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app_4.R
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library(ggplot2)
library(shiny)
library(dplyr)
library(DT)
# read in data
dogs <- read.csv("dogs.csv")
# define UI
ui <- fluidPage(theme = shinythemes::shinytheme("united"), #add a theme!
# use shiny feedback
shinyFeedback::useShinyFeedback(),
# navbar page layout with pages at top
navbarPage("Dog Breed Explorer",
# page 1
tabPanel("Explore dog breeds",
# sidebar layout
sidebarLayout(
# sidebar
sidebarPanel(
h2("Breed Characteristics"),
# INPPUT: checkbox for dog sizes
shinyWidgets::checkboxGroupButtons(
inputId = "size",
size = "sm",
label = h4("Size of dog"),
choices = c("X-Small","Small","Medium","Large", "X-Large"),
selected = c("X-Small","Small","Medium","Large", "X-Large"),
individual = TRUE,
status = "primary",
checkIcon = list(
yes = icon("square-check"),
no = icon("square")
)),
# INPUT: drop down options for dog coat type
selectInput("coat", label = h4("Type of coat"),
choices = unique(dogs$coat_type),
selected = unique(dogs$coat_type),
multiple = TRUE),
h2("Personality traits"),
h5("Scored from 1:5, with 5 indicating the HIGHEST level e.g. most friendly, most playful"),
br(), # blank row
# INPUT: sliders for personality traits
# sliders
sliderInput("openness_level",
label = "Opennness with strangers",
min = 1,
max = 5,
value = c(1,5)),
sliderInput("playfulness_level",
label = "Playfulness",
min = 1,
max = 5,
value = c(1,5)),
sliderInput("energy_level",
label = "Energy levels",
min = 1,
max = 5,
value = c(1,5)),
sliderInput("barking_level",
label = "Barking tendencies",
min = 1,
max = 5,
value = c(1,5)),
shiny::actionButton(inputId = "goButton", "Filter dogs!")
# action button to initiate filtering
# shinyWidgets::actionBttn("goButton", "Filter dogs!",
# color = "success",
# style = "pill")
),
# main panel
mainPanel(
h3("Most popular dogs"),
# OUTPUT: datatable showing most popular dogs within criteria
dataTableOutput("rank_table")
))),
# page 2
tabPanel("Compare dog breeds",
# sidebar layout
sidebarLayout(
sidebarPanel(
h2("Select Dog Breed(s)"),
# INPUT: drop down for different breeds
selectInput("breed", label = h4("Select dog breed(s)"),
choices = unique(dogs$breed),
selected =unique(dogs$breed)[c(25,83)], # 2 selected as default
multiple = TRUE) # multiple selections possible
),
# main panel
mainPanel(
tabsetPanel(
# OUTPUT: radar plot of personality traits for given breed(s)
tabPanel("Personality", plotOutput("radar_pers", height = "800px", width="900px")),
# OUTPUT: bar plot of other traits for given breed(s)
tabPanel("Other characteristics", plotOutput("bar_characteristics", height = "600px")),
# OUTPUT: line plot of popularity for given breed(s) over time
tabPanel("Popularity over time", plotOutput("popularity", height = "600px"))
))))))
# server
server <- function(input, output) {
# create datatable with dog popularity rankings
output$rank_table <- DT::renderDataTable({
# Take a dependency on input$goButton
input$goButton
# creating a dataframe with the input filters applied
df <- dogs %>%
filter(openness_to_strangers >= isolate(input$openness_level[1])) %>%
filter(openness_to_strangers <= isolate(input$openness_level[2])) %>%
filter(energy_level >= isolate(input$energy_level[1])) %>%
filter(energy_level <= isolate(input$energy_level[2])) %>%
filter(barking_level >= isolate(input$barking_level[1])) %>%
filter(barking_level <= isolate(input$barking_level[2])) %>%
filter(playfulness_level >= isolate(input$playfulness_level[1])) %>%
filter(playfulness_level <= isolate(input$playfulness_level[2])) %>%
filter(size_category %in% isolate(input$size)) %>%
filter(coat_type %in% isolate(input$coat)) %>%
select(breed, x2020_rank, image) %>%
arrange(x2020_rank) %>%
rename(rank = x2020_rank) %>%
select(rank, breed, image) %>%
mutate(image = paste0("<img src=", "'", image, "'", " height='72'>"))
