my question is related both to Is there a way to match the colors of a column in a ggplot bar chart to a different ggplot bar chart? and to ggplot conditional scale_fill_manual colors but I could not find the exact solution from either.
Is there a way to fill in a color scheme based on the column name? And then the other variables will be filled in by a default palette? So for example in my code below I would like Sepal.Length.Sum to be filled in by a specific color palette and then the rest of the data to be filled in by a default palette.
# 1.0 Loading Libraries ----
#install.packages("reshape2")
# tidyverse contains: dplyr, ggplot2, tidyr, stringr, forcats, tibble, purrr, readr
library(tidyverse)
# shiny integrates user interface elements and reactivity
library(shiny)
# shinydashboard allows us to actually build the dashboard
library(shinydashboard)
# shinyWidgets offers custom widgets and other components to enhance your shiny applications.
library(shinyWidgets)
# tidyquant for financial analysis. Has nice ggplot2 themes
library(tidyquant)
# DT is used for making tables
library(DT)
library(reshape2)
# 2.0 Load Data ----
# Here I am importing the data I need for analysis
data <- iris
# 3.0 Cleaning Data ----
iris_summed <- data %>%
group_by(Species) %>%
summarize(Petal.Width.Sum = sum(Petal.Width),
Petal.Length.Sum = sum(Petal.Length),
Sepal.Width.Sum = sum(Sepal.Width),
Sepal.Length.Sum = sum(Sepal.Length)) %>%
ungroup() %>%
reshape2::melt(measure.vars = c("Sepal.Length.Sum", "Sepal.Width.Sum", "Petal.Length.Sum", "Petal.Width.Sum"),
variable.name = "Characteristics") %>%
mutate(value = value %>% as.numeric()) %>%
rename(Numerical = value)
iris_palettes <- iris_summed %>%
distinct(Species, Characteristics) %>%
mutate(iris_fill = case_when(Species == "setosa" ~ "#33CCCC",
Species == "versicolor" ~ "#00A499",
Species == "virginica" ~ "#CC000"))
# 4.0 Shiny User Interface ----
# Here I am outlining just the UI part of the Shiny dashboard
ui <- dashboardPage(title = "Iris Data Evaulation", skin = "blue",
dashboardHeader(title = "Iris Dashboard"),
dashboardSidebar(
sidebarMenu(
sidebarSearchForm("searchtext", "buttonSearch", "Search"),
menuItem("Iris Dataset",
tabName = "iris_dataset",
icon = icon("fas fa-chart-bar"))
)
),
dashboardBody(
tabItems(
tabItem(tabName = "iris_dataset",
fluidRow(box(width = 3,
height = 400,
selectInput(inputId = "iris_id",
label = h5(strong("Iris Information")),
choices = unique(data$Species),
selected = "")),
box(width = 9,
height = 400,
h5(strong("Iris Breakdown")),
DT::dataTableOutput("iris_table"),
style = "height:400px; overflow-y: scroll"),
box(width = 12,
height = 400,
title = "Iris Chart",
status = "primary",
solidHeader = T,
plotOutput("iris_chart"))
)))))
# 5.0 Shiny Server ----
# Here I am outlining just the server part of the Shiny dashboard
server <- function(input, output, session) {
iris_tbl <- reactive({
data %>%
filter(Species %in% input$iris_id)
})
output$iris_table <- DT::renderDataTable({
iris_tbl()},
rownames = FALSE,
extensions = "FixedHeader",
options = list(
scrollX = TRUE,
scrollY = "450px",
autoWidth = TRUE,
fixedHeader = TRUE,
pageLength = 10,
lengthMenu = c(10, 15),
dom = "pt"
))
iris_filter <- reactive({
iris_summed %>%
filter(Species %in% input$iris_id)
})
output$iris_chart <- renderPlot({
plot_1 <- iris_filter()
if(isTRUE(plot_1$Characteristics) && plot_1$Characteristics == "Sepal.Length.Sum"){
actual_plot <- iris_filter() %>%
ggplot(aes(Species, Numerical, fill = Characteristics)) +
geom_col(width = 0.5) +
scale_fill_manual(values = iris_palettes) +
theme_tq()+
labs(
title = "Characteristics Per Iris Species",
x = "Iris Species",
y = "Iris Characteristics"
)}
else {
actual_plot <- iris_filter() %>%
ggplot(aes(Species, Numerical, fill = Characteristics)) +
geom_col(width = 0.5) +
scale_fill_tq() +
theme_tq()+
labs(
title = "Characteristics Per Iris Species",
x = "Iris Species",
y = "Iris Characteristics"
)
}
actual_plot
})
}
# 6.0 Connecting UI with Server ----
shinyApp(ui, server)
