R course

Daniel Vaulot

2023-01-21

Data visualization

R - Session 03

• Graph types
• Grammar of graphics
• Playing with ggplot2
• Multiple graphs
• ggplot2 syntax

Intro to Data vizualisation

Installation and Resources

Packages

• ggplot2
• patchwork

Download

• R-session-03.zip

Reading

• Chapter 28 of R for data science

Resources

• Fundamental of data visualization
• Data visualization: practical introduction

Workflow

Graph purposes

• Analysis graphs
  • design to see patterns, trends
  • aid the process of data description
  • interpretation

• Presentation graphs
  • design to attract attention
  • make a point
  • illustrate a conclusion

Source: Michael Friendly

Graph types

Jitter

• Two variables numerical

Bubble

• Two variables numerical
• Add another variable numerical

Animate

• Two variables numerical
• One variable numerical
• One variable categorical
• Animate another variable

Times series

• Line graph

Bargraphs

• One variable categorical
• One variable numerical

Bargraphs

• Rotate

Bargraphs

• Two variable categorical
• One variable numerical

Boxplots

• One variable categorical
• One variable numerical but with many values

Treemaps

• One variable categorical
• One variable numerical
• Much better than pie charts

3D

• Three variable numerical
• Avoid unless it is a simple shape

Contours

• Three variable numerical
• Better than 3D

Many…

• Choose as a function of what you want to analyze or the story you want to tell
• https://www.r-graph-gallery.com/all-graphs/

ggplot2

@allison_horst

Initialize

Load necessary libraries

library("readxl") # Import the data from Excel file

library("dplyr")  # filter and reformat data frames

library("ggplot2") # graphics

library("patchwork") # arrange graphics

Read the data

samples <- readxl::read_excel("data/CARBOM data.xlsx", 
                         sheet = "Samples_boat") %>% 
           tidyr::fill(station)

sample number	transect	station	date	time	depth	level	latitude	longitude	picoeuks	nanoeuks	phosphates	nitrates	temperature	salinity
10	1	81	2013-11-13	1899-12-31 01:00:00	140	Deep	-27.42	-44.72	3278	1232	0.20	0.26	17.3	35.9
11	1	85	2013-11-13	1899-12-31 13:30:00	110	Deep	-26.80	-45.30	16312	1615	0.29	0.22	21.3	36.5
120	2	96	2013-11-18	1899-12-31 23:50:00	5	Surf	-27.39	-47.82	1150	75	0.43	0.19	23.1	33.5
121	2	96	2013-11-18	1899-12-31 23:50:00	30	Deep	-27.39	-47.82	1737	218	0.43	0.23	22.6	33.7
122	2	96	2013-11-18	1899-12-31 23:50:00	50	Deep	-27.39	-47.82	853	234	0.56	0.21	20.3	35.9
125	2	98	2013-11-18	1899-12-31 05:00:00	5	Surf	-27.59	-47.39	3086	1300	0.29	0.25	23.1	35.7
126	2	98	2013-11-18	1899-12-31 05:00:00	50	Deep	-27.59	-47.39	1217	782	0.25	0.20	23.7	37.2
127	2	98	2013-11-18	1899-12-31 05:00:00	85	Deep	-27.59	-47.39	3420	226	0.25	0.47	22.9	37.0
13	1	86	2013-11-13	1899-12-31 17:00:00	105	Deep	-26.33	-45.41	6366	1007	0.34	0.15	20.9	36.3
140	2	101	2013-11-18	1899-12-31 12:00:00	5	Surf	-27.79	-46.96	500	366	0.29	0.14	23.5	36.5

A simple plot

• Choose the data set
• Choose the geometric representation
• Choose the aesthetics : x,y, color, shape etc…

# All functions are from ggplot2 package unless specified

ggplot(data=samples) + 
    geom_point(mapping = aes(x=phosphates, 
                            y=nitrates))

The grammar of graphics

 ggplot(data=samples) + 
  geom_point(mapping = aes(x=phosphates, 
                           y=nitrates))

