如何校正图宽度+R中的正确标签位置(ggplot2)

e3bfsja2  于 2023-01-10  发布在  其他
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我需要你的帮助，我在R标记我的项目，我有一些问题。
这个情节很奇怪，因为一些很长的名字，比如第二个，所以情节很单薄，看起来很奇怪，而且小标题也不完整。我试着在情节中使用out. with ="100%"，但是仍然很奇怪

另一个问题是，这里的第一个条很长，所以我不能把标签放在右边。但是底部的条很短，我不能把它们放在条里面。我该怎么解决这个问题

以下是数据集：https://www.kaggle.com/datasets/thedevastator/uncovering-insights-to-college-majors-and-their?select=all-ages.csv
这里是我的整个代码（图像情节代码是由大箭头标识）

install.packages("tidyverse")
install.packages("scales")
require(tidyverse)
require(scales)

collegeData <- read_csv("all-ages.csv")
collegeWomenData <-read_csv("women-stem.csv")
head(collegeData)
str(collegeData)

                #Number students

##Number of students per major category     #Melhorar labels  #V
collegeData %>%
  group_by(Major_category) %>% 
  summarize(Total= sum(Total)) %>% 
  ggplot() +
  geom_col(mapping=aes(x=Total, y= reorder(Major_category, Total)),fill="darkblue")+
  labs(title= "Number of students per Major Category")+ xlab("Total of students") + ylab("Major category")+
  theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+ theme(axis.title.y = element_text(margin = margin(t = 0, r = 25, b = 0, l = 0)))+
  scale_x_continuous(labels = comma)+
  geom_text(aes(x = Total, y = reorder(Major_category, Total), label= format(Total,digits=5)), color="white",fontface="bold",hjust = 1.1)

  
  

#Top 15 majors with more students 
collegeData %>%
  arrange(desc(Total)) %>% 
  slice(1:15) %>% 
  ggplot() +
  geom_col(mapping=aes(x=Total, y= reorder(Major, Total)), fill="darkblue")+
  labs(title= "Number of students per Major Category")+ xlab("Total of students") + ylab("Major category")+
  theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)),axis.title.y = element_text(margin = margin(t = 0, r = 25, b = 0, l = 0)),plot.title = element_text(hjust = 0.5))+
  scale_x_continuous(labels = comma)+
  geom_text(aes(x = Total, y = reorder(Major, Total), label= format(Total,digits=5)), color="white",fontface="bold",hjust = 1.1)

#Top 15 majors with less students    # Second IMAGE <-------------------------------------------------- 
collegeData %>%
  arrange(Total) %>% 
  slice(1:15) %>% 
  ggplot() +
  geom_col(mapping=aes(x=Total, y= reorder(Major, Total)),fill="darkblue")+
  labs(title= "Number of students per Major Category")+ xlab("Total of students") + ylab("Major category")+
  theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)),axis.title.y = element_text(margin = margin(t = 0, r = 25, b = 0, l = 0)),plot.title = element_text(hjust = 0.5))+
  scale_x_continuous(labels = comma)+
  geom_text(aes(x = Total, y = reorder(Major, Total), label= format(Total,digits=5)), color="white",fontface="bold",hjust = 1.1)

                                #Women

#Percentage of female by major STEM category
collegeWomenData %>% 
  group_by(Major_category) %>%
  summarize(ShareWomen=mean(ShareWomen)) %>% 
  ggplot()+
  geom_col(mapping=aes(x=ShareWomen, y= reorder(Major_category, ShareWomen)),fill="darkblue")+
  labs(title= "Percentage of females by STEM category")+ xlab("Percentage of females") + ylab("Category")+
  theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)),axis.title.y = element_text(margin = margin(t = 0, r = 25, b = 0, l = 0)),plot.title = element_text(hjust = 0.5))+
  scale_x_continuous(labels=scales::percent_format())+
  geom_text(aes(x = ShareWomen, y = reorder(Major_category, ShareWomen), label= paste0(format(ShareWomen*100,digits=3), "%")), color="white",fontface="bold",hjust = 1.1)

