if (!require("pacman")) install.packages("pacman")Lade nötiges Paket: pacmanpacman::p_load(haven, psych,
sjmisc, sjPlot, writexl,
tidyverse, multilevelTools)2 Kapitel 2: Datenaufbereitung
2.1 Pakete installieren und laden
2.2 Daten einlesen
Mit load() können wir .RData Dateien einlesen (für weitergehende Infos s. auch Einführung in R, Kapitel 3.2.4.)
load("../data/df_cfa_wide.RData")2.3 Daten ansehen
Der Datensatz hat 131 Spalten (ncol()) und 100 Zeilen (nrow()) (eine pro Person).
Wie wir mit names() sehen, gibt es die Variablen id für die Personidentifikation (jede Person hat ihre eigene Nummer), a1-a5, b1-b5, und c1-c3. Da jeder Tag (1-10 Tage) von jeder Variable seine eigene Spalte bekommt (t1-t10) gibt es viele Spalten und wir nennen das Datenformat daher breites Datenformat ( wide format).
a,b und c bilden jeweils eine Skala mit 5 bzw. bei c 3 Indikatoren.
Mit head() können wir einen Blick in die Daten werfen.
ncol(df_cfa_wide)[1] 131nrow(df_cfa_wide)[1] 100names(df_cfa_wide) [1] "id" "a1_t1" "a1_t2" "a1_t3" "a1_t4" "a1_t5" "a1_t6" "a1_t7"
[9] "a1_t8" "a1_t9" "a1_t10" "a2_t1" "a2_t2" "a2_t3" "a2_t4" "a2_t5"
[17] "a2_t6" "a2_t7" "a2_t8" "a2_t9" "a2_t10" "a3_t1" "a3_t2" "a3_t3"
[25] "a3_t4" "a3_t5" "a3_t6" "a3_t7" "a3_t8" "a3_t9" "a3_t10" "a4_t1"
[33] "a4_t2" "a4_t3" "a4_t4" "a4_t5" "a4_t6" "a4_t7" "a4_t8" "a4_t9"
[41] "a4_t10" "a5_t1" "a5_t2" "a5_t3" "a5_t4" "a5_t5" "a5_t6" "a5_t7"
[49] "a5_t8" "a5_t9" "a5_t10" "b1_t1" "b1_t2" "b1_t3" "b1_t4" "b1_t5"
[57] "b1_t6" "b1_t7" "b1_t8" "b1_t9" "b1_t10" "b2_t1" "b2_t2" "b2_t3"
[65] "b2_t4" "b2_t5" "b2_t6" "b2_t7" "b2_t8" "b2_t9" "b2_t10" "b3_t1"
[73] "b3_t2" "b3_t3" "b3_t4" "b3_t5" "b3_t6" "b3_t7" "b3_t8" "b3_t9"
[81] "b3_t10" "b4_t1" "b4_t2" "b4_t3" "b4_t4" "b4_t5" "b4_t6" "b4_t7"
[89] "b4_t8" "b4_t9" "b4_t10" "b5_t1" "b5_t2" "b5_t3" "b5_t4" "b5_t5"
[97] "b5_t6" "b5_t7" "b5_t8" "b5_t9" "b5_t10" "c1_t1" "c1_t2" "c1_t3"
[105] "c1_t4" "c1_t5" "c1_t6" "c1_t7" "c1_t8" "c1_t9" "c1_t10" "c2_t1"
[113] "c2_t2" "c2_t3" "c2_t4" "c2_t5" "c2_t6" "c2_t7" "c2_t8" "c2_t9"
[121] "c2_t10" "c3_t1" "c3_t2" "c3_t3" "c3_t4" "c3_t5" "c3_t6" "c3_t7"
[129] "c3_t8" "c3_t9" "c3_t10"head(df_cfa_wide)| id | a1_t1 | a1_t2 | a1_t3 | a1_t4 | a1_t5 | a1_t6 | a1_t7 | a1_t8 | a1_t9 | a1_t10 | a2_t1 | a2_t2 | a2_t3 | a2_t4 | a2_t5 | a2_t6 | a2_t7 | a2_t8 | a2_t9 | a2_t10 | a3_t1 | a3_t2 | a3_t3 | a3_t4 | a3_t5 | a3_t6 | a3_t7 | a3_t8 | a3_t9 | a3_t10 | a4_t1 | a4_t2 | a4_t3 | a4_t4 | a4_t5 | a4_t6 | a4_t7 | a4_t8 | a4_t9 | a4_t10 | a5_t1 | a5_t2 | a5_t3 | a5_t4 | a5_t5 | a5_t6 | a5_t7 | a5_t8 | a5_t9 | a5_t10 | b1_t1 | b1_t2 | b1_t3 | b1_t4 | b1_t5 | b1_t6 | b1_t7 | b1_t8 | b1_t9 | b1_t10 | b2_t1 | b2_t2 | b2_t3 | b2_t4 | b2_t5 | b2_t6 | b2_t7 | b2_t8 | b2_t9 | b2_t10 | b3_t1 | b3_t2 | b3_t3 | b3_t4 | b3_t5 | b3_t6 | b3_t7 | b3_t8 | b3_t9 | b3_t10 | b4_t1 | b4_t2 | b4_t3 | b4_t4 | b4_t5 | b4_t6 | b4_t7 | b4_t8 | b4_t9 | b4_t10 | b5_t1 | b5_t2 | b5_t3 | b5_t4 | b5_t5 | b5_t6 | b5_t7 | b5_t8 | b5_t9 | b5_t10 | c1_t1 | c1_t2 | c1_t3 | c1_t4 | c1_t5 | c1_t6 | c1_t7 | c1_t8 | c1_t9 | c1_t10 | c2_t1 | c2_t2 | c2_t3 | c2_t4 | c2_t5 | c2_t6 | c2_t7 | c2_t8 | c2_t9 | c2_t10 | c3_t1 | c3_t2 | c3_t3 | c3_t4 | c3_t5 | c3_t6 | c3_t7 | c3_t8 | c3_t9 | c3_t10 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 3.336252 | 2.149828 | 1.613518 | 3.351085 | 3.320900 | 4.173775 | 3.653771 | 1.926106 | 5.621661 | 4.948758 | 3.380463 | 5.110309 | 3.004133 | 3.852294 | 3.694431 | 5.658948 | 4.973751 | 4.999227 | 5.123906 | 6.090460 | 3.223528 | 5.678413 | 2.503233 | 4.972103 | 4.703663 | 3.273027 | 3.187604 | 3.664482 | 5.464105 | 3.765162 | 4.399347 | 2.765556 | 1.898476 | 2.612551 | 3.476859 | 4.192700 | 2.856953 | 4.112356 | 4.579488 | 3.134414 | 3.542602 | 2.387270 | 2.807670 | 3.233779 | 4.210036 | 3.565438 | 3.293944 | 3.909548 | 5.635526 | 4.620410 | 3.370896 | 2.486919 | 0.991958 | 2.248097 | 1.953658 | 1.544111 | 2.103968 | 1.763424 | 3.087552 | 1.618470 | 1.697640 | 0.952220 | -0.685685 | 0.004118 | 0.675750 | -0.373186 | 1.938302 | 0.447036 | 3.860272 | 1.211009 | 0.584167 | 2.101517 | 0.003147 | 0.711264 | 0.921623 | 1.303905 | 1.201749 | 0.899882 | 1.797242 | 0.734580 | 1.576649 | 0.331000 | -0.517730 | 2.185949 | 0.284884 | 1.687402 | 1.388842 | 0.160632 | 2.144261 | 1.205662 | 2.847195 | 2.552073 | 1.955846 | 2.028044 | 2.361978 | 2.539967 | 2.125510 | 1.206909 | 4.844395 | 4.320178 | 4.325797 | 3.364003 | 2.582169 | 2.918614 | 2.829237 | 4.141474 | 2.711044 | 4.787052 | 4.594232 | 2.511473 | 1.030847 | 5.711729 | 2.659834 | 3.163276 | 3.910432 | 5.234767 | 5.515933 | 5.206142 | 4.431722 | 4.953160 | 3.822098 | 3.613752 | 2.155132 | 2.463234 | 2.739723 | 3.409232 | 3.488225 | 1.453246 | 4.117747 | 3.745528 |
