Data Visualization
✍️ The Grammar of Graphics
 
Dr Thiyanga Talagala
1

The Grammar of Graphics

description of the image

Acknowledgement: Justin Matejke and George Fitzmaurice, Autodesk Research, Canada

Grammar of Graphics

knitrhex

Packages

library(tidyverse) # To obtain ggplot2
library(magrittr)

knitrhex rmarkdown

Dataset

library(gapminder)
glimpse(gapminder)

Rows: 1,704
Columns: 6
$ country   <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
$ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, …
$ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
$ lifeExp   <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…
$ pop       <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
$ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …

Plotting with R8

Base R

using plot() function

Using ggplot2: grammar of graphics

ggplot2 package: qplot() function
- qplot: quick plot
- very similar to how you graph with plot() function
ggplot2 package: ggplot() function
- fully utilize the power of grammar

Grammar

English

Nouns
Article
Adjective
Verb
Adverb
Proposition

Graphics

knitrhex

English

The little monkey hangs confidently by a branch.

Article: The
Adjective: little
Noun: monkey
Verb: hangs
Adverb: Confidently
Proposition: by
Noun: a branch

Graphics

ggplot(iris)+
  aes(x = Sepal.Length,
      y = Sepal.Width)+
  geom_point()

Elements of ggplot2 object

Data
Aesthetics: x, y, col
Geometrics: geom_point, geom_boxplot

Elements of ggplot2 object

knitrhex

Data: data
Aesthetics: aes
Geometrics: geom_*

Making your first plot with ggplot15

Data: data to be plotted

knitrhex

'data.frame':    150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

Data

ggplot(iris)

Aesthetics: mapping variables

knitrhex

x
y
colour
shape

Data + Aesthetics

ggplot(iris,
aes(x =S epal.Length, 
     y = Sepal.Width))

Geometrics

knitrhex

geom_point
geom_boxplot

Data + Aesthetics + Geometrics

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width))+
     geom_point()

Data + Aesthetics + Geometrics

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width))+
  geom_point()

knitrhex

Data + Aesthetics + Geometrics

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width))+
     geom_point(col = "forestgreen")

Data + Aesthetics + Geometrics

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width))+
    geom_point(col = "forestgreen",
     shape = 8)

Data + Aesthetics + Geometrics

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width,
     col = Species))+
    geom_point()

Data + Aesthetics + Geometrics

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width,
    col= Species))+
    geom_point(
     shape = 3
    )

Data + Aesthetics + Geometrics

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width,
    col = Species))+
    geom_point()

knitrhex

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width,
    col = Species))+
    geom_point()+
     facet_grid(~Species)

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width,
    col = Species))+
    geom_point()+
     facet_grid(Species ~.)

Statistics

knitrhex

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width,
    col = Species))+
    geom_point()+
    facet_wrap(~Species)+
     stat_smooth(method = "lm", se = F, col ="red")

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width,
    col = Species))+
    geom_point()+
    facet_wrap( ~ Species)+
     stat_smooth(method = "lm", se = T, col = "red")

Coordinate

knitrhex

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width,
    col = Species)) +
    geom_point() +
    facet_wrap( ~ Species) +
    stat_smooth(method = "lm", se = T, col = "red") + 
     coord_equal()

Theme

knitrhex

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width,
    col = Species)) +
    geom_point() +
    facet_wrap( ~ Species) +
    stat_smooth(method = "lm", se = T, col ="red") + 
    coord_equal() + 
     theme(legend.position = "bottom")

Scale

knitrhex

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width,
    col = Species)) +
    geom_point() +
    facet_wrap( ~ Species) +
    stat_smooth(method = "lm", se = T, col = "red") + 
    coord_equal() + 
    theme(legend.position = "bottom") +
     scale_color_manual(values = c("#1b9e77", "#d95f02", "#7570b3"))

titles and axes labels

ggplot(iris,
aes(x = Sepal.Length, 
    y = Sepal.Width,
    col = Species)) +
    geom_point() +
    facet_wrap( ~ Species) +
    stat_smooth(method = "lm", se = T, col = "red") + 
    coord_equal() + 
    theme(legend.position = "bottom") +
    scale_color_manual(values = c("#1b9e77", "#d95f02", "#7570b3"))+
labs(title="Scatter plot of Sepal Length  vs Sepal Width",
        x ="Sepal Length (cm)", y = "Sepal Width (cm)")

