---
title: "Code in Chapter 02: Exploratory Data Analysis"
author: "Antonio Paez and Genevieve Boisjoly"
output:
  rmdformats::robobook:
    highlight: kate
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r ch02-load-packages-and-data, include=FALSE, cache=FALSE}
library(biscale) # Tools and Palettes for Bivariate Thematic Mapping
library(cowplot) # Streamlined Plot Theme and Plot Annotations for 'ggplot2'
library(discrtr) # A companion package for the book Introduction to Discrete Choice Analysis with `R`
library(dplyr) # A Grammar of Data Manipulation
library(ggalluvial) # Alluvial Plots in 'ggplot2'
library(ggplot2) # Create Elegant Data Visualisations Using the Grammar of Graphics
library(ggspatial) # Spatial Data Framework for ggplot2
library(kableExtra) # Construct Complex Table with 'kable' and Pipe Syntax
library(sf) # Simple Features for R
library(stargazer) # Well-Formatted Regression and Summary Statistics Tables
library(treemapify) # Draw Treemaps in 'ggplot2'
data("mc_commute_long")
```

```{r ch02-prepare-data-example-map, include=FALSE}
# Convert the table to simple features and set the
# coordinate referencing system to epsg 4326 for long-lat
mc_commute_wide.sf <- mc_commute_wide %>%
  st_as_sf(coords = c("LONG", "LAT"),
           crs = 4326)

# Filter out rural type
urban_suburban <- urban_types %>% 
  filter(Type == "Urban" | Type == "Suburban") %>%
  st_transform(4326)

# Select observations that are in urban_suburban
mc_commute_wide.sf <- mc_commute_wide.sf[urban_suburban, ]

# Extract the bounding box of the spatial data
bbox <- st_bbox(mc_commute_wide.sf)

# Use the bounding box to crop the spatial data
urban_suburban <- urban_suburban %>% 
  st_crop(bbox)

# Create a table with a bivariate classification
# based on `street_density` and `sidewalk_density`
data <- bi_class(mc_commute_wide.sf, 
                 x = street_density,
                 y = sidewalk_density,
                 style = "quantile", 
                 dim = 2)
```

```{r ch02-example-table, echo=FALSE}
# Pipe the table to function `select()`
mc_commute_wide %>%
  # Select columns
  select(street_density, sidewalk_density, LAT, LONG, choice, gender, shared, family) %>%
  # Slice (filter) 8 rows at the head of the table
  slice_head(n = 8) %>%
  # Use `kable()` to tabluate
  kable("latex",
        booktabs = TRUE,
        digits = 3,
        caption = "\\label{tab:ch02-commute-table}First eight rows of selected variables in data frame") %>%
  kable_styling(latex_options = c("scale_down", "striped", "Hold"))
```

```{r ch02-example-map, echo=FALSE, fig.cap="\\label{fig:ch02-map}Multidimensional data visualization: map"}
# Create a ggplot object and name it `map`
map <- ggplot() +
  # `geom_sf()` is used to plot geographical geometric objects
  # The input needs to be a simple features object
  # Plot the spatial features in object `urban_suburban`
  geom_sf(data = urban_suburban,
          # Fill the polygons with color according to `Type` 
          aes(fill = Type),
          alpha = 0.2) +
  # Plot the spatial feature in `data`
  geom_sf(data = data, 
          # Color the objects using the bivariate classification
          # we created earlier
          aes(color = bi_class),
          size = 2.5,
          show.legend = FALSE) +
  # Plot the spatial features in `data` with size = 2 and shape = 1
  geom_sf(data = data,
          size = 2,
          shape = 1) +
  # Select color palette for bivariate scale
  bi_scale_color(pal = "DkViolet", 
                 dim = 2) +
  # Select color palette for filling polygons
  scale_fill_brewer(palette = "Dark2") +
  annotation_scale(pad_x = unit(10.5, "cm"),
                   pad_y = unit(5, "cm")) +
  # Select theme for plot
  bi_theme() +
  # Modify the theme
  theme(legend.position = "bottom",
        legend.justification = "left",
        legend.text = element_text(size = 9),
        legend.title = element_blank())

