Calculate A Mean with Confidence Intervals

This function gives you a mean with 95 percent CIs

mean_ci(df, var, wt, ci = 0.95)

Arguments

df	Name of the Dataset
var	Variable to find the mean of
wt	Weight to be applied
ci	Confidence Interval, expressed as a decimal. i.e. .84. Defaults to .95

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 %>%
   mean_ci(gender)
#> # A tibble: 1 x 7
#>    mean    sd     n level     se lower upper
#>   <dbl> <dbl> <int> <dbl>  <dbl> <dbl> <dbl>
#> 1  1.54 0.499   500  0.05 0.0223  1.49  1.58

# Weighted Means    

cces %>%
   mean_ci(gender, wt = commonweight_vv)
#> # A tibble: 1 x 6
#>    mean    sd     n     se lower upper
#>   <dbl> <dbl> <int>  <dbl> <dbl> <dbl>
#> 1  1.50 0.499   500 0.0223  1.46  1.54

# Change the Confidence Interval

cces %>%
   mean_ci(gender, ci = .84)
#> # A tibble: 1 x 7
#>    mean    sd     n level     se lower upper
#>   <dbl> <dbl> <int> <dbl>  <dbl> <dbl> <dbl>
#> 1  1.54 0.499   500  0.16 0.0223  1.50  1.57