Recompile exercises

chapter-18-exercises/exercise-1/README.md

@@ -8,7 +8,7 @@ You will need to create and work with multiple files for this exercise, followin
 ## 1. Wrangle Data
 You will need to implement a script to download the relevant movie review from the NY Times. Follow the instructions in the provided `exercise.R` file. 
 
-_This wrangling is the same as that used in Chapter 11 exercise-2; you can and should reuse that content._
+_This wrangling is the same as that used in Chapter 14 exercise-2; you can and should reuse that content._
 
 Note that you will need to register for an API key at **<https://developer.nytimes.com/signup>**. Save this in a file `apikey.R`, and be sure to modify your `.gitignore` file to list your `apikey.R` file as one to not be committed.
 

chapter-19-exercises/exercise-5/README.md

@@ -0,0 +1,7 @@
+# Exercise 5
+In this exercise, you'll build a simple Shiny application that can communicate between a server and a user-interface. The final product will be a scatterplot in which you can select which columns of the `mpg` dataset to display, and how you want the graph to be rendered (using shiny widgets):
+
+![final product scatterplot](imgs/final-plot.png)
+
+To complete the exercise, you'll need to work in both the `ui.R` and `server.R` files in RStudio, and follow the instructions there.
+

chapter-19-exercises/exercise-5/server.R

@@ -0,0 +1,17 @@
+### Exercise 5 ###
+
+library(shiny)
+library(ggplot2)
+# Create a shiny server that creates a scatterplot. 
+
+# It should use an `input` with features: `x_var`, `y_var`, `color`, and `size`
+# Save the result of `renderPlot` to output$scatter
+
+    # Store `x` and `y` values to plot
+
+
+    # Store the title of the graph in a variable
+
+
+    # Create ggplot scatter
+

chapter-19-exercises/exercise-5/ui.R

@@ -0,0 +1,24 @@
+# UI for scatterplot
+library(shiny)
+
+# Get a vector of column names (from `mpg`) to use as select inputs
+select_values <- colnames(mpg)
+
+# Create a shinyUI with a `fluidPage` layout
+
+  # Add a page header
+
+
+  # Add a `selectInput` for the `x` variable
+
+
+  # Add a `selectInput` for the `y` variable
+
+
+  # Add a `sliderInput` to set the `size` of each point
+
+
+  # Add a `selectInput` that allows you to select a color from a list of choices
+
+
+  # Plot the output with the name "scatter"

chapter-19-exercises/exercise-6/README.md

@@ -0,0 +1,16 @@
+# Exercise 6
+In this exercise, you'll practice building an interactive Plotly map in a Shiny application. 
+
+As in previous exercises, you should fork and clone this repository, then follow the instructions below.
+
+## server.R
+Your `server.R` file already loads the data you need, as well as scripts for building a map. Inside your `shinyServer`, you should do the following:
+
+- Replace the static input `'population'` with a dynamic value that comes from your UI
+
+## ui.R
+Your `ui.R` file already has a `tabPanel` built displaying your map. In this section, you should add another `tabPanel` for your scatter-plot by doing the following:
+
+- Add a `selectInput` (with a proper id) that allows you to select a variable to map (`population`, `votes`, or `ratio`)
+
+Also, make sure you look at the `build_map.R` file and **understand how it is using variables to make a dynamic application**.

