Quantitative Methods HS22
Marc Gauch
2022-11-17
1 Preparation
if (!require(tidyverse)){
install.packages("tidyverse")
library(tidyverse)
}2 Load Data
2.1 Load from CSV
litdata <- read_csv("DataLit_R.csv", show_col_types = FALSE)
litdata <- as_tibble(litdata)2.2 First inspection of data
2.2.1 Summary
summary(litdata)## id submitdate lastpage startlanguage
## Min. : 1 Length:313 Min. :-1.000 Length:313
## 1st Qu.: 81 Class :character 1st Qu.: 2.000 Class :character
## Median :162 Mode :character Median : 5.000 Mode :character
## Mean :163 Mean : 3.556
## 3rd Qu.:245 3rd Qu.: 5.000
## Max. :327 Max. : 5.000
## NA's :108
## seed startdate datestamp W001
## Min. :5.647e+06 Length:313 Length:313 Length:313
## 1st Qu.:5.568e+08 Class :character Class :character Class :character
## Median :1.086e+09 Mode :character Mode :character Mode :character
## Mean :1.081e+09
## 3rd Qu.:1.637e+09
## Max. :2.147e+09
##
## W002 W003 W004 W005
## Length:313 Length:313 Length:313 Length:313
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## W006 W007 W008 W009
## Length:313 Length:313 Length:313 Length:313
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## K001 K002 K003 K004
## Length:313 Length:313 Length:313 Length:313
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## K005 K006 K007 K008
## Length:313 Length:313 Length:313 Length:313
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## K009 TK001_01 TK001_02 TK001_03
## Length:313 Length:313 Length:313 Length:313
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## TK001_04 TK002_01 TK002_02 TK002_03
## Length:313 Length:313 Length:313 Length:313
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## TK002_04 TK003_01 TK003_02 TK003_03
## Length:313 Length:313 Length:313 Length:313
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## TK003_04 TK004_01 TK004_02 TK004_03
## Length:313 Length:313 Length:313 Length:313
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## TK004_04 TK005_01 TK005_02 TK005_03
## Length:313 Length:313 Length:313 Length:313
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## TK005_04 TK006_01 TK006_02 TK006_03
## Length:313 Length:313 Length:313 Length:313
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## TK006_04 H001_001 H001_002 H001_003
## Length:313 Length:313 Length:313 Length:313
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## H001_004 H001_005 H001_006 H001_007
## Length:313 Length:313 Length:313 Length:313
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## H002 H003 H004 H004_other
## Length:313 Length:313 Length:313 Length:313
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## H005 H005_other H006 H007
## Length:313 Mode:logical Min. :2012 Min. :1971
## Class :character NA's:313 1st Qu.:2018 1st Qu.:1991
## Mode :character Median :2020 Median :1995
## Mean :2019 Mean :1993
## 3rd Qu.:2020 3rd Qu.:1998
## Max. :2021 Max. :2002
## NA's :201 NA's :207
## H008 R1
## Length:313 Length:313
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## 2.2.2 Glimpse
glimpse(litdata)## Rows: 313
## Columns: 66
## $ id <dbl> 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, …
## $ submitdate <chr> "10/25/2021 11:07:44", NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ lastpage <dbl> 5, NA, NA, 2, 2, NA, NA, NA, 2, 1, NA, NA, 5, NA, NA, NA…
## $ startlanguage <chr> "en", "de", "de", "de", "en", "de", "de", "de", "en", "d…
## $ seed <dbl> 664891087, 334145431, 683903577, 2082427237, 438283320, …
## $ startdate <chr> "10/25/2021 11:07:40", "10/25/2021 11:33:06", "10/25/202…
## $ datestamp <chr> "10/25/2021 11:07:44", "10/25/2021 11:33:06", "10/25/202…
## $ W001 <chr> NA, NA, NA, "Stimme voll zu5", "3", NA, NA, NA, "Stimme …
## $ W002 <chr> NA, NA, NA, "Stimme voll zu5", "3", NA, NA, NA, "4", "2"…
## $ W003 <chr> NA, NA, NA, "4", "3", NA, NA, NA, "4", "4", NA, NA, "Sti…
## $ W004 <chr> NA, NA, NA, "Stimme voll zu5", "Stimme voll zu5", NA, NA…
## $ W005 <chr> NA, NA, NA, "Stimme voll zu5", "Stimme voll zu5", NA, NA…
## $ W006 <chr> NA, NA, NA, "4", "4", NA, NA, NA, "4", "4", NA, NA, "4",…
## $ W007 <chr> NA, NA, NA, "4", "Stimme voll zu5", NA, NA, NA, "Stimme …
## $ W008 <chr> NA, NA, NA, "Stimme voll zu5", "4", NA, NA, NA, "Stimme …
## $ W009 <chr> NA, NA, NA, "Stimme voll zu5", "Stimme voll zu5", NA, NA…
## $ K001 <chr> NA, NA, NA, "4", "4", NA, NA, NA, "4", NA, NA, NA, "3", …
## $ K002 <chr> NA, NA, NA, "3", "3", NA, NA, NA, "4", NA, NA, NA, "4", …
## $ K003 <chr> NA, NA, NA, "3", "Stimme voll zu5", NA, NA, NA, "3", NA,…
## $ K004 <chr> NA, NA, NA, "3", "4", NA, NA, NA, "4", NA, NA, NA, "3", …
## $ K005 <chr> NA, NA, NA, "4", "3", NA, NA, NA, "4", NA, NA, NA, "3", …
## $ K006 <chr> NA, NA, NA, "4", "4", NA, NA, NA, "4", NA, NA, NA, "4", …
## $ K007 <chr> NA, NA, NA, "4", "3", NA, NA, NA, "3", NA, NA, NA, "4", …
## $ K008 <chr> NA, NA, NA, "4", "3", NA, NA, NA, "4", NA, NA, NA, "Stim…
## $ K009 <chr> NA, NA, NA, "4", "2", NA, NA, NA, "4", NA, NA, NA, "Stim…
## $ TK001_01 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "4", NA,…
## $ TK001_02 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK001_03 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK001_04 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "4", NA,…
## $ TK002_01 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "4", NA,…
## $ TK002_02 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK002_03 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK002_04 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "4", NA,…
## $ TK003_01 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "3", NA,…
## $ TK003_02 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "4", NA,…
## $ TK003_03 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK003_04 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK004_01 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK004_02 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK004_03 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK004_04 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "4", NA,…
## $ TK005_01 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK005_02 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK005_03 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK005_04 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK006_01 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK006_02 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK006_03 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Stimme …
## $ TK006_04 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "4", NA,…
## $ H001_001 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Ja", NA…
## $ H001_002 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Nicht G…
## $ H001_003 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Ja", NA…
## $ H001_004 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Nicht G…
## $ H001_005 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Nicht G…
## $ H001_006 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Nicht G…
## $ H001_007 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Nicht G…
## $ H002 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Ja", NA…
## $ H003 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Bibliot…
## $ H004 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Gesundh…
## $ H004_other <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ H005 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Bachelo…
## $ H005_other <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ H006 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ H007 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ H008 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Weiblic…
## $ R1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Die Fra…2.2.3 Print
print(litdata)## # A tibble: 313 × 66
