Overview

This presentation was prepared for the Course Project: Shiny Application and Reproducible Pitch

The application is running here.

The data used, ui.R and Server.R code for this app are in my github repository.

The dashboard displays the climate change of Kenya from 1991 to 2016. The climatic factors in this context are Temperature (in degree celsius) and Rainfall (mm). The application also displays the Summaries of the temperature and rainfall and also the map of Kenya showing the population and the County when you hover the circles.

About the Application.

It is a simple application where you can choose the variable to view the plot for from the drop down menu on the sidebar panel. You can also see the averages for the respective variables spanning all years in form of bar plot below the line plot. They are measurements of either temperature in degree celsius or rainfall in mm.

ui.R Code

library(shiny)
library(plotly)
library(leaflet)

ui <- fluidPage(
 titlePanel("Kenyan Climate"),
 sidebarLayout(
  sidebarPanel(
   uiOutput("rname"),
   uiOutput("year"),
   radioButtons("period", "Choose a period:",  c("All the time" = "all", "From to Years" = "yearbegin"),
                selected = "all"),
   uiOutput("slider")),
  mainPanel(
   tabsetPanel( type = "tabs", tabPanel( "Plots", plotlyOutput("plot1"),
   plotlyOutput("barplot1")),
   tabPanel("Summaries", verbatimTextOutput('Summ'),
            plotlyOutput("boxplot1"),
            plotlyOutput("boxplot2")),
   tabPanel("Map", leafletOutput("keMap", width = "1200px", height = "800px")))))

server.R Code

library(shiny)
library(plotly)
library(leaflet)
server <- function(input, output){
 library(tidyverse)
 library(janitor)
 library(psych)
 #Data 
 df1 <- read_csv("data/kenya-climate-data-1991-2016-temp-degress-celcius.csv")
 df2 <- read_csv("data/kenya-climate-data-1991-2016-rainfallmm.csv")
 ke_data <- read_csv("data/ke.csv")
 data_climate <- merge(df1,df2) %>% clean_names() %>% 
  separate(month_average, into = c("month", "other")) %>% 
  select(-other) %>%
  mutate(Month = factor(month, levels = month.abb))
  output$rname <- renderUI({selectInput("rname", "Choose a variable:", as.list(names(data_climate))[3:4])}) 
  output$slider <- renderUI({
  if (input$period == "yearbegin"){ sliderInput("slider", label = "Years range",
  min = 1991, max = 2016, value = c(2001,2006), sep = "")} })
 output$plot1 <- renderPlotly({
 if (input$period == "all") { dt <- data_climate}
 if (input$period == "yearbegin") { dt <- data_climate %>% filter(year%in%input$slider[1]:input$slider[2])}
 plot_ly(dt, x = ~as.factor(year), y = ~dt[,input$rname], color = ~month,
 type = "scatter", mode = "lines") %>% 
 layout(title = paste("Kenya Climate (", input$rname, " )", sep = ""),
 xaxis = list(title = "Period in Years"), yaxis = list(title = input$rname))})
 output$barplot1 <- renderPlotly({
  p <- data_climate %>% 
   select(year, month, col_main = input$rname) %>% 
   group_by(month) %>% 
   summarise(Mean = mean(col_main)) %>% 
   mutate(month = reorder(month, Mean)) %>% 
   ggplot(aes(x = month, y = Mean, fill = month))+
   geom_bar(stat = "identity")+
   labs(y = paste("Mean ", str_split(str_to_title(input$rname), pattern = "_")[[1]][1]," (",
                  str_split(str_to_title(input$rname), pattern = "_")[[1]][2],")", sep = ""))+
   coord_flip()
  ggplotly(p)})
 output$Summ <- renderPrint({
  data_climate %>% 
   select("Temperature (Celsius)" = temperature_celsius, `Rainfall (mm)` = rainfall_mm) %>% 
   describe()})
 output$keMap <- renderLeaflet({
 my_map <- ke_data %>% leaflet() %>% addTiles() %>% 
  addCircleMarkers(lat = ~lat, lng = ~lng, weight = 1, radius = sqrt(ke_data$population) * 0.03125, 
                   popup = paste(ke_data$city,"<br>Pop: ",ke_data$population))
 my_map})
 output$boxplot1 <- renderPlotly({
  data_climate %>% 
   plot_ly(y = ~temperature_celsius, color = ~month, type = "box") %>% 
   layout(showlegend = FALSE, xaxis = list(title = "Temperature (<sup>o</sup>C)"))})
   output$boxplot2 <- renderPlotly({
  data_climate %>% 
   plot_ly(y = ~rainfall_mm, color = ~month, type = "box") %>% 
   layout(showlegend = FALSE, xaxis = list(title = "Rainfall (mm)"))
 })
}