| 2 | 3.707018 | 2.377418 | 1.854018 | 4.632683 | 2.479164 | 2.234632 | 4.485121 | 4.011457 | 2.427658 | 2.600831 | 3.996684 | 3.074568 | 3.716554 | 3.796472 | 3.108752 | 2.922636 | 4.654462 | 4.431057 | 3.497468 | 4.130753 | 0.810690 | 2.874266 | 3.669414 | 2.478778 | 2.406462 | 2.515853 | 3.129976 | 3.718570 | 2.542158 | 2.606330 | 3.515638 | 4.128262 | 3.577047 | 4.258751 | 4.238509 | 3.267878 | 4.119021 | 2.712250 | 2.601381 | 5.293957 | 2.907960 | 2.834590 | 3.827659 | 5.278379 | 3.967381 | 4.134327 | 5.923659 | 4.251589 | 3.292468 | 3.639972 | 2.323243 | 2.042416 | 2.601619 | 0.235322 | 1.990456 | 3.388960 | 0.995331 | 3.325457 | 2.207927 | 0.221311 | 2.990608 | 1.476811 | 2.349784 | 3.097647 | 2.047705 | 2.722032 | 3.112804 | 1.368452 | 2.251611 | 1.587188 | 1.013232 | 2.926646 | 2.095343 | 2.858416 | 3.019904 | 1.726097 | 3.515165 | 1.348090 | 1.890474 | 2.311975 | 1.190393 | 2.184150 | 1.549624 | 0.452129 | 1.513757 | 1.432716 | 1.950500 | 2.007023 | 0.249291 | -0.504312 | 2.010378 | 4.018477 | 3.410691 | 3.063017 | 3.897992 | 2.469268 | 2.906900 | 2.914951 | 0.608605 | 2.239330 | 2.576924 | 1.208644 | 2.606353 | 3.886131 | 3.856964 | 3.332155 | 0.597512 | 2.731819 | 2.110466 | 2.687502 | -1.710033 | -0.670529 | -0.330814 | -0.601643 | -0.251286 | 0.439045 | 0.734464 | 0.066167 | -0.849054 | -0.757472 | 0.351828 | 0.095795 | 0.897598 | 2.615174 | 0.455632 | 2.211615 | -0.235576 | 0.389909 | -0.160126 | 1.833746 |
| 3 | 5.177071 | -0.128053 | 2.797632 | 0.303081 | 4.314881 | 2.337009 | 3.575595 | 1.818855 | -0.306364 | 1.614770 | 2.958702 | 2.735971 | 2.978813 | 4.107814 | 4.661105 | 3.033462 | 2.912859 | 3.754350 | 1.986925 | 2.453018 | 5.346752 | 4.501127 | 4.825683 | 4.908117 | 5.724673 | 5.153263 | 4.766531 | 5.198076 | 1.471673 | 3.829653 | 4.772178 | 2.755005 | 4.204091 | 2.631057 | 4.744125 | 3.983818 | 2.875329 | 3.684137 | 1.252706 | 1.649124 | 5.782683 | 4.648184 | 3.675335 | 5.918703 | 5.729757 | 5.027148 | 4.950459 | 5.555384 | 2.874046 | 2.074345 | 3.034341 | 5.163917 | 1.010752 | 1.666880 | 1.295466 | 1.477020 | 1.009490 | 1.175458 | 1.284057 | 3.434230 | 4.263902 | 3.116927 | 2.598624 | 2.281766 | 4.053404 | 2.417430 | 1.154442 | 2.357182 | -0.103946 | 2.599341 | 3.981286 | 1.433954 | 2.329805 | 3.582351 | 2.509821 | -0.341738 | 1.558373 | 2.687843 | 1.337345 | 3.732375 | 3.988386 | 2.509786 | 2.058585 | 3.740040 | 3.256067 | 3.500106 | 0.782067 | 3.601466 | 0.922850 | 4.635307 | 3.024556 | 1.150083 | 3.052157 | 3.163149 | 2.655710 | 0.093386 | -0.073910 | 2.227515 | 1.712568 | 2.898763 | 2.820979 | 1.664689 | 2.688626 | 2.657114 | 1.381627 | 2.417342 | 1.443182 | 2.776111 | 2.248535 | 2.684371 | 1.748407 | 2.869058 | 1.052509 | 3.174517 | 0.118054 | 0.789280 | 2.679447 | 0.740977 | 1.135104 | 0.980832 | 1.537544 | 2.950929 | 1.429920 | 2.534687 | 3.792833 | 1.161559 | 0.105494 | 2.915895 | 1.714465 | 2.815621 |
| 4 | 4.705740 | 3.597286 | 2.948942 | 2.098839 | 2.783596 | 4.420878 | 1.358347 | 3.848268 | 3.714631 | 2.284839 | 4.038396 | 4.916627 | 2.739167 | 3.144930 | 2.224095 | 2.968036 | 2.365223 | 3.665594 | 2.585768 | 3.208773 | 5.110747 | 4.059650 | 3.327694 | 4.411642 | 3.937307 | 5.154507 | 3.126869 | 4.554551 | 3.766840 | 4.039097 | 5.965881 | 5.027371 | 4.130869 | 5.175652 | 4.037358 | 5.245027 | 3.205133 | 3.004498 | 6.786242 | 4.499281 | 3.958800 | 3.838211 | 3.211688 | 2.475119 | 2.191741 | 2.052676 | 2.687921 | 2.079803 | 3.929772 | 1.621818 | 3.621642 | 3.197431 | 2.838179 | 1.280215 | 1.746913 | 2.267367 | 1.597599 | 2.441468 | 1.211542 | 4.138991 | 1.342898 | 1.821522 | 1.688056 | 2.463308 | 2.030884 | 0.815056 | 0.953910 | 1.729489 | 1.332197 | 3.093290 | 2.400477 | 2.873127 | 1.997939 | 1.791812 | -0.960833 | 0.893946 | 0.647417 | 1.110916 | 1.748169 | 2.121472 | 1.952886 | 2.904973 | 0.878772 | 1.269630 | 0.835469 | -1.070358 | -0.541286 | 1.063053 | 1.485312 | 3.607293 | 4.027168 | 3.804839 | 2.787383 | 2.807136 | 2.706166 | 2.527843 | 1.675010 | 2.839636 | 3.124758 | 4.327591 | -0.571315 | 2.285224 | 1.057377 | 1.803651 | 0.992669 | 1.980404 | 3.310448 | 1.662047 | 2.537811 | 1.245361 | 1.490392 | 0.456182 | 1.328019 | 1.697168 | 0.693560 | 0.252673 | 2.728439 | 1.103803 | 1.332902 | 0.163830 | 0.043749 | 0.744745 | 0.204956 | -0.370906 | -1.022587 | -0.403501 | 1.095947 | 0.274934 | 1.170815 | -1.658070 |
| 5 | 3.949976 | 3.330208 | 5.413867 | 3.719810 | 5.798861 | 3.088709 | 4.012137 | 5.875070 | 3.181456 | 4.931577 | 3.317796 | 5.810359 | 5.343310 | 3.928448 | 3.798973 | 3.932334 | 2.986737 | 6.926680 | 4.932457 | 6.716081 | 3.673230 | 3.927261 | 4.515421 | 3.791699 | 4.846026 | 3.398835 | 2.273474 | 4.371252 | 1.762524 | 3.275910 | 5.841838 | 8.072948 | 6.721671 | 6.987111 | 5.944237 | 4.489158 | 4.921571 | 7.603350 | 6.165390 | 6.983403 | 3.389670 | 5.893949 | 3.749111 | 4.411794 | 2.873486 | 2.611568 | 1.286889 | 4.646997 | 3.107310 | 4.433750 | 1.701253 | 0.278520 | 3.249563 | 2.181162 | 3.107517 | 0.092424 | 1.985821 | 1.740418 | 3.187945 | 2.244310 | 3.361174 | 3.497546 | 3.372372 | 2.430237 | 5.397523 | 3.270945 | 3.053623 | 1.919141 | 3.688502 | 2.961518 | 4.337413 | 3.496237 | 5.441549 | 3.444545 | 4.486709 | 4.455020 | 2.470650 | 5.844872 | 4.583409 | 6.108861 | 4.302733 | 2.764837 | 3.876676 | 3.527337 | 5.128605 | 2.163503 | 1.886539 | 1.734753 | 3.437857 | 3.740089 | 4.179862 | 3.775715 | 5.299768 | 2.953386 | 3.807893 | 2.537854 | 2.901724 | 4.785203 | 5.926126 | 4.928503 | 3.053878 | 1.885364 | 4.448834 | 3.900540 | 4.728735 | 2.129385 | 3.805631 | 2.089345 | 3.743529 | 2.992289 | 1.790360 | 1.515488 | 3.618075 | 3.781413 | 4.205560 | 1.303834 | 3.673459 | 3.327604 | 3.160806 | 3.722098 | 1.817841 | 2.362278 | 2.812572 | 4.554119 | 3.287976 | 3.279677 | 1.435943 | 3.242930 | 0.500366 | 5.079764 |
| 6 | 2.274255 | 2.290222 | 1.376191 | 2.173916 | 2.099371 | 3.312339 | 2.172030 | 2.595873 | 2.289308 | 1.580778 | 2.787734 | 2.067549 | 2.180852 | 3.302369 | 1.863362 | 3.760236 | 2.294264 | 2.135892 | 1.550506 | 2.432511 | 3.703579 | 2.047107 | 3.065654 | 1.978181 | 2.963593 | 2.955959 | 3.468359 | 4.343268 | 3.525407 | 3.837417 | 4.151001 | 0.835873 | 1.884775 | 2.608722 | 3.012065 | 2.776904 | 2.578840 | 3.399579 | 3.577152 | 3.039595 | 1.512579 | 0.621510 | 1.092427 | 1.911605 | 1.368582 | 2.511084 | 0.839038 | 3.272104 | 1.121144 | 1.740897 | 2.996994 | -1.267884 | 2.586945 | 2.184757 | -0.124601 | 2.601747 | 1.747253 | 2.520691 | 4.051763 | 2.738223 | 1.022146 | -1.129770 | 1.219736 | -0.032099 | 2.843606 | -1.122089 | 0.739502 | 2.138073 | 0.272516 | 0.625661 | 2.134281 | 0.231331 | 2.861858 | 1.303966 | 1.199804 | 1.300905 | 1.312581 | 2.266003 | 3.106999 | 2.869239 | 2.308207 | 1.068449 | 2.414964 | 1.894137 | 2.681821 | 0.512435 | 2.125864 | 2.787195 | 2.595406 | 2.191860 | 2.267998 | 0.203488 | 2.181351 | 1.670791 | 0.222494 | 0.947280 | 2.464313 | 2.733245 | 2.599860 | 1.042442 | 1.648781 | 0.598068 | 0.225724 | 0.521410 | 1.669001 | 1.384766 | 2.299040 | 2.014711 | 2.088084 | 0.651604 | 2.356883 | 0.718648 | 1.309239 | 2.011107 | 1.617811 | 3.844703 | 1.838274 | 2.215482 | 2.364317 | 1.437971 | 2.745956 | 3.067538 | 3.096565 | 2.927631 | 4.206480 | 3.969140 | 5.377585 | 2.509150 | 2.817378 | 1.073844 |
2.4 Daten transformieren: Langformat
Als erstes transformieren wir die Daten vom Breit- ins Langformat, so dass jede Messung (Tag 1-Tag 10) eine eigene Zeile bekommt. Diese Variable nennen wir “time”. Im ersten Schritt machen wir mit pivot_longer() den Datensatz seeehr lang, es bekommt nämlich jede Messung von jeder Variable ihre eigenen Zeile. Wir machen den Datensatz dann im zweiten Schritt mit pivot_wider() wieder etwas breiter mit dem Ziel, eine Zeile pro Person und Tag zu bekommen, und jeweils eine Spalte pro Item.
Die Funktionsweise von pivot_longer() und pivot_wider() ist in der Einführung zu R, Kapitel 4.3 ausführlicher beschrieben.
