4.7 Coding missing values

SSCC - Social Science Computing Cooperative

Supporting Statistical Analysis for Research

These exercises use the PSID.csv data set that was imported in the prior section.

Import the PSID.csv data set.

library(tidyverse)

psid_path <- file.path("..", "datasets", "PSID.csv")
psid_in <- read_csv(psid_path, col_types = cols())

Warning: Missing column names filled in: 'X1' [1]

psid_in <-
  rename(
    psid_in,
    obs_num = X1,
    intvw_num = intnum,
    person_id = persnum,
    marital_status = married
    )

psid <-
  psid_in %>%
  select(-obs_num)
glimpse(psid)

Observations: 4,856
Variables: 8
$ intvw_num      <dbl> 4, 4, 4, 4, 5, 6, 6, 7, 7, 7, 10, 10, 10, 11, 1...
$ person_id      <dbl> 4, 6, 7, 173, 2, 4, 172, 4, 170, 171, 3, 171, 1...
$ age            <dbl> 39, 35, 33, 39, 47, 44, 38, 38, 39, 37, 48, 47,...
$ educatn        <dbl> 12, 12, 12, 10, 9, 12, 16, 9, 12, 11, 13, 12, 1...
$ earnings       <dbl> 77250, 12000, 8000, 15000, 6500, 6500, 7000, 50...
$ hours          <dbl> 2940, 2040, 693, 1904, 1683, 2024, 1144, 2080, ...
$ kids           <dbl> 2, 2, 1, 2, 5, 2, 3, 4, 3, 5, 98, 3, 0, 0, 2, 0...
$ marital_status <chr> "married", "divorced", "married", "married", "m...

Code NAs for the kids variable.

In the prepratory exercies it was seen that there are values that are varry large for the kids variable, larger than 90. Change these to NA.
```
psid <-
  psid %>%
  mutate(
    kids = if_else(kids < 90, kids, NA_real_)
  )
```

Display observations that contain missing values in the Kids variable.

psid %>%
  filter(is.na(kids)) %>%
  select(intvw_num, person_id, age, educatn, kids, marital_status) %>%
  arrange(person_id, age) %>%
  print(n = 15)

# A tibble: 118 x 6
   intvw_num person_id   age educatn  kids marital_status
       <dbl>     <dbl> <dbl>   <dbl> <dbl> <chr>         
 1      8937         1    36      12    NA never married 
 2      9302         1    37       8    NA divorced      
 3      7660         1    50       8    NA divorced      
 4      2704         2    39       0    NA no histories  
 5      5806         2    45       0    NA no histories  
 6       878         2    47      12    NA divorced      
 7      8444         2    48       8    NA no histories  
 8      8652         2    48       1    NA married       
 9      5474         2    49       5    NA married       
10      7269         2    49      11    NA separated     
11      9004         2    49      99    NA married       
12      1709         2    50       0    NA no histories  
13      5413         2    50      17    NA married       
14      7207         2    50      99    NA married       
15      8955         2    50      11    NA married       
# ... with 103 more rows