Load packages:
library(tidytuesdayR)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(purrr)
library(forcats)
library(ggplot2)
library(ggpubr)Load data:
ttData <- tidytuesdayR::tt_load('2025-03-18')
dat <- ttData$palmtrees
dat
#> # A tibble: 2,557 × 29
#> spec_name acc_genus acc_species palm_tribe palm_subfamily climbing
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Acanthophoenix crin… Acanthop… crinita Areceae Arecoideae climbing
#> 2 Acanthophoenix rous… Acanthop… rousselii Areceae Arecoideae climbing
#> 3 Acanthophoenix rubra Acanthop… rubra Areceae Arecoideae climbing
#> 4 Acoelorrhaphe wrigh… Acoelorr… wrightii Trachycar… Coryphoideae climbing
#> 5 Acrocomia aculeata Acrocomia aculeata Cocoseae Arecoideae climbing
#> 6 Acrocomia crispa Acrocomia crispa Cocoseae Arecoideae climbing
#> 7 Acrocomia emensis Acrocomia emensis Cocoseae Arecoideae climbing
#> 8 Acrocomia glaucesce… Acrocomia glaucescens Cocoseae Arecoideae climbing
#> 9 Acrocomia hassleri Acrocomia hassleri Cocoseae Arecoideae climbing
#> 10 Acrocomia intumesce… Acrocomia intumescens Cocoseae Arecoideae climbing
#> # ℹ 2,547 more rows
#> # ℹ 23 more variables: acaulescent <chr>, erect <chr>, stem_solitary <chr>,
#> # stem_armed <chr>, leaves_armed <chr>, max_stem_height_m <dbl>,
#> # max_stem_dia_cm <dbl>, understorey_canopy <chr>, max_leaf_number <dbl>,
#> # max__blade__length_m <dbl>, max__rachis__length_m <dbl>,
#> # max__petiole_length_m <dbl>, average_fruit_length_cm <dbl>,
#> # min_fruit_length_cm <dbl>, max_fruit_length_cm <dbl>, …
glimpse(dat)
#> Rows: 2,557
#> Columns: 29
#> $ spec_name <chr> "Acanthophoenix crinita", "Acanthophoenix rous…
#> $ acc_genus <chr> "Acanthophoenix", "Acanthophoenix", "Acanthoph…
#> $ acc_species <chr> "crinita", "rousselii", "rubra", "wrightii", "…
#> $ palm_tribe <chr> "Areceae", "Areceae", "Areceae", "Trachycarpea…
#> $ palm_subfamily <chr> "Arecoideae", "Arecoideae", "Arecoideae", "Cor…
#> $ climbing <chr> "climbing", "climbing", "climbing", "climbing"…
#> $ acaulescent <chr> "acaulescent", "acaulescent", "acaulescent", "…
#> $ erect <chr> "non-erect", "non-erect", "non-erect", "non-er…
#> $ stem_solitary <chr> "non-solitary", "non-solitary", "non-solitary"…
#> $ stem_armed <chr> "non-armed", "non-armed", "non-armed", "armed"…
#> $ leaves_armed <chr> "non-armed", "non-armed", "non-armed", "non-ar…
#> $ max_stem_height_m <dbl> 10.0, 25.0, 15.0, 9.1, 12.0, 18.0, 0.0, NA, 0.…
#> $ max_stem_dia_cm <dbl> 20.0, 30.0, 18.0, 15.0, 50.0, 35.0, NA, NA, NA…
#> $ understorey_canopy <chr> "canopy", "canopy", "canopy", "canopy", "canop…
#> $ max_leaf_number <dbl> 15, NA, 20, 25, 30, 15, NA, NA, 6, NA, NA, NA,…
#> $ max__blade__length_m <dbl> 2.30, 3.00, 3.10, 1.30, 3.50, 3.00, NA, NA, 0.…
#> $ max__rachis__length_m <dbl> NA, NA, 3.00, 0.70, 2.50, NA, NA, NA, 0.54, NA…
#> $ max__petiole_length_m <dbl> NA, NA, NA, 0.65, NA, 0.65, NA, NA, 0.51, NA, …
#> $ average_fruit_length_cm <dbl> 0.65, 2.00, 1.00, 0.70, 4.25, 2.50, 2.00, NA, …
#> $ min_fruit_length_cm <dbl> 0.6, NA, NA, NA, 3.5, NA, NA, NA, 1.5, 3.8, NA…
#> $ max_fruit_length_cm <dbl> 0.7, NA, NA, NA, 5.0, NA, NA, NA, 3.0, 5.4, NA…
#> $ average_fruit_width_cm <dbl> 0.50, 0.80, 0.70, 0.70, 4.60, 1.80, 2.00, NA, …
#> $ min_fruit_width_cm <dbl> NA, NA, NA, 0.5, 3.8, NA, NA, NA, 1.5, 3.8, NA…
#> $ max_fruit_width_cm <dbl> NA, NA, NA, 0.9, 5.4, NA, NA, NA, 3.0, 5.4, NA…
#> $ fruit_size_categorical <chr> "small", "small", "small", "small", "large", "…
#> $ fruit_shape <chr> NA, "ovoid", "ovoid", "ovoid", "ovoid", "globo…
#> $ fruit_color_description <chr> "black", "black", "black", "orange-brown; beco…
#> $ main_fruit_colors <chr> "black", "black", "black", "brown; black", "gr…
#> $ conspicuousness <chr> "cryptic", "cryptic", "cryptic", "cryptic", "c…Let’s create a summary for all categorical variables with less than 10 elements:
colNms <- dat |>
map_if(.p = function(x) is.character(x) | is.factor(x), ~ unique(.x)) |>
keep(~ length(.x) < 10) |>
names()
colNms <- colNms[colNms != "palm_subfamily"]
datL <- vector("list", length(colNms))
names(datL) <- colNms
for (i in seq_along(datL)) {
datL[[i]] <- as.data.frame(table(dat[,colNms[i], drop = FALSE]))
}Let’s create a plot for each of them:
plots <- vector("list", length(colNms))
for (i in seq_along(colNms)) {
plots[[i]] <- datL[[colNms[i]]] |>
ggplot(aes(
x = reorder(.data[[colNms[i]]], -Freq),
y = Freq
)) +
geom_col(aes(fill = .data[[colNms[i]]])) +
xlab(colNms[i]) +
ylab("Count") +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
}
ggarrange(
plotlist = plots
)
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.4.3 (2025-02-28)
#> os Ubuntu 24.04.2 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language en
#> collate C.UTF-8
#> ctype C.UTF-8
#> tz UTC
#> date 2025-03-23
#> pandoc 3.1.11 @ /opt/hostedtoolcache/pandoc/3.1.11/x64/ (via rmarkdown)
#> quarto NA
#>
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#> * ── Packages attached to the search path.
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