英文:
How can I create a grouped bar plot visualisation in R using the given dataset
问题
如何在R中为以下数据集创建一个分组条形图
我正在使用这个数据集：
full_trains <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-02-26/full_trains.csv")
我想要找到每年的最高、最低和平均出发延误时间。以下是用于此目的的脚本和数据输出（附在下面）：
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()
有人可以帮我创建一个类似于Excel中的数据输出的可视化吗？您可以参考以下图像：
英文:
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 
?
答案1
得分: 1
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")
英文:
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")
答案2
得分: 0
这个问题是否解答了您的疑问？
英文:
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)
答案3
得分: 0
你所提到的是一个“分组条形图”，而不是“直方图”。在R中完成这个操作，首先需要将表格转换为长格式（使用pivot_longer()在tidyverse中或其他选项，如reshape2::melt()）。然后，将其输入到一个ggplot对象中，使用geom_bar()，您需要设置stat = "identity"和position = position_dodge()以获得您想要的精确类型的分组条形图：
英文:
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:
答案4
得分: 0
以下是翻译好的部分：
使用ggplot2，您可以模仿您想要的Excel版本；调整参数值以满足您想要的设计。
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())
创建于2023-05-22，使用reprex v2.0.2
英文:
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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<sup>Created on 2023-05-22 with reprex v2.0.2</sup>
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