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Fundamental of Data Science for EESS





R session 05 - Data viz

Daniel Vaulot

2021-02-15

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Outline

  • Graph types
  • Grammar of graphics
  • Playing with ggplot2
  • Multiple graphs
  • ggplot2 syntax
  • Your turn
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Installation and Resources

Packages

  • ggplot2
  • cowplot

Download

  • R-session-04.zip

Resources

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Data vizualization

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Data vizualization

Graph purposes

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Data vizualization

Graph purposes

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

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 - http://datavis.ca/courses/RGraphics/

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Graph types

Jitter

  • Two variables numerical
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Graph types

Jitter

  • Two variables numerical

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Graph types

Bubble

  • Two variables numerical
  • Add another variable numerical

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Graph types

Animate

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

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Graph types

Times series

  • Line graph

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Graph types

Bargraphs

  • One variable categorical
  • One variable numerical

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Graph types

Bargraphs

  • Rotate

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Graph types

Bargraphs

  • Two variable categorical
  • One variable numerical

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Graph types

Boxplots

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

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Graph types

Treemaps

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

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Graph types

3D

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

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Graph types

Contours

  • Three variable numerical
  • Better than 3D

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Graph types

Many...

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Wooclap - Quizz on Data wrangling

https://www.wooclap.com/R01

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ggplot2

@allison_horst

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Initialize

Load necessary libraries

library("readxl") # Import the data from Excel file
library("dplyr")  # filter and reformat data frames
library("ggplot2") # graphics
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Initialize

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
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ggplot2

A simple plot

  • Choose the data set
  • Choose the geometric representation
  • Choose the aesthetics : x,y, color, shape etc...
ggplot(data=samples) + 
geom_point(mapping = aes(x=phosphates, 
                         y=nitrates))
  • All functions are from ggplot2 package unless specified
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ggplot2

A simple plot

  • Choose the data set
  • Choose the geometric representation
  • Choose the aesthetics : x,y, color, shape etc...
ggplot(data=samples) + 
geom_point(mapping = aes(x=phosphates, 
                         y=nitrates))
  • All functions are from ggplot2 package unless specified

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ggplot2

The grammar of graphics

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
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ggplot2

Syntax

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

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ggplot2

Alternatively

 ggplot(data=samples, 
        mapping = aes(x=phosphates, 
                      y=nitrates)) + 
  geom_point()
  • If different geometries origniate from different datasets or have different mapping the datasets or the mapping must be called inside the geom function.

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ggplot2

Alternatively

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

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ggplot2

Make dot size bigger

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

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ggplot2

Make dot size bigger

 ggplot(samples,
        aes(x=phosphates, 
            y=nitrates)) + 
  geom_point(size=5)
  • Add: size=5 outside of the aesthetics function

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ggplot2

Color according to depth level (discrete)

 ggplot(samples,
        aes(x=phosphates, 
            y=nitrates,
            color=level)) + 
  geom_point(size=5)
  • The mapping aesthetics must be an argument of the aes function
  • geom_point(color=level, size=5) will generate an error...

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ggplot2

Color according to depth (continuous)

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

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ggplot2

Symbol according to transect (continuous)

 ggplot(samples,
        aes(x=phosphates, 
            y=nitrates,
            color=depth,
            shape=transect)) + 
  geom_point(size=5)
  • Add: shape=transect 
Error: A continuous variable can not be mapped to shape

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ggplot2

Symbol according to transect (continuous)

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

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ggplot2

Panels depending on one variable

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

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ggplot2

Adding a regression line

 ggplot(samples,
        aes(x=phosphates, 
            y=nitrates, 
            color=level)) +
  geom_point(size=5) +
  geom_smooth(mapping = aes(x=phosphates, 
                            y=nitrates), 
              method="lm")
  • Add: geom_smooth()
  • You can choose the type of smoothing "lm" is for linear model

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ggplot2

Adding a regression line

 ggplot(samples,
        aes(x=phosphates, 
            y=nitrates)) +
  geom_point(aes(color=level), 
             size=5) +
  geom_smooth(mapping = aes(x=phosphates, 
                            y=nitrates), 
              method="lm")
  • If the mapping is in the ggplot function is for all the geom....

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ggplot2

Finalizing the graph

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

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Putting several graphs together

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

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Putting several graphs together

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

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Putting several graphs together

Package patchwork

library(patchwork)
(g1 + g2)/g1

See also packages :

  • gridExtra
  • cowplot

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Putting several graphs together

Package patchwork

  • Adding annotation
  • Collecting legends
g1 / g2 +
  plot_annotation(tag_levels = 'A') +
  plot_layout(guides = 'collect')

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ggplot2 syntax

Anatomy of a plot

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ggplot2 syntax

Geometries

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ggplot2 syntax

Continuous x and y

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ggplot2 syntax

Plotting error

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ggplot2 syntax

Discrete x - Continuous y

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ggplot2 syntax

Continuous x

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ggplot2 syntax

3D

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ggplot2 syntax

Modifying axis and scales

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ggplot2 syntax

Palettes

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ggplot2 syntax

Palettes

  • Use color blind friendly palettes
    • viridis (e.g. scale_colour_viridis_c)
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ggplot2 syntax

Themes

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Extensions

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Recap

  • Exploratory vs. final
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Recap

  • Exploratory vs. final
  • Decide what element is fixed and what varies
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Recap

  • Exploratory vs. final
  • Decide what element is fixed and what varies
  • It takes time to get what you want...
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Next time: Create maps

What you will learn :

  • Create simple maps
  • Create interactive maps
  • Create thematic maps

Install

  • rworldmap
  • leaflet
  • sf
  • raster
  • spData
  • tmap
  • ggplot2

Reading list

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Outline

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