## id submit…¹ lastp…² start…³ seed start…⁴ dates…⁵ W001 W002 W003 W004
## <dbl> <chr> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1 10/25/2… 5 en 6.65e8 10/25/… 10/25/… <NA> <NA> <NA> <NA>
## 2 2 <NA> NA de 3.34e8 10/25/… 10/25/… <NA> <NA> <NA> <NA>
## 3 3 <NA> NA de 6.84e8 10/25/… 10/25/… <NA> <NA> <NA> <NA>
## 4 5 <NA> 2 de 2.08e9 10/25/… 10/25/… Stim… Stim… 4 Stim…
## 5 6 <NA> 2 en 4.38e8 10/25/… 10/25/… 3 3 3 Stim…
## 6 7 <NA> NA de 2.15e9 10/25/… 10/25/… <NA> <NA> <NA> <NA>
## 7 8 <NA> NA de 1.74e9 10/25/… 10/25/… <NA> <NA> <NA> <NA>
## 8 9 <NA> NA de 4.64e8 10/25/… 10/25/… <NA> <NA> <NA> <NA>
## 9 10 <NA> 2 en 1.09e9 10/25/… 10/25/… Stim… 4 4 4
## 10 11 <NA> 1 de 1.55e9 10/25/… 10/25/… 3 2 4 4
## # … with 303 more rows, 55 more variables: W005 <chr>, W006 <chr>, W007 <chr>,
## # W008 <chr>, W009 <chr>, K001 <chr>, K002 <chr>, K003 <chr>, K004 <chr>,
## # K005 <chr>, K006 <chr>, K007 <chr>, K008 <chr>, K009 <chr>, TK001_01 <chr>,
## # TK001_02 <chr>, TK001_03 <chr>, TK001_04 <chr>, TK002_01 <chr>,
## # TK002_02 <chr>, TK002_03 <chr>, TK002_04 <chr>, TK003_01 <chr>,
## # TK003_02 <chr>, TK003_03 <chr>, TK003_04 <chr>, TK004_01 <chr>,
## # TK004_02 <chr>, TK004_03 <chr>, TK004_04 <chr>, TK005_01 <chr>, …2.2.4 Head
head(litdata)## # A tibble: 6 × 66
## id submitd…¹ lastp…² start…³ seed start…⁴ dates…⁵ W001 W002 W003 W004
## <dbl> <chr> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1 10/25/20… 5 en 6.65e8 10/25/… 10/25/… <NA> <NA> <NA> <NA>
## 2 2 <NA> NA de 3.34e8 10/25/… 10/25/… <NA> <NA> <NA> <NA>
## 3 3 <NA> NA de 6.84e8 10/25/… 10/25/… <NA> <NA> <NA> <NA>
## 4 5 <NA> 2 de 2.08e9 10/25/… 10/25/… Stim… Stim… 4 Stim…
## 5 6 <NA> 2 en 4.38e8 10/25/… 10/25/… 3 3 3 Stim…
## 6 7 <NA> NA de 2.15e9 10/25/… 10/25/… <NA> <NA> <NA> <NA>
## # … with 55 more variables: W005 <chr>, W006 <chr>, W007 <chr>, W008 <chr>,
## # W009 <chr>, K001 <chr>, K002 <chr>, K003 <chr>, K004 <chr>, K005 <chr>,
## # K006 <chr>, K007 <chr>, K008 <chr>, K009 <chr>, TK001_01 <chr>,
## # TK001_02 <chr>, TK001_03 <chr>, TK001_04 <chr>, TK002_01 <chr>,
## # TK002_02 <chr>, TK002_03 <chr>, TK002_04 <chr>, TK003_01 <chr>,
## # TK003_02 <chr>, TK003_03 <chr>, TK003_04 <chr>, TK004_01 <chr>,
## # TK004_02 <chr>, TK004_03 <chr>, TK004_04 <chr>, TK005_01 <chr>, …3 Data cleaning
3.1 Converting Strings to numbers and Keine Antwort zu NaN
litdata <- litdata %>%
mutate_all(~ replace(., . == "Stimme voll zu5", 5)) %>%
mutate_all(~ replace(., . == "Stimme überhaupt nicht zu1", 1)) %>%
mutate_all(~ replace(., . == "Keine Antwort-", NaN))3.2 Make it numeric
The following code will NOT be run. The Idea is to show a way to automatically edit all columns. It works but some columns are NOT numeric.
# All colnames that exist
litdataColnames <- colnames(litdata)
# the ones we don't want to change
litdataNonNumericCols <- c("submitdate", "startlanguage", "startdate", "datestamp", "lastpage", "seed")
# the colnames that should be changed
litdataColsToMakeNumeric <- litdataColnames[!(litdataColnames %in% litdataNonNumericCols)]
print(litdataColsToMakeNumeric)
litdataColsToMakeNumeric <- c("R1")
for (col in litdataColsToMakeNumeric) {
litdata[[col]] <- as.numeric(litdata[[col]])
}First we rename all the columns
litdata <- litdata %>% rename(
"A1" = "W001",
"A2" = "W002",
"A3" = "W003",
"A4" = "W004",
"A5" = "W005",
"A6" = "W006",
"A7" = "W007",
"A8" = "W008",
"A9" = "W009",
"B1" = "K001",
"B2" = "K002",
"B3" = "K003",
"B4" = "K004",
"B5" = "K005",
"B6" = "K006",
"B7" = "K007",
"B8" = "K008",
"B9" = "K009",
"C1_1" = "TK001_01",
"C1_2" = "TK001_02",
"C1_3" = "TK001_03",