# Retrieve the legend of the plot
legend <- bi_legend(pal = "DkViolet",
                    dim = 2,
                    xlab = "Higher Street Den ",
                    ylab = "Higher Sidewalk Den ",
                    size = 5)

# Render the plot by layering the map and the legend
ggdraw() +
  draw_plot(map, 0, 0, 1, 1) +
  draw_plot(legend, 0.65, 0.1, 0.3, 0.3)
```

```{r ch02-prepare-data-example-alluvial, include=FALSE}
# Convert data to alluvia: pipe `mc_commute_wide` to `mutate()`
mc_commute_alluvia <- mc_commute_wide %>%
  # Code the living arrangements
  mutate(living_arrangements = case_when(shared == "Living in Shared Accommodations" ~ "Shared",
                                         family == "Living with Family" ~ "Family",
                                         TRUE ~ "Other")) %>%
  # Select three variables
  select(gender, living_arrangements, choice) %>%
  # Use `group_by()` to summarize the count of cases
  group_by(gender, living_arrangements, choice) %>%
  # Summarize the number of cases
  summarize(frequency = n(),
            .groups = "drop")
```

```{r ch02-commute-table-frequency, echo=FALSE}
# Pipe table to `kable()` for tabulation
mc_commute_alluvia %>%
  kable("latex",
        booktabs = TRUE,
        caption = "\\label{tab:ch02-commute-table-frequency}Table of frequencies by three categorical variables") %>%
  kable_styling(latex_options = c("striped", "Hold"))
```

```{r ch02-example-alluvial, echo=FALSE, fig.cap="\\label{fig:ch02-alluvial}Multidimensional data visualization: alluvial plot"}
# Create a ggplot object using table `mc_commute_alluvia`
ggplot(data = mc_commute_alluvia,
       # Map the frequency to the y-axis
       aes(y = frequency, 
           # Specify the variables to map to the x-axis (the stratum)
           axis1 = gender,
           axis2 = living_arrangements,
           axis4 = choice)) +
  # Add geometric objects of type alluvium
  geom_alluvium(aes(fill = gender),
                width = 1/12,
                color = "black") +
  # Add geometric objects of type stratum
  geom_stratum(width = 1/3, 
               fill = "black", 
               color = "grey") +
  # Lable the stratum
  geom_text(stat = "stratum", 
            aes(label = after_stat(stratum)), 
            color = "white",
            size = 3) +
  # Specify parameters for the scale in the x-axis
  scale_x_discrete(limits = c("Gender", 
                              "Living Arrangements",
                              "Choice"), 
                   expand = c(.05, .05)) +
  # Select palette for alluvia
  scale_fill_brewer(type = "qual", 
                    palette = "Dark2") +
  # Select theme for plot
  theme_minimal()
```

```{r ch02-hello-world}
# Function `print()` displays the argument on the screen. 
print("Is that a bar chart in your pocket, or are you just happy to see me?")
```

```{r ch02-clear-workspace}
# Function `rm()` removes objects from the environment
# i.e., it clears the working memory
rm(list = ls())
```

```{r ch02-load-packages, message=FALSE}
library(discrtr) # A companion package for the book Introduction to Discrete Choice Analysis with `R`
library(dplyr) # A Grammar of Data Manipulation 
library(ggplot2) # Create Elegant Data Visualisations Using the Grammar of Graphics
library(gplots) # Various R Programming Tools for Plotting Data  
library(ggmosaic) # Mosaic Plots in the 'ggplot2' Framework  
```

```{r ch02-load-data}
# Load the data set
data("mc_commute_wide",
     package = "discrtr") 
```

