Introduction

• ggplot2 is a data visualization package for the statistical programming language R. Created by Hadley Wickham in 2005
• ggplot2 stands for Grammar of Graphics

Agenda

• ggplot2 presenation : Install and load the package
• Basic ggplot2 graphs (Density, Bar, …)
• Case study

Installation

• To install R packages in RStudio: GUI versus R Console :

1. Using the GUI: Go to the Packages tab and click Install
2. Using the R Console: install.packages(“package_name”)

• Try this R Code: install.packages(“ggplot2”)

Loading an R Package For Use

• Once you’ve installed an R package, it’s then bundled with R and RStudio.
• However, to access these files you must load your R package.
• Try this R Code: library(ggplot2)

General structure

ggplot(data = <DATA>),  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

ggplot2 structure exemple (1)

library(ggplot2)
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

ggplot2 structure exemple (2)

library(ggplot2)
library(gapminder)
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp))  

ggplot2 structure exemple (3)

library(ggplot2)
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp))  + geom_point()

ggplot2 structure exemple (4)

ggplot(gapminder, aes(x=gdpPercap, y=lifeExp)) + geom_point() + scale_x_log10()

ggplot2 full syntax

ggplot(data = data_frame) + aes(x = variable_1, y = variable_2, lty = , color = ) 
+ geom_line() + labs(title = "Title of Graph", x = "new x label", y = "new y label") + 
theme(panel.background = element_rect(fill = "background_color")) + xlim() +
ylim()

List of datasets

Gapminder: For each of 142 countries, the package provides values for life expectancy, GDP per capita, and population, every five years, from 1952 to 2007.

airquality: Daily air quality measurements in New York, May to September 1973.

mtcars: Motor Trend Car Road Tests.

mpg: Mileage per gallon performances of various cars.

Loading the data set to process

install.packages(“gapminder”)

library(gapminder)
library(datasets)

Viewing the Data as a Spreadsheet

library(gapminder)
View(gapminder)

Viewing the Data as a Spreadsheet

View(airquality)

Descritption of datasets (1)

str(gapminder)

## Classes 'tbl_df', 'tbl' and 'data.frame':    1704 obs. of  6 variables:
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num  779 821 853 836 740 ...

library(tidyverse)
glimpse(gapminder)

## Observations: 1,704
## Variables: 6
## $ country   <fct> Afghanistan, Afghanistan, Afghanistan, Afghanistan, ...
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia...
## $ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992...
## $ lifeExp   <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.8...
## $ pop       <int> 8425333, 9240934, 10267083, 11537966, 13079460, 1488...
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 78...

Descritption of datasets (2)

length(unique(gapminder$country))

## [1] 142

unique(gapminder$country)