Every graph can be described as a combination of independent building blocks:

• data: a data frame: quantitative, categorical; local or data base query
• aesthetic mapping of variables into visual properties: size, color, x, y
• geometric objects (“geom”): points, lines, areas, arrows, …
• coordinate system (“coord”): Cartesian, log, polar, map

Alternatively

• Move mapping into ggplot function

 ggplot(data=samples, 
        mapping = aes(x=phosphates, 
                      y=nitrates)) + 
  geom_point()

Alternatively

• Remove function arguments

 ggplot(samples, 
        aes(x=phosphates, 
            y=nitrates)) + 
  geom_point()

Makes dots bigger

• Add: size=5 outside of the aesthetics function

 ggplot(samples,
        aes(x=phosphates, 
            y=nitrates)) + 
  geom_point(size=5)

Color according to depth level (discrete)

• The mapping aesthetics must be an argument of the aes function
• geom_point(color=level, size=5) will generate an error…

 ggplot(samples,
        aes(x=phosphates, 
            y=nitrates,
            color=level)) + 
  geom_point(size=5)

Color according to depth level (discrete)

• The mapping aesthetics must be an argument of the aes function
• geom_point(color=level, size=5) will generate an error…

 ggplot(samples,
        aes(x=phosphates, 
            y=nitrates)) + 
  geom_point(color=level, size=5)

Error in list2(na.rm = na.rm, ...): objet 'level' introuvable

Color according to depth (continuous)

• The mapping aesthetics must be an argument of the aes function
• Add: color=depth

 ggplot(samples,
        aes(x=phosphates, 
            y=nitrates,
            color=depth)) + 
  geom_point(size=5)

Symbol according to transect (continuous)

• Add: shape=transect

 ggplot(samples,
        aes(x=phosphates, 
            y=nitrates,
            color=depth,
            shape=transect)) + 
  geom_point(size=5)

Error in `geom_point()`:
! Problem while computing aesthetics.
i Error occurred in the 1st layer.
Caused by error in `scale_f()`:
! A continuous variable cannot be mapped to the shape aesthetic
i choose a different aesthetic or use `scale_shape_binned()`

Symbol according to transect (continuous)

• Add: shape=as.character(transect)

 ggplot(samples,
        aes(x=phosphates, 
            y=nitrates,
            color=depth,
            shape=as.character(transect))) + 
  geom_point(size=5)

Panels depending on one variable

 ggplot(samples,
        aes(x=phosphates, 
            y=nitrates)) +
  geom_point() +
  facet_wrap(~ level) 

Adding a regression line

• Add: geom_smooth()
• You can choose the type of smoothing “lm” is for linear model

 ggplot(samples,
        aes(x=phosphates, 
            y=nitrates, 
            color=level)) +
  geom_point(size=5) +
  geom_smooth(mapping = aes(x=phosphates, 
                            y=nitrates), 
              method="lm")

Adding a regression line

• If the mapping is in the ggplot function is for all the geom….

 ggplot(samples,
        aes(x=phosphates, 
            y=nitrates)) +
  geom_point(aes(color=level), 
             size=5) +
  geom_smooth(mapping = aes(x=phosphates, 
                            y=nitrates), 
              method="lm")

Finalizing the graph

• Adding labels and legends

 ggplot(samples) + 
  geom_point(mapping = aes(x=phosphates, 
                           y=nitrates,
                           color=level), 
             size=5) +
  geom_smooth(mapping = aes(x=phosphates, 
                            y=nitrates), 
              method="lm") +
  xlab("Phosphates") + 
  ylab("Nitrates") + 
  ggtitle("CARBOM cruise")

Multigraphs (patchwork package)

