Data Visualization with ggplot2 (Part 3)

Introduction

• Course notes from the Data Vizualization with ggplot2 (Part 3) course on DataCamp

Whats Covered

• Statistical plots
  • Aesthetics review,
  • box plots, density plots
  • multiple groups/variables
• Plots for specific data types (Part 1)
  • graphics of large data
  • Ternary plots
  • Network plots
  • Diagnostic plots
• Plots for specific data types (Part 2)
  • choropleths
  • cartographic maps
  • animations
• ggplot2 internals
  • grid graphics, grid grapshics in ggplot2
  • ggplot objects
  • gridExtra
• Data Munging and Visualization Case Study
  • Bag plot case study, weather case study

   

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Statistical plots

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Introduction

– Refresher (1)

# Create movies_small
library(ggplot2movies)
set.seed(123)
movies_small <- movies[sample(nrow(movies), 1000), ]
movies_small$rating <- factor(round(movies_small$rating))

# Explore movies_small with str()
str(movies_small)

## Classes 'tbl_df', 'tbl' and 'data.frame':    1000 obs. of  24 variables:
##  $ title      : chr  "Fair and Worm-er" "Shelf Life" "House: After Five Years of Living" "Three Long Years" ...
##  $ year       : int  1946 2000 1955 2003 1963 1992 1999 1972 1994 1985 ...
##  $ length     : int  7 4 11 76 103 107 87 84 127 94 ...
##  $ budget     : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ rating     : Factor w/ 10 levels "1","2","3","4",..: 7 7 6 8 8 5 4 8 5 5 ...
##  $ votes      : int  16 11 15 11 103 28 105 9 37 28 ...
##  $ r1         : num  0 0 14.5 4.5 4.5 4.5 14.5 0 4.5 4.5 ...
##  $ r2         : num  0 0 0 0 4.5 0 4.5 0 4.5 0 ...
##  $ r3         : num  0 0 4.5 4.5 0 4.5 4.5 0 14.5 4.5 ...
##  $ r4         : num  0 0 4.5 0 4.5 4.5 4.5 0 4.5 14.5 ...
##  $ r5         : num  4.5 4.5 0 0 4.5 0 4.5 14.5 24.5 4.5 ...
##  $ r6         : num  4.5 24.5 34.5 4.5 4.5 0 14.5 0 4.5 14.5 ...
##  $ r7         : num  64.5 4.5 24.5 0 14.5 4.5 14.5 14.5 14.5 14.5 ...
##  $ r8         : num  14.5 24.5 4.5 4.5 14.5 24.5 14.5 24.5 14.5 14.5 ...
##  $ r9         : num  0 0 0 14.5 14.5 24.5 14.5 14.5 4.5 4.5 ...
##  $ r10        : num  14.5 24.5 14.5 44.5 44.5 24.5 14.5 44.5 4.5 24.5 ...
##  $ mpaa       : chr  "" "" "" "" ...
##  $ Action     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Animation  : int  1 0 0 0 0 0 0 0 0 0 ...
##  $ Comedy     : int  1 0 0 1 0 1 1 1 0 0 ...
##  $ Drama      : int  0 0 0 0 1 0 0 0 1 1 ...
##  $ Documentary: int  0 0 1 0 0 0 0 0 0 0 ...
##  $ Romance    : int  0 0 0 0 0 0 1 0 0 0 ...
##  $ Short      : int  1 1 1 0 0 0 0 0 0 0 ...

# Build a scatter plot with mean and 95% CI
ggplot(movies_small, aes(x = rating, y = votes)) +
  geom_point() +
  stat_summary(fun.data = "mean_cl_normal",
               geom = "crossbar",
               width = 0.2,
               col = "red") +
  scale_y_log10()

– Refresher (2)

str(diamonds)

## Classes 'tbl_df', 'tbl' and 'data.frame':    53940 obs. of  10 variables:
##  $ carat  : num  0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
##  $ cut    : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
##  $ color  : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
##  $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
##  $ depth  : num  61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
##  $ table  : num  55 61 65 58 58 57 57 55 61 61 ...
##  $ price  : int  326 326 327 334 335 336 336 337 337 338 ...
##  $ x      : num  3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
##  $ y      : num  3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
##  $ z      : num  2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...

