我正试图在反应函数中过滤数据集，这样我就可以上下移动滑块并更改数据集的大小。
我使用名为"sliderfordata"的滑块作为名为"yeardata"的反应函数的输入，因此我使用该反应函数作为输出$chart的数据输入。我正在使用的数据集也是mtcars。
错误消息：：松夹错误(x(：无法松夹环境
当我选择一个文件作为数据上传时，就会发生这种情况，该文件映射到从文件路径读取数据文件的reactivefunc。
如有任何帮助，我们将不胜感激！
服务器代码
library("dplyr")
server <- function(input, output, session) {
yeardata <- reactive({
mtcarslist <- reactivefunc()
df <- mtcarslist %>%
filter(mpg >= input$sliderfordata)
})
output$chart <- reactive({
dataframe <- yeardata()
gggraph <-
ggplot(dataframe, aes(x = mpg , y = hp))
gggraph <- gggraph + geom_point()
gggraph
})
reactivefunc <- reactive(
csv <- read.csv(input$file$datapath)
)
# scatter plot the mtcars dataset - mpg vs hp
output$graph <- renderPlot({
# filename = input$file$datapath
# csv <- read.csv(filename)
# If more than 1 category, you can do this (put in a if state)
# If filepath is put in
csvread <- reactivefunc()
x_axis <- input$xaxis
y_axis <- input$yaxis
rangemin <- as.numeric(input$val1)
rangemax <- as.numeric(input$val2)
# csv <-
# csvread %>%
# filter(
# x_axis <- csvread$x_axis < as.numeric(input$val2)
# # y_axis <- csvread$y_axis < as.numeric(input$val2)
# )
size <- input$size
color <- input$color
gg <-
ggplot(reactivefunc(), aes_string(x = x_axis, y = y_axis, size = size, colour = color))
gg <- gg + geom_point()
gg
})
output$hist <- renderPlot({
x_axis <- input$xaxis
csvread <- reactivefunc()
ggplot(reactivefunc(), aes_string(x= x_axis)) + geom_histogram(bins = input$bins)
})
# To display the mtcars dataset on the left side in the app
output$data <- renderTable({
reactivefunc()
})
output$mytable = DT::renderDataTable(reactivefunc(), selection = list(target = 'row+column'))
# brushedPoints returns the row of data under the brush
# brush information comes from ui to server using brushId and can be accessed using input$brushId
output$data_brush <- renderTable({
n = nrow(brushedPoints(reactivefunc(), brush = input$plot_brush)) # row count will be 0 when no selection made by the brush
if(n==0)
return()
else
brushedPoints(reactivefunc(), brush = input$plot_brush) # return rows
# argument allRows = TRUE can also be used
## It will add another column (selected_) to the actual dataset. True indicates that data point
# corresponding to that row was under the brush. False means data corresponding to that row wasn't selected by brush
})
}
UI代码：
library(shiny)
library(ggplot2)
library(shinydashboard)
## ui code starts here ##
data <- read.csv(file.choose())
datacolnames <- colnames(data)
datarownames<- data[,1]
datacolnames <- datacolnames[-1]
length <- length(datacolnames)
length
data
# Works!@
maxvector <- c()
for (i in 2:length) {
maxvector <- append(maxvector, max(data[,i]))
i <- i + 1
}
maxvector <- max(maxvector)
maxvector
minvector <- c()
for (i in 2:length) {
minvector <- append(minvector, min(data[,i]))
i <- i + 1
}
minvector <- min(minvector)
minvector
# max <- max(data[,c(:11]))
# min <- min(data[,2])
# max
ui <-
dashboardPage(
dashboardHeader(),
dashboardSidebar(),
dashboardBody(
h4("Interactive plots - select data points in plot - return the rows of data that are selected by brush"),
# brush argument will enable the brush, sends the data point information to the server side
# at the server side the data points under the brush related information can be read through input$BRUSHID
plotOutput(outputId = "graph", brush = "plot_brush"), # brush ID is plot_brush
plotOutput("chart"),
fluidRow(
box(width = 5,title = "Charting", status = "warning", solidHeader = T,
fileInput("file", "Upload the File"),
h5("Max file size to upload is 5 MB"),
radioButtons("sep", "Seperator", choices = c(Comma = ",", Period = ".")),
selectInput("xaxis","Select the Model number", datacolnames),
selectInput("yaxis", "Select the Type number", datacolnames),
selectInput("size", "Select the Size", datacolnames),
selectInput("color", "Select the Color", datacolnames),
textInput("val1", "Type in the Beginning Value you Want to see", minvector),
textInput("val2", "Type in the End Value you want to see", maxvector),
sliderInput("bins", "Data Range", min = minvector, max = maxvector, 10),
sliderInput("sliderfordata", "Range of X Data Values", min = 10, max = 100, value = 10)
# sliderInput("y", "Range of Y Data Values", min = 10, max = 100, value = c(10,100))
# Instead of sliders, we could have text inputs that map to the indexing
# sliderInput("obs", "Data Range:",
# min = minvector, max = maxvector, value = c(0,472))
),
column(width=5, tags$b(tags$i("Rows corresponding to datapoints under brush")), tableOutput("data_brush"), offset = 2)
),
fluidPage(
plotOutput("hist")
),
# left side actual dataset and right side the rows for datapoints selected by brush
# defined the width of each column and also some styling (bold & italics) using tags
fluidPage(
box(width = 12, tags$b(tags$i("Actual Dataset")), DT::dataTableOutput("mytable"))
)
)
)
# download the data button
问题似乎是对图表输出使用了reactive函数而不是renderPlot函数。对于任何有这个问题并寻求可能答案的人。