Your turn

Dataset: gapminder

Visualize the relationship between life expectancy, GDP per capita and continent in 2007.

gapminder2007 <- gapminder %>%
  filter(year == 2007)
ggplot(gapminder2007,
aes(x = lifeExp, y = gdpPercap, col=continent)) +
    geom_point() + theme(legend.position = "bottom") + 
labs(title = "Relationship between life expectancy and GPD per capita by continent - 2007",
        x ="life expectancy at birth, in years",
        y = "GDP per capita (US$, inflation-adjusted)")

Add a vertical line

gapminder2007 <- gapminder %>%
  filter(year == 2007)
ggplot(gapminder2007,
aes(x = lifeExp, y = gdpPercap, col=continent)) +
    geom_point() + 
     geom_vline(xintercept = 70)

Add a horizontal line

gapminder2007 <- gapminder %>%
  filter(year == 2007)
ggplot(gapminder2007,
aes(x = lifeExp, y = gdpPercap, col=continent)) +
    geom_point() + 
     geom_hline(yintercept = 20000)

Add a diagonal line

gapminder2007 <- gapminder %>%
  filter(year == 2007)
ggplot(gapminder2007,
aes(x = lifeExp, y = gdpPercap, col=continent)) +
    geom_point() + 
     geom_abline(intercept = 20, slope=200)

All Geoms

 [1] "geom_abline"            "geom_area"              "geom_bar"              
 [4] "geom_bin_2d"            "geom_bin2d"             "geom_blank"            
 [7] "geom_boxplot"           "geom_col"               "geom_contour"          
[10] "geom_contour_filled"    "geom_count"             "geom_crossbar"         
[13] "geom_curve"             "geom_density"           "geom_density_2d"       
[16] "geom_density_2d_filled" "geom_density2d"         "geom_density2d_filled" 
[19] "geom_dotplot"           "geom_errorbar"          "geom_errorbarh"        
[22] "geom_freqpoly"          "geom_function"          "geom_hex"              
[25] "geom_histogram"         "geom_hline"             "geom_jitter"           
[28] "geom_label"             "geom_line"              "geom_linerange"        
[31] "geom_map"               "geom_path"              "geom_point"            
[34] "geom_pointrange"        "geom_polygon"           "geom_qq"               
[37] "geom_qq_line"           "geom_quantile"          "geom_raster"           
[40] "geom_rect"              "geom_ribbon"            "geom_rug"              
[43] "geom_segment"           "geom_sf"                "geom_sf_label"         
[46] "geom_sf_text"           "geom_smooth"            "geom_spoke"            
[49] "geom_step"              "geom_text"              "geom_tile"             
[52] "geom_violin"            "geom_vline"

geom_boxplot

ggplot(gapminder2007, aes(x=lifeExp, y=continent)) +
geom_boxplot()

geom_boxplot

ggplot(gapminder2007, aes(x=lifeExp, y=continent, color=continent)) +
geom_boxplot()

geom_boxplot

ggplot(gapminder2007, aes(x=lifeExp, y=continent, fill=continent)) +
geom_boxplot()

geom_boxplot

ggplot(gapminder2007, aes(x=lifeExp, y=continent)) +
geom_boxplot(fill="forestgreen")

geom_boxplot

ggplot(gapminder2007, aes(x=lifeExp, y=continent)) +
geom_boxplot(fill="forestgreen", alpha=0.5)

geom_point

ggplot(gapminder2007, aes(x=lifeExp, y=continent)) +
  geom_point()

geom_jitter

ggplot(gapminder2007, aes(x=lifeExp, y=continent)) +
  geom_jitter()

geom_jitter + geom_boxplot

ggplot(gapminder2007, aes(x=lifeExp, y=continent)) +
  geom_jitter() +
  geom_boxplot()

geom_jitter + geom_boxplot

ggplot(gapminder2007, aes(x=lifeExp, y=continent)) +
  geom_jitter() +
  geom_boxplot(alpha=0.5)

geom_jitter + geom_boxplot

ggplot(gapminder2007, aes(x=lifeExp, y=continent)) +
  geom_boxplot() + 
  geom_jitter()