# Reproduce the plot
ggplot(diamonds, aes(x = carat, y = price, col = color)) +
  geom_point(alpha = 0.5, size = 0.5, shape = 16) +
  scale_x_log10(expression(log[10](Carat)), limits = c(0.1, 10)) +
  scale_y_log10(expression(log[10](Price)), limits = c(100, 100000)) +
  scale_color_brewer(palette = "YlOrRd") +
  coord_equal() +
  theme_classic()

– Refresher (3)

# Add smooth layer and facet the plot
ggplot(diamonds, aes(x = carat, y = price, col = color)) +
  stat_smooth(method = "lm") + 
  scale_x_log10(expression(log[10](Carat)), limits = c(0.1,10)) +
  scale_y_log10(expression(log[10](Price)), limits = c(100,100000)) +
  scale_color_brewer(palette = "YlOrRd") +
  coord_equal() +
  theme_classic()

Box Plots

– Transformations

# movies_small is available

# Add a boxplot geom
d <- ggplot(movies_small, aes(x = rating, y = votes)) +
  geom_point() +
  geom_boxplot() +
  stat_summary(fun.data = "mean_cl_normal",
               geom = "crossbar",
               width = 0.2,
               col = "red")

# Untransformed plot
d

# Transform the scale
d + scale_y_log10()

# Transform the coordinates
## coord_trans is different to scale transformations in that it occurs after statistical transformation and will affect the visual appearance of geoms - there is no guarantee that straight lines will continue to be straight.

## This does not work in my case. 
## It likely is from the statitics having a zero value.
## d + coord_trans(y = "log10")

# It works fine without the stats layer
ggplot(movies_small, aes(x = rating, y = votes)) +
  geom_point() +
  geom_boxplot() +
  coord_trans(y = "log10")

This is the example from the documentation which actually works

# Three ways of doing transformation in ggplot:

#  * by transforming the data
ggplot(diamonds, aes(log10(carat), log10(price))) +
  geom_point()

#  * by transforming the scales
ggplot(diamonds, aes(carat, price)) +
  geom_point() +
  scale_x_log10() +
  scale_y_log10()

#  * by transforming the coordinate system:
ggplot(diamonds, aes(carat, price)) +
  geom_point() +
  coord_trans(x = "log10", y = "log10")

# The difference between transforming the scales and
# transforming the coordinate system is that scale
# transformation occurs BEFORE statistics, and coordinate
# transformation afterwards.  Coordinate transformation also
# changes the shape of geoms:

d <- subset(diamonds, carat > 0.5)

ggplot(d, aes(carat, price)) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_x_log10() +
  scale_y_log10()

ggplot(d, aes(carat, price)) +
  geom_point() +
  geom_smooth(method = "lm") +
  coord_trans(x = "log10", y = "log10")

– Cut it up!

# Plot object p
p <- ggplot(diamonds, aes(x = carat, y = price))

# Use cut_interval
p + geom_boxplot(aes(group = cut_interval(carat, n = 10)))

# Use cut_number
p + geom_boxplot(aes(group = cut_number(carat, n = 10)))

# Use cut_width
p + geom_boxplot(aes(group = cut_width(carat, width = 0.25)))

• If you only have continuous variables, you can convert them into ordinal variables using any of the following functions:
  • cut_interval(x, n) makes n groups from vector x with equal range.
  • cut_number(x, n) makes n groups from vector x with (approximately) equal numbers of observations.
  • cut_width(x, width) makes groups of width width from vector x.

– Understanding quartiles

plot_quart <- function(n) {
  set.seed(123)
  playData <- data.frame(raw.values = rnorm(n, 1, 6))

  quan.summary <- data.frame(t(sapply(1:9, function(x) quantile(playData$raw.values, type = x))))
  names(quan.summary) <- c("Min", "Q1", "Median", "Q3", "Max")
  quan.summary$Type <- as.factor(1:9)

  library(reshape2)
  quan.summary <- melt(quan.summary, id = "Type")
  quan.summary <- list(quartiles = quan.summary, values = playData)

  ggplot(quan.summary$quartiles, aes(x = Type, y = value, col = variable)) +
    geom_point() +
    geom_rug(data = quan.summary$values, aes(y = raw.values), sides = "l", inherit.aes = F)
}

plot_quart(4)

plot_quart(10)

plot_quart(50)

plot_quart(100)

• The IQR becomes more consistent across methods as the sample size increases

Density Plots

– geom_density()

load('data/test_datasets.RData')
str(ch1_test_data)

## 'data.frame':    200 obs. of  3 variables:
##  $ norm   : num  -0.5605 -0.2302 1.5587 0.0705 0.1293 ...
##  $ bimodal: num  0.199 -0.688 -2.265 -1.457 -2.414 ...
##  $ uniform: num  -0.117 -0.537 -1.515 -1.812 -0.949 ...

