我想在这些列中过滤具有特定值的所选选定列。我已尝试使用checkboxgroupinput执行此操作。在我的数据集中,这些列的值为0,1。所以我想只选择值为1的列。
library(shinydashboard)
library(shiny)
library(dplyr)
ui <- fluidPage(
checkboxGroupInput("variable", "Variables to show:",
choices = names(mtcars)),
tableOutput("data")
)
server <- function(input, output, session) {
data<-reactive({
mtcars[input$variable==1.00]
})
output$data <- renderTable({
data()
}, rownames = TRUE)
}
shinyApp(ui, server)
答案 0 :(得分:0)
当您访问checkboxGroupInput
时,您会看到一个带有所选项目名称的向量。您可以使用它来选择数据框中的列,然后对它们应用条件:
mtcars[input$variable] == 1
# if "vs" and "am" are checked, this is equivalent to:
mtcars[c('vs','am')] == 1
vs am
Mazda RX4 FALSE TRUE
Mazda RX4 Wag FALSE TRUE
Datsun 710 TRUE TRUE
Hornet 4 Drive TRUE FALSE
Hornet Sportabout FALSE FALSE
Valiant TRUE FALSE
Duster 360 FALSE FALSE
Merc 240D TRUE FALSE
Merc 230 TRUE FALSE
...
下一步取决于您是否要选择所选列的任何与值匹配的行或所有列。您可以逐行apply
any
或all
功能将此2D数据帧缩减为可用于选择行的单个逻辑向量。出于您的目的,只需将c('vs','am')
替换为input$variable
:
# Selects rows where ALL of the selected columns have a value of 1
mtcars[apply(mtcars[c('vs','am')] == 1, 1, all),]
mpg cyl disp hp drat wt qsec vs am gear carb
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# Selects rows where ANY of the selected columns have a value of 1
mtcars[apply(mtcars[c('vs','am')] == 1, 1, any),]
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2