library(tidyverse)
library(tidytuesdayR)
library(tidyplots)
library(janitor)
library(ggpubr)
library(here)
# choosing a dataset randomly
set.seed(4)
ttyears <- c(2018:2025)
ttweeks <- c(1:52)
# choose a year at random
chosen_year <- sample(ttyears, size = 1)
# choose at week at random
chosen_week <- sample(ttweeks, size = 1)
# read the data from that year/week
df <- tidytuesdayR::tt_load(chosen_year, chosen_week)
# print dataset
print(df)
palm <- df[[1]]
options(scipen = 999)The data for this plot comes from a 2025 TidyTuesday challenge. The Week 11 data that year was about Palm Trees.
palm |>
ggplot(aes(y = max_stem_height_m, x = max_leaf_number)) +
geom_jitter() +
scale_y_continuous(limits = c(0,65), breaks = seq(0,80,20), expand = c(0,0)) +
scale_x_continuous(limits = c(0,80), breaks = seq(0,80,20), expand = c(0,0)) +
geom_smooth(method = "lm", se = FALSE, colour = "red") +
labs(y = "Maximum height (m)", x = "Maximum leaf number",
title = "Taller palm trees have more leaves",
caption = "TidyTuesday 2025 Week 11 | Data from `palmtrees` R package") +
theme_pubr(base_family = "Lato") +
stat_cor(method = "pearson", p.accuracy = 0.001, r.accuracy = 0.01, label.x = 1) 
palm |>
tidyplot(y = max_stem_height_m, x = max_leaf_number) |>
add_data_points_jitter(colour = "black", size = 2) |>
add_curve_fit(method = "lm", colour = "red", linewidth = 1, se = FALSE) |>
adjust_y_axis(limits = c(0,65), breaks = seq(0,80,20)) |>
adjust_x_axis(limits = c(0,80), breaks = seq(0,80,20)) |>
adjust_font(fontsize = 14, family = "Lato") |>
adjust_title("Taller palm trees have more leaves", fontsize = 16) |>
adjust_x_axis_title("Maximum leaf number") |>
adjust_y_axis_title("Maximum height (m)") |>
adjust_caption("TidyTuesday 2025 Week 11 | Data from `palmtrees` R package") |>
adjust_size(unit = "mm", width = 160, height = 100) |>
add_annotation_text("R = 0.42, p < 0.001", x = 10, y = 62, fontsize = 12) |>
save_plot(here::here("charts26", "2026-04-04_slope","tidyfeatured.png")) 