multiple selection in checkboxGroupInput and plotting in shiny - r

Question

In my shiny app I have a checkboxGroupInput
How should I do the plot command in server function, in a way that I plot the TurbInt_mean against MeanWindSpeed_mean and add lines (curves) to the plot by user selection ?
I have tried to summaries my shiny app as reproduce-able code as follow (you have to first load the sample data that I have provided)
library(shiny)
ui <- fluidPage(
checkboxGroupInput("variable", "Select IEC Classes for TI",c("A Plus" = "ap","A" = "a","B" = "b","C"="c")),
plotOutput("plotmeanTI",width = "100%") )
server <- function(input, output, session){
output$plotmeanTI <- renderPlot({
plot(as.matrix(TI_plot[,1]),as.matrix(TI_plot[,2]),t='o',ylim=c(0,1),xaxs="i",
xlab="Mean Wind Speed", ylab="<TI>")
if(input$variable=="ap"){lines(as.matrix(TI_plot[,1]),TI_plot$NTM_A_Plus_mean,col=6)}
if(input$variable=="a"){lines(as.matrix(TI_plot[,1]),TI_plot$NTM_A_mean,col=2)}
if(input$variable=="b"){lines(as.matrix(TI_plot[,1]),TI_plot$NTM_B_mean,col=3)}
if(input$variable=="c"){lines(as.matrix(TI_plot[,1]),TI_plot$NTM_C_mean,col=4)}
})
}
shinyApp(ui=ui,server=server)
If user select 1, one curve should be added, if select more than one, I want to have multiple curves added to my plot.I can do it for single selection like I have explained in my code, but when I have multi selection it does not work.
My data set looks like :
dput(TI_plot)
structure(list(MeanWindSpeed_mean = c(0.292023070097604, 1.12011882699226,
2.0283906614786, 3.00947886508396, 4.01428066037736, 5.01250749719984,
6.0080377166157, 7.00777409860191, 8.0049941822883, 9.00201938353988,
9.99646762244478, 10.9883558855227, 11.9798700705476, 12.976996101646,
13.9653724394786, 14.9495068163593, 15.9628459343795, 16.9708685581934,
17.9623943661972, 18.992621231979, 19.9643220338983, 20.9834693877551,
22.0170278637771, 22.9658904109589, 24.0025266903915, 24.9935025380711
), TurbInt_mean = c(3.02705430346051, 0.420402191213343, 0.264195029831388,
0.215109260166585, 0.18794121258946, 0.16699392997796, 0.148261539245668,
0.134479958525654, 0.122038442146089, 0.110595865904036, 0.097103704211826,
0.0836329541372291, 0.0708397249149876, 0.0622491842333237, 0.0591184473929236,
0.0611678829190056, 0.0652080242510699, 0.0690131441806601, 0.073762588028169,
0.0756961992136304, 0.0805696610169492, 0.0817446428571429, 0.0830263157894737,
0.0827277397260274, 0.0749537366548043, 0.0765532994923858),
NTM_A_Plus_mean = c(Inf, 1.10260388189292, 0.642329939163608,
0.473065816856713, 0.387417559923049, 0.336769624752903,
0.303163441845455, 0.27908457313955, 0.261084722917897, 0.247090026094941,
0.235918715179959, 0.226796351934008, 0.219190019655214,
0.212713243118379, 0.20720881268079, 0.202452008587075, 0.19816685602934,
0.19441329542209, 0.191131377464549, 0.188086340606011, 0.185500707351721,
0.18304730715887, 0.180790073836667, 0.178898058874634, 0.177002145398197,
0.175335040729601), NTM_A_mean = c(Inf, 0.98009233946037,
0.570959945923208, 0.420502948317078, 0.344371164376044,
0.299350777558136, 0.269478614973738, 0.248075176124045,
0.232075309260353, 0.219635578751059, 0.209705524604408,
0.201596757274674, 0.194835573026857, 0.189078438327448,
0.184185611271814, 0.179957340966289, 0.176148316470525,
0.172811818152969, 0.169894557746266, 0.167187858316455,
0.164889517645975, 0.162708717474551, 0.160702287854815,
0.159020496777452, 0.157335240353953, 0.155853369537423),
NTM_B_mean = c(Inf, 0.857580797027824, 0.499589952682807,
0.367940079777444, 0.301324768829038, 0.261931930363369,
0.23579378810202, 0.217065779108539, 0.203065895602809, 0.192181131407176,
0.183492334028857, 0.176397162615339, 0.1704811263985, 0.165443633536517,
0.161162409862837, 0.157462673345503, 0.154129776911709,
0.151210340883848, 0.148657738027983, 0.146289376026898,
0.144278327940228, 0.142370127790232, 0.140614501872963,
0.139142934680271, 0.137668335309708, 0.136371698345246),
NTM_C_mean = c(Inf, 0.735069254595278, 0.428219959442406,
0.315377211237809, 0.258278373282033, 0.224513083168602,
0.202108961230303, 0.186056382093034, 0.174056481945265,
0.164726684063294, 0.157279143453306, 0.151197567956005,
0.146126679770143, 0.141808828745586, 0.13813920845386, 0.134968005724717,
0.132111237352894, 0.129608863614727, 0.127420918309699,
0.125390893737341, 0.123667138234481, 0.122031538105913,
0.120526715891111, 0.119265372583089, 0.118001430265464,
0.116890027153068)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -26L), .Names = c("MeanWindSpeed_mean",
"TurbInt_mean", "NTM_A_Plus_mean", "NTM_A_mean", "NTM_B_mean",
"NTM_C_mean"))
the head of TI_plot is like :
head(TI_plot)
# A tibble: 6 x 6
MeanWindSpeed_mean TurbInt_mean NTM_A_Plus_mean NTM_A_mean NTM_B_mean NTM_C_mean
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0.2920231 3.0270543 Inf Inf Inf Inf
2 1.1201188 0.4204022 1.1026039 0.9800923 0.8575808 0.7350693
3 2.0283907 0.2641950 0.6423299 0.5709599 0.4995900 0.4282200
4 3.0094789 0.2151093 0.4730658 0.4205029 0.3679401 0.3153772
5 4.0142807 0.1879412 0.3874176 0.3443712 0.3013248 0.2582784
6 5.0125075 0.1669939 0.3367696 0.2993508 0.2619319 0.2245131

