758157 0 0 german
4 2 2 3 2 10.578846 0 0 german
5 3 2 2 2 9.894364 1 0 german
6 3 3 3 2 10.446850 0 0 german
7 4 4 1 2 10.897605 1 0 german
8 3 3 2 1 7.977382 1 0 german
9 3 3 2 1 10.688379 NA 0 german
10 3 3 2 2 9.105864 0 0 german
11 2 1 3 3 8.540117 0 0 german
12 2 2 3 3 11.841954 0 0 german
13 2 2 3 4 11.647655 0 0 german
14 2 3 3 3 11.745306 0 0 german
15 2 3 3 3 9.970630 0 0 german
16 3 3 2 1 8.186501 NA 0 german
17 2 2 2 2 7.773473 1 0 german
18 2 2 3 2 NA 1 0 german
19 2 2 3 3 9.339448 0 0 german
20 4 4 1 1 NA 1 0 german
21 4 3 2 2 9.831947 0 0 german
22 2 3 3 3 9.890266 1 0 german
23 2 3 2 2 11.501609 0 0 german
24 2 NA 3 2 9.341404 0 0 german
25 3 3 2 2 11.540030 1 0 german
26 2 3 3 3 10.098491 1 0 german
27 4 3 2 2 10.851320 1 0 german
28 3 2 2 2 6.735228 1 0 german
29 3 4 2 2 10.690059 1 0 german
30 3 2 2 3 9.746659 1 0 german
31 2 1 4 4 8.983450 0 0 german
32 2 NA 3 3 11.557894 0 0 german
33 2 2 3 2 8.057455 1 0 german
34 2 2 3 3 10.134752 0 0 german
35 2 3 3 2 8.096160 0 0 german
36 4 3 1 1 9.404151 NA 0 german
37 3 3 3 3 12.156118 0 0 german
38 2 2 3 2 9.600157 0 0 german
39 2 3 3 3 11.591400 0 0 german
40 3 4 2 2 10.691193 1 0 german
41 3 3 3 2 12.622380 0 0 german
42 3 2 2 2 10.043774 1 0 german
43 4 3 2 2 8.452508 0 0 german
44 4 3 2 2 12.029754 1 0 german
45 3 2 3 3 8.213722 1 0 german
46 2 3 2 3 11.654911 0 0 german
47 4 3 1 1 9.086209 1 0 german
48 3 NA 3 3 9.535470 1 0 german
49 2 3 3 3 9.617718 0 0 german
50 2 3 2 2 10.347768 1 0 german
51 2 1 3 3 10.735626 0 1 german
52 3 3 3 2 10.903281 1 1 german
53 2 NA 3 3 10.265831 1 1 german
54 2 3 4 3 10.824752 0 1 german
55 3 3 3 3 9.282584 0 1 german
56 2 3 2 2 NA 0 1 german
57 2 3 3 3 8.747505 1 1 german
58 2 2 3 4 8.992998 1 1 german
59 2 2 3 3 12.632563 1 1 german
60 2 2 3 3 11.015445 1 1 german
61 3 3 2 2 9.529734 1 1 german
62 4 3 1 1 11.569437 1 1 german
63 4 3 2 2 11.626011 1 1 german
64 2 NA 3 3 12.371110 0 1 german
65 2 2 2 3 8.712209 1 1 german
66 1 2 3 2 12.271368 0 1 german
67 3 3 2 2 11.514336 1 1 german
68 4 4 2 1 NA 0 1 german
69 2 2 2 2 10.858827 1 1 german
70 2 2 3 4 11.416986 0 1 german
71 2 NA 1 2 10.722411 0 1 german
72 2 3 3 3 9.417725 1 1 german
73 4 3 1 2 10.788379 1 1 german
74 2 3 2 2 7.527881 1 1 german
75 2 2 3 3 7.697320 0 1 german
76 3 4 3 2 9.723288 1 1 german
77 2 2 3 3 8.457035 NA 1 german
78 3 3 3 3 9.552697 1 1 german
79 3 3 3 3 10.766935 1 1 german
80 3 NA 2 2 8.086932 1 1 german
81 2 3 2 2 10.027268 0 1 german
82 4 3 2 2 11.108152 1 1 german
83 3 3 2 2 9.364853 1 1 german
84 2 NA 4 4 9.229549 0 1 german
85 4 3 1 1 8.791437 0 1 german
86 2 2 3 3 14.699514 1 1 german
87 1 2 4 3 9.310963 0 1 german
88 2 NA 2 2 7.862333 0 1 german
89 3 4 1 2 9.164070 0 1 german
90 3 2 3 2 9.904690 0 1 german
91 1 2 3 3 9.949049 0 1 german
92 1 2 3 2 10.874213 0 1 german
93 2 3 3 3 9.589440 0 1 german
94 2 2 3 3 8.396720 0 1 german
95 1 2 3 3 8.633062 1 1 german
96 3 4 2 2 10.174559 1 1 german
97 3 2 2 2 NA 1 1 german
98 4 3 2 2 6.022669 1 1 german
99 3 3 2 2 10.701638 1 1 german
100 3 NA 2 2 NA 0 1 german
101 2 3 3 3 11.536565 0 1 german
102 3 3 2 3 NA 1 1 german
103 2 2 3 3 7.082311 1 1 german
104 3 3 2 3 7.990254 1 1 german
105 2 2 3 3 13.223930 1 1 german
106 2 3 2 3 8.847114 0 1 german
107 2 NA 3 3 11.477532 0 1 german
108 3 4 1 1 8.809008 NA 1 german
109 2 2 3 2 8.917741 1 1 german
110 2 2 3 4 9.059646 0 1 german
111 2 2 3 3 NA 1 1 german
112 2 NA 2 2 7.801995 1 1 german
113 2 NA 3 3 NA 1 1 german
114 4 3 1 1 NA 1 1 german
115 2 2 3 3 8.336241 0 1 german
116 3 3 2 2 12.158201 1 1 german
117 2 2 2 3 NA 1 1 german
118 2 2 4 3 11.067872 1 1 german
119 3 NA 2 2 11.700517 0 1 german
120 3 2 3 2 9.562434 0 1 german
121 3 3 3 2 8.977396 0 1 german
122 3 2 3 2 10.162202 0 1 german
123 3 3 3 2 9.885336 0 1 german
124 3 2 3 3 12.560990 1 1 german
125 2 2 4 4 11.087431 1 1 german
126 3 3 4 3 10.371456 1 1 german
127 3 4 1 2 9.319275 1 1 german
128 2 2 2 3 11.008828 1 1 german
129 1 2 3 2 9.656535 0 1 german
130 1 2 3 4 10.032404 0 1 german
131 2 2 3 3 6.974716 1 1 german
132 3 2 2 3 9.168019 1 1 german
133 2 1 3 4 10.658201 1 1 german
134 3 2 2 2 10.723573 1 1 german
135 1 2 4 3 9.564175 1 1 german
136 2 3 3 2 9.440463 1 1 german
137 1 2 3 4 8.574188 1 1 german
138 2 2 3 3 8.336412 0 1 german
139 3 2 2 2 11.316498 0 1 german
140 4 3 2 2 10.089194 1 1 german
141 3 3 2 2 11.326115 0 1 german
142 2 3 2 2 10.544879 1 1 german
143 4 3 2 2 8.278763 0 1 german
144 3 3 2 2 9.165442 0 1 german
145 3 4 2 2 14.840083 0 1 german
146 4 4 1 1 9.177470 0 1 german
147 2 3 1 1 10.392509 1 1 german
148 3 3 2 3 10.074265 1 1 german
149 2 NA 2 2 10.394414 0 1 german
150 2 3 2 2 NA 0 1 german
151 2 2 3 3 11.303224 0 2 german
152 3 3 2 2 8.301101 0 2 german
153 3 4 2 2 11.552966 1 2 german
154 3 3 2 2 10.420355 1 2 german
155 3 4 2 2 12.648300 1 2 german
156 2 2 3 2 9.985757 1 2 german
157 2 3 2 2 10.504636 0 2 german
158 1 NA 4 4 NA 1 2 german
159 2 2 3 3 9.576623 0 2 german
160 3 3 2 2 12.855773 1 2 german
161 2 1 3 4 10.131071 0 2 german
162 2 3 3 3 11.018442 1 2 german
163 3 3 2 2 10.910317 1 2 german
164 1 NA 3 4 9.682431 0 2 german
165 3 2 3 3 10.402108 1 2 german
166 2 2 3 2 11.020302 0 2 german
167 2 2 3 3 10.862003 1 2 german
168 3 NA 2 1 12.021632 0 2 german
169 2 NA 2 3 11.145560 1 2 german
170 3 NA 1 2 11.157485 1 2 german
171 3 3 2 1 11.156488 1 2 german
172 1 2 3 3 11.774043 0 2 german
173 3 2 3 2 9.014673 0 2 german
174 4 4 1 1 9.755259 1 2 german
175 3 3 3 3 7.710862 0 2 german
176 1 1 4 4 11.418964 1 2 german
177 1 2 4 3 10.702477 0 2 german
178 3 2 3 2 11.237905 1 2 german
179 3 1 2 2 9.225546 0 2 german
180 3 3 2 2 9.673228 0 2 german
181 2 3 2 2 9.358897 0 2 german
182 3 2 3 3 9.252146 1 2 german
183 2 2 3 3 8.616693 0 2 german
184 3 4 2 2 10.721936 0 2 german
185 3 3 2 2 11.054393 0 2 german
186 1 1 4 4 8.094315 1 2 german
187 3 3 1 2 9.231779 0 2 german
188 3 3 2 2 8.547865 1 2 german
189 3 3 3 3 10.345779 0 2 german
190 1 2 3 3 13.226910 NA 2 german
191 2 2 3 4 10.411992 0 2 german
192 3 3 2 3 8.971620 1 2 german
193 1 2 4 4 10.898386 0 2 german
194 2 2 3 3 10.625394 1 2 german
195 2 2 3 4 6.357136 0 2 german
196 1 2 3 3 12.102026 1 2 german
197 2 3 3 2 NA 0 2 german
198 2 3 3 3 6.586317 1 2 german
199 3 3 3 2 9.050529 1 2 german
200 2 2 3 3 12.723541 1 2 german
201 2 2 3 3 9.753327 NA 2 german
202 2 2 2 2 13.303115 1 2 german
203 2 3 2 2 NA 0 2 german
204 3 3 2 2 11.445677 0 2 german
205 2 2 2 2 9.306572 0 2 german
206 3 1 3 3 8.328087 0 2 german
207 3 4 1 2 10.359319 0 2 german
208 4 3 1 1 NA 0 2 german
209 2 2 3 2 9.748836 1 2 german
210 2 2 3 3 11.357677 0 2 german
211 3 3 2 2 9.790493 1 2 german
212 3 3 3 3 8.325024 0 2 german
213 2 2 2 2 9.281008 1 2 german
214 3 2 2 2 13.164568 0 2 german
215 2 2 2 2 7.828867 0 2 german
216 3 3 3 3 10.665805 0 2 german
217 2 1 3 3 10.635206 1 2 german
218 2 NA 3 2 7.969089 1 2 german
219 2 2 2 3 8.923574 0 2 german
220 2 2 2 2 7.883175 NA 2 german
221 4 NA 2 1 8.577328 1 2 german
222 2 NA 4 4 8.257213 1 2 german
223 3 3 3 2 10.397873 1 2 german
224 4 3 2 2 10.241358 1 2 german
225 3 3 2 1 9.874614 1 2 german
226 2 3 2 2 9.251216 0 2 german
227 3 2 2 2 11.927287 0 2 german
228 4 4 2 2 10.327432 0 2 german
229 3 2 3 2 6.475004 0 2 german
230 2 3 3 3 11.338842 0 2 german
231 2 3 2 2 NA 1 2 german
232 3 3 2 2 7.933710 0 2 german
233 2 3 3 3 10.721250 1 2 german
234 3 2 3 3 8.086553 0 2 german
235 2 3 3 3 12.252961 NA 2 german
236 3 3 2 2 9.883740 NA 2 german
237 2 2 2 3 11.260712 0 2 german
238 4 3 2 1 NA 1 2 german
239 3 NA 2 2 13.991412 1 2 german
240 2 3 2 3 9.606693 1 2 german
241 2 2 2 2 9.965807 1 2 german
242 3 3 2 2 6.665730 NA 2 german
243 2 2 2 2 11.588374 1 2 german
244 2 1 3 3 12.054486 0 2 german
245 2 1 3 2 8.551229 1 2 german
246 1 1 4 4 14.758472 1 2 german
247 3 3 2 1 10.601619 1 2 german
248 2 3 2 3 9.608768 1 2 german
249 2 2 3 4 10.631563 0 2 german
250 3 4 1 1 9.195155 0 2 german
251 3 2 3 3 12.153778 1 2 german
252 2 1 4 4 11.594112 1 2 german
253 3 3 2 2 12.749052 0 2 german
254 2 1 3 3 10.904641 1 2 german
255 2 2 3 3 9.674235 NA 2 german
256 3 3 1 1 9.382583 1 2 german
257 2 2 4 3 10.075463 1 2 german
258 1 1 3 2 NA 0 2 german
259 3 NA 3 3 NA 1 2 german
260 3 3 2 2 10.003249 0 2 german
261 2 2 3 3 10.357645 0 2 german
262 1 2 3 3 11.427154 0 2 german
263 1 2 4 3 11.141758 1 2 german
264 2 2 3 3 11.717822 0 2 german
265 3 3 2 1 8.591719 1 2 german
266 4 3 2 2 7.199316 1 2 german
267 2 2 3 3 8.511259 NA 2 german
268 1 NA 4 4 13.183838 1 2 german
269 3 NA 2 2 9.723661 0 2 german
270 3 2 1 1 9.901684 1 2 german
271 2 2 2 3 11.543191 1 2 german
272 3 2 3 2 9.905296 0 2 german
273 3 3 2 2 10.804370 0 2 german
274 3 3 1 1 9.135004 1 2 german
275 3 NA 2 2 9.937281 1 2 german
276 2 2 4 4 8.449205 NA 2 german
277 4 4 2 2 10.110370 1 2 german
278 2 3 3 3 8.910521 1 2 german
279 3 3 2 2 11.335096 0 2 german
280 2 3 3 3 10.872968 1 2 german
281 3 3 2 2 9.550622 0 2 german
282 1 NA 4 4 NA 1 2 german
283 1 NA 3 4 12.176753 0 2 german
284 2 2 2 2 9.628909 0 2 german
285 2 2 3 3 13.948810 0 2 german
286 2 NA 3 3 12.154323 1 2 german
287 3 2 2 2 9.487855 1 2 german
288 3 3 3 2 10.250928 0 2 german
289 3 2 3 3 10.038493 0 2 german
290 3 2 2 2 9.110477 NA 2 german
291 3 3 2 3 11.294503 1 2 german
292 2 2 3 3 12.974758 0 2 german
293 3 3 2 2 10.285506 0 2 german
294 3 2 3 3 10.156128 1 2 german
295 3 4 3 3 10.477511 0 2 german
296 2 2 3 4 11.463436 0 2 german
297 2 3 2 2 10.958489 0 2 german
298 3 2 2 2 8.164545 0 2 german
299 2 3 3 3 10.140617 0 2 german
300 3 NA 2 2 9.653269 1 2 german
301 1 1 3 3 NA 0 3 german
302 2 3 2 3 9.384978 1 3 german
303 2 2 3 3 7.993749 1 3 german
304 2 3 3 3 6.955896 1 3 german
305 4 3 2 2 10.535078 0 3 german
306 4 3 1 2 NA 1 3 german
307 2 2 2 2 9.815517 NA 3 german
308 4 4 1 1 10.504347 1 3 german
309 2 3 2 2 9.939982 0 3 german
310 3 3 3 3 10.389400 1 3 german
311 2 2 3 3 9.448678 1 3 german
312 2 4 1 2 10.221263 1 3 german
313 2 NA 2 2 7.693476 0 3 german
314 3 3 2 1 11.177018 1 3 german
315 2 2 3 2 9.339305 0 3 german
316 3 2 2 2 6.542470 1 3 german
317 4 3 1 2 8.953333 1 3 german
318 3 2 3 3 NA 0 3 german
319 2 1 3 3 7.581036 0 3 german
320 3 3 2 1 9.966598 NA 3 german
321 3 2 3 3 10.881096 1 3 german
322 2 2 4 3 9.503126 1 3 german
323 3 3 2 2 10.542913 1 3 german
324 2 1 4 3 7.226064 1 3 german
325 2 2 3 3 NA 0 3 german
326 3 3 1 2 10.473550 1 3 german
327 3 2 2 2 NA 1 3 german
328 3 3 2 2 10.490264 0 3 german
329 3 3 2 1 11.740022 1 3 german
330 3 3 2 2 11.473238 0 3 german
331 3 3 2 3 12.689666 NA 3 german
332 3 3 2 2 NA 0 3 german
333 3 3 2 2 7.619042 0 3 german
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comment
1 Hi, i like playing video games.
2 Hello, I am strong like Hulk.
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4 i like avocados!
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381 no
382 no
383 nope
384 no!
385 no!
386 nopeee
387 no!
388 nopeee
389 no...
390 nopeee
391 no!
392 ...
393 nope
394 nope
395 no!
396 no!
397 no...
398 no!
399 no
400 nopeee
401 no
402 ...
403 nopeee
404 no!
405 ...
406 no
407 no...
408 no...
409 nopeee
410 no
411 no!
412 no
413 nopeee
414 ...
415 no!
416 nope
417 no
418 nopeee
419 no
420 nopeee
421 no...
422 no!
423 nope
424 no
425 no...
426 no
427 ...
428 no!
429 nopeee
430 no
431 nopeee
432 nopeee
433 ...
434 nope
435 no
436 nopeee
437 nope
439 no!
440 nopeee
441 ...
442 no
443 no!
444 nope
445 no!
446 no
447 no!
448 ...
449 ...
450 nopeee
451 nopeee
452 no
453 no...
454 no
455 ...
456 nope
457 no!
458 nopeee
459 no
460 no
461 nope
462 no
463 nope
464 nope
465 no
466 no
467 nopeee
468 ...
469 nope
470 nope
471 nope
472 ...
473 nopeee
474 no...
475 no
476 ...
477 nopeee
478 nope
479 no!
480 no...
481 ...
482 nope
483 nope
484 nope
485 no...
486 ...
487 no
488 nopeee
489 nopeee
490 no!
491 no...
492 ...
493 ...
495 no
496 no...
497 nopeee
498 ...
499 nopeee
500 no!
501 no!
502 nope
503 nope
504 no
505 nope
506 no
507 no
508 no
509 no...
510 ...
511 no!
512 no
513 nope
514 nopeee
515 ...
516 ...
517 no!
518 nopeee
519 nope
520 nope
521 no...
522 no
523 nope
524 no...
525 nopeee
526 no
527 nope
528 nopeee
529 ...
530 nopeee
531 ...
532 no...
533 ...
534 no!
535 no
536 no!
537 nope
538 nopeee
539 nopeee
540 no
541 no
542 no...
543 no!
544 no...
545 nopeee
546 nope
547 nopeee
548 no...
549 nopeee
550 nope
551 nope
552 no...
553 no!
554 nopeee
555 nopeee
556 no
557 no
558 nopeee
559 no...
560 nope
561 nopeee
562 nopeee
563 no
564 no
565 no
566 ...
567 ...
568 no!
569 no...
570 ...
571 no!
572 no
573 nopeee
574 nopeee
575 ...
576 no...
577 nopeee
578 ...
579 nope
580 no...
581 no!
582 ...
583 ...
584 no!
585 no
586 nope
587 ...
588 ...
589 ...
590 no...
591 nopeee
592 nopeee
593 ...
594 ...
595 ...
596 nope
597 ...
598 no!
599 ...
600 no!
601 nopeee
602 nopeee
603 no
604 ...
605 no!
606 no!
607 no!
608 no
609 no...
610 no
611 nope
612 nope
613 no
614 no!
615 no
616 no
617 no
618 nope
619 nopeee
620 no...
621 no
622 nopeee
623 nopeee
624 nopeee
625 no!
626 ...
627 no
628 nopeee
629 ...
630 no...
631 no...
632 no...
633 nopeee
634 nopeee
635 nope
636 no!
637 ...
638 no
639 nope
640 nope
641 no
642 nope
643 ...
644 ...
645 nope
646 nope
647 nopeee
648 nopeee
649 no
650 ...
651 ...
652 nopeee
653 no!
654 nopeee
655 no
656 no...
657 no
658 no!
659 no...
660 no!
661 no...
662 no
663 no...
664 ...
665 no!
666 no...
667 no!
668 nope
669 nopeee
670 ...
671 no
672 ...
673 no
674 no
675 ...
676 ...
677 nopeee
678 ...
679 nope
680 no...
681 nope
682 no...
683 nopeee
684 nope
685 nope
686 no...
687 nopeee
688 nope
689 no...
690 ...
691 no
692 no!
693 no
694 no
695 no!
696 no...
697 ...
698 nopeee
699 nope
700 ...
701 no
702 no!
703 no...
704 no...
705 nopeee
706 nopeee
707 nope
708 nopeee
709 nopeee
710 no
711 ...
712 ...
713 no!
714 nope
715 no...
716 ...
717 no!
718 nopeee
719 no...
720 nopeee
721 nopeee
722 nopeee
723 nope
724 no...
725 nopeee
726 nope
727 no...
728 no...
729 nope
730 no
731 no!
732 no
733 nope
734 no
735 no
736 no...
737 no...
738 nopeee
739 no
740 nopeee
741 no
742 no
743 nope
744 nopeee
745 no...
746 nope
747 no
748 no
749 no!
750 no...The base::unique function eliminated 2 additional observations that are no real duplicates…Hence, it is important to carefully applying the base::unique function and checking that it does not eliminate different observations with the same response pattern (only real duplicates)!
In the first step, we identify the duplicated id values with the base::duplicated function and adding a new column (dupID) using the dplyr::mutate function to the data set. Then we filter (using the dplyr::filter()) observations based on the returned TRUE values of the base::duplicated function Lastly, we select the id column (using dplyr::select) and base::unlist and base::unname it.
whichIDdplyr <- exDatID |>
dplyr::mutate(dupID = duplicated(id)) |>
dplyr::filter(dupID == TRUE) |>
dplyr::select(id) |>
unlist() |>
unname()
whichIDdplyr[1] 7 3 4 5- Select only the the rows with duplicated
idvalues (dplyr::filter) and show the (ordered,dplyr::arrange) data set.
msc1 msc2 msc3 msc4 age sex edu fLang id comment
1 2 2 3 3 12.758157 0 0 german 3 i like apples!
2 2 2 3 3 12.758157 0 0 german 3 i like apples!
3 2 2 3 2 10.578846 0 0 german 4 i like avocados!
4 2 2 3 2 10.578846 0 0 german 4 i like avocados!
5 3 2 2 2 9.894364 1 0 german 5 bye...
6 3 2 2 2 9.894364 1 0 german 5 bye...