df_cfa_superlong <- df_cfa_wide |>
pivot_longer(
cols = -id, # All columns except id
names_to = c("variable", "time"), # Namensgebende Spalten sollen heissen: variable, time
names_sep = "_t"
# Namensgebende Spalten anhand "_t_ separieren (z.B. "a1_t1" --> gespalten in "a1" und "1", "_t" ist der Separator)
)
head(df_cfa_superlong)| id | variable | time | value |
|---|---|---|---|
| 1 | a1 | 1 | 3.336252 |
| 1 | a1 | 2 | 2.149828 |
| 1 | a1 | 3 | 1.613518 |
| 1 | a1 | 4 | 3.351085 |
| 1 | a1 | 5 | 3.320900 |
| 1 | a1 | 6 | 4.173775 |
df_cfa_long <- df_cfa_superlong |>
pivot_wider(names_from = variable, #namen aus variable
values_from = value) |> # werte aus "value" (Standardname)
mutate(time = as.numeric(time)) # time ist eine Zahl von 1-10, wurde aber zuvor als Character-Vector abgespeichert
head(df_cfa_long)| id | time | a1 | a2 | a3 | a4 | a5 | b1 | b2 | b3 | b4 | b5 | c1 | c2 | c3 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 3.336252 | 3.380463 | 3.223528 | 4.399347 | 3.542602 | 3.370896 | 1.697640 | 0.584167 | 1.576649 | 2.847195 | 4.325797 | 1.030847 | 3.822098 |
| 1 | 2 | 2.149828 | 5.110309 | 5.678413 | 2.765556 | 2.387270 | 2.486919 | 0.952220 | 2.101517 | 0.331000 | 2.552073 | 3.364003 | 5.711729 | 3.613752 |
| 1 | 3 | 1.613518 | 3.004133 | 2.503233 | 1.898476 | 2.807670 | 0.991958 | -0.685685 | 0.003147 | -0.517730 | 1.955846 | 2.582169 | 2.659834 | 2.155132 |
| 1 | 4 | 3.351085 | 3.852294 | 4.972103 | 2.612551 | 3.233779 | 2.248097 | 0.004118 | 0.711264 | 2.185949 | 2.028044 | 2.918614 | 3.163276 | 2.463234 |
| 1 | 5 | 3.320900 | 3.694431 | 4.703663 | 3.476859 | 4.210036 | 1.953658 | 0.675750 | 0.921623 | 0.284884 | 2.361978 | 2.829237 | 3.910432 | 2.739723 |
| 1 | 6 | 4.173775 | 5.658948 | 3.273027 | 4.192700 | 3.565438 | 1.544111 | -0.373186 | 1.303905 | 1.687402 | 2.539967 | 4.141474 | 5.234767 | 3.409232 |
Das Ergebnis:
- eine Zeile pro Person (id) und Messung (time). Die Spalten id und time identifizieren also für jede Zeile, von welcher Person und welchem Tag die Werte in den folgenden Spalten kommen.
- eine Spalte pro Variable (a1-a5, b1-b5, c1-c3)
2.5 Daten transformieren: Skalenscores erstellen
Als nächstes können wir mittels summarise() die Skalenscores erstellen. Wir verwenden eine simple Form der Skalenerstellung bei dem der Mittelwert aller vorhandenen Items einer Skala rowMeans(...) verwendet wird.
df_cfa_long_scores <- df_cfa_long |> group_by(id, time) |>
summarise(
a = rowMeans(across(starts_with("a")), na.rm = TRUE),
b = rowMeans(across(starts_with("b")), na.rm = TRUE),
c = rowMeans(across(starts_with("c")), na.rm = TRUE),
.groups = "drop" # group_by() wieder aufheben für den finalen Datensatz
)(Best-practice ist es bei fehlenden Daten genau hinzuschauen und nur dann einen Skalenwert zu erstellen, wenn die Personen zu einen gewissen Prozentsatz aller Items eine Antwort geben (etwa 2/3). Eine solche Funktion könnten wir programmieren, lassen es aber für das Beispiel weg.)
2.6 Daten transformieren: Zentrierung
Für die spätere Verwendung zerlegen wir die Rohvariablen mittels person-mean Zentrierung. Wir zentrieren wir die Skalenvariablen, die täglich gemessen werden (aber nicht Baseline-Variablen), mittels de_mean(). de_mean() nimmt als Argumente (a) mit Komma getrennte Namen der Variablen, die wir zentrieren wollen (mehrere auf einmal ist möglich), (b) mittels grp = Argument die identifizierende Variable für die Gruppenzugehörigkeit in Anführungszeichen ("id".
df_cfa_long_scores <- df_cfa_long_scores |>
de_mean(a,b, c, grp = "id")
head(df_cfa_long_scores)| id | time | a | b | c | a_dm | b_dm | c_dm | a_gm | b_gm | c_gm |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 3.576438 | 2.0153094 | 3.059581 | -0.2566380 | 0.4476044 | -0.5267811 | 3.833076 | 1.567705 | 3.586362 |
| 1 | 2 | 3.618275 | 1.6847458 | 4.229828 | -0.2148012 | 0.1170408 | 0.6434662 | 3.833076 | 1.567705 | 3.586362 |
| 1 | 3 | 2.365406 | 0.3495072 | 2.465712 | -1.4676704 | -1.2181978 | -1.1206501 | 3.833076 | 1.567705 | 3.586362 |
| 1 | 4 | 3.604362 | 1.4354944 | 2.848375 | -0.2287140 | -0.1322106 | -0.7379871 | 3.833076 | 1.567705 | 3.586362 |
| 1 | 5 | 3.881178 | 1.2395786 | 3.159797 | 0.0481014 | -0.3281264 | -0.4265645 | 3.833076 | 1.567705 | 3.586362 |
| 1 | 6 | 4.172778 | 1.3404398 | 4.261824 | 0.3397012 | -0.2272652 | 0.6754625 | 3.833076 | 1.567705 | 3.586362 |
Als Ergebnis erhalten wir die zusätzlichen Variablen a, b, c jeweils mit “_dm” und “_gm”. Was verbirgt sich dahinter? Wir haben einen Datensatz mit den unzentrierten / Rohvariablen der Skalen (ohne Suffix), den zentrierten Variablen (Suffix _dm) und den Mittelwerten der Personen (Suffix _gm), den wir zur weiteren Verwendung auch abspeichern.
Damit ist die Transformation der Daten abgeschlossen! Wir können die Datensätze - “df_cfa_long” für die Items und “df_cfa_long_scores” für die Skalen nun abspeichern.
save(df_cfa_long, df_cfa_long_scores, file = "../data/df_cfa_long.RData")2.7 Überprüfe dein Verständnis
beispiel_zentrierung <- df_cfa_long_scores |>
filter(id == 1) |>
select(id, time, a, a_dm, a_gm)
beispiel_zentrierung| id | time | a | a_dm | a_gm |
|---|---|---|---|---|
| 1 | 1 | 3.576438 | -0.2566380 | 3.833076 |
| 1 | 2 | 3.618275 | -0.2148012 | 3.833076 |
| 1 | 3 | 2.365406 | -1.4676704 | 3.833076 |
| 1 | 4 | 3.604362 | -0.2287140 | 3.833076 |
| 1 | 5 | 3.881178 | 0.0481014 | 3.833076 |
| 1 | 6 | 4.172778 | 0.3397012 | 3.833076 |
| 1 | 7 | 3.593205 | -0.2398718 | 3.833076 |
| 1 | 8 | 3.722344 | -0.1107326 | 3.833076 |
| 1 | 9 | 5.284937 | 1.4518608 | 3.833076 |
| 1 | 10 | 4.511841 | 0.6787644 | 3.833076 |
Warum ist der Wert, den Person 1 in “a_gm” hat, in jeder Zeile gleich, nicht aber bei “a_dm” und “a”? Wie müsste man “a” transformieren, damit man auf “a_gm” und “a_dm” kommt? Denke an die mathematischen Operationen die du in mutate() eingeben müsstest, wie Plus, Minus ( +, -, /, …) und die Funktion für Mittelwert ( mean()).
