An All-Purpose Count Function

This function gives you a simple count with percentages that is weighted

ct(df, var, wt, cum = FALSE, show_na = TRUE)

Arguments

df	Name of the Dataset
var	Variable to Count
wt	Weighting Variable
cum	Will add a cumulative total if set to TRUE
show_na	Will remove the NAs before the calculation if set to TRUE, defaults to FALSE

Examples

cces <- read_csv("https://raw.githubusercontent.com/ryanburge/blocks/master/cces.csv")
#> Warning: Missing column names filled in: 'X1' [1]
#> Parsed with column specification:
#> cols(
#>   X1 = col_double(),
#>   V101 = col_double(),
#>   race = col_double(),
#>   gender = col_double(),
#>   commonweight_vv = col_double()
#> )
cces %>%
   ct(race)
#> # A tibble: 8 x 3
#>    race     n   pct
#>   <dbl> <int> <dbl>
#> 1     1   368 0.736
#> 2     2    54 0.108
#> 3     3    38 0.076
#> 4     4    13 0.026
#> 5     5     7 0.014
#> 6     6     9 0.018
#> 7     7     8 0.016
#> 8     8     3 0.006

# With a weight 
cces %>%
     ct(race, wt = commonweight_vv)
#> # A tibble: 8 x 3
#>    race       n   pct
#>   <dbl>   <dbl> <dbl>
#> 1     1 348.    0.758
#> 2     2  43.9   0.096
#> 3     3  34.0   0.074
#> 4     4   7.09  0.015
#> 5     5   3.87  0.008
#> 6     6  15.2   0.033
#> 7     7   6.33  0.014
#> 8     8   0.704 0.002

cces %>%
   mutate(race2 = frcode(race == 1 ~ "White",
                     race == 2 ~ "Black",
                     race == 3 ~ "Hispanic",
                      race == 4 ~ "Asian")) %>%
  ct(race2, show_na = FALSE, commonweight_vv)
#> # A tibble: 4 x 3
#>   race2         n   pct
#>   <fct>     <dbl> <dbl>
#> 1 White    348.   0.804
#> 2 Black     43.9  0.101
#> 3 Hispanic  34.0  0.078
#> 4 Asian      7.09 0.016

# Cumulative Counts       

cces %>%
     ct(race, cum = TRUE)
#> # A tibble: 8 x 5
#>    race     n   pct cum_n cum_pct
#>   <dbl> <int> <dbl> <int>   <dbl>
#> 1     1   368 0.736   368   0.736
#> 2     2    54 0.108   422   0.844
#> 3     3    38 0.076   460   0.92 
#> 4     4    13 0.026   473   0.946
#> 5     5     7 0.014   480   0.96 
#> 6     6     9 0.018   489   0.978
#> 7     7     8 0.016   497   0.994
#> 8     8     3 0.006   500   1