Summarize one variable in a dataset, by another categorical variable

Source: R/ff_summ_bygroup.R

Generate distributional and other statistics for a particular continuous variable, categorized by some discrete variables. Wage by gender for example.

ff_summ_allvars_bygroup(
  df,
  vars.group,
  var.numeric,
  str.stats.group = "main",
  ar.perc = c(0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99),
  str.stats.specify = NULL,
  boo.overall.stats = TRUE
)

Arguments

df

dataframe input dataframe of interest

vars.group

list of strings containing grouping variables, could be gender and age groups for example

var.numeric

string variable name of continuous quantitative variable to summarize

str.stats.group

string what type of statistics to consider see line 31 and below

ar.perc

array of percentiles to calculate, only calculated if str.stats.group = 'mainperc'

Value

a list of various variables

  • df_table_grp_stats - A dataframe where each row is a combination of categories, and columns are categories and statistics

  • df_row_grp_stats - A single row with all statistics

  • df_overall_stats - A dataframe with non-grouped overall summaries

  • df_row_stats_all - A named list of all statistics generated

Author

Fan Wang, http://fanwangecon.github.io

Examples

data(mtcars)
df_mtcars <- mtcars
df <- df_mtcars
vars.group <- c('am', 'vs')
var.numeric <- 'mpg'
str.stats.group <- 'allperc'
ar.perc <- c(0.01, 0.05, 0.10, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99)
ls_summ_by_group <- ff_summ_bygroup(df, vars.group, var.numeric, str.stats.group, ar.perc)
#> Warning: `funs()` was deprecated in dplyr 0.8.0.
#> Please use a list of either functions or lambdas: 
#> 
#>   # Simple named list: 
#>   list(mean = mean, median = median)
#> 
#>   # Auto named with `tibble::lst()`: 
#>   tibble::lst(mean, median)
#> 
#>   # Using lambdas
#>   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
#> Warning: attributes are not identical across measure variables;
#> they will be dropped
df_table_grp_stats <- ls_summ_by_group$df_table_grp_stats
df_row_grp_stats <- ls_summ_by_group$df_row_grp_stats
df_overall_stats <- ls_summ_by_group$df_overall_stats
df_row_stats_all <- ls_summ_by_group$df_row_stats_all
print(df_table_grp_stats)
#> # A tibble: 4 x 21
#> # Groups:   am [2]
#>      am    vs  mean median    sd   IQR   mad  `1%`  `5%` `10%` `25%` `50%` `75%`
#>   <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     0     0  15.0   15.2  2.77  2.57  2.30  10.4  10.4  10.7  14.0  15.2  16.6
#> 2     0     1  20.7   21.4  2.47  3.5   3.26  17.8  17.9  18.0  18.6  21.4  22.2
