Previously.

install.packages("dplyr", repos = "http://cran.us.r-project.org")
## Installing package into 'C:/Users/Uriel/Documents/R/win-library/3.4'
## (as 'lib' is unspecified)
## package 'dplyr' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\Uriel\AppData\Local\Temp\RtmpAfJ0v3\downloaded_packages
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
install.packages("haven", repos = "http://cran.us.r-project.org")
## Installing package into 'C:/Users/Uriel/Documents/R/win-library/3.4'
## (as 'lib' is unspecified)
## package 'haven' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\Uriel\AppData\Local\Temp\RtmpAfJ0v3\downloaded_packages
library(haven)
install.packages("curl", repos = "http://cran.us.r-project.org")
## Installing package into 'C:/Users/Uriel/Documents/R/win-library/3.4'
## (as 'lib' is unspecified)
## package 'curl' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\Uriel\AppData\Local\Temp\RtmpAfJ0v3\downloaded_packages
acs<-read_dta("https://github.com/coreysparks/data/blob/master/usa_00045.dta?raw=true")

1) Estimate the mean, median, standard deviation and sample size of “incwage” variable for the entire sample.

acs%>%
  mutate(incwage2= ifelse(incwage%in%c(999998,999999), NA, incwage))%>%
  summarise(mean(incwage2, na.rm=T), median(incwage2, na.rm=T), sd(incwage2, na.rm=T), n=n())
## # A tibble: 1 x 4
##   `mean(incwage2, na.rm = T)` `median(incwage2, na.rm = T)`
##                         <dbl>                         <dbl>
## 1                    27489.69                          7000
## # ... with 2 more variables: `sd(incwage2, na.rm = T)` <dbl>, n <int>

2) Calculate these summary statistics by level of completed education for people who are currently in the labor force by sex and over age 25.

acs%>%
  mutate(incwage2=ifelse(incwage%in%c(999998,999999), NA, incwage))%>%
  mutate(sex2=ifelse(sex==1, "Male", "Female"))%>%
  mutate(edurec=case_when(acs$educd %in% c(0:61)~"No High school", acs$educd %in% c(62:64)~"High school",
                          acs$educd %in% c(65:100)~"Some collage", acs$educd %in% c(101:116)~"Collage graduate",
                          acs$educd == 999 ~"Missing"))%>%
  filter(labforce==2, age>=25)%>%
  group_by(sex2, edurec)%>%
  summarise(mean(incwage2), median(incwage2), sd(incwage2), n=n())
## # A tibble: 8 x 6
## # Groups:   sex2 [?]
##     sex2           edurec `mean(incwage2)` `median(incwage2)`
##    <chr>            <chr>            <dbl>              <dbl>
## 1 Female Collage graduate         57775.23              48000
## 2 Female      High school         25607.74              21600
## 3 Female   No High school         18332.41              15000
## 4 Female     Some collage         32798.44              28000
## 5   Male Collage graduate         92236.55              70000
## 6   Male      High school         37929.02              32000
## 7   Male   No High school         28148.86              23000
## 8   Male     Some collage         48098.73              40000
## # ... with 2 more variables: `sd(incwage2)` <dbl>, n <int>