```{r ch02-relabel-child-vehind}
# Pipe table `mc_commute_wide`
mc_commute_wide <- mc_commute_wide %>%
  # Function `mutate()` creates new columns in the table; mutate
  # the table to convert variables `child` and `vehind` to factors
  # with more informative labels
  mutate(child = factor(child, 
                        levels=c("Yes", 
                                 "No"), 
                        # Give the factor categories more descriptive labels
                        labels=c("Living with a child",
                                 "Not living with a child")),
         # Relabel `vehind` variable
         vehind = factor(vehind, 
                         levels=c("No", 
                                  "Yes"),
                         # Give the factor categories more descriptive labels
                         labels=c("No ind. vehicle access",
                                  "Ind. vehicle access"))) 
```

```{r ch02-summary-mc_commute_wide}
summary(mc_commute_wide)
```

```{r ch02-summary-shelters-SD}
summary(mc_commute_wide$Shelters_SD)
```

```{r ch02-recode-shelters}
# Pipe data frame `mc_mode_wide` to next function
mc_commute_wide <- mc_commute_wide %>% 
  # Use mutate to create a new variable
  # Function `case_when()` is a vectorized form of if-else statements
  mutate(Shelters = case_when(Shelters_SD == 1 ~ -2,
                              Shelters_D == 1 ~ -1,
                              Shelters_A == 1 ~ 1,
                              Shelters_SA == 1 ~ 2,
                              TRUE ~ 0))
```

```{r ch02-recode-shelters-as-factor}
mc_commute_wide <- mc_commute_wide %>%
  # Use `mutate()` to modify the content of an existing variable
  mutate(Shelters = factor(Shelters, 
                           levels = c(-2, -1, 0, 1, 2),
                           labels = c("Strongly Disagree", 
                                      "Disagree", 
                                      "Neutral",
                                      "Agree", 
                                      "Strongly Agree"),
                           # The factor is an ordinal variable
                           ordered = TRUE))

summary(mc_commute_wide$Shelters)
```

```{r ch02-bar-chart-shelters}
# Pipe table `mc_Commute_wide to `ggplot()`
mc_commute_wide %>% 
  # Create a ggplot object with the table that was piped
  # and map the variable `Shelters` to the x-axis
  ggplot(aes(x = Shelters)) + 
  # Add a geometric object of type bar; we do not need
  # to specify the y-axis because the height of the bar
  # will be the statistic for the corresponding categorical
  # outcome
  geom_bar(color = "black",
           fill = "white") +
  # The function `labs()` adds labels to part of the plot, for instance the x and y axes
  labs(x = "Public transport facilities of good quality",
       y = "Number of respondents")
```

```{r ch02-lollipop-plot-shelters}
# Pipe table `mc_commute_wide`
mc_commute_wide %>%
  # Use `group_by()` to group the table by the values
  # of variable `Shelters`
  group_by(Shelters) %>%
  # Summarize: calculate the number n() of cases by 
  # category of `Shelters`
  summarize(n = n()) %>%
  # Pipe the result to `ggplot()`; map `Shelters` to the x-axis
  # and map the number of cases to y; to create segments map
  # the end of the segment to y = 0 and keep it constant on x,
  # this will create vertical line
  ggplot(aes(x = Shelters,
             xend = Shelters,
             y = n,
             yend = 0)) + 
  # Add geometric featues of type point
  geom_point(color = "black",
             fill = "white",
             size = 6) +
  # Add geometric features of type segment (line segments)
  geom_segment(size = 1) +
  # Label the axes
  labs(x = "Public transport facilities of good quality",
       y = "Number of respondents")
```

```{r ch02-treemap-of-choice}
library(treemapify)

# Pipe table
mc_commute_wide %>%
  # Group table based on `choice`
  group_by(choice) %>%
  # Count the number of cases by `choice` and pipe to `ggplot()`
  summarize(n = n()) %>%
  # Map the color of the rectangles to the variable `choice` and 
  # their area to the number of cases
  ggplot(aes(fill = choice, 
             area = n)) +
  # Layer geometric object of type treemap
  geom_treemap() + 
  # Add labels
  labs(title = "Trips by mode", 
       fill="Mode")
```