chapter-19-exercises/exercise-6/data/electoral_college.csv

@@ -0,0 +1 @@
+state,votes,populationAlabama,9,4858979Alaska,3,738432Arizona,11,6828065Arkansas,6,2978204California,55,39144818Colorado,9,5456574Connecticut,7,3590886Delaware,3,945934Florida,29,20271272Georgia,16,10214860Hawaii,4,1431603Idaho,4,1654930Illinois,20,12859995Indiana,11,6619680Iowa,6,3123899Kansas,6,2911641Kentucky,8,4425092Louisiana,8,4670724Maine,4,1329328Maryland,10,6006401Massachusetts,11,6794422Michigan,16,9922576Minnesota,10,5489594Mississippi,6,2992333Missouri,10,6083672Montana,3,1032949Nebraska,5,1896190Nevada,6,2890845New Hampshire,4,1330608New Jersey,14,8958013New Mexico,5,2085109New York,29,19795791North Carolina,15,10042802North Dakota,3,756927Ohio,18,11613423Oklahoma,7,3911338Oregon,7,4028977Pennsylvania,20,12802503Rhode Island,4,1056298South Carolina,9,4896146South Dakota,3,858469Tennessee,11,6600299Texas,38,27469114Utah,6,2995919Vermont,3,626042Virginia,13,8382993Washington,12,7170351 District of Columbia,3,672228West Virginia,5,1844128Wisconsin,10,5771337Wyoming,3,586107

chapter-19-exercises/exercise-6/data/state_codes.csv

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+code,stateAL,AlabamaAK,AlaskaAZ,ArizonaAR,ArkansasCA,CaliforniaCO,ColoradoCT,ConnecticutDE,DelawareFL,FloridaGA,GeorgiaHI,HawaiiID,IdahoIL,IllinoisIN,IndianaIA,IowaKS,KansasKY,KentuckyLA,LouisianaME,MaineMD,MarylandMA,MassachusettsMI,MichiganMN,MinnesotaMS,MississippiMO,MissouriMT,MontanaNE,NebraskaNV,NevadaNH,New HampshireNJ,New JerseyNM,New MexicoNY,New YorkNC,North CarolinaND,North DakotaOH,OhioOK,OklahomaOR,OregonPA,PennsylvaniaRI,Rhode IslandSC,South CarolinaSD,South DakotaTN,TennesseeTX,TexasUT,UtahVT,VermontVA,VirginiaWA,WashingtonWV,West VirginiaWI,WisconsinWY,Wyoming

chapter-19-exercises/exercise-6/scripts/build_map.R

@@ -0,0 +1,32 @@
+# BuildMap file: function that returns a plotly map
+library(plotly)
+library(stringr)
+
+# BuildMap function: fill this in with a function that returns a map:
+# Derived from: https://plot.ly/r/choropleth-maps/
+
+build_map <- function(data, map_var) {
+  # give state boundaries a white border
+  l <- list(color = toRGB("white"), width = 2)
+
+  # specify some map projection/options
+  g <- list(
+    scope = 'usa',
+    projection = list(type = 'albers usa'),
+    showlakes = TRUE,
+    lakecolor = toRGB('white')
+  )
+
+  # Plot
+  p <- plot_geo(data, locationmode = 'USA-states') %>%
+    add_trace(
+      z = data[, map_var], text = ~state, locations = ~code,
+      color = data[, map_var], colors = 'Purples'
+    ) %>%
+    colorbar(title = "Color Title") %>%
+    layout(
+      title = str_to_title(map_var),
+      geo = g
+    )
+   return(p)
+}

chapter-19-exercises/exercise-6/server.R

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+# server.R
+library(dplyr)
+library(shiny)
+library(plotly)
+
+# Read in data
+source('./scripts/build_map.R')
+df <- read.csv('./data/electoral_college.csv', stringsAsFactors = FALSE)
+state_codes <- read.csv('./data/state_codes.csv', stringsAsFactors = FALSE)
+
+# Join together state.codes and df
+joined_data <- left_join(df, state_codes, by="state")
+
+# Compute the electoral votes per 100K people in each state
+joined_data <- joined_data %>% mutate(ratio = votes/population * 100000)
+
+# Start shinyServer
+shinyServer(function(input, output) { 
+
+  # Render a plotly object that returns your map
+  output$map <- renderPlotly({ 
+      return(build_map(joined_data, "population"))
+  })
+})

chapter-19-exercises/exercise-6/ui.R

@@ -0,0 +1,13 @@
+# Libraries
+library(plotly)
+library(shiny)
+
+# ui.R
+shinyUI(fluidPage(
+  mainPanel(
+    # Add a selectInput (with a proper id) that allows you to select a variable to map
+
+    # Plot the map
+    plotlyOutput("map")
+  )
+))