7 3 3 3 2 10.446850 0 0 german 7 nopeee
8 4 4 1 2 10.897605 1 0 german 7 no!exDatIDclean <- exDatID |>
dplyr::mutate(dupID = duplicated(id)) |>
dplyr::mutate(id = ifelse(id == 7 & comment == "nopeee", 6, id)) |>
dplyr::mutate(dupID = ifelse(id == 7, FALSE, dupID)) |>
dplyr::filter(dupID == FALSE) |>
dplyr::mutate(dupID = NULL)
nrow(exDatIDclean)[1] 752exDatIDclean msc1 msc2 msc3 msc4 age sex edu fLang id
1 2 3 2 2 9.815538 0 0 german 1
2 3 2 1 1 8.980194 1 0 german 2
3 2 2 3 3 12.758157 0 0 german 3
4 2 2 3 2 10.578846 0 0 german 4
5 3 2 2 2 9.894364 1 0 german 5
6 3 3 3 2 10.446850 0 0 german 6
7 4 4 1 2 10.897605 1 0 german 7
8 3 3 2 1 7.977382 1 0 german 8
9 3 3 2 1 10.688379 NA 0 german 9
10 3 3 2 2 9.105864 0 0 german 10
11 2 1 3 3 8.540117 0 0 german 11
12 2 2 3 3 11.841954 0 0 german 12
13 2 2 3 4 11.647655 0 0 german 13
14 2 3 3 3 11.745306 0 0 german 14
15 2 3 3 3 9.970630 0 0 german 15
16 3 3 2 1 8.186501 NA 0 german 16
17 2 2 2 2 7.773473 1 0 german 17
18 2 2 3 2 NA 1 0 german 18
19 2 2 3 3 9.339448 0 0 german 19
20 4 4 1 1 NA 1 0 german 20
21 4 3 2 2 9.831947 0 0 german 21
22 2 3 3 3 9.890266 1 0 german 22
23 2 3 2 2 11.501609 0 0 german 23
24 2 NA 3 2 9.341404 0 0 german 24
25 3 3 2 2 11.540030 1 0 german 25
26 2 3 3 3 10.098491 1 0 german 26
27 4 3 2 2 10.851320 1 0 german 27
28 3 2 2 2 6.735228 1 0 german 28
29 3 4 2 2 10.690059 1 0 german 29
30 3 2 2 3 9.746659 1 0 german 30
31 2 1 4 4 8.983450 0 0 german 31
32 2 NA 3 3 11.557894 0 0 german 32
33 2 2 3 2 8.057455 1 0 german 33
34 2 2 3 3 10.134752 0 0 german 34
35 2 3 3 2 8.096160 0 0 german 35
36 4 3 1 1 9.404151 NA 0 german 36
37 3 3 3 3 12.156118 0 0 german 37
38 2 2 3 2 9.600157 0 0 german 38
39 2 3 3 3 11.591400 0 0 german 39
40 3 4 2 2 10.691193 1 0 german 40
41 3 3 3 2 12.622380 0 0 german 41
42 3 2 2 2 10.043774 1 0 german 42
43 4 3 2 2 8.452508 0 0 german 43
44 4 3 2 2 12.029754 1 0 german 44
45 3 2 3 3 8.213722 1 0 german 45
46 2 3 2 3 11.654911 0 0 german 46
47 4 3 1 1 9.086209 1 0 german 47
48 3 NA 3 3 9.535470 1 0 german 48
49 2 3 3 3 9.617718 0 0 german 49
50 2 3 2 2 10.347768 1 0 german 50
51 2 1 3 3 10.735626 0 1 german 51
52 3 3 3 2 10.903281 1 1 german 52
53 2 NA 3 3 10.265831 1 1 german 53
54 2 3 4 3 10.824752 0 1 german 54
55 3 3 3 3 9.282584 0 1 german 55
56 2 3 2 2 NA 0 1 german 56
57 2 3 3 3 8.747505 1 1 german 57
58 2 2 3 4 8.992998 1 1 german 58
59 2 2 3 3 12.632563 1 1 german 59
60 2 2 3 3 11.015445 1 1 german 60
61 3 3 2 2 9.529734 1 1 german 61
62 4 3 1 1 11.569437 1 1 german 62
63 4 3 2 2 11.626011 1 1 german 63
64 2 NA 3 3 12.371110 0 1 german 64
65 2 2 2 3 8.712209 1 1 german 65
66 1 2 3 2 12.271368 0 1 german 66
67 3 3 2 2 11.514336 1 1 german 67
68 4 4 2 1 NA 0 1 german 68
69 2 2 2 2 10.858827 1 1 german 69
70 2 2 3 4 11.416986 0 1 german 70
71 2 NA 1 2 10.722411 0 1 german 71
72 2 3 3 3 9.417725 1 1 german 72
73 4 3 1 2 10.788379 1 1 german 73
74 2 3 2 2 7.527881 1 1 german 74
75 2 2 3 3 7.697320 0 1 german 75
76 3 4 3 2 9.723288 1 1 german 76
77 2 2 3 3 8.457035 NA 1 german 77
78 3 3 3 3 9.552697 1 1 german 78
79 3 3 3 3 10.766935 1 1 german 79
80 3 NA 2 2 8.086932 1 1 german 80
81 2 3 2 2 10.027268 0 1 german 81
82 4 3 2 2 11.108152 1 1 german 82
83 3 3 2 2 9.364853 1 1 german 83
84 2 NA 4 4 9.229549 0 1 german 84
85 4 3 1 1 8.791437 0 1 german 85
86 2 2 3 3 14.699514 1 1 german 86
87 1 2 4 3 9.310963 0 1 german 87
88 2 NA 2 2 7.862333 0 1 german 88
89 3 4 1 2 9.164070 0 1 german 89
90 3 2 3 2 9.904690 0 1 german 90
91 1 2 3 3 9.949049 0 1 german 91
92 1 2 3 2 10.874213 0 1 german 92
93 2 3 3 3 9.589440 0 1 german 93
94 2 2 3 3 8.396720 0 1 german 94
95 1 2 3 3 8.633062 1 1 german 95
96 3 4 2 2 10.174559 1 1 german 96
97 3 2 2 2 NA 1 1 german 97
98 4 3 2 2 6.022669 1 1 german 98
99 3 3 2 2 10.701638 1 1 german 99
100 3 NA 2 2 NA 0 1 german 100
101 2 3 3 3 11.536565 0 1 german 101
102 3 3 2 3 NA 1 1 german 102
103 2 2 3 3 7.082311 1 1 german 103
104 3 3 2 3 7.990254 1 1 german 104
105 2 2 3 3 13.223930 1 1 german 105
106 2 3 2 3 8.847114 0 1 german 106
107 2 NA 3 3 11.477532 0 1 german 107
108 3 4 1 1 8.809008 NA 1 german 108
109 2 2 3 2 8.917741 1 1 german 109
110 2 2 3 4 9.059646 0 1 german 110
111 2 2 3 3 NA 1 1 german 111
112 2 NA 2 2 7.801995 1 1 german 112
113 2 NA 3 3 NA 1 1 german 113
114 4 3 1 1 NA 1 1 german 114
115 2 2 3 3 8.336241 0 1 german 115
116 3 3 2 2 12.158201 1 1 german 116
117 2 2 2 3 NA 1 1 german 117
118 2 2 4 3 11.067872 1 1 german 118
119 3 NA 2 2 11.700517 0 1 german 119
120 3 2 3 2 9.562434 0 1 german 120
121 3 3 3 2 8.977396 0 1 german 121
122 3 2 3 2 10.162202 0 1 german 122
123 3 3 3 2 9.885336 0 1 german 123
124 3 2 3 3 12.560990 1 1 german 124
125 2 2 4 4 11.087431 1 1 german 125
126 3 3 4 3 10.371456 1 1 german 126
127 3 4 1 2 9.319275 1 1 german 127
128 2 2 2 3 11.008828 1 1 german 128
129 1 2 3 2 9.656535 0 1 german 129
130 1 2 3 4 10.032404 0 1 german 130
131 2 2 3 3 6.974716 1 1 german 131
132 3 2 2 3 9.168019 1 1 german 132
133 2 1 3 4 10.658201 1 1 german 133
134 3 2 2 2 10.723573 1 1 german 134
135 1 2 4 3 9.564175 1 1 german 135
136 2 3 3 2 9.440463 1 1 german 136
137 1 2 3 4 8.574188 1 1 german 137
138 2 2 3 3 8.336412 0 1 german 138
139 3 2 2 2 11.316498 0 1 german 139
140 4 3 2 2 10.089194 1 1 german 140
141 3 3 2 2 11.326115 0 1 german 141
142 2 3 2 2 10.544879 1 1 german 142
143 4 3 2 2 8.278763 0 1 german 143
144 3 3 2 2 9.165442 0 1 german 144
145 3 4 2 2 14.840083 0 1 german 145
146 4 4 1 1 9.177470 0 1 german 146
147 2 3 1 1 10.392509 1 1 german 147
148 3 3 2 3 10.074265 1 1 german 148
149 2 NA 2 2 10.394414 0 1 german 149
150 2 3 2 2 NA 0 1 german 150
151 2 2 3 3 11.303224 0 2 german 151
152 3 3 2 2 8.301101 0 2 german 152
153 3 4 2 2 11.552966 1 2 german 153
154 3 3 2 2 10.420355 1 2 german 154
155 3 4 2 2 12.648300 1 2 german 155
156 2 2 3 2 9.985757 1 2 german 156
157 2 3 2 2 10.504636 0 2 german 157
158 1 NA 4 4 NA 1 2 german 158
159 2 2 3 3 9.576623 0 2 german 159
160 3 3 2 2 12.855773 1 2 german 160
161 2 1 3 4 10.131071 0 2 german 161
162 2 3 3 3 11.018442 1 2 german 162
163 3 3 2 2 10.910317 1 2 german 163
164 1 NA 3 4 9.682431 0 2 german 164
165 3 2 3 3 10.402108 1 2 german 165
166 2 2 3 2 11.020302 0 2 german 166
167 2 2 3 3 10.862003 1 2 german 167
168 3 NA 2 1 12.021632 0 2 german 168
169 2 NA 2 3 11.145560 1 2 german 169
170 3 NA 1 2 11.157485 1 2 german 170
171 3 3 2 1 11.156488 1 2 german 171
172 1 2 3 3 11.774043 0 2 german 172
173 3 2 3 2 9.014673 0 2 german 173
174 4 4 1 1 9.755259 1 2 german 174
175 3 3 3 3 7.710862 0 2 german 175
176 1 1 4 4 11.418964 1 2 german 176
177 1 2 4 3 10.702477 0 2 german 177
178 3 2 3 2 11.237905 1 2 german 178
179 3 1 2 2 9.225546 0 2 german 179
180 3 3 2 2 9.673228 0 2 german 180
181 2 3 2 2 9.358897 0 2 german 181
182 3 2 3 3 9.252146 1 2 german 182
183 2 2 3 3 8.616693 0 2 german 183
184 3 4 2 2 10.721936 0 2 german 184
185 3 3 2 2 11.054393 0 2 german 185
186 1 1 4 4 8.094315 1 2 german 186
187 3 3 1 2 9.231779 0 2 german 187
188 3 3 2 2 8.547865 1 2 german 188
189 3 3 3 3 10.345779 0 2 german 189
190 1 2 3 3 13.226910 NA 2 german 190
191 2 2 3 4 10.411992 0 2 german 191
192 3 3 2 3 8.971620 1 2 german 192
193 1 2 4 4 10.898386 0 2 german 193
194 2 2 3 3 10.625394 1 2 german 194
195 2 2 3 4 6.357136 0 2 german 195
196 1 2 3 3 12.102026 1 2 german 196
197 2 3 3 2 NA 0 2 german 197
198 2 3 3 3 6.586317 1 2 german 198
199 3 3 3 2 9.050529 1 2 german 199
200 2 2 3 3 12.723541 1 2 german 200
201 2 2 3 3 9.753327 NA 2 german 201
202 2 2 2 2 13.303115 1 2 german 202
203 2 3 2 2 NA 0 2 german 203
204 3 3 2 2 11.445677 0 2 german 204
205 2 2 2 2 9.306572 0 2 german 205
206 3 1 3 3 8.328087 0 2 german 206
207 3 4 1 2 10.359319 0 2 german 207
208 4 3 1 1 NA 0 2 german 208
209 2 2 3 2 9.748836 1 2 german 209
210 2 2 3 3 11.357677 0 2 german 210
211 3 3 2 2 9.790493 1 2 german 211
212 3 3 3 3 8.325024 0 2 german 212
213 2 2 2 2 9.281008 1 2 german 213
214 3 2 2 2 13.164568 0 2 german 214
215 2 2 2 2 7.828867 0 2 german 215
216 3 3 3 3 10.665805 0 2 german 216
217 2 1 3 3 10.635206 1 2 german 217
218 2 NA 3 2 7.969089 1 2 german 218
219 2 2 2 3 8.923574 0 2 german 219
220 2 2 2 2 7.883175 NA 2 german 220
221 4 NA 2 1 8.577328 1 2 german 221
222 2 NA 4 4 8.257213 1 2 german 222
223 3 3 3 2 10.397873 1 2 german 223
224 4 3 2 2 10.241358 1 2 german 224
225 3 3 2 1 9.874614 1 2 german 225
226 2 3 2 2 9.251216 0 2 german 226
227 3 2 2 2 11.927287 0 2 german 227
228 4 4 2 2 10.327432 0 2 german 228
229 3 2 3 2 6.475004 0 2 german 229
230 2 3 3 3 11.338842 0 2 german 230
231 2 3 2 2 NA 1 2 german 231
232 3 3 2 2 7.933710 0 2 german 232
233 2 3 3 3 10.721250 1 2 german 233
234 3 2 3 3 8.086553 0 2 german 234
235 2 3 3 3 12.252961 NA 2 german 235
236 3 3 2 2 9.883740 NA 2 german 236
237 2 2 2 3 11.260712 0 2 german 237
238 4 3 2 1 NA 1 2 german 238
239 3 NA 2 2 13.991412 1 2 german 239
240 2 3 2 3 9.606693 1 2 german 240
241 2 2 2 2 9.965807 1 2 german 241
242 3 3 2 2 6.665730 NA 2 german 242
243 2 2 2 2 11.588374 1 2 german 243
244 2 1 3 3 12.054486 0 2 german 244
245 2 1 3 2 8.551229 1 2 german 245
246 1 1 4 4 14.758472 1 2 german 246
247 3 3 2 1 10.601619 1 2 german 247
248 2 3 2 3 9.608768 1 2 german 248
249 2 2 3 4 10.631563 0 2 german 249
250 3 4 1 1 9.195155 0 2 german 250
251 3 2 3 3 12.153778 1 2 german 251
252 2 1 4 4 11.594112 1 2 german 252
253 3 3 2 2 12.749052 0 2 german 253
254 2 1 3 3 10.904641 1 2 german 254
255 2 2 3 3 9.674235 NA 2 german 255
256 3 3 1 1 9.382583 1 2 german 256
257 2 2 4 3 10.075463 1 2 german 257
258 1 1 3 2 NA 0 2 german 258
259 3 NA 3 3 NA 1 2 german 259
260 3 3 2 2 10.003249 0 2 german 260
261 2 2 3 3 10.357645 0 2 german 261
262 1 2 3 3 11.427154 0 2 german 262
263 1 2 4 3 11.141758 1 2 german 263
264 2 2 3 3 11.717822 0 2 german 264
265 3 3 2 1 8.591719 1 2 german 265
266 4 3 2 2 7.199316 1 2 german 266
267 2 2 3 3 8.511259 NA 2 german 267
268 1 NA 4 4 13.183838 1 2 german 268
269 3 NA 2 2 9.723661 0 2 german 269
270 3 2 1 1 9.901684 1 2 german 270
271 2 2 2 3 11.543191 1 2 german 271
272 3 2 3 2 9.905296 0 2 german 272
273 3 3 2 2 10.804370 0 2 german 273
274 3 3 1 1 9.135004 1 2 german 274
275 3 NA 2 2 9.937281 1 2 german 275
276 2 2 4 4 8.449205 NA 2 german 276
277 4 4 2 2 10.110370 1 2 german 277
278 2 3 3 3 8.910521 1 2 german 278
279 3 3 2 2 11.335096 0 2 german 279
280 2 3 3 3 10.872968 1 2 german 280
281 3 3 2 2 9.550622 0 2 german 281
282 1 NA 4 4 NA 1 2 german 282
283 1 NA 3 4 12.176753 0 2 german 283
284 2 2 2 2 9.628909 0 2 german 284
285 2 2 3 3 13.948810 0 2 german 285
286 2 NA 3 3 12.154323 1 2 german 286
287 3 2 2 2 9.487855 1 2 german 287
288 3 3 3 2 10.250928 0 2 german 288
289 3 2 3 3 10.038493 0 2 german 289
290 3 2 2 2 9.110477 NA 2 german 290
291 3 3 2 3 11.294503 1 2 german 291
292 2 2 3 3 12.974758 0 2 german 292
293 3 3 2 2 10.285506 0 2 german 293
294 3 2 3 3 10.156128 1 2 german 294
295 3 4 3 3 10.477511 0 2 german 295
296 2 2 3 4 11.463436 0 2 german 296
297 2 3 2 2 10.958489 0 2 german 297
298 3 2 2 2 8.164545 0 2 german 298
299 2 3 3 3 10.140617 0 2 german 299
300 3 NA 2 2 9.653269 1 2 german 300
301 1 1 3 3 NA 0 3 german 301
302 2 3 2 3 9.384978 1 3 german 302
303 2 2 3 3 7.993749 1 3 german 303
304 2 3 3 3 6.955896 1 3 german 304
305 4 3 2 2 10.535078 0 3 german 305
306 4 3 1 2 NA 1 3 german 306
307 2 2 2 2 9.815517 NA 3 german 307
308 4 4 1 1 10.504347 1 3 german 308
309 2 3 2 2 9.939982 0 3 german 309
310 3 3 3 3 10.389400 1 3 german 310
311 2 2 3 3 9.448678 1 3 german 311
312 2 4 1 2 10.221263 1 3 german 312
313 2 NA 2 2 7.693476 0 3 german 313
314 3 3 2 1 11.177018 1 3 german 314
315 2 2 3 2 9.339305 0 3 german 315
316 3 2 2 2 6.542470 1 3 german 316
317 4 3 1 2 8.953333 1 3 german 317
318 3 2 3 3 NA 0 3 german 318
319 2 1 3 3 7.581036 0 3 german 319
320 3 3 2 1 9.966598 NA 3 german 320
321 3 2 3 3 10.881096 1 3 german 321
322 2 2 4 3 9.503126 1 3 german 322
323 3 3 2 2 10.542913 1 3 german 323
324 2 1 4 3 7.226064 1 3 german 324
325 2 2 3 3 NA 0 3 german 325
326 3 3 1 2 10.473550 1 3 german 326
327 3 2 2 2 NA 1 3 german 327
328 3 3 2 2 10.490264 0 3 german 328
329 3 3 2 1 11.740022 1 3 german 329
330 3 3 2 2 11.473238 0 3 german 330
331 3 3 2 3 12.689666 NA 3 german 331
332 3 3 2 2 NA 0 3 german 332
333 3 3 2 2 7.619042 0 3 german 333
334 3 2 2 2 9.525225 1 3 german 334
335 3 2 2 2 9.301695 1 3 german 335
336 1 1 4 4 10.235494 1 3 german 336
337 3 2 2 3 NA 0 3 german 337
338 3 2 3 3 10.078303 0 3 german 338
339 3 3 2 2 NA 1 3 german 339
340 2 3 2 2 9.613427 0 3 german 340
341 2 3 2 2 10.440469 1 3 german 341
342 1 2 4 3 NA 0 3 german 342
343 2 2 3 3 9.632019 0 3 german 343
344 3 NA 2 1 11.285079 0 3 german 344
345 3 4 2 2 12.780121 NA 3 german 345
346 3 3 2 3 10.358045 0 3 german 346
347 3 3 2 3 9.452587 0 3 german 347
348 2 2 3 3 10.616298 0 3 german 348
349 2 2 3 4 10.531881 1 3 german 349
350 3 2 3 3 10.917065 0 3 german 350
351 3 4 2 2 6.753558 1 3 german 351
352 3 3 1 1 9.081102 1 3 german 352
353 2 2 3 3 9.324282 0 3 german 353
354 3 4 2 2 9.556236 1 3 german 354
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comment
1 Hi, i like playing video games.
2 Hello, I am strong like Hulk.
3 i like apples!
4 i like avocados!
5 bye...
6 nopeee
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327 no
328 no
329 nopeee
330 no!
331 nope
332 nopeee
333 nopeee
334 no...
335 nope
336 nope
337 no
338 no!
339 no!
340 nopeee
341 nope
342 no
343 nopeee
344 nopeee
345 nope
346 no
347 no!
348 ...
349 nope
350 no!
351 nopeee
352 no!
353 nopeee
354 no!
355 nope
356 no...
357 nope
358 ...
359 no...
360 nope
361 no...
362 no...
363 nope
364 no!
365 nope
366 no...
367 nope
368 nopeee
369 ...
370 ...
371 nopeee
372 no...
373 ...
374 nopeee
375 nopeee
376 no
377 no
378 no!
379 no!
380 no
381 no
382 no
383 nope
384 no!
385 no!
386 nopeee
387 no!
388 nopeee
389 no...
390 nopeee
391 no!
392 ...
393 nope
394 nope
395 no!
396 no!
397 no...
398 no!
399 no
400 nopeee
401 no
402 ...
403 nopeee
404 no!
405 ...
406 no
407 no...
408 no...
409 nopeee
410 no
411 no!
412 no
413 nopeee
414 ...
415 no!
416 nope
417 no
418 nopeee
419 no
420 nopeee
421 no...
422 no!
423 nope
424 no
425 no...
426 no
427 ...
428 no!
429 nopeee
430 no
431 nopeee
432 nopeee
433 ...
434 nope
435 no
436 nopeee
437 nope
438 no
439 no!
440 nopeee
441 ...
442 no
443 no!
444 nope
445 no!
446 no
447 no!
448 ...
449 ...
450 nopeee
451 nopeee
452 no
453 no...
454 no
455 ...
456 nope
457 no!
458 nopeee
459 no
460 no
461 nope
462 no
463 nope
464 nope
465 no
466 no
467 nopeee
468 ...
469 nope
470 nope
471 nope
472 ...
473 nopeee
474 no...
475 no
476 ...
477 nopeee
478 nope
479 no!
480 no...
481 ...
482 nope
483 nope
484 nope
485 no...
486 ...
487 no
488 nopeee
489 nopeee
490 no!
491 no...
492 ...
493 ...
494 no
495 no
496 no...
497 nopeee
498 ...
499 nopeee
500 no!
501 no!
502 nope
503 nope
504 no
505 nope
506 no
507 no
508 no
509 no...
510 ...
511 no!
512 no
513 nope
514 nopeee
515 ...
516 ...
517 no!
518 nopeee
519 nope
520 nope
521 no...
522 no
523 nope
524 no...
525 nopeee
526 no
527 nope
528 nopeee
529 ...
530 nopeee
531 ...
532 no...
533 ...
534 no!
535 no
536 no!
537 nope
538 nopeee
539 nopeee
540 no
541 no
542 no...
543 no!
544 no...
545 nopeee
546 nope
547 nopeee
548 no...
549 nopeee
550 nope
551 nope
552 no...
553 no!
554 nopeee
555 nopeee
556 no
557 no
558 nopeee
559 no...
560 nope
561 nopeee
562 nopeee
563 no
564 no
565 no
566 ...
567 ...
568 no!
569 no...
570 ...
571 no!
572 no
573 nopeee
574 nopeee
575 ...
576 no...
577 nopeee
578 ...
579 nope
580 no...
581 no!
582 ...
583 ...
584 no!
585 no
586 nope
587 ...
588 ...
589 ...
590 no...
591 nopeee
592 nopeee
593 ...
594 ...
595 ...
596 nope
597 ...
598 no!
599 ...
600 no!
601 nopeee
602 nopeee
603 no
604 ...
605 no!
606 no!
607 no!
608 no
609 no...
610 no
611 nope
612 nope
613 no
614 no!
615 no
616 no
617 no
618 nope
619 nopeee
620 no...
621 no
622 nopeee
623 nopeee
624 nopeee
625 no!
626 ...
627 no
628 nopeee
629 ...
630 no...
631 no...
632 no...
633 nopeee
634 nopeee
635 nope
636 no!
637 ...
638 no
639 nope
640 nope
641 no
642 nope
643 ...
644 ...
645 nope
646 nope
647 nopeee
648 nopeee
649 no
650 ...
651 ...
652 nopeee
653 no!
654 nopeee
655 no
656 no...
657 no
658 no!
659 no...
660 no!
661 no...
662 no
663 no...
664 ...
665 no!
666 no...
667 no!
668 nope
669 nopeee
670 ...
671 no
672 ...
673 no
674 no
675 ...
676 ...
677 nopeee
678 ...
679 nope
680 no...
681 nope
682 no...
683 nopeee
684 nope
685 nope
686 no...
687 nopeee
688 nope
689 no...
690 ...
691 no
692 no!
693 no
694 no
695 no!
696 no...
697 ...
698 nopeee
699 nope
700 ...
701 no
702 no!
703 no...
704 no...
705 nopeee
706 nopeee
707 nope
708 nopeee
709 nopeee
710 no
711 ...
712 ...
713 no!
714 nope
715 no...
716 ...
717 no!
718 nopeee
719 no...
720 nopeee
721 nopeee
722 nopeee
723 nope
724 no...
725 nopeee
726 nope
727 no...
728 no...
729 nope
730 no
731 no!
732 no
733 nope
734 no
735 no
736 no...
737 no...
738 nopeee
739 no
740 nopeee
741 no
742 no
743 nope
744 nopeee
745 no...
746 nope
747 no
748 no
749 no!
750 no...