#Top 15 majors with least females
collegeWomenData %>% 
  arrange(ShareWomen) %>% 
  slice(1:15) %>% 
  ggplot()+
  geom_col(mapping=aes(x=ShareWomen, y= reorder(Major, ShareWomen)),fill="darkblue")+
  labs(title= "Top 15 STEM majors with the lowest female percentage")+ xlab("Percentage of females") + ylab("Major")+
  theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)),axis.title.y = element_text(margin = margin(t = 0, r = 25, b = 0, l = 0)),plot.title = element_text(hjust = 0.5))+
  scale_x_continuous(labels=scales::percent_format())+
  geom_text(aes(x = ShareWomen, y = reorder(Major, ShareWomen), label= paste0(format(ShareWomen*100,digits=3), "%")), color="white",fontface="bold",hjust = 1.1)

#Top 15 majors with most females
collegeWomenData %>% 
  arrange(desc(ShareWomen)) %>% 
  slice(1:15) %>% 
  ggplot()+
  geom_col(mapping=aes(x=ShareWomen, y= reorder(Major, ShareWomen)),fill="darkblue")+
  labs(title= "Top 15 STEM majors with the highest female percentage")+ xlab("Percentage of females") + ylab("Major")+
  theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)),axis.title.y = element_text(margin = margin(t = 0, r = 25, b = 0, l = 0)),plot.title = element_text(hjust = 0.5))+
  scale_x_continuous(labels=scales::percent_format())+
  geom_text(aes(x = ShareWomen, y = reorder(Major, ShareWomen), label= paste0(format(ShareWomen*100,digits=3), "%")), color="white",fontface="bold",hjust = 1.1)

  
                        #Unemployment rate

#Unemployment rate per major category   
collegeData %>%
  group_by(Major_category) %>% 
  summarise(average_rate= mean(Unemployment_rate)) %>% 
  ggplot()+
  geom_col(mapping=aes(x=average_rate, y=reorder(Major_category, average_rate)), fill="darkblue")+
  theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)),axis.title.y = element_text(margin = margin(t = 0, r = 25, b = 0, l = 0)),plot.title = element_text(hjust = 0.5))+
  labs(title= "Unemployment rate per major category") + xlab("Unemployment rate")+ ylab("Major category")+
  scale_x_continuous(labels=scales::percent_format())+
  geom_text(aes(x = average_rate, y = reorder(Major_category, average_rate), label= paste0(format(average_rate*100,digits=3), "%")), color="white",fontface="bold",hjust = 1.1)
  

#Top 15 major with biggest Unemployment rate
collegeData %>%
  group_by(Major) %>%   
  summarise(average_rate= mean(Unemployment_rate)) %>%
  arrange(desc(average_rate)) %>%
  slice(1:15) %>% 
  ggplot()+
  geom_col(mapping=aes(x=average_rate, y=reorder(Major, average_rate)), fill="darkblue")+
  theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)) , axis.title.y = element_text(margin = margin(t = 0, r = 25, b = 0, l = 0)),plot.title = element_text(hjust = 0.5))+
  labs(title= "Top 15 majors with the biggest unemployment rate") + xlab("Unemployment rate")+ ylab("Major")+
  scale_x_continuous(labels=scales::percent_format())+
  geom_text(aes(x = average_rate, y = reorder(Major, average_rate), label= paste0(format(average_rate*100,digits=3), "%")), color="white",fontface="bold",hjust = 1.1)

#Top 15 major with smallest Unemployment rate
collegeData %>%
  group_by(Major) %>%   
  summarise(average_rate= mean(Unemployment_rate)) %>%
  arrange(average_rate) %>%
  slice(1:15) %>% 
  ggplot()+
  geom_col(mapping=aes(x=average_rate, y=reorder(Major, average_rate)), fill="darkblue")+
  theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)) , axis.title.y = element_text(margin = margin(t = 0, r = 25, b = 0, l = 0)),plot.title = element_text(hjust = 0.5))+
  labs(title= "Top 15 majors with the smallest unemployment rate") + xlab("Unemployment rate")+ ylab("Major")+
  scale_x_continuous(labels=scales::percent_format())+
  geom_text(aes(x = average_rate, y = reorder(Major, average_rate), label= paste0(format(average_rate*100,digits=3), "%")), color="white",fontface="bold",hjust = 1.1)

                #Salary

#Salary per major category   
collegeData %>%
  group_by(Major_category) %>% 
  summarise(average_salary= mean(Median)) %>% 
  ggplot()+
  geom_col(mapping=aes(x=average_salary, y=reorder(Major_category, average_salary)), fill="darkblue")+
  theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)) , axis.title.y = element_text(margin = margin(t = 0, r = 25, b = 0, l = 0)),plot.title = element_text(hjust = 0.5))+
  labs(title= "Average salary per major category") + xlab("Salary")+ ylab("Major category") + scale_x_continuous(labels=scales::dollar_format())+
  geom_text(aes(x = average_salary, y = reorder(Major_category, average_salary), label= paste0("$", format(average_salary,digits=5))), color="white",fontface="bold",hjust = 1.1)

