Data Visualization

Plotting in R

There are 3 main types of plotting in R:

1. base
2. lattice
3. ggplot2

For this workshop series we are focusing on ggplot2 package for learning how to create publication quality plots.

Setting up (packages and color palettes)

Installing required packages

install.packages("ggplot2")

Loading packages

library(ggplot2)

Installing and loading multiple packages at once

# Create a vector of package names
all.lib<-c("reshape2","ggplot2","gridExtra","ggpubr","ggthemes", "scales","RColorBrewer","tidyverse","hrbrthemes","extrafont","ggtext") 

# install packages at once 
#install.packages(all.lib)

# Load packages at once
lapply(all.lib,require,character.only=TRUE)

Sidenote about apply functions

• lapply() function is useful for performing operations on list objects and returns a list object of same length of original set. lappy() returns a list of the similar length as input list object, each element of which is the result of applying FUN to the corresponding element of list. Lapply in R takes list, vector or data frame as input and gives output in list.

• sapply() function takes list, vector or data frame as input and gives output in vector or matrix. It is useful for operations on list objects and returns a list object of same length of original set. Sapply function in R does the same job as lapply() function but returns a vector.

# Creating a data frame 
# lapply(list, function)

x <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE,FALSE,FALSE,TRUE))

# compute the list mean for each list element
lapply(x, mean)

$a
[1] 5.5

$beta
[1] 4.535125

$logic
[1] 0.5

# sapply(list, function)

Color palettes in R

R colorbrewer is a good option

library(RColorBrewer)
par(mar=c(3,4,2,2))
display.brewer.all()

Creating your own color palette

# COLOR PALETTES
pastel.colpal<-c("#355070", "#6d597a", "#b56576", "#e56b6f", "#eaac8b","#a44200")

sage.colpal<-c("#cb997e", "#ddbea9", "#ffe8d6", "#b7b7a4", "#6b705c","#585123")

tog<-rbind(pastel.colpal,sage.colpal)

#show_col(tog,labels = FALSE)
show_col(tog,cex_label=0.5)

Dataset: Working with Inbuild dataset

data()  # Exploring different inbuild dataset
data("iris") # attaching inbuild dataset
head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

# Iris data set is a dataset with 150 observation with 5 variables as columns
# Each observation is a row (150)
# Each variable is a column (5): Sepal.Length; Sepal.Width, Petal.Length, Petal.Width and Species
#View(iris) # Viewing dataset
str(iris) # structure of data

'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 ...

dim(iris) # dimensions rows and columns

[1] 150   5

ggplot2

ggplot2 function works with figures in layers. We are first calling the ggplot function to create a new plot.

Adding a base layer:

ggplot(data = iris, mapping = aes(x = Species , y = Sepal.Length))

Figure 1. Base Layer in ggplot2

ggplot tells R, we are working with a new plot. All the parameters and arguments given inside of the ggplot function are global parameters.

Two arguments passed:

• data : what data to work with
• aes function : how variables in the data map to aesthetic properties of the figure, in this case the x and y locations

Adding geom_point function:

ggplot(data = iris, mapping = aes(x = Species , y = Petal.Length))+
  geom_point()

Figure 2. Adding geom plot

Changing aes function by adding color:

ggplot(data = iris, mapping = aes(x = Species , y = Petal.Length, color=Species)) +
  geom_point()

Figure 3. Adding color

geom_line

Changing the geom parameters from point to line:

ggplot(data = iris, mapping = aes(x = Species , y = Petal.Length, color=Species)) +
 geom_line()

Figure 4. Adding line

geom_boxplot

Changing the geom parameters from point to boxplot:

ggplot(data = iris, mapping = aes(x = Species , y = Petal.Length, color=Species)) +
 geom_boxplot()

Figure 5. Adding boxplot

Adding more layer

Adding additional layers of points on top of boxplots:

ggplot(data = iris, mapping = aes(x = Species , y = Petal.Length, color=Species)) +
 geom_boxplot() +
 geom_point()

Figure 6. Data on Boxplots

HYPOTHESIS: How is sepal length varying with petal length?

Adding group aesthetics

We are testing for the following hypothesis. Hypothesis: How is sepal length varying with petal length?

ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length)) +
 geom_point()

Figure 7. Sepal length variation with petal length

Adding a line on top of points:

ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length)) +
 geom_point()+
 geom_line()

Figure 8. Sepal length variation with petal length, line added

The above graph doesn’t make sense unless you are looking for an overall pattern without breaking it down by species.