"C1_4" = "TK001_04",
"C2_1" = "TK002_01",
"C2_2" = "TK002_02",
"C2_3" = "TK002_03",
"C2_4" = "TK002_04",
"C3_1" = "TK003_01",
"C3_2" = "TK003_02",
"C3_3" = "TK003_03",
"C3_4" = "TK003_04",
"C4_1" = "TK004_01",
"C4_2" = "TK004_02",
"C4_3" = "TK004_03",
"C4_4" = "TK004_04",
"C5_1" = "TK005_01",
"C5_2" = "TK005_02",
"C5_3" = "TK005_03",
"C5_4" = "TK005_04",
"C6_1" = "TK006_01",
"C6_2" = "TK006_02",
"C6_3" = "TK006_03",
"C6_4" = "TK006_04",
"D1_1" = "H001_001",
"D1_2" = "H001_002",
"D1_3" = "H001_003",
"D1_4" = "H001_004",
"D1_5" = "H001_005",
"D1_6" = "H001_006",
"D1_7" = "H001_007",
"D2" = "H002",
"D3" = "H003",
"D4" = "H004",
"D4_comment" = "H004_other",
"D5" = "H005",
"D5_comment" = "H005_other",
"D6" = "H006",
"D7" = "H007",
"D8" = "H008",
"E1" = "R1"
)Then we change the datatype and fix the values
litdata$A1 <- as.numeric(litdata$A1)
litdata$A2 <- as.numeric(litdata$A2)
litdata$A3 <- as.numeric(litdata$A3)
litdata$A4 <- as.numeric(litdata$A4)
litdata$A5 <- as.numeric(litdata$A5)
litdata$A6 <- as.numeric(litdata$A6)
litdata$A7 <- as.numeric(litdata$A7)
litdata$A8 <- as.numeric(litdata$A8)
litdata$A9 <- as.numeric(litdata$A9)
litdata$B1 <- as.numeric(litdata$B1)
litdata$B2 <- as.numeric(litdata$B2)
litdata$B3 <- as.numeric(litdata$B3)
litdata$B4 <- as.numeric(litdata$B4)
litdata$B5 <- as.numeric(litdata$B5)
litdata$B6 <- as.numeric(litdata$B6)
litdata$B7 <- as.numeric(litdata$B7)
litdata$B8 <- as.numeric(litdata$B8)
litdata$B9 <- as.numeric(litdata$B9)
litdata$C1_1 <- as.numeric(litdata$C1_1)
litdata$C1_2 <- as.numeric(litdata$C1_2)
litdata$C1_3 <- as.numeric(litdata$C1_3)
litdata$C1_4 <- as.numeric(litdata$C1_4)
litdata$C2_1 <- as.numeric(litdata$C2_1)
litdata$C2_2 <- as.numeric(litdata$C2_2)
litdata$C2_3 <- as.numeric(litdata$C2_3)
litdata$C2_4 <- as.numeric(litdata$C2_4)
litdata$C3_1 <- as.numeric(litdata$C3_1)
litdata$C3_2 <- as.numeric(litdata$C3_2)
litdata$C3_3 <- as.numeric(litdata$C3_3)
litdata$C3_4 <- as.numeric(litdata$C3_4)
litdata$C4_1 <- as.numeric(litdata$C4_1)
litdata$C4_2 <- as.numeric(litdata$C4_2)
litdata$C4_3 <- as.numeric(litdata$C4_3)
litdata$C4_4 <- as.numeric(litdata$C4_4)
litdata$C5_1 <- as.numeric(litdata$C5_1)
litdata$C5_2 <- as.numeric(litdata$C5_2)
litdata$C5_3 <- as.numeric(litdata$C5_3)
litdata$C5_4 <- as.numeric(litdata$C5_4)
litdata$C6_1 <- as.numeric(litdata$C6_1)
litdata$C6_2 <- as.numeric(litdata$C6_2)
litdata$C6_3 <- as.numeric(litdata$C6_3)
litdata$C6_4 <- as.numeric(litdata$C6_4)
litdata <- litdata %>% mutate(D1_1 = ifelse(D1_1 == "Ja", TRUE, ifelse(D1_1 == "Nicht Gewählt", FALSE, D1_1)))
litdata$D1_1 <- as.logical(litdata$D1_1)
litdata <- litdata %>% mutate(D1_2 = ifelse(D1_2 == "Ja", TRUE, ifelse(D1_2 == "Nicht Gewählt", FALSE, D1_2)))
litdata$D1_2 <- as.logical(litdata$D1_2)
litdata <- litdata %>% mutate(D1_3 = ifelse(D1_3 == "Ja", TRUE, ifelse(D1_3 == "Nicht Gewählt", FALSE, D1_3)))
litdata$D1_3 <- as.logical(litdata$D1_3)
litdata <- litdata %>% mutate(D1_4 = ifelse(D1_4 == "Ja", TRUE, ifelse(D1_4 == "Nicht Gewählt", FALSE, D1_4)))
litdata$D1_4 <- as.logical(litdata$D1_4)
litdata <- litdata %>% mutate(D1_5 = ifelse(D1_5 == "Ja", TRUE, ifelse(D1_5 == "Nicht Gewählt", FALSE, D1_5)))
litdata$D1_5 <- as.logical(litdata$D1_5)
litdata <- litdata %>% mutate(D1_6 = ifelse(D1_6 == "Ja", TRUE, ifelse(D1_6 == "Nicht Gewählt", FALSE, D1_6)))
litdata$D1_6 <- as.logical(litdata$D1_6)
litdata <- litdata %>% mutate(D1_7 = ifelse(D1_7 == "Ja", TRUE, ifelse(D1_7 == "Nicht Gewählt", FALSE, D1_7)))
litdata$D1_7 <- as.logical(litdata$D1_7)
litdata <- litdata %>% mutate(D2 = ifelse(D2 == "Ja", TRUE, ifelse(D2 == "Nein", FALSE, D2)))
litdata$D2 <- as.logical(litdata$D2)
# skipping D3 because it's just a free text
litdata$D4 <- as.factor(litdata$D4)
# skipping D4_comment because it's a free text
litdata$D5 <- as.factor(litdata$D5)