##   [1] Afghanistan              Albania                 
##   [3] Algeria                  Angola                  
##   [5] Argentina                Australia               
##   [7] Austria                  Bahrain                 
##   [9] Bangladesh               Belgium                 
##  [11] Benin                    Bolivia                 
##  [13] Bosnia and Herzegovina   Botswana                
##  [15] Brazil                   Bulgaria                
##  [17] Burkina Faso             Burundi                 
##  [19] Cambodia                 Cameroon                
##  [21] Canada                   Central African Republic
##  [23] Chad                     Chile                   
##  [25] China                    Colombia                
##  [27] Comoros                  Congo, Dem. Rep.        
##  [29] Congo, Rep.              Costa Rica              
##  [31] Cote d'Ivoire            Croatia                 
##  [33] Cuba                     Czech Republic          
##  [35] Denmark                  Djibouti                
##  [37] Dominican Republic       Ecuador                 
##  [39] Egypt                    El Salvador             
##  [41] Equatorial Guinea        Eritrea                 
##  [43] Ethiopia                 Finland                 
##  [45] France                   Gabon                   
##  [47] Gambia                   Germany                 
##  [49] Ghana                    Greece                  
##  [51] Guatemala                Guinea                  
##  [53] Guinea-Bissau            Haiti                   
##  [55] Honduras                 Hong Kong, China        
##  [57] Hungary                  Iceland                 
##  [59] India                    Indonesia               
##  [61] Iran                     Iraq                    
##  [63] Ireland                  Israel                  
##  [65] Italy                    Jamaica                 
##  [67] Japan                    Jordan                  
##  [69] Kenya                    Korea, Dem. Rep.        
##  [71] Korea, Rep.              Kuwait                  
##  [73] Lebanon                  Lesotho                 
##  [75] Liberia                  Libya                   
##  [77] Madagascar               Malawi                  
##  [79] Malaysia                 Mali                    
##  [81] Mauritania               Mauritius               
##  [83] Mexico                   Mongolia                
##  [85] Montenegro               Morocco                 
##  [87] Mozambique               Myanmar                 
##  [89] Namibia                  Nepal                   
##  [91] Netherlands              New Zealand             
##  [93] Nicaragua                Niger                   
##  [95] Nigeria                  Norway                  
##  [97] Oman                     Pakistan                
##  [99] Panama                   Paraguay                
## [101] Peru                     Philippines             
## [103] Poland                   Portugal                
## [105] Puerto Rico              Reunion                 
## [107] Romania                  Rwanda                  
## [109] Sao Tome and Principe    Saudi Arabia            
## [111] Senegal                  Serbia                  
## [113] Sierra Leone             Singapore               
## [115] Slovak Republic          Slovenia                
## [117] Somalia                  South Africa            
## [119] Spain                    Sri Lanka               
## [121] Sudan                    Swaziland               
## [123] Sweden                   Switzerland             
## [125] Syria                    Taiwan                  
## [127] Tanzania                 Thailand                
## [129] Togo                     Trinidad and Tobago     
## [131] Tunisia                  Turkey                  
## [133] Uganda                   United Kingdom          
## [135] United States            Uruguay                 
## [137] Venezuela                Vietnam                 
## [139] West Bank and Gaza       Yemen, Rep.             
## [141] Zambia                   Zimbabwe                
## 142 Levels: Afghanistan Albania Algeria Angola Argentina ... Zimbabwe

Part 1: Density Plots (geom_density())

Simple

dsp <- ggplot(airquality, aes(x = Ozone)) +
  geom_density()
dsp

Part 1: Density Plots (geom_density())

Simple

# Customising axis labels
dsp <- dsp + scale_x_continuous(name = "Mean ozone in parts per billion") +
  scale_y_continuous(name = "Density")
dsp

Part 1: Density Plots (geom_density())

Simple

# ggplot also allows for the use of multiline names 
dsp <- dsp + scale_x_continuous(name = "Mean ozone in\nparts per billion")
dsp

Part 1: Density Plots (geom_density())

Complexe

fill <- "forestgreen"
line <- "tomato1"

dsp <- ggplot(airquality, aes(x = Ozone)) +
  geom_density(fill = fill, colour = line) +
  scale_x_continuous(name = "Mean ozone in\nparts per billion",
                     breaks = seq(0, 200, 25),
                     limits=c(0, 200)) +
  scale_y_continuous(name = "Density") +
  ggtitle("Density plot of mean ozone")
dsp

Part 1: Density Plots (geom_density())

Complexe

ggplot(data=gapminder, aes(x=lifeExp, fill=continent)) +
  geom_density(alpha=0.3)

Part 3: Bar charts (geom_bar()/ geom_col())

ggplot(gapminder, aes(x=continent)) + geom_bar()

Part 3: Bar charts (geom_bar()/ geom_col())

ggplot(gapminder, aes(x=continent, fill=continent)) + geom_bar()

Part 3: Bar charts (geom_bar()/ geom_col())

by_continent <- gapminder %>% filter(year == 2007) %>% group_by(continent) %>% 
summarize(medianGdpPercap = median(gdpPercap))

ggplot(by_continent , aes(x = continent, y = medianGdpPercap)) + geom_col()

Part 3: Bar charts (geom_histogram())

ggplot(airquality) + geom_histogram(mapping=aes(Ozone))

Part 3: Bar charts (geom_histogram())

gapminder_2007 <- gapminder %>% filter(year == 2007)
ggplot(gapminder_2007 , aes(x = pop)) + geom_histogram() + scale_x_log10()

Part 3: Bar charts (geom_histogram())

ggplot(gapminder_2007) + geom_histogram(mapping=aes(gdpPercap))