751 Hi, i like playing video games.
752 Hello, I am strong like Hulk.Similar to the base::unique function, the dplyr package provides a function dplyr::distinct() which can be used to keep only unique rows.
msc1 msc2 msc3 msc4 age sex edu fLang
1 2 3 2 2 9.815538 0 0 german
2 3 2 1 1 8.980194 1 0 german
3 2 2 3 3 12.758157 0 0 german
4 2 2 3 2 10.578846 0 0 german
5 3 2 2 2 9.894364 1 0 german
6 3 3 3 2 10.446850 0 0 german
7 4 4 1 2 10.897605 1 0 german
8 3 3 2 1 7.977382 1 0 german
9 3 3 2 1 10.688379 NA 0 german
10 3 3 2 2 9.105864 0 0 german
11 2 1 3 3 8.540117 0 0 german
12 2 2 3 3 11.841954 0 0 german
13 2 2 3 4 11.647655 0 0 german
14 2 3 3 3 11.745306 0 0 german
15 2 3 3 3 9.970630 0 0 german
16 3 3 2 1 8.186501 NA 0 german
17 2 2 2 2 7.773473 1 0 german
18 2 2 3 2 NA 1 0 german
19 2 2 3 3 9.339448 0 0 german
20 4 4 1 1 NA 1 0 german
21 4 3 2 2 9.831947 0 0 german
22 2 3 3 3 9.890266 1 0 german
23 2 3 2 2 11.501609 0 0 german
24 2 NA 3 2 9.341404 0 0 german
25 3 3 2 2 11.540030 1 0 german
26 2 3 3 3 10.098491 1 0 german
27 4 3 2 2 10.851320 1 0 german
28 3 2 2 2 6.735228 1 0 german
29 3 4 2 2 10.690059 1 0 german
30 3 2 2 3 9.746659 1 0 german
31 2 1 4 4 8.983450 0 0 german
32 2 NA 3 3 11.557894 0 0 german
33 2 2 3 2 8.057455 1 0 german
34 2 2 3 3 10.134752 0 0 german
35 2 3 3 2 8.096160 0 0 german
36 4 3 1 1 9.404151 NA 0 german
37 3 3 3 3 12.156118 0 0 german
38 2 2 3 2 9.600157 0 0 german
39 2 3 3 3 11.591400 0 0 german
40 3 4 2 2 10.691193 1 0 german
41 3 3 3 2 12.622380 0 0 german
42 3 2 2 2 10.043774 1 0 german
43 4 3 2 2 8.452508 0 0 german
44 4 3 2 2 12.029754 1 0 german
45 3 2 3 3 8.213722 1 0 german
46 2 3 2 3 11.654911 0 0 german
47 4 3 1 1 9.086209 1 0 german
48 3 NA 3 3 9.535470 1 0 german
49 2 3 3 3 9.617718 0 0 german
50 2 3 2 2 10.347768 1 0 german
51 2 1 3 3 10.735626 0 1 german
52 3 3 3 2 10.903281 1 1 german
53 2 NA 3 3 10.265831 1 1 german
54 2 3 4 3 10.824752 0 1 german
55 3 3 3 3 9.282584 0 1 german
56 2 3 2 2 NA 0 1 german
57 2 3 3 3 8.747505 1 1 german
58 2 2 3 4 8.992998 1 1 german
59 2 2 3 3 12.632563 1 1 german
60 2 2 3 3 11.015445 1 1 german
61 3 3 2 2 9.529734 1 1 german
62 4 3 1 1 11.569437 1 1 german
63 4 3 2 2 11.626011 1 1 german
64 2 NA 3 3 12.371110 0 1 german
65 2 2 2 3 8.712209 1 1 german
66 1 2 3 2 12.271368 0 1 german
67 3 3 2 2 11.514336 1 1 german
68 4 4 2 1 NA 0 1 german
69 2 2 2 2 10.858827 1 1 german
70 2 2 3 4 11.416986 0 1 german
71 2 NA 1 2 10.722411 0 1 german
72 2 3 3 3 9.417725 1 1 german
73 4 3 1 2 10.788379 1 1 german
74 2 3 2 2 7.527881 1 1 german
75 2 2 3 3 7.697320 0 1 german
76 3 4 3 2 9.723288 1 1 german
77 2 2 3 3 8.457035 NA 1 german
78 3 3 3 3 9.552697 1 1 german
79 3 3 3 3 10.766935 1 1 german
80 3 NA 2 2 8.086932 1 1 german
81 2 3 2 2 10.027268 0 1 german
82 4 3 2 2 11.108152 1 1 german
83 3 3 2 2 9.364853 1 1 german
84 2 NA 4 4 9.229549 0 1 german
85 4 3 1 1 8.791437 0 1 german
86 2 2 3 3 14.699514 1 1 german
87 1 2 4 3 9.310963 0 1 german
88 2 NA 2 2 7.862333 0 1 german
89 3 4 1 2 9.164070 0 1 german
90 3 2 3 2 9.904690 0 1 german
91 1 2 3 3 9.949049 0 1 german
92 1 2 3 2 10.874213 0 1 german
93 2 3 3 3 9.589440 0 1 german
94 2 2 3 3 8.396720 0 1 german
95 1 2 3 3 8.633062 1 1 german
96 3 4 2 2 10.174559 1 1 german
97 3 2 2 2 NA 1 1 german
98 4 3 2 2 6.022669 1 1 german
99 3 3 2 2 10.701638 1 1 german
100 3 NA 2 2 NA 0 1 german
101 2 3 3 3 11.536565 0 1 german
102 3 3 2 3 NA 1 1 german
103 2 2 3 3 7.082311 1 1 german
104 3 3 2 3 7.990254 1 1 german
105 2 2 3 3 13.223930 1 1 german
106 2 3 2 3 8.847114 0 1 german
107 2 NA 3 3 11.477532 0 1 german
108 3 4 1 1 8.809008 NA 1 german
109 2 2 3 2 8.917741 1 1 german
110 2 2 3 4 9.059646 0 1 german
111 2 2 3 3 NA 1 1 german
112 2 NA 2 2 7.801995 1 1 german
113 2 NA 3 3 NA 1 1 german
114 4 3 1 1 NA 1 1 german
115 2 2 3 3 8.336241 0 1 german
116 3 3 2 2 12.158201 1 1 german
117 2 2 2 3 NA 1 1 german
118 2 2 4 3 11.067872 1 1 german
119 3 NA 2 2 11.700517 0 1 german
120 3 2 3 2 9.562434 0 1 german
121 3 3 3 2 8.977396 0 1 german
122 3 2 3 2 10.162202 0 1 german
123 3 3 3 2 9.885336 0 1 german
124 3 2 3 3 12.560990 1 1 german
125 2 2 4 4 11.087431 1 1 german
126 3 3 4 3 10.371456 1 1 german
127 3 4 1 2 9.319275 1 1 german
128 2 2 2 3 11.008828 1 1 german
129 1 2 3 2 9.656535 0 1 german
130 1 2 3 4 10.032404 0 1 german
131 2 2 3 3 6.974716 1 1 german
132 3 2 2 3 9.168019 1 1 german
133 2 1 3 4 10.658201 1 1 german
134 3 2 2 2 10.723573 1 1 german
135 1 2 4 3 9.564175 1 1 german
136 2 3 3 2 9.440463 1 1 german
137 1 2 3 4 8.574188 1 1 german
138 2 2 3 3 8.336412 0 1 german
139 3 2 2 2 11.316498 0 1 german
140 4 3 2 2 10.089194 1 1 german
141 3 3 2 2 11.326115 0 1 german
142 2 3 2 2 10.544879 1 1 german
143 4 3 2 2 8.278763 0 1 german
144 3 3 2 2 9.165442 0 1 german
145 3 4 2 2 14.840083 0 1 german
146 4 4 1 1 9.177470 0 1 german
147 2 3 1 1 10.392509 1 1 german
148 3 3 2 3 10.074265 1 1 german
149 2 NA 2 2 10.394414 0 1 german
150 2 3 2 2 NA 0 1 german
151 2 2 3 3 11.303224 0 2 german
152 3 3 2 2 8.301101 0 2 german
153 3 4 2 2 11.552966 1 2 german
154 3 3 2 2 10.420355 1 2 german
155 3 4 2 2 12.648300 1 2 german
156 2 2 3 2 9.985757 1 2 german
157 2 3 2 2 10.504636 0 2 german
158 1 NA 4 4 NA 1 2 german
159 2 2 3 3 9.576623 0 2 german
160 3 3 2 2 12.855773 1 2 german
161 2 1 3 4 10.131071 0 2 german
162 2 3 3 3 11.018442 1 2 german
163 3 3 2 2 10.910317 1 2 german
164 1 NA 3 4 9.682431 0 2 german
165 3 2 3 3 10.402108 1 2 german
166 2 2 3 2 11.020302 0 2 german
167 2 2 3 3 10.862003 1 2 german
168 3 NA 2 1 12.021632 0 2 german
169 2 NA 2 3 11.145560 1 2 german
170 3 NA 1 2 11.157485 1 2 german
171 3 3 2 1 11.156488 1 2 german
172 1 2 3 3 11.774043 0 2 german
173 3 2 3 2 9.014673 0 2 german
174 4 4 1 1 9.755259 1 2 german
175 3 3 3 3 7.710862 0 2 german
176 1 1 4 4 11.418964 1 2 german
177 1 2 4 3 10.702477 0 2 german
178 3 2 3 2 11.237905 1 2 german
179 3 1 2 2 9.225546 0 2 german
180 3 3 2 2 9.673228 0 2 german
181 2 3 2 2 9.358897 0 2 german
182 3 2 3 3 9.252146 1 2 german
183 2 2 3 3 8.616693 0 2 german
184 3 4 2 2 10.721936 0 2 german
185 3 3 2 2 11.054393 0 2 german
186 1 1 4 4 8.094315 1 2 german
187 3 3 1 2 9.231779 0 2 german
188 3 3 2 2 8.547865 1 2 german
189 3 3 3 3 10.345779 0 2 german
190 1 2 3 3 13.226910 NA 2 german
191 2 2 3 4 10.411992 0 2 german
192 3 3 2 3 8.971620 1 2 german
193 1 2 4 4 10.898386 0 2 german
194 2 2 3 3 10.625394 1 2 german
195 2 2 3 4 6.357136 0 2 german
196 1 2 3 3 12.102026 1 2 german
197 2 3 3 2 NA 0 2 german
198 2 3 3 3 6.586317 1 2 german
199 3 3 3 2 9.050529 1 2 german
200 2 2 3 3 12.723541 1 2 german
201 2 2 3 3 9.753327 NA 2 german
202 2 2 2 2 13.303115 1 2 german
203 2 3 2 2 NA 0 2 german
204 3 3 2 2 11.445677 0 2 german
205 2 2 2 2 9.306572 0 2 german
206 3 1 3 3 8.328087 0 2 german
207 3 4 1 2 10.359319 0 2 german
208 4 3 1 1 NA 0 2 german
209 2 2 3 2 9.748836 1 2 german
210 2 2 3 3 11.357677 0 2 german
211 3 3 2 2 9.790493 1 2 german
212 3 3 3 3 8.325024 0 2 german
213 2 2 2 2 9.281008 1 2 german
214 3 2 2 2 13.164568 0 2 german
215 2 2 2 2 7.828867 0 2 german
216 3 3 3 3 10.665805 0 2 german
217 2 1 3 3 10.635206 1 2 german
218 2 NA 3 2 7.969089 1 2 german
219 2 2 2 3 8.923574 0 2 german
220 2 2 2 2 7.883175 NA 2 german
221 4 NA 2 1 8.577328 1 2 german
222 2 NA 4 4 8.257213 1 2 german
223 3 3 3 2 10.397873 1 2 german
224 4 3 2 2 10.241358 1 2 german
225 3 3 2 1 9.874614 1 2 german
226 2 3 2 2 9.251216 0 2 german
227 3 2 2 2 11.927287 0 2 german
228 4 4 2 2 10.327432 0 2 german
229 3 2 3 2 6.475004 0 2 german
230 2 3 3 3 11.338842 0 2 german
231 2 3 2 2 NA 1 2 german
232 3 3 2 2 7.933710 0 2 german
233 2 3 3 3 10.721250 1 2 german
234 3 2 3 3 8.086553 0 2 german
235 2 3 3 3 12.252961 NA 2 german
236 3 3 2 2 9.883740 NA 2 german
237 2 2 2 3 11.260712 0 2 german
238 4 3 2 1 NA 1 2 german
239 3 NA 2 2 13.991412 1 2 german
240 2 3 2 3 9.606693 1 2 german
241 2 2 2 2 9.965807 1 2 german
242 3 3 2 2 6.665730 NA 2 german
243 2 2 2 2 11.588374 1 2 german
244 2 1 3 3 12.054486 0 2 german
245 2 1 3 2 8.551229 1 2 german
246 1 1 4 4 14.758472 1 2 german
247 3 3 2 1 10.601619 1 2 german
248 2 3 2 3 9.608768 1 2 german
249 2 2 3 4 10.631563 0 2 german
250 3 4 1 1 9.195155 0 2 german
251 3 2 3 3 12.153778 1 2 german
252 2 1 4 4 11.594112 1 2 german
253 3 3 2 2 12.749052 0 2 german
254 2 1 3 3 10.904641 1 2 german
255 2 2 3 3 9.674235 NA 2 german
256 3 3 1 1 9.382583 1 2 german
257 2 2 4 3 10.075463 1 2 german
258 1 1 3 2 NA 0 2 german
259 3 NA 3 3 NA 1 2 german
260 3 3 2 2 10.003249 0 2 german
261 2 2 3 3 10.357645 0 2 german
262 1 2 3 3 11.427154 0 2 german
263 1 2 4 3 11.141758 1 2 german
264 2 2 3 3 11.717822 0 2 german
265 3 3 2 1 8.591719 1 2 german
266 4 3 2 2 7.199316 1 2 german
267 2 2 3 3 8.511259 NA 2 german
268 1 NA 4 4 13.183838 1 2 german
269 3 NA 2 2 9.723661 0 2 german
270 3 2 1 1 9.901684 1 2 german
271 2 2 2 3 11.543191 1 2 german
272 3 2 3 2 9.905296 0 2 german
273 3 3 2 2 10.804370 0 2 german
274 3 3 1 1 9.135004 1 2 german
275 3 NA 2 2 9.937281 1 2 german
276 2 2 4 4 8.449205 NA 2 german
277 4 4 2 2 10.110370 1 2 german
278 2 3 3 3 8.910521 1 2 german
279 3 3 2 2 11.335096 0 2 german
280 2 3 3 3 10.872968 1 2 german
281 3 3 2 2 9.550622 0 2 german
282 1 NA 4 4 NA 1 2 german
283 1 NA 3 4 12.176753 0 2 german
284 2 2 2 2 9.628909 0 2 german
285 2 2 3 3 13.948810 0 2 german
286 2 NA 3 3 12.154323 1 2 german
287 3 2 2 2 9.487855 1 2 german
288 3 3 3 2 10.250928 0 2 german
289 3 2 3 3 10.038493 0 2 german
290 3 2 2 2 9.110477 NA 2 german
291 3 3 2 3 11.294503 1 2 german
292 2 2 3 3 12.974758 0 2 german
293 3 3 2 2 10.285506 0 2 german
294 3 2 3 3 10.156128 1 2 german
295 3 4 3 3 10.477511 0 2 german
296 2 2 3 4 11.463436 0 2 german
297 2 3 2 2 10.958489 0 2 german
298 3 2 2 2 8.164545 0 2 german
299 2 3 3 3 10.140617 0 2 german
300 3 NA 2 2 9.653269 1 2 german
301 1 1 3 3 NA 0 3 german
302 2 3 2 3 9.384978 1 3 german
303 2 2 3 3 7.993749 1 3 german
304 2 3 3 3 6.955896 1 3 german
305 4 3 2 2 10.535078 0 3 german
306 4 3 1 2 NA 1 3 german
307 2 2 2 2 9.815517 NA 3 german
308 4 4 1 1 10.504347 1 3 german
309 2 3 2 2 9.939982 0 3 german
310 3 3 3 3 10.389400 1 3 german
311 2 2 3 3 9.448678 1 3 german
312 2 4 1 2 10.221263 1 3 german
313 2 NA 2 2 7.693476 0 3 german
314 3 3 2 1 11.177018 1 3 german
315 2 2 3 2 9.339305 0 3 german
316 3 2 2 2 6.542470 1 3 german
317 4 3 1 2 8.953333 1 3 german
318 3 2 3 3 NA 0 3 german
319 2 1 3 3 7.581036 0 3 german
320 3 3 2 1 9.966598 NA 3 german
321 3 2 3 3 10.881096 1 3 german
322 2 2 4 3 9.503126 1 3 german
323 3 3 2 2 10.542913 1 3 german
324 2 1 4 3 7.226064 1 3 german
325 2 2 3 3 NA 0 3 german
326 3 3 1 2 10.473550 1 3 german
327 3 2 2 2 NA 1 3 german
328 3 3 2 2 10.490264 0 3 german
329 3 3 2 1 11.740022 1 3 german
330 3 3 2 2 11.473238 0 3 german
331 3 3 2 3 12.689666 NA 3 german
332 3 3 2 2 NA 0 3 german
333 3 3 2 2 7.619042 0 3 german
334 3 2 2 2 9.525225 1 3 german
335 3 2 2 2 9.301695 1 3 german
336 1 1 4 4 10.235494 1 3 german
337 3 2 2 3 NA 0 3 german
338 3 2 3 3 10.078303 0 3 german
339 3 3 2 2 NA 1 3 german
340 2 3 2 2 9.613427 0 3 german
341 2 3 2 2 10.440469 1 3 german
342 1 2 4 3 NA 0 3 german
343 2 2 3 3 9.632019 0 3 german
344 3 NA 2 1 11.285079 0 3 german
345 3 4 2 2 12.780121 NA 3 german
346 3 3 2 3 10.358045 0 3 german
347 3 3 2 3 9.452587 0 3 german
348 2 2 3 3 10.616298 0 3 german
349 2 2 3 4 10.531881 1 3 german
350 3 2 3 3 10.917065 0 3 german
351 3 4 2 2 6.753558 1 3 german
352 3 3 1 1 9.081102 1 3 german
353 2 2 3 3 9.324282 0 3 german
354 3 4 2 2 9.556236 1 3 german
355 3 3 2 3 8.404846 1 3 german
356 3 3 2 2 9.835171 1 3 german
357 3 3 2 1 NA 1 3 german
358 3 3 2 2 9.347660 1 3 german
359 2 NA 3 3 7.015727 0 3 german
360 3 3 2 2 8.146446 0 3 german
361 3 3 1 2 7.774709 1 3 german
362 3 3 2 2 6.083013 1 3 german
363 2 3 3 3 8.618334 1 3 german
364 2 NA 3 2 10.029465 0 3 german
365 3 NA 2 1 10.539754 1 3 german
366 2 3 3 3 11.498595 0 3 german
367 2 2 4 4 NA 0 3 german
368 3 4 2 2 11.885905 0 3 german
369 2 1 3 3 10.151313 0 3 german
370 2 3 2 3 8.703436 1 3 german
371 1 2 3 4 11.715882 1 3 german
372 2 2 3 3 10.497316 NA 3 german
373 2 2 3 2 9.260014 0 3 german
374 2 1 4 4 12.764876 0 3 german
375 2 3 3 3 11.855908 0 3 german
376 2 2 3 3 11.346669 1 3 german
377 2 3 3 2 10.437059 0 3 german
378 3 3 2 3 NA 0 3 german
379 3 NA 2 2 8.338840 1 3 german
380 3 2 2 3 11.449698 0 3 german
381 3 3 2 2 NA 0 3 german
382 2 NA 2 2 10.331462 0 3 german
383 3 3 2 1 10.240093 1 3 german
384 3 3 3 3 12.629684 0 3 german
385 3 3 2 2 10.858224 0 3 german
386 2 3 3 3 NA 1 3 german
387 2 3 3 3 11.439490 NA 3 german
388 3 3 2 2 8.881271 1 3 german
389 3 NA 3 3 10.293081 1 3 german
390 3 3 3 3 10.192251 0 3 german
391 3 3 1 2 11.065900 0 3 german
392 3 3 2 2 10.998406 1 3 german
393 2 2 3 2 8.552346 1 3 german
394 4 3 1 2 9.412805 0 3 german
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comment
1 Hi, i like playing video games.
2 Hello, I am strong like Hulk.
3 i like apples!
4 i like avocados!
5 bye...
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569 no!
570 no
571 nopeee
572 nopeee
573 ...
574 no...
575 nopeee
576 ...
577 nope
578 no...
579 no!
580 ...
581 ...
582 no!
583 no
584 nope
585 ...
586 ...
587 ...
588 no...
589 nopeee
590 nopeee
591 ...
592 ...
593 ...
594 nope
595 ...
596 no!
597 ...
598 no!
599 nopeee
600 nopeee
601 no
602 ...
603 no!
604 no!
605 no!
606 no
607 no...
608 no
609 nope
610 nope
611 no
612 no!
613 no
614 no
615 no
616 nope
617 nopeee
618 no...
619 no
620 nopeee
621 nopeee
622 nopeee
623 no!
624 ...
625 no
626 nopeee
627 ...
628 no...
629 no...
630 no...
631 nopeee
632 nopeee
633 nope
634 no!
635 ...
636 no
637 nope
638 nope
639 no
640 nope
641 ...
642 ...
643 nope
644 nope
645 nopeee
646 nopeee
647 no
648 ...
649 ...
650 nopeee
651 no!
652 nopeee
653 no
654 no...
655 no
656 no!
657 no...
658 no!
659 no...
660 no
661 no...
662 ...
663 no!
664 no...
665 no!
666 nope
667 nopeee
668 ...
669 no
670 ...
671 no
672 no
673 ...
674 ...
675 nopeee
676 ...
677 nope
678 no...
679 nope
680 no...
681 nopeee
682 nope
683 nope
684 no...
685 nopeee
686 nope
687 no...
688 ...
689 no
690 no!
691 no
692 no
693 no!
694 no...
695 ...
696 nopeee
697 nope
698 ...
699 no
700 no!
701 no...
702 no...
703 nopeee
704 nopeee
705 nope
706 nopeee
707 nopeee
708 no
709 ...
710 ...
711 no!
712 nope
713 no...
714 ...
715 no!
716 nopeee
717 no...
718 nopeee
719 nopeee
720 nopeee
721 nope
722 no...
723 nopeee
724 nope
725 no...
726 no...
727 nope
728 no
729 no!
730 no
731 nope
732 no
733 no
734 no...
735 no...
736 nopeee
737 no
738 nopeee
739 no
740 no
741 nope
742 nopeee
743 no...
744 nope
745 no
746 no
747 no!
748 no...The dplyr::distinct function eliminated 2 additional observations that are no real duplicates…Hence, it is important to carefully applying the dplyr::distinct function and checking that it does not eliminate different observations with the same response pattern (only real duplicates)!
7.5 Handling missing data
Missing data is a common problem in most behavioral science research (Enders, 2010, 2023; Schafer & Graham, 2002). Especially, in questionnaire surveys it is unavoidable that values are missing because a field of the questionnaire was not filled in or a person dropped out of the study. Therefore, it is essential to examine the data with regard to missing data. By missing values, we refer to cells that could have a value, but whose value is not available (see section on Data Types in the Introduction to R & RStudio part).
The procedure encompasses 3 2 steps:
- Define missing values
-
Examine missing values
- (Omit missing values → usually not recommended)
7.5.1 Define missing values
In other statistical software programs (e.g., SPSS) or in study planning, missing values are often defined as numeric values (e.g., -9, -88 or -99)2 or as empty characters/strings (i.e., "" or " "). In R, missing values are represented by the symbol NA (not available). This means, the first step is to ensure that all missing values are declared as NA in the data set.
Assign -99 & -88 values
!! Do not do this with real data !! !! Do not do this with real data !! !! Do not do this with real data !!
The example data set exDat does also not contain any missing values that are not NA values. Hence, we have to create them. With the following code, we assign the values -99 to the first 5 rows of the variable age and -88 to the first 4 rows of the variables sex and edu (and store it in a new data set exDatMis).
exDatMis <- exDat
exDatMis[1:5, "age"] <- -99
exDatMis[1:4,c("sex", "edu")] <- -88
head(exDatMis, 6) msc1 msc2 msc3 msc4 age sex edu fLang id
1 2 3 2 2 -99.00000 -88 -88 german 1
2 3 2 1 1 -99.00000 -88 -88 german 2
3 2 2 3 3 -99.00000 -88 -88 german 3
4 2 2 3 2 -99.00000 -88 -88 german 4
5 3 2 2 2 -99.00000 1 0 german 5
6 3 3 3 2 10.44685 0 0 german 6!! Do not do this with real data !! !! Do not do this with real data !! !! Do not do this with real data !!