# skipping D5_comment because it's a free text
# can't be a number as there is a 2010 or earlier option.
litdata$D6 <- as.factor(litdata$D6)
litdata$D7 <- as.numeric(litdata$D7)
litdata$D8 <- as.factor(litdata$D8)
# skipping E1 because it's a free text3.3 Second inspection of data
3.3.1 Summary
summary(litdata)## id submitdate lastpage startlanguage
## Min. : 1 Length:313 Min. :-1.000 Length:313
## 1st Qu.: 81 Class :character 1st Qu.: 2.000 Class :character
## Median :162 Mode :character Median : 5.000 Mode :character
## Mean :163 Mean : 3.556
## 3rd Qu.:245 3rd Qu.: 5.000
## Max. :327 Max. : 5.000
## NA's :108
## seed startdate datestamp A1
## Min. :5.647e+06 Length:313 Length:313 Min. :2.000
## 1st Qu.:5.568e+08 Class :character Class :character 1st Qu.:4.000
## Median :1.086e+09 Mode :character Mode :character Median :5.000
## Mean :1.081e+09 Mean :4.515
## 3rd Qu.:1.637e+09 3rd Qu.:5.000
## Max. :2.147e+09 Max. :5.000
## NA's :117
## A2 A3 A4 A5
## Min. :1.000 Min. :2.000 Min. :1.000 Min. :2.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :5.000 Median :5.000
## Mean :3.955 Mean :4.246 Mean :4.523 Mean :4.411
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :114 NA's :114 NA's :114 NA's :116
## A6 A7 A8 A9
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :5.000 Median :4.000 Median :4.000
## Mean :4.347 Mean :4.824 Mean :3.581 Mean :3.663
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :117 NA's :114 NA's :122 NA's :120
## B1 B2 B3 B4
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.293 Mean :2.851 Mean :2.863 Mean :3.221
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :132 NA's :132 NA's :131 NA's :132
## B5 B6 B7 B8 B9
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:2.00 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :4.000 Median :3.00 Median :3.000
## Mean :3.409 Mean :2.956 Mean :3.522 Mean :2.72 Mean :2.657
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.75 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000
## NA's :132 NA's :132 NA's :131 NA's :131 NA's :132
## C1_1 C1_2 C1_3 C1_4
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:2.000
## Median :4.000 Median :4.000 Median :5.000 Median :3.000
## Mean :3.606 Mean :4.153 Mean :4.336 Mean :2.956
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :204 NA's :202 NA's :200 NA's :200
## C2_1 C2_2 C2_3 C2_4
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:2.000
## Median :3.000 Median :4.000 Median :5.000 Median :3.000
## Mean :3.409 Mean :4.055 Mean :4.279 Mean :3.071
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :203 NA's :204 NA's :202 NA's :201
## C3_1 C3_2 C3_3 C3_4 C4_1
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.00 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :4.000 Median :5.00 Median :3.000 Median :4.000
## Mean :3.495 Mean :4.189 Mean :4.42 Mean :3.321 Mean :3.759
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.00 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000
## NA's :202 NA's :202 NA's :201 NA's :201 NA's :201
## C4_2 C4_3 C4_4 C5_1 C5_2
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.00 1st Qu.:3.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :3.00 Median :4.000 Median :4.000
## Mean :4.279 Mean :4.396 Mean :3.33 Mean :3.727 Mean :4.183
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.00 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000
## NA's :202 NA's :202 NA's :201 NA's :203 NA's :204
## C5_3 C5_4 C6_1 C6_2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:4.000
## Median :5.000 Median :3.000 Median :4.000 Median :4.000
## Mean :4.369 Mean :3.255 Mean :3.609 Mean :4.136
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :202 NA's :203 NA's :203 NA's :203