# Calculating density: d
d <- density(ch1_test_data$norm, bw = "nrd0", kernel = "gaussian")

# Use which.max() to calculate mode
mode <- d$x[which.max(d$y)]

# Finish the ggplot call
ggplot(ch1_test_data, aes(x = norm)) +
  geom_density() +
  geom_rug() +
  geom_vline(xintercept = mode, col = "red")

– Combine density plots and histogram

# ch1_test_data is available

# Arguments you'll need later on
fun_args <- list(mean = mean(ch1_test_data$norm), sd = sd(ch1_test_data$norm))

# Finish the ggplot
ggplot(ch1_test_data, aes(x = norm)) + 
  geom_histogram(aes(y = ..density..)) + 
  geom_density(col = "red") + 
  stat_function(
    fun = dnorm, 
    args = fun_args, 
    col = "blue")

– Adjusting density plots

# small_data is available
small_data <- structure(list(x = c(-3.5, 0, 0.5, 6)), .Names = "x", row.names = c(NA, 
-4L), class = "data.frame")

# Get the bandwith
get_bw <- density(small_data$x)$bw

# Basic plotting object
p <- ggplot(small_data, aes(x = x)) +
  geom_rug() +
  coord_cartesian(ylim = c(0,0.5))

# Create three plots
p + geom_density()

p + geom_density(adjust = 0.25)

p + geom_density(bw = 0.25 * get_bw)

# Create two plots
## rectangular kernel
p + geom_density(kernel = "r")

## epanechnikov kernel
p + geom_density(kernel = "e")

Multiple Groups/Variables

– Box plots with varying width

# Finish the plot
ggplot(diamonds, aes(x = cut, y = price, col = color)) + 
  geom_boxplot(varwidth = T) + 
  facet_grid(. ~ color)

– Mulitple density plots

# ch1_test_data and ch1_test_data2 are available
str(ch1_test_data)

## 'data.frame':    200 obs. of  3 variables:
##  $ norm   : num  -0.5605 -0.2302 1.5587 0.0705 0.1293 ...
##  $ bimodal: num  0.199 -0.688 -2.265 -1.457 -2.414 ...
##  $ uniform: num  -0.117 -0.537 -1.515 -1.812 -0.949 ...

str(ch1_test_data2)

## 'data.frame':    400 obs. of  2 variables:
##  $ dist : Factor w/ 2 levels "norm","bimodal": 1 1 1 1 1 1 1 1 1 1 ...
##  $ value: num  -0.5605 -0.2302 1.5587 0.0705 0.1293 ...

# Plot with ch1_test_data
ggplot(ch1_test_data, aes(x = norm)) +
  geom_rug() + 
  geom_density()

# Plot two distributions with ch1_test_data2
ggplot(ch1_test_data2, aes(x = value, fill = dist, col = dist)) +
  geom_rug(alpha = 0.6) + 
  geom_density(alpha = 0.6)

– Multiple density plots (2)

# Individual densities
ggplot(mammals[mammals$vore == "Insectivore", ], 
    aes(x = sleep_total, fill = vore)) +
  geom_density(col = NA, alpha = 0.35) +
  scale_x_continuous(limits = c(0, 24)) +
  coord_cartesian(ylim = c(0, 0.3))

# With faceting
ggplot(mammals, aes(x = sleep_total, fill = vore)) +
  geom_density(col = NA, alpha = 0.35) +
  scale_x_continuous(limits = c(0, 24)) +
  coord_cartesian(ylim = c(0, 0.3)) +
  facet_wrap( ~ vore, nrow = 2)

# Note that by default, the x ranges fill the scale
ggplot(mammals, aes(x = sleep_total, fill = vore)) +
  geom_density(col = NA, alpha = 0.35) +
  scale_x_continuous(limits = c(0, 24)) +
  coord_cartesian(ylim = c(0, 0.3))

# Trim each density plot individually
ggplot(mammals, aes(x = sleep_total, fill = vore)) +
  geom_density(col = NA, alpha = 0.35, trim = T) +
  scale_x_continuous(limits=c(0,24)) +
  coord_cartesian(ylim = c(0, 0.3))

– Weighted density plots

# Unweighted density plot from before
ggplot(mammals, aes(x = sleep_total, fill = vore)) +
  geom_density(col = NA, alpha = 0.35) +
  scale_x_continuous(limits = c(0, 24)) +
  coord_cartesian(ylim = c(0, 0.3))

# Unweighted violin plot
ggplot(mammals, aes(x = vore, y = sleep_total, fill = vore)) +
  geom_violin()

# Calculate weighting measure
library(dplyr)
mammals2 <- mammals %>%
  group_by(vore) %>%
  mutate(n = n() / nrow(mammals)) -> mammals

str(mammals2, give.attr = F)