geom_jitter + geom_boxplot

ggplot(gapminder2007, aes(x=lifeExp, y=continent, fill=continent)) +
  geom_boxplot() + 
  geom_jitter()

geom_jitter + geom_boxplot

ggplot(gapminder2007, aes(x=lifeExp, y=continent, fill=continent)) +
  geom_boxplot() + 
  geom_jitter(aes(col=continent))

geom_jitter + geom_boxplot (outlier.shape = NA)

ggplot(gapminder2007, aes(x = lifeExp, y = continent, fill = continent)) +
   geom_boxplot(outlier.shape = NA) + 
  geom_jitter(aes(col = continent))

geom_jitter + geom_boxplot

ggplot(gapminder2007, aes(x=lifeExp, y=continent, fill=continent, col=continent))+
  geom_boxplot(outlier.shape = NA) + 
  geom_jitter(aes(col=continent))

geom_jitter + geom_boxplot

ggplot(gapminder2007, 
aes(x=lifeExp, y=continent, fill=continent, col=continent))+
   geom_boxplot(outlier.shape = NA, alpha=0.2) + 
  geom_jitter(aes(col=continent))

geom_jitter + geom_boxplot + coord_flip

ggplot(gapminder2007, 
aes(x=lifeExp, y=continent, fill=continent, col=continent))+
  geom_boxplot(outlier.shape = NA, alpha=0.2) + 
  geom_jitter(aes(col=continent)) +
   coord_flip()

geom_boxplot

ggplot(gapminder2007, aes(y=lifeExp))+
  geom_boxplot()

ggplot(gapminder2007, 
aes(y = lifeExp))+
  geom_boxplot() + facet_wrap(~continent, ncol = 5)

geom_density

ggplot(gapminder2007, 
aes(x=lifeExp))+
   geom_density() + 
  facet_wrap(~continent, ncol=5)

Your turn

Modify the code below to obtain the following plot.

ggplot(gapminder2007, 
aes(x=lifeExp))+
 geom_density()

geom_histogram

ggplot(gapminder2007, 
aes(x=lifeExp))+
   geom_histogram()

geom_bar

ggplot(gapminder2007, 
aes(x=continent))+ 
 geom_bar()

Your turn

Modify the code below to obtain the following plot.

ggplot(gapminder2007, 
aes(x=continent))+ 
 geom_bar()

geom_bar (stat="identity")

Method 1

cut.percent <- data.frame(cut=c("Fair", "Good", "Very Good", "Premium", 
                                "Ideal"), percent=c(3, 9, 22.4, 25.6, 40))
cut.percent

        cut percent
1      Fair     3.0
2      Good     9.0
3 Very Good    22.4
4   Premium    25.6
5     Ideal    40.0

ggplot(data=cut.percent, aes(x=cut, y=percent)) +
  geom_bar(stat="identity")

geom_col

Method 2

cut.percent <- data.frame(cut=c("Fair", "Good", "Very Good", "Premium", 
                                "Ideal"), percent=c(3, 9, 22.4, 25.6, 40))
cut.percent

        cut percent
1      Fair     3.0
2      Good     9.0
3 Very Good    22.4
4   Premium    25.6
5     Ideal    40.0

ggplot(data=cut.percent, aes(x=cut, y=percent)) +
  geom_col()

Change the order of levels

Method 2

cut.percent <- data.frame(cut=c("Fair", "Good", "Very Good", "Premium", 
                                "Ideal"), percent=c(3, 9, 22.4, 25.6, 40))
cut.percent$cut <- factor(cut.percent$cut, 
                  levels = c("Fair", "Good", "Very Good", 
                             "Premium", "Ideal"))

ggplot(data=cut.percent, aes(x=cut, y=percent)) +
  geom_col()

geom_point

gapminder %>%
filter(country == "India") %>%
ggplot(aes(x = year, y = gdpPercap)) +
geom_point()

geom_line

gapminder %>%
filter(country == "India") %>%
ggplot(aes(x = year, y = gdpPercap)) +
geom_line()

geom_line + geom_point

gapminder %>%
filter(country == "India") %>%
ggplot(aes(x = year, y = gdpPercap)) +
geom_line() + 
geom_point()

Your turn

Modify the code below to obtain the following plot.

gapminder %>% filter(country == "India") %>%
ggplot(aes(x = year, y = gdpPercap)) + geom_line() + geom_point()