Lösung
Die Variable a ist die Rohvariable, die den gemessenen Wert auf der Skala an jedem Tag angibt. Variablen mit “_gm” und “_dm” werden von sjmisc::de_mean() erstellt. Variablen mit “_gm” stehen für den Mittelwert der Person über alle Tage hinweg. Variablen mit “_dm” stehen für die täglichen Abweichungen vom Mittelwert der Person. “a_dm” ergibt sich aus: \(a = a - a\_gm\). Wir können dies auch in R replizieren mit:
beispiel_zentrierung |>
mutate(a_gm_manuell = mean(a),
a_dm_manuell = a - a_gm_manuell)| id | time | a | a_dm | a_gm | a_gm_manuell | a_dm_manuell |
|---|---|---|---|---|---|---|
| 1 | 1 | 3.576438 | -0.2566380 | 3.833076 | 3.833076 | -0.2566380 |
| 1 | 2 | 3.618275 | -0.2148012 | 3.833076 | 3.833076 | -0.2148012 |
| 1 | 3 | 2.365406 | -1.4676704 | 3.833076 | 3.833076 | -1.4676704 |
| 1 | 4 | 3.604362 | -0.2287140 | 3.833076 | 3.833076 | -0.2287140 |
| 1 | 5 | 3.881178 | 0.0481014 | 3.833076 | 3.833076 | 0.0481014 |
| 1 | 6 | 4.172778 | 0.3397012 | 3.833076 | 3.833076 | 0.3397012 |
| 1 | 7 | 3.593205 | -0.2398718 | 3.833076 | 3.833076 | -0.2398718 |
| 1 | 8 | 3.722344 | -0.1107326 | 3.833076 | 3.833076 | -0.1107326 |
| 1 | 9 | 5.284937 | 1.4518608 | 3.833076 | 3.833076 | 1.4518608 |
| 1 | 10 | 4.511841 | 0.6787644 | 3.833076 | 3.833076 | 0.6787644 |
2.8 Übung Datenaufbereitung
Schau dir den Datensatz df_cfa_exercise an und repliziere die Schritte oben:
- Daten in Langformat transformieren und als
df_uebung_lang <-zuweisen. - Skalenscores für x und y erstellen und als
df_uebung_lang_scores <-zuweisen. - Skalenscores für x und y zentrieren (weiterhin in
df_uebung_lang_scores).
Du kannst dazu den Code von oben wiederverwenden und auf den hier verwendeten Datensatz und seine Variablen anpassen.
load("../data/df_cfa_exercise.RData")
head(df_cfa_exercise)| id | x1_t1 | x1_t2 | x1_t3 | x1_t4 | x1_t5 | x1_t6 | x1_t7 | x1_t8 | x1_t9 | x1_t10 | x2_t1 | x2_t2 | x2_t3 | x2_t4 | x2_t5 | x2_t6 | x2_t7 | x2_t8 | x2_t9 | x2_t10 | x3_t1 | x3_t2 | x3_t3 | x3_t4 | x3_t5 | x3_t6 | x3_t7 | x3_t8 | x3_t9 | x3_t10 | x4_t1 | x4_t2 | x4_t3 | x4_t4 | x4_t5 | x4_t6 | x4_t7 | x4_t8 | x4_t9 | x4_t10 | x5_t1 | x5_t2 | x5_t3 | x5_t4 | x5_t5 | x5_t6 | x5_t7 | x5_t8 | x5_t9 | x5_t10 | y1_t1 | y1_t2 | y1_t3 | y1_t4 | y1_t5 | y1_t6 | y1_t7 | y1_t8 | y1_t9 | y1_t10 | y2_t1 | y2_t2 | y2_t3 | y2_t4 | y2_t5 | y2_t6 | y2_t7 | y2_t8 | y2_t9 | y2_t10 | y3_t1 | y3_t2 | y3_t3 | y3_t4 | y3_t5 | y3_t6 | y3_t7 | y3_t8 | y3_t9 | y3_t10 | y4_t1 | y4_t2 | y4_t3 | y4_t4 | y4_t5 | y4_t6 | y4_t7 | y4_t8 | y4_t9 | y4_t10 | y5_t1 | y5_t2 | y5_t3 | y5_t4 | y5_t5 | y5_t6 | y5_t7 | y5_t8 | y5_t9 | y5_t10 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 4.927017 | 3.484359 | 5.209319 | 3.777327 | 2.822955 | 2.874990 | 4.399279 | 3.999338 | 2.829229 | 4.534257 | 3.569244 | 3.716360 | 4.025572 | 2.601702 | 4.917407 | 4.417907 | 5.836183 | 2.584864 | 2.416726 | 3.428733 | 3.123268 | 2.200183 | 2.416466 | 2.600930 | 2.179303 | 3.370835 | 5.833981 | 3.574207 | 2.399534 | 1.756436 | 5.759150 | 4.865658 | 5.106690 | 4.142291 | 4.984553 | 4.902819 | 7.088255 | 6.024145 | 4.119966 | 4.134776 | 4.700258 | 3.579269 | 5.881804 | 4.111085 | 4.600078 | 4.055922 | 4.496880 | 4.373719 | 4.322336 | 3.912015 | 4.329297 | 3.597471 | 3.552542 | 3.052100 | 2.592308 | 3.082656 | 4.184377 | 4.064089 | 3.642032 | 2.777390 | 4.477198 | 4.454955 | 4.760682 | 3.288172 | 5.678996 | 4.679951 | 4.928690 | 5.166207 | 3.485148 | 3.454632 | 2.848577 | 5.551165 | 3.769263 | 3.649328 | 2.271035 | 5.264214 | 3.832915 | 3.875455 | 2.525079 | 1.163129 | 4.678402 | 4.682683 | 3.607138 | 4.363976 | 3.401883 | 5.139966 | 5.210060 | 4.713772 | 3.424733 | 2.631818 | 3.480544 | 3.221823 | 3.853132 | 3.120772 | 3.842651 | 2.815425 | 3.273848 | 3.126467 | 3.647887 | 3.895862 |
| 2 | 5.024072 | 2.470240 | 2.259750 | 3.383971 | 3.405628 | 2.925394 | 3.722530 | 3.104064 | 4.068848 | 2.923166 | 3.661601 | 3.732475 | 2.084769 | 3.978517 | 2.855392 | 3.381118 | 3.865699 | 3.911571 | 3.102266 | 2.858461 | 5.186708 | 1.540100 | 3.453976 | 1.671304 | 4.397883 | 4.996236 | 3.609226 | 2.266511 | 5.332266 | 5.041631 | 2.577130 | 1.182058 | 2.648178 | 2.580964 | 4.378121 | 2.015839 | 2.477425 | 2.718602 | 2.512741 | 3.104794 | 2.425656 | 2.302399 | 1.261294 | 2.294336 | 0.776553 | 3.169805 | 2.850572 | 2.683466 | 2.756426 | 3.199966 | 3.488302 | 3.348598 | 1.567981 | 2.689270 | 2.093631 | 0.946151 | 2.760766 | 1.974521 | 0.788326 | 1.546632 | 3.613495 | 3.002835 | 2.618250 | 3.474268 | 4.107470 | 3.950412 | 3.461374 | 3.162900 | 2.504398 | 3.957139 | 3.269670 | 2.924702 | 2.951385 | 1.403080 | 1.747677 | 3.671060 | 2.755273 | 2.860291 | 3.582403 | 2.855433 | 4.296437 | 4.459933 | 3.877955 | 3.542746 | 4.623289 | 5.768592 | 2.820453 | 4.347125 | 5.008321 | 5.628651 | 3.446137 | 3.354074 | 0.216899 | 1.984132 | 2.551348 | 1.010641 | 3.200017 | 1.600360 | 1.541323 | 3.561905 |