```{r ch02-recode-housing-as-factor}
mc_commute_wide <- mc_commute_wide %>%
  # Use `mutate()` to convert variable `housing` to a factor
  mutate(housing = case_when(shared != "No" ~ "shared",
                             family != "No" ~ "family",
                             TRUE ~ "solo"),
         housing = factor(housing))

summary(mc_commute_wide$housing)
```

```{r ch02-summary-mode-sidewalk}
# Pipe table `mc_commute_wide` to `select()`
mc_commute_wide %>% 
  # Select variables `choice` and `sidewalk_density`
  select(choice, 
         sidewalk_density) %>% 
  summary()
```

```{r ch02-boxplot-choice-sidewalk-density}
mc_commute_wide %>%
  # Map `choice` to the x-axis and `sidewalk_density` to the y-axis
  ggplot(aes(x = choice, 
             y = sidewalk_density)) + 
  # Boxplots are useful for visualizing the relationship between 
  # one categorical and one quantitative variable
  geom_boxplot()
```

```{r}
mc_commute_wide %>%
  ggplot(aes(x = choice, 
             y = sidewalk_density)) + 
  geom_boxplot() +
  # Label the axes
  labs(x="Mode", 
       # The expression function allows us to include superscripts 
       # and subscripts in labels and titles
       y = expression("Sidewalk density (km/km"^2*")")) 
```

```{r ch02-violin-plot-choice-sidewalk}
mc_commute_wide %>% 
  ggplot(aes(x = choice,
             y = sidewalk_density,
             # Map the color of the polygons to `choice`
             fill = choice)) +
  # Add a geometric object of type violin
  geom_violin(trim = FALSE) + 
  # Add geometric object of type boxplot
  geom_boxplot(width = 0.1, 
               fill = "white") +
  labs(x="Mode",  
       y = expression("Sidewalk density (km/km"^2*")"), 
       # Add a label for the fill
       fill = "Mode")
```

```{r ch02-ridge-plots, message=FALSE}
library(ggridges)

mc_commute_wide %>%
  ggplot(aes(x = sidewalk_density,
             y = choice,
             # Map the color of the polygons to `choice`
             fill = choice)) +
  # Add geometric object of type ridges with jittered points 
  geom_density_ridges(jittered_points = TRUE,
                      bandwidth = 3.5,
                      position = position_points_jitter(width = 0.05, 
                                                        height = 0),
                      point_shape = '|', 
                      point_size = 3, 
                      point_alpha = 1, 
                      alpha = 0.7) +
  labs(y="Mode",  
       x = expression("Sidewalk density (km/km"^2*")"), 
       # Add a label for the fill
       fill = "Mode")
```

```{r ch02-summary-child}
summary(mc_commute_wide$child)
```

```{r ch02-balloon-plot-mode-by-child}
# Create a table with the two variables of interest
tableau <- table(mc_commute_wide$choice, 
                 mc_commute_wide$child) 

balloonplot(as.table(tableau),
            # The parameters below control the aspect of the table
            # Labels
            xlab = "Mode",
            ylab = "Dependent minor(s)",
            # Adjust maximum dot size
            dotsize = 3/max(strwidth(19),
                            strheight(19)),
            # Symbol used for the dots
            dotcolor = "skyblue",
            text.size = 0.65,
            # Title of plot
            main = "Mode as a function of dependent minors in household",
            # Display the values in the cells
            label = TRUE,
            label.size = 0.80,
            # Scale balloons by volume (or diameter) 
            scale.method = c("volume"),
            # Scale balloons relative to zero
            scale.range = c("absolute"),
            # Space for column/row labels
            colmar = 1,
            rowmar = 2,
            # Display zeros if present
            show.zeros = TRUE,
            # Display row and column sums
            show.margins = TRUE,
            # Display cumulative margins as cascade plots
            cum.margins = TRUE)
```

