How do I get my shinyApp to read in my data file?

I have my server.R, ui.R and csv file all in my github repo. When I run my shinyapp from R using

shiny::runGitHub("st558projectfinal","cgcomora")

I get an error that object 'crimes' can't be found. Any suggestions on what I am doing wrong? I have tried moving my code that reads in the csv file and initializes variables such as northeast to the area above the shinyServer() line but that isn't working either. Here is my server.R file. Any help is greatly appreciated!

shinyServer(function(input, output,session){

  crimes <- read.csv("cgcomora/st558projectfinal/hate_crimes.csv")
  # initialize variables for each region of the U.S. 
  northeast <- c("Maine","New Hampshire","Vermont",
                 "Massachusetts","Rhode Island",
                 "Connecticut","New York","New Jersey",
                 "Pennsylvania")
  midwest <- c("Ohio","Michigan","Indiana","Illinois",
               "Wisconsin","Minnesota","Iowa","Missouri",
               "North Dakota", "South Dakota","Nebraska",
               "Kansas")
  south <- c("Texas","Oklahoma","Arkansas","Louisiana",
             "Mississippi","Tennesee","Alabama","Georgia",
             "Florida","West Virginia", "Kentucky",
             "Delaware","Maryland","Virginia","North Carolina",
             "South Carolina","District of Columbia")
  west <- c("Washington","Idaho","Montana","Wyoming",
            "Oregon","California","Nevada","Utah",
            "Arizona","Colorado","New Mexico",
            "Alaska","Hawaii")
  region <- vector()
  for (i in 1:length(crimes$state)){
    if(is.element(crimes$state[i], northeast)==TRUE){
      region[i]= "northeast"}
    else if(is.element(crimes$state[i], midwest)==TRUE){
      region[i]= "midwest"}
    else if(is.element(crimes$state[i], west)==TRUE){
      region[i] = "west"}
    else {region[i] = "south"}
  }   

  #define new column in data set with region assigned to state
  crimes$region <- region
  #create dataframe for unsupervised learning
  df <- column_to_rownames(crimes, var = "state")
  df <- na.omit(df)
  df <- select(df, -12)
  df_scale <- scale(df)
     output$introduction <- renderText({
        "This is my introduction"
    })

     output$data <- renderText({
       "This page allows you to create some basic graphical
       and numeric summaries including a scatter plot, boxplot,
       and statistical summary for any of the variables in the 
       'crimes' dataset."
     })

    selectedData <- reactive({
      crimes %>% filter(region ==input$region)
    })
    selectData2 <- reactive({
      crimes[ , c("region",input$y)]
    })

    selectData3 <- reactive({
      crimes[ , c("region", input$y2)]
    })

    mydata <- reactive({
      crimes[ ,input$var]
    })

    mycluster <- reactive({
      kmeans(df_scale, centers = input$k, nstart = 25)
    })

    d <- reactive({
      dist(df, method = input$dmethod)
    })

    clusterplot <- reactive({
      plot(hclust(d(), method = input$cmethod))
    })

    modelmethod <- reactive({
      input$regmethod
    })

    p <- reactive({
      ggplot(crimes, aes_string(x = input$x,
                                y = input$y)) + 
        geom_point(size = 3) + 
        theme(axis.text.x = element_blank(),
              axis.ticks.x = element_blank(),
              axis.text.y = element_blank())
    })

    output$save <- downloadHandler(
      filename = "save.png",
      content = function(file){
        ggsave(p(), filename = file)
      })

    dt <- reactive({
      input$mytable1
    })

    output$table <- downloadHandler(
      filename = "table.csv",
      content = function(file){
        s = input$mytable1_rows_all
        write.csv(df[s, ,drop = FALSE],file,row.names = TRUE)
      }
    )

    #render scatter plot
    output$scatterplot <- renderPlot({
      scatter <- ggplot(crimes, aes_string(x = input$x,
                                        y = input$y)) + 
        geom_point(size = 3) + 
        theme(axis.text.x = element_blank(),
              axis.ticks.x = element_blank(),
              axis.text.y = element_blank())
      scatter
    })

    output$hover_info <- renderPrint({
      cat("input$plot_hover:\n")
      str(input$plot_hover$coords_img)
    })

    output$boxplot <- renderPlot({
      ggplot(selectData3(), aes(x = region, y = selectData3()[ ,input$y2])) + 
        geom_boxplot(aes(fill = region)) + ylab(input$y2)+
        theme(axis.text.x = element_blank(),
              axis.ticks.x = element_blank(),
              axis.text.y = element_blank())
    })

    #render numerical summary
    output$summary <- renderPrint({
      dataset <- na.omit(mydata())
      summary(dataset)
    })

    output$cluster <- renderPlot({
      fviz_cluster(mycluster(),data = df)
    })

    output$tree <- renderPlot({
      clusterplot()
    })

    #render data table with ability to filter
    output$mytable1 <- DT::renderDataTable({
      DT::datatable(df,class = "display nowrap compact",
                    filter = "top")
    })

    output$model <- renderPrint({
      myformula <- as.formula(paste(input$yvar,"~",input$xvar))
      model <- train(myformula, df,
                     method = modelmethod(),
                     trControl = trainControl(
                       method = "cv", number = 10,
                       verboseIter = TRUE))
      print(model)
    })

})