chapter-19-exercises/exercise-7/README.md

@@ -0,0 +1,24 @@
+# Exercise 7
+In this exercise, you'll practice building an a multi-tab Shiny application. The final product should look like this:
+
+![scatter plot in shiny app](imgs/scatter.png)
+
+The Map panel is already built for you to model. Your scatter panel should enable users to search for a state in the scatterplot.
+
+As in previous exercises, you should fork and clone this repository, then follow the instructions below.
+
+## server.R
+Your `server.R` file already loads the data you need, as well as scripts for building a map and a scatter plot. Inside your `shinyServer`, you should do the following:
+
+- Create a `scatter` property on your `output` object. That property should be a `renderPlotly` object that returns a scatterplot (`build_scatter`)
+- Make sure to pass your data and search string (i.e., `input$search`) to your `build_scatter` function.
+
+## ui.R
+Your `ui.R` file already has a `tabPanel` built displaying your map. In this section, you should add another `tabPanel` for your scatter-plot by doing the following:
+
+- Create a `tabPanel` to show your scatter plot
+- Add a `titlePanel` to your tab
+- Create a `sidebarLayout` for this tab (page)
+- Create a `sidebarPanel` for your controls
+- In your `sidebarPanel`, make a `textInput` widget for searching for a state in your scatter plot
+- Create a `mainPanel`, in which you should display your plotly Scatter plot

chapter-19-exercises/exercise-7/data/electoral_college.csv

@@ -0,0 +1 @@
+state,votes,populationAlabama,9,4858979Alaska,3,738432Arizona,11,6828065Arkansas,6,2978204California,55,39144818Colorado,9,5456574Connecticut,7,3590886Delaware,3,945934Florida,29,20271272Georgia,16,10214860Hawaii,4,1431603Idaho,4,1654930Illinois,20,12859995Indiana,11,6619680Iowa,6,3123899Kansas,6,2911641Kentucky,8,4425092Louisiana,8,4670724Maine,4,1329328Maryland,10,6006401Massachusetts,11,6794422Michigan,16,9922576Minnesota,10,5489594Mississippi,6,2992333Missouri,10,6083672Montana,3,1032949Nebraska,5,1896190Nevada,6,2890845New Hampshire,4,1330608New Jersey,14,8958013New Mexico,5,2085109New York,29,19795791North Carolina,15,10042802North Dakota,3,756927Ohio,18,11613423Oklahoma,7,3911338Oregon,7,4028977Pennsylvania,20,12802503Rhode Island,4,1056298South Carolina,9,4896146South Dakota,3,858469Tennessee,11,6600299Texas,38,27469114Utah,6,2995919Vermont,3,626042Virginia,13,8382993Washington,12,7170351 District of Columbia,3,672228West Virginia,5,1844128Wisconsin,10,5771337Wyoming,3,586107

chapter-19-exercises/exercise-7/data/state_codes.csv

@@ -0,0 +1 @@
+code,stateAL,AlabamaAK,AlaskaAZ,ArizonaAR,ArkansasCA,CaliforniaCO,ColoradoCT,ConnecticutDE,DelawareFL,FloridaGA,GeorgiaHI,HawaiiID,IdahoIL,IllinoisIN,IndianaIA,IowaKS,KansasKY,KentuckyLA,LouisianaME,MaineMD,MarylandMA,MassachusettsMI,MichiganMN,MinnesotaMS,MississippiMO,MissouriMT,MontanaNE,NebraskaNV,NevadaNH,New HampshireNJ,New JerseyNM,New MexicoNY,New YorkNC,North CarolinaND,North DakotaOH,OhioOK,OklahomaOR,OregonPA,PennsylvaniaRI,Rhode IslandSC,South CarolinaSD,South DakotaTN,TennesseeTX,TexasUT,UtahVT,VermontVA,VirginiaWA,WashingtonWV,West VirginiaWI,WisconsinWY,Wyoming