To demonstrate the effect, we calculate the mean of the variable age in the exDat and exDatMis data set:
To set the numeric values of -99 or -88 to NA, we may use one of the following two approaches:
For a single variable:
exDatMis$age[exDatMis$age == -99] <- NAFor multiple variables:
colToNa <- c("sex", "edu")
exDatMis[colToNa][exDatMis[colToNa] == -88] <- NAFor the whole data set:
exDatMis[exDatMis == -99] <- NAThe na_if() function from the dplyr package (Wickham, François, et al., 2023) is designed to to convert (specific) values to NA.
For a single variable:
exDatMis$age <- dplyr::na_if(exDatMis$age, -99)For multiple variables:
For the whole data set:
But note that na_if() is meant for use with vectors rather than entire data frames.
7.5.2 Examine missing values
The is.na function indicates which elements are missing. It requires an input x that can be e.g., a vector, data.frame or list. The values that are returned depend on the input. For example, when you pass a vector to the function, it returns a logical vector of the same length as its argument x, containing TRUE for those elements marked NA or, for numeric or complex vectors, NaN, and FALSE otherwise.
In the following, we show how the is.na function may be applied to variables within a data set. To count the missing values (or to be more specific the returned TRUE values), we use the sum and colSums functions.
Tip: summary() of a data.frame
The summary() function returns beside some descriptive statistics (e.g., minimum, maximum, mean, …), also the amount of NAs of each variable of the given data set.
summary(exDat) msc1 msc2 msc3 msc4
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.00
Median :3.000 Median :3.000 Median :2.000 Median :2.00
Mean :2.519 Mean :2.544 Mean :2.488 Mean :2.48
3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.00
Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.00
NA's :70
age sex edu fLang
Min. : 5.440 Min. :0.0000 Min. :0.000 Length:750
1st Qu.: 9.052 1st Qu.:0.0000 1st Qu.:2.000 Class :character
Median :10.030 Median :0.0000 Median :3.000 Mode :character
Mean :10.035 Mean :0.4979 Mean :2.667
3rd Qu.:11.018 3rd Qu.:1.0000 3rd Qu.:4.000
Max. :30.000 Max. :1.0000 Max. :4.000
NA's :80 NA's :39
id
Min. : 1.0
1st Qu.:188.2
Median :375.5
Mean :375.5
3rd Qu.:562.8
Max. :750.0
For a single variable:
For multiple variables:
For the whole data set:
For a single variable:
For multiple variables:
For the whole data set:
Exercise: Count the missing values!
7.5.3 Omit missing values
Although omitting or deleting missing values is a common practice, this is not recommended to deal with missing data and should–in most scenarios–be avoided altogether (Schafer & Graham, 2002). Two “state-of-the-art” missing data methods are maximum likelihood estimation (e.g., implemented in the lavaan package, see Rosseel et al., 2023) and multiple imputation (e.g., implemented in the mice package, see van Buuren & Groothuis-Oudshoorn, 2021; for good introductions to this topic, see Enders, 2010).
Documentation of missing data
For the sake of documentation it is important to report the percentage of missing data of the variables in the data set.
Nevertheless, if you would like to omit missing values anyway, there are several functions to do this in R. Also, it is important to note that ignoring or omitting missing values are often the default options in R.
For example, by negating the is.na function (i.e., !is.na), those rows that do not contain NAs on a variable or data set are kept.
For a single variable:
exDat[!is.na(exDat$age), ] msc1 msc2 msc3 msc4 age sex edu fLang id
1 2 3 2 2 9.815538 0 0 german 1
2 3 2 1 1 8.980194 1 0 german 2
3 2 2 3 3 12.758157 0 0 german 3
4 2 2 3 2 10.578846 0 0 german 4
5 3 2 2 2 9.894364 1 0 german 5
6 3 3 3 2 10.446850 0 0 german 6
7 4 4 1 2 10.897605 1 0 german 7
8 3 3 2 1 7.977382 1 0 german 8
9 3 3 2 1 10.688379 NA 0 german 9
10 3 3 2 2 9.105864 0 0 german 10
11 2 1 3 3 8.540117 0 0 german 11
12 2 2 3 3 11.841954 0 0 german 12
13 2 2 3 4 11.647655 0 0 german 13
14 2 3 3 3 11.745306 0 0 german 14
15 2 3 3 3 9.970630 0 0 german 15
16 3 3 2 1 8.186501 NA 0 german 16
17 2 2 2 2 7.773473 1 0 german 17
19 2 2 3 3 9.339448 0 0 german 19
21 4 3 2 2 9.831947 0 0 german 21
22 2 3 3 3 9.890266 1 0 german 22
23 2 3 2 2 11.501609 0 0 german 23
24 2 NA 3 2 9.341404 0 0 german 24
25 3 3 2 2 11.540030 1 0 german 25
26 2 3 3 3 10.098491 1 0 german 26
27 4 3 2 2 10.851320 1 0 german 27
28 3 2 2 2 6.735228 1 0 german 28
29 3 4 2 2 10.690059 1 0 german 29
30 3 2 2 3 9.746659 1 0 german 30
31 2 1 4 4 8.983450 0 0 german 31
32 2 NA 3 3 11.557894 0 0 german 32
33 2 2 3 2 8.057455 1 0 german 33
34 2 2 3 3 10.134752 0 0 german 34
35 2 3 3 2 8.096160 0 0 german 35
36 4 3 1 1 9.404151 NA 0 german 36
37 3 3 3 3 12.156118 0 0 german 37
38 2 2 3 2 9.600157 0 0 german 38
39 2 3 3 3 11.591400 0 0 german 39
40 3 4 2 2 10.691193 1 0 german 40
41 3 3 3 2 12.622380 0 0 german 41
42 3 2 2 2 10.043774 1 0 german 42
43 4 3 2 2 8.452508 0 0 german 43
44 4 3 2 2 12.029754 1 0 german 44
45 3 2 3 3 8.213722 1 0 german 45
46 2 3 2 3 11.654911 0 0 german 46
47 4 3 1 1 9.086209 1 0 german 47
48 3 NA 3 3 9.535470 1 0 german 48
49 2 3 3 3 9.617718 0 0 german 49
50 2 3 2 2 10.347768 1 0 german 50
51 2 1 3 3 10.735626 0 1 german 51
52 3 3 3 2 10.903281 1 1 german 52
53 2 NA 3 3 10.265831 1 1 german 53
54 2 3 4 3 10.824752 0 1 german 54
55 3 3 3 3 9.282584 0 1 german 55
57 2 3 3 3 8.747505 1 1 german 57
58 2 2 3 4 8.992998 1 1 german 58
59 2 2 3 3 12.632563 1 1 german 59
60 2 2 3 3 11.015445 1 1 german 60
61 3 3 2 2 9.529734 1 1 german 61
62 4 3 1 1 11.569437 1 1 german 62
63 4 3 2 2 11.626011 1 1 german 63
64 2 NA 3 3 12.371110 0 1 german 64
65 2 2 2 3 8.712209 1 1 german 65
66 1 2 3 2 12.271368 0 1 german 66
67 3 3 2 2 11.514336 1 1 german 67
69 2 2 2 2 10.858827 1 1 german 69
70 2 2 3 4 11.416986 0 1 german 70
71 2 NA 1 2 10.722411 0 1 german 71
72 2 3 3 3 9.417725 1 1 german 72
73 4 3 1 2 10.788379 1 1 german 73
74 2 3 2 2 7.527881 1 1 german 74
75 2 2 3 3 7.697320 0 1 german 75
76 3 4 3 2 9.723288 1 1 german 76
77 2 2 3 3 8.457035 NA 1 german 77
78 3 3 3 3 9.552697 1 1 german 78
79 3 3 3 3 10.766935 1 1 german 79
80 3 NA 2 2 8.086932 1 1 german 80
81 2 3 2 2 10.027268 0 1 german 81
82 4 3 2 2 11.108152 1 1 german 82
83 3 3 2 2 9.364853 1 1 german 83
84 2 NA 4 4 9.229549 0 1 german 84
85 4 3 1 1 8.791437 0 1 german 85
86 2 2 3 3 14.699514 1 1 german 86
87 1 2 4 3 9.310963 0 1 german 87
88 2 NA 2 2 7.862333 0 1 german 88
89 3 4 1 2 9.164070 0 1 german 89
90 3 2 3 2 9.904690 0 1 german 90
91 1 2 3 3 9.949049 0 1 german 91
92 1 2 3 2 10.874213 0 1 german 92
93 2 3 3 3 9.589440 0 1 german 93
94 2 2 3 3 8.396720 0 1 german 94
95 1 2 3 3 8.633062 1 1 german 95
96 3 4 2 2 10.174559 1 1 german 96
98 4 3 2 2 6.022669 1 1 german 98
99 3 3 2 2 10.701638 1 1 german 99
101 2 3 3 3 11.536565 0 1 german 101
103 2 2 3 3 7.082311 1 1 german 103
104 3 3 2 3 7.990254 1 1 german 104
105 2 2 3 3 13.223930 1 1 german 105
106 2 3 2 3 8.847114 0 1 german 106
107 2 NA 3 3 11.477532 0 1 german 107
108 3 4 1 1 8.809008 NA 1 german 108
109 2 2 3 2 8.917741 1 1 german 109
110 2 2 3 4 9.059646 0 1 german 110
112 2 NA 2 2 7.801995 1 1 german 112
115 2 2 3 3 8.336241 0 1 german 115
116 3 3 2 2 12.158201 1 1 german 116
118 2 2 4 3 11.067872 1 1 german 118
119 3 NA 2 2 11.700517 0 1 german 119
120 3 2 3 2 9.562434 0 1 german 120
121 3 3 3 2 8.977396 0 1 german 121
122 3 2 3 2 10.162202 0 1 german 122
123 3 3 3 2 9.885336 0 1 german 123
124 3 2 3 3 12.560990 1 1 german 124
125 2 2 4 4 11.087431 1 1 german 125
126 3 3 4 3 10.371456 1 1 german 126
127 3 4 1 2 9.319275 1 1 german 127
128 2 2 2 3 11.008828 1 1 german 128
129 1 2 3 2 9.656535 0 1 german 129
130 1 2 3 4 10.032404 0 1 german 130
131 2 2 3 3 6.974716 1 1 german 131
132 3 2 2 3 9.168019 1 1 german 132
133 2 1 3 4 10.658201 1 1 german 133
134 3 2 2 2 10.723573 1 1 german 134
135 1 2 4 3 9.564175 1 1 german 135
136 2 3 3 2 9.440463 1 1 german 136
137 1 2 3 4 8.574188 1 1 german 137
138 2 2 3 3 8.336412 0 1 german 138
139 3 2 2 2 11.316498 0 1 german 139
140 4 3 2 2 10.089194 1 1 german 140
141 3 3 2 2 11.326115 0 1 german 141
142 2 3 2 2 10.544879 1 1 german 142
143 4 3 2 2 8.278763 0 1 german 143
144 3 3 2 2 9.165442 0 1 german 144
145 3 4 2 2 14.840083 0 1 german 145
146 4 4 1 1 9.177470 0 1 german 146
147 2 3 1 1 10.392509 1 1 german 147
148 3 3 2 3 10.074265 1 1 german 148
149 2 NA 2 2 10.394414 0 1 german 149
151 2 2 3 3 11.303224 0 2 german 151
152 3 3 2 2 8.301101 0 2 german 152
153 3 4 2 2 11.552966 1 2 german 153
154 3 3 2 2 10.420355 1 2 german 154
155 3 4 2 2 12.648300 1 2 german 155
156 2 2 3 2 9.985757 1 2 german 156
157 2 3 2 2 10.504636 0 2 german 157
159 2 2 3 3 9.576623 0 2 german 159
160 3 3 2 2 12.855773 1 2 german 160
161 2 1 3 4 10.131071 0 2 german 161
162 2 3 3 3 11.018442 1 2 german 162
163 3 3 2 2 10.910317 1 2 german 163
164 1 NA 3 4 9.682431 0 2 german 164
165 3 2 3 3 10.402108 1 2 german 165
166 2 2 3 2 11.020302 0 2 german 166
167 2 2 3 3 10.862003 1 2 german 167
168 3 NA 2 1 12.021632 0 2 german 168
169 2 NA 2 3 11.145560 1 2 german 169
170 3 NA 1 2 11.157485 1 2 german 170
171 3 3 2 1 11.156488 1 2 german 171
172 1 2 3 3 11.774043 0 2 german 172
173 3 2 3 2 9.014673 0 2 german 173
174 4 4 1 1 9.755259 1 2 german 174
175 3 3 3 3 7.710862 0 2 german 175
176 1 1 4 4 11.418964 1 2 german 176
177 1 2 4 3 10.702477 0 2 german 177
178 3 2 3 2 11.237905 1 2 german 178
179 3 1 2 2 9.225546 0 2 german 179
180 3 3 2 2 9.673228 0 2 german 180
181 2 3 2 2 9.358897 0 2 german 181
182 3 2 3 3 9.252146 1 2 german 182
183 2 2 3 3 8.616693 0 2 german 183
184 3 4 2 2 10.721936 0 2 german 184
185 3 3 2 2 11.054393 0 2 german 185
186 1 1 4 4 8.094315 1 2 german 186
187 3 3 1 2 9.231779 0 2 german 187
188 3 3 2 2 8.547865 1 2 german 188
189 3 3 3 3 10.345779 0 2 german 189
190 1 2 3 3 13.226910 NA 2 german 190
191 2 2 3 4 10.411992 0 2 german 191
192 3 3 2 3 8.971620 1 2 german 192
193 1 2 4 4 10.898386 0 2 german 193
194 2 2 3 3 10.625394 1 2 german 194
195 2 2 3 4 6.357136 0 2 german 195
196 1 2 3 3 12.102026 1 2 german 196
198 2 3 3 3 6.586317 1 2 german 198
199 3 3 3 2 9.050529 1 2 german 199
200 2 2 3 3 12.723541 1 2 german 200
201 2 2 3 3 9.753327 NA 2 german 201
202 2 2 2 2 13.303115 1 2 german 202
204 3 3 2 2 11.445677 0 2 german 204
205 2 2 2 2 9.306572 0 2 german 205
206 3 1 3 3 8.328087 0 2 german 206
207 3 4 1 2 10.359319 0 2 german 207
209 2 2 3 2 9.748836 1 2 german 209
210 2 2 3 3 11.357677 0 2 german 210
211 3 3 2 2 9.790493 1 2 german 211
212 3 3 3 3 8.325024 0 2 german 212
213 2 2 2 2 9.281008 1 2 german 213
214 3 2 2 2 13.164568 0 2 german 214
215 2 2 2 2 7.828867 0 2 german 215
216 3 3 3 3 10.665805 0 2 german 216
217 2 1 3 3 10.635206 1 2 german 217
218 2 NA 3 2 7.969089 1 2 german 218
219 2 2 2 3 8.923574 0 2 german 219
220 2 2 2 2 7.883175 NA 2 german 220
221 4 NA 2 1 8.577328 1 2 german 221
222 2 NA 4 4 8.257213 1 2 german 222
223 3 3 3 2 10.397873 1 2 german 223
224 4 3 2 2 10.241358 1 2 german 224
225 3 3 2 1 9.874614 1 2 german 225
226 2 3 2 2 9.251216 0 2 german 226
227 3 2 2 2 11.927287 0 2 german 227
228 4 4 2 2 10.327432 0 2 german 228
229 3 2 3 2 6.475004 0 2 german 229
230 2 3 3 3 11.338842 0 2 german 230
232 3 3 2 2 7.933710 0 2 german 232
233 2 3 3 3 10.721250 1 2 german 233
234 3 2 3 3 8.086553 0 2 german 234
235 2 3 3 3 12.252961 NA 2 german 235
236 3 3 2 2 9.883740 NA 2 german 236
237 2 2 2 3 11.260712 0 2 german 237
239 3 NA 2 2 13.991412 1 2 german 239
240 2 3 2 3 9.606693 1 2 german 240
241 2 2 2 2 9.965807 1 2 german 241
242 3 3 2 2 6.665730 NA 2 german 242
243 2 2 2 2 11.588374 1 2 german 243
244 2 1 3 3 12.054486 0 2 german 244
245 2 1 3 2 8.551229 1 2 german 245
246 1 1 4 4 14.758472 1 2 german 246
247 3 3 2 1 10.601619 1 2 german 247
248 2 3 2 3 9.608768 1 2 german 248
249 2 2 3 4 10.631563 0 2 german 249
250 3 4 1 1 9.195155 0 2 german 250
251 3 2 3 3 12.153778 1 2 german 251
252 2 1 4 4 11.594112 1 2 german 252
253 3 3 2 2 12.749052 0 2 german 253
254 2 1 3 3 10.904641 1 2 german 254
255 2 2 3 3 9.674235 NA 2 german 255
256 3 3 1 1 9.382583 1 2 german 256
257 2 2 4 3 10.075463 1 2 german 257
260 3 3 2 2 10.003249 0 2 german 260
261 2 2 3 3 10.357645 0 2 german 261
262 1 2 3 3 11.427154 0 2 german 262
263 1 2 4 3 11.141758 1 2 german 263
264 2 2 3 3 11.717822 0 2 german 264
265 3 3 2 1 8.591719 1 2 german 265
266 4 3 2 2 7.199316 1 2 german 266
267 2 2 3 3 8.511259 NA 2 german 267
268 1 NA 4 4 13.183838 1 2 german 268
269 3 NA 2 2 9.723661 0 2 german 269
270 3 2 1 1 9.901684 1 2 german 270
271 2 2 2 3 11.543191 1 2 german 271
272 3 2 3 2 9.905296 0 2 german 272
273 3 3 2 2 10.804370 0 2 german 273
274 3 3 1 1 9.135004 1 2 german 274
275 3 NA 2 2 9.937281 1 2 german 275
276 2 2 4 4 8.449205 NA 2 german 276
277 4 4 2 2 10.110370 1 2 german 277
278 2 3 3 3 8.910521 1 2 german 278
279 3 3 2 2 11.335096 0 2 german 279
280 2 3 3 3 10.872968 1 2 german 280
281 3 3 2 2 9.550622 0 2 german 281
283 1 NA 3 4 12.176753 0 2 german 283
284 2 2 2 2 9.628909 0 2 german 284
285 2 2 3 3 13.948810 0 2 german 285
286 2 NA 3 3 12.154323 1 2 german 286
287 3 2 2 2 9.487855 1 2 german 287
288 3 3 3 2 10.250928 0 2 german 288
289 3 2 3 3 10.038493 0 2 german 289
290 3 2 2 2 9.110477 NA 2 german 290
291 3 3 2 3 11.294503 1 2 german 291
292 2 2 3 3 12.974758 0 2 german 292
293 3 3 2 2 10.285506 0 2 german 293
294 3 2 3 3 10.156128 1 2 german 294
295 3 4 3 3 10.477511 0 2 german 295
296 2 2 3 4 11.463436 0 2 german 296
297 2 3 2 2 10.958489 0 2 german 297
298 3 2 2 2 8.164545 0 2 german 298
299 2 3 3 3 10.140617 0 2 german 299
300 3 NA 2 2 9.653269 1 2 german 300
302 2 3 2 3 9.384978 1 3 german 302
303 2 2 3 3 7.993749 1 3 german 303
304 2 3 3 3 6.955896 1 3 german 304
305 4 3 2 2 10.535078 0 3 german 305
307 2 2 2 2 9.815517 NA 3 german 307
308 4 4 1 1 10.504347 1 3 german 308
309 2 3 2 2 9.939982 0 3 german 309
310 3 3 3 3 10.389400 1 3 german 310
311 2 2 3 3 9.448678 1 3 german 311
312 2 4 1 2 10.221263 1 3 german 312
313 2 NA 2 2 7.693476 0 3 german 313
314 3 3 2 1 11.177018 1 3 german 314
315 2 2 3 2 9.339305 0 3 german 315
316 3 2 2 2 6.542470 1 3 german 316
317 4 3 1 2 8.953333 1 3 german 317
319 2 1 3 3 7.581036 0 3 german 319
320 3 3 2 1 9.966598 NA 3 german 320
321 3 2 3 3 10.881096 1 3 german 321
322 2 2 4 3 9.503126 1 3 german 322
323 3 3 2 2 10.542913 1 3 german 323
324 2 1 4 3 7.226064 1 3 german 324
326 3 3 1 2 10.473550 1 3 german 326
328 3 3 2 2 10.490264 0 3 german 328
329 3 3 2 1 11.740022 1 3 german 329
330 3 3 2 2 11.473238 0 3 german 330
331 3 3 2 3 12.689666 NA 3 german 331
333 3 3 2 2 7.619042 0 3 german 333
334 3 2 2 2 9.525225 1 3 german 334
335 3 2 2 2 9.301695 1 3 german 335
336 1 1 4 4 10.235494 1 3 german 336
338 3 2 3 3 10.078303 0 3 german 338
340 2 3 2 2 9.613427 0 3 german 340
341 2 3 2 2 10.440469 1 3 german 341
343 2 2 3 3 9.632019 0 3 german 343
344 3 NA 2 1 11.285079 0 3 german 344
345 3 4 2 2 12.780121 NA 3 german 345
346 3 3 2 3 10.358045 0 3 german 346
347 3 3 2 3 9.452587 0 3 german 347
348 2 2 3 3 10.616298 0 3 german 348
349 2 2 3 4 10.531881 1 3 german 349
350 3 2 3 3 10.917065 0 3 german 350
351 3 4 2 2 6.753558 1 3 german 351
352 3 3 1 1 9.081102 1 3 german 352
353 2 2 3 3 9.324282 0 3 german 353
354 3 4 2 2 9.556236 1 3 german 354
355 3 3 2 3 8.404846 1 3 german 355
356 3 3 2 2 9.835171 1 3 german 356
358 3 3 2 2 9.347660 1 3 german 358
359 2 NA 3 3 7.015727 0 3 german 359
360 3 3 2 2 8.146446 0 3 german 360
361 3 3 1 2 7.774709 1 3 german 361
362 3 3 2 2 6.083013 1 3 german 362
363 2 3 3 3 8.618334 1 3 german 363
364 2 NA 3 2 10.029465 0 3 german 364
365 3 NA 2 1 10.539754 1 3 german 365
366 2 3 3 3 11.498595 0 3 german 366
368 3 4 2 2 11.885905 0 3 german 368
369 2 1 3 3 10.151313 0 3 german 369
370 2 3 2 3 8.703436 1 3 german 370
371 1 2 3 4 11.715882 1 3 german 371
372 2 2 3 3 10.497316 NA 3 german 372
373 2 2 3 2 9.260014 0 3 german 373
374 2 1 4 4 12.764876 0 3 german 374
375 2 3 3 3 11.855908 0 3 german 375
376 2 2 3 3 11.346669 1 3 german 376
377 2 3 3 2 10.437059 0 3 german 377