## Classes 'grouped_df', 'tbl_df', 'tbl' and 'data.frame':  76 obs. of  3 variables:
##  $ vore       : Factor w/ 4 levels "Carnivore","Herbivore",..: 1 4 2 4 2 2 1 1 2 2 ...
##  $ sleep_total: num  12.1 17 14.4 14.9 4 14.4 8.7 10.1 3 5.3 ...
##  $ n          : num  0.25 0.263 0.421 0.263 0.421 ...

str(mammals, give.attr = F)

## Classes 'grouped_df', 'tbl_df', 'tbl' and 'data.frame':  76 obs. of  3 variables:
##  $ vore       : Factor w/ 4 levels "Carnivore","Herbivore",..: 1 4 2 4 2 2 1 1 2 2 ...
##  $ sleep_total: num  12.1 17 14.4 14.9 4 14.4 8.7 10.1 3 5.3 ...
##  $ n          : num  0.25 0.263 0.421 0.263 0.421 ...

# Weighted density plot
ggplot(mammals, aes(x = sleep_total, fill = vore)) +
  geom_density(aes(weight = n), col = NA, alpha = 0.35) +
  scale_x_continuous(limits = c(0, 24)) + 
  coord_cartesian(ylim = c(0, 0.3))

# Weighted violin plot
ggplot(mammals, aes(x = vore, y = sleep_total, fill = vore)) +
  geom_violin(aes(weight = n), col = NA)

– 2D density plots (1)

# Base layers
p <- ggplot(faithful, aes(x = waiting, y = eruptions)) +
  scale_y_continuous(limits = c(1, 5.5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(40, 100), expand = c(0, 0)) +
  coord_fixed(60 / 4.5)

# 1 - Use geom_density_2d()
p + geom_density_2d()

# 2 - Use stat_density_2d() with arguments
p + stat_density_2d(aes(col = ..level..), h = c(5, 0.5))

– 2D density plots (2)

# Load in the viridis package
library(viridis)

# Add viridis color scale
ggplot(faithful, aes(x = waiting, y = eruptions)) +
  scale_y_continuous(limits = c(1, 5.5), expand = c(0,0)) +
  scale_x_continuous(limits = c(40, 100), expand = c(0,0)) +
  coord_fixed(60/4.5) +
  stat_density_2d(geom = "tile", aes(fill = ..density..), h=c(5,.5), contour = FALSE) +
  scale_fill_viridis()

   

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Plots for specific data types (Part 1)

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Graphics of Large Data

– Pair plots and correlation matrices

# pairs
pairs(iris[1:4])

# chart.Correlation
library(PerformanceAnalytics)

chart.Correlation(iris[1:4])

# ggpairs
library(GGally)

mtcars_fact <- mtcars %>%
  mutate(
    cyl = as.factor(cyl),
    vs = as.factor(vs),
    am = as.factor(am),
    gear = as.factor(gear),
    carb = as.factor(carb)
  )

ggpairs(mtcars_fact[1:3])

– Create a correlation matrix in ggplot2

library(ggplot2)
library(reshape2)

cor_list <- function(x) {
  L <- M <- cor(x)
  
  M[lower.tri(M, diag = TRUE)] <- NA
  M <- melt(M)
  names(M)[3] <- "points"
  
  L[upper.tri(L, diag = TRUE)] <- NA
  L <- melt(L)
  names(L)[3] <- "labels"
  
  merge(M, L)
}

# Calculate xx with cor_list
library(dplyr)

xx <- iris %>%
  group_by(Species) %>%
  do(cor_list(.[1:4])) 

# Finish the plot
ggplot(xx, aes(x = Var1, y = Var2)) +
  geom_point(
    aes(col = points, size = abs(points)), 
    shape = 16
    ) +
  geom_text(
    aes(col = labels, size = abs(labels), label = round(labels, 2))
    ) +
  scale_size(range = c(0, 6)) +
  scale_color_gradient2("r", limits = c(-1, 1)) +
  scale_y_discrete("", limits = rev(levels(xx$Var1))) +
  scale_x_discrete("") +
  guides(size = FALSE) +
  geom_abline(slope = -1, intercept = nlevels(xx$Var1) + 1) +
  coord_fixed() +
  facet_grid(. ~ Species) +
  theme(axis.text.y = element_text(angle = 45, hjust = 1),
        axis.text.x = element_text(angle = 45, hjust = 1),
        strip.background = element_blank())