Adding grouping

ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length, group=Species)) +
 geom_point() +
 geom_line()

Figure 9. Sepal length variation with petal length, grouped by species

Changing aesthetics within layers

ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length, group=Species)) +
 geom_point() +
 geom_line(aes(color=Species))

Figure 10. Sepal length variation with petal length, grouped by species, colored line

Changing transparency

ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length, group=Species)) +
 geom_point(alpha = 0.5) +
 geom_line(aes(color=Species))

Figure 11. Sepal length variation with petal length, grouped by species, transparency added

Adding transformation

geom_smooth: fitting a relationship between data; by default formula is y~x and method is loess

Without grouping

This doesn’t make sense unless you are looking for an overall pattern in the data.

ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length)) +
 geom_point(alpha = 0.5) +
 geom_line(aes(color=Species))+
 geom_smooth()

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Figure 12. Trends in data, without grouping

With grouping

If you are looking for a relationship between sepal length and petal length for each species

ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length, group = Species)) +
 geom_point(alpha = 0.5) +
 geom_line(aes(color=Species))+
 geom_smooth()

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Figure 13. Trends in data, with grouping

Linear relationship

ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length, group = Species)) +
 geom_point(alpha = 0.5) +
 geom_line(aes(color=Species))+
 geom_smooth(method = "lm",size=1)

`geom_smooth()` using formula = 'y ~ x'

Figure 14. Linear relationship in data, without grouping

Multipanel figures

ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length)) +
  geom_point(alpha = 0.5) +
  geom_line(aes(color=Species))+
  geom_smooth(method = "lm",size=1)+
  facet_wrap(~Species)

`geom_smooth()` using formula = 'y ~ x'

Figure 15. Multipanel figure, facet wrap

Changing figure aesthetics

Colors

ggplot(data = iris, mapping = aes(x = Species , y = Petal.Length, color=Species)) +
  geom_point()+
  scale_color_manual(values=pastel.colpal)

Figure 16. Changing colors

Understanding colors

Here only line changes color since geom_line has the color aesthetics

ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length)) +
  geom_point(alpha = 0.5) +
  geom_line(aes(color=Species))+
  geom_smooth(method = "lm",size=1)+
  facet_wrap(~Species)+
  scale_color_manual(values=pastel.colpal)

`geom_smooth()` using formula = 'y ~ x'

Figure 17. Color aesthetics for only lines

Let’s specify color as a global aesthetic

ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length, color=Species)) +
  geom_point(alpha = 0.5) +
  geom_line(aes(color=Species))+
  geom_smooth(method = "lm",size=0.4,color="black")+
  facet_wrap(~Species)+
  scale_color_manual(values=pastel.colpal)

`geom_smooth()` using formula = 'y ~ x'

Figure 18. Color as global aesthetic

Changing x and y labels

using xlab(), ylab()

ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length, color=Species)) +
  geom_point(alpha = 0.5) +
  geom_line(aes(color=Species))+
  geom_smooth(method = "lm",size=0.4,color="black")+
  facet_wrap(~Species)+
  scale_color_manual(values=pastel.colpal)+
  xlab("This is Sepal Length") +
  ylab("This s Petal Length") 

`geom_smooth()` using formula = 'y ~ x'

Figure 19. Changing x and y label

Adding themes

ggthemes package has several pre-specified themes

ipsum theme

library(hrbrthemes)
#| label: fig-tweentyfig
#| fig-cap: "Ipsum theme"
#| fig-align: "center"
ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length, color=Species)) +
  geom_point(alpha = 0.5) +
  geom_line()+
  geom_smooth(method = "lm",size=0.4,color="black")+
  facet_wrap(~Species)+
  scale_color_manual(values=pastel.colpal)+
  xlab("This is Sepal Length") +
  ylab("This s Petal Length") +
  theme_ipsum()

`geom_smooth()` using formula = 'y ~ x'

Classic theme

library(ggthemes)
#| label: fig-twentyonefig
#| fig-cap: "Classic theme"
#| fig-align: "center"
ggplot(data = iris, mapping = aes(x = Sepal.Length, y = Petal.Length, color=Species)) +
  geom_point(alpha = 0.5) +
  geom_line(aes(color=Species))+
  geom_smooth(method = "lm",size=0.4,color="black")+
  facet_wrap(~Species)+
  scale_color_manual(values=pastel.colpal)+
  xlab("This is Sepal Length") +
  ylab("This s Petal Length") +
  theme_classic()

`geom_smooth()` using formula = 'y ~ x'

