All,
In order to graph data in many groups(categories):
data(iris)
library(dplyr)
iris_new <- select(iris, -Species)
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
geom_point(data = iris_new, colour = "grey70") +
geom_point(aes(colour = Species)) +
facet_wrap(~Species)
I didn't make interactive plots before, but I would like to know a way to allow me to make the above graph interactively. For example, in stead of showing the data using facet, I would like to have a bottom-like thing or scroll down list function that I can click on to highlight the data of different groups interactively. Each time I click on a certain group name (like those used for the legend), I can see the group data are highlighted and other data are greyed out. Any ideas here? Thank you.
You can make interactive displays via shiny. See here: https://shiny.rstudio.com/
Here's code that you can run:
library(shiny)
library(dplyr)
library(ggplot2)
data(iris)
ui <- fluidPage(
selectInput('species','Species',c("setosa","versicolor","virginica")),
plotOutput("plot")
)
server <- function(input, output) {
iris_new <- select(iris, -Species)
output$plot <- renderPlot({
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
geom_point(data = iris_new, colour = "grey70") +
geom_point(data=iris[iris$Species==input$species,],aes(colour = Species))
})
}
shinyApp(ui = ui, server = server)
Related
I am trying to format my ggplotly tooltip in a percent stacked barchart. In order to do this, I am editing a 'text' parameter in 'aes'
library(scales)
library(tidyverse)
library(plotly)
library(ggplot2)
library(shiny)
#Define dataframe
weeknum <- c(1,1,1,1,2,2,2,2,3,3,3,3)
channel <- rep(c("a", "b"), 6)
product <- c(rep("x",6), rep("y",6))
data <- data.frame(weeknum, channel, product)
# Define UI
ui <- fluidPage(theme = shinytheme("flatly"),
mainPanel(
h1("plot"),
plotlyOutput(outputId = "plot_1", height = "800px")
))
# Define server function
server <- function(input, output) {
output$plot_1 <- renderPlotly({
p1 <- data %>%
ggplot(aes(x=weeknum, fill=channel, text = paste('share:', percent(..count..), "<br> channel", fill))) +
geom_bar(position = "fill", stat ='count')+
facet_grid(rows = vars(product))
fig1 <- ggplotly(p1, tooltip = "text")
fig1
})
}
# Create Shiny object
shinyApp(ui = ui, server = server)
so here I got only count * 100% in the tooltip. I know I need to divide it by a dynamic height of a bar, because in this dashboard I'm gonna use some filters. Question is how can I do this? (..count..)/sum(..count..) doesn't work.
Since you did not provide a reproducible example, I created one using mtcars data set. This is how I would approach your task.
library(ggplot2)
library(dplyr)
plot <- mtcars %>%
count(cyl, am, name = "count") %>%
mutate(across(c(cyl, am), as.character)) %>%
ggplot(
aes(x = cyl, fill= am, y = count,
text = paste('share:', scales::percent(count/sum(count)), '<br>AM:', am)
)
) +
geom_col(position = "fill")
plotly::ggplotly(plot, tooltip = "text")
I am working on a simple shiny app.
Here is my data.
library(data.table)
library(ggthemes)
library(ggplot2)
library(shiny)
tempList <- list()
for(i in 1989:1991){
temp <- as.data.frame(cbind(runif(10,-10.85, 20.02),runif(10, 49.82,59.47)))
temp$value <- rnorm(10)
temp$Year <-i
tempList[[i]] <- temp
}
my.df <- rbindlist(tempList)
names(my.df)[1:2] <- c('lon', 'lat')
I want to make a shiny app that displays the raster for each year based on which year the users select
ui <- fluidPage(
titlePanel('My dat'),
sliderInput('yearRef','Select Year',min=1989,max=1991,value=1),
plotOutput(outputId = 'test')
)
server <- function(input, output) {
tempI <- reactive({my.df %>% dplyr::filter(Year == input$yearRef)})
output$test <- renderPlot({
ggplot() + geom_raster(data = tempI, aes(x = lon, y = lat, fill = value)) +
theme_map() + coord_equal() + scale_fill_viridis_c(option = 'C')
})
}
shinyApp(ui, server)
It gives me an error that tempI is not a dataframe which I understand is causing since tempI is class reactiveExpr. How do I correct it?
when working with reactive expressions in shiny you have to use paranthesis.
In your case:
renderPlot({
ggplot() + geom_raster(data = tempI(), aes(x = lon, y = lat, fill = value)) +
theme_map() + coord_equal() + scale_fill_viridis_c(option = 'C')
})
You can think of tempI() as a function that knows when its return-value is outdated. As soon as this happens (i.e. as soon as the user changes the slider)
tempI has to be reevaluated. Hence it works like a function. This also justifies the name reactive.
