Daten-Import
Bevor wir in R – oder auch in jedem anderen Statistik-Programm – arbeiten könnne, müssen wir unsere eifrig gesammelten Daten irgendwie ins Programm kriegen. In R war das für einige Datenformate lange Zeit eine schmerzhafte Angelegenheit, was das Arbeiten in R nicht gerade attraktiv gemacht hat. Das Tidyverse jedoch greift uns bei dieser Aufgabe unter die Arme und der Import ist mit einer Zeile Code (versprochen!) abgeschlossen.
Wir behandeln hier die gängigsten Datei-Typen: Comma-Separated Values (.csv), Excel-Dateien (.xlsx), SPSS-Dateien (.sav) und R-Objekt-Dateien (.rds). Jeder Datei-Typ hat seine Stärken und Schwächen, welche beim Import eine Rolle spielen können. CSV-Dateien sind eigentlich ideal zum Speichern von Daten, weil so ziemich jedes Programm, das mit Daten umgeht, entweder CSV-Daten einlesen, ausgeben oder abspeichern kann. Dabei werden die einzelnen Datenwerte durch Kommata getrennt abgespeichert (daher der Name). Jedes Komma zeigt also an, wann eine neue Spalte beginnt. Tricky wird es im deutschen Sprachraum, weil das Komma hier als Dezimaltrennzeichen genutzt wird. Die Zahl 1,5 würde also in 2 Spalten aufgeteilt werden. Da auch wir eine Daseinsberechtigung haben, und nicht alles ins englische Format überfüreh wollen, ist das Trennzeichen für CSV-Dateien bei uns kein Komma, sondern ein Semikolon (“;”).
Nach den CSV-Dateien werden wohl Excel-Dateien die am häufigsten verwendeten sein, wenn es um den Austausch von Daten geht. Natürlich kann man Excel-Dateien über “Speichern unter” als CSV abspeichern (etwas, das nach jeder Änderung dann immer wieder gemacht werden muss), aber den Schritt kann man sich sparen und direkt Excel-Dateien importieren.
Auch SPSS hat sein eigenes Datenformat, war ja klar. R hat sogar zwei, aber das RDS-Format bietet einige Vorteile, weshalb wir auch nur dieses behandeln.
Je nach Datei-Typ müssen wir unterschiedliche Pakete laden. Für CSV- und RDS-Dateien benötigen wir nur das Tidyverse. Für Excel-Dateien wird das Paket readxl verwendet, für SPSS-Dateien haven.
Im GitHub-Repository findest Du im Ordner “data” fünf Beispiel-Dateien (csv_data.csv, csv2_data.csv, excel_data.csv, spss_data.sav und rds_data.rds) in unterschiedlichen Formaten, mit denen wir den Daten-Import üben werden.
Comma-Separated Values (.csv)
International
CSV-Dateien importieren wir mit dem Befehl read_csv().
## Parsed with column specification:
## cols(
## id = col_double(),
## age = col_double(),
## sex = col_character(),
## group = col_character(),
## test_score = col_double(),
## bdi = col_double(),
## well_being = col_double()
## )## # A tibble: 530 x 7
## id age sex group test_score bdi well_being
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1 24 Male Waitlist 14.8 7 50
## 2 2 47 Male Waitlist 19.0 4 25
## 3 3 24 Female Placebo 14.6 6 42
## 4 4 28 Female Waitlist 15.4 8 59
## 5 5 43 Male Waitlist 16.2 3 12
## 6 6 25 Male Treatment 22.0 8 20
## 7 7 22 Female Placebo 16.0 7 33
## 8 8 17 Female Treatment 22.4 5 14
## 9 9 44 Female Treatment 23.0 3 32
## 10 10 20 Female Waitlist 16.5 8 37
## # ... with 520 more rowsZu Beginn kriegen wir eine Information dazu, wie R die Datei eingelesen hat. Es gibt nämlich mehrere Daten-Typen. read_csv() versucht nun selbst zu erraten, um welchen Daten-Typen sich in den Variablen verstecken und gibt seinen Vorschlag als Info ab. col_double() bedeutet, dass R diese Spalte als Zahl eingelesen hat, col_character() dass eine Spalte als Zeichenfolge behandelt wird.