Ternary Plots

– Proportional/stacked bar plots

# Explore africa
load('data/africa.RData')
str(africa)

## 'data.frame':    40093 obs. of  3 variables:
##  $ Sand: num  24 36 56 52 65 43 42 47 57 51 ...
##  $ Silt: num  12 14 18 21 3 14 22 19 15 14 ...
##  $ Clay: num  64 50 26 27 32 43 36 34 28 35 ...

africa_sample <- sample_n(africa, 50)
str(africa_sample)

## 'data.frame':    50 obs. of  3 variables:
##  $ Sand: num  42 28 83 40 35 24 29 30 75 92 ...
##  $ Silt: num  15 29 6 14 10 22 20 4 10 6 ...
##  $ Clay: num  43 43 11 46 55 54 51 66 15 2 ...

# Add an ID column from the row.names
africa_sample$ID <- row.names(africa_sample)
head(africa_sample)

##       Sand Silt Clay    ID
## 34893   42   15   43 34893
## 85261   28   29   43 85261
## 64201   83    6   11 64201
## 52595   40   14   46 52595
## 44793   35   10   55 44793
## 79175   24   22   54 79175

# Gather africa_sample
library(tidyr)
africa_sample_tidy <- gather(africa_sample, key, value, -ID)
head(africa_sample_tidy)

##      ID  key value
## 1 34893 Sand    42
## 2 85261 Sand    28
## 3 64201 Sand    83
## 4 52595 Sand    40
## 5 44793 Sand    35
## 6 79175 Sand    24

# Finish the ggplot command
ggplot(africa_sample_tidy, aes(x = factor(ID), y = value, fill = key)) +
  geom_col() +
  coord_flip()

– Producing ternary plots

# Load ggtern
library(ggtern)

# Build ternary plot
str(africa)

## 'data.frame':    40093 obs. of  3 variables:
##  $ Sand: num  24 36 56 52 65 43 42 47 57 51 ...
##  $ Silt: num  12 14 18 21 3 14 22 19 15 14 ...
##  $ Clay: num  64 50 26 27 32 43 36 34 28 35 ...

ggtern(africa, aes(x = Sand, y = Silt, z = Clay)) +
  geom_point(shape = 16, alpha = 0.2)

– Adjusting ternary plots

# ggtern and ggplot2 are loaded
# Original plot:
ggtern(africa, aes(x = Sand, y = Silt, z = Clay)) +
  geom_point(shape = 16, alpha = 0.2)

# Plot 1
ggtern(africa, aes(x = Sand, y = Silt, z = Clay)) +
  geom_density_tern()

# Plot 2
ggtern(africa, aes(x = Sand, y = Silt, z = Clay)) +
  stat_density_tern(geom = 'polygon', aes(fill = ..level.., alpha = ..level..)) +
  guides(fill = FALSE)

Network Plots

– Build the network (1)

# Load geomnet & examine structure of madmen
library(geomnet)
str(madmen)

## List of 2
##  $ edges   :'data.frame':    39 obs. of  2 variables:
##   ..$ Name1: Factor w/ 9 levels "Betty Draper",..: 1 1 2 2 2 2 2 2 2 2 ...
##   ..$ Name2: Factor w/ 39 levels "Abe Drexler",..: 15 31 2 4 5 6 8 9 11 21 ...
##  $ vertices:'data.frame':    45 obs. of  2 variables:
##   ..$ label : Factor w/ 45 levels "Abe Drexler",..: 5 9 16 23 26 32 33 38 39 17 ...
##   ..$ Gender: Factor w/ 2 levels "female","male": 1 2 2 1 2 1 2 2 2 2 ...

## This is a much better way to see whats in each list. Love it. 
library(purrr)
madmen %>% map(head)

## $edges
##          Name1            Name2
## 1 Betty Draper    Henry Francis
## 2 Betty Draper       Random guy
## 3   Don Draper          Allison
## 4   Don Draper Bethany Van Nuys
## 5   Don Draper     Betty Draper
## 6   Don Draper   Bobbie Barrett
## 
## $vertices
##           label Gender
## 1  Betty Draper female
## 2    Don Draper   male
## 3   Harry Crane   male
## 4 Joan Holloway female
## 5    Lane Pryce   male
## 6   Peggy Olson female

# Merge edges and vertices
mmnet <- merge(madmen$edges, madmen$vertices,
               by.x = "Name1", by.y = "label",
               all = TRUE)

