distrr provides some tools to estimate and manage empirical distributions. In particular, one of the main features of distrr is the creation of data cubes of estimated statistics, that include all the combinations of the variables of interest. The package makes strong usage of the tools provided by dplyr, which is a grammar of data manipulation.
The main functions to create a data cube are dcc5() and dcc6()
(dcc stands for data cube creation).
The data cube creation is like:
data |>
group_by(some variables) |>
summarise(one or more estimated statistic)
in dplyr terms, but the operation is done for each possible combination of the variables used for grouping. The result will be a data frame in “tidy form”. See some examples in the Usage section below.
# From CRAN
install.packages("distrr")
# Or the development version from GitHub:
# install.packages("remotes")
remotes::install_github("gibonet/distrr")
Consider the invented_wages dataset:
library(distrr)
str(invented_wages)
#> tibble [1,000 × 5] (S3: tbl_df/tbl/data.frame)
#> $ gender : Factor w/ 2 levels "men","women": 1 2 1 2 1 1 1 2 2 2 ...
#> $ sector : Factor w/ 2 levels "secondary","tertiary": 2 1 2 2 1 1 2 1 2 1 ...
#> $ education : Factor w/ 3 levels "I","II","III": 3 2 2 2 2 1 3 1 2 2 ...
#> $ wage : num [1:1000] 8400 4200 5100 7400 4300 4900 5400 2900 4500 3000 ...
#> $ sample_weights: num [1:1000] 105 32 36 12 21 46 79 113 34 32 ...If we want to count the number of observations and estimate the average wage by gender, with dplyr we can do:
library(dplyr)
invented_wages |>
group_by(gender) |>
summarise(n = n(), av_wage = mean(wage)) |>
ungroup()
#> # A tibble: 2 × 3
#> gender n av_wage
#> <fct> <int> <dbl>
#> 1 men 547 5435.
#> 2 women 453 4441.We can estimate the same statistics but grouped by education by changing
the argument inside group_by:
invented_wages |>
group_by(education) |>
summarise(n = n(), av_wage = mean(wage)) |>
ungroup()
#> # A tibble: 3 × 3
#> education n av_wage
#> <fct> <int> <dbl>
#> 1 I 172 3774.
#> 2 II 719 5099.
#> 3 III 109 6139.and estimate the statistics by gender and education including both
variables in group_by:
invented_wages |>
group_by(gender, education) |>
summarise(n = n(), av_wage = mean(wage)) |>
ungroup()
#> `summarise()` has grouped output by 'gender'. You can override using the
#> `.groups` argument.
#> # A tibble: 6 × 4
#> gender education n av_wage
#> <fct> <fct> <int> <dbl>
#> 1 men I 60 4627.
#> 2 men II 409 5278.
#> 3 men III 78 6886.
#> 4 women I 112 3317.
#> 5 women II 310 4865.
#> 6 women III 31 4261.With dcc5 we can perform all the steps above with one call:
invented_wages |>
dcc5(.variables = c("gender", "education"), av_wage = ~mean(wage))
#> | | | 0% | |=================================== | 50% | |======================================================================| 100%
#> # A tibble: 12 × 4
#> gender education n av_wage
#> * <fct> <fct> <int> <dbl>
#> 1 Totale Totale 1000 4985.
#> 2 Totale I 172 3774.
#> 3 Totale II 719 5099.
#> 4 Totale III 109 6139.
#> 5 men Totale 547 5435.
#> 6 men I 60 4627.
#> 7 men II 409 5278.
#> 8 men III 78 6886.
#> 9 women Totale 453 4441.
#> 10 women I 112 3317.
#> 11 women II 310 4865.
#> 12 women III 31 4261.The resulting data frame contains a column for each grouping variable, and the estimations of all the combinations of the variables:
- by gender
- by education
- by gender and education
- plus the same statistics for all the dataset, without any grouping
(this can be set with the argument
.all, which by default isTRUE).
Note that in the result there are some rows where the variables take the
value "Totale". When a variable has this value, it means that the
subset of the data considered in that row contains all the values of the
variable. For example, the first row of the result of dcc5 contains
the estimations for all the dataset. The value "Totale" can be changed
with the argument .total.
The same result of dcc5 can be produced by dcc6, with a slightly
different approach.
# Set a list of function calls
list_of_funs <- list(
n = ~n(),
av_wage = ~mean(wage),
weighted_av_wage = ~weighted.mean(wage, sample_weights)
)
# Set the grouping variables
vars <- c("gender", "education")
# And create the data cube with dcc6
invented_wages |>
dcc6(.variables = vars, .funs_list = list_of_funs, .total = "TOTAL")
#> | | | 0% | |=================================== | 50% | |======================================================================| 100%
#> # A tibble: 12 × 5
#> gender education n av_wage weighted_av_wage
#> * <fct> <fct> <int> <dbl> <dbl>
#> 1 TOTAL TOTAL 1000 4985. 4645.
#> 2 TOTAL I 172 3774. 3527.
#> 3 TOTAL II 719 5099. 4917.
#> 4 TOTAL III 109 6139. 5885.
#> 5 men TOTAL 547 5435. 5323.
#> 6 men I 60 4627. 4681.
#> 7 men II 409 5278. 5129.
#> 8 men III 78 6886. 6173.
#> 9 women TOTAL 453 4441. 3614.
#> 10 women I 112 3317. 3227.
#> 11 women II 310 4865. 4225.
#> 12 women III 31 4261. 4388.Compared to the results obtained with dcc5, we added the weighted
average of wages and changed the "Totale" value to "TOTAL".