379 3 NA 2 2 8.338840 1 3 german 379
380 3 2 2 3 11.449698 0 3 german 380
382 2 NA 2 2 10.331462 0 3 german 382
383 3 3 2 1 10.240093 1 3 german 383
384 3 3 3 3 12.629684 0 3 german 384
385 3 3 2 2 10.858224 0 3 german 385
387 2 3 3 3 11.439490 NA 3 german 387
388 3 3 2 2 8.881271 1 3 german 388
389 3 NA 3 3 10.293081 1 3 german 389
390 3 3 3 3 10.192251 0 3 german 390
391 3 3 1 2 11.065900 0 3 german 391
392 3 3 2 2 10.998406 1 3 german 392
393 2 2 3 2 8.552346 1 3 german 393
394 4 3 1 2 9.412805 0 3 german 394
395 2 2 3 3 9.894383 NA 3 german 395
398 3 2 3 3 9.242431 1 3 german 398
399 2 2 3 3 10.957836 NA 3 german 399
400 3 3 3 3 10.084902 1 3 german 400
401 3 2 2 2 10.097882 0 3 german 401
402 3 3 1 1 11.672478 0 3 german 402
403 3 4 2 1 13.353267 0 3 german 403
404 1 2 4 4 10.107733 0 3 german 404
405 2 3 3 3 10.847647 0 3 german 405
406 3 3 1 1 8.989002 1 3 german 406
407 4 3 2 2 9.767775 0 3 german 407
408 3 3 3 2 8.104969 1 3 german 408
409 3 3 2 2 11.940227 0 3 german 409
410 2 1 4 3 8.396467 1 3 german 410
411 2 NA 2 3 9.219042 0 3 german 411
412 2 2 2 3 9.927729 1 3 german 412
413 3 3 2 2 9.120070 0 3 german 413
414 3 2 2 2 9.359507 1 3 german 414
415 3 2 3 3 7.340234 0 3 german 415
416 3 3 2 2 8.565667 1 3 german 416
417 3 2 2 3 11.829203 1 3 german 417
418 2 2 2 2 9.444976 1 3 german 418
419 1 1 4 4 8.163760 1 3 german 419
420 1 2 2 3 7.971904 0 3 german 420
422 2 2 3 2 10.435981 1 3 german 422
423 1 2 3 3 11.167197 0 3 german 423
424 4 3 2 2 9.076633 0 3 german 424
425 3 3 2 2 9.734855 0 3 german 425
426 3 3 1 1 9.520808 0 3 german 426
427 2 2 2 2 8.750194 0 3 german 427
428 2 2 3 3 10.203757 1 3 german 428
429 3 3 2 2 8.855370 0 3 german 429
430 3 2 3 2 13.023492 1 3 german 430
433 2 2 3 4 8.328651 1 3 german 433
434 3 2 3 2 9.358613 0 3 german 434
435 4 3 2 2 11.184436 0 3 german 435
436 2 2 4 3 10.608993 NA 3 german 436
437 2 NA 3 3 12.301002 0 3 german 437
439 4 4 1 1 9.609236 0 3 german 439
440 2 3 2 2 9.303808 0 3 german 440
441 3 3 2 2 10.739005 0 3 german 441
443 2 3 3 3 10.028578 1 3 german 443
444 4 4 2 2 10.703093 1 3 german 444
446 3 2 3 3 10.775893 1 3 german 446
447 2 2 3 3 10.714497 0 3 german 447
448 3 2 2 2 9.476227 0 3 german 448
449 4 NA 1 1 7.254288 1 3 german 449
450 2 2 3 2 5.788306 NA 3 german 450
451 3 3 2 2 9.860913 0 3 german 451
452 3 4 1 2 9.406198 1 3 german 452
453 3 3 2 2 7.358570 0 3 german 453
454 3 4 2 2 9.143064 1 3 german 454
455 3 4 2 2 11.833885 0 3 german 455
456 2 2 4 3 10.510292 1 3 german 456
457 4 3 2 2 11.219608 1 3 german 457
458 2 2 3 3 13.257474 0 3 german 458
459 2 2 3 4 7.569693 0 3 german 459
460 2 2 4 3 11.684286 1 3 german 460
461 1 2 4 4 10.056906 NA 3 german 461
462 2 2 3 3 9.203611 1 3 german 462
463 2 2 3 3 11.462454 1 3 german 463
464 3 2 3 3 8.608740 1 3 german 464
465 3 3 3 2 8.550825 0 3 german 465
467 2 NA 3 3 9.467938 1 3 german 467
468 3 3 2 2 9.638242 0 3 german 468
469 4 3 2 2 11.559736 0 3 german 469
470 2 3 2 2 9.289579 1 3 german 470
471 2 2 4 3 10.660731 0 3 german 471
472 1 2 4 4 8.653708 1 3 german 472
473 3 NA 2 3 11.267748 0 3 german 473
474 3 NA 2 2 6.386290 0 3 german 474
475 2 2 2 2 10.394297 0 3 german 475
477 2 1 3 3 11.588138 1 3 german 477
478 3 2 3 3 9.701818 1 3 german 478
479 2 2 3 3 10.131820 0 3 german 479
480 3 2 2 3 11.851048 0 3 german 480
481 2 2 3 3 8.094906 0 3 german 481
482 2 3 2 3 6.522122 0 3 german 482
483 2 NA 4 4 9.492688 1 3 german 483
484 3 2 2 2 7.171610 1 3 german 484
485 4 3 1 2 10.449040 1 3 german 485
486 3 3 2 2 8.565564 1 3 german 486
487 2 3 2 3 9.692163 0 3 german 487
488 2 3 3 2 10.544790 0 3 german 488
489 1 1 4 4 9.152848 1 3 german 489
490 3 NA 2 3 11.417658 0 3 german 490
491 2 NA 3 3 10.335521 1 3 german 491
492 3 4 2 2 12.157326 1 3 german 492
493 2 2 4 3 8.071642 1 3 german 493
496 2 2 2 2 9.657121 1 3 german 496
497 2 2 3 3 8.421820 1 3 german 497
498 2 2 3 2 11.234864 0 3 german 498
499 3 3 3 2 11.684493 1 3 german 499
500 2 NA 3 3 10.551730 1 3 german 500
501 3 2 3 3 9.518572 0 4 german 501
503 3 2 2 2 9.394796 1 4 german 503
504 3 2 2 2 10.871842 0 4 german 504
505 3 3 1 1 11.807168 1 4 german 505
507 2 3 3 3 11.295793 0 4 german 507
508 3 2 3 3 9.704389 1 4 german 508
509 3 3 2 1 12.982000 1 4 german 509
510 3 2 2 3 8.051677 0 4 german 510
511 2 3 3 3 10.134297 1 4 german 511
512 2 3 2 2 10.915921 0 4 german 512
513 4 3 2 3 9.464469 NA 4 german 513
514 3 2 3 3 9.377179 1 4 german 514
515 2 NA 3 3 6.628830 0 4 german 515
516 2 2 3 3 9.799799 0 4 german 516
517 2 2 3 3 9.352013 0 4 german 517
518 3 2 3 3 7.578805 0 4 german 518
519 2 3 3 2 8.955748 0 4 german 519
520 3 3 1 2 7.345759 1 4 german 520
521 2 3 3 2 9.038828 0 4 german 521
522 3 2 3 3 6.462564 0 4 german 522
523 3 3 2 2 8.710578 0 4 german 523
524 2 2 3 3 8.078165 1 4 german 524
525 3 3 3 3 9.308461 0 4 german 525
526 2 3 3 2 9.621561 0 4 german 526
527 2 3 2 2 10.468559 1 4 german 527
528 2 2 4 3 12.527006 0 4 german 528
529 1 1 4 4 9.713401 0 4 german 529
530 2 3 2 3 12.449355 1 4 german 530
531 4 3 1 2 10.171527 1 4 german 531
532 2 2 4 3 11.592511 1 4 german 532
533 2 2 3 3 9.746639 0 4 german 533
534 2 2 2 3 11.539024 0 4 german 534
535 3 4 1 1 12.662389 1 4 german 535
536 3 3 2 2 8.558018 1 4 german 536
537 3 4 1 2 12.490442 0 4 german 537
538 3 4 2 2 8.849825 1 4 german 538
540 2 2 2 3 9.856794 1 4 german 540
542 2 3 2 3 10.455994 0 4 german 542
543 3 3 1 2 8.953355 0 4 german 543
544 3 3 2 2 10.233466 1 4 german 544
545 2 NA 3 2 11.345757 0 4 german 545
546 3 3 2 1 11.511366 0 4 german 546
547 3 3 2 2 7.030045 0 4 german 547
548 2 1 4 4 11.833564 0 4 german 548
549 2 NA 4 3 10.347853 0 4 german 549
550 2 3 2 2 10.669845 1 4 german 550
551 3 3 3 2 11.945497 0 4 german 551
552 3 3 1 1 7.631776 0 4 german 552
553 2 2 3 3 7.929047 0 4 german 553
554 2 3 2 2 10.655136 1 4 german 554
555 4 4 1 1 11.651111 1 4 german 555
556 3 3 2 2 9.499631 0 4 german 556
557 2 3 2 3 9.869376 1 4 german 557
558 3 3 1 2 10.573739 0 4 german 558
559 2 2 3 4 6.777733 0 4 german 559
560 3 3 2 1 10.292540 0 4 german 560
561 3 2 3 2 8.420615 0 4 german 561
564 3 NA 2 2 11.011815 NA 4 german 564
565 3 3 2 2 9.567283 1 4 german 565
566 3 2 2 2 9.700664 1 4 german 566
567 2 2 4 3 9.035493 1 4 german 567
568 2 2 3 3 6.920118 0 4 german 568
569 3 NA 2 2 12.785713 0 4 german 569
571 4 4 1 1 11.561428 0 4 german 571
572 3 2 2 2 10.479614 1 4 german 572
573 3 2 2 3 12.062231 0 4 german 573
574 3 3 3 2 11.258923 1 4 german 574
575 2 2 3 3 8.535113 1 4 german 575
576 3 2 2 2 12.190370 NA 4 german 576
577 3 3 2 2 9.513192 0 4 german 577
581 1 2 3 3 7.840196 1 4 german 581
583 2 3 3 3 11.716523 1 4 german 583
584 2 2 3 3 10.115714 0 4 german 584
585 2 1 2 3 10.487666 1 4 german 585
586 3 3 2 2 10.129045 1 4 german 586
587 4 3 2 2 10.213173 0 4 german 587
588 1 1 3 3 12.114688 1 4 german 588
589 2 2 3 3 11.655211 0 4 german 589
590 1 2 4 4 10.474828 1 4 german 590
591 2 3 3 2 9.222274 0 4 german 591
592 3 3 3 3 10.053230 0 4 german 592
593 2 1 3 3 8.360811 NA 4 german 593
595 3 3 3 2 10.611019 0 4 german 595
596 3 3 2 3 11.952722 NA 4 german 596
598 2 3 3 3 12.403272 0 4 german 598
600 4 3 1 1 30.000000 0 4 german 600
601 3 4 2 2 9.675024 NA 4 german 601
602 3 2 3 3 9.797451 0 4 german 602
604 2 3 3 3 5.697734 1 4 german 604
605 4 3 3 2 11.315240 0 4 german 605
606 3 3 2 2 9.398728 0 4 german 606
608 3 4 2 1 7.712797 0 4 german 608
609 4 3 2 2 11.397924 1 4 german 609
610 2 NA 3 3 10.497554 1 4 german 610
611 2 NA 2 2 9.537385 0 4 german 611
612 2 1 4 3 8.434417 1 4 german 612
613 2 2 3 3 9.741397 1 4 german 613
615 2 NA 2 2 10.524864 1 4 german 615
616 3 3 2 2 8.498451 0 4 german 616
617 1 2 3 3 10.447842 0 4 german 617
618 3 3 3 3 6.916193 0 4 german 618
619 3 2 3 3 7.967729 0 4 german 619
620 2 2 3 3 10.971877 0 4 german 620
621 1 2 3 3 10.036674 1 4 german 621
622 4 4 1 2 11.171656 0 4 german 622
623 2 2 3 3 9.809559 1 4 german 623
624 3 2 2 2 8.970928 0 4 german 624
625 2 2 4 4 10.715082 0 4 german 625
626 3 3 3 2 7.928970 1 4 german 626
627 2 3 3 3 6.909529 1 4 german 627
628 2 3 3 2 10.799244 0 4 german 628
629 3 NA 2 2 6.011167 0 4 german 629
630 2 2 2 1 12.505498 0 4 german 630
631 4 4 1 1 9.779263 1 4 german 631
632 3 3 2 3 9.474470 0 4 german 632
633 2 2 3 3 12.139497 0 4 german 633
634 3 3 3 2 11.067315 0 4 german 634
635 3 4 2 3 8.320202 0 4 german 635
636 3 2 2 2 7.182648 1 4 german 636
637 2 2 4 4 10.130177 0 4 german 637
638 3 3 3 3 12.496180 0 4 german 638
639 2 2 3 3 12.592766 0 4 german 639
640 2 2 2 3 9.907003 0 4 german 640
641 3 2 3 3 11.737818 1 4 german 641
642 3 2 2 1 8.821091 0 4 german 642
643 2 3 3 2 6.860783 0 4 german 643
644 3 2 3 3 10.268927 1 4 german 644
645 3 3 2 2 10.266885 1 4 german 645
646 3 3 2 2 5.763412 0 4 german 646
647 3 2 2 2 7.066767 1 4 german 647
648 3 3 3 2 9.180560 0 4 german 648
649 3 3 2 2 12.972046 1 4 german 649
650 1 NA 2 3 8.906204 1 4 german 650
651 3 3 2 2 11.411023 1 4 ger 651
652 2 2 2 3 10.049973 0 4 ger 652
653 2 2 3 2 11.337266 0 4 ger 653
654 3 3 3 3 13.679508 0 4 ger 654
655 2 3 3 3 8.168574 1 4 ger 655
656 3 3 2 2 12.273152 NA 4 ger 656
657 2 3 3 3 9.269406 0 4 ger 657
659 3 3 2 2 8.932260 1 4 ger 659
660 3 3 1 2 7.922082 0 4 ger 660
661 2 2 3 3 11.825439 0 4 ger 661
663 2 1 3 3 13.418967 0 4 ger 663
664 3 2 3 3 12.795091 0 4 ger 664
665 2 3 3 3 9.076854 0 4 ger 665
666 2 2 2 2 9.791845 1 4 ger 666
667 2 3 2 2 7.201118 0 4 ger 667
669 3 3 3 3 11.357964 1 4 ger 669
670 3 3 2 2 10.881354 0 4 ger 670
671 2 1 3 3 10.379563 0 4 ger 671
673 3 NA 2 2 9.222115 NA 4 ger 673
674 3 3 3 2 8.146615 1 4 ger 674
675 2 3 2 2 9.175585 0 4 ger 675
676 2 2 4 4 12.131668 1 4 ger 676
677 2 NA 2 2 10.870383 0 4 ger 677
678 1 2 3 3 11.473233 1 4 ger 678
679 3 3 2 3 9.506655 1 4 ger 679
680 2 3 2 2 10.030170 0 4 ger 680
681 3 NA 2 2 10.886894 NA 4 ger 681
682 1 1 3 4 10.668823 0 4 ger 682
683 2 3 3 3 9.386863 1 4 ger 683
684 2 2 3 3 7.093356 1 4 ger 684
685 2 2 3 3 12.640182 1 4 ger 685
686 1 2 3 3 8.807493 1 4 ger 686
687 3 3 3 3 14.291764 0 4 ger 687
689 3 3 2 3 5.439760 0 4 ger 689
691 2 2 3 3 8.718412 1 4 ger 691
692 3 3 2 2 8.041810 1 4 ger 692
694 3 2 3 3 10.982985 1 4 ger 694
695 1 2 2 3 10.007353 0 4 ger 695
696 3 2 2 2 8.467930 0 4 ger 696
697 4 4 1 2 10.328330 NA 4 ger 697
698 2 2 3 3 10.826550 1 4 ger 698
699 3 2 2 2 9.543067 1 4 ger 699
700 3 3 1 2 10.025428 1 4 germn 700
701 2 3 3 3 10.668717 1 4 italian 701
702 3 3 3 3 10.518729 1 4 italian 702
703 3 3 2 2 10.808743 1 4 italian 703
704 2 2 2 2 7.725368 1 4 italian 704
705 3 3 2 3 10.498354 1 4 italian 705
707 3 4 2 2 11.808316 1 4 italian 707
708 2 2 4 4 9.354355 0 4 italian 708
709 1 1 4 4 6.893923 0 4 italian 709
710 2 3 2 3 10.715373 1 4 italian 710
712 2 3 2 3 10.579138 1 4 french 712
713 3 2 3 3 7.743470 1 4 french 713
715 3 3 1 2 11.443266 1 4 french 715
716 3 2 3 3 10.739886 0 4 french 716
717 3 2 3 3 9.666964 NA 4 french 717
719 4 3 2 2 9.963483 1 4 french 719
720 3 3 3 3 12.146356 0 4 french 720
722 1 1 4 4 12.437140 0 4 <NA> 722
723 3 3 2 2 9.361663 0 4 <NA> 723
725 4 3 2 2 10.954406 0 4 <NA> 725
726 2 3 3 3 8.813882 1 4 <NA> 726
727 2 2 3 3 8.399134 1 4 <NA> 727
729 3 NA 2 2 10.716499 1 4 <NA> 729
731 2 NA 3 3 9.827114 0 4 <NA> 731
732 3 3 2 2 10.646740 1 4 <NA> 732
733 2 4 2 1 10.350377 0 4 <NA> 733
734 3 3 2 2 10.019486 1 4 <NA> 734
735 3 2 2 2 9.739026 1 4 <NA> 735
736 2 3 3 4 8.826410 0 4 <NA> 736
737 2 2 3 3 8.191695 1 4 <NA> 737
738 2 2 3 3 9.700108 0 4 <NA> 738
739 2 2 3 3 8.267847 0 4 <NA> 739
740 3 4 2 2 8.238960 1 4 <NA> 740
741 3 4 2 3 8.657699 1 4 741
742 2 2 3 3 12.724873 1 4 742
743 1 1 3 3 9.056560 1 4 743
744 2 1 3 3 10.776392 1 4 744
745 3 2 2 3 11.332372 0 4 745
746 3 3 2 2 9.605044 0 4 746
747 3 3 2 2 12.553403 0 4 747
748 3 3 3 2 8.607291 1 4 748
750 3 3 3 2 12.253184 1 4 750For the whole data set:
The na.omit function returns the object with incomplete cases removed.
exDatnoNA <- na.omit(exDat)
exDatnoNA msc1 msc2 msc3 msc4 age sex edu fLang id
1 2 3 2 2 9.815538 0 0 german 1
2 3 2 1 1 8.980194 1 0 german 2
3 2 2 3 3 12.758157 0 0 german 3
4 2 2 3 2 10.578846 0 0 german 4
5 3 2 2 2 9.894364 1 0 german 5
6 3 3 3 2 10.446850 0 0 german 6
7 4 4 1 2 10.897605 1 0 german 7
8 3 3 2 1 7.977382 1 0 german 8
10 3 3 2 2 9.105864 0 0 german 10
11 2 1 3 3 8.540117 0 0 german 11
12 2 2 3 3 11.841954 0 0 german 12
13 2 2 3 4 11.647655 0 0 german 13
14 2 3 3 3 11.745306 0 0 german 14
15 2 3 3 3 9.970630 0 0 german 15
17 2 2 2 2 7.773473 1 0 german 17
19 2 2 3 3 9.339448 0 0 german 19
21 4 3 2 2 9.831947 0 0 german 21
22 2 3 3 3 9.890266 1 0 german 22
23 2 3 2 2 11.501609 0 0 german 23
25 3 3 2 2 11.540030 1 0 german 25
26 2 3 3 3 10.098491 1 0 german 26
27 4 3 2 2 10.851320 1 0 german 27
28 3 2 2 2 6.735228 1 0 german 28
29 3 4 2 2 10.690059 1 0 german 29
30 3 2 2 3 9.746659 1 0 german 30
31 2 1 4 4 8.983450 0 0 german 31
33 2 2 3 2 8.057455 1 0 german 33
34 2 2 3 3 10.134752 0 0 german 34
35 2 3 3 2 8.096160 0 0 german 35
37 3 3 3 3 12.156118 0 0 german 37
38 2 2 3 2 9.600157 0 0 german 38
39 2 3 3 3 11.591400 0 0 german 39
40 3 4 2 2 10.691193 1 0 german 40
41 3 3 3 2 12.622380 0 0 german 41
42 3 2 2 2 10.043774 1 0 german 42
43 4 3 2 2 8.452508 0 0 german 43
44 4 3 2 2 12.029754 1 0 german 44
45 3 2 3 3 8.213722 1 0 german 45
46 2 3 2 3 11.654911 0 0 german 46
47 4 3 1 1 9.086209 1 0 german 47
49 2 3 3 3 9.617718 0 0 german 49
50 2 3 2 2 10.347768 1 0 german 50
51 2 1 3 3 10.735626 0 1 german 51
52 3 3 3 2 10.903281 1 1 german 52
54 2 3 4 3 10.824752 0 1 german 54
55 3 3 3 3 9.282584 0 1 german 55
57 2 3 3 3 8.747505 1 1 german 57
58 2 2 3 4 8.992998 1 1 german 58
59 2 2 3 3 12.632563 1 1 german 59
60 2 2 3 3 11.015445 1 1 german 60
61 3 3 2 2 9.529734 1 1 german 61
62 4 3 1 1 11.569437 1 1 german 62
63 4 3 2 2 11.626011 1 1 german 63
65 2 2 2 3 8.712209 1 1 german 65
66 1 2 3 2 12.271368 0 1 german 66
67 3 3 2 2 11.514336 1 1 german 67
69 2 2 2 2 10.858827 1 1 german 69
70 2 2 3 4 11.416986 0 1 german 70
72 2 3 3 3 9.417725 1 1 german 72
73 4 3 1 2 10.788379 1 1 german 73
74 2 3 2 2 7.527881 1 1 german 74
75 2 2 3 3 7.697320 0 1 german 75
76 3 4 3 2 9.723288 1 1 german 76
78 3 3 3 3 9.552697 1 1 german 78
79 3 3 3 3 10.766935 1 1 german 79
81 2 3 2 2 10.027268 0 1 german 81
82 4 3 2 2 11.108152 1 1 german 82
83 3 3 2 2 9.364853 1 1 german 83
85 4 3 1 1 8.791437 0 1 german 85
86 2 2 3 3 14.699514 1 1 german 86
87 1 2 4 3 9.310963 0 1 german 87
89 3 4 1 2 9.164070 0 1 german 89
90 3 2 3 2 9.904690 0 1 german 90
91 1 2 3 3 9.949049 0 1 german 91
92 1 2 3 2 10.874213 0 1 german 92
93 2 3 3 3 9.589440 0 1 german 93
94 2 2 3 3 8.396720 0 1 german 94
95 1 2 3 3 8.633062 1 1 german 95
96 3 4 2 2 10.174559 1 1 german 96
98 4 3 2 2 6.022669 1 1 german 98
99 3 3 2 2 10.701638 1 1 german 99
101 2 3 3 3 11.536565 0 1 german 101
103 2 2 3 3 7.082311 1 1 german 103
104 3 3 2 3 7.990254 1 1 german 104
105 2 2 3 3 13.223930 1 1 german 105
106 2 3 2 3 8.847114 0 1 german 106
109 2 2 3 2 8.917741 1 1 german 109
110 2 2 3 4 9.059646 0 1 german 110
115 2 2 3 3 8.336241 0 1 german 115
116 3 3 2 2 12.158201 1 1 german 116
118 2 2 4 3 11.067872 1 1 german 118
120 3 2 3 2 9.562434 0 1 german 120
121 3 3 3 2 8.977396 0 1 german 121
122 3 2 3 2 10.162202 0 1 german 122
123 3 3 3 2 9.885336 0 1 german 123
124 3 2 3 3 12.560990 1 1 german 124
125 2 2 4 4 11.087431 1 1 german 125
126 3 3 4 3 10.371456 1 1 german 126
127 3 4 1 2 9.319275 1 1 german 127
128 2 2 2 3 11.008828 1 1 german 128
129 1 2 3 2 9.656535 0 1 german 129
130 1 2 3 4 10.032404 0 1 german 130
131 2 2 3 3 6.974716 1 1 german 131
132 3 2 2 3 9.168019 1 1 german 132
133 2 1 3 4 10.658201 1 1 german 133
134 3 2 2 2 10.723573 1 1 german 134
135 1 2 4 3 9.564175 1 1 german 135
136 2 3 3 2 9.440463 1 1 german 136
137 1 2 3 4 8.574188 1 1 german 137
138 2 2 3 3 8.336412 0 1 german 138
139 3 2 2 2 11.316498 0 1 german 139
140 4 3 2 2 10.089194 1 1 german 140
141 3 3 2 2 11.326115 0 1 german 141
142 2 3 2 2 10.544879 1 1 german 142
143 4 3 2 2 8.278763 0 1 german 143
144 3 3 2 2 9.165442 0 1 german 144
145 3 4 2 2 14.840083 0 1 german 145
146 4 4 1 1 9.177470 0 1 german 146
147 2 3 1 1 10.392509 1 1 german 147
148 3 3 2 3 10.074265 1 1 german 148