Dieser Befehl öffnet jedoch nur die Datei. Um sie zu speichern, müssen wir sie einem Objekt zuweisen, dessen Namen wir frei wählen können.
## Parsed with column specification:
## cols(
## id = col_double(),
## age = col_double(),
## sex = col_character(),
## group = col_character(),
## test_score = col_double(),
## bdi = col_double(),
## well_being = col_double()
## )## # A tibble: 530 x 7
## id age sex group test_score bdi well_being
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1 24 Male Waitlist 14.8 7 50
## 2 2 47 Male Waitlist 19.0 4 25
## 3 3 24 Female Placebo 14.6 6 42
## 4 4 28 Female Waitlist 15.4 8 59
## 5 5 43 Male Waitlist 16.2 3 12
## 6 6 25 Male Treatment 22.0 8 20
## 7 7 22 Female Placebo 16.0 7 33
## 8 8 17 Female Treatment 22.4 5 14
## 9 9 44 Female Treatment 23.0 3 32
## 10 10 20 Female Waitlist 16.5 8 37
## # ... with 520 more rowsWenn Du in RStudio arbeitest, wird dir aufgefallen sein, dass dir noch ein anderer, sehr ähnlich klingender Befehl vorgeschlagen wird, nämlich read.csv(). Das ist die Basis-Variante, die R nativ zur Verfügung stellt. Allerdings wird der Import relativ unübersichtlich dargestellt und wir erhalten ohne weiteres Zutun keine Informationen zu den Variablen-Typen.
## id age sex group test_score bdi well_being
## 1 1 24 Male Waitlist 14.830963 7 50
## 2 2 47 Male Waitlist 18.951326 4 25
## 3 3 24 Female Placebo 14.619546 6 42
## 4 4 28 Female Waitlist 15.438800 8 59
## 5 5 43 Male Waitlist 16.181193 3 12
## 6 6 25 Male Treatment 22.037545 8 20
## 7 7 22 Female Placebo 15.983710 7 33
## 8 8 17 Female Treatment 22.366219 5 14
## 9 9 44 Female Treatment 23.026597 3 32
## 10 10 20 Female Waitlist 16.530477 8 37
## 11 11 28 Male Treatment 14.598394 5 15
## 12 12 46 Female Waitlist 18.594622 6 57
## 13 13 43 Male Placebo 20.850671 4 31
## 14 14 48 Male Treatment 20.520692 7 19
## 15 15 32 Male Treatment 28.278799 2 21
## 16 16 45 Female Treatment 20.037016 4 40
## 17 17 30 Female Placebo 9.963999 3 50
## 18 18 45 Male Waitlist 14.552438 1 52
## 19 19 21 Male Waitlist 15.447021 4 58
## 20 20 38 Female Treatment 17.896427 1 46
## 21 21 47 Male Treatment 22.458752 3 20
## 22 22 44 Male Placebo 21.351758 1 13
## 23 23 36 Female Placebo 14.896804 3 46
## 24 24 29 Female Placebo 18.709113 3 22
## 25 25 45 Female Waitlist 20.140872 6 22
## 26 26 48 Male Placebo 17.808666 5 45
## 27 27 41 Female Placebo 18.734497 5 14
## 28 28 49 Male Treatment 16.802089 5 41
## 29 29 30 Male Treatment 20.425132 1 39
## 30 30 48 Male Waitlist 24.605413 4 57
## 31 31 30 Male Waitlist 16.878033 3 19
## 32 32 20 Female Treatment 22.497833 5 59
## 33 33 45 Male Placebo 14.416401 2 13
## 34 34 19 Male Waitlist 24.052620 2 53
## 35 35 17 Male Treatment 25.681463 2 22
## 36 36 41 Male Placebo 16.137472 9 15
## 37 37 49 Female Waitlist 20.557112 1 24
## 38 38 18 Female Placebo 20.312771 4 41
## 39 39 22 Male Placebo 16.465839 2 47
## 40 40 27 Female Treatment 20.988159 3 40
## 41 41 44 Female Treatment 24.229824 3 41
## 42 42 42 Male Placebo 15.449402 4 26
## 43 43 50 Female Placebo 21.268919 3 35
## 44 44 32 Female Treatment 25.625651 8 16
## 45 45 20 Female Placebo 18.058238 6 59
## 46 46 32 Female Placebo 17.631963 1 24
## 47 47 33 Male Waitlist 22.110356 8 18
## 48 48 32 Male Placebo 