```{r ch02-balloon-plot-mode-by-housing}
tableau <- table(mc_commute_wide$choice, 
                 mc_commute_wide$housing)

balloonplot(as.table(tableau),
            # The parameters below control the aspect of the table
            # Labels
            xlab = "Mode",
            ylab = "Living arrangement",
            # Adjust maximum dot size
            dotsize = 3/max(strwidth(19),
                            strheight(19)),
            # Symbol used for the dots
            dotcolor = "skyblue",
            text.size = 0.65,
            # Title of plot
            main = "Mode as a function of living arrangement",
            # Display the values in the cells
            label = TRUE,
            label.size = 0.80,
            # Scale balloons by volume (or diameter) 
            scale.method = c("volume"),
            # Scale balloons relative to zero
            scale.range = c("absolute"),
            # Space for column/row labels
            colmar = 1,
            rowmar = 2,
            # Display zeros if present
            show.zeros = TRUE,
            # Display row and column sums
            show.margins = TRUE,
            # Display cumulative margins as cascade plots
            cum.margins = TRUE)
```

```{r ch02-balloon-plot-child-by-housing}
tableau <- table(mc_commute_wide$child, 
                 mc_commute_wide$housing)

balloonplot(as.table(tableau),
            # The parameters below control the aspect of the table
            # Labels
            xlab = "Living arrangement",
            ylab = "Dependent minor(s)",
            # Adjust maximum dot size
            dotsize = 3/max(strwidth(19),
                            strheight(19)),
            # Symbol used for the dots
            dotcolor = "skyblue",
            text.size = 0.50,
            # Title of plot
            main = "Minors in household and living arrangement",
            # Display the values in the cells
            label = TRUE,
            label.size = 0.80,
            # Scale balloons by volume (or diameter) 
            scale.method = c("volume"),
            # Scale balloons relative to zero
            scale.range = c("absolute"),
            # Space for column/row labels
            colmar = 1,
            rowmar = 2,
            # Display zeros if present
            show.zeros = TRUE,
            # Display row and column sums
            show.margins = TRUE,
            # Display cumulative margins as cascade plots
            cum.margins = TRUE)
```

```{r ch02-mosaic-child-by-mode, warning=FALSE}
mc_commute_wide %>%
  ggplot() +
  # Add geometric object of type mosaic
  # Map the interaction between `choice` and `child` to the x-axis 
  geom_mosaic(aes(x = product(choice, 
                              child), 
                  fill = choice)) + 
  # Add labels
  labs(x = "Dependent minor(s)",  
       y = "Mode", 
       fill = "Mode")
```

```{r ch02-mosaic-number-of-alternatives-by-mode}
ggplot(data = mc_commute_wide) +
  # Add geometric object of type mosaic
  # Map the interaction between `choice` and `numna` to the x-axis 
  geom_mosaic(aes(x = product(choice, 
                              numna), 
                  # Color rectangles based on `choice`
                  fill = choice)) + 
  # Add labels
  labs(x = "Number of alternatives",  
       y = "Mode", 
       fill = "Mode")
```

```{r ch02-tile-plot-mode-housing}
# Pipe table to next function
mc_commute_wide %>% 
  # Group observations by `choice` and `housing`
  group_by(choice, 
           housing) %>%
  # Calculate number of cases by combination of `choice` and `housing`
  summarize(n = n(),
            .groups = "drop") %>%
  ggplot(aes(x = choice, 
             y = housing)) +
  # Add geometric object of type tile, map the color
  # of tiles to `n`, the number of cases
  geom_tile(aes(fill = n)) +
  # Add labels
  labs(x = "Mode",  
       y = "Living arrangement", 
       fill = "Number of respondents")
```

```{r ch02-stacked-bar-plot-choice-by-like-neighborhood}
mc_commute_wide %>%
  ggplot(aes(x = like_active_neighborhood, 
             fill = choice)) + 
  geom_bar(position = "fill") +
  labs(y = "Proportion",
       x = "Like active neighborhood", 
       fill="Mode")
```

