# INBO CODING CLUB

6 October 2022

Welcome!

## Share your code snippet

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As an **example**:

library(tidyverse)

(*you can copy paste this example and add your code further down*)

## Yellow sticky notes

No yellow sticky notes online. Put your name + " | " and add a "*" each time you solve a challenge (see below).

## Participants

Name | Challenges
--- | ---
Damiano Oldoni |
Lynn Pallemaerts |***
Cécile Herr |***
Hans Van Calster | *
Sarah Broos|
Adriaan Seynaeve | *
Raïsa Carmen | ***
Els Lommelen |
Floris Vanderhaeghe | ***
Wouter Van Landuyt
Laura Marquez | *
Toon Westra | ***

## Challenge 1

### floris

3. `shinyApp(ui = ui, server = server)`
4.
5. `input` defined and generated by `sliderInput()` in `ui`, output defined by `renderPlot()` in the `server` function and picked up in `ui` by `plotOutput()`
6. Change the default `app.R` as follows:

--- a/src/myfirstshinyapplication/app.R
+++ b/src/myfirstshinyapplication/app.R
@@ -37,13 +37,14 @@ server <- function(input, output) {
 
     output$distPlot <- renderPlot({
         # generate bins based on input$bins from ui.R
-        x    <- faithful[, 2]
+        x    <- faithful[, 1]
         bins <- seq(min(x), max(x), length.out = input$bins + 1)
 
         # draw the histogram with the specified number of bins
         hist(x, breaks = bins, col = 'darkgray', border = 'white',
-             xlab = 'Waiting time to next eruption (in mins)',
-             main = 'Histogram of waiting times')
+             xlab = 'minutes',
+             ylab = 'number of eruptions',
+             main = 'Distribution of eruption times')
     })
 }

7. Change `app.R` as follows:

--- a/src/myfirstshinyapplication/app.R
+++ b/src/myfirstshinyapplication/app.R
@@ -22,7 +22,7 @@ ui <- fluidPage(
                         "Number of bins:",
                         min = 1,
                         max = 50,
-                        value = 30)
+                        value = 20)
         ),
 
         # Show a plot of the generated distribution

## Challenge 2

### floris

Note that my solution uses an external folder for the data (the original data folder of the coding club project); this works but actually the app should be self-contained, i.e. have the data folder inside the app folder.

<details><summary><code>app.R</code> (click to expand)</summary>

#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
#    http://shiny.rstudio.com/
#

library(shiny)

butter <- read.csv("../../data/20221006/20221006_butterflies_data.txt")
butter$species <- factor(butter$species)
butter$biotope <- factor(butter$biotope)

# Define UI for application that draws a histogram
ui <- fluidPage(

    # Application title
    titlePanel("Butterflies in different biotopes"),

    # Sidebar with a slider input for number of bins
    sidebarLayout(
        sidebarPanel(
            radioButtons("buttons",
                        "Biotope:",
                        levels(butter$biotope)),
            selectInput("dropdown",
                        "Species:",
                        levels(butter$species))
        ),

        # Show a plot of the generated distribution
        mainPanel(
           textOutput("textoutput")
        )
    )
)

# Define server logic required to draw a histogram
server <- function(input, output) {

    output$textoutput <- renderText(
        paste("You selected biotope",
        input$buttons,
        "and species",
        input$dropdown)
    )
}

# Run the application
shinyApp(ui = ui, server = server)
    

</details>

### Hans:

#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
#    http://shiny.rstudio.com/
#
library(tidyverse)


## CHALLENGE 3

# code to be on top of app.R

# read dataset (path to be checked)
butterfly_data <- read_csv(here::here("./data/20221006/20221006_butterflies_data.txt", na = ""))
# biotopes
biotopes <- unique(butterfly_data$biotope)
# species
sp <- unique(butterfly_data$species)

biotopes_list <- as.list(biotopes)
biotopes_list <- set_names(biotopes_list, biotopes)

species_list <- as.list(sp)
species_list <- set_names(species_list, sp)


library(shiny)