# df <- dogs %>%
# filter(openness_to_strangers >= input$openness_level[1]) %>%
# filter(openness_to_strangers <= input$openness_level[2]) %>%
# filter(energy_level >= input$energy_level[1]) %>%
# filter(energy_level <= input$energy_level[2]) %>%
# filter(barking_level >= input$barking_level[1]) %>%
# filter(barking_level <= input$barking_level[2]) %>%
# filter(playfulness_level >= input$playfulness_level[1]) %>%
# filter(playfulness_level <= input$playfulness_level[2]) %>%
# filter(size_category %in% input$size) %>%
# filter(coat_type %in% input$coat) %>%
# select(breed, x2020_rank, image) %>%
# arrange(x2020_rank) %>%
# rename(rank = x2020_rank) %>%
# select(rank, breed, image) %>%
# mutate(image = paste0("<img src=", "'", image, "'", " height='72'></img>"))
# rendering a datatable using that data
DT::datatable(df, rownames = FALSE, options = list(pageLength = 10), escape=FALSE) # escape is false to allow HTML code in images
})
# reactive dataframe with selected dogs
selected_dogs <- reactive({
dogs <- dogs %>%
filter(breed %in% input$breed)
})
output$popularity <-renderPlot({
selected_dogs() %>%
select(breed, contains("_rank")) %>%
tidyr::pivot_longer(-breed, names_to ="Year", values_to="Popularity Ranking") %>%
mutate(Year = gsub("x", "", Year)) %>%
mutate(Year = gsub("_rank", "", Year)) %>%
select(breed, Year, `Popularity Ranking`) %>%
rename(Breed = breed) %>%
ggplot(aes(x=Year, y=`Popularity Ranking`, group=Breed, colour=Breed)) +
geom_line(linewidth=3) +
scale_y_reverse() +
theme_minimal() +
theme(text = element_text(size = 16)) +
scale_colour_manual(values = c(rgb(0.2,0.5,0.5,0.9), rgb(0.8,0.2,0.5,0.9) , rgb(0.7,0.5,0.1,0.9))
)
})
# render radar plot with personality traits
output$radar_pers <-renderPlot({
# select relevant columns of reactive dataframe
data <- selected_dogs()[,c(1,6:8, 14:21)]
# user feedback
# more_than_3 <- length(input$breed) > 3 #logical value
# shinyFeedback::feedbackWarning(inputId = "breed", show = more_than_3, text = "Please select three or fewer breeds to compare!")
# make breed the row name
rownames(data) <- paste0(data$breed)
data <- data %>%
select(-breed)
# add two lines to dataframe - with 11 columns
# all values for first line should be max value in radar
# all values for second line should be min value in radar
data <- rbind(rep(5,11) , rep(0,11), data)
# check data
head(data)
# Color vector
colors_border=c( rgb(0.2,0.5,0.5,0.9), rgb(0.8,0.2,0.5,0.9) , rgb(0.7,0.5,0.1,0.9) )
colors_in=c( rgb(0.2,0.5,0.5,0.4), rgb(0.8,0.2,0.5,0.4) , rgb(0.7,0.5,0.1,0.4) )
# plot with default options:
fmsb::radarchart(data , axistype=1 ,
#custom polygon
pcol=colors_border , pfcol=colors_in , plwd=4 , plty=1,
#custom the grid
cglcol="grey", cglty=1, axislabcol="grey", caxislabels=seq(1,5,1), cglwd=0.8,
#custom labels
vlcex=0.8
) +
theme_minimal() +
theme(text = element_text(size = 16))
legend(x=0.8, y=0.8, legend = rownames(data[-c(1,2),]), bty = "n", pch=20 , col=colors_in , text.col = "grey", cex=1.2, pt.cex=3)
})
# render bar plot with characteristics
output$bar_characteristics <-renderPlot({
selected_dogs()[,c(1,5,9:11)] %>% # select relevant columns
mutate(size_category = gsub("Medium", 3, size_category)) %>% #convert text to numerical values
mutate(size_category = gsub("X-Large", 5, size_category)) %>%
mutate(size_category = gsub("X-Small", 1, size_category)) %>%
mutate(size_category = gsub("Large", 4, size_category)) %>%
mutate(size_category = gsub("Small", 2, size_category)) %>%
mutate(size_category = as.numeric(size_category)) %>%
tidyr::pivot_longer(-breed, names_to="characteristic", values_to="score") %>%
ggplot(aes(x=characteristic, y=score, fill=breed)) +
geom_line(aes(group = characteristic), linewidth=2) +
geom_point(aes(color = breed), size=7, alpha=0.7) +
coord_flip() +
theme_minimal() +
labs(y = "Lower -> Higher Scores", x= "") +
theme(text = element_text(size = 16)) +
scale_colour_manual(values = c(rgb(0.2,0.5,0.5,0.9), rgb(0.8,0.2,0.5,0.9) , rgb(0.7,0.5,0.1,0.9))
)
})
}
# Run the application
shinyApp(ui = ui, server = server)