Data Wrangling + Data Visualization

avglifeExp <- gapminder %>%
  group_by(continent, year) %>%
  summarise(meanlifeExp=mean(lifeExp))
avglifeExp

# A tibble: 60 × 3
# Groups:   continent [5]
   continent  year meanlifeExp
   <fct>     <int>       <dbl>
 1 Africa     1952        39.1
 2 Africa     1957        41.3
 3 Africa     1962        43.3
 4 Africa     1967        45.3
 5 Africa     1972        47.5
 6 Africa     1977        49.6
 7 Africa     1982        51.6
 8 Africa     1987        53.3
 9 Africa     1992        53.6
10 Africa     1997        53.6
# … with 50 more rows

Your turn

Write an R code to reproduce the plot below.

Hint: use avglifeExp

Your turn

Write an R code to reproduce the plot below.

Your turn

Write an R code to reproduce the plot below.

Hint: Next slide

gapminder %>%
  ggplot(aes(y=log(lifeExp), x=log(gdpPercap), color=continent)) + 
  geom_point() + 
  labs(y = "log(Life Expectancy)",
       x = "log(GDP per capita)")

Your turn

Write an R code to reproduce the plot below.

geom_point

ggplot(gapminder, aes(x=year, y=gdpPercap, colour=continent))+geom_point()

geom_smooth

ggplot(gapminder, aes(x=year, y=gdpPercap, colour=continent))+
geom_smooth()

Your turn

Write an R code to reproduce the plot below.

Your turn

Write an R code to reproduce the plot below.

Your turn

Write an R code to visualize the shape of standard normal distribution.

Hint: dnorm

Recapaes
x
y
colour
size

geom arguments
colour
fill
size
alpha
shape

91

Recapgeom
geom_point
geom_jitter
geom_line
geom_bar
geom_col
geom_histogram
geom_smooth
geom_density
geom_abline
geom_vline
geom_hline

other elements
labs
coord_equal
coord_flip
scale_colour_manual
labs
facet_wrap
theme

92

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Data Visualization

✍️ The Grammar of Graphics

Dr Thiyanga Talagala

Today's menu

Grammar of Graphics

Packages

Dataset

Plotting with R

Base R

Using ggplot2: grammar of graphics

Grammar

English

Graphics

English

Graphics

Elements of ggplot2 object

Elements of ggplot2 object

Making your first plot with ggplot

Data: data to be plotted

Data

Aesthetics: mapping variables

Data + Aesthetics

Geometrics

Data + Aesthetics + Geometrics

Data + Aesthetics + Geometrics

Data + Aesthetics + Geometrics

Data + Aesthetics + Geometrics

Data + Aesthetics + Geometrics

Data + Aesthetics + Geometrics

Data + Aesthetics + Geometrics

Facets: small multiples

Data + Aesthetics + Geometrics + Facets

Data + Aesthetics + Geometrics + Facets

Statistics

Data + Aesthetics + Geometrics + Facets + Statistics

Data + Aesthetics + Geometrics + Facets + Statistics

Coordinate

Data + Aesthetics + Geometrics + Facets + Statistics + Coordinate

Theme

Data + Aesthetics + Geometrics + Facets + Statistics + Coordinate+ Theme

Scale

Data + Aesthetics + Geometrics + Facets + Statistics + Coordinate + Theme + Scale

titles and axes labels

Your turn

Add a vertical line

Add a horizontal line

Add a diagonal line

All Geoms

geom_boxplot

geom_boxplot

geom_boxplot

geom_boxplot

geom_boxplot

geom_point

geom_jitter

geom_jitter + geom_boxplot

geom_jitter + geom_boxplot

geom_jitter + geom_boxplot

geom_jitter + geom_boxplot

geom_jitter + geom_boxplot

geom_jitter + geom_boxplot (outlier.shape = NA)

geom_jitter + geom_boxplot

geom_jitter + geom_boxplot

geom_jitter + geom_boxplot + coord_flip

geom_boxplot

geom_boxplot + facet_wrap

geom_density

Your turn

geom_histogram

geom_bar

Your turn

geom_bar (stat="identity")

geom_col

Change the order of levels

geom_point

geom_line

geom_line + geom_point

Your turn

Data Wrangling + Data Visualization

Your turn