19.083754 3 25
## 49 49 23 Male Treatment 15.371831 4 59
## 50 50 26 Male Placebo 22.901227 4 46
## 51 51 44 Male Waitlist 13.780166 3 56
## 52 52 47 Female Treatment 18.529491 3 47
## 53 53 47 Female Treatment 14.404215 3 36
## 54 54 27 Male Placebo 19.129989 4 57
## 55 55 40 Male Waitlist 18.903438 5 35
## 56 56 42 Female Treatment 23.578581 8 52
## 57 57 22 Male Treatment 18.987187 10 38
## 58 58 18 Female Waitlist 16.601481 2 18
## 59 59 18 Male Treatment 18.519873 1 38
## 60 60 44 Male Waitlist 15.646308 2 58
## 61 61 21 Male Waitlist 18.543104 2 24
## 62 62 24 Male Placebo 24.027461 1 33
## 63 63 40 Male Waitlist 21.249020 4 52
## 64 64 38 Female Treatment 25.376193 3 13
## 65 65 43 Male Waitlist 16.181173 2 59
## 66 66 17 Female Placebo 22.969084 5 29
## 67 67 42 Male Waitlist 16.807646 6 19
## 68 68 23 Male Treatment 23.436641 6 16
## 69 69 49 Male Waitlist 17.557085 2 57
## 70 70 41 Female Treatment 30.308846 11 38
## 71 71 28 Male Placebo 20.256123 4 51
## 72 72 17 Male Waitlist 16.870011 4 44
## 73 73 46 Male Treatment 19.286927 1 29
## 74 74 19 Male Treatment 20.711221 7 15
## 75 75 29 Male Treatment 24.012376 1 39
## 76 76 23 Male Treatment 21.209376 2 22
## 77 77 31 Male Waitlist 16.756785 3 25
## 78 78 36 Female Treatment 16.372833 0 40
## 79 79 16 Female Waitlist 19.662887 4 18
## 80 80 40 Female Waitlist 23.532984 4 34
## 81 81 50 Female Treatment 20.600158 1 41
## 82 82 38 Female Waitlist 25.512852 3 57
## 83 83 24 Female Treatment 12.801259 8 44
## 84 84 38 Female Treatment 22.265600 6 51
## 85 85 40 Female Treatment 28.278386 6 39
## 86 86 42 Female Placebo 19.677741 4 19
## 87 87 34 Male Placebo 19.176906 3 55
## 88 88 48 Female Waitlist 23.611693 4 41
## 89 89 29 Female Waitlist 21.662897 2 57
## 90 90 39 Male Treatment 15.391568 3 21
## 91 91 42 Female Placebo 15.188511 4 44
## 92 92 17 Male Treatment 22.202550 1 44
## 93 93 44 Female Placebo 15.788929 3 14
## 94 94 49 Male Treatment 13.969373 5 14
## 95 95 46 Male Treatment 23.018800 9 20
## 96 96 46 Female Placebo 17.933293 3 32
## 97 97 33 Female Waitlist 21.673127 5 53
## 98 98 30 Female Waitlist 11.932595 3 55
## 99 99 18 Female Treatment 21.858603 2 59
## 100 100 33 Male Placebo 14.373729 4 60
## 101 101 40 Female Placebo 17.519324 6 42
## 102 102 17 Male Waitlist 20.009557 12 31
## 103 103 50 Male Placebo 18.654740 2 20
## 104 104 39 Male Placebo 25.991534 7 38
## 105 105 21 Female Waitlist 23.607069 4 50
## 106 106 36 Female Waitlist 18.910912 9 13
## 107 107 38 Female Placebo 17.784416 7 19
## 108 108 48 Female Waitlist 22.188541 2 26
## 109 109 36 Male Treatment 15.579388 1 59
## 110 110 17 Female Waitlist 14.961379 7 14
## 111 111 24 Female Treatment 18.759443 6 54
## 112 112 21 Male Treatment 16.491228 3 59
## 113 113 33 Male Waitlist 14.070121 4 21
## 114 114 49 Female Waitlist 14.678054 3 17
## 115 115 35 Female Waitlist 16.776493 4 33
## 116 116 19 Female Treatment 17.166675 3 51
## 117 117 30 Male Placebo 14.959392 2 55
## 118 118 26 Female Waitlist 16.345063 5 43
## 119 119 50 Female Waitlist 24.255593 4 43
## 120 120 35 Male Waitlist 22.930437 9 55
## 121 121 42 Female Waitlist 22.914002 3 57
## 122 122 50 Female Waitlist 20.155140 3 27
## 123 123 25 Female Waitlist 23.323915 2 49
## 124 124 33 