I'm trying to create an easy shiny dashboard. I'm using the next data frame:
df <- data.frame(Age = c(18,20,25,30,40),
Salary = c(18000, 20000, 25000, 30000, 40000),
Date = as.Date(c("2006-01-01", "2008-01-01", "2013-01-01", "2018-01-01", "2028-01-01")))
save(df, file = "data.Rdata")
And the code for doing the shiny app is the following:
library(shiny)
library(ggplot2)
load("C:/.../data.RData")
ui <- fluidPage(
sidebarLayout(
# Inputs
sidebarPanel(
# Select variable for y-axis
selectInput(inputId = "y",
label = "Y-axis:",
choices = names(df),
selected = "Salary"),
# Select variable for x-axis
selectInput(inputId = "x",
label = "X-axis:",
choices = names(df),
selected = "Date")
),
# Outputs
mainPanel(
plotOutput(outputId = "scatterplot")
)
)
)
server <- function(input, output) {
output$scatterplot <- renderPlot({
ggplot(data = df, aes(x = input$x, y = input$y)) +
geom_line()
})
}
shinyApp(ui = ui, server = server)
This is what I get on my plot:
And this is what I'm expecting:
I'm not sure what I'm missing on my code.
Try with:
output$scatterplot <- renderPlot({
ggplot(data = df, aes(x = df[, input$x], y = df[, input$y])) +
geom_line()
})
or simply by using:
output$scatterplot <- renderPlot({
ggplot(data = df, aes_string(x = input$x, y = input$y)) +
geom_line()
})
Related
I am trying to filter the dataframe that I use for my graph based on the input values from two sliders. I have sliders that select a range for temperature and wind speed in a given NFL game. (Each row of the dataframe is a quarterback's performance in a game along with game weather and QB measurables, so at least two rows per game.) How do I take the output from the sliders and filter the dataframe based on that? For example, how do I filter df$temperature based on the slider with id "z"?
library(shiny)
library(dplyr)
library(ggplot2)
library(tidyr)
df = read.csv("Combined_QB_Game_Data.csv")
df[df == "--"] = NA
df$Passes.Completed = as.double(df$Passes.Completed)
df$Passes.Attempted = as.double(df$Passes.Attempted)
df$Completion.Percentage = as.double(df$Completion.Percentage)
df$Passing.Yards = as.double(df$Passing.Yards)
df$Passing.Yards.Per.Attempt = as.double(df$Passing.Yards.Per.Attempt)
df$TD.Passes = as.double(df$TD.Passes)
df$Sacks = as.double(df$Sacks)
ui = fluidPage(
titlePanel("QB Performance"),
sidebarLayout(
sidebarPanel(
selectInput(inputId = "x",
label = "Options:",
choices = c("Ht", "Wt",
"Forty", "Vertical", "BenchReps",
"BroadJump", "Cone", "Shuttle", "Round", "Pick"),
selected = "Ht"),
selectInput(inputId = "y",
label = "Options2:",
choices = c("Passer.Rating","Passes.Completed","Passes.Attempted","Completion.Percentage","Passing.Yards","Passing.Yards.Per.Attempt","TD.Passes","Ints","Sacks"),
selected = "Passer.Rating"),
sliderInput("z", "Tempurature",
min = 0, max = 100, value = c(25, 75)),
sliderInput("a", "Wind",
min = 0, max = 30, value = c(5, 25))
),
mainPanel(
plotOutput(outputId = "scatterplot")
)
)
)
server = function(input, output) {
output$scatterplot = renderPlot({
p = ggplot(data = df) +
aes_string(x = input$x, y = input$y) +
geom_point()+
geom_smooth(method = "lm")
plot(p)
})
}
shinyApp(ui, server)
One possibility is to treat the data in a reactive conductor:
ggdata <- reactive({
bounds <- input$z
df %>% filter(temperature > bounds[1], temperature < bounds[2])
})
and then use it in the renderPlot:
output$scatterplot = renderPlot({
ggplot(data = ggdata()) +
aes_string(x = input$x, y = input$y) +
geom_point() +
geom_smooth(method = "lm")
})
I'm new in programming language especially R.