```{r ch02-ridge-plot-revisited}
mc_commute_wide %>%
  ggplot(aes(x = sidewalk_density,
             # choice is mapped to the y axis
             y = choice,
             # choice is also mapped to the fill color!
             fill = choice)) +
  # Add geometric object of type density ridges
  geom_density_ridges(jittered_points = TRUE,
                      bandwidth = 3.5,
                      position = position_points_jitter(width = 0.05, 
                                                        height = 0),
                      point_shape = '|', 
                      point_size = 3, 
                      point_alpha = 1, 
                      alpha = 0.7)+
  # Add labels
  labs(y="Mode",  
       x = expression("Sidewalk density (km/km"^2*")"), 
       fill = "Mode")
```

```{r ch02-ridge-plot-with-additional-data}
ggplot(data = mc_commute_wide, 
       aes(x = sidewalk_density,
           y = choice,
           # By mapping the fill color to `vehind` 
           # we introduce an additional data dimension to the plot
           fill = vehind)) +
  geom_density_ridges(jittered_points = TRUE,
                      bandwidth = 3.5,
                      position = position_points_jitter(width = 0.05, 
                                                        height = 0),
                      point_shape = '|', 
                      point_size = 3, 
                      point_alpha = 1, 
                      alpha = 0.7) +
  labs(y="Mode",  
       x = expression("Sidewalk density (km/km"^2*")"),
       fill = "Individual access to a vehicle")      
```

```{r ch02-treemap-with-three-variables}
ggplot(data = mc_commute_wide %>%
         # Group by choice and gender
         group_by(choice, 
                  gender) %>%
         # Summarize to obtain the number of responses by choice-gender combination
         # and the mean of sidewalk density for each group
         summarize(n = n(), 
                   sidewalk_density = mean(sidewalk_density),
                   .groups = "drop"), 
       # Map the area of the rectangles to the number of responses, the fill color 
       # to mean sidewalk density, and group rectangles by choice
       aes(area = n,
           fill = sidewalk_density,
           label = gender,
           subgroup = choice)) +
  # Create main treemap
  geom_treemap()  +
  # Plot borders of subgroups
  geom_treemap_subgroup_border(size = 5)+ 
  # Add labels
  geom_treemap_subgroup_text(fontface = "bold", 
                             colour = "white", 
                             place = "topleft",
                             size = 10,
                             grow = FALSE) +
  geom_treemap_text(fontface = "italic", 
                    colour = "lightgray", 
                    place = "bottomright",
                    size = 10,
                    grow = FALSE) +
  labs(title = "Trips by Mode-Gender and sidewalk density", fill = expression("Sidewalk density (km/km"^2*")"))
```

```{r ch02-alluvial-prep}
# data preparation
mc_commute_alluvia <- mc_commute_wide %>%
  mutate(living_arrangments = case_when(shared == "Living in Shared Accommodations" ~ "Shared",
                                        family == "Living with Family" ~ "Family",
                                        TRUE ~ "Other")) %>%
  select(gender, living_arrangments, choice) %>%
  group_by(gender, living_arrangments, choice) %>%
  summarize(frequency = n(),
            .groups = "drop")

```

```{r ch02-alluvial-plot}
# plot
mc_commute_alluvia %>%
  ggplot(aes(y = frequency, 
             axis1 = gender,
             axis2 = living_arrangments,
             axis4 = choice)) +
  geom_alluvium(aes(fill = gender),
                width = 1/12,
                color = "black") +
  geom_stratum(width = 1/3, 
               fill = "black", 
               color = "grey") +
  geom_text(stat = "stratum", 
            aes(label = after_stat(stratum)), 
            color = "white",
            size = 3) +
  scale_x_discrete(limits = c("Gender", 
                              "Living Arrangements",
                              "Choice"), 
                   expand = c(.05, .05)) +
  scale_fill_brewer(type = "qual", 
                    palette = "Dark2") +
  theme_minimal()
```