151 2 2 3 3 11.303224 0 2 german 151
152 3 3 2 2 8.301101 0 2 german 152
153 3 4 2 2 11.552966 1 2 german 153
154 3 3 2 2 10.420355 1 2 german 154
155 3 4 2 2 12.648300 1 2 german 155
156 2 2 3 2 9.985757 1 2 german 156
157 2 3 2 2 10.504636 0 2 german 157
159 2 2 3 3 9.576623 0 2 german 159
160 3 3 2 2 12.855773 1 2 german 160
161 2 1 3 4 10.131071 0 2 german 161
162 2 3 3 3 11.018442 1 2 german 162
163 3 3 2 2 10.910317 1 2 german 163
165 3 2 3 3 10.402108 1 2 german 165
166 2 2 3 2 11.020302 0 2 german 166
167 2 2 3 3 10.862003 1 2 german 167
171 3 3 2 1 11.156488 1 2 german 171
172 1 2 3 3 11.774043 0 2 german 172
173 3 2 3 2 9.014673 0 2 german 173
174 4 4 1 1 9.755259 1 2 german 174
175 3 3 3 3 7.710862 0 2 german 175
176 1 1 4 4 11.418964 1 2 german 176
177 1 2 4 3 10.702477 0 2 german 177
178 3 2 3 2 11.237905 1 2 german 178
179 3 1 2 2 9.225546 0 2 german 179
180 3 3 2 2 9.673228 0 2 german 180
181 2 3 2 2 9.358897 0 2 german 181
182 3 2 3 3 9.252146 1 2 german 182
183 2 2 3 3 8.616693 0 2 german 183
184 3 4 2 2 10.721936 0 2 german 184
185 3 3 2 2 11.054393 0 2 german 185
186 1 1 4 4 8.094315 1 2 german 186
187 3 3 1 2 9.231779 0 2 german 187
188 3 3 2 2 8.547865 1 2 german 188
189 3 3 3 3 10.345779 0 2 german 189
191 2 2 3 4 10.411992 0 2 german 191
192 3 3 2 3 8.971620 1 2 german 192
193 1 2 4 4 10.898386 0 2 german 193
194 2 2 3 3 10.625394 1 2 german 194
195 2 2 3 4 6.357136 0 2 german 195
196 1 2 3 3 12.102026 1 2 german 196
198 2 3 3 3 6.586317 1 2 german 198
199 3 3 3 2 9.050529 1 2 german 199
200 2 2 3 3 12.723541 1 2 german 200
202 2 2 2 2 13.303115 1 2 german 202
204 3 3 2 2 11.445677 0 2 german 204
205 2 2 2 2 9.306572 0 2 german 205
206 3 1 3 3 8.328087 0 2 german 206
207 3 4 1 2 10.359319 0 2 german 207
209 2 2 3 2 9.748836 1 2 german 209
210 2 2 3 3 11.357677 0 2 german 210
211 3 3 2 2 9.790493 1 2 german 211
212 3 3 3 3 8.325024 0 2 german 212
213 2 2 2 2 9.281008 1 2 german 213
214 3 2 2 2 13.164568 0 2 german 214
215 2 2 2 2 7.828867 0 2 german 215
216 3 3 3 3 10.665805 0 2 german 216
217 2 1 3 3 10.635206 1 2 german 217
219 2 2 2 3 8.923574 0 2 german 219
223 3 3 3 2 10.397873 1 2 german 223
224 4 3 2 2 10.241358 1 2 german 224
225 3 3 2 1 9.874614 1 2 german 225
226 2 3 2 2 9.251216 0 2 german 226
227 3 2 2 2 11.927287 0 2 german 227
228 4 4 2 2 10.327432 0 2 german 228
229 3 2 3 2 6.475004 0 2 german 229
230 2 3 3 3 11.338842 0 2 german 230
232 3 3 2 2 7.933710 0 2 german 232
233 2 3 3 3 10.721250 1 2 german 233
234 3 2 3 3 8.086553 0 2 german 234
237 2 2 2 3 11.260712 0 2 german 237
240 2 3 2 3 9.606693 1 2 german 240
241 2 2 2 2 9.965807 1 2 german 241
243 2 2 2 2 11.588374 1 2 german 243
244 2 1 3 3 12.054486 0 2 german 244
245 2 1 3 2 8.551229 1 2 german 245
246 1 1 4 4 14.758472 1 2 german 246
247 3 3 2 1 10.601619 1 2 german 247
248 2 3 2 3 9.608768 1 2 german 248
249 2 2 3 4 10.631563 0 2 german 249
250 3 4 1 1 9.195155 0 2 german 250
251 3 2 3 3 12.153778 1 2 german 251
252 2 1 4 4 11.594112 1 2 german 252
253 3 3 2 2 12.749052 0 2 german 253
254 2 1 3 3 10.904641 1 2 german 254
256 3 3 1 1 9.382583 1 2 german 256
257 2 2 4 3 10.075463 1 2 german 257
260 3 3 2 2 10.003249 0 2 german 260
261 2 2 3 3 10.357645 0 2 german 261
262 1 2 3 3 11.427154 0 2 german 262
263 1 2 4 3 11.141758 1 2 german 263
264 2 2 3 3 11.717822 0 2 german 264
265 3 3 2 1 8.591719 1 2 german 265
266 4 3 2 2 7.199316 1 2 german 266
270 3 2 1 1 9.901684 1 2 german 270
271 2 2 2 3 11.543191 1 2 german 271
272 3 2 3 2 9.905296 0 2 german 272
273 3 3 2 2 10.804370 0 2 german 273
274 3 3 1 1 9.135004 1 2 german 274
277 4 4 2 2 10.110370 1 2 german 277
278 2 3 3 3 8.910521 1 2 german 278
279 3 3 2 2 11.335096 0 2 german 279
280 2 3 3 3 10.872968 1 2 german 280
281 3 3 2 2 9.550622 0 2 german 281
284 2 2 2 2 9.628909 0 2 german 284
285 2 2 3 3 13.948810 0 2 german 285
287 3 2 2 2 9.487855 1 2 german 287
288 3 3 3 2 10.250928 0 2 german 288
289 3 2 3 3 10.038493 0 2 german 289
291 3 3 2 3 11.294503 1 2 german 291
292 2 2 3 3 12.974758 0 2 german 292
293 3 3 2 2 10.285506 0 2 german 293
294 3 2 3 3 10.156128 1 2 german 294
295 3 4 3 3 10.477511 0 2 german 295
296 2 2 3 4 11.463436 0 2 german 296
297 2 3 2 2 10.958489 0 2 german 297
298 3 2 2 2 8.164545 0 2 german 298
299 2 3 3 3 10.140617 0 2 german 299
302 2 3 2 3 9.384978 1 3 german 302
303 2 2 3 3 7.993749 1 3 german 303
304 2 3 3 3 6.955896 1 3 german 304
305 4 3 2 2 10.535078 0 3 german 305
308 4 4 1 1 10.504347 1 3 german 308
309 2 3 2 2 9.939982 0 3 german 309
310 3 3 3 3 10.389400 1 3 german 310
311 2 2 3 3 9.448678 1 3 german 311
312 2 4 1 2 10.221263 1 3 german 312
314 3 3 2 1 11.177018 1 3 german 314
315 2 2 3 2 9.339305 0 3 german 315
316 3 2 2 2 6.542470 1 3 german 316
317 4 3 1 2 8.953333 1 3 german 317
319 2 1 3 3 7.581036 0 3 german 319
321 3 2 3 3 10.881096 1 3 german 321
322 2 2 4 3 9.503126 1 3 german 322
323 3 3 2 2 10.542913 1 3 german 323
324 2 1 4 3 7.226064 1 3 german 324
326 3 3 1 2 10.473550 1 3 german 326
328 3 3 2 2 10.490264 0 3 german 328
329 3 3 2 1 11.740022 1 3 german 329
330 3 3 2 2 11.473238 0 3 german 330
333 3 3 2 2 7.619042 0 3 german 333
334 3 2 2 2 9.525225 1 3 german 334
335 3 2 2 2 9.301695 1 3 german 335
336 1 1 4 4 10.235494 1 3 german 336
338 3 2 3 3 10.078303 0 3 german 338
340 2 3 2 2 9.613427 0 3 german 340
341 2 3 2 2 10.440469 1 3 german 341
343 2 2 3 3 9.632019 0 3 german 343
346 3 3 2 3 10.358045 0 3 german 346
347 3 3 2 3 9.452587 0 3 german 347
348 2 2 3 3 10.616298 0 3 german 348
349 2 2 3 4 10.531881 1 3 german 349
350 3 2 3 3 10.917065 0 3 german 350
351 3 4 2 2 6.753558 1 3 german 351
352 3 3 1 1 9.081102 1 3 german 352
353 2 2 3 3 9.324282 0 3 german 353
354 3 4 2 2 9.556236 1 3 german 354
355 3 3 2 3 8.404846 1 3 german 355
356 3 3 2 2 9.835171 1 3 german 356
358 3 3 2 2 9.347660 1 3 german 358
360 3 3 2 2 8.146446 0 3 german 360
361 3 3 1 2 7.774709 1 3 german 361
362 3 3 2 2 6.083013 1 3 german 362
363 2 3 3 3 8.618334 1 3 german 363
366 2 3 3 3 11.498595 0 3 german 366
368 3 4 2 2 11.885905 0 3 german 368
369 2 1 3 3 10.151313 0 3 german 369
370 2 3 2 3 8.703436 1 3 german 370
371 1 2 3 4 11.715882 1 3 german 371
373 2 2 3 2 9.260014 0 3 german 373
374 2 1 4 4 12.764876 0 3 german 374
375 2 3 3 3 11.855908 0 3 german 375
376 2 2 3 3 11.346669 1 3 german 376
377 2 3 3 2 10.437059 0 3 german 377
380 3 2 2 3 11.449698 0 3 german 380
383 3 3 2 1 10.240093 1 3 german 383
384 3 3 3 3 12.629684 0 3 german 384
385 3 3 2 2 10.858224 0 3 german 385
388 3 3 2 2 8.881271 1 3 german 388
390 3 3 3 3 10.192251 0 3 german 390
391 3 3 1 2 11.065900 0 3 german 391
392 3 3 2 2 10.998406 1 3 german 392
393 2 2 3 2 8.552346 1 3 german 393
394 4 3 1 2 9.412805 0 3 german 394
398 3 2 3 3 9.242431 1 3 german 398
400 3 3 3 3 10.084902 1 3 german 400
401 3 2 2 2 10.097882 0 3 german 401
402 3 3 1 1 11.672478 0 3 german 402
403 3 4 2 1 13.353267 0 3 german 403
404 1 2 4 4 10.107733 0 3 german 404
405 2 3 3 3 10.847647 0 3 german 405
406 3 3 1 1 8.989002 1 3 german 406
407 4 3 2 2 9.767775 0 3 german 407
408 3 3 3 2 8.104969 1 3 german 408
409 3 3 2 2 11.940227 0 3 german 409
410 2 1 4 3 8.396467 1 3 german 410
412 2 2 2 3 9.927729 1 3 german 412
413 3 3 2 2 9.120070 0 3 german 413
414 3 2 2 2 9.359507 1 3 german 414
415 3 2 3 3 7.340234 0 3 german 415
416 3 3 2 2 8.565667 1 3 german 416
417 3 2 2 3 11.829203 1 3 german 417
418 2 2 2 2 9.444976 1 3 german 418
419 1 1 4 4 8.163760 1 3 german 419
420 1 2 2 3 7.971904 0 3 german 420
422 2 2 3 2 10.435981 1 3 german 422
423 1 2 3 3 11.167197 0 3 german 423
424 4 3 2 2 9.076633 0 3 german 424
425 3 3 2 2 9.734855 0 3 german 425
426 3 3 1 1 9.520808 0 3 german 426
427 2 2 2 2 8.750194 0 3 german 427
428 2 2 3 3 10.203757 1 3 german 428
429 3 3 2 2 8.855370 0 3 german 429
430 3 2 3 2 13.023492 1 3 german 430
433 2 2 3 4 8.328651 1 3 german 433
434 3 2 3 2 9.358613 0 3 german 434
435 4 3 2 2 11.184436 0 3 german 435
439 4 4 1 1 9.609236 0 3 german 439
440 2 3 2 2 9.303808 0 3 german 440
441 3 3 2 2 10.739005 0 3 german 441
443 2 3 3 3 10.028578 1 3 german 443
444 4 4 2 2 10.703093 1 3 german 444
446 3 2 3 3 10.775893 1 3 german 446
447 2 2 3 3 10.714497 0 3 german 447
448 3 2 2 2 9.476227 0 3 german 448
451 3 3 2 2 9.860913 0 3 german 451
452 3 4 1 2 9.406198 1 3 german 452
453 3 3 2 2 7.358570 0 3 german 453
454 3 4 2 2 9.143064 1 3 german 454
455 3 4 2 2 11.833885 0 3 german 455
456 2 2 4 3 10.510292 1 3 german 456
457 4 3 2 2 11.219608 1 3 german 457
458 2 2 3 3 13.257474 0 3 german 458
459 2 2 3 4 7.569693 0 3 german 459
460 2 2 4 3 11.684286 1 3 german 460
462 2 2 3 3 9.203611 1 3 german 462
463 2 2 3 3 11.462454 1 3 german 463
464 3 2 3 3 8.608740 1 3 german 464
465 3 3 3 2 8.550825 0 3 german 465
468 3 3 2 2 9.638242 0 3 german 468
469 4 3 2 2 11.559736 0 3 german 469
470 2 3 2 2 9.289579 1 3 german 470
471 2 2 4 3 10.660731 0 3 german 471
472 1 2 4 4 8.653708 1 3 german 472
475 2 2 2 2 10.394297 0 3 german 475
477 2 1 3 3 11.588138 1 3 german 477
478 3 2 3 3 9.701818 1 3 german 478
479 2 2 3 3 10.131820 0 3 german 479
480 3 2 2 3 11.851048 0 3 german 480
481 2 2 3 3 8.094906 0 3 german 481
482 2 3 2 3 6.522122 0 3 german 482
484 3 2 2 2 7.171610 1 3 german 484
485 4 3 1 2 10.449040 1 3 german 485
486 3 3 2 2 8.565564 1 3 german 486
487 2 3 2 3 9.692163 0 3 german 487
488 2 3 3 2 10.544790 0 3 german 488
489 1 1 4 4 9.152848 1 3 german 489
492 3 4 2 2 12.157326 1 3 german 492
493 2 2 4 3 8.071642 1 3 german 493
496 2 2 2 2 9.657121 1 3 german 496
497 2 2 3 3 8.421820 1 3 german 497
498 2 2 3 2 11.234864 0 3 german 498
499 3 3 3 2 11.684493 1 3 german 499
501 3 2 3 3 9.518572 0 4 german 501
503 3 2 2 2 9.394796 1 4 german 503
504 3 2 2 2 10.871842 0 4 german 504
505 3 3 1 1 11.807168 1 4 german 505
507 2 3 3 3 11.295793 0 4 german 507
508 3 2 3 3 9.704389 1 4 german 508
509 3 3 2 1 12.982000 1 4 german 509
510 3 2 2 3 8.051677 0 4 german 510
511 2 3 3 3 10.134297 1 4 german 511
512 2 3 2 2 10.915921 0 4 german 512
514 3 2 3 3 9.377179 1 4 german 514
516 2 2 3 3 9.799799 0 4 german 516
517 2 2 3 3 9.352013 0 4 german 517
518 3 2 3 3 7.578805 0 4 german 518
519 2 3 3 2 8.955748 0 4 german 519
520 3 3 1 2 7.345759 1 4 german 520
521 2 3 3 2 9.038828 0 4 german 521
522 3 2 3 3 6.462564 0 4 german 522
523 3 3 2 2 8.710578 0 4 german 523
524 2 2 3 3 8.078165 1 4 german 524
525 3 3 3 3 9.308461 0 4 german 525
526 2 3 3 2 9.621561 0 4 german 526
527 2 3 2 2 10.468559 1 4 german 527
528 2 2 4 3 12.527006 0 4 german 528
529 1 1 4 4 9.713401 0 4 german 529
530 2 3 2 3 12.449355 1 4 german 530
531 4 3 1 2 10.171527 1 4 german 531
532 2 2 4 3 11.592511 1 4 german 532
533 2 2 3 3 9.746639 0 4 german 533
534 2 2 2 3 11.539024 0 4 german 534
535 3 4 1 1 12.662389 1 4 german 535
536 3 3 2 2 8.558018 1 4 german 536
537 3 4 1 2 12.490442 0 4 german 537
538 3 4 2 2 8.849825 1 4 german 538
540 2 2 2 3 9.856794 1 4 german 540
542 2 3 2 3 10.455994 0 4 german 542
543 3 3 1 2 8.953355 0 4 german 543
544 3 3 2 2 10.233466 1 4 german 544
546 3 3 2 1 11.511366 0 4 german 546
547 3 3 2 2 7.030045 0 4 german 547
548 2 1 4 4 11.833564 0 4 german 548
550 2 3 2 2 10.669845 1 4 german 550
551 3 3 3 2 11.945497 0 4 german 551
552 3 3 1 1 7.631776 0 4 german 552
553 2 2 3 3 7.929047 0 4 german 553
554 2 3 2 2 10.655136 1 4 german 554
555 4 4 1 1 11.651111 1 4 german 555
556 3 3 2 2 9.499631 0 4 german 556
557 2 3 2 3 9.869376 1 4 german 557
558 3 3 1 2 10.573739 0 4 german 558
559 2 2 3 4 6.777733 0 4 german 559
560 3 3 2 1 10.292540 0 4 german 560
561 3 2 3 2 8.420615 0 4 german 561
565 3 3 2 2 9.567283 1 4 german 565
566 3 2 2 2 9.700664 1 4 german 566
567 2 2 4 3 9.035493 1 4 german 567
568 2 2 3 3 6.920118 0 4 german 568
571 4 4 1 1 11.561428 0 4 german 571
572 3 2 2 2 10.479614 1 4 german 572
573 3 2 2 3 12.062231 0 4 german 573
574 3 3 3 2 11.258923 1 4 german 574
575 2 2 3 3 8.535113 1 4 german 575
577 3 3 2 2 9.513192 0 4 german 577
581 1 2 3 3 7.840196 1 4 german 581
583 2 3 3 3 11.716523 1 4 german 583
584 2 2 3 3 10.115714 0 4 german 584
585 2 1 2 3 10.487666 1 4 german 585
586 3 3 2 2 10.129045 1 4 german 586
587 4 3 2 2 10.213173 0 4 german 587
588 1 1 3 3 12.114688 1 4 german 588
589 2 2 3 3 11.655211 0 4 german 589
590 1 2 4 4 10.474828 1 4 german 590
591 2 3 3 2 9.222274 0 4 german 591
592 3 3 3 3 10.053230 0 4 german 592
595 3 3 3 2 10.611019 0 4 german 595
598 2 3 3 3 12.403272 0 4 german 598
600 4 3 1 1 30.000000 0 4 german 600
602 3 2 3 3 9.797451 0 4 german 602
604 2 3 3 3 5.697734 1 4 german 604
605 4 3 3 2 11.315240 0 4 german 605
606 3 3 2 2 9.398728 0 4 german 606
608 3 4 2 1 7.712797 0 4 german 608
609 4 3 2 2 11.397924 1 4 german 609
612 2 1 4 3 8.434417 1 4 german 612
613 2 2 3 3 9.741397 1 4 german 613
616 3 3 2 2 8.498451 0 4 german 616
617 1 2 3 3 10.447842 0 4 german 617
618 3 3 3 3 6.916193 0 4 german 618
619 3 2 3 3 7.967729 0 4 german 619
620 2 2 3 3 10.971877 0 4 german 620
621 1 2 3 3 10.036674 1 4 german 621
622 4 4 1 2 11.171656 0 4 german 622
623 2 2 3 3 9.809559 1 4 german 623
624 3 2 2 2 8.970928 0 4 german 624
625 2 2 4 4 10.715082 0 4 german 625
626 3 3 3 2 7.928970 1 4 german 626
627 2 3 3 3 6.909529 1 4 german 627
628 2 3 3 2 10.799244 0 4 german 628
630 2 2 2 1 12.505498 0 4 german 630
631 4 4 1 1 9.779263 1 4 german 631
632 3 3 2 3 9.474470 0 4 german 632
633 2 2 3 3 12.139497 0 4 german 633
634 3 3 3 2 11.067315 0 4 german 634
635 3 4 2 3 8.320202 0 4 german 635
636 3 2 2 2 7.182648 1 4 german 636
637 2 2 4 4 10.130177 0 4 german 637
638 3 3 3 3 12.496180 0 4 german 638
639 2 2 3 3 12.592766 0 4 german 639
640 2 2 2 3 9.907003 0 4 german 640
641 3 2 3 3 11.737818 1 4 german 641
642 3 2 2 1 8.821091 0 4 german 642
643 2 3 3 2 6.860783 0 4 german 643
644 3 2 3 3 10.268927 1 4 german 644
645 3 3 2 2 10.266885 1 4 german 645
646 3 3 2 2 5.763412 0 4 german 646
647 3 2 2 2 7.066767 1 4 german 647
648 3 3 3 2 9.180560 0 4 german 648
649 3 3 2 2 12.972046 1 4 german 649
651 3 3 2 2 11.411023 1 4 ger 651
652 2 2 2 3 10.049973 0 4 ger 652
653 2 2 3 2 11.337266 0 4 ger 653
654 3 3 3 3 13.679508 0 4 ger 654
655 2 3 3 3 8.168574 1 4 ger 655
657 2 3 3 3 9.269406 0 4 ger 657
659 3 3 2 2 8.932260 1 4 ger 659
660 3 3 1 2 7.922082 0 4 ger 660
661 2 2 3 3 11.825439 0 4 ger 661
663 2 1 3 3 13.418967 0 4 ger 663
664 3 2 3 3 12.795091 0 4 ger 664
665 2 3 3 3 9.076854 0 4 ger 665
666 2 2 2 2 9.791845 1 4 ger 666
667 2 3 2 2 7.201118 0 4 ger 667
669 3 3 3 3 11.357964 1 4 ger 669
670 3 3 2 2 10.881354 0 4 ger 670
671 2 1 3 3 10.379563 0 4 ger 671
674 3 3 3 2 8.146615 1 4 ger 674
675 2 3 2 2 9.175585 0 4 ger 675
676 2 2 4 4 12.131668 1 4 ger 676
678 1 2 3 3 11.473233 1 4 ger 678
679 3 3 2 3 9.506655 1 4 ger 679
680 2 3 2 2 10.030170 0 4 ger 680
682 1 1 3 4 10.668823 0 4 ger 682
683 2 3 3 3 9.386863 1 4 ger 683
684 2 2 3 3 7.093356 1 4 ger 684
685 2 2 3 3 12.640182 1 4 ger 685
686 1 2 3 3 8.807493 1 4 ger 686
687 3 3 3 3 14.291764 0 4 ger 687
689 3 3 2 3 5.439760 0 4 ger 689
691 2 2 3 3 8.718412 1 4 ger 691
692 3 3 2 2 8.041810 1 4 ger 692
694 3 2 3 3 10.982985 1 4 ger 694
695 1 2 2 3 10.007353 0 4 ger 695
696 3 2 2 2 8.467930 0 4 ger 696
698 2 2 3 3 10.826550 1 4 ger 698
699 3 2 2 2 9.543067 1 4 ger 699
700 3 3 1 2 10.025428 1 4 germn 700
701 2 3 3 3 10.668717 1 4 italian 701
702 3 3 3 3 10.518729 1 4 italian 702
703 3 3 2 2 10.808743 1 4 italian 703
704 2 2 2 2 7.725368 1 4 italian 704
705 3 3 2 3 10.498354 1 4 italian 705
707 3 4 2 2 11.808316 1 4 italian 707
708 2 2 4 4 9.354355 0 4 italian 708
709 1 1 4 4 6.893923 0 4 italian 709
710 2 3 2 3 10.715373 1 4 italian 710
712 2 3 2 3 10.579138 1 4 french 712
713 3 2 3 3 7.743470 1 4 french 713
715 3 3 1 2 11.443266 1 4 french 715
716 3 2 3 3 10.739886 0 4 french 716
719 4 3 2 2 9.963483 1 4 french 719
720 3 3 3 3 12.146356 0 4 french 720
741 3 4 2 3 8.657699 1 4 741
742 2 2 3 3 12.724873 1 4 742
743 1 1 3 3 9.056560 1 4 743
744 2 1 3 3 10.776392 1 4 744
745 3 2 2 3 11.332372 0 4 745
746 3 3 2 2 9.605044 0 4 746
747 3 3 2 2 12.553403 0 4 747
748 3 3 3 2 8.607291 1 4 748
750 3 3 3 2 12.253184 1 4 750To exclude observations that do have missing values, we may use the filter function from the dplyr package.
