Import data

# excel file
data <- read_excel("Salaries.xlsx")
data
## # A tibble: 397 × 6
##    rank      discipline yrs.since.phd yrs.service sex    salary
##    <chr>     <chr>              <dbl>       <dbl> <chr>   <dbl>
##  1 Prof      B                     19          18 Male   139750
##  2 Prof      B                     20          16 Male   173200
##  3 AsstProf  B                      4           3 Male    79750
##  4 Prof      B                     45          39 Male   115000
##  5 Prof      B                     40          41 Male   141500
##  6 AssocProf B                      6           6 Male    97000
##  7 Prof      B                     30          23 Male   175000
##  8 Prof      B                     45          45 Male   147765
##  9 Prof      B                     21          20 Male   119250
## 10 Prof      B                     18          18 Female 129000
## # … with 387 more rows

Pivoting

Long to wide form

data %>%
    pivot_longer(cols = c("rank", "sex"))
## # A tibble: 794 × 6
##    discipline yrs.since.phd yrs.service salary name  value   
##    <chr>              <dbl>       <dbl>  <dbl> <chr> <chr>   
##  1 B                     19          18 139750 rank  Prof    
##  2 B                     19          18 139750 sex   Male    
##  3 B                     20          16 173200 rank  Prof    
##  4 B                     20          16 173200 sex   Male    
##  5 B                      4           3  79750 rank  AsstProf
##  6 B                      4           3  79750 sex   Male    
##  7 B                     45          39 115000 rank  Prof    
##  8 B                     45          39 115000 sex   Male    
##  9 B                     40          41 141500 rank  Prof    
## 10 B                     40          41 141500 sex   Male    
## # … with 784 more rows

Wide to long form

This does not work for my data set.

Seperating Uniting

Seperate a column

data2 <- data %>%
    separate(col = rank, into = c("rank_salary"))

Unite two cikumns

Missing Values

This does not work for my data set. There is no with NA.