Adding figure title

Using ggtitle,

ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length, color=Species,group=Species)) +
  geom_point(alpha = 0.5) +
  geom_line(aes(color=Species))+
  geom_smooth(method = "lm",size=0.4,color="black")+
  #facet_wrap(~Species)+
  scale_color_manual(values=pastel.colpal)+
  xlab("Sepal Length") +
  ylab("Petal Length") +
  ggtitle("Relationship between Sepal Length and Petal Length")+
  theme_classic()

`geom_smooth()` using formula = 'y ~ x'

Figure 20. Adding figure title

Adding more theme elements

fig1<-ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length,color=Species,group=Species)) +
  geom_point(alpha = 0.5) +
  geom_line(aes(color=Species))+
  geom_smooth(method = "lm",size=0.4,color="black")+
  #facet_wrap(~Species)+
  scale_color_manual(values=pastel.colpal)+
  xlab("Sepal Length") +
  ylab("Petal Length") +
  ggtitle("Relationship between Sepal Length and Petal Length")+
  theme_ipsum()+
  theme(axis.ticks.x=element_blank(),
        legend.title = element_text(size=14,face = "bold"),
        #legend.text = element_markdown(size=12,face="bold"),
        axis.text.x = element_text(size=14,face="bold"),
        axis.text.y = element_text(size=14,face="bold"),
        axis.title.y = element_text(size=18,face="bold"),
        axis.title.x = element_text(size = 18,face="bold"))

fig1

`geom_smooth()` using formula = 'y ~ x'

Figure 21. Adding more elements

Saving and publishing plots

figure1<-ggplot(data = iris, mapping = aes(x = Sepal.Length , y = Petal.Length, color=Species,group=Species)) +
  geom_point(alpha = 0.5) +
  geom_line(aes(color=Species))+
  geom_smooth(method = "lm",size=0.4,color="black")+
  #facet_wrap(~Species)+
  scale_color_manual(values=pastel.colpal)+
  xlab("Sepal Length") +
  ylab("Petal Length") +
  ggtitle("Relationship between Sepal Length and Petal Length")+
  theme_ipsum()+
  theme(axis.ticks.x=element_blank(),
        legend.title = element_text(size=14,face = "bold"),
       # legend.text = element_markdown(size=12,face="bold"),
        axis.text.x = element_text(size=14,face="bold"),
        axis.text.y = element_text(size=14,face="bold"),
        axis.title.y = element_text(size=18,face="bold"),
        axis.title.x = element_text(size = 18,face="bold"))

figure1

`geom_smooth()` using formula = 'y ~ x'

ggsave(filename="My_publishable_figure.jpeg", plot=figure1, width=10, height=6,dpi=700)

`geom_smooth()` using formula = 'y ~ x'

Figure 22. Saving and publishing plot

Multiple plots together in R

You can add multiple figures together from different data sets as well.

Using geom plot using mtcars data

# Renaming the figures
## Geom plot using mtcars data

fig1<-ggplot(mtcars, aes(x=as.factor(cyl), y=mpg)) + 
    geom_boxplot(fill="slateblue", alpha=0.2) + 
    xlab("cyl")
fig1

Figure 23. mtcars boxplot

Building a dataset with different distributions

data <- data.frame(
  type = c( rep("variable 1", 1000), rep("variable 2", 1000) ),
  value = c( rnorm(1000), rnorm(1000, mean=4) ))

fig2<- data %>%
  ggplot( aes(x=value, fill=type)) +
  geom_histogram( color="#e9ecef", alpha=0.6, position = 'identity') +
  scale_fill_manual(values=c("#69b3a2", "#404080")) +
  theme_ipsum() +
  labs(fill="")
fig2

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Figure 24. dataset with different distributions

Loading dataset from github

path<-"https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/1_OneNum.csv"

data <- read.table(path, header=TRUE)

# Make the histogram
fig3<- data %>%
  filter( price<300 ) %>%
  ggplot( aes(x=price)) +
  geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.8)
fig3

Figure 25. Data from github

Loading dataset from your computer

# Note you will have to specify a dataset here from your computer for this to work
data("Orange")
write.csv(Orange,"newlysaved.csv",row.names = FALSE)
data<-read.csv(file = "newlysaved.csv")

fig4<- data %>%
  ggplot(aes(x=Tree,y=circumference,color=Tree))+
  geom_boxplot(aes(factor(Tree)))
fig4

Figure 26. Using dataset from your computer

all.fig<-ggarrange(nrow = 2,ncol = 2, 
                  fig1,fig2,fig3,fig4,
                  labels = c("a)","b)","c)","d)"))

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

all.fig

#ggsave(filename="MultipleFigures.jpeg", plot=all.fig, width=15, height=8,dpi=600)

Figure 27. Multiple plot together