I have data frame and want to show top 3 of my data and sort from the biggest value using bar chart. I have tried some codes but failed to create proper chart. Here is my latest code :
library(shiny)
library(plotly)
my_data <- data.frame(x1 = c("a","b", "c","d","e","f","g","h"),
x2 = c(200, 200, 100,200,200,100,200,100),
x3 = c(100,400,500,50,100,300,100,50))
df1 <- my_data[order(my_data$x3),] #order by x3 value, to create rank
ui <- tabPanel("Test",
sidebarLayout(
sidebarPanel(
selectInput(inputId = "why",
label = "1. Select",
choices = df1$x2),
),
mainPanel(plotlyOutput("test"))
))
server <- function(input, output, session) {
output$test <- renderPlotly({
df2 <- df1 %>%
filter(x2 ==input$why) #filter by x2
p <-ggplot(df2,
aes(x = x1, y=x3)) +
geom_bar(stat = "identity")
fig <- ggplotly(p)
fig
})}
shinyApp(ui = ui, server = server)
the bar chart I created was not ordered correctly (based on x3 values), and I also only want to show top 3 of my data
To filter for the top 3 rows you could use dplyr::slice_max and to reorder your bars use e.g. reorder. Simply reordering the dataset will not work.
library(shiny)
library(dplyr)
library(plotly)
ui <- tabPanel(
"Test",
sidebarLayout(
sidebarPanel(
selectInput(
inputId = "why",
label = "1. Select",
choices = unique(df1$x2),
selected = 200
),
),
mainPanel(plotlyOutput("test"))
)
)
server <- function(input, output, session) {
output$test <- renderPlotly({
df2 <- df1 %>%
filter(x2 == input$why) %>%
slice_max(x3, n = 3, with_ties = FALSE)
p <- ggplot(
df2,
aes(x = reorder(x1, -x3), y = x3)
) +
geom_bar(stat = "identity")
fig <- ggplotly(p)
fig
})
}
shinyApp(ui = ui, server = server)
#>
#> Listening on http://127.0.0.1:8022
I know the question is already answered, but I encourage you to keep your server function as small as possible and try to wrap long series of code into functions.
Here is an example of what I mean
library(tidyverse)
library(shiny)
library(plotly)
my_data <- data.frame(x1 = c("a","b", "c","d","e","f","g","h"),
x2 = c(200, 200, 100,200,200,100,200,100),
x3 = c(100,400,500,50,100,300,100,50))
df1 <- my_data[order(my_data$x3),] #order by x3 value, to create rank
myPlot <- function(data, input) {
df <- data |>
filter(x2 == input) #filter by x2
p <-ggplot(df, aes(x = reorder(x1, -x3), y=x3)) +
geom_bar(stat = "identity")
return(ggplotly(p))
}
ui <- tabPanel("Test",
sidebarLayout(
sidebarPanel(
selectInput(inputId = "why",
label = "1. Select",
choices = df1$x2),
),
mainPanel(plotlyOutput("test"))
))
server <- function(input, output, session) {
output$test <- renderPlotly({
myPlot(df1, input$why)
})
}
shinyApp(ui = ui, server = server)
The ggplot just shows a vertical line of values that doesn't change when I try changing the x axis selection. The only thing on the x axis is the word "column" when I try to change the x axis, instead of the values of df$column according to what's selected.
df_variable <- df
df_colnames <- colnames(df)
xaxis_input <- selectInput(
inputId = "xaxis",
label = "Feature of Interest",
choices = df_colnames,
selected = df_colnames['default']
)
ui <- fluidPage(
titlePanel("DF"),
xaxis_input,
plotOutput(
outputId = "df_plot",
)
)
server <- function(input, output) {
output$df_plot <- renderPlot({
plot <- ggplot(data = df) +
geom_point(aes(x = input$xaxis, y = some_other_col))
return(plot)
})
}
input$xaxis is a string, so you cannot use it directly inside aes().
Try using aes_string() instead.