Male Placebo 15.446671 2 55
## 125 125 41 Male Placebo 23.644645 1 13
## 126 126 30 Male Waitlist 24.649189 2 48
## 127 127 46 Male Treatment 26.058207 2 12
## 128 128 40 Male Placebo 19.040639 5 44
## 129 129 29 Female Placebo 23.001233 4 26
## 130 130 45 Female Treatment 15.738587 2 51
## 131 131 50 Male Treatment 16.838571 5 44
## 132 132 41 Male Treatment 21.184144 2 28
## 133 133 21 Female Treatment 23.467592 2 44
## 134 134 22 Female Waitlist 20.336577 4 38
## 135 135 46 Female Treatment 24.611880 6 26
## 136 136 17 Female Treatment 13.752675 8 46
## 137 137 40 Male Placebo 24.766789 2 13
## 138 138 29 Male Treatment 23.749299 2 44
## 139 139 43 Male Waitlist 22.794793 6 25
## 140 140 26 Male Waitlist 28.446327 2 12
## 141 141 23 Female Treatment 17.973373 13 34
## 142 142 46 Male Placebo 23.963685 2 36
## 143 143 22 Male Waitlist 11.514562 3 36
## 144 144 17 Male Treatment 15.901428 11 36
## 145 145 38 Male Waitlist 12.755775 2 22
## 146 146 41 Female Treatment 22.384958 2 59
## 147 147 47 Female Placebo 25.983820 3 31
## 148 148 33 Male Treatment 22.174039 7 55
## 149 149 45 Female Waitlist 14.405200 3 44
## 150 150 26 Female Treatment 25.163400 9 53
## 151 151 26 Female Waitlist 26.624734 3 27
## 152 152 31 Male Treatment 27.244304 4 40
## 153 153 35 Male Waitlist 19.786399 3 39
## 154 154 49 Female Waitlist 19.441433 3 48
## 155 155 48 Female Placebo 17.253233 0 27
## 156 156 43 Female Waitlist 21.523004 4 16
## 157 157 24 Female Placebo 25.556499 2 32
## 158 158 45 Male Placebo 18.618811 7 54
## 159 159 49 Female Treatment 15.918905 3 38
## 160 160 44 Male Waitlist 8.417106 2 51
## 161 161 18 Male Waitlist 11.556624 11 47
## 162 162 50 Female Waitlist 19.767451 2 32
## 163 163 40 Female Placebo 19.376419 10 43
## 164 164 48 Female Treatment 26.919202 3 21
## 165 165 22 Male Treatment 27.346637 6 52
## 166 166 46 Female Treatment 22.562643 4 13
## 167 167 16 Female Placebo 24.853248 2 20
## 168 168 26 Male Treatment 22.911675 2 15
## 169 169 19 Female Placebo 21.240379 4 14
## 170 170 18 Female Waitlist 26.346302 1 18
## 171 171 41 Female Placebo 21.784256 1 28
## 172 172 26 Female Treatment 12.233351 3 41
## 173 173 47 Female Waitlist 23.818266 0 35
## 174 174 28 Male Placebo 16.256383 2 45
## 175 175 33 Female Placebo 20.522907 5 52
## 176 176 37 Male Treatment 10.670618 11 60
## 177 177 17 Male Waitlist 18.074535 1 16
## 178 178 33 Male Waitlist 21.044331 2 30
## 179 179 28 Male Treatment 16.082789 3 35
## 180 180 38 Female Treatment 28.038135 6 28
## 181 181 36 Female Waitlist 18.438616 3 27
## 182 182 37 Female Placebo 22.948672 4 52
## 183 183 37 Female Treatment 17.957416 2 57
## 184 184 16 Female Treatment 17.867703 5 24
## 185 185 24 Male Treatment 21.025227 7 28
## 186 186 29 Male Waitlist 22.534694 3 50
## 187 187 22 Male Treatment 20.118505 7 20
## 188 188 25 Female Waitlist 18.623502 6 43
## 189 189 31 Female Treatment 15.106704 9 22
## 190 190 36 Male Placebo 13.173582 2 50
## 191 191 47 Male Treatment 22.607622 1 44
## 192 192 47 Male Waitlist 16.533083 4 13
## 193 193 46 Male Placebo 19.178714 4 14
## 194 194 18 Female Treatment 15.637437 2 14
## 195 195 36 Female Waitlist 23.503519 2 37
## 196 196 24 Female Waitlist 19.693115 10 36
## 197 197 19 Female Treatment 