# Examine structure of mmnet
head(mmnet)

##          Name1            Name2 Gender
## 1 Betty Draper    Henry Francis female
## 2 Betty Draper       Random guy female
## 3   Don Draper          Allison   male
## 4   Don Draper Bethany Van Nuys   male
## 5   Don Draper     Betty Draper   male
## 6   Don Draper   Bobbie Barrett   male

str(mmnet)

## 'data.frame':    75 obs. of  3 variables:
##  $ Name1 : Factor w/ 45 levels "Betty Draper",..: 1 1 2 2 2 2 2 2 2 2 ...
##  $ Name2 : Factor w/ 39 levels "Abe Drexler",..: 15 31 2 4 5 6 8 9 11 21 ...
##  $ Gender: Factor w/ 2 levels "female","male": 1 1 2 2 2 2 2 2 2 2 ...

– Build the network (2)

# geomnet is pre-loaded

# Merge edges and vertices
mmnet <- merge(madmen$edges, madmen$vertices,
               by.x = "Name1", by.y = "label",
               all = TRUE)

head(mmnet)

##          Name1            Name2 Gender
## 1 Betty Draper    Henry Francis female
## 2 Betty Draper       Random guy female
## 3   Don Draper          Allison   male
## 4   Don Draper Bethany Van Nuys   male
## 5   Don Draper     Betty Draper   male
## 6   Don Draper   Bobbie Barrett   male

# Finish the ggplot command
ggplot(data = mmnet, aes(from_id = Name1, to_id = Name2)) +
  geom_net(
    aes(col = Gender),
    size = 6,
    linewidth = 1,
    labelon = T,
    fontsize = 3,
    labelcolour = "black")

– Adjusting the network

# geomnet is pre-loaded
library(ggmap)

# Merge edges and vertices
mmnet <- merge(madmen$edges, madmen$vertices,
               by.x = "Name1", by.y = "label",
               all = TRUE)

# Tweak the network plot
ggplot(data = mmnet, aes(from_id = Name1, to_id = Name2)) +
  geom_net(
    aes(col = Gender),
    size = 6,
    linewidth = 1,
    labelon = TRUE,
    fontsize = 3,
    labelcolour = "black",
    directed = T) +
  scale_color_manual(values = c("#FF69B4", "#0099ff")) +
  xlim(c(-0.05, 1.05)) +
  theme_nothing() +
  theme(legend.key = element_blank())

Diagnostic Plots

– Autoplot on linear models

# Create linear model: res
res <- lm(Volume ~ Girth, data = trees)

# Plot res
plot(res)

# Import ggfortify and use autoplot()
library(ggfortify)
autoplot(res, ncol = 2)

– ggfortify - time series

# ggfortify and Canada are available

# Inspect structure of Canada
str(Canada)

##  Time-Series [1:84, 1:4] from 1980 to 2001: 930 930 930 931 933 ...
##  - attr(*, "dimnames")=List of 2
##   ..$ : NULL
##   ..$ : chr [1:4] "e" "prod" "rw" "U"

head(Canada)

## [1] 929.6105 929.8040 930.3184 931.4277 932.6620 933.5509

# Call plot() on Canada
plot(Canada)

# Call autoplot() on Canada
autoplot(Canada)

– Distance matrices and Multi-Dimensional Scaling (MDS)

# ggfortify and eurodist are available
str(eurodist)

## Class 'dist'  atomic [1:210] 3313 2963 3175 3339 2762 ...
##   ..- attr(*, "Size")= num 21
##   ..- attr(*, "Labels")= chr [1:21] "Athens" "Barcelona" "Brussels" "Calais" ...

# Autoplot + ggplot2 tweaking
autoplot(eurodist) + 
  coord_fixed()

# Autoplot of MDS
autoplot(cmdscale(eurodist, eig = TRUE), 
         label = TRUE, 
         label.size = 3, 
         size = 0)

– Plotting K-means clustering

# Perform clustering
iris_k <- kmeans(iris[-5], 3)

# Autoplot: color according to cluster
autoplot(iris_k, data = iris, frame = T)

# Autoplot: above, plus shape according to species
autoplot(iris_k, data = iris, frame = T, shape = 'Species')

   

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Plots for specific data types (Part 2)

---

Choropleths

– Working with maps from the maps package: USA

library(maps)
library(ggmap)

# maps, ggplot2, and ggmap are pre-loaded
# Use map_data() to create usa and inspect
usa <- map_data("usa")
str(usa)

## 'data.frame':    7243 obs. of  6 variables:
##  $ long     : num  -101 -101 -101 -101 -101 ...
##  $ lat      : num  29.7 29.7 29.7 29.6 29.6 ...
##  $ group    : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ order    : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ region   : chr  "main" "main" "main" "main" ...
##  $ subregion: chr  NA NA NA NA ...