#> 3     1     0  19.8   20.4  4.01  4.22  3.85  15.0  15.2  15.4  16.8  20.4  21  
#> 4     1     1  28.4   30.4  4.76  6.35  4.60  21.5  21.8  22.2  25.0  30.4  31.4
#> # ... with 8 more variables: 90% <dbl>, 95% <dbl>, 99% <dbl>, min <dbl>,
#> #   max <dbl>, first <dbl>, last <dbl>, n.distinct <int>
print(df_row_grp_stats)
#> # A tibble: 1 x 76
#>   `mpg.am.vs.0.0.1%` `mpg.am.vs.0.0.10%` `mpg.am.vs.0.0.25%` `mpg.am.vs.0.0.5%`
#>                <dbl>               <dbl>               <dbl>              <dbl>
#> 1               10.4                10.7                14.0               10.4
#> # ... with 72 more variables: mpg.am.vs.0.0.50% <dbl>, mpg.am.vs.0.0.75% <dbl>,
#> #   mpg.am.vs.0.0.90% <dbl>, mpg.am.vs.0.0.95% <dbl>, mpg.am.vs.0.0.99% <dbl>,
#> #   mpg.am.vs.0.0.IQR <dbl>, mpg.am.vs.0.0.first <dbl>,
#> #   mpg.am.vs.0.0.last <dbl>, mpg.am.vs.0.0.mad <dbl>, mpg.am.vs.0.0.max <dbl>,
#> #   mpg.am.vs.0.0.mean <dbl>, mpg.am.vs.0.0.median <dbl>,
#> #   mpg.am.vs.0.0.min <dbl>, mpg.am.vs.0.0.n.distinct <dbl>,
#> #   mpg.am.vs.0.0.sd <dbl>, mpg.am.vs.0.1.1% <dbl>, ...
print(df_overall_stats)
#>   mpg.mean mpg.median   mpg.sd mpg.IQR mpg.mad mpg.1% mpg.5% mpg.10% mpg.25%
#> 1 20.09062       19.2 6.026948   7.375 5.41149   10.4 11.995   14.34  15.425
#>   mpg.50% mpg.75% mpg.90% mpg.95% mpg.99% mpg.min mpg.max mpg.first mpg.last
#> 1    19.2    22.8   30.09    31.3  33.435    10.4    33.9        21     21.4
#>   mpg.n_distinct
#> 1             25
print(df_row_stats_all)
#> $`mpg.am.vs.0.0.1%`
#> [1] 10.4
#> 
#> $`mpg.am.vs.0.0.10%`
#> [1] 10.69
#> 
#> $`mpg.am.vs.0.0.25%`
#> [1] 14.05
#> 
#> $`mpg.am.vs.0.0.5%`
#> [1] 10.4
#> 
#> $`mpg.am.vs.0.0.50%`
#> [1] 15.2
#> 
#> $`mpg.am.vs.0.0.75%`
#> [1] 16.625
#> 
#> $`mpg.am.vs.0.0.90%`
#> [1] 18.56
#> 
#> $`mpg.am.vs.0.0.95%`
#> [1] 18.925
#> 
#> $`mpg.am.vs.0.0.99%`
#> [1] 19.145
#> 
#> $mpg.am.vs.0.0.IQR
#> [1] 2.575
#> 
#> $mpg.am.vs.0.0.first
#> [1] 10.4
#> 
#> $mpg.am.vs.0.0.last
#> [1] 19.2
#> 
#> $mpg.am.vs.0.0.mad
#> [1] 2.29803
#> 
#> $mpg.am.vs.0.0.max
#> [1] 19.2
#> 
#> $mpg.am.vs.0.0.mean
#> [1] 15.05
#> 
#> $mpg.am.vs.0.0.median
#> [1] 15.2
#> 
#> $mpg.am.vs.0.0.min
#> [1] 10.4
#> 
#> $mpg.am.vs.0.0.n.distinct
#> [1] 10
#> 
#> $mpg.am.vs.0.0.sd
#> [1] 2.774396
#> 
#> $`mpg.am.vs.0.1.1%`
#> [1] 17.818
#> 
#> $`mpg.am.vs.0.1.10%`
#> [1] 17.98
#> 
#> $`mpg.am.vs.0.1.25%`
#> [1] 18.65
#> 
#> $`mpg.am.vs.0.1.5%`
#> [1] 17.89
#> 
#> $`mpg.am.vs.0.1.50%`
#> [1] 21.4
#> 
#> $`mpg.am.vs.0.1.75%`
#> [1] 22.15
#> 
#> $`mpg.am.vs.0.1.90%`
#> [1] 23.44
#> 
#> $`mpg.am.vs.0.1.95%`
#> [1] 23.92
