R/cor_tables.R
plot_distributions.RdParticularly in exploratory data analysis, it can be instructive to see histograms or density charts. This function works with both regular data frames and survey design objects, properly accounting for survey weights when present.
plot_distributions(
data,
var_names = NULL,
plot_type = c("auto", "histogram", "density"),
hist_align_y = FALSE,
plot_theme = NULL
)A dataframe or survey design object. If var_names is NULL, all numeric
variables in data will be used, otherwise those included in var_names will be selected.
For survey design objects, survey weights will be properly incorporated using
svyhist() and svysmooth().
A named character vector with new variable names or a tibble as provided by get_rename_tribbles()
If provided, only variables included here will be plotted. Apart from that, this will only determine the names of the list items, so it is most relevant if the output is to be combined with a correlation matrix, e.g., from cor_matrix()
Type of plot that should be produced - histogram or density plot. If auto,
histograms are produced for variables that take fewer than 10 unique values, density plots for others. If a number is provided,
that number is used as the maximum number of unique values for which a histogram is used.
Should histograms use the same y-axis, so that bin heights are comparable? Defaults to FALSE
Additional theme_ commands to be added to each plot
A list of plots
if (FALSE) { # \dontrun{
# Regular data
plot_distributions(mtcars, var_names = c(wt = "Weight", mpg = "Efficiency",
am = "Transmission", gear = "Gears"))
# Survey data
library(survey)
library(srvyr)
data(api)
dstrat <- apistrat %>% as_survey_design(1, strata = stype, fpc = fpc, weight = pw)
plot_distributions(dstrat, var_names = c(enroll = "Enrollment", api00 = "API Score"))
} # }