Chapter 12

Other aesthetics

Author

Aditya Dahiya

Published

January 4, 2024

This chapter has no exercises, so we use the plot created in Chapter 11 to customize some aesthetics explained in this Chapter.

Code

12.1 Size & 12.2 Shape

Let us customize the size and shape aesthetics in map of Delhi with Movie Theater Locations ( Figure 1 ), made in Chapter 11 (Section 11.7), and change the shapes, colours and sizes to produce Figure 2 .

Code
#==============================================================================#
# Library Load-in---------------------------------------------------------------
#==============================================================================#
library(tidyverse)      # Data wrangling and plotting
library(osmdata)        # Wrapper for Overpass API from Open Street Maps
library(janitor)        # Cleaning names
library(sf)             # For plotting maps
library(here)           # Files location and loading
library(paletteer)      # Lots of Color Palettes in R
library(colorspace)     # Lightening and Darkening Colors
library(showtext)       # Using Fonts More Easily in R Graphs
library(fontawesome)    # Social Media icons
library(ggtext)         # Markdown Text in ggplot2
library(patchwork)      # For compiling plots
library(magick)         # Work with Images and Logos
library(ggimage)        # Background Image


#==============================================================================#
# Data Load-in------------------------------------------------------------------
#==============================================================================#

del_mov <- read_csv("https://raw.githubusercontent.com/HarshaDevulapalli/indian-movie-theatres/master/indian-movie-theatres.csv") |> 
  filter(city == "Delhi")

main_roads <- st_read(here::here("data", 
                                 "delhi_osm",
                                 "delhi_main_roads.shp"))
st_crs(main_roads) <- "WGS 84"

minor_roads <- st_read(here::here("data", 
                                  "delhi_osm",
                                  "delhi_minor_roads.shp")) 
st_crs(minor_roads) <- "WGS 84"

very_minor_roads <- st_read(here::here("data", 
                                       "delhi_osm",
                                       "delhi_veryminor_roads.shp")) 
st_crs(very_minor_roads) <- "WGS 84"

#==============================================================================#
# Data Wrangling----------------------------------------------------------------
#==============================================================================#

coords <- del_mov |> 
  summarise(
    top = max(lat),
    bottom = min(lat),
    left = min(lon),
    right = max(lon)
  ) |> 
  as_vector()

# Adjust to remove the one leftmost (westward) cniema hall - outlier
percentage_removal_factor = 0.2
coords[3] <- coords[3] + 
  ((coords[4] - coords[3]) * percentage_removal_factor)

# Impute average value to NAs

impute_na <- median(del_mov$average_ticket_price, na.rm = TRUE)
del_mov <- del_mov |> 
  mutate(average_ticket_price = 
           if_else(is.na(average_ticket_price),
                   impute_na,
                   average_ticket_price))


#==============================================================================#
# Options & Visualization Parameters--------------------------------------------
#==============================================================================#

# Load fonts
font_add_google("Limelight", 
                family = "title_font")       # Font for titles
font_add_google("Saira Extra Condensed", 
                family = "caption_font")     # Font for the caption
font_add_google("Bree Serif", 
                family = "body_font")        # Font for plot text
showtext_auto()

# Colour Palette
mypal_c <- paletteer::scale_colour_paletteer_c("ggthemes::Purple")
mypal <- paletteer::paletteer_d("rcartocolor::Purp")

# Define colours
low_col <- mypal[4]                   # Low colour
hi_col <- mypal[6]                    # High colour
bg_col <- mypal[3] |> lighten(0.9)    # Background Colour
text_col <- mypal[1] |> darken(0.6)   # Colour for the text
text_hil <- mypal[6] |> darken(0.4)   # Colour for the title

# Caption stuff
sysfonts::font_add(family = "Font Awesome 6 Brands",
                   regular = here::here("docs", "Font Awesome 6 Brands-Regular-400.otf"))
github <- "&#xf09b"
github_username <- "aditya-dahiya"
xtwitter <- "&#xe61b"
xtwitter_username <- "@adityadahiyaias"
linkedin <- "&#xf08c"
linkedin_username <- "dr-aditya-dahiya-ias"
social_caption <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{github};</span> <span style='color: {text_col}'>{github_username}  </span> <span style='font-family:\"Font Awesome 6 Brands\";'>{xtwitter};</span> <span style='color: {text_col}'>{xtwitter_username}</span> <span style='font-family:\"Font Awesome 6 Brands\";'>{linkedin};</span> <span style='color: {text_col}'>{linkedin_username}</span>")

# Add text to plot--------------------------------------------------------------
plot_title <- "Movie Theatres in Delhi"

subtitle_text <- "Most theatres are in North or South Delhi, with a central vacant band around Lutyens Delhi !"
plot_subtitle <- paste(strwrap(subtitle_text, 100), collapse = "\n")

plot_caption <- paste0("**Data:** Harsha Devulapalli  |  ", "**Graphics:** ", social_caption)