Part 4: Scatterplots (geom_point())

library(ggplot2)
library(gapminder)
library(tidyverse)
gapminder_2007 <- gapminder %>% filter(year == 2007)
ggplot(gapminder_2007 , aes(x = gdpPercap, y = lifeExp)) + geom_point()

Part 4: Scatterplots (geom_point())

ggplot(gapminder_2007 , aes(x = gdpPercap, y = lifeExp)) + geom_point() + scale_x_log10() +
scale_y_log10()

Part 5: Line Type

life_expec_sex_race <- read_csv("E:/rworkshop/data/life_expec_sex_race.csv")
ggplot(life_expec_sex_race) + aes(x = Year, y = Avg_Life_Expec, color = Sex, lty = Race) +
geom_line() + scale_linetype_manual(values = c("longdash", "dotdash", "twodash"))

Part 5: Line Type

Scale Limits

life_expec_sex <- life_expec_sex_race[,-2]
ggplot(life_expec_sex) + aes(x = Year, y = Avg_Life_Expec, color = Sex) +
geom_line() + xlim(1915, 1920) + ylim(35, 60)

Part 5: Line Type

Coloring

ggplot(gapminder_2007 , aes(x = gdpPercap, y = lifeExp, color = continent, size = pop)) + 
geom_point() + scale_x_log10() + scale_y_log10()

Part 5: Line Type

Coloring

ggplot(life_expec_sex_race) + aes(x = Year, y = Avg_Life_Expec, color = Sex, lty = Race) +
geom_line() + scale_color_manual(values = c("magenta", "orange", "blue")) +
scale_linetype_manual(values = c("longdash", "dotdash", "dotted"))

Part 5: Line Type

Coloring

ggplot(life_expec_sex_race) + aes(x = Year, y = Avg_Life_Expec, lty = Race) + 
geom_line() + scale_linetype_manual(values = c(1,3, 2)) + facet_wrap(~Sex, ncol = 3) + 
theme(panel.background = element_rect(fill = "white"))

Part 5: Line Type

data <- gapminder %>% group_by(year) %>% summarize(medianGdpPercap = median(gdpPercap))
ggplot(data , aes(x = year, y = medianGdpPercap)) + geom_line() + expand_limits(y = 0)

Part 5: Faceting

One colum

ggplot(gapminder , aes(x =gdpPercap, y = lifeExp, color = continent )) + 
geom_point() + scale_x_log10() + scale_y_log10() + facet_wrap(~ continent, ncol = 1)

Part 5: Faceting

Five colum

ggplot(gapminder , aes(x =gdpPercap, y = lifeExp, color = continent )) + 
geom_point() + scale_x_log10() + scale_y_log10() + facet_wrap(~ continent, ncol = 5)

Part 5: Faceting

Change axis title of graph

ggplot(life_expec_sex_race) + aes(x = Year, y = Avg_Life_Expec, lty = Race) + 
geom_line() + scale_linetype_manual(values = c(1,2,3)) + facet_wrap(~Sex, ncol = 2) + 
labs(title = "AbidjanRUsers", x = "Années",
y = "Average Life Expectancy (Years)") + 
theme(panel.background = element_rect(fill = "white"))

Part 5: Faceting

Change titel position, ncol=1(library(grid)

p<-ggplot(life_expec_sex_race) + aes(x = Year, y = Avg_Life_Expec, lty = Race) + 
geom_line() + scale_linetype_manual(values = c(1,3, 10)) + facet_wrap(~Sex, ncol = 3) + 
labs(title = " ", x = "Années",
y = "Average Life Expectancy (Years)") + 
theme(panel.background = element_rect(fill = "white"))
p

Part 5: Faceting

Change titel position, ncol=1(library(grid)

library(grid)
library(ggplot2)
p+ theme(plot.margin=unit(c(0.5, 1, 2, 0.5), "lines"))
grid.text("United States Life Expectancy: 100 Years of Change", x = unit(0.5, "npc"), y = unit(0, "npc"),
          vjust = -0.5, gp = gpar(cex=1.2))

p

Part 6: Boxblot (geom_boxplot())

ggplot(gapminder_2007, aes(x = continent, y = gdpPercap)) + geom_boxplot() + scale_y_log10()+ ggtitle("Comparing GDP per capita across continents")

