How can I create a grouped bar plot visualisation in R using the given dataset

How to create a grouped bar plot for the following dataset in R

I'm using this dataset:

full_trains <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-02-26/full_trains.csv")

I wanted to find the Wthe highest, lowest, and average departure delay time for each year. Here's the script for that and data output (attached)

Summary_statistics <- full_trains %>%
  group_by(year) %>%
  summarise(min_ave_time = min(journey_time_avg),
            max_ave_time = max(journey_time_avg),
            mean_ave_time = mean(journey_time_avg)) %>%
            ungroup()

Can someone please help me create a visualisation for this data output similar to ?

I'm a big fan of the Rnvd3 package for such charts because of the interactivity it offers. In particular the grouped/stacked effect is really funny.

library(Rnvd3)
library(dplyr)
library(tidyr)

full_trains <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-02-26/full_trains.csv")

Summary_statistics <- full_trains %>%
  group_by(year) %>%
  summarise(min_ave_time = min(journey_time_avg),
            max_ave_time = max(journey_time_avg),
            mean_ave_time = mean(journey_time_avg)) %>%
  ungroup()

dat <- Summary_statistics %>% 
  pivot_longer(-year, names_to = "variable", values_to = "average_time") 


multiBarChart(
  data = dat, 
  average_time ~ year,
  by = "variable",
  height = "500px"
)

Does this answer your question?

library(tidyverse)
library(ggplot2)

full_trains <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-02-26/full_trains.csv")

full_trains %>% 
  ggplot(aes(x = journey_time_avg)) + 
    geom_histogram() + 
    facet_wrap(~year)

What you are referring to is a "grouped bar plot" and not "histogram". To do it in R you first need to transform your table into long format (using pivot_longer() in tidyverse or other options like reshape2::melt(). Then feed this into a ggplot item with geom_bar(), you have to set stat = "identity" and position = position_dodge() to get the exact type of grouped bar plot that you want:

library(dplyr)

Summary_statistics %>% 
  pivot_longer(-year, names_to = "variable", values_to = "average_time") %>% 
  ggplot(aes(x = year, y = average_time, fill = variable)) + 
  geom_bar(stat = "identity", position = position_dodge())

output:

With ggplot2 you can mimic your desired excel version; adjust argument values to suit your desired design.

library(dplyr)
library(tidyr)
library(ggplot2)

Summary_statistics <- 
  full_trains  |> 
  group_by(year)  |> 
  summarise(min_ave_time = min(journey_time_avg),
            max_ave_time = max(journey_time_avg),
            mean_ave_time = mean(journey_time_avg)) |> 
  ungroup() |> 
  pivot_longer(-year, names_to = "var", values_to = "avg_time") 


ggplot(Summary_statistics, aes(year, avg_time, fill = var)) + 
  geom_col(position = position_dodge2(width = 0.8)) +
  geom_text(aes(label = round(avg_time, 1)), 
            vjust = -0.3, 
            position = position_dodge2(width = 0.9)) +
  labs(x = NULL,
       y = NULL,
       fill = NULL) +
  theme_minimal() +
  theme(legend.position = "bottom",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        panel.grid.major.x = element_blank())

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^{Created on 2023-05-22 with reprex v2.0.2}