#Top15 worst paying majors
collegeData %>%
  arrange(Median) %>% 
  slice(1:15) %>% 
  ggplot()+
  geom_col(mapping=aes(x=Median, y=reorder(Major, Median)), fill="darkblue")+
  theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)) , axis.title.y = element_text(margin = margin(t = 0, r = 25, b = 0, l = 0)),plot.title = element_text(hjust = 0.5))+
  labs(title= "Top 15 major with lowest average salaries") + xlab("Salary")+ ylab("Major") + scale_x_continuous(labels=scales::dollar_format())+
  geom_text(aes(x = Median, y = reorder(Major, Median), label= paste0("$",Median)), color="white",fontface="bold",hjust = 1.1)

#Top 15 best paying majors      #FIRST IMAGE <----------------------------------------------------
collegeData %>%
  arrange(desc(Median)) %>% 
  slice(1:15) %>% 
  ggplot()+
  geom_col(mapping=aes(x=Median, y=reorder(Major, Median)), fill="darkblue")+
  theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)) , axis.title.y = element_text(margin = margin(t = 0, r = 25, b = 0, l = 0)),plot.title = element_text(hjust = 0.5))+
  labs(title= "Top 15 major with highest average salaries") + xlab("Salary")+ ylab("Major") + scale_x_continuous(labels=scales::dollar_format())+
  geom_text(aes(x = Median, y = reorder(Major, Median), label= paste0("$",Median)), color="white",fontface="bold",hjust = 1.1)

对于宽度问题，我尝试了="100%"和其他值。对于标签问题，我尝试了将值放在不同的位置（左、中、右），但似乎没有一个在视觉上是可以的

r

1条答案

按热度按时间
0pizxfdo

0pizxfdo1#

你可以做很多事情，但我不知道什么对你最有效。
首先，可以在scale_ _continuous中使用expand（无论是x还是y）来扩展绘图限制。
对于一个长标题，你可以添加一个回车符（也可以是新行，下一行，* 按回车 *）。当使用这样的图（水平条）时，另一个选择是将标题在整个空间居中。查看theme中的附加调用。
对于长名称，您可以修改它们。在这种情况下，对于某些图，文本全部大写。在其他情况下，您可以混合大小写。如果您采用全部大写的图并重新格式化文本，则会更适合。（注意数据中添加的mutate和对库stringr的调用。）

collegeWomenData %>% 
  arrange(ShareWomen) %>% 
  mutate(Major = str_to_title(Major)) %>% 
  slice(1:15) %>% 
  ggplot(aes(x = reorder(Major, ShareWomen), y = ShareWomen,
             label = label_percent()(ShareWomen))) +
  geom_col(fill = "darkblue") +
  geom_text(hjust = 0, nudge_y = .01) + 
  labs(title= "Top 15 STEM majors with the lowest female percentage") + 
  ylab("Percentage of females") + xlab("Major") +
  theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)),
        axis.title.y = element_text(margin = margin(t = 0, r = 25, b = 0, l = 0)),
        plot.title = element_text(hjust = 0.5),
        plot.title.position = "plot")+
  scale_y_continuous(labels = percent_format(),
                     expand = expansion(mult = c(0, .3))) +
  coord_flip()

你可以将这些相同的原则应用于你在问题中使用的另一个图。

collegeData %>%
  group_by(Major_category) %>% 
  summarize(Total = sum(Total)) %>% 
  ggplot(aes(x = reorder(Major_category, Total), y = Total, label = Total)) +
  geom_col(fill = "darkblue") +
  geom_text(hjust = 0, nudge_y = 100000) +
  labs(title= "Number of students per Major Category") +
  ylab("Total of students") + xlab("Major category") +
  theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)),
        axis.title.y = element_text(margin = margin(t = 0, r = 25, b = 0, l = 0)),
        plot.title = element_text(hjust = 0.5),
        plot.title.position = "plot") + 
  scale_y_continuous(labels = comma,
                     expand = expansion(mult = c(0, .25))) +
  coord_flip()

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