18.837736 4 23
## 198 198 48 Male Waitlist 15.924889 8 14
## 199 199 43 Female Treatment 15.093246 2 41
## 200 200 36 Female Waitlist 25.811519 9 52
## 201 201 38 Female Waitlist 18.818594 0 38
## 202 202 49 Female Treatment 23.406162 4 22
## 203 203 41 Female Waitlist 15.703774 2 44
## 204 204 32 Female Placebo 17.606009 2 27
## 205 205 48 Female Treatment 16.508025 7 32
## 206 206 31 Male Treatment 19.163628 1 40
## 207 207 50 Male Waitlist 20.086610 4 50
## 208 208 45 Female Placebo 18.461053 8 54
## 209 209 36 Female Placebo 22.770488 1 45
## 210 210 34 Male Treatment 29.122881 12 56
## 211 211 25 Female Placebo 12.498740 3 60
## 212 212 25 Female Waitlist 18.161649 4 30
## 213 213 43 Female Treatment 18.926686 0 46
## 214 214 33 Male Placebo 18.422332 3 31
## 215 215 46 Female Treatment 16.713769 4 20
## 216 216 17 Female Waitlist 20.016918 4 49
## 217 217 30 Male Placebo 20.089724 4 34
## 218 218 29 Female Waitlist 16.338159 1 24
## 219 219 38 Female Treatment 23.214371 2 22
## 220 220 17 Male Treatment 18.142627 2 36
## 221 221 39 Male Treatment 21.796199 7 55
## 222 222 31 Male Placebo 15.416649 5 25
## 223 223 47 Male Waitlist 20.014228 1 57
## 224 224 30 Female Placebo 25.899107 2 34
## 225 225 38 Male Placebo 24.501029 2 21
## 226 226 43 Male Waitlist 19.407325 3 35
## 227 227 22 Male Treatment 16.789511 2 47
## 228 228 18 Female Treatment 19.443477 2 37
## 229 229 17 Female Placebo 12.674474 4 40
## 230 230 22 Male Placebo 20.994411 4 41
## 231 231 34 Male Treatment 20.535739 4 30
## 232 232 44 Male Waitlist 21.127682 9 59
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## 234 234 49 Male Placebo 18.433091 8 28
## 235 235 22 Female Treatment 16.084158 6 54
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## 237 237 43 Female Treatment 15.930302 3 22
## 238 238 31 Male Treatment 15.482508 4 37
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## 240 240 46 Female Waitlist 25.260506 5 35
## 241 241 27 Male Waitlist 14.967710 6 13
## 242 242 35 Male Treatment 22.323641 10 49
## 243 243 28 Female Waitlist 13.674219 6 19
## 244 244 45 Male Placebo 16.138770 5 56
## 245 245 17 Male Waitlist 23.056194 3 54
## 246 246 23 Female Waitlist 25.782987 5 46
## 247 247 27 Female Waitlist 23.714016 4 36
## 248 248 30 Male Treatment 24.234724 3 18
## 249 249 34 Female Placebo 24.472456 1 53
## 250 250 32 Female Placebo 14.575209 4 31
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## 253 253 26 Male Treatment 19.592365 1 45
## 254 254 33 Male Placebo 16.349466 2 25
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## 256 256 16 Male Waitlist 17.311270 5 56
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## 261 261 38 Male Waitlist 11.364917 2 43
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## 267 267 46 Male Treatment 19.599735 2 14
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## 269 269 32 Male Placebo 18.626982 3 15
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## 276 276 31 Female Placebo 18.744161 5 58
## 277 277 45 Male Placebo 28.264763 7 24
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## 281 281 31 Male Placebo 24.305054 7 59
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## 284 284 20 Female Placebo 23.508046 2 54