Answer 1

We could use switch
library(shiny)
ui <- fluidPage(
checkboxGroupInput("variable", "Select IEC Classes for TI",c("A Plus" = "ap","A" = "a","B" = "b","C"="c"),
selected = c("A Plus" = "ap")),
plotOutput("plotmeanTI",width = "100%")
)
server <- function(input, output, session){
output$plotmeanTI <- renderPlot({
f1 <- function(nm1){
switch(nm1,
ap = lines(TI_plot[[1]],TI_plot$NTM_A_Plus_mean,col=6),
a = lines(TI_plot[[1]],TI_plot$NTM_A_mean,col=2),
b = lines(TI_plot[[1]],TI_plot$NTM_B_mean,col=3),
c = lines(TI_plot[[1]],TI_plot$NTM_C_mean,col=4)
)
}
if(is.null(input$variable)) {
plot(TI_plot[[1]], TI_plot[[2]],t='o',ylim=c(0,1),xaxs="i",
xlab="Mean Wind Speed", ylab="<TI>")
} else {
plot(TI_plot[[1]], TI_plot[[2]],t='o',ylim=c(0,1),xaxs="i",
xlab="Mean Wind Speed", ylab="<TI>")
f1(input$variable)
}
})
}
shinyApp(ui=ui,server=server)
-output
Using ggplot2
library(shiny)
library(ggplot2)
library(tidyr)
library(dplyr)
ui <- fluidPage(
checkboxGroupInput("variable", "Select IEC Classes for TI",c("A Plus" = "ap","A" = "a","B" = "b","C"="c"),
selected = c("A Plus" = "ap")),
plotOutput("plotmeanTI",width = "100%") )
server <- function(input, output, session){
output$plotmeanTI <- renderPlot({
keyvaldata <- data.frame(key = c('NTM_A_Plus_mean', 'NTM_A_mean', 'NTM_B_mean', 'NTM_C_mean' ),
Var = c('ap', 'a', 'b', 'c'), stringsAsFactors = FALSE)
p1 <- gather(TI_plot, key, val, -MeanWindSpeed_mean, -TurbInt_mean) %>%
left_join(., keyvaldata) %>%
filter(Var %in% input$variable) %>%
ggplot(., aes(MeanWindSpeed_mean, TurbInt_mean, colour = Var)) +
geom_line() +
geom_line(aes(y =val)) +
labs(x = "Mean Wind Speed", y = "<TI>") +
theme_bw()
if(is.null(input$variable)) {
ggplot(TI_plot, aes(MeanWindSpeed_mean, TurbInt_mean)) +
geom_line() +
labs(x = "Mean Wind Speed", y = "<TI>") +
theme_bw()
} else {
p1
}
})
}
shinyApp(ui=ui,server=server)
-output