Chapter 6: Data Visualisation with `ggplot2` 2

Goals:
Learn additional visualisation techniques.
  • Adjusting theme elements
  • Annotating the plots
  • facet_wrap() and grid_plot()
  • Savings plots

Read time: 15 Minutes

Adjusting theme elements

Everything in ggplot can be adjusted. Lets just go through a few of them to show you how its done, and you can explore them as you go.

Theme elements:

Code

data %>%
  ggplot(aes(CD28, age, color = log(CCR5_MFI))) +
  geom_point()+
  theme_classic()+
  theme(axis.text = element_text(size=10),
        axis.title = element_text(size=12), 
        legend.position = "inside", legend.justification = c(.99,.99))

Changing the scale:

Code

data %>%
  ggplot(aes(CD28, age)) +
  geom_point()+
  theme_bw()+
  ylim(0,100)

Changing the colours manually:

Code

data %>%
  mutate(percent_CCR5 = 100*CCR5/CD4) %>%
  ggplot(aes(
    x = group, 
    y = percent_CCR5, 
    fill = group
  ))+
  geom_boxplot(width=0.5) + 
  geom_jitter(width=0.1, alpha=0.2)+
  labs( y= "CCR5 %", x= "Tissue x Layer")+
  scale_fill_manual(values = c("blue", "blue4", "red", "red4", "green", "green4"))

Code

  theme_classic()

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##   ..$ arrow        : logi FALSE
##   ..$ inherit.blank: logi TRUE
##   ..- attr(*, "class")= chr [1:2] "element_line" "element"
##  $ rect                            :List of 5
##   ..$ fill         : chr "white"
##   ..$ colour       : chr "black"
##   ..$ linewidth    : num 0.5
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##   ..- attr(*, "class")= chr [1:2] "element_rect" "element"
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##   ..- attr(*, "class")= chr [1:2] "element_text" "element"
##  $ title                           : NULL
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##  - attr(*, "class")= chr [1:2] "theme" "gg"
##  - attr(*, "complete")= logi TRUE
##  - attr(*, "validate")= logi TRUE

Code

data %>% mutate(percent_HLADR = HLADR/CD4) %>%
  ggplot(aes(x=CD28, y=age, color = percent_HLADR)) +
  geom_point()+
  theme_bw()+
  scale_color_gradient(high = "#FF0000", low = "#0000FF") # you can search hex numbers, or match colours to a powerpoint presentation. You can use digital colour meter on Mac

Code

data %>% mutate(percent_HLADR = HLADR/CD4) %>%
  ggplot(aes(x=CD28, y=age, color = percent_HLADR)) +
  geom_point()+
  theme_bw()+
  scale_color_viridis_c() # you can search through the options for these, as well as other scale_color and scale_fill options.

Annotating the plot

Horizontal and Vertical lines:

I’ve added vertical lines (vlines), where you can specify the xintercepts to draw lines through.

I also added “annotate” layers where you can specify “text” or “segment” layers, with x, y and label arguments for

Code

data %>%
  mutate(percent_CCR5 = 100*CCR5/CD4) %>%
  ggplot(aes(
    x = group, 
    y = percent_CCR5
  ))+
  geom_boxplot(width=0.5) + 
  geom_jitter(width=0.1, alpha=0.2)+
  labs( y= "CCR5 %", x= "Tissue x Layer")+
  geom_vline(xintercept = c(2,4)+.5)+
  annotate("segment", 
           x=c(1:6)-.25, xend = c(1:6)+.25, 
           y=data%>% mutate(percent_CCR5 = 100*CCR5/CD4) %>% group_by(group) %>% summarise(mean = mean(percent_CCR5)) %>% select(mean) %>% unlist(), color="red", size=2)+
  annotate("text", 
           x=1:6, 
           y=data%>% mutate(percent_CCR5 = 100*CCR5/CD4) %>% group_by(group) %>% summarise(mean = mean(percent_CCR5)) %>% select(mean) %>% unlist(),
           label = data%>% mutate(percent_CCR5 = 100*CCR5/CD4)%>%group_by(group)%>%summarise(mean=mean(percent_CCR5)) %>% select(mean) %>% unlist() %>%round(),
           hjust = -2, color="red")+
    theme_classic()