| 3 | 3.661946 | 4.515898 | 2.627455 | 2.097116 | 3.295632 | 4.674215 | 3.854248 | 3.430167 | 3.423922 | 4.346313 | 3.369146 | 2.583655 | 2.805899 | 2.629365 | 2.759572 | 2.753171 | 1.063016 | 2.180655 | 3.397097 | 3.797167 | 3.784757 | 3.774945 | 1.163201 | 2.880376 | 2.329117 | 5.686030 | 3.881392 | 3.907517 | 3.697921 | 3.270770 | 2.040071 | 4.645036 | 2.312931 | 2.106188 | 3.517961 | 3.099783 | 3.312862 | 4.134669 | 3.522531 | 2.951239 | 3.113105 | 2.513375 | 2.887626 | 4.867711 | 4.787919 | 2.084980 | 4.140326 | 3.036390 | 4.304286 | 3.375258 | 1.956426 | 2.710257 | 2.345528 | 2.025919 | 1.763310 | 1.487463 | 1.435715 | 3.255219 | 3.430917 | 2.560493 | 2.972647 | 3.841999 | 2.321647 | 5.547948 | 2.755463 | 3.135102 | 1.252126 | 1.455243 | 0.126571 | 0.754453 | 3.451330 | 2.634979 | 2.051891 | 3.677636 | 4.382111 | 0.630656 | 2.520796 | 2.908761 | -0.251480 | 1.706827 | 4.334217 | 3.310795 | 3.634877 | 3.788040 | 3.997747 | 3.418240 | 1.451858 | 5.332129 | 3.107325 | 2.796257 | 3.441736 | 1.732713 | 4.678564 | 3.527282 | 4.910696 | 3.960887 | 2.782757 | 3.427556 | 4.629581 | 4.084214 |
| 4 | 1.933604 | 1.835414 | 4.246882 | 3.229524 | 2.456309 | 2.818070 | 2.602155 | 3.215303 | 4.200392 | 2.272876 | 0.706912 | -0.801259 | 1.640656 | 1.587674 | 1.470820 | 2.430678 | 1.819882 | 1.695119 | 1.338284 | 0.605444 | 0.133074 | 0.158037 | 1.792941 | 1.790514 | 1.269408 | 3.353523 | 2.179509 | 1.409529 | 0.888444 | 1.224938 | 0.326793 | 0.174798 | 2.335327 | 1.525558 | -0.955707 | 3.610542 | 1.960530 | -0.865120 | 1.385375 | 1.430312 | 0.685265 | -0.230309 | 0.633548 | 1.199429 | 0.310757 | 2.375445 | 1.869323 | 0.641068 | 1.674369 | 1.870899 | 3.087726 | 0.985564 | 0.784520 | 2.725897 | 0.850097 | 0.685824 | 1.168844 | -0.239602 | 2.799497 | 2.131838 | 1.596182 | 1.458913 | 3.630159 | 2.387079 | 0.462230 | 0.650202 | 2.356301 | 0.755215 | 2.026497 | 2.441231 | 2.790539 | 2.575200 | 3.509842 | 3.116568 | 3.452843 | 3.093494 | 1.836035 | 2.222184 | 2.761235 | 4.237117 | 3.075454 | 1.881486 | 2.553940 | 1.045851 | 3.202694 | 4.161503 | 2.702880 | 0.817310 | 2.733675 | 1.369864 | 0.687845 | 2.150121 | 1.293763 | 0.737558 | 0.793559 | 1.293628 | 1.312178 | -1.925199 | 0.168729 | 1.409099 |
| 5 | 2.274807 | 2.908011 | 3.179267 | 2.941155 | 3.710268 | 2.717757 | 3.510822 | 1.550497 | 2.728217 | 4.947419 | 4.312907 | 1.624997 | 3.450676 | 3.296389 | 4.639991 | 4.428850 | 4.773291 | 2.273139 | 2.820446 | 3.868664 | 4.675249 | 2.412898 | 3.691474 | 4.295843 | 4.288994 | 3.679321 | 3.664403 | 3.449730 | 4.627416 | 5.374093 | 3.114936 | 0.937293 | 3.231832 | 2.031238 | 2.033217 | 0.704127 | 1.766480 | 1.422281 | 1.951835 | 3.429262 | 2.336261 | 3.616157 | 3.631813 | 3.258421 | 5.976209 | 4.089613 | 2.896231 | 4.596477 | 4.129096 | 7.093438 | 3.593002 | 3.190479 | 2.211968 | 2.500292 | 3.999353 | 2.826291 | 2.768957 | 2.979500 | 3.155079 | 3.074007 | 2.688911 | 2.680939 | 4.108605 | 2.347372 | 3.353903 | 3.417996 | 3.602248 | 3.722665 | 1.867266 | 4.398309 | 2.672452 | 3.234202 | 4.512234 | 1.011391 | 2.736290 | 2.495329 | 2.283781 | 0.979636 | 2.101990 | 3.646597 | 4.168885 | 6.011056 | 5.576807 | 4.452524 | 5.109755 | 3.539751 | 5.203918 | 6.884093 | 6.758122 | 4.835822 | 1.016782 | 1.853578 | 2.498811 | 3.096292 | 2.572159 | 2.496271 | 1.515257 | 1.183772 | 3.329594 | 2.744476 |
| 6 | 3.184660 | 4.046734 | 3.540150 | 4.059281 | 2.374465 | 2.067087 | 3.390182 | 3.492470 | 3.661657 | 1.892471 | 2.910669 | 2.351059 | 0.726541 | 1.928325 | -0.062322 | 1.306896 | 0.272615 | 1.463914 | 2.076900 | 2.569456 | 5.019716 | 2.328710 | 2.837884 | 4.043339 | 1.947459 | 2.449519 | 3.065598 | 4.321089 | 2.036927 | 1.389769 | 3.537501 | 4.318936 | 1.631771 | 3.685972 | 3.609319 | 1.645394 | 2.917829 | 2.317283 | 2.267146 | 3.963144 | 3.536972 | 3.295575 | 3.075710 | 3.460675 | 3.224478 | 1.660497 | 3.093049 | 2.421162 | 2.188736 | 3.557481 | 0.309582 | 1.857645 | 1.819382 | 1.085073 | 2.427644 | 1.988539 | 2.246552 | 2.295530 | 1.075097 | 1.388195 | 1.656496 | 1.752528 | 2.418624 | 1.340024 | 0.824050 | 0.698072 | 1.970136 | 2.064690 | 1.795599 | 4.439921 | 1.967216 | 0.477582 | 1.303589 | 0.223835 | 1.302689 | 1.254710 | 2.383013 | 0.978524 | 2.745955 | 1.923409 | 1.194165 | 1.793615 | 0.785669 | 0.374648 | 1.989626 | 3.421168 | 2.360255 | 0.441367 | 2.033105 | 3.299585 | 1.283497 | 1.845475 | 1.331906 | 1.425406 | 2.937331 | 0.823984 | 2.306800 | 2.113772 | 2.127941 | 1.507769 |
