如何更改 geom_bar 上的范围

Question

我有一个闪亮的应用程序，它应该显示硅谷工人的数量（第 count 列）和 race 之间的依赖关系。我希望当我在左侧选择 gender、job category 和 company 时，图表上将显示 count 个具有不同 races 的工人。现在我的图表的计数范围仅为 0 到 1，显示的图表不正确。

这是我的代码：

library(shiny)
library(ggplot2)
library(dplyr)

bcl <- read.csv(file = "E:/country/data/reveal.csv", colClasses =   c("character", "integer", "factor", "factor", "factor", "integer"), na.strings = c("na", "NA")) %>% na.omit()

ui <- fluidPage(
titlePanel("Silicon Valley Diversity Data"),
sidebarLayout(
  sidebarPanel(
  img(src = "silicon.png", height = 150, width = 250),br(),
  em("Choose company, job category and gender"),br(),
     radioButtons("genderInput", "gender", 
                         choices = list("male" = "male", 
                                         "female" = "female"),
                          selected = "male"),
     radioButtons("jobInput","Job category",
                  choices = c(
                    "First/Mid officials & Mgrs",
                    "Professionals",
                    "Administrative support",
                    "Sales workers"
                  ),
                  selected = "Technicians"
                ),
                selectInput("companyInput", "company",
                            choices = c("Adobe", "Cisco",  "Facebook", "Google", "HP", "Intel", "Twitter"))
              ),
              mainPanel(plotOutput("coolplot"),
                        br(), br(),
                        tableOutput("results"))
            ))

server <- function(input, output) {
output$coolplot <- renderPlot({
filtered <-
  bcl %>%
  filter(
    gender == input$genderInput,
    job_category == input$jobInput,
    company == input$companyInput
  )
ggplot(filtered, aes(race)) +
  geom_bar(fill = "#9f3e74")
})
}

shinyApp(ui = ui, server = server)

这是我的应用程序：

Answer 1

试试这个：

library(shiny)
library(ggplot2)
library(dplyr)

# Load the data
df <- read.csv(file = "reveal.csv",
    colClasses = c("character", "integer", "factor", "factor", "factor", "integer"),
    na.strings = c("na", "NA"))

# Clean the data
bcl <- df %>% 
  na.omit() %>% 
  select(-year) %>% 
  filter(count != 0) %>%
  filter(job_category != "Totals", 
         job_category != "Previous_totals",
         race != "Overall_totals", 
         race != "Two_or_more_races")

ui <- fluidPage(
  titlePanel("Silicon Valley Diversity Data"),
  sidebarLayout(
    sidebarPanel(
      img(
        src = "silicon.png",
        height = 150,
        width = 250
      ),
      br(),
      em("Choose company, job category and gender"),
      br(),
      radioButtons(
        "genderInput",
        "gender",
        choices = list("male" = "male",
                       "female" = "female"),
        selected = "male"
      ),
      selectInput(
        "jobInput",
        "Job category",
        choices = unique(bcl$job_category)  # get choices programmatically
      ),
      selectInput(
        "companyInput",
        "company",
        choices = unique(bcl$company)  # get choices programmatically
      )
    ),
    mainPanel(plotOutput("coolplot"),
              br(), br(),
              tableOutput("results"))
  )
)

server <- function(input, output) {

  output$coolplot <- renderPlot({

    filtered <- filter(
      bcl,
      company == input$companyInput,
      gender == input$genderInput,
      job_category == input$jobInput)

    df <- filtered[rep(row.names(filtered), filtered$count), 1:4]  #  prepare data for plotting

    ggplot(df, aes(race)) + geom_bar() + coord_flip()

  })
}

shinyApp(ui = ui, server = server)

总而言之，解决方案涉及对数据进行更多清理，以及在 server 中执行过滤后进行一些最终操作。我还冒昧地对 ui 进行了一些更改。我希望你喜欢它。

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