First graph

 g1 <- ggplot(samples) + 
  geom_point(mapping = aes(x=phosphates, 
                           y=nitrates,color=
                             level), size=5) +
  geom_smooth(mapping = aes(x=phosphates, 
                            y=nitrates), 
              method="lm") +
  xlab("Phosphates") + 
  ylab("Nitrates")

 g1

Second graph

 g2<- ggplot(samples) + 
  geom_point(mapping = aes(x=nanoeuks, 
                           y=picoeuks,
                           color=level), 
             size=5) +
  geom_smooth(mapping = aes(nanoeuks, 
                            y=picoeuks), 
              method="lm") +
  xlab("Pico-eukaryotes") + 
  ylab("Nano-eukaryotes") 

 g2

Package patchwork

• https://patchwork.data-imaginist.com/index.html
• See also packages :
  • gridExtra
  • cowplot

library(patchwork)
(g1 / g2)

Package patchwork

• Adding annotation
• Collecting legends

g1 / g2 +
  plot_annotation(tag_levels = 'A') +
  plot_layout(guides = 'collect')

ggplot2 syntax

Anatomy of a plot

Geometries

Continuous x and y

Plotting error

Discrete x - Continuous y

Continuous x

3D

Modifying axis and scales

Palettes

• Package tmaptools : https://github.com/mtennekes/tmaptools
  • Function : palette_explorer()
• Package paletteer : https://github.com/EmilHvitfeldt/paletteer
  • More than 1000 palettes

Palettes

• Use color blind friendly palettes
  • viridis (e.g. scale_colour_viridis_c())

Themes

Extensions

Let’s do a graph

Your mission

Reproduce graph on right

• Only transect 2
• One panel per station
• Increasing depth
• Log scale for x
• White background

Instructions

• Work by group of 2 (1 expert, 1 less expert)
• Send code and results by element.io

Your turn

Step 1

• basic plot

ggplot(filter(samples, 
               transect==2 & !is.na(depth)), 
        aes(y=depth, x=picoeuks))  + 
geom_point(size=3)

Step 2

• facet_wrap

ggplot(filter(samples, 
             transect==2 & !is.na(depth)), 
      aes(y=depth, x=picoeuks))  + 
geom_point(size=3) +
facet_wrap(~ station) 

Step 3

• link points together (! use geom_path())

ggplot(filter(samples, 
             transect==2 & !is.na(depth)), 
      aes(y=depth, x=picoeuks))  + 
geom_point(size=3) +
facet_wrap(~ station) +
geom_path()  

Step 4

• reverse y scale

ggplot(filter(samples, 
             transect==2 & !is.na(depth)), 
      aes(y=depth, x=picoeuks))  + 
geom_point(size=3) +
facet_wrap(~ station) +
geom_path() +
scale_y_reverse()  

Step 5

• add theme

ggplot(filter(samples, 
             transect==2 & !is.na(depth)), 
      aes(y=depth, x=picoeuks))  + 
geom_point(size=3) +
facet_wrap(~ station) +
geom_path() +
scale_y_reverse() +
theme_bw()

Step 6

• add legends

ggplot(filter(samples, 
             transect==2 & !is.na(depth)), 
      aes(y=depth, x=picoeuks))  + 
geom_point(size=3) +
facet_wrap(~ station) +
geom_path() +
scale_y_reverse() +
theme_bw() +
ggtitle("Abundance of pico-eukaryotes per station on transect 2") +
xlab("Pico-eukaryote per mL") +
ylab("Depth (m)") 

Step 7

• change scales

ggplot(filter(samples, 
             transect==2 & !is.na(depth)), 
      aes(y=depth, x=picoeuks))  + 
geom_point(size=3) +
facet_wrap(~ station) +
geom_path() +
scale_y_reverse() +
theme_bw() +
ggtitle("Abundance of pico-eukaryotes per station on transect 2") +
xlab("Pico-eukaryote per mL") +
ylab("Depth (m)") +
scale_x_log10(limits= c(100,10000)) +
annotation_logticks(sides="b") 

Recap

• Conceptualize your graph before coding

• Decide what element is fixed and what varies

• It takes time to get what you want…

• Exploratory vs. final

Next time: Markdown and Quarto