## C6_3 C6_4 D1_1 D1_2
## Min. :1.000 Min. :1.000 Mode :logical Mode :logical
## 1st Qu.:4.000 1st Qu.:3.000 FALSE:64 FALSE:69
## Median :5.000 Median :3.000 TRUE :51 TRUE :46
## Mean :4.245 Mean :3.191 NA's :198 NA's :198
## 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
## NA's :203 NA's :203
## D1_3 D1_4 D1_5 D1_6
## Mode :logical Mode :logical Mode :logical Mode :logical
## FALSE:30 FALSE:81 FALSE:108 FALSE:115
## TRUE :85 TRUE :34 TRUE :7 NA's :198
## NA's :198 NA's :198 NA's :198
##
##
##
## D1_7 D2 D3
## Mode :logical Mode :logical Length:313
## FALSE:115 FALSE:12 Class :character
## NA's :198 TRUE :103 Mode :character
## NA's :198
##
##
##
## D4
## Berufe des Managements und der Administration, des Bank- und Versicherungsgewerbes und des Rechtswesens: 29
## Gesundheits-, Lehr- und Kulturberufe, Wissenschaftler : 24
## Technische Berufe sowie Informatikberufe : 24
## Berufe des Gastgewerbes und Berufe zur Erbringung persönlicher Dienstleistungens- und Verkehrsberufe : 13
## - : 8
## (Other) : 5
## NA's :210
## D4_comment D5 D5_comment
## Length:313 Bachelor Information Science : 40 Min. : NA
## Class :character Master Information and Data Management: 10 1st Qu.: NA
## Mode :character Bachelor Multimedia Production : 8 Median : NA
## Bachelor Tourismus : 8 Mean :NaN
## Bachelor Betriebsökonomie : 7 3rd Qu.: NA
## (Other) : 40 Max. : NA
## NA's :200 NA's :313
## D6 D7 D8 E1
## 2020 : 34 Min. :1971 männlich: 39 Length:313
## 2018 : 26 1st Qu.:1991 Weiblich: 75 Class :character
## 2021 : 25 Median :1995 NA's :199 Mode :character
## 2019 : 20 Mean :1993
## 2017 : 4 3rd Qu.:1998
## (Other): 3 Max. :2002
## NA's :201 NA's :2073.3.2 Glimpse
glimpse(litdata)## Rows: 313
## Columns: 66
## $ id <dbl> 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, …
## $ submitdate <chr> "10/25/2021 11:07:44", NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ lastpage <dbl> 5, NA, NA, 2, 2, NA, NA, NA, 2, 1, NA, NA, 5, NA, NA, NA…
## $ startlanguage <chr> "en", "de", "de", "de", "en", "de", "de", "de", "en", "d…
## $ seed <dbl> 664891087, 334145431, 683903577, 2082427237, 438283320, …
## $ startdate <chr> "10/25/2021 11:07:40", "10/25/2021 11:33:06", "10/25/202…
## $ datestamp <chr> "10/25/2021 11:07:44", "10/25/2021 11:33:06", "10/25/202…
## $ A1 <dbl> NA, NA, NA, 5, 3, NA, NA, NA, 5, 3, NA, NA, 5, NA, NA, N…
## $ A2 <dbl> NA, NA, NA, 5, 3, NA, NA, NA, 4, 2, NA, NA, 5, NA, NA, N…
## $ A3 <dbl> NA, NA, NA, 4, 3, NA, NA, NA, 4, 4, NA, NA, 5, NA, NA, N…
## $ A4 <dbl> NA, NA, NA, 5, 5, NA, NA, NA, 4, 4, NA, NA, 5, NA, NA, N…
## $ A5 <dbl> NA, NA, NA, 5, 5, NA, NA, NA, 4, 4, NA, NA, 4, NA, NA, N…
## $ A6 <dbl> NA, NA, NA, 4, 4, NA, NA, NA, 4, 4, NA, NA, 4, NA, NA, N…
## $ A7 <dbl> NA, NA, NA, 4, 5, NA, NA, NA, 5, 3, NA, NA, 5, NA, NA, N…
## $ A8 <dbl> NA, NA, NA, 5, 4, NA, NA, NA, 5, 4, NA, NA, 5, NA, NA, N…
## $ A9 <dbl> NA, NA, NA, 5, 5, NA, NA, NA, 5, 5, NA, NA, 5, NA, NA, N…
## $ B1 <dbl> NA, NA, NA, 4, 4, NA, NA, NA, 4, NA, NA, NA, 3, NA, NA, …
## $ B2 <dbl> NA, NA, NA, 3, 3, NA, NA, NA, 4, NA, NA, NA, 4, NA, NA, …
## $ B3 <dbl> NA, NA, NA, 3, 5, NA, NA, NA, 3, NA, NA, NA, 4, NA, NA, …
## $ B4 <dbl> NA, NA, NA, 3, 4, NA, NA, NA, 4, NA, NA, NA, 3, NA, NA, …
## $ B5 <dbl> NA, NA, NA, 4, 3, NA, NA, NA, 4, NA, NA, NA, 3, NA, NA, …
## $ B6 <dbl> NA, NA, NA, 4, 4, NA, NA, NA, 4, NA, NA, NA, 4, NA, NA, …
## $ B7 <dbl> NA, NA, NA, 4, 3, NA, NA, NA, 3, NA, NA, NA, 4, NA, NA, …
## $ B8 <dbl> NA, NA, NA, 4, 3, NA, NA, NA, 4, NA, NA, NA, 5, NA, NA, …
## $ B9 <dbl> NA, NA, NA, 4, 2, NA, NA, NA, 4, NA, NA, NA, 5, NA, NA, …
## $ C1_1 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, N…
## $ C1_2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C1_3 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C1_4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, N…
## $ C2_1 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, N…
## $ C2_2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C2_3 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C2_4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, N…
## $ C3_1 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3, NA, N…
## $ C3_2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, N…
## $ C3_3 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C3_4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C4_1 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C4_2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C4_3 