Part 6: Boxblot (geom_boxplot())

ggplot(gapminder_2007, aes(x = continent, y = gdpPercap)) + geom_boxplot() + scale_y_log10()

Color

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

Color

ggplot(mtcars, aes(wt, mpg)) +
  geom_point(shape = 21, color = "cadetblue4", fill = "lightgoldenrod1", size = 5, stroke = 3)

Shape

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))

Regression Line

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class)) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))

Regression Line

ggplot(data = gapminder_2007) + 
  geom_point(mapping = aes(x = gdpPercap, y = lifeExp, color = continent  )) + 
  geom_smooth(mapping = aes(x = gdpPercap, y = lifeExp))

Regression Line

ggplot(gapminder_2007, aes(gdpPercap, lifeExp)) +
  geom_point() +
  geom_smooth(method = lm, formula = y ~ splines::bs(x, 3), se = FALSE)

Regression Line

ggplot(gapminder_2007, aes(gdpPercap, lifeExp)) +
  geom_point() +
  geom_smooth(method = "auto", se = FALSE)

Required Packages

library(ggrepel) #Add point labels
library(ggplot2) #Main package for graph
library(ggthemes)#Themes for formating

library(extrafont)#Adding more font format (this package is optional because it will took sometimes for install all the font)

## Registering fonts with R
library(grid) #Add grid line
library(cowplot) #Add annotation

The Economist dataset

library(readr)
EconomistData <- read_csv("E:/rworkshop/data/EconomistData.csv")
View(EconomistData)

Cleaning up

EconomistData$Region <- factor(EconomistData$Region,
                     levels = c("EU W. Europe",
                                "Americas",
                                "Asia Pacific",
                                "East EU Cemt Asia",
                                "MENA",
                                "SSA"),
                     labels = c("OECD",
                                "Americas",
                                "Asia &\nOceania",
                                "Central &\nEastern Europe",
                                "Middle East &\nnorth Africa",
                                "Sub-Saharan\nAfrica"))

Basic graph

graph1 <-  ggplot(EconomistData, aes (x=CPI, y=HDI))
graph1 + geom_point(aes(color = Region))

Basic graph

graph1 + geom_point(aes(color = Region),
                    shape=21, 
                    fill= "White")

Basic graph

g2 <- graph1 + geom_point(aes(color = Region),
                          shape=21, 
                          fill= "White",
                          size =3, 
                          stroke=1.5)
                        
ggdraw(g2)

Fit line

geom_line

g2 + geom_line(method = "lm", 
                 formula = y ~log(x))

Fit line

geom_smooth

g2 + geom_smooth(method = "lm", 
                 formula = y ~log(x))

Fit line

geom_smooth

g2 + geom_smooth(method = "lm",
                 formula = y ~log(x), 
                 se=FALSE)

Fit line

Change the color of the line

g3 <- g2 + geom_smooth(aes(fill="red"),method = "lm", 
                       formula = y ~log(x),
                       se=FALSE, 
                       linetype=1 , 
                       color= "Red") 

ggdraw(g3)

Labeling point

All Countries

g3 + geom_text(aes(label = Country))

Labeling point

identify some countries

point_1 <- c( "Venezuela", "Iraq", "Myanmar", "Sudan",
                    "Afghanistan", "Congo", "Greece", "Argentina", 
                    "India", "Italy",
                    "Botswana", "Cape Verde", "Bhutan", "Rwanda", "France",
                    "United States",  "Britain", "Barbados", "Norway", 
                    "New Zealand", "Singapore", "Côte d'Ivoire", "Ghana", "Burkina Fasa")
point_2 <- c("Russia","Brazil","Spain","Germany", "Japan","China","South Africa")
point_3 <-  c( "Venezuela", "Iraq", "Myanmar", "Sudan",
               "Afghanistan", "Congo", "Greece", "Argentina", 
               "India", "Italy",
               "Botswana", "Cape Verde", "Bhutan", "Rwanda", "France",
               "United States",  "Britain", "Barbados", "Norway", 
               "New Zealand", "Singapore","Russia","Brazil","Spain","Germany", "Japan","China","South Africa")

Labeling point

identify some countries

g3 +geom_text(data=EconomistData[EconomistData$Country %in% point_1,], aes(label=Country))

