The Happy data from GSS

The General Social Survey (GSS) has been run by NORC every other year since 1972 to keep track of current opinions across the United States.

An excerpt of the GSS data called happy is available from the classdata package:

remotes::install_github("heike/classdata")

library(classdata)
head(happy)

##   year age         degree       finrela         happy    health       marital
## 1 1972  23       bachelor       average not too happy      good never married
## 2 1972  70 lt high school above average not too happy      fair       married
## 3 1972  48    high school       average  pretty happy excellent       married
## 4 1972  27       bachelor       average not too happy      good       married
## 5 1972  61    high school above average  pretty happy      good       married
## 6 1972  26    high school above average  pretty happy      good never married
##      sex polviews          partyid wtssall wtssnr
## 1 female     <NA>     ind,near dem       7   1147
## 2   male     <NA> not str democrat      54   1147
## 3 female     <NA>      independent      54   1147
## 4 female     <NA> not str democrat      54   1147
## 5 female     <NA>  strong democrat      54   1147
## 6   male     <NA>     ind,near dem       7   1147

You can find a codebook with explanations for each of the variables at https://gssdataexplorer.norc.org/

Helper functions (1)

• n() provides the number of rows of a subset:

library(dplyr)
happy %>% group_by(sex) %>% summarise(n = n())

## # A tibble: 3 × 2
##   sex        n
##   <fct>  <int>
## 1 male   30350
## 2 female 38404
## 3 <NA>      92

• tally() is a combination of summarise and n

happy %>% group_by(sex) %>% tally()

## # A tibble: 3 × 2
##   sex        n
##   <fct>  <int>
## 1 male   30350
## 2 female 38404
## 3 <NA>      92

Helper functions (2)

• count() is a further shortcut of group_by and tally:

happy %>% count(sex, degree)

##       sex         degree     n
## 1    male lt high school  6021
## 2    male    high school 14828
## 3    male junior college  1637
## 4    male       bachelor  4951
## 5    male       graduate  2828
## 6    male           <NA>    85
## 7  female lt high school  7766
## 8  female    high school 19942
## 9  female junior college  2400
## 10 female       bachelor  5551
## 11 female       graduate  2641
## 12 female           <NA>   104
## 13   <NA> lt high school    46
## 14   <NA>    high school    22
## 15   <NA> junior college     1
## 16   <NA>       bachelor     9
## 17   <NA>       graduate     7
## 18   <NA>           <NA>     7

• count() doesn't introduce any grouping

Grouping and Ungrouping

• ungroup removes a grouping structure from a data set

• necessary to make changes to a grouping variable (such as re-ordering or re-labelling)