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C4_4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, N…
## $ C5_1 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C5_2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C5_3 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C5_4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C6_1 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C6_2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C6_3 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, N…
## $ C6_4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, N…
## $ D1_1 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, TRUE, NA…
## $ D1_2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, FALSE, N…
## $ D1_3 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, TRUE, NA…
## $ D1_4 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, FALSE, N…
## $ D1_5 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, FALSE, N…
## $ D1_6 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, FALSE, N…
## $ D1_7 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, FALSE, N…
## $ D2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, TRUE, NA…
## $ D3 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Bibliot…
## $ D4 <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Gesundh…
## $ D4_comment <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ D5 <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, Bachelor…
## $ D5_comment <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ D6 <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ D7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ D8 <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, Weiblich…
## $ E1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Die Fra…3.3.3 Print
print(litdata)## # A tibble: 313 × 66
## id submit…¹ lastp…² start…³ seed start…⁴ dates…⁵ A1 A2 A3 A4
## <dbl> <chr> <dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 10/25/2… 5 en 6.65e8 10/25/… 10/25/… NA NA NA NA
## 2 2 <NA> NA de 3.34e8 10/25/… 10/25/… NA NA NA NA
## 3 3 <NA> NA de 6.84e8 10/25/… 10/25/… NA NA NA NA
## 4 5 <NA> 2 de 2.08e9 10/25/… 10/25/… 5 5 4 5
## 5 6 <NA> 2 en 4.38e8 10/25/… 10/25/… 3 3 3 5
## 6 7 <NA> NA de 2.15e9 10/25/… 10/25/… NA NA NA NA
## 7 8 <NA> NA de 1.74e9 10/25/… 10/25/… NA NA NA NA
## 8 9 <NA> NA de 4.64e8 10/25/… 10/25/… NA NA NA NA
## 9 10 <NA> 2 en 1.09e9 10/25/… 10/25/… 5 4 4 4
## 10 11 <NA> 1 de 1.55e9 10/25/… 10/25/… 3 2 4 4
## # … with 303 more rows, 55 more variables: A5 <dbl>, A6 <dbl>, A7 <dbl>,
## # A8 <dbl>, A9 <dbl>, B1 <dbl>, B2 <dbl>, B3 <dbl>, B4 <dbl>, B5 <dbl>,
## # B6 <dbl>, B7 <dbl>, B8 <dbl>, B9 <dbl>, C1_1 <dbl>, C1_2 <dbl>, C1_3 <dbl>,
## # C1_4 <dbl>, C2_1 <dbl>, C2_2 <dbl>, C2_3 <dbl>, C2_4 <dbl>, C3_1 <dbl>,
## # C3_2 <dbl>, C3_3 <dbl>, C3_4 <dbl>, C4_1 <dbl>, C4_2 <dbl>, C4_3 <dbl>,
## # C4_4 <dbl>, C5_1 <dbl>, C5_2 <dbl>, C5_3 <dbl>, C5_4 <dbl>, C6_1 <dbl>,
## # C6_2 <dbl>, C6_3 <dbl>, C6_4 <dbl>, D1_1 <lgl>, D1_2 <lgl>, D1_3 <lgl>, …3.3.4 Head
head(litdata)## # A tibble: 6 × 66
## id submitd…¹ lastp…² start…³ seed start…⁴ dates…⁵ A1 A2 A3 A4
## <dbl> <chr> <dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 10/25/20… 5 en 6.65e8 10/25/… 10/25/… NA NA NA NA
## 2 2 <NA> NA de 3.34e8 10/25/… 10/25/… NA NA NA NA
## 3 3 <NA> NA de 6.84e8 10/25/… 10/25/… NA NA NA NA
## 4 5 <NA> 2 de 2.08e9 10/25/… 10/25/… 5 5 4 5
## 5 6 <NA> 2 en 4.38e8 10/25/… 10/25/… 3 3 3 5
## 6 7 <NA> NA de 2.15e9 10/25/… 10/25/… NA NA NA NA
## # … with 55 more variables: A5 <dbl>, A6 <dbl>, A7 <dbl>, A8 <dbl>, A9 <dbl>,
## # B1 <dbl>, B2 <dbl>, B3 <dbl>, B4 <dbl>, B5 <dbl>, B6 <dbl>, B7 <dbl>,
## # B8 <dbl>, B9 <dbl>, C1_1 <dbl>, C1_2 <dbl>, C1_3 <dbl>, C1_4 <dbl>,
## # C2_1 <dbl>, C2_2 <dbl>, C2_3 <dbl>, C2_4 <dbl>, C3_1 <dbl>, C3_2 <dbl>,
## # C3_3 <dbl>, C3_4 <dbl>, C4_1 <dbl>, C4_2 <dbl>, C4_3 <dbl>, C4_4 <dbl>,
## # C5_1 <dbl>, C5_2 <dbl>, C5_3 <dbl>, C5_4 <dbl>, C6_1 <dbl>, C6_2 <dbl>,
## # C6_3 <dbl>, C6_4 <dbl>, D1_1 <lgl>, D1_2 <lgl>, D1_3 <lgl>, D1_4 <lgl>, …4 Selbststudium 1
Berechnen Sie die Häufigkeiten für die Variablen W003, K003, H001_001, H005, H007 und H008.