chapter-19-exercises/exercise-7/scripts/build_map.R

@@ -0,0 +1,35 @@
+# BuildMap file: function that returns a plotly map
+library(plotly)
+library(stringr)
+
+# BuildMap function: fill this in with a function that returns a map:
+# Derived from: https://plot.ly/r/choropleth-maps/
+
+build_map <- function(data, map.var) {
+  # give state boundaries a white border
+  l <- list(color = toRGB("white"), width = 2)
+
+  # specify some map projection/options
+  g <- list(
+    scope = 'usa',
+    projection = list(type = 'albers usa'),
+    showlakes = TRUE,
+    lakecolor = toRGB('white')
+  )
+
+  # Make equation for map color / text
+  var.equation <- paste0('~', map.var)
+
+  # Plot
+  p <- plot_geo(data, locationmode = 'USA-states') %>%
+    add_trace(
+      z = data[,map.var], text = ~state, locations = ~code,
+      color = data[,map.var], colors = 'Purples'
+    ) %>%
+    colorbar(title = "Color Title") %>%
+    layout(
+      title = str_to_title(map.var),
+      geo = g
+    )
+   return(p)
+}

chapter-19-exercises/exercise-7/scripts/build_scatter.R

@@ -0,0 +1,27 @@
+# Build Scatter file: function that returns a plotly Scatter plot
+library(plotly)
+library(stringr)
+
+### Build Scatter ###
+build_scatter <- function(data,  search = "", xvar = "population", yvar = "votes") {
+  # Get x and y max
+  xmax <- max(data[,xvar]) * 1.5
+  ymax <- max(data[,yvar]) * 1.5
+
+  # Filter data based on search 
+  data <- data %>% 
+    filter(grepl(search, state))
+
+  # Plot data
+  plot_ly(x = data[, xvar],
+            y = data[, yvar], 
+            mode="markers", 
+            marker = list(
+              opacity = .4, 
+              size = 10
+            )) %>% 
+    layout(xaxis = list(range = c(0, xmax), title = xvar), 
+           yaxis = list(range = c(0, ymax), title = yvar)
+    ) %>% 
+    return()
+}

chapter-19-exercises/exercise-7/server.R

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+# server.R
+library(dplyr)
+
+# Read in data
+source('./scripts/build_map.R')
+source('./scripts/build_scatter.R')
+df <- read.csv('./data/electoral_college.csv', stringsAsFactors = FALSE)
+state_codes <- read.csv('./data/state_codes.csv', stringsAsFactors = FALSE)
+
+# Join together state.codes and df
+joined_data <- left_join(df, state_codes, by="state")
+
+# Compute the electoral votes per 100K people in each state
+joined_data <- joined_data %>% mutate(ratio = votes/population * 100000)
+
+# Start shinyServer
+shinyServer(function(input, output) { 
+
+  # Render a plotly object that returns your map
+  output$map <- renderPlotly({ 
+      return(build_map(joined_data, input$mapvar))
+  }) 
+
+
+})

chapter-19-exercises/exercise-7/ui.R

@@ -0,0 +1,36 @@
+# ui.R
+library(shiny)
+library(plotly)
+shinyUI(navbarPage(
+  "Electoral College",
+  # Create a tab panel for your map
+  tabPanel(
+    "Map",
+    titlePanel("Electoral College Votes"),
+    # Create sidebar layout
+    sidebarLayout(
+
+      # Side panel for controls
+      sidebarPanel(
+
+        # Input to select variable to map
+        selectInput(
+          "mapvar",
+          label = "Variable to Map",
+          choices = list(
+            "Population" = "population",
+            "Electoral Votes" = "votes",
+            "Votes / Population" = "ratio"
+          )
+        )
+      ),
+
+      # Main panel: display plotly map
+      mainPanel(
+        plotlyOutput("map")
+      )
+    )
+  )
+
+  # Create a tabPanel to show your scatter plot
+))