Supporting Statistical Analysis for Research
4.5 Dropping unneeded observations
These exercises use the PSID.csv data set
that was imported in the prior section.
Import the
PSID.csvdata 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...Display some of the observations where there are more than 90 kids in the household. Chose several of the pertinent variables to display.
psid %>% filter(kids > 90) %>% select(person_id, age, educatn, kids, marital_status) %>% print(n = 15)# A tibble: 118 x 5 person_id age educatn kids marital_status <dbl> <dbl> <dbl> <dbl> <chr> 1 3 48 13 98 divorced 2 186 41 12 98 married 3 178 49 12 98 married 4 5 34 99 99 no histories 5 3 34 12 98 divorced 6 2 47 12 98 divorced 7 182 49 12 99 no histories 8 3 48 3 98 never married 9 21 49 0 99 no histories 10 177 40 0 98 married 11 3 45 12 98 married 12 2 50 0 99 no histories 13 171 49 0 98 divorced 14 173 40 9 98 divorced 15 175 37 0 98 divorced # ... with 103 more rowsCreate a copy of the data frame that removes the observations where
marriedwasno historyorNA/DF. You may have combined these categories into a missing category in the preparatory exercises.psid_copy <- psid %>% filter( marital_status != "no history", marital_status != "NA/DF" ) psid_copy %>% select(person_id, age, educatn, kids, marital_status) %>% print(n = 15)# A tibble: 4,847 x 5 person_id age educatn kids marital_status <dbl> <dbl> <dbl> <dbl> <chr> 1 4 39 12 2 married 2 6 35 12 2 divorced 3 7 33 12 1 married 4 173 39 10 2 married 5 2 47 9 5 married 6 4 44 12 2 married 7 172 38 16 3 married 8 4 38 9 4 divorced 9 170 39 12 3 married 10 171 37 11 5 married 11 3 48 13 98 divorced 12 171 47 12 3 married 13 178 40 12 0 separated 14 171 38 16 0 married 15 3 41 12 2 married # ... with 4,832 more rows