You can learn more about reactive expressions here.
I have a dataset with home values for all 50 states over ~30 years. Columns include State, Year, Value, etc. I am trying to create an interactive Shiny app where the user can select certain states so they will be the only ones displayed in the plot. I have successfully created the plot of all states independently where Year is on the x-axis and Value is on the y-axis, colored by State, and also have successfully subset the dataset so that only one state plots.
I am new to Shiny and having issues having anything other than the Input checkBox feature work in this. Is there something obvious I am missing?
ui <- fluidPage(
checkboxGroupInput(inputId = "state", label = "States", choices = levels(AllData2$STATE),
plotOutput(outputId = "hist")))
server <- function(input, output) {
output$hist <- renderPlot({
plot(data = subset(AllData2, AllData2 == input$state), mapping = aes(x = Date, y = HomeValue,
color = STATE) + geom_line(size=2, alpha=0.8) +
scale_y_continuous(breaks = seq(0, 1000000, 50000)) +
scale_x_continuous(breaks = seq(1975, 2020, 5)) +
ylab("Value in Dollars") + xlab("Year"))
})
}
shinyApp(ui = ui, server = server)
I get no output in my Shiny App except the checkbox options. Thank you for any help.
There are only syntax errors in your code. Many of them:
You have included plotOutput() inside the checkbox group, please place it outside it.
Use ggplot() instead of plot()
You have included everything inside plot() If you use ggplot() the syntax is: ggplot(data=AllData,mapping=aes())+geom_line()+scale_y_continuous()+scale_x_continuous()+labs(x="This is X label",y="This is ylab",color="This is the color legend label")
Your code will work after fixing these problems
Just copy paste this if you want instant result:
library(shiny)
library(ggplot2)
library(dplyr)
ui <- fluidPage(
column(12,checkboxGroupInput(inputId = "state", label = "States", choices = c(1,2,3,4,5))),
column(12,plotOutput(outputId = "hist")))
server <- function(input, output) {
output$hist <- renderPlot({
ggplot(data = subset(AllData2, AllData2$STATE %in% input$state), mapping = aes(x = Date, y = HomeValue,
color = STATE)) + geom_line(size=2, alpha=0.8) +
scale_y_continuous(breaks = seq(0, 1000000, 50000)) +
scale_x_continuous(breaks = seq(1975, 2020, 5)) +labs(x="Value in Dollars",y="Year")
})
}
shinyApp(ui = ui, server = server)
When I do a facet_grid in ggplotly() for a Shiny App, with a large number of faceting groups, the plot is messed up. However it works correctly outside Shiny.
How can I fix this?
I suspect it is linked to the Y scale but I couldn't find the solution.
Here's a reproducible example based on diamonds example from plotly.
Comparison of Shiny vs non Shiny outputs : Comparison of facet_grid outside and within Shiny
Code
Outside Shiny:
library(ggplot2)
data(diamonds, package = "ggplot2")
# new faceting group
diamonds$rdmGroup <- as.factor(sample(LETTERS, dim(diamonds)[1], replace=TRUE))
# subset of diamonds
diamonds <- diamonds[sample(nrow(diamonds), 1000),]
ggplot(diamonds , aes_string(x = diamonds$x, y = diamonds$y, color = diamonds$x)) +
geom_point() + facet_grid(rdmGroup~.) +
guides(color=FALSE) +
labs(x = "X", y="Y")
The same code in a Shiny App:
library(shiny)
library(plotly)
library(ggplot2)
data(diamonds, package = "ggplot2")
# new faceting group
diamonds$rdmGroup <- as.factor(sample(LETTERS, dim(diamonds)[1], replace=TRUE))
# subset of diamonds
diamonds <- diamonds[sample(nrow(diamonds), 1000),]
ui <- fluidPage(
headerPanel("Diamonds Explorer"),
sidebarPanel(
sliderInput('plotHeight', 'Height of plot (in pixels)',
min = 100, max = 2000, value = 1000)
),
mainPanel(
plotlyOutput('trendPlot')
)
)
server <- function(input, output) {
output$trendPlot <- renderPlotly({
p <- ggplot(diamonds, aes_string(x = diamonds$x, y =diamonds$y, color = diamonds$x)) +
geom_point()+ facet_grid(rdmGroup~., scales = "free_y") +
labs(x = "X", y="Y")
ggplotly(p) %>%
layout(height = input$plotHeight, autosize=TRUE)
})
}
shinyApp(ui, server)
PS: I used aes_string() instead of aes() intentionally as I need it in my real app.