## 285 285 44 Male Treatment 23.214027 3 55
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## 288 288 26 Male Treatment 18.723762 7 14
## 289 289 18 Male Placebo 14.618085 5 42
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## 291 291 41 Male Treatment 18.798679 5 37
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## 295 295 31 Male Placebo 16.432054 5 57
## 296 296 36 Female Treatment 14.840605 7 22
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## 426 426 35 Male Waitlist 17.942245 1 55
## 427 427 27 Female Placebo 21.961905 6 19
## 428 428 35 Male Treatment 14.312453 6 28
## 429 429 20 Female Waitlist 19.652386 5 34
## 430 430 37 Female Waitlist 18.109733 1 15
## 431 431 50 Female Placebo 13.354369 5 29
## 432 432 38 Female Waitlist 22.229775 2 46
## 433 433 46 Male Treatment 25.066251 6 46
## 434 434 19 Male Waitlist 20.046887 2 36
## 435 435 44 Male Waitlist 25.409209 5 44
## 436 436 28 Male Waitlist 14.160332 2 14
## 437 437 21 Male Waitlist 22.110333 9 25
## 438 438 20 Female Placebo 21.946626 1 13
## 439 439 38 Female Waitlist 18.231621 5 26
## 440 440 27 Female Treatment 24.818577 1 34
## 441 441 30 Male Waitlist 21.781023 1 42
## 442 442 31 Female Placebo 18.509878 4 21
## 443 443 43 Male Placebo 17.994107 2 38
## 444 444 28 Female Treatment 16.823179 2 27
## 445 445 18 Female Treatment 20.967382 2 20
## 446 446 39 Male Placebo 12.001395 10 44
## 447 447 22 Female Treatment 14.509140 16 15
## 448 448 34 Male Treatment 11.609299 10 20
## 449 449 35 Male Placebo 20.276881 7 52
## 450 450 43 Female Treatment 21.367308 5 13
## 451 451 21 Male Treatment 28.648329 3 38
## 452 452 49 Male Waitlist 24.746938 3 31
## 453 453 31 Female Placebo 23.077753 5 40
## 454 454 43 Female Waitlist 21.973095 6 34
## 455 455 39 Male Treatment 20.917162 3 17
## 456 456 44 Male Treatment 21.387537 2 46
## 457 457 39 Male Waitlist 21.154447 2 19
## 458 458 28 Female Placebo 19.159983 4 34
## 459 459 29 Female Waitlist 21.248572 2 38
## 460 460 32 Male Treatment 22.005122 3 25
## 461 461 46 Female Waitlist 25.021638 4 38
## 462 462 30 Female Placebo 21.769572 5 58
## 463 463 17 Female Placebo 23.588788 1 30
## 464 464 27 Female Treatment 19.974765 6 27
## 465 465 25 Female Placebo 12.377031 3 60
## 466 466 29 Male Treatment 24.215494 2 34
## 467 467 21 Female Treatment 17.410842 3 28
## 468 468 27 Male Waitlist 16.932460 2 57
## 469 469 37 Male Waitlist 20.807675 4 56
## 470 470 36 Female Placebo 12.636720 7 43
## 471 471 48 Female Treatment 23.764568 1 32
## 472 472 36 Male Waitlist 19.813318 11 49
## 473 473 36 Female Treatment 19.503054 6 42
## 474 474 19 Male Placebo 20.826843 7 34
## 475 475 18 Male Waitlist 26.741348 3 49
## 476 476 34 Male Placebo 15.514271 1 17
## 477 477 18 Female Waitlist 12.333065 6 23
## 478 478 22 Male Placebo 20.687795 2 50
## 479 479 18 Female Treatment 16.211733 12 47
## 480 480 27 Male Treatment 16.746482 3 32
## 481 481 47 Male Waitlist 13.731471 3 40
## 482 482 33 Male Waitlist 19.851468 3 43
## 483 483 42 Male Placebo 20.754198 1 17
## 484 484 40 Female Treatment 22.964284 3 21
## 485 485 48 Male Waitlist 12.999226 1 36
## 486 486 27 Male Waitlist 15.270534 5 36
## 487 487 30 Male Treatment 21.138963 2 28
## 488 488 29 Male Waitlist 15.731455 13 53
## 489 489 34 Male