# 1. Daten in Langformat transformieren - funktionen: pivot_longer(), pivot_wider()
# 2. Skalenscores für X und Y erstellen - funktionen: group_by(), summarise()
# 3. Skalenscores für X und Y zentrieren - Funktionen: de_mean()
Lösung
load("../data/df_cfa_exercise.RData")
head(df_cfa_exercise)| id | x1_t1 | x1_t2 | x1_t3 | x1_t4 | x1_t5 | x1_t6 | x1_t7 | x1_t8 | x1_t9 | x1_t10 | x2_t1 | x2_t2 | x2_t3 | x2_t4 | x2_t5 | x2_t6 | x2_t7 | x2_t8 | x2_t9 | x2_t10 | x3_t1 | x3_t2 | x3_t3 | x3_t4 | x3_t5 | x3_t6 | x3_t7 | x3_t8 | x3_t9 | x3_t10 | x4_t1 | x4_t2 | x4_t3 | x4_t4 | x4_t5 | x4_t6 | x4_t7 | x4_t8 | x4_t9 | x4_t10 | x5_t1 | x5_t2 | x5_t3 | x5_t4 | x5_t5 | x5_t6 | x5_t7 | x5_t8 | x5_t9 | x5_t10 | y1_t1 | y1_t2 | y1_t3 | y1_t4 | y1_t5 | y1_t6 | y1_t7 | y1_t8 | y1_t9 | y1_t10 | y2_t1 | y2_t2 | y2_t3 | y2_t4 | y2_t5 | y2_t6 | y2_t7 | y2_t8 | y2_t9 | y2_t10 | y3_t1 | y3_t2 | y3_t3 | y3_t4 | y3_t5 | y3_t6 | y3_t7 | y3_t8 | y3_t9 | y3_t10 | y4_t1 | y4_t2 | y4_t3 | y4_t4 | y4_t5 | y4_t6 | y4_t7 | y4_t8 | y4_t9 | y4_t10 | y5_t1 | y5_t2 | y5_t3 | y5_t4 | y5_t5 | y5_t6 | y5_t7 | y5_t8 | y5_t9 | y5_t10 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 4.927017 | 3.484359 | 5.209319 | 3.777327 | 2.822955 | 2.874990 | 4.399279 | 3.999338 | 2.829229 | 4.534257 | 3.569244 | 3.716360 | 4.025572 | 2.601702 | 4.917407 | 4.417907 | 5.836183 | 2.584864 | 2.416726 | 3.428733 | 3.123268 | 2.200183 | 2.416466 | 2.600930 | 2.179303 | 3.370835 | 5.833981 | 3.574207 | 2.399534 | 1.756436 | 5.759150 | 4.865658 | 5.106690 | 4.142291 | 4.984553 | 4.902819 | 7.088255 | 6.024145 | 4.119966 | 4.134776 | 4.700258 | 3.579269 | 5.881804 | 4.111085 | 4.600078 | 4.055922 | 4.496880 | 4.373719 | 4.322336 | 3.912015 | 4.329297 | 3.597471 | 3.552542 | 3.052100 | 2.592308 | 3.082656 | 4.184377 | 4.064089 | 3.642032 | 2.777390 | 4.477198 | 4.454955 | 4.760682 | 3.288172 | 5.678996 | 4.679951 | 4.928690 | 5.166207 | 3.485148 | 3.454632 | 2.848577 | 5.551165 | 3.769263 | 3.649328 | 2.271035 | 5.264214 | 3.832915 | 3.875455 | 2.525079 | 1.163129 | 4.678402 | 4.682683 | 3.607138 | 4.363976 | 3.401883 | 5.139966 | 5.210060 | 4.713772 | 3.424733 | 2.631818 | 3.480544 | 3.221823 | 3.853132 | 3.120772 | 3.842651 | 2.815425 | 3.273848 | 3.126467 | 3.647887 | 3.895862 |
| 2 | 5.024072 | 2.470240 | 2.259750 | 3.383971 | 3.405628 | 2.925394 | 3.722530 | 3.104064 | 4.068848 | 2.923166 | 3.661601 | 3.732475 | 2.084769 | 3.978517 | 2.855392 | 3.381118 | 3.865699 | 3.911571 | 3.102266 | 2.858461 | 5.186708 | 1.540100 | 3.453976 | 1.671304 | 4.397883 | 4.996236 | 3.609226 | 2.266511 | 5.332266 | 5.041631 | 2.577130 | 1.182058 | 2.648178 | 2.580964 | 4.378121 | 2.015839 | 2.477425 | 2.718602 | 2.512741 | 3.104794 | 2.425656 | 2.302399 | 1.261294 | 2.294336 | 0.776553 | 3.169805 | 2.850572 | 2.683466 | 2.756426 | 3.199966 | 3.488302 | 3.348598 | 1.567981 | 2.689270 | 2.093631 | 0.946151 | 2.760766 | 1.974521 | 0.788326 | 1.546632 | 3.613495 | 3.002835 | 2.618250 | 3.474268 | 4.107470 | 3.950412 | 3.461374 | 3.162900 | 2.504398 | 3.957139 | 3.269670 | 2.924702 | 2.951385 | 1.403080 | 1.747677 | 3.671060 | 2.755273 | 2.860291 | 3.582403 | 2.855433 | 4.296437 | 4.459933 | 3.877955 | 3.542746 | 4.623289 | 5.768592 | 2.820453 | 4.347125 | 5.008321 | 5.628651 | 3.446137 | 3.354074 | 0.216899 | 1.984132 | 2.551348 | 1.010641 | 3.200017 | 1.600360 | 1.541323 | 3.561905 |
| 3 | 3.661946 | 4.515898 | 2.627455 | 2.097116 | 3.295632 | 4.674215 | 3.854248 | 3.430167 | 3.423922 | 4.346313 | 3.369146 | 2.583655 | 2.805899 | 2.629365 | 2.759572 | 2.753171 | 1.063016 | 2.180655 | 3.397097 | 3.797167 | 3.784757 | 3.774945 | 1.163201 | 2.880376 | 2.329117 | 5.686030 | 3.881392 | 3.907517 | 3.697921 | 3.270770 | 2.040071 | 4.645036 | 2.312931 | 2.106188 | 3.517961 | 3.099783 | 3.312862 | 4.134669 | 3.522531 | 2.951239 | 3.113105 | 2.513375 | 2.887626 | 4.867711 | 4.787919 | 2.084980 | 4.140326 | 3.036390 | 4.304286 | 3.375258 | 1.956426 | 2.710257 | 2.345528 | 2.025919 | 1.763310 | 1.487463 | 1.435715 | 3.255219 | 3.430917 | 2.560493 | 2.972647 | 3.841999 | 2.321647 | 5.547948 | 2.755463 | 3.135102 | 1.252126 | 1.455243 | 0.126571 | 0.754453 | 3.451330 | 2.634979 | 2.051891 | 3.677636 | 4.382111 | 0.630656 | 2.520796 | 2.908761 | -0.251480 | 1.706827 | 4.334217 | 3.310795 | 3.634877 | 3.788040 | 3.997747 | 3.418240 | 1.451858 | 5.332129 | 3.107325 | 2.796257 | 3.441736 | 1.732713 | 4.678564 | 3.527282 | 4.910696 | 3.960887 | 2.782757 | 3.427556 | 4.629581 | 4.084214 |