4.1 Data
displayFunction1 <- function(table, column) {
tmp <- table[column]
tmp <- rename(tmp, value = all_of(column))
tmp <- tmp %>%
count(value) %>%
mutate(percentage = prop.table(n)*100)
print(tmp, n = 100)
ggplot(tmp,
aes(x = value, y=n)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
}A3 (W003)
displayFunction1(litdata, "A3")## # A tibble: 6 × 3
## value n percentage
## <dbl> <int> <dbl>
## 1 2 6 1.92
## 2 3 29 9.27
## 3 4 74 23.6
## 4 5 90 28.8
## 5 NA 112 35.8
## 6 NaN 2 0.639## Warning: Removed 2 rows containing missing values (`position_stack()`).
B3 (K003)
displayFunction1(litdata, "B3")## # A tibble: 7 × 3
## value n percentage
## <dbl> <int> <dbl>
## 1 1 16 5.11
## 2 2 47 15.0
## 3 3 71 22.7
## 4 4 42 13.4
## 5 5 6 1.92
## 6 NA 130 41.5
## 7 NaN 1 0.319## Warning: Removed 2 rows containing missing values (`position_stack()`).
D1_1 (H001_001)
displayFunction1(litdata, "D1_1")## # A tibble: 3 × 3
## value n percentage
## <lgl> <int> <dbl>
## 1 FALSE 64 20.4
## 2 TRUE 51 16.3
## 3 NA 198 63.3
D5 (H005)
displayFunction1(litdata, "D5")## # A tibble: 18 × 3
## value n percentage
## <fct> <int> <dbl>
## 1 Bachelor Architektur 1 0.319
## 2 Bachelor Bauingenieurwesen 2 0.639
## 3 Bachelor Betriebsökonomie 7 2.24
## 4 Bachelor Computational and Data Science 2 0.639
## 5 Bachelor Digital Business Management 6 1.92
## 6 Bachelor Information Science 40 12.8
## 7 Bachelor Mobile Robotics 1 0.319
## 8 Bachelor Multimedia Production 8 2.56
## 9 Bachelor Photonics 3 0.958
## 10 Bachelor Service Innovation and Design 5 1.60
## 11 Bachelor Sport Management 7 2.24
## 12 Bachelor Tourismus 8 2.56
## 13 CAS Sport Management 4.0 1 0.319
## 14 MAS Information Science 1 0.319
## 15 Master Information and Data Management 10 3.19
## 16 Master New Business 7 2.24
## 17 Master Tourism and Change 4 1.28
## 18 <NA> 200 63.9
D7 (H007)
displayFunction1(litdata, "D7")## # A tibble: 24 × 3
## value n percentage
## <dbl> <int> <dbl>
## 1 1971 1 0.319
## 2 1972 1 0.319
## 3 1973 1 0.319
## 4 1974 3 0.958
## 5 1980 1 0.319
## 6 1984 1 0.319
## 7 1985 4 1.28
## 8 1987 5 1.60
## 9 1988 3 0.958
## 10 1989 2 0.639
## 11 1990 4 1.28
## 12 1991 5 1.60
## 13 1992 6 1.92
## 14 1993 8 2.56
## 15 1994 2 0.639
## 16 1995 10 3.19
## 17 1996 3 0.958
## 18 1997 14 4.47
## 19 1998 15 4.79
## 20 1999 7 2.24
## 21 2000 7 2.24
## 22 2001 2 0.639
## 23 2002 1 0.319
## 24 NA 207 66.1## Warning: Removed 1 rows containing missing values (`position_stack()`).
Die Warnung ist resultiert daraus, dass es sehr viele NA gibt.
D8 (H008)
displayFunction1(litdata, "D8")## # A tibble: 3 × 3
## value n percentage
## <fct> <int> <dbl>
## 1 männlich 39 12.5
## 2 Weiblich 75 24.0
## 3 <NA> 199 63.6