Placebo 17.605946 9 52
## 490 490 37 Male Treatment 15.919131 4 58
## 491 491 20 Female Treatment 25.566831 6 33
## 492 492 19 Female Treatment 20.907985 4 32
## 493 493 22 Male Placebo 24.329630 5 32
## 494 494 31 Female Waitlist 21.404479 1 54
## 495 495 46 Male Placebo 22.384332 1 23
## 496 496 48 Male Placebo 19.569929 2 45
## 497 497 16 Male Treatment 21.362089 3 37
## 498 498 28 Male Waitlist 20.823564 2 55
## 499 499 32 Male Placebo 17.790241 10 59
## 500 500 36 Male Placebo 16.682421 5 28
## 501 501 19 Male Waitlist 20.897070 1 30
## 502 502 25 Female Treatment 26.004790 4 34
## 503 503 42 Female Treatment 16.136324 3 20
## 504 504 34 Female Waitlist 21.963424 7 36
## 505 505 32 Female Waitlist 21.996091 11 15
## 506 506 39 Male Waitlist 15.839340 1 35
## 507 507 33 Female Placebo 18.501674 8 18
## 508 508 24 Male Treatment 22.778783 2 52
## 509 509 20 Male Placebo 25.496369 5 40
## 510 510 21 Male Treatment 22.381152 6 40
## 511 511 37 Female Treatment 16.422991 1 51
## 512 512 37 Male Treatment 21.885998 3 34
## 513 513 31 Male Waitlist 22.950648 3 39
## 514 514 29 Male Waitlist 22.508998 7 55
## 515 515 25 Female Placebo 26.954884 6 54
## 516 516 47 Female Treatment 23.129663 6 27
## 517 517 41 Female Waitlist 24.696012 2 12
## 518 518 18 Male Treatment 18.386584 5 12
## 519 519 17 Female Waitlist 16.726535 5 46
## 520 520 30 Male Waitlist 22.869176 9 27
## 521 521 42 Female Treatment 10.648731 2 30
## 522 522 29 Male Placebo 20.773746 4 53
## 523 523 50 Male Treatment 22.097858 6 22
## 524 524 24 Female Placebo 20.782215 12 41
## 525 525 39 Female Treatment 19.184498 3 29
## 526 526 16 Female Waitlist 20.632837 1 54
## 527 527 37 Female Waitlist 13.438727 6 31
## 528 528 42 Female Placebo 23.168809 3 47
## 529 529 28 Male Waitlist 16.298207 2 43
## 530 530 41 Female Placebo 24.077541 3 15Deswegen kann man an dieser Stelle gerne im Tidyverse bleiben.
Deutschsprachiger Raum
Im deutschen Sprachraum (und auch noch in anderen Gebieten, in denen das Komma schon als Dezimaltrennzeichen verwendet wird) wird anstatt des Kommas das Semikolon zum Trennen von Werten genutzt. Wenn man sich die Datei in einem Text-Editor genauer anschaut, wird man genau das feststellen. Hat man eine solche Datei, nutzt man einfach read_csv2().
## Using ',' as decimal and '.' as grouping mark. Use read_delim() for more control.## Parsed with column specification:
## cols(
## id = col_double(),
## age = col_double(),
## sex = col_character(),
## group = col_character(),
## test_score = col_double(),
## bdi = col_double(),
## well_being = col_double()
## )## # A tibble: 530 x 7
## id age sex group test_score bdi well_being
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1 24 Male Waitlist 14.8 7 50
## 2 2 47 Male Waitlist 19.0 4 25
## 3 3 24 Female Placebo 14.6 6 42
## 4 4 28 Female Waitlist 15.4 8 59
## 5 5 43 Male Waitlist 16.2 3 12
## 6 6 25 Male Treatment 22.0 8 20
## 7 7 22 Female Placebo 16.0 7 33
## 8 8 17 Female Treatment 22.4 5 14
## 9 9 44 Female Treatment 23.0 3 32
## 10 10 20 Female Waitlist 16.5 8 37
## # ... with 520 more rowsAuch hier wieder nicht vergessen, den importierten Datensatz einem Objekt zuzuweisen!
Excel (.xlsx)
Excel-Dateien könnnen mit der Funktion read_excel() geöffnet werden.