Labeling point

Avoiding overlapping the data point

g4 <- g3 + geom_text_repel(data=EconomistData[EconomistData$Country %in% point_1,],
                     aes(label=Country))

Labeling point

Avoiding overlapping the data point

g4 + geom_text_repel(data=EconomistData[EconomistData$Country %in% point_2,],
                     aes(label=Country))

Labeling point

Avoiding overlapping the data point

g5 <- g4 + geom_text_repel(data=EconomistData[EconomistData$Country %in% point_2,],
                     aes(label=Country),
                     box.padding = unit(1.75, 'lines'))
ggdraw(g5)

Legend box

Color

g6 <- g5 +  scale_color_manual( values = c("#23576E", "#099FDB", 
                                            "#29B00E", "#208F84", 
                                            "#F55840", "#924F3E")) +
      scale_fill_manual(name='My Lines', values=c("red"),labels=c("R^2=52%"))

Legend box

Position

g6 + theme(legend.position="top")

Legend box

Position

g7 <- g6 + theme(legend.position="top",
                 legend.title = element_blank(),
                 legend.box = "horizontal" ,
                 legend.text=element_text(size=8.5)) +
  guides(col = guide_legend(nrow = 1))
ggdraw(g7)

Grid line

Position

g8 <- g7 + theme(panel.grid.minor = element_blank(), 
        panel.grid.major = element_line(color = "red", size = 0.5),
        panel.grid.major.x = element_blank(),
        panel.background = element_blank(),
        line = element_blank())
ggdraw(g8)

X-axis and Y axis

Normal

g9 <- g8 + scale_x_continuous(expand = c(0, 0),
                        limits=c(-.2,10.2),
                        breaks=seq(0,10,1), 
                        name = "Corruption Perception Index (10=Least corrupt)") +
  scale_y_continuous(expand = c(0, 0),
                     limits=c(0.2,1),
                     breaks=seq(0.2,1,0.1), 
                     name = "Human Development Index,2011 (1=best)")
ggdraw(g9)

X-axis and Y axis

Font

g10 <-g9 +theme(axis.ticks.length = unit(.15, "cm"),
        axis.ticks.y = element_blank(),
        axis.title.x = element_text(color="black", 
                                    size=10,
                                    face="italic"),
        axis.title.y = element_text(color="black",
                                    size=10,
                                    face="italic"))
ggdraw(g10)

Title and Footnote

g11 <- g10+ ggtitle("Corruption and human development\n") + 
  theme(plot.title = element_text(hjust = -0.15, 
                                  vjust=2.12, 
                                  colour="black",
                                  size = 14,
                                  face="bold"))
ggdraw(g11)

Title and Footnote

g12 <-  add_sub(g11,"Source:Transparency International; UN Human Development report",
                x = -0.07,
                hjust = 0,
                fontface = "plain",
                size= 10) 
ggdraw(g12)

At this point you should have

• Created density plots and histograms
• Generated bar plots and box plots
• Customise ggplot2 graph
• Created scatter plots with smoothing lines and labels
• Mapped social science data

Scripts

https://honingds.com/blog/data-visualization-using-ggplot2/#ftoc-line-plots

https://ggplot.library.duke.edu/#aesthetics__tweaking

http://rstudio-pubs-static.s3.amazonaws.com/284329_c7e660636fec4a42a09eed968dc47f32.html

http://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html

Data source 1

gapminder

https://cran.r-project.org/web/packages/gapminder/README.html

NCHS - Death rates and life expectancy at birt

https://catalog.data.gov/dataset/age-adjusted-death-rates-and-life-expectancy-at-birth-all-races-both-sexes-united-sta-1900

landdata-states.csv

https://github.com/IQSS/dss-workshops/blob/master/R/Rgraphics/dataSets/landdata-states.csv

Data source 2

The Economist Dataset

http://tutorials.iq.harvard.edu/R/Rgraphics.zip

Economic Data freely available online

https://www.economicsnetwork.ac.uk/data_sets

Data source 3

Motor Trend Car Road Tests

https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/mtcars.html

Exploration of MPG Dataset

https://rpubs.com/shailesh/mpg-exploration

https://www.rdocumentation.org/packages/ggplot2/versions/3.2.1/topics/mpg