1 Motivation
Often times, in explorative data analysis, one would like to plot the distribution of the relevant variables. Whereas ggplot provides handy tools to plot one variable after each other, it would come handy to plot ’em all in one go.
Of course, there are many ways to achieve this comfort. This posts presents some of them in brevity.
2 Load packages
library(tidyverse) # data wrangling3 Load data
Demo data.
d_path <- "https://raw.githubusercontent.com/sebastiansauer/pradadata/master/data-raw/extra.csv"
d <- read_csv(d_path)
head(d)
#> # A tibble: 6 × 34
#> timestamp code i01 i02r i03 i04 i05 i06r i07 i08 i09 i10
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 11.03.2015 … HSC 3 3 3 3 4 4 3 2 3 1
#> 2 11.03.2015 … ERB 2 2 1 2 3 2 2 3 3 1
#> 3 11.03.2015 … ADP 3 4 1 4 4 1 3 2 3 1
#> 4 11.03.2015 … KHB 3 3 2 4 3 3 3 3 3 2
#> 5 11.03.2015 … PTG 4 3 1 4 4 3 4 2 3 4
#> 6 11.03.2015 … ABL 3 2 1 4 2 3 4 3 3 3
#> # … with 22 more variables: n_facebook_friends <dbl>, n_hangover <dbl>,
#> # age <dbl>, sex <chr>, extra_single_item <dbl>, time_conversation <dbl>,
#> # presentation <chr>, n_party <dbl>, clients <chr>, extra_vignette <chr>,
#> # i21 <lgl>, extra_vignette2 <dbl>, major <chr>, smoker <chr>,
#> # sleep_week <dbl>, sleep_wend <dbl>, clients_freq <dbl>, extra_mean <dbl>,
#> # extra_md <dbl>, extra_aad <dbl>, extra_mode <dbl>, extra_iqr <dbl>Select the items (columns) we would like to plot the distribution for:
d_items <-
d %>%
select(matches("i\\d\\d")) %>%
select(-i21)d_item_names <-
names(d_items)
d_item_names
#> [1] "i01" "i02r" "i03" "i04" "i05" "i06r" "i07" "i08" "i09" "i10"4 Let’s plot
4.1 Way 1
map(d_item_names,
~ ggplot(data = d_items) + geom_bar(aes(x = .data[[.x]])) + labs(y = .x))
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4.2 Way 2
d_items %>%
pivot_longer(everything(), names_to = "item") %>%
group_by(item) %>%
group_split() %>%
map(~ {ggplot(., aes(x = value)) + geom_bar()})
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4.3 Way 3
d_items %>%
pivot_longer(everything(), names_to = "item") %>%
mutate(item = factor(item, levels = d_item_names)) %>%
ggplot(aes(x = value)) +
geom_bar() +
facet_wrap(~ item)
4.4 Way 4
library(DataExplorer)
d_items %>%
plot_histogram()
5 Reproducibility
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