Step 1: Load ACS Mircrodata from Github

Load data from Sparks Github data file usa_00045.dta

Step 2: Recode incwage to remove missing variables to calculate mean

ipums%>%
  mutate(mywage = ifelse(incwage %in% c(999998,999999),NA, incwage)) %>%
  summarise(meanold = mean(incwage), meannew = mean(mywage, na.rm=T), n=n())
## # A tibble: 1 x 3
##    meanold  meannew      n
##      <dbl>    <dbl>  <int>
## 1 205672.4 27489.69 300552

Step 3: Compare “incwage” median to “mywage” median

ipums%>%
    mutate(mywage = ifelse(incwage %in% c(999998,999999),NA, incwage)) %>%
    summarise(medianold = median(incwage), mediannew = median(mywage, na.rm =T), n=n())
## # A tibble: 1 x 3
##   medianold mediannew      n
##       <dbl>     <dbl>  <int>
## 1     20000      7000 300552

Step 4: Compare “incwage” and “mywage” standard deviations

ipums%>%
  mutate(mywage = ifelse(incwage %in% c(999998,999999),NA, incwage)) %>%
  summarise(sdold = sd(incwage), sdnew = sd(mywage, na.rm=T), n=n())
## # A tibble: 1 x 3
##      sdold   sdnew      n
##      <dbl>   <dbl>  <int>
## 1 378988.6 50665.1 300552

Step 5: Create categories for persons 25 years and older in the labor force, by sex

ipums%>%
  mutate(mywage = ifelse(incwage %in% c(999998,999999),NA, incwage)) %>%
  mutate(edurec = case_when(.$educd %in% c(0:61)~"nohs",
       .$educd %in% c(62:64)~"hs",
       .$educd %in% c(65:100)~"somecoll",
       .$educd %in% c(101:116)~"collgrad",
       .$educd ==999 ~ "missing")) %>%
  mutate(sexrec = ifelse(sex==1, "male", "female")) %>%
  filter(labforce==2, age >= 25) %>%
  group_by(sexrec, edurec) %>%
  summarise(meaninc = mean(mywage, na.rm=T), medianinc = median(mywage, na.rm=T), sdinc = sd(mywage, na.rm=T), n=n())
## # A tibble: 8 x 6
## # Groups:   sexrec [?]
##   sexrec   edurec  meaninc medianinc    sdinc     n
##    <chr>    <chr>    <dbl>     <dbl>    <dbl> <int>
## 1 female collgrad 57775.23     48000 56722.86 23539
## 2 female       hs 25607.74     21600 26361.37 13454
## 3 female     nohs 18332.41     15000 23212.05  3797
## 4 female somecoll 32798.44     28000 31673.19 19404
## 5   male collgrad 92236.55     70000 98040.42 23860
## 6   male       hs 37929.02     32000 37056.63 17412
## 7   male     nohs 28148.86     23000 32879.64  6394
## 8   male somecoll 48098.73     40000 47090.02 19201