Introduction

Whats Covered

Part A:

  • 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

Part B:

  • Plots for specific data types (Part 2)
    • choropleths
    • cartographic maps
    • animations

Part C:

  • ggplot2 internals
    • grid graphics, grid grapshics in ggplot2
    • ggplot objects
    • gridExtra
  • Data Munging and Visualization Case Study
    • Bag plot case study, weather case study

Libraries and Data

source("create_datasets.R")
load('data/test_datasets.RData')

library(readr)
library(dplyr)
library(ggplot2)
library(purrr)

library(ggplot2movies)
library(viridis)
library(GGally)
library(ggtern)
library(ggthemes)
library(geomnet)
library(ggmap)
library(ggfortify)

   

Statistical plots

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

  • Notice how the IQR becomes more consistent across methods as the sample size increases
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)

Density Plots

– geom_density()

# test_datasets.RData has been loaded

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

There are three parameters that you may be tempted to adjust in a density plot:

  • bw - the smoothing bandwidth to be used, see ?density for details
  • adjust - adjustment of the bandwidth, see density for details
  • kernel - kernel used for density estimation, defined as
    • “g” = gaussian
    • “r” = rectangular
    • “t” = triangular
    • “e” = epanechnikov
    • “b” = biweight
    • “c” = cosine
    • “o” = optcosine
# 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

  • One way to represent the sample size, n, is to use variable witdths for the boxes.
# 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

  • When plotting a single variable, the density plots (and their bandwidths) are calculated separate for each variable (see the plot from the previous exercise, provided).
  • However, when you compare several variables (such as eating habits) it’s useful to see the density of each subset in relation to the whole data set.
# 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
## I remove the ylim because the y scale changes here
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))

# 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()

   

Plots for specific data types (Part 1)

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')
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 ...
head(africa_sample)
##       Sand Silt Clay
## 34893   42   15   43
## 85261   28   29   43
## 64201   83    6   11
## 52595   40   14   46
## 44793   35   10   55
## 79175   24   22   54
# Add an ID column from the row.names
africa_sample$ID <- row.names(africa_sample)

# 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

# The ggtern library is loaded

# 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

# 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)

  • Just playing around and trying to make my own pretty plot
    • I want something like the faithful density but on the tern
    • This is a start
## I want to see all the points on there
ggtern(africa, aes(x = Sand, y = Silt, z = Clay)) +
  geom_point(alpha = 0.1, color = "navyblue", size = .5) + 
  stat_density_tern(
    geom = 'polygon', 
    aes(fill = ..level.., alpha = ..level..),
    bins = 100
    ) +
  guides(alpha = FALSE) + 
  scale_fill_viridis()

Network Plots

– Build the network (1)

# Load geomnet & examine structure of madmen
# The geomnet library is loaded
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 %>% purrr::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
# ggmap is already loaded

# 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 is from the ggfortify library
autoplot(Canada)

– Distance matrices and Multi-Dimensional Scaling (MDS)

  • The cmdscale() function from the stats package performs Classical Multi-Dimensional Scaling and returns point coodinates as a matrix.
  • Although autoplot() will work on this object, it will produce a heatmap, and not a scatter plot.
  • However, if either eig = TRUE, add = TRUE or x.ret = TRUE is specified, cmdscale() will return a list instead of matrix.
  • In these cases, the list method for autoplot() in the ggfortify package can deal with the output.
  • Specifics on multi-dimensional scaling is beyond the scope of this course, however details on the method and these arguments can be found in the help pages ?cmdscale.
# 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

  • You must explicitly pass the original data to the autoplot function via the data argument, since kmeans objects don’t contain the original data.
  • This kmeans cluster is wrong. Its very differnt from what I saw in the datacamp exercise. Not sure why.
# 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')