Note that some_other_col should also be a string.
server <- function(input, output) {
output$df_plot <- renderPlot({
plot <- ggplot(data = df) +
geom_point(aes_string(x = input$xaxis, y = "some_other_col"))
return(plot)
})
A full working example:
library(shiny)
library(ggplot2)
df <- iris
df_colnames <- colnames(df)
xaxis_input <- selectInput(
inputId = "xaxis",
label = "Feature of Interest",
choices = df_colnames
)
ui <- fluidPage(
titlePanel("DF"),
xaxis_input,
plotOutput(
outputId = "df_plot",
)
)
server <- function(input, output) {
output$df_plot <- renderPlot({
plot <- ggplot(data = df) +
geom_point(aes_string(x = input$xaxis, y = "Sepal.Width"))
return(plot)
})
}
# Run the application
shinyApp(ui = ui, server = server)
I'd like to include the reactive outputs of two data sets as different geom_lines in the same ggplotly figure. The code runs as expected when only one reactive data.frame is included as a geom_line. Why not two?
ui <- fluidPage(
sidebarLayout(
selectInput("Var1",
label = "Variable", #DATA CHOICE 1
selected = 10,
choices = c(10:100)),
selectInput("Var1",
label = "Variable2", #DATA CHOICE 2
selected = 10,
choices = c(10:100))
# Show a plot of the generated distribution
),
mainPanel(
plotlyOutput('plot') #Draw figure
)
)
server <- function(input, output) {
out <- reactive({
data.frame(x = rnorm(input$Var1), #Build data set 1
y = 1:input$Var1)
})
out2 <- reactive({
data.frame(x = rnorm(input$Var2), #Build data set 2
y = 1:input$Var2)
})
output$plot <- renderPlotly({
p <- ggplot() +
geom_line(data = out(), aes(x = x, y = y)) #Add both data sets in one ggplot
geom_line(data = out2(), aes(x = x, y = y), color = "red")
ggplotly(p)
})
}
# Run the application
shinyApp(ui = ui, server = server)
When you put the data into long format and give each group a group identifier it seems to work. Note that you should be able to change sliderInput back to selectInput - this was one of the entries I toggled during testing, but the choice of UI widget should not matter.
This works -- code can be simplified inside the reactive from here:
library(plotly)
ui <- fluidPage(
sidebarLayout(
sliderInput("Var1",
label = "Variable", #DATA CHOICE 1
min=10, max=100, value=10),
sliderInput("Var2",
label = "Variable2", #DATA CHOICE 2
min=10, max=100, value=10),
),
mainPanel(
plotlyOutput('plot') #Draw figure
)
)
server <- function(input, output) {
out <- reactive({
x1 <- rnorm(input$Var1)
y1 <- seq(1:input$Var1)
x2 <- rnorm(input$Var2)
y2 <- seq(1:input$Var2)
xx <- c(x1,x2)
yy <- c(y1,y2)
gg <- c( rep(1,length(y1)), rep(2,length(y2)) )
df <- data.frame(cbind(xx,yy,gg))
df
})
output$plot <- renderPlotly({
p <- ggplot() +
geom_line(data=out(), aes(x = xx, y = yy, group=gg, colour=gg))
ggplotly(p)
})
}
shinyApp(ui = ui, server = server)
I am new to R and Shiny package. I have a csv file with 4 col and 600 rows and I am trying to plot some graphs using ggplot2.
My ui and server codes are like:
dt<-read.csv('file.csv')
server <- function(input, output) {
output$aPlot <- renderPlot({
ggplot(data = dt, aes(x = Col1, y = Col2, group = 'Col3', color = 'Col4')) + geom_point()
})
}
ui <- fluidPage(sidebarLayout(
sidebarPanel(
sliderInput("Obs", "Log FC", min = 1, max = 600, value = 100)
),
mainPanel(plotOutput("aPlot")) ))
Here, I can get the ggplot output but I don't know how to use this slider input to control the number of rows to be read i.e., I want this "Obs" input to define the size of Col1 to be used in the graph.
Try something like this, example here is with mtcars dataset:
library(shiny)
library(ggplot2)
dt <- mtcars[,1:4]
ui <- fluidPage(
sidebarPanel(
sliderInput("Obs", "Log FC", min = 1, max = nrow(dt), value = nrow(dt)-10)
),
mainPanel(plotOutput("aPlot"))
)
server <- function(input, output) {
mydata <- reactive({
dt[1:as.numeric(input$Obs),]
})
output$aPlot <- renderPlot({
test <- mydata()
ggplot(data = test, aes(x = test[,1], y = test[,2], group = names(test)[3], color = names(test)[4])) + geom_point()
})
}
shinyApp(ui = ui, server = server)
Change your server to:
server <- function(input, output) {
observe({
dt_plot <- dt[1:input$Obs,]
output$aPlot <- renderPlot({
ggplot(data = dt_plot, aes(x = Col1, y = Col2, group = 'Col3', color = 'Col4')) + geom_point()
})
})
}