# Define UI for application that draws a histogram
ui <- fluidPage(

    # Application title
    titlePanel("Butterflies biotope"),

    # Sidebar with a slider input for number of bins
    sidebarLayout(
        sidebarPanel(
            radioButtons("biotope",
                         label = "Biotope",
                         choices = biotopes_list),
            selectInput("species",
                        label = "Species",
                        choices = species_list)
            ),

        # Show a plot of the generated distribution
        mainPanel(
           textOutput("selected_choices")
        )
    )
)

# Define server logic required to draw a histogram
server <- function(input, output) {
  output$selected_choices <- renderText(sprintf("You have selected the biotope %s and the species %s",
                     input$biotope,
                     input$species))
}

# Run the application
shinyApp(ui = ui, server = server)

## Challenge 3

### Lynn:

library(shiny)
library(tidyverse)

df <- read_csv("./20221006_butterflies_data.txt", na = "")
biotopes <- unique(df$biotope)
sp <- unique(df$species)


# Define UI for application that draws a histogram
ui <- fluidPage(
  
  #Application title
  titlePanel("Butterfly biotopes"),
  
  #Sidebar input
  sidebarLayout(sidebarPanel(radioButtons(inputId = "btp",
                                          label = "Select biotope:",
                                          choices = c("Agriculture" = biotopes[1],
                                                      "Forest" = biotopes[2],
                                                      "Open" = biotopes[3],
                                                      "Urban" = biotopes[4]),
                                          selected = character(0)),
                             radioButtons(inputId = "sp",
                                          label = "Select species:",
                                          choices = c("Limenitis camilla" = sp[1],
                                                      "Pararge aegeria" = sp[2]),
                                          selected = character(0))),
                
                #Main panel
                mainPanel(textOutput(outputId = "infoText"),
                          plotOutput(outputId = "bioPlot"),
                          tableOutput(outputId = "tbl")))
  )

# Define server logic required to draw a histogram
server <- function(input, output) {
  
  output$infoText <- renderText({
    infoText <- paste0("You have selected the biotope ",
                       input$btp,
                       " and the species ",
                       input$sp,
                       ".")
  })
  
  output$bioPlot <- renderPlot({
    df %>%
      filter(species == input$sp,
             biotope == input$btp) %>%
      ggplot(aes(x = year,
                 y = meanArea)) +
      geom_point() +
      geom_smooth() +
      labs(title = paste("Distribution of ", input$sp, "- biotope:", input$btp),
           y = "Area (%)") +
      facet_wrap(~ region)
  })
  
  output$tbl <- renderTable({
    df %>%
      filter(species == input$sp,
             biotope == input$btp)
  },
  striped = T,
  bordered = T)
}

# Run the application 
shinyApp(ui = ui, server = server)

### floris

Change `app.R` from challenge 2 as follows:

--- a/src/shinyapp_challenge_2_3/app.R
+++ b/src/shinyapp_challenge_2_3/app.R
@@ -8,6 +8,7 @@
 #
 
 library(shiny)
+library(ggplot2)
 
 butter <- read.csv("../../data/20221006/20221006_butterflies_data.txt")
 butter$species <- factor(butter$species)
@@ -32,7 +33,7 @@ ui <- fluidPage(
 
         # Show a plot of the generated distribution
         mainPanel(
-            textOutput("textoutput")
+            plotOutput("plots")
         )
     )
 )
@@ -40,12 +41,19 @@ ui <- fluidPage(
 # Define server logic required to draw a histogram
 server <- function(input, output) {
 
-    output$textoutput <- renderText(
-        paste("You selected biotope",
-              input$buttons,
-              "and species",
-              input$dropdown)
-    )
+    output$plots <- renderPlot({
+        ggplot(subset(butter,
+                      species == input$dropdown &
+                          biotope == input$buttons),
+               aes(x = year,
+                   y = meanArea)) +
+            geom_point() +
+            geom_smooth() +
+            labs(title = paste("Distribution of", input$dropdown, "in biotope", input$buttons),
+                 y = "Area (%)") +
+            facet_wrap(~ region)
+
+    })
 }
 
 # Run the application