```{r ch02-facet-wrap-ridge-plot}
mc_commute_wide %>%
  ggplot(aes(x = sidewalk_density,
             y = choice,
             fill = vehind)) +
  geom_density_ridges(jittered_points = TRUE,
                      bandwidth = 3.5,
                      position = position_points_jitter(width = 0.05, 
                                                        height = 0),
                      point_shape = '|', 
                      point_size = 3, 
                      point_alpha = 1, 
                      alpha = 0.7) +
  labs(y="Mode",  
       x = expression("Sidewalk density (km/km"^2*")"),
       fill = "Individual access to a vehicle")  +
  # `facet_wrap()` creates subplots after partitioning the data set
  # by the variable(s) specified, in this case `vehind`
  facet_wrap(~ vehind)
```

```{r ch02-facet-wrap-barplot-gender}
mc_commute_wide %>% 
  ggplot(aes(x = child, 
             fill = choice)) + 
  geom_bar(position = "fill") +
  labs(y = "Proportion",
       x = "", 
       fill="Mode") + 
  # Facet the plots based on `gender`
  facet_wrap(~ gender) +
  theme(axis.text.x = element_text(size = 7))
```

```{r ch02-facet-wrap-barplot-child}
mc_commute_wide %>%
  ggplot(aes(x = gender, 
             fill = choice)) + 
  geom_bar(position = "fill") +
  labs(y = "Proportion",
       x = "Gender", 
       fill="Mode") + 
  # Facet the plots based on `child`
  facet_wrap(~ child)
```

```{r ch02-facet-grid-barplot-vehind-child}
mc_commute_wide %>%
  ggplot(aes(x = gender, 
             fill = choice)) +
  geom_bar(position = "fill") +
  labs(y = "Proportion",
       x = "Gender", 
       fill="Mode") +
  # `facet_grid()` creates a "matrix" of subplots
  # with the variable on the left spread on the 
  # x-axis and the one on the right on the y-axis
  facet_grid(vehind ~ child)
```
```{r ch02-number-observations}
table(mc_commute_wide$vehind, 
      mc_commute_wide$child)
```

```{r ch02-facet-wrap-barplot-fun-gender}
mc_commute_wide %>%
  ggplot() + 
  (aes(x = gender, 
       fill = choice)) +
  geom_bar(position = "fill") +
  labs(y = "Proportion",
       x = "Getting there is fun", 
       fill = "Mode") + 
  facet_wrap(~ getting_there_fun)
```

```{r ch02-facet-wrap-barplot-fun-child}
mc_commute_wide %>%
  ggplot(aes(x = getting_there_fun, 
             fill = choice)) + 
  geom_bar(position = "fill") +
  labs(y = "Proportion",
       x = "Getting there is fun",
       fill = "Mode") +
  facet_wrap(~ child)
```

```{r ch02-facet-wrap-mosaic}
mc_commute_wide %>%
  ggplot() + 
  geom_mosaic(aes(x = product(choice,
                              getting_there_fun), 
                  fill = choice)) + 
  facet_wrap(~ child)+
  labs(y = "Proportion",
       x = "Getting there is fun", 
       fill = "Mode") 
```

```{r ch02-facet-wrap-alternatives}
names(mc_commute_wide)[names(mc_commute_wide) == "numna"] <- "Alternatives" # Renaming variable

mc_commute_wide %>%
  ggplot(aes(x = available.Walk)) + 
  labs(y = "Proportion",
       x = "Walk is available") +
  geom_bar(color="black", 
           fill="white") + 
  facet_wrap(~ Alternatives, 
             labeller = label_both)
```

```{r ch02-facet-wrap-walk-available}
mc_commute_wide %>%
  ggplot(aes(x = Alternatives)) + 
  labs(y = "Proportion",
       x = "Number of alternatives")+
  geom_bar(color = "black", 
           fill = "white") + 
  facet_wrap(~ available.Walk, 
             labeller = label_both)
```

```{r ch02-exercise-load-data}
library(mlogit)
data("Car")
```