#==============================================================================#
# Data Visualization------------------------------------------------------------
#==============================================================================#




  
g <- ggplot() +
  geom_sf(
    data = 
      main_roads |> 
      mutate(geometry = st_simplify(
        geometry, 
        dTolerance = 50,
        preserveTopology = TRUE)),
    mapping = aes(geometry = geometry),
    color = low_col,
    linewidth = 1,
    alpha = 0.4) +
  geom_sf(
    data = 
      minor_roads |> 
      mutate(geometry = st_simplify(
        geometry, 
        dTolerance = 1,
        preserveTopology = TRUE)),
    color = low_col,
    linewidth = 0.7,
    alpha = 0.3) +
  geom_sf(
    data = 
      very_minor_roads |> 
      mutate(geometry = st_simplify(
        geometry, 
        dTolerance = 10,
        preserveTopology = TRUE)),
    color = low_col,
    linewidth = 0.3,
    alpha = 0.2) +
  geom_point(
    data = del_mov,
    mapping = aes(
      x = lon,
      y = lat,
      size = total_seats,
      fill = average_ticket_price
    ),
    pch = 21,
    color = text_hil,
    alpha = 0.6
  ) +
  ggrepel::geom_text_repel(
    data = del_mov,
    mapping = aes(
      x = lon,
      y = lat,
      label = theatre_name
    ),
    alpha = 0.95,
    family = "body_font",
    colour = text_col,
    seed = 42,
    size = 10,
    segment.color = text_col
  ) +
  coord_sf(
    xlim = coords[c("left", "right")],
    ylim = coords[c("bottom", "top")],
    expand = FALSE) +
  scale_fill_paletteer_c("ggthemes::Purple") +
  scale_size_continuous(range = c(1, 15)) +
  labs(title = plot_title,
       subtitle = plot_subtitle,
       caption = plot_caption,
       fill = "Average Ticket Price (in Rs.)",
       size = "Total Number of Seats") +
  theme_void() + 
  guides(fill = guide_colorbar(title.position = "top",
                               barheight = unit(0.5, "cm"),
                               barwidth = unit(8, "cm")),
         size = guide_legend(title.position = "top",
                             keywidth = unit(0.5, "cm"),
                             keyheight = unit(0.5, "cm"),
                             label.hjust = 0)) +
  theme(
    plot.caption =  element_textbox(family = "caption_font",
                                    hjust = 0.5,
                                    colour = text_col,
                                    size = unit(40, "cm")),
    plot.title   =     element_text(hjust = 0.5,
                                    size = unit(175, "cm"),
                                    margin = margin(0.3,0,0.2,0, 
                                                    unit = "cm"),
                                    family = "title_font",
                                    face = "bold",
                                    colour = text_col),
    plot.subtitle    = element_text(hjust = 0.5,
                                    size = unit(50, "cm"),
                                    family = "body_font",
                                    colour = text_col,
                                    margin = margin(0,0,0.2,0, 
                                                    unit = "cm")),
    plot.background =  element_rect(fill = bg_col,
                                    color = bg_col,
                                    linewidth = 0),
    legend.position = "bottom",
    legend.text = element_text(hjust = 0.5,
                               size = unit(40, "cm"),
                               family = "body_font",
                               colour = text_col),
    legend.title = element_text(hjust = 0.5,
                                size = 50,
                                family = "body_font",
                                colour = text_col,
                                margin = margin(0,0,0,0)),
    legend.box.margin = margin(0,0,0.5,0, unit = "cm"),
    legend.box = "horizontal",
    legend.spacing.y = unit(0.2, "cm")
  )




#=============================================================================#
# Image Saving-----------------------------------------------------------------
#=============================================================================#


ggsave(
  filename = here::here("docs", "delhimovies_tidy.png"),
  plot = g,
  device = "png", 
  dpi = "retina", 
  width = unit(10, "cm"), 
  height = unit(10, "cm"),
  bg = bg_col
)

Figure 1: Original Plot produced in Chapter 11

Figure 2: Plot produced by customizing the Shape and Size Scales in ggplot2

12.3 Line width & 12.4 Line type

The Figure 3 shows the use of custom scales in line-type and line-width as aesthetics.

Code
economics |>
  select(date, psavert, uempmed) |> 
  pivot_longer(
    cols = c(psavert, uempmed),
    names_to = "indicator",
    values_to = "value"
  ) |> 
  ggplot(aes(x = date,
             y = value,
             linetype = indicator, 
             linewidth = indicator)) +
  geom_line() +
  scale_linetype_manual(
    values = c(1, 3),
    labels = c(
      "Personal savings rate (%)",
      "Median duration of unemployment, in weeks"
    ),
    name = NULL
  ) +
  scale_linewidth_manual(
    values = c(1, 0.5)
  ) +
  guides(
    linewidth = "none",
    linetype = guide_legend(
      override.aes = list(linewidth = 1),
      keywidth = unit(2, "cm")
    )
  ) +
  labs(
    x = NULL, y = NULL,
    title = "USA: Comparing Unemployment and Savings Rate trends"
  ) +
  cowplot::theme_half_open() +
  theme(
    legend.position = "bottom",
    axis.line = element_line(arrow = arrow(length = unit(3, "mm")))
  )

Figure 3: Using scale_linetype() to manually control linetype as an aesthetic