The first thing to note is that the problem has nothing to do with Shiny but rather your use of ggplotly. The problem can be replicated with just:
library(ggplot2)
library(plotly)
data(diamonds, package = "ggplot2")
# new faceting group
diamonds$rdmGroup <- as.factor(sample(LETTERS, dim(diamonds)[1], replace=TRUE))
# subset of diamonds
diamonds <- diamonds[sample(nrow(diamonds), 1000),]
p <- ggplot(diamonds , aes_string(x = diamonds$x, y = diamonds$y, color = diamonds$x)) +
geom_point() + facet_grid(rdmGroup~.)
ggplotly(p)
though you will need something to view the output in, which may well be shiny.
In answer to your question, the problem seems to be that you cannot have more than 25 facets. If you remove any single group from rdmGroup then the plotly output works fine e.g.
diamonds <- subset(diamonds, rdmGroup != "Q")
To update your shiny example:
library(shiny)
library(plotly)
library(ggplot2)
data(diamonds, package = "ggplot2")
# new faceting group
diamonds$rdmGroup <- as.factor(sample(LETTERS, dim(diamonds)[1], replace=TRUE))
# subset of diamonds
diamonds <- diamonds[sample(nrow(diamonds), 1000),]
diamonds <- subset(diamonds, rdmGroup != "Q")
ui <- fluidPage(
headerPanel("Diamonds Explorer"),
sidebarPanel(
sliderInput('plotHeight', 'Height of plot (in pixels)',
min = 100, max = 2000, value = 1000)
),
mainPanel(
plotlyOutput('trendPlot')
)
)
server <- function(input, output) {
output$trendPlot <- renderPlotly({
p <- ggplot(diamonds, aes_string(x = diamonds$x, y =diamonds$y, color = diamonds$x)) +
geom_point()+ facet_grid(rdmGroup~., scales = "free_y") +
labs(x = "X", y="Y")
ggplotly(p) %>%
layout(height = input$plotHeight, autosize=TRUE)
})
}
shinyApp(ui, server)
provides the following output:
A workaround could be to simply have more than one plot, splitting the dataset into groups of 25.
EDIT: I did some more research and the plot stops displaying as expected when the panel margins are too large to allow all of the plots to display. You can display all 26 by reducing the panel.spacing.y but this will only go so far depending on how many rows you need:
p <- ggplot(diamonds, aes_string(x = diamonds$x, y =diamonds$y, color = diamonds$x)) +
geom_point()+ facet_grid(rdmGroup~., scales = "free_y") +
labs(x = "X", y="Y") + theme(panel.spacing.y = unit(0.2, "lines"))
Just discovering shiny apps but this is driving me insane.......I have looked at numerous examples of server.R and ui.R code and cannot figure out what I am doing wrong. Apologies in advance if it's something very basic..........
Taking the iris dataset as an example, I want to plot one column against another, something simple using qplot or preferably ggplot
However, using qplot I get this:
and using ggplot2, I get the error:
I don't think I need the reactive function as I'm not subsetting the dataset, just extracting columns to plot.
server.R code
library(shiny)
library(shinyapps)
library(ggplot2)
shinyServer(function(input, output, session) {
output$text1 <- renderText({input$id1})
output$text2 <- renderText({input$select1})
output$plot1 <- renderPlot({
g <- qplot(Sepal.Length, input$select1, data = iris)
print(g)
})
})
or using ggplot function to replace the qplot call
g <- ggplot(iris, aes(x = Sepal.Length, y = input$select1))
g <- g + geom_line(col = "green", lwd =1) +
labs(x = "Date", y = "Ranking") +
theme_bw() + scale_y_reverse()
ui.R code
library(shiny)
library(shinyapps)
data(iris)
opts <- unique(colnames(iris))
opts <- opts[-1] ## want to keep Sepal.Length as the x values
shinyUI(pageWithSidebar(
headerPanel('test with iris database'),
sidebarPanel(
selectInput(inputId = "select1", label = "select",
choices = opts),
textInput(inputId = "id1", label = "Input Text", "")
),
mainPanel(
p('Output text1'),
textOutput('text1'),
textOutput('text2'),
plotOutput('plot1')
)
))
Change your aes statement to aes_string and make x a string. This should fix the problem.
g <- ggplot(iris, aes_string(x = "Sepal.Length", y = input$select1))
g <- g + geom_line(col = "green", lwd =1) +
labs(x = "Date", y = "Ranking") +
theme_bw() + scale_y_reverse()