#> 
#> $`mpg.am.vs.0.1.99%`
#> [1] 24.304
#> 
#> $mpg.am.vs.0.1.IQR
#> [1] 3.5
#> 
#> $mpg.am.vs.0.1.first
#> [1] 17.8
#> 
#> $mpg.am.vs.0.1.last
#> [1] 24.4
#> 
#> $mpg.am.vs.0.1.mad
#> [1] 3.26172
#> 
#> $mpg.am.vs.0.1.max
#> [1] 24.4
#> 
#> $mpg.am.vs.0.1.mean
#> [1] 20.74286
#> 
#> $mpg.am.vs.0.1.median
#> [1] 21.4
#> 
#> $mpg.am.vs.0.1.min
#> [1] 17.8
#> 
#> $mpg.am.vs.0.1.n.distinct
#> [1] 7
#> 
#> $mpg.am.vs.0.1.sd
#> [1] 2.471071
#> 
#> $`mpg.am.vs.1.0.1%`
#> [1] 15.04
#> 
#> $`mpg.am.vs.1.0.10%`
#> [1] 15.4
#> 
#> $`mpg.am.vs.1.0.25%`
#> [1] 16.775
#> 
#> $`mpg.am.vs.1.0.5%`
#> [1] 15.2
#> 
#> $`mpg.am.vs.1.0.50%`
#> [1] 20.35
#> 
#> $`mpg.am.vs.1.0.75%`
#> [1] 21
#> 
#> $`mpg.am.vs.1.0.90%`
#> [1] 23.5
#> 
#> $`mpg.am.vs.1.0.95%`
#> [1] 24.75
#> 
#> $`mpg.am.vs.1.0.99%`
#> [1] 25.75
#> 
#> $mpg.am.vs.1.0.IQR
#> [1] 4.225
#> 
#> $mpg.am.vs.1.0.first
#> [1] 15
#> 
#> $mpg.am.vs.1.0.last
#> [1] 26
#> 
#> $mpg.am.vs.1.0.mad
#> [1] 3.85476
#> 
#> $mpg.am.vs.1.0.max
#> [1] 26
#> 
#> $mpg.am.vs.1.0.mean
#> [1] 19.75
#> 
#> $mpg.am.vs.1.0.median
#> [1] 20.35
#> 
#> $mpg.am.vs.1.0.min
#> [1] 15
#> 
#> $mpg.am.vs.1.0.n.distinct
#> [1] 5
#> 
#> $mpg.am.vs.1.0.sd
#> [1] 4.008865
#> 
#> $`mpg.am.vs.1.1.1%`
#> [1] 21.484
#> 
#> $`mpg.am.vs.1.1.10%`
#> [1] 22.24
#> 
#> $`mpg.am.vs.1.1.25%`
#> [1] 25.05
#> 
#> $`mpg.am.vs.1.1.5%`
#> [1] 21.82
#> 
#> $`mpg.am.vs.1.1.50%`
#> [1] 30.4
#> 
#> $`mpg.am.vs.1.1.75%`
#> [1] 31.4
#> 
#> $`mpg.am.vs.1.1.90%`
#> [1] 33
#> 
#> $`mpg.am.vs.1.1.95%`
#> [1] 33.45
#> 
#> $`mpg.am.vs.1.1.99%`
#> [1] 33.81
#> 
#> $mpg.am.vs.1.1.IQR
#> [1] 6.35
#> 
#> $mpg.am.vs.1.1.first
#> [1] 21.4
#> 
#> $mpg.am.vs.1.1.last
#> [1] 33.9
#> 
#> $mpg.am.vs.1.1.mad
#> [1] 4.59606
#> 
#> $mpg.am.vs.1.1.max
#> [1] 33.9
#> 
#> $mpg.am.vs.1.1.mean
#> [1] 28.37143
#> 
#> $mpg.am.vs.1.1.median
#> [1] 30.4
#> 
#> $mpg.am.vs.1.1.min
#> [1] 21.4
#> 
#> $mpg.am.vs.1.1.n.distinct
#> [1] 6
#> 
#> $mpg.am.vs.1.1.sd
#> [1] 4.757701
#> 
#> $mpg.mean
#> [1] 20.09062
#> 
#> $mpg.median
#> [1] 19.2
#> 
#> $mpg.sd
#> [1] 6.026948
#> 
#> $mpg.IQR
#> [1] 7.375
#> 
#> $mpg.mad
#> [1] 5.41149
#> 
#> $`mpg.1%`
#>   1% 
#> 10.4 
#> 
#> $`mpg.5%`
#>     5% 
#> 11.995 
#> 
#> $`mpg.10%`
#>   10% 
#> 14.34 
#> 
#> $`mpg.25%`
#>    25% 
#> 15.425 
#> 
#> $`mpg.50%`
#>  50% 
#> 19.2 
#> 
#> $`mpg.75%`
#>  75% 
#> 22.8 
#> 
#> $`mpg.90%`
#>   90% 
#> 30.09 
#> 
#> $`mpg.95%`
#>  95% 
#> 31.3 
#> 
#> $`mpg.99%`
#>    99% 
#> 33.435 
#> 
#> $mpg.min
#> [1] 10.4
#> 
#> $mpg.max
#> [1] 33.9
#> 
#> $mpg.first
#> [1] 21
#> 
#> $mpg.last
#> [1] 21.4
#> 
#> $mpg.n_distinct
#> [1] 25
#>