| 4 | 1.933604 | 1.835414 | 4.246882 | 3.229524 | 2.456309 | 2.818070 | 2.602155 | 3.215303 | 4.200392 | 2.272876 | 0.706912 | -0.801259 | 1.640656 | 1.587674 | 1.470820 | 2.430678 | 1.819882 | 1.695119 | 1.338284 | 0.605444 | 0.133074 | 0.158037 | 1.792941 | 1.790514 | 1.269408 | 3.353523 | 2.179509 | 1.409529 | 0.888444 | 1.224938 | 0.326793 | 0.174798 | 2.335327 | 1.525558 | -0.955707 | 3.610542 | 1.960530 | -0.865120 | 1.385375 | 1.430312 | 0.685265 | -0.230309 | 0.633548 | 1.199429 | 0.310757 | 2.375445 | 1.869323 | 0.641068 | 1.674369 | 1.870899 | 3.087726 | 0.985564 | 0.784520 | 2.725897 | 0.850097 | 0.685824 | 1.168844 | -0.239602 | 2.799497 | 2.131838 | 1.596182 | 1.458913 | 3.630159 | 2.387079 | 0.462230 | 0.650202 | 2.356301 | 0.755215 | 2.026497 | 2.441231 | 2.790539 | 2.575200 | 3.509842 | 3.116568 | 3.452843 | 3.093494 | 1.836035 | 2.222184 | 2.761235 | 4.237117 | 3.075454 | 1.881486 | 2.553940 | 1.045851 | 3.202694 | 4.161503 | 2.702880 | 0.817310 | 2.733675 | 1.369864 | 0.687845 | 2.150121 | 1.293763 | 0.737558 | 0.793559 | 1.293628 | 1.312178 | -1.925199 | 0.168729 | 1.409099 |
| 5 | 2.274807 | 2.908011 | 3.179267 | 2.941155 | 3.710268 | 2.717757 | 3.510822 | 1.550497 | 2.728217 | 4.947419 | 4.312907 | 1.624997 | 3.450676 | 3.296389 | 4.639991 | 4.428850 | 4.773291 | 2.273139 | 2.820446 | 3.868664 | 4.675249 | 2.412898 | 3.691474 | 4.295843 | 4.288994 | 3.679321 | 3.664403 | 3.449730 | 4.627416 | 5.374093 | 3.114936 | 0.937293 | 3.231832 | 2.031238 | 2.033217 | 0.704127 | 1.766480 | 1.422281 | 1.951835 | 3.429262 | 2.336261 | 3.616157 | 3.631813 | 3.258421 | 5.976209 | 4.089613 | 2.896231 | 4.596477 | 4.129096 | 7.093438 | 3.593002 | 3.190479 | 2.211968 | 2.500292 | 3.999353 | 2.826291 | 2.768957 | 2.979500 | 3.155079 | 3.074007 | 2.688911 | 2.680939 | 4.108605 | 2.347372 | 3.353903 | 3.417996 | 3.602248 | 3.722665 | 1.867266 | 4.398309 | 2.672452 | 3.234202 | 4.512234 | 1.011391 | 2.736290 | 2.495329 | 2.283781 | 0.979636 | 2.101990 | 3.646597 | 4.168885 | 6.011056 | 5.576807 | 4.452524 | 5.109755 | 3.539751 | 5.203918 | 6.884093 | 6.758122 | 4.835822 | 1.016782 | 1.853578 | 2.498811 | 3.096292 | 2.572159 | 2.496271 | 1.515257 | 1.183772 | 3.329594 | 2.744476 |
| 6 | 3.184660 | 4.046734 | 3.540150 | 4.059281 | 2.374465 | 2.067087 | 3.390182 | 3.492470 | 3.661657 | 1.892471 | 2.910669 | 2.351059 | 0.726541 | 1.928325 | -0.062322 | 1.306896 | 0.272615 | 1.463914 | 2.076900 | 2.569456 | 5.019716 | 2.328710 | 2.837884 | 4.043339 | 1.947459 | 2.449519 | 3.065598 | 4.321089 | 2.036927 | 1.389769 | 3.537501 | 4.318936 | 1.631771 | 3.685972 | 3.609319 | 1.645394 | 2.917829 | 2.317283 | 2.267146 | 3.963144 | 3.536972 | 3.295575 | 3.075710 | 3.460675 | 3.224478 | 1.660497 | 3.093049 | 2.421162 | 2.188736 | 3.557481 | 0.309582 | 1.857645 | 1.819382 | 1.085073 | 2.427644 | 1.988539 | 2.246552 | 2.295530 | 1.075097 | 1.388195 | 1.656496 | 1.752528 | 2.418624 | 1.340024 | 0.824050 | 0.698072 | 1.970136 | 2.064690 | 1.795599 | 4.439921 | 1.967216 | 0.477582 | 1.303589 | 0.223835 | 1.302689 | 1.254710 | 2.383013 | 0.978524 | 2.745955 | 1.923409 | 1.194165 | 1.793615 | 0.785669 | 0.374648 | 1.989626 | 3.421168 | 2.360255 | 0.441367 | 2.033105 | 3.299585 | 1.283497 | 1.845475 | 1.331906 | 1.425406 | 2.937331 | 0.823984 | 2.306800 | 2.113772 | 2.127941 | 1.507769 |
# 1. Daten in Langformat transformieren - funktionen: pivot_longer(), pivot_wider()
df_uebung_superlang <- df_cfa_exercise |>
pivot_longer(
cols = -id, # All columns except id
names_to = c("variable", "time"),
names_sep = "_t"
)
df_uebung_lang <- df_uebung_superlang |>
pivot_wider(names_from = variable,
values_from = value) |>
mutate(time = as.numeric(time))
# 2. Skalenscores für X und Y erstellen - funktionen: group_by(), summarise()
df_uebung_lang_scores <- df_uebung_lang |> group_by(id, time) |>
summarise(
x = rowMeans(across(starts_with("x")), na.rm = TRUE),
y = rowMeans(across(starts_with("y")), na.rm = TRUE),
.groups = "drop" # group_by() wieder aufheben für den finalen Datensatz
)
# 3. Skalenscores für X und Y zentrieren - Funktionen: de_mean()
df_uebung_lang_scores <- df_uebung_lang_scores |>
de_mean(x, y, grp = "id")
head(df_uebung_lang_scores)| id | time | x | y | x_dm | y_dm | x_gm | y_gm |
|---|---|---|---|---|---|---|---|
| 1 | 1 | 4.415787 | 3.962804 | 0.3959958 | 0.1601657 | 4.019792 | 3.802638 |
| 1 | 2 | 3.569166 | 4.301619 | -0.4506258 | 0.4989815 | 4.019792 | 3.802638 |
| 1 | 3 | 4.527970 | 3.908551 | 0.5081786 | 0.1059135 | 4.019792 | 3.802638 |
| 1 | 4 | 3.446667 | 3.494870 | -0.5731246 | -0.3077683 | 4.019792 | 3.802638 |
| 1 | 5 | 3.900859 | 3.557375 | -0.1189324 | -0.2452633 | 4.019792 | 3.802638 |
| 1 | 6 | 3.924495 | 4.196442 | -0.0952970 | 0.3938045 | 4.019792 | 3.802638 |
2.9 Abspeichern der gebildeten Skalen
Zum Schluss speichern wir die Ergebnisse (sowohl die Items als auch die Skalen in Langformat) der Übung ab.
save(df_uebung_lang, df_uebung_lang_scores, file = "../data/df_uebung.RData")