For a single variable:
msc1 msc2 msc3 msc4 age sex edu fLang id
1 2 3 2 2 9.815538 0 0 german 1
2 3 2 1 1 8.980194 1 0 german 2
3 2 2 3 3 12.758157 0 0 german 3
4 2 2 3 2 10.578846 0 0 german 4
5 3 2 2 2 9.894364 1 0 german 5
6 3 3 3 2 10.446850 0 0 german 6
7 4 4 1 2 10.897605 1 0 german 7
8 3 3 2 1 7.977382 1 0 german 8
9 3 3 2 1 10.688379 NA 0 german 9
10 3 3 2 2 9.105864 0 0 german 10
11 2 1 3 3 8.540117 0 0 german 11
12 2 2 3 3 11.841954 0 0 german 12
13 2 2 3 4 11.647655 0 0 german 13
14 2 3 3 3 11.745306 0 0 german 14
15 2 3 3 3 9.970630 0 0 german 15
16 3 3 2 1 8.186501 NA 0 german 16
17 2 2 2 2 7.773473 1 0 german 17
18 2 2 3 3 9.339448 0 0 german 19
19 4 3 2 2 9.831947 0 0 german 21
20 2 3 3 3 9.890266 1 0 german 22
21 2 3 2 2 11.501609 0 0 german 23
22 2 NA 3 2 9.341404 0 0 german 24
23 3 3 2 2 11.540030 1 0 german 25
24 2 3 3 3 10.098491 1 0 german 26
25 4 3 2 2 10.851320 1 0 german 27
26 3 2 2 2 6.735228 1 0 german 28
27 3 4 2 2 10.690059 1 0 german 29
28 3 2 2 3 9.746659 1 0 german 30
29 2 1 4 4 8.983450 0 0 german 31
30 2 NA 3 3 11.557894 0 0 german 32
31 2 2 3 2 8.057455 1 0 german 33
32 2 2 3 3 10.134752 0 0 german 34
33 2 3 3 2 8.096160 0 0 german 35
34 4 3 1 1 9.404151 NA 0 german 36
35 3 3 3 3 12.156118 0 0 german 37
36 2 2 3 2 9.600157 0 0 german 38
37 2 3 3 3 11.591400 0 0 german 39
38 3 4 2 2 10.691193 1 0 german 40
39 3 3 3 2 12.622380 0 0 german 41
40 3 2 2 2 10.043774 1 0 german 42
41 4 3 2 2 8.452508 0 0 german 43
42 4 3 2 2 12.029754 1 0 german 44
43 3 2 3 3 8.213722 1 0 german 45
44 2 3 2 3 11.654911 0 0 german 46
45 4 3 1 1 9.086209 1 0 german 47
46 3 NA 3 3 9.535470 1 0 german 48
47 2 3 3 3 9.617718 0 0 german 49
48 2 3 2 2 10.347768 1 0 german 50
49 2 1 3 3 10.735626 0 1 german 51
50 3 3 3 2 10.903281 1 1 german 52
51 2 NA 3 3 10.265831 1 1 german 53
52 2 3 4 3 10.824752 0 1 german 54
53 3 3 3 3 9.282584 0 1 german 55
54 2 3 3 3 8.747505 1 1 german 57
55 2 2 3 4 8.992998 1 1 german 58
56 2 2 3 3 12.632563 1 1 german 59
57 2 2 3 3 11.015445 1 1 german 60
58 3 3 2 2 9.529734 1 1 german 61
59 4 3 1 1 11.569437 1 1 german 62
60 4 3 2 2 11.626011 1 1 german 63
61 2 NA 3 3 12.371110 0 1 german 64
62 2 2 2 3 8.712209 1 1 german 65
63 1 2 3 2 12.271368 0 1 german 66
64 3 3 2 2 11.514336 1 1 german 67
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426 2 3 2 2 9.289579 1 3 german 470
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440 4 3 1 2 10.449040 1 3 german 485
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449 2 2 2 2 9.657121 1 3 german 496
450 2 2 3 3 8.421820 1 3 german 497
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458 2 3 3 3 11.295793 0 4 german 507
459 3 2 3 3 9.704389 1 4 german 508
460 3 3 2 1 12.982000 1 4 german 509
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490 2 2 2 3 9.856794 1 4 german 540
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538 2 3 3 3 12.403272 0 4 german 598
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553 1 2 3 3 10.447842 0 4 german 617
554 3 3 3 3 6.916193 0 4 german 618
555 3 2 3 3 7.967729 0 4 german 619
556 2 2 3 3 10.971877 0 4 german 620
557 1 2 3 3 10.036674 1 4 german 621
558 4 4 1 2 11.171656 0 4 german 622
559 2 2 3 3 9.809559 1 4 german 623
560 3 2 2 2 8.970928 0 4 german 624
561 2 2 4 4 10.715082 0 4 german 625
562 3 3 3 2 7.928970 1 4 german 626
563 2 3 3 3 6.909529 1 4 german 627
564 2 3 3 2 10.799244 0 4 german 628
565 3 NA 2 2 6.011167 0 4 german 629
566 2 2 2 1 12.505498 0 4 german 630
567 4 4 1 1 9.779263 1 4 german 631
568 3 3 2 3 9.474470 0 4 german 632
569 2 2 3 3 12.139497 0 4 german 633
570 3 3 3 2 11.067315 0 4 german 634
571 3 4 2 3 8.320202 0 4 german 635
572 3 2 2 2 7.182648 1 4 german 636
573 2 2 4 4 10.130177 0 4 german 637
574 3 3 3 3 12.496180 0 4 german 638
575 2 2 3 3 12.592766 0 4 german 639
576 2 2 2 3 9.907003 0 4 german 640
577 3 2 3 3 11.737818 1 4 german 641
578 3 2 2 1 8.821091 0 4 german 642
579 2 3 3 2 6.860783 0 4 german 643
580 3 2 3 3 10.268927 1 4 german 644
581 3 3 2 2 10.266885 1 4 german 645
582 3 3 2 2 5.763412 0 4 german 646
583 3 2 2 2 7.066767 1 4 german 647
584 3 3 3 2 9.180560 0 4 german 648
585 3 3 2 2 12.972046 1 4 german 649
586 1 NA 2 3 8.906204 1 4 german 650
587 3 3 2 2 11.411023 1 4 ger 651
588 2 2 2 3 10.049973 0 4 ger 652
589 2 2 3 2 11.337266 0 4 ger 653
590 3 3 3 3 13.679508 0 4 ger 654
591 2 3 3 3 8.168574 1 4 ger 655
592 3 3 2 2 12.273152 NA 4 ger 656
593 2 3 3 3 9.269406 0 4 ger 657
594 3 3 2 2 8.932260 1 4 ger 659
595 3 3 1 2 7.922082 0 4 ger 660
596 2 2 3 3 11.825439 0 4 ger 661
597 2 1 3 3 13.418967 0 4 ger 663
598 3 2 3 3 12.795091 0 4 ger 664
599 2 3 3 3 9.076854 0 4 ger 665
600 2 2 2 2 9.791845 1 4 ger 666
601 2 3 2 2 7.201118 0 4 ger 667
602 3 3 3 3 11.357964 1 4 ger 669
603 3 3 2 2 10.881354 0 4 ger 670
604 2 1 3 3 10.379563 0 4 ger 671
605 3 NA 2 2 9.222115 NA 4 ger 673
606 3 3 3 2 8.146615 1 4 ger 674
607 2 3 2 2 9.175585 0 4 ger 675
608 2 2 4 4 12.131668 1 4 ger 676
609 2 NA 2 2 10.870383 0 4 ger 677
610 1 2 3 3 11.473233 1 4 ger 678
611 3 3 2 3 9.506655 1 4 ger 679
612 2 3 2 2 10.030170 0 4 ger 680
613 3 NA 2 2 10.886894 NA 4 ger 681
614 1 1 3 4 10.668823 0 4 ger 682
615 2 3 3 3 9.386863 1 4 ger 683
616 2 2 3 3 7.093356 1 4 ger 684
617 2 2 3 3 12.640182 1 4 ger 685
618 1 2 3 3 8.807493 1 4 ger 686
619 3 3 3 3 14.291764 0 4 ger 687
620 3 3 2 3 5.439760 0 4 ger 689
621 2 2 3 3 8.718412 1 4 ger 691
622 3 3 2 2 8.041810 1 4 ger 692
623 3 2 3 3 10.982985 1 4 ger 694
624 1 2 2 3 10.007353 0 4 ger 695
625 3 2 2 2 8.467930 0 4 ger 696
626 4 4 1 2 10.328330 NA 4 ger 697
627 2 2 3 3 10.826550 1 4 ger 698
628 3 2 2 2 9.543067 1 4 ger 699
629 3 3 1 2 10.025428 1 4 germn 700
630 2 3 3 3 10.668717 1 4 italian 701
631 3 3 3 3 10.518729 1 4 italian 702
632 3 3 2 2 10.808743 1 4 italian 703
633 2 2 2 2 7.725368 1 4 italian 704
634 3 3 2 3 10.498354 1 4 italian 705
635 3 4 2 2 11.808316 1 4 italian 707
636 2 2 4 4 9.354355 0 4 italian 708
637 1 1 4 4 6.893923 0 4 italian 709
638 2 3 2 3 10.715373 1 4 italian 710
639 2 3 2 3 10.579138 1 4 french 712
640 3 2 3 3 7.743470 1 4 french 713
641 3 3 1 2 11.443266 1 4 french 715
642 3 2 3 3 10.739886 0 4 french 716
643 3 2 3 3 9.666964 NA 4 french 717
644 4 3 2 2 9.963483 1 4 french 719
645 3 3 3 3 12.146356 0 4 french 720
646 1 1 4 4 12.437140 0 4 <NA> 722
647 3 3 2 2 9.361663 0 4 <NA> 723
648 4 3 2 2 10.954406 0 4 <NA> 725
649 2 3 3 3 8.813882 1 4 <NA> 726
650 2 2 3 3 8.399134 1 4 <NA> 727
651 3 NA 2 2 10.716499 1 4 <NA> 729
652 2 NA 3 3 9.827114 0 4 <NA> 731
653 3 3 2 2 10.646740 1 4 <NA> 732
654 2 4 2 1 10.350377 0 4 <NA> 733
655 3 3 2 2 10.019486 1 4 <NA> 734
656 3 2 2 2 9.739026 1 4 <NA> 735
657 2 3 3 4 8.826410 0 4 <NA> 736
658 2 2 3 3 8.191695 1 4 <NA> 737
659 2 2 3 3 9.700108 0 4 <NA> 738
660 2 2 3 3 8.267847 0 4 <NA> 739
661 3 4 2 2 8.238960 1 4 <NA> 740
662 3 4 2 3 8.657699 1 4 741
663 2 2 3 3 12.724873 1 4 742
664 1 1 3 3 9.056560 1 4 743
665 2 1 3 3 10.776392 1 4 744
666 3 2 2 3 11.332372 0 4 745
667 3 3 2 2 9.605044 0 4 746
668 3 3 2 2 12.553403 0 4 747
669 3 3 3 2 8.607291 1 4 748
670 3 3 3 2 12.253184 1 4 750For the whole data set:
msc1 msc2 msc3 msc4 age sex edu fLang id
1 2 3 2 2 9.815538 0 0 german 1
2 3 2 1 1 8.980194 1 0 german 2
3 2 2 3 3 12.758157 0 0 german 3
4 2 2 3 2 10.578846 0 0 german 4
5 3 2 2 2 9.894364 1 0 german 5
6 3 3 3 2 10.446850 0 0 german 6
7 4 4 1 2 10.897605 1 0 german 7
8 3 3 2 1 7.977382 1 0 german 8
10 3 3 2 2 9.105864 0 0 german 10
11 2 1 3 3 8.540117 0 0 german 11
12 2 2 3 3 11.841954 0 0 german 12
13 2 2 3 4 11.647655 0 0 german 13
14 2 3 3 3 11.745306 0 0 german 14
15 2 3 3 3 9.970630 0 0 german 15
17 2 2 2 2 7.773473 1 0 german 17
19 2 2 3 3 9.339448 0 0 german 19
21 4 3 2 2 9.831947 0 0 german 21
22 2 3 3 3 9.890266 1 0 german 22
23 2 3 2 2 11.501609 0 0 german 23
25 3 3 2 2 11.540030 1 0 german 25
26 2 3 3 3 10.098491 1 0 german 26
27 4 3 2 2 10.851320 1 0 german 27
28 3 2 2 2 6.735228 1 0 german 28
29 3 4 2 2 10.690059 1 0 german 29
30 3 2 2 3 9.746659 1 0 german 30
31 2 1 4 4 8.983450 0 0 german 31
33 2 2 3 2 8.057455 1 0 german 33
34 2 2 3 3 10.134752 0 0 german 34
35 2 3 3 2 8.096160 0 0 german 35
37 3 3 3 3 12.156118 0 0 german 37
38 2 2 3 2 9.600157 0 0 german 38
39 2 3 3 3 11.591400 0 0 german 39
40 3 4 2 2 10.691193 1 0 german 40
41 3 3 3 2 12.622380 0 0 german 41
42 3 2 2 2 10.043774 1 0 german 42
43 4 3 2 2 8.452508 0 0 german 43
44 4 3 2 2 12.029754 1 0 german 44
45 3 2 3 3 8.213722 1 0 german 45
46 2 3 2 3 11.654911 0 0 german 46
47 4 3 1 1 9.086209 1 0 german 47
49 2 3 3 3 9.617718 0 0 german 49
50 2 3 2 2 10.347768 1 0 german 50
51 2 1 3 3 10.735626 0 1 german 51
52 3 3 3 2 10.903281 1 1 german 52
54 2 3 4 3 10.824752 0 1 german 54
55 3 3 3 3 9.282584 0 1 german 55
57 2 3 3 3 8.747505 1 1 german 57
58 2 2 3 4 8.992998 1 1 german 58
59 2 2 3 3 12.632563 1 1 german 59
60 2 2 3 3 11.015445 1 1 german 60
61 3 3 2 2 9.529734 1 1 german 61
62 4 3 1 1 11.569437 1 1 german 62
63 4 3 2 2 11.626011 1 1 german 63
65 2 2 2 3 8.712209 1 1 german 65
66 1 2 3 2 12.271368 0 1 german 66
67 3 3 2 2 11.514336 1 1 german 67
69 2 2 2 2 10.858827 1 1 german 69
70 2 2 3 4 11.416986 0 1 german 70
72 2 3 3 3 9.417725 1 1 german 72
73 4 3 1 2 10.788379 1 1 german 73
74 2 3 2 2 7.527881 1 1 german 74
75 2 2 3 3 7.697320 0 1 german 75
76 3 4 3 2 9.723288 1 1 german 76
78 3 3 3 3 9.552697 1 1 german 78
79 3 3 3 3 10.766935 1 1 german 79
81 2 3 2 2 10.027268 0 1 german 81
82 4 3 2 2 11.108152 1 1 german 82
83 3 3 2 2 9.364853 1 1 german 83
85 4 3 1 1 8.791437 0 1 german 85
86 2 2 3 3 14.699514 1 1 german 86
87 1 2 4 3 9.310963 0 1 german 87
89 3 4 1 2 9.164070 0 1 german 89
90 3 2 3 2 9.904690 0 1 german 90
91 1 2 3 3 9.949049 0 1 german 91
92 1 2 3 2 10.874213 0 1 german 92
93 2 3 3 3 9.589440 0 1 german 93
94 2 2 3 3 8.396720 0 1 german 94
95 1 2 3 3 8.633062 1 1 german 95
96 3 4 2 2 10.174559 1 1 german 96
98 4 3 2 2 6.022669 1 1 german 98
99 3 3 2 2 10.701638 1 1 german 99
101 2 3 3 3 11.536565 0 1 german 101
103 2 2 3 3 7.082311 1 1 german 103
104 3 3 2 3 7.990254 1 1 german 104
105 2 2 3 3 13.223930 1 1 german 105
106 2 3 2 3 8.847114 0 1 german 106
109 2 2 3 2 8.917741 1 1 german 109
110 2 2 3 4 9.059646 0 1 german 110
115 2 2 3 3 8.336241 0 1 german 115
116 3 3 2 2 12.158201 1 1 german 116
118 2 2 4 3 11.067872 1 1 german 118
120 3 2 3 2 9.562434 0 1 german 120
121 3 3 3 2 8.977396 0 1 german 121
122 3 2 3 2 10.162202 0 1 german 122
123 3 3 3 2 9.885336 0 1 german 123
124 3 2 3 3 12.560990 1 1 german 124
125 2 2 4 4 11.087431 1 1 german 125
126 3 3 4 3 10.371456 1 1 german 126
127 3 4 1 2 9.319275 1 1 german 127
128 2 2 2 3 11.008828 1 1 german 128
129 1 2 3 2 9.656535 0 1 german 129
130 1 2 3 4 10.032404 0 1 german 130
131 2 2 3 3 6.974716 1 1 german 131
132 3 2 2 3 9.168019 1 1 german 132
133 2 1 3 4 10.658201 1 1 german 133
134 3 2 2 2 10.723573 1 1 german 134
135 1 2 4 3 9.564175 1 1 german 135
136 2 3 3 2 9.440463 1 1 german 136
137 1 2 3 4 8.574188 1 1 german 137
138 2 2 3 3 8.336412 0 1 german 138
139 3 2 2 2 11.316498 0 1 german 139
140 4 3 2 2 10.089194 1 1 german 140
141 3 3 2 2 11.326115 0 1 german 141
142 2 3 2 2 10.544879 1 1 german 142
143 4 3 2 2 8.278763 0 1 german 143
144 3 3 2 2 9.165442 0 1 german 144
145 3 4 2 2 14.840083 0 1 german 145
146 4 4 1 1 9.177470 0 1 german 146
147 2 3 1 1 10.392509 1 1 german 147
148 3 3 2 3 10.074265 1 1 german 148
151 2 2 3 3 11.303224 0 2 german 151
152 3 3 2 2 8.301101 0 2 german 152
153 3 4 2 2 11.552966 1 2 german 153
154 3 3 2 2 10.420355 1 2 german 154
155 3 4 2 2 12.648300 1 2 german 155
156 2 2 3 2 9.985757 1 2 german 156
157 2 3 2 2 10.504636 0 2 german 157
159 2 2 3 3 9.576623 0 2 german 159
160 3 3 2 2 12.855773 1 2 german 160
161 2 1 3 4 10.131071 0 2 german 161
162 2 3 3 3 11.018442 1 2 german 162
163 3 3 2 2 10.910317 1 2 german 163
165 3 2 3 3 10.402108 1 2 german 165
166 2 2 3 2 11.020302 0 2 german 166
167 2 2 3 3 10.862003 1 2 german 167
171 3 3 2 1 11.156488 1 2 german 171
172 1 2 3 3 11.774043 0 2 german 172
173 3 2 3 2 9.014673 0 2 german 173
174 4 4 1 1 9.755259 1 2 german 174
175 3 3 3 3 7.710862 0 2 german 175
176 1 1 4 4 11.418964 1 2 german 176
177 1 2 4 3 10.702477 0 2 german 177
178 3 2 3 2 11.237905 1 2 german 178
179 3 1 2 2 9.225546 0 2 german 179
180 3 3 2 2 9.673228 0 2 german 180
181 2 3 2 2 9.358897 0 2 german 181
182 3 2 3 3 9.252146 1 2 german 182
183 2 2 3 3 8.616693 0 2 german 183
184 3 4 2 2 10.721936 0 2 german 184
185 3 3 2 2 11.054393 0 2 german 185
186 1 1 4 4 8.094315 1 2 german 186
187 3 3 1 2 9.231779 0 2 german 187
188 3 3 2 2 8.547865 1 2 german 188
189 3 3 3 3 10.345779 0 2 german 189
191 2 2 3 4 10.411992 0 2 german 191
192 3 3 2 3 8.971620 1 2 german 192
193 1 2 4 4 10.898386 0 2 german 193
194 2 2 3 3 10.625394 1 2 german 194
195 2 2 3 4 6.357136 0 2 german 195
196 1 2 3 3 12.102026 1 2 german 196
198 2 3 3 3 6.586317 1 2 german 198
199 3 3 3 2 9.050529 1 2 german 199
200 2 2 3 3 12.723541 1 2 german 200
202 2 2 2 2 13.303115 1 2 german 202
204 3 3 2 2 11.445677 0 2 german 204
205 2 2 2 2 9.306572 0 2 german 205
206 3 1 3 3 8.328087 0 2 german 206
207 3 4 1 2 10.359319 0 2 german 207
209 2 2 3 2 9.748836 1 2 german 209
210 2 2 3 3 11.357677 0 2 german 210
211 3 3 2 2 9.790493 1 2 german 211
212 3 3 3 3 8.325024 0 2 german 212
213 2 2 2 2 9.281008 1 2 german 213
214 3 2 2 2 13.164568 0 2 german 214
215 2 2 2 2 7.828867 0 2 german 215
216 3 3 3 3 10.665805 0 2 german 216
217 2 1 3 3 10.635206 1 2 german 217
219 2 2 2 3 8.923574 0 2 german 219
223 3 3 3 2 10.397873 1 2 german 223
224 4 3 2 2 10.241358 1 2 german 224
225 3 3 2 1 9.874614 1 2 german 225
226 2 3 2 2 9.251216 0 2 german 226
227 3 2 2 2 11.927287 0 2 german 227
228 4 4 2 2 10.327432 0 2 german 228
229 3 2 3 2 6.475004 0 2 german 229
230 2 3 3 3 11.338842 0 2 german 230
232 3 3 2 2 7.933710 0 2 german 232
233 2 3 3 3 10.721250 1 2 german 233
234 3 2 3 3 8.086553 0 2 german 234
237 2 2 2 3 11.260712 0 2 german 237
240 2 3 2 3 9.606693 1 2 german 240
241 2 2 2 2 9.965807 1 2 german 241
243 2 2 2 2 11.588374 1 2 german 243
244 2 1 3 3 12.054486 0 2 german 244
245 2 1 3 2 8.551229 1 2 german 245
246 1 1 4 4 14.758472 1 2 german 246
247 3 3 2 1 10.601619 1 2 german 247
248 2 3 2 3 9.608768 1 2 german 248
249 2 2 3 4 10.631563 0 2 german 249
250 3 4 1 1 9.195155 0 2 german 250
251 3 2 3 3 12.153778 1 2 german 251
252 2 1 4 4 11.594112 1 2 german 252
253 3 3 2 2 12.749052 0 2 german 253
254 2 1 3 3 10.904641 1 2 german 254
256 3 3 1 1 9.382583 1 2 german 256
257 2 2 4 3 10.075463 1 2 german 257
260 3 3 2 2 10.003249 0 2 german 260
261 2 2 3 3 10.357645 0 2 german 261
262 1 2 3 3 11.427154 0 2 german 262
263 1 2 4 3 11.141758 1 2 german 263
264 2 2 3 3 11.717822 0 2 german 264
265 3 3 2 1 8.591719 1 2 german 265
266 4 3 2 2 7.199316 1 2 german 266
270 3 2 1 1 9.901684 1 2 german 270
271 2 2 2 3 11.543191 1 2 german 271
272 3 2 3 2 9.905296 0 2 german 272
273 3 3 2 2 10.804370 0 2 german 273
274 3 3 1 1 9.135004 1 2 german 274
277 4 4 2 2 10.110370 1 2 german 277
278 2 3 3 3 8.910521 1 2 german 278
279 3 3 2 2 11.335096 0 2 german 279
280 2 3 3 3 10.872968 1 2 german 280
281 3 3 2 2 9.550622 0 2 german 281
284 2 2 2 2 9.628909 0 2 german 284
285 2 2 3 3 13.948810 0 2 german 285
287 3 2 2 2 9.487855 1 2 german 287
288 3 3 3 2 10.250928 0 2 german 288
289 3 2 3 3 10.038493 0 2 german 289
291 3 3 2 3 11.294503 1 2 german 291
292 2 2 3 3 12.974758 0 2 german 292
293 3 3 2 2 10.285506 0 2 german 293
294 3 2 3 3 10.156128 1 2 german 294
295 3 4 3 3 10.477511 0 2 german 295
296 2 2 3 4 11.463436 0 2 german 296
297 2 3 2 2 10.958489 0 2 german 297
298 3 2 2 2 8.164545 0 2 german 298
299 2 3 3 3 10.140617 0 2 german 299
302 2 3 2 3 9.384978 1 3 german 302
303 2 2 3 3 7.993749 1 3 german 303
304 2 3 3 3 6.955896 1 3 german 304
305 4 3 2 2 10.535078 0 3 german 305
308 4 4 1 1 10.504347 1 3 german 308
309 2 3 2 2 9.939982 0 3 german 309
310 3 3 3 3 10.389400 1 3 german 310
311 2 2 3 3 9.448678 1 3 german 311
312 2 4 1 2 10.221263 1 3 german 312
314 3 3 2 1 11.177018 1 3 german 314
315 2 2 3 2 9.339305 0 3 german 315
316 3 2 2 2 6.542470 1 3 german 316
317 4 3 1 2 8.953333 1 3 german 317
319 2 1 3 3 7.581036 0 3 german 319
321 3 2 3 3 10.881096 1 3 german 321
322 2 2 4 3 9.503126 1 3 german 322
323 3 3 2 2 10.542913 1 3 german 323
324 2 1 4 3 7.226064 1 3 german 324
326 3 3 1 2 10.473550 1 3 german 326
328 3 3 2 2 10.490264 0 3 german 328
329 3 3 2 1 11.740022 1 3 german 329
330 3 3 2 2 11.473238 0 3 german 330
333 3 3 2 2 7.619042 0 3 german 333
334 3 2 2 2 9.525225 1 3 german 334
335 3 2 2 2 9.301695 1 3 german 335
336 1 1 4 4 10.235494 1 3 german 336
338 3 2 3 3 10.078303 0 3 german 338
340 2 3 2 2 9.613427 0 3 german 340
341 2 3 2 2 10.440469 1 3 german 341
343 2 2 3 3 9.632019 0 3 german 343
346 3 3 2 3 10.358045 0 3 german 346
347 3 3 2 3 9.452587 0 3 german 347
348 2 2 3 3 10.616298 0 3 german 348
349 2 2 3 4 10.531881 1 3 german 349
350 3 2 3 3 10.917065 0 3 german 350
351 3 4 2 2 6.753558 1 3 german 351
352 3 3 1 1 9.081102 1 3 german 352
353 2 2 3 3 9.324282 0 3 german 353
354 3 4 2 2 9.556236 1 3 german 354
355 3 3 2 3 8.404846 1 3 german 355
356 3 3 2 2 9.835171 1 3 german 356
358 3 3 2 2 9.347660 1 3 german 358
360 3 3 2 2 8.146446 0 3 german 360
361 3 3 1 2 7.774709 1 3 german 361
362 3 3 2 2 6.083013 1 3 german 362
363 2 3 3 3 8.618334 1 3 german 363
366 2 3 3 3 11.498595 0 3 german 366
368 3 4 2 2 11.885905 0 3 german 368
369 2 1 3 3 10.151313 0 3 german 369