head(usa, 20)

##         long      lat group order region subregion
## 1  -101.4078 29.74224     1     1   main      <NA>
## 2  -101.3906 29.74224     1     2   main      <NA>
## 3  -101.3620 29.65056     1     3   main      <NA>
## 4  -101.3505 29.63911     1     4   main      <NA>
## 5  -101.3219 29.63338     1     5   main      <NA>
## 6  -101.3047 29.64484     1     6   main      <NA>
## 7  -101.3047 29.62192     1     7   main      <NA>
## 8  -101.3161 29.58181     1     8   main      <NA>
## 9  -101.3047 29.56462     1     9   main      <NA>
## 10 -101.2817 29.57035     1    10   main      <NA>
## 11 -101.2703 29.59327     1    11   main      <NA>
## 12 -101.2646 29.61619     1    12   main      <NA>
## 13 -101.2474 29.61046     1    13   main      <NA>
## 14 -101.2302 29.59327     1    14   main      <NA>
## 15 -101.2359 29.55316     1    15   main      <NA>
## 16 -101.2531 29.53597     1    16   main      <NA>
## 17 -101.2416 29.50733     1    17   main      <NA>
## 18 -101.2015 29.50733     1    18   main      <NA>
## 19 -101.1557 29.47868     1    19   main      <NA>
## 20 -101.1041 29.47295     1    20   main      <NA>

table(usa$group)

## 
##    1    2    3    4    5    6    7    8    9   10 
## 6886   36   30   16   10  168   17   17   19   44

# Build the map
ggplot(usa, aes(x = long, y = lat, group = group)) +
  geom_polygon() +
  coord_map() +
  theme_nothing()

– Working with maps from the maps package: adding points

# usa, cities, and all required packages are available
library(readr)
cities <- read_tsv('https://assets.datacamp.com/production/course_862/datasets/US_Cities.txt')

head(usa)

##        long      lat group order region subregion
## 1 -101.4078 29.74224     1     1   main      <NA>
## 2 -101.3906 29.74224     1     2   main      <NA>
## 3 -101.3620 29.65056     1     3   main      <NA>
## 4 -101.3505 29.63911     1     4   main      <NA>
## 5 -101.3219 29.63338     1     5   main      <NA>
## 6 -101.3047 29.64484     1     6   main      <NA>

head(cities)

## # A tibble: 6 x 5
##         City      State Pop_est     lat      long
##        <chr>      <chr>   <int>   <dbl>     <dbl>
## 1     Eugene     Oregon  163460 44.0567 -123.1162
## 2      Salem     Oregon  164549 44.9237 -123.0231
## 3  Hillsboro     Oregon  102347 45.5167 -122.9833
## 4 Santa Rosa California  174972 38.4468 -122.7061
## 5   Portland     Oregon  632309 45.5370 -122.6500
## 6  Vancouver Washington  172860 45.6372 -122.5965

## Need this to get the theme_map() and scale_color_viridis() functions
library(ggthemes)
library(viridis)

# Finish plot 1
ggplot(usa, aes(x = long, y = lat, group = group)) +
  geom_polygon() +
  geom_point(data = cities, aes(group = State, size = Pop_est),
             col = "red", shape = 16, alpha = 0.6) +
  coord_map() +
  theme_map()

# Arrange cities
library(dplyr)
cities_arr <- arrange(cities, Pop_est)

# Copy-paste plot 1 and adapt
ggplot(usa, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "grey90") +
  geom_point(data = cities_arr, aes(group = State, col = Pop_est),
             shape = 16, size = 2) +
  coord_map() +
  theme_map() +
  scale_color_viridis()

– State choropleth

# pop and all required packages are available

# Use map_data() to create state
state <- map_data("state")
head(state)

##        long      lat group order  region subregion
## 1 -87.46201 30.38968     1     1 alabama      <NA>
## 2 -87.48493 30.37249     1     2 alabama      <NA>
## 3 -87.52503 30.37249     1     3 alabama      <NA>
## 4 -87.53076 30.33239     1     4 alabama      <NA>
## 5 -87.57087 30.32665     1     5 alabama      <NA>
## 6 -87.58806 30.32665     1     6 alabama      <NA>

# Map of states
ggplot(state, aes(x = long, y = lat, fill = region, group = group)) +
  geom_polygon(col = "white") +
  coord_map() +
  theme_nothing()