## # A tibble: 530 x 7
## id age sex group test_score bdi well_being
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1 24 Male Waitlist 14.8 7 50
## 2 2 47 Male Waitlist 19.0 4 25
## 3 3 24 Female Placebo 14.6 6 42
## 4 4 28 Female Waitlist 15.4 8 59
## 5 5 43 Male Waitlist 16.2 3 12
## 6 6 25 Male Treatment 22.0 8 20
## 7 7 22 Female Placebo 16.0 7 33
## 8 8 17 Female Treatment 22.4 5 14
## 9 9 44 Female Treatment 23.0 3 32
## 10 10 20 Female Waitlist 16.5 8 37
## # ... with 520 more rowsAuch hier wieder nicht vergessen, den importierten Datensatz einem Objekt zuzuweisen!
SPSS (.sav)
SPSS-Dateien können mit der Funktion read_spss() geöffnet werden.
## # A tibble: 530 x 7
## id age sex group test_score bdi well_being
## <dbl> <dbl> <dbl+lbl> <dbl+lbl> <dbl> <dbl> <dbl>
## 1 1 21 1 [Male] 1 [Waitlist] 19.5 6 37
## 2 2 18 2 [Female] 2 [Treatment] 25.1 1 32
## 3 3 37 1 [Male] 1 [Waitlist] 18.4 5 37
## 4 4 36 1 [Male] 1 [Waitlist] 18.4 6 55
## 5 5 45 1 [Male] 1 [Waitlist] 23.7 3 38
## 6 6 37 2 [Female] 3 [Placebo] 13.4 3 36
## 7 7 32 1 [Male] 2 [Treatment] 18.5 2 21
## 8 8 32 2 [Female] 3 [Placebo] 25.4 3 30
## 9 9 20 2 [Female] 2 [Treatment] 15.3 3 32
## 10 10 41 1 [Male] 1 [Waitlist] 20.1 1 16
## # ... with 520 more rowsDie Variablen sex und group sehen relativ komisch aus, weil hier anscheindn Zahlen und Worte abgespeichert wurden. In SPSS speichert man Faktoren, indem einem selbst gewähltem Faktor-Level ein -Label gegeben wird. Diese behält read_spss() freundlicherweise beide für uns (zu erkennen am Typ <dbl + lbl>), damit wir damit später selbst umgehen können.
R (.rds)
Das Beste kommt zum Schluss: RDS-Dateien können mit read_rds() geöffnet werden.
## # A tibble: 530 x 7
## id age sex group test_score bdi well_being
## <int> <dbl> <fct> <fct> <dbl> <dbl> <dbl>
## 1 1 21 Male Waitlist 19.5 6 37
## 2 2 18 Female Treatment 25.1 1 32
## 3 3 37 Male Waitlist 18.4 5 37
## 4 4 36 Male Waitlist 18.4 6 55
## 5 5 45 Male Waitlist 23.7 3 38
## 6 6 37 Female Placebo 13.4 3 36
## 7 7 32 Male Treatment 18.5 2 21
## 8 8 32 Female Placebo 25.4 3 30
## 9 9 20 Female Treatment 15.3 3 32
## 10 10 41 Male Waitlist 20.1 1 16
## # ... with 520 more rowsDas Schöne an diesem Weg des Speicherns und Importierens ist, dass wir automatisch die korrekten Daten-Typen in den Variablen mit abgespeichert und eingelesen werden. Die Variablen sex und group sind nämlich Faktoren (zu erkennen am Typ <fct>), was durch das Einlesen des Datensatzes mit read_rds() berücksichtigt wurde. Wenn man noch einmal in die obigen Beispiele schaut, wird deutlich, dass das in allen anderen Methoden nicht geklappt hat. Teilt man Daten zwischen R-Nutzern, kann man das RDS-Format ohne weiteres ans Herz legen!