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

facet_wrap() and Group

Facet wrap lets you split your points by a categorical value

Facet_wrap():

Code

data %>% mutate(percent_HLADR = 100*HLADR/CD4) %>%
  ggplot(aes(x=CD28, y=age, color = percent_HLADR)) +
  geom_point(aes(shape=layer))+
  theme_bw()+
  scale_color_gradient(high = "#FF0000", low = "#0000FF")+
  facet_wrap(~tissue)

plot_grid():

Code

library(cowplot)

## 
## Attaching package: 'cowplot'

## The following object is masked from 'package:lubridate':
## 
##     stamp

Code

plot1 = data %>%
  mutate(percent_CCR5 = 100*CCR5/CD4) %>%
  ggplot(aes(
    x = group, 
    y = percent_CCR5
  ))+
  geom_boxplot(width=0.5) + 
  geom_jitter(width=0.1, alpha=0.2)+
  labs( y= "CCR5 %", x= "Tissue x Layer", title="Percent CCR5")+
  geom_vline(xintercept = c(2,4)+.5)+
  theme_classic()
plot2 = data %>%
  ggplot(aes(
    x = group, 
    y = log(CCR5_MFI)
  ))+
  geom_boxplot(width=0.5) + 
  geom_jitter(width=0.1, alpha=0.2)+
  labs( y= "log(CCR5 MFI)", x= "Tissue x Layer", title="CCR5 MFI")+
  geom_vline(xintercept = c(2,4)+.5)+
  theme_classic()

plot_grid(plot1,plot2, nrow=2) #or change the rows. Add 50 plots if you want to adn specify how you arrange them by rows and/or columns

Saving plots

You can save plots in a few ways.

pdf/png/tiff/jpeg():

Use ?png to get the specific options, but here is a simple example of generating a multipage pdf.

You call the function to “open” the pdf, plot your figures, and then close the pdf using dev.off()

Code

pdf(file="CCR5_results.pdf", widht = 7, height=7) 

data %>%
  mutate(percent_CCR5 = 100*CCR5/CD4) %>%
  ggplot(aes(
    x = group, 
    y = percent_CCR5
  ))+
  geom_boxplot(width=0.5) + 
  geom_jitter(width=0.1, alpha=0.2)+
  labs( y= "CCR5 %", x= "Tissue x Layer", title="Percent CCR5")+
  geom_vline(xintercept = c(2,4)+.5)+
  theme_classic()

data %>%
  ggplot(aes(
    x = group, 
    y = log(CCR5_MFI)
  ))+
  geom_boxplot(width=0.5) + 
  geom_jitter(width=0.1, alpha=0.2)+
  labs( y= "log(CCR5 MFI)", x= "Tissue x Layer", title="CCR5 MFI")+
  geom_vline(xintercept = c(2,4)+.5)+
  theme_classic()

dev.off()

ggsave:

ggsave is quite simple and allows you to very easily control resolutions and size.

Code

ggsave("CCR5_percent.png", 
       plot1, 
       path = "C:/1_Yc/r-analysis-user-group/plots", 
       width = 7, height = 5, units = c('cm'), 
       dpi=300)

Some final hints

  • You can use show.legend=F inside geom layers to remove the legends
  • You can store your initial plots as plot = ggplot(...)+geom_...
    and then append layers onto this.

Next Chapter →

Chapter 6: Data Visualisation with ggplot2 2

Adjusting theme elements

Annotating the plot

facet_wrap() and Group

Saving plots

Some final hints

Chapter 6: Data Visualisation with `ggplot2` 2