# Merge state and pop: state2
state2 <- merge(state, pop)
head(state2)

##    region      long      lat group order subregion Pop_est
## 1 alabama -87.46201 30.38968     1     1      <NA> 4858979
## 2 alabama -87.48493 30.37249     1     2      <NA> 4858979
## 3 alabama -87.52503 30.37249     1     3      <NA> 4858979
## 4 alabama -87.53076 30.33239     1     4      <NA> 4858979
## 5 alabama -87.57087 30.32665     1     5      <NA> 4858979
## 6 alabama -87.58806 30.32665     1     6      <NA> 4858979

# Map of states with populations
ggplot(state2, aes(x = long, y = lat, fill = Pop_est, group = group)) +
  geom_polygon(col = "white") +
  coord_map() +
  theme_map()

– Map from shapefiles

# Import shape information: germany
library(rgdal)
germany <- readOGR(dsn = 'data/shape_files', layer = "DEU_adm1")

## OGR data source with driver: ESRI Shapefile 
## Source: "data/shape_files", layer: "DEU_adm1"
## with 16 features
## It has 16 fields

# fortify germany: bundes
bundes <- fortify(germany)

# Plot map of germany
ggplot(bundes, aes(x = long, y = lat, group = group)) +
    geom_polygon(fill = "blue", col = "white") +
    coord_map() +
    theme_nothing()

– Choropleth from shapefiles

# germany, bundes and unemp are available
head(bundes)

##       long      lat order  hole piece id group
## 1 9.650460 49.77634     1 FALSE     1  0   0.1
## 2 9.650968 49.76515     2 FALSE     1  0   0.1
## 3 9.656839 49.76145     3 FALSE     1  0   0.1
## 4 9.640400 49.75014     4 FALSE     1  0   0.1
## 5 9.652028 49.74276     5 FALSE     1  0   0.1
## 6 9.652208 49.73903     6 FALSE     1  0   0.1

# re-add state names to bundes
bundes$state <- factor(as.numeric(bundes$id))
levels(bundes$state) <- germany$NAME_1

head(bundes)

##       long      lat order  hole piece id group             state
## 1 9.650460 49.77634     1 FALSE     1  0   0.1 Baden-Württemberg
## 2 9.650968 49.76515     2 FALSE     1  0   0.1 Baden-Württemberg
## 3 9.656839 49.76145     3 FALSE     1  0   0.1 Baden-Württemberg
## 4 9.640400 49.75014     4 FALSE     1  0   0.1 Baden-Württemberg
## 5 9.652028 49.74276     5 FALSE     1  0   0.1 Baden-Württemberg
## 6 9.652208 49.73903     6 FALSE     1  0   0.1 Baden-Württemberg

head(unemp)

##                state unemployment
## 1             Bayern          3.7
## 2  Baden-Württemberg          4.0
## 3    Rheinland-Pfalz          5.4
## 4             Hessen          5.8
## 5      Niedersachsen          6.5
## 6 Schleswig-Holstein          6.7

# Merge bundes and unemp: bundes_unemp
bundes_unemp <- merge(bundes, unemp)

# Update the ggplot call
ggplot(bundes_unemp, aes(x = long, y = lat, group = group, fill = unemployment)) +
  geom_polygon() +
  coord_map() +
  theme_map()

Cartographic Maps

– Different templates

– Mapping points onto a cartographic map

– Using a bounding box

– Combine cartographic and choropleth maps

Animations

– The population pyramid

– Animations with gganimate

   

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ggplot2 internals

---

Grid Graphics

– Viewport basics (1)

– Viewport basics (2)

– Build a plot from scratch (1)

– Build a plot from scratch (2)

– Modifying a plot with grid.edit

Grid Graphics in ggplot2

– Exploring the gTable

– Modifying the gTable

ggplot Objects

– Exploring ggplot objects

– ggplot_build and ggplot_gtable

– Extracting details

gridExtra

– Arranging plots (1)

– Arranging plots (2)

   

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Data Munging and Visualization Case Study

---

Case Study I - Bag Plot

– Base package bag plot

– Multilayer ggplot2 bag plot

– Creating ggproto functions

– Creating stat_bag()

– Use stat_bag()

Case Study II - Weather (Part 1)

– Step 1: Read in data and examine

– Step 2: Summarize history

– Step 3: Plot history

– Step 4: Plot present

– Step 5: Find new record highs

– Step 6: Efficiently calculate record highs and lows

– Step 7: Custom legend

Case Study II - Weather (Part 2)

– Step 1: clean_weather()

– Step 2: Historical data

– Step 3: Present data

– Step 4: Extremes

– Step 5: Re-use plotting style

Wrap-up