370 2 3 2 3 8.703436 1 3 german 370
371 1 2 3 4 11.715882 1 3 german 371
373 2 2 3 2 9.260014 0 3 german 373
374 2 1 4 4 12.764876 0 3 german 374
375 2 3 3 3 11.855908 0 3 german 375
376 2 2 3 3 11.346669 1 3 german 376
377 2 3 3 2 10.437059 0 3 german 377
380 3 2 2 3 11.449698 0 3 german 380
383 3 3 2 1 10.240093 1 3 german 383
384 3 3 3 3 12.629684 0 3 german 384
385 3 3 2 2 10.858224 0 3 german 385
388 3 3 2 2 8.881271 1 3 german 388
390 3 3 3 3 10.192251 0 3 german 390
391 3 3 1 2 11.065900 0 3 german 391
392 3 3 2 2 10.998406 1 3 german 392
393 2 2 3 2 8.552346 1 3 german 393
394 4 3 1 2 9.412805 0 3 german 394
398 3 2 3 3 9.242431 1 3 german 398
400 3 3 3 3 10.084902 1 3 german 400
401 3 2 2 2 10.097882 0 3 german 401
402 3 3 1 1 11.672478 0 3 german 402
403 3 4 2 1 13.353267 0 3 german 403
404 1 2 4 4 10.107733 0 3 german 404
405 2 3 3 3 10.847647 0 3 german 405
406 3 3 1 1 8.989002 1 3 german 406
407 4 3 2 2 9.767775 0 3 german 407
408 3 3 3 2 8.104969 1 3 german 408
409 3 3 2 2 11.940227 0 3 german 409
410 2 1 4 3 8.396467 1 3 german 410
412 2 2 2 3 9.927729 1 3 german 412
413 3 3 2 2 9.120070 0 3 german 413
414 3 2 2 2 9.359507 1 3 german 414
415 3 2 3 3 7.340234 0 3 german 415
416 3 3 2 2 8.565667 1 3 german 416
417 3 2 2 3 11.829203 1 3 german 417
418 2 2 2 2 9.444976 1 3 german 418
419 1 1 4 4 8.163760 1 3 german 419
420 1 2 2 3 7.971904 0 3 german 420
422 2 2 3 2 10.435981 1 3 german 422
423 1 2 3 3 11.167197 0 3 german 423
424 4 3 2 2 9.076633 0 3 german 424
425 3 3 2 2 9.734855 0 3 german 425
426 3 3 1 1 9.520808 0 3 german 426
427 2 2 2 2 8.750194 0 3 german 427
428 2 2 3 3 10.203757 1 3 german 428
429 3 3 2 2 8.855370 0 3 german 429
430 3 2 3 2 13.023492 1 3 german 430
433 2 2 3 4 8.328651 1 3 german 433
434 3 2 3 2 9.358613 0 3 german 434
435 4 3 2 2 11.184436 0 3 german 435
439 4 4 1 1 9.609236 0 3 german 439
440 2 3 2 2 9.303808 0 3 german 440
441 3 3 2 2 10.739005 0 3 german 441
443 2 3 3 3 10.028578 1 3 german 443
444 4 4 2 2 10.703093 1 3 german 444
446 3 2 3 3 10.775893 1 3 german 446
447 2 2 3 3 10.714497 0 3 german 447
448 3 2 2 2 9.476227 0 3 german 448
451 3 3 2 2 9.860913 0 3 german 451
452 3 4 1 2 9.406198 1 3 german 452
453 3 3 2 2 7.358570 0 3 german 453
454 3 4 2 2 9.143064 1 3 german 454
455 3 4 2 2 11.833885 0 3 german 455
456 2 2 4 3 10.510292 1 3 german 456
457 4 3 2 2 11.219608 1 3 german 457
458 2 2 3 3 13.257474 0 3 german 458
459 2 2 3 4 7.569693 0 3 german 459
460 2 2 4 3 11.684286 1 3 german 460
462 2 2 3 3 9.203611 1 3 german 462
463 2 2 3 3 11.462454 1 3 german 463
464 3 2 3 3 8.608740 1 3 german 464
465 3 3 3 2 8.550825 0 3 german 465
468 3 3 2 2 9.638242 0 3 german 468
469 4 3 2 2 11.559736 0 3 german 469
470 2 3 2 2 9.289579 1 3 german 470
471 2 2 4 3 10.660731 0 3 german 471
472 1 2 4 4 8.653708 1 3 german 472
475 2 2 2 2 10.394297 0 3 german 475
477 2 1 3 3 11.588138 1 3 german 477
478 3 2 3 3 9.701818 1 3 german 478
479 2 2 3 3 10.131820 0 3 german 479
480 3 2 2 3 11.851048 0 3 german 480
481 2 2 3 3 8.094906 0 3 german 481
482 2 3 2 3 6.522122 0 3 german 482
484 3 2 2 2 7.171610 1 3 german 484
485 4 3 1 2 10.449040 1 3 german 485
486 3 3 2 2 8.565564 1 3 german 486
487 2 3 2 3 9.692163 0 3 german 487
488 2 3 3 2 10.544790 0 3 german 488
489 1 1 4 4 9.152848 1 3 german 489
492 3 4 2 2 12.157326 1 3 german 492
493 2 2 4 3 8.071642 1 3 german 493
496 2 2 2 2 9.657121 1 3 german 496
497 2 2 3 3 8.421820 1 3 german 497
498 2 2 3 2 11.234864 0 3 german 498
499 3 3 3 2 11.684493 1 3 german 499
501 3 2 3 3 9.518572 0 4 german 501
503 3 2 2 2 9.394796 1 4 german 503
504 3 2 2 2 10.871842 0 4 german 504
505 3 3 1 1 11.807168 1 4 german 505
507 2 3 3 3 11.295793 0 4 german 507
508 3 2 3 3 9.704389 1 4 german 508
509 3 3 2 1 12.982000 1 4 german 509
510 3 2 2 3 8.051677 0 4 german 510
511 2 3 3 3 10.134297 1 4 german 511
512 2 3 2 2 10.915921 0 4 german 512
514 3 2 3 3 9.377179 1 4 german 514
516 2 2 3 3 9.799799 0 4 german 516
517 2 2 3 3 9.352013 0 4 german 517
518 3 2 3 3 7.578805 0 4 german 518
519 2 3 3 2 8.955748 0 4 german 519
520 3 3 1 2 7.345759 1 4 german 520
521 2 3 3 2 9.038828 0 4 german 521
522 3 2 3 3 6.462564 0 4 german 522
523 3 3 2 2 8.710578 0 4 german 523
524 2 2 3 3 8.078165 1 4 german 524
525 3 3 3 3 9.308461 0 4 german 525
526 2 3 3 2 9.621561 0 4 german 526
527 2 3 2 2 10.468559 1 4 german 527
528 2 2 4 3 12.527006 0 4 german 528
529 1 1 4 4 9.713401 0 4 german 529
530 2 3 2 3 12.449355 1 4 german 530
531 4 3 1 2 10.171527 1 4 german 531
532 2 2 4 3 11.592511 1 4 german 532
533 2 2 3 3 9.746639 0 4 german 533
534 2 2 2 3 11.539024 0 4 german 534
535 3 4 1 1 12.662389 1 4 german 535
536 3 3 2 2 8.558018 1 4 german 536
537 3 4 1 2 12.490442 0 4 german 537
538 3 4 2 2 8.849825 1 4 german 538
540 2 2 2 3 9.856794 1 4 german 540
542 2 3 2 3 10.455994 0 4 german 542
543 3 3 1 2 8.953355 0 4 german 543
544 3 3 2 2 10.233466 1 4 german 544
546 3 3 2 1 11.511366 0 4 german 546
547 3 3 2 2 7.030045 0 4 german 547
548 2 1 4 4 11.833564 0 4 german 548
550 2 3 2 2 10.669845 1 4 german 550
551 3 3 3 2 11.945497 0 4 german 551
552 3 3 1 1 7.631776 0 4 german 552
553 2 2 3 3 7.929047 0 4 german 553
554 2 3 2 2 10.655136 1 4 german 554
555 4 4 1 1 11.651111 1 4 german 555
556 3 3 2 2 9.499631 0 4 german 556
557 2 3 2 3 9.869376 1 4 german 557
558 3 3 1 2 10.573739 0 4 german 558
559 2 2 3 4 6.777733 0 4 german 559
560 3 3 2 1 10.292540 0 4 german 560
561 3 2 3 2 8.420615 0 4 german 561
565 3 3 2 2 9.567283 1 4 german 565
566 3 2 2 2 9.700664 1 4 german 566
567 2 2 4 3 9.035493 1 4 german 567
568 2 2 3 3 6.920118 0 4 german 568
571 4 4 1 1 11.561428 0 4 german 571
572 3 2 2 2 10.479614 1 4 german 572
573 3 2 2 3 12.062231 0 4 german 573
574 3 3 3 2 11.258923 1 4 german 574
575 2 2 3 3 8.535113 1 4 german 575
577 3 3 2 2 9.513192 0 4 german 577
581 1 2 3 3 7.840196 1 4 german 581
583 2 3 3 3 11.716523 1 4 german 583
584 2 2 3 3 10.115714 0 4 german 584
585 2 1 2 3 10.487666 1 4 german 585
586 3 3 2 2 10.129045 1 4 german 586
587 4 3 2 2 10.213173 0 4 german 587
588 1 1 3 3 12.114688 1 4 german 588
589 2 2 3 3 11.655211 0 4 german 589
590 1 2 4 4 10.474828 1 4 german 590
591 2 3 3 2 9.222274 0 4 german 591
592 3 3 3 3 10.053230 0 4 german 592
595 3 3 3 2 10.611019 0 4 german 595
598 2 3 3 3 12.403272 0 4 german 598
600 4 3 1 1 30.000000 0 4 german 600
602 3 2 3 3 9.797451 0 4 german 602
604 2 3 3 3 5.697734 1 4 german 604
605 4 3 3 2 11.315240 0 4 german 605
606 3 3 2 2 9.398728 0 4 german 606
608 3 4 2 1 7.712797 0 4 german 608
609 4 3 2 2 11.397924 1 4 german 609
612 2 1 4 3 8.434417 1 4 german 612
613 2 2 3 3 9.741397 1 4 german 613
616 3 3 2 2 8.498451 0 4 german 616
617 1 2 3 3 10.447842 0 4 german 617
618 3 3 3 3 6.916193 0 4 german 618
619 3 2 3 3 7.967729 0 4 german 619
620 2 2 3 3 10.971877 0 4 german 620
621 1 2 3 3 10.036674 1 4 german 621
622 4 4 1 2 11.171656 0 4 german 622
623 2 2 3 3 9.809559 1 4 german 623
624 3 2 2 2 8.970928 0 4 german 624
625 2 2 4 4 10.715082 0 4 german 625
626 3 3 3 2 7.928970 1 4 german 626
627 2 3 3 3 6.909529 1 4 german 627
628 2 3 3 2 10.799244 0 4 german 628
630 2 2 2 1 12.505498 0 4 german 630
631 4 4 1 1 9.779263 1 4 german 631
632 3 3 2 3 9.474470 0 4 german 632
633 2 2 3 3 12.139497 0 4 german 633
634 3 3 3 2 11.067315 0 4 german 634
635 3 4 2 3 8.320202 0 4 german 635
636 3 2 2 2 7.182648 1 4 german 636
637 2 2 4 4 10.130177 0 4 german 637
638 3 3 3 3 12.496180 0 4 german 638
639 2 2 3 3 12.592766 0 4 german 639
640 2 2 2 3 9.907003 0 4 german 640
641 3 2 3 3 11.737818 1 4 german 641
642 3 2 2 1 8.821091 0 4 german 642
643 2 3 3 2 6.860783 0 4 german 643
644 3 2 3 3 10.268927 1 4 german 644
645 3 3 2 2 10.266885 1 4 german 645
646 3 3 2 2 5.763412 0 4 german 646
647 3 2 2 2 7.066767 1 4 german 647
648 3 3 3 2 9.180560 0 4 german 648
649 3 3 2 2 12.972046 1 4 german 649
651 3 3 2 2 11.411023 1 4 ger 651
652 2 2 2 3 10.049973 0 4 ger 652
653 2 2 3 2 11.337266 0 4 ger 653
654 3 3 3 3 13.679508 0 4 ger 654
655 2 3 3 3 8.168574 1 4 ger 655
657 2 3 3 3 9.269406 0 4 ger 657
659 3 3 2 2 8.932260 1 4 ger 659
660 3 3 1 2 7.922082 0 4 ger 660
661 2 2 3 3 11.825439 0 4 ger 661
663 2 1 3 3 13.418967 0 4 ger 663
664 3 2 3 3 12.795091 0 4 ger 664
665 2 3 3 3 9.076854 0 4 ger 665
666 2 2 2 2 9.791845 1 4 ger 666
667 2 3 2 2 7.201118 0 4 ger 667
669 3 3 3 3 11.357964 1 4 ger 669
670 3 3 2 2 10.881354 0 4 ger 670
671 2 1 3 3 10.379563 0 4 ger 671
674 3 3 3 2 8.146615 1 4 ger 674
675 2 3 2 2 9.175585 0 4 ger 675
676 2 2 4 4 12.131668 1 4 ger 676
678 1 2 3 3 11.473233 1 4 ger 678
679 3 3 2 3 9.506655 1 4 ger 679
680 2 3 2 2 10.030170 0 4 ger 680
682 1 1 3 4 10.668823 0 4 ger 682
683 2 3 3 3 9.386863 1 4 ger 683
684 2 2 3 3 7.093356 1 4 ger 684
685 2 2 3 3 12.640182 1 4 ger 685
686 1 2 3 3 8.807493 1 4 ger 686
687 3 3 3 3 14.291764 0 4 ger 687
689 3 3 2 3 5.439760 0 4 ger 689
691 2 2 3 3 8.718412 1 4 ger 691
692 3 3 2 2 8.041810 1 4 ger 692
694 3 2 3 3 10.982985 1 4 ger 694
695 1 2 2 3 10.007353 0 4 ger 695
696 3 2 2 2 8.467930 0 4 ger 696
698 2 2 3 3 10.826550 1 4 ger 698
699 3 2 2 2 9.543067 1 4 ger 699
700 3 3 1 2 10.025428 1 4 germn 700
701 2 3 3 3 10.668717 1 4 italian 701
702 3 3 3 3 10.518729 1 4 italian 702
703 3 3 2 2 10.808743 1 4 italian 703
704 2 2 2 2 7.725368 1 4 italian 704
705 3 3 2 3 10.498354 1 4 italian 705
707 3 4 2 2 11.808316 1 4 italian 707
708 2 2 4 4 9.354355 0 4 italian 708
709 1 1 4 4 6.893923 0 4 italian 709
710 2 3 2 3 10.715373 1 4 italian 710
712 2 3 2 3 10.579138 1 4 french 712
713 3 2 3 3 7.743470 1 4 french 713
715 3 3 1 2 11.443266 1 4 french 715
716 3 2 3 3 10.739886 0 4 french 716
719 4 3 2 2 9.963483 1 4 french 719
720 3 3 3 3 12.146356 0 4 french 720
741 3 4 2 3 8.657699 1 4 741
742 2 2 3 3 12.724873 1 4 742
743 1 1 3 3 9.056560 1 4 743
744 2 1 3 3 10.776392 1 4 744
745 3 2 2 3 11.332372 0 4 745
746 3 3 2 2 9.605044 0 4 746
747 3 3 2 2 12.553403 0 4 747
748 3 3 3 2 8.607291 1 4 748
750 3 3 3 2 12.253184 1 4 7507.6 Plausibility checks
So-called plausibility checks are performed in order to detect:
- structural errors → e.g., the range of a lickert scale is from 1 to 4, but the value 5 occurs
- theoretical inconsistencies → e.g., 30 years of age in sample of primary students
- (statistical) outliers → see Excursus on outlier in the following
Excursus: Outliers
According to wikipedia, outliers can be defined as a data point that differs significantly from other observations.
Outliers may occur due to different reasons:
- data entry errors
- measurement error
- “true” extreme values
Outliers must be detected, because they may introduce bias into parameter estimates of statistical models and hence, compromise the validity of the results.
Different ways of detecting outliers:
- Z-score (e.g., above or below \(3SD\))
- Visualization (e.g., box plots, histograms, or scatter plot)
- Mahalanobis distance
- …
Plausibility checks can be performed both analytically and graphically. Graphics can be created with base R (R Core Team, 2023), or packages such as lattice (Sarkar, 2021) and ggplot2 (Wickham, Chang, et al., 2023) that are specifically designed for data visualization. Although base R graphics are useful (especially for creating simple plots), the ggplot2 package is more powerful and flexible for creating complex visualizations. You may load the package via:
Note that ggplot2 package (Wickham, Chang, et al., 2023) is part of the tidyverse and nicely works with the dplyr package (Wickham, François, et al., 2023). For an introduction see the tidyverse documentation of ggplot2.
To perform plausibility checks analytically, the base::range function is a useful starting point to quickly examine the minimum and maximum of variables that are no characters/string variables (i.e., !is.character).
- Identify all non-character variables using the
!is.characterfunction within abase::lapplyloop. Becausebase::lapplyreturns a list, we need tobase::unlistthe output to get a logical vector.
- Then we can apply the
base::rangefunction to the respective variable within theexDatMis.
Tip: The apply function
The base::apply function is designed to apply functions (e.g., base::range) over array margins (e.g., rows or columns) and needs the following arguments (copied from the function description):
-
X: an array, including a matrix. -
MARGIN: a vector giving the subscripts which the function will be applied over. E.g., for a matrix 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows and columns. Where X has named dimnames, it can be a character vector selecting dimension names. -
FUN: the function to be applied: see ‘Details’.
In the dplyr package we first use the select function (i.e., to select the variables) in combination with where function to identify all non-character variables by stating !is.character. Then applying the base::range function on very variable in the data set.
exDatMis |>
dplyr::select(
dplyr::where(~!is.character(.))
) |>
dplyr::summarise_all("range", na.rm = TRUE)Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
dplyr 1.1.0.
ℹ Please use `reframe()` instead.
ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
always returns an ungrouped data frame and adjust accordingly.
ℹ The deprecated feature was likely used in the dplyr package.
Please report the issue at <https://github.com/tidyverse/dplyr/issues>. msc1 msc2 msc3 msc4 age sex edu id
1 1 1 1 1 5.43976 0 0 1
2 4 4 4 4 30.00000 1 4 750
Exercise: Set the value 30 of the age variable to NA.
- Identify the observation with
age == 30, using thebase::whichfunction.
- Set the value 30 of the variable
agetoNA.
exDat[which(exDat$age == 30),"age"] <- NA7.6.1 Categorical variables
7.6.1.1 Frequencies/Counts
To do further plausibility checks on categorical variables, we may examine counts (at each combination) of factor levels (e.g., examine extreme responses in one category).
The base::table function builds a contingency table of the counts at each combination of factor levels.
Note that by default, NAs are not included in the base::table function. To include NAs, we need to change the useNA argument to "ifany" (only if the count is positive) or "always" (even for zero counts).
table(exDatMis$edu, useNA = "always")
0 1 2 3 4 <NA>
46 100 150 200 250 4 The dplyr approach is using the count function that counts the unique values of one or more variables.
exDatMis |>
dplyr::count(edu) edu n
1 0 46
2 1 100
3 2 150
4 3 200
5 4 250
6 NA 47.6.1.2 Graphical inspection using bar charts
Bar charts are one way of graphically checking the plausibility of discrete variables.
barplot(
height = table(exDat$edu, useNA = "always"),
names.arg = c(0:4, "NA"),
#main = "Education in example data set (barplot)", # plot title
xlab = "edu", # x-axis label
ylab = "Count", # y-axis label
ylim = c(0, 300) # y-axis range
)
exDat |>
dplyr::count(edu) |>
ggplot(aes(x = edu, y = n)) +
geom_bar(stat = "identity", color = "black", fill = "lightgrey") +
geom_text(aes(label=paste("n = ", n)), vjust=1.6, color="black", size=3.5)+
labs(x = "edu", y = "Count") +
theme_classic()
7.6.2 Continuous variables
Continuous variables are checked by examining the distribution of the variables. It is reasonable to start checking the range (i.e., minimum & maximum) of the variable (using the base::range function, see e.g., above).
7.6.2.1 Distribution parameters/measures
Furthermore, the following Distribution parameters/measures might be informative:
- location: mean, median →
base::mean,stats::median - dispersion: standard deviation, variance →
stats::sd,stats::var - shape (asymmetry): skewness →
moments::skewness - shape (tailedness): kurtosis →
moments::kurtosis
In the following, we show how to examine the skewness and kurtosis. The calculation of the other parameters is introduced in the section on Descriptive statistics and item analysis.
moments::skewness(exDat$age,
na.rm = T)[1] -0.06354551moments::kurtosis(exDat$age,
na.rm = T)[1] 3.085664What are the cutoffs for skewness and kurtosis? It depends on the specific context…
- Skewness: 0 (perfect symmetry) → cutoff: 1-2
- \(> 0\): right-skewed distributions
- \(< 0\): left-skewed distributions
- Kurtosis: 3 (perfect normal distribution) → cutoff: 6-7
- \(> 3\): more peaked distribution
- \(< 3\): a flatter distribution
(Reference needed)
7.6.2.2 Graphical inspection using histogram
Histograms are graphical representations of frequency (or relative frequency) distributions of variables and are used to visualize the shape of a distribution.
ggplot(data = exDat, #na.omit
aes(x = age)) +
geom_histogram(bins = 100,
binwidth = .5,
color = "black",
fill = "white") +
geom_vline(data = exDat, aes(xintercept = mean(age, na.rm = T)),
linetype="dashed",
color="red") +
scale_x_continuous(limits = c(
min(exDat$age,
na.rm = T)-1,
max(exDat$age,
na.rm = T)+1)) +
theme_minimal()
7.6.2.3 Graphical inspection using boxplots
Boxplots allow visualization of the most important robust measures of location and dispersion.
boxplot(exDat$age,
#main = "plot title",
xlab = "Variables", # x-axis label
ylab = "Range" # y-axis label
)
ggplot(data = exDat, #na.omit
aes(y = age, x = factor(0))) +
geom_boxplot(width=.5, outlier.color = "red") +
#geom_jitter(width = .25, color = "grey", alpha = .2) +
scale_x_discrete(breaks = NULL) +
xlab(NULL) +
theme_bw()
How to add a new variable is explained in more detail in the section on Data transformation.↩︎
Note that this is also recommended when designing a codebook, see here: https://datawizkb.leibniz-psychology.org↩︎