R Graphics

library(tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 2.2.1     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.5
✔ tidyr   0.8.1     ✔ stringr 1.3.1
✔ readr   1.1.1     ✔ forcats 0.3.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

options.orig <- options(repr.plot.width=6, repr.plot.height=4)

Simulate some data for plotting

n <- 100
x <- sort(runif(n))
y <- x^2 + x + 3 + 0.2*rnorm(n)
z <- x^2
g <- as.factor(sample(1:4, n, replace=T))
df <- tibble(x=x, y=y, z=z, g=g)

Grammar of Graphics

ggplot2 uses a Grammar of Graphics convention to define plots. See The fundamentals of ``gggplot2` explained <https://www.aridhia.com/technical-tutorials/the-fundamentals-of-ggplot-explained/>`__ for a simple epxlanation.

The main concents are

• data source
• map data elements to visual characteristics (aes)
• type of visual elements in plot (geom)
• layers (+)
• facet to automatically show “group by” plots
• scale to controls how the mapping is made
• statistics to add summary data to the plot
• themes to define the look and feel of the plot

g0 <- ggplot(df, aes(x=x, y=y))

g1 <- g0 +
geom_point(aes(col=z)) +
geom_smooth(method='lm',
            formula=y ~ poly(x, 2, raw=TRUE))

g2 <- g1 +
labs(title="ggplot2",
     subtitle="Graphics example",
     caption="HTS 2018",
     x="x-coordinate",
     y="y-coordiante")

g3 <- g2 +
guides(col=FALSE)

g0

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g1

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g2

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g3

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Interpration

• What is the data source?
• What mappings are applid to the plot as a whole? To the points?
• What labels are specified and where do they appear?
• Wthat geometric objects are used for plotitng?
• What statistics are added to the plot?
• How was layering used to build the plot incrementally?
• How were partially completed plot stages saved?

Themes

g3 + theme_minimal()

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g3 + theme_linedraw()

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g3 + theme_dark()

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Facets

g3 + facet_wrap(facets='g', nrow = 2)

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g3 + facet_grid(~ g)

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g3 + facet_grid(g ~ .)

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Scales

g4 <- ggplot(df, aes(x=x, y=y, col=as.factor(g)) ) +
geom_point() +
geom_smooth(method='lm',
            formula=y ~ poly(x, 2, raw=TRUE)) +
labs(title="ggplot2",
     subtitle="Graphics example",
     caption="HTS 2018",
     x="x-coordinate",
     y="y-coordiante") +
facet_grid(~ g) +
guides(col=FALSE)

Colors

g4

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g4 + scale_color_brewer()

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g4 + scale_color_brewer(type = 'seq', palette = 'Reds')

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g4 + scale_color_brewer(type = 'qual', palette = 2)

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Palettes available from brewer

The 3 plots show color maps that are

1. Sequential
2. Qualitiative
3. Divergent

library(RColorBrewer)

options.orig <- options(repr.plot.width=6, repr.plot.height=12)
display.brewer.all()

options.orig <- options(repr.plot.width=6, repr.plot.height=4)

Scales can be used for changing the mapping of color, fill, hue, size, coordinates …

Here we scale the y-coordinate to show log values.

g4 + scale_y_log10()

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Geoms

g0 + geom_point()

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g0 + geom_line()

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ggplot(df, aes(x=z, fill=g)) +
geom_density(aes(alpha=0.1)) +
guides(alpha=F)

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g0 +
geom_density_2d() +
geom_rug()

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head(df)

x	y	z	g
0.004293157	3.013839	1.843120e-05	1
0.008484842	2.584769	7.199254e-05	4
0.011955994	2.853660	1.429458e-04	2
0.031500574	2.667081	9.922861e-04	3
0.036965139	3.097624	1.366421e-03	1
0.042308528	3.006366	1.790012e-03	3

geom_bar shows counts by default

ggplot(df, aes(x=g)) +
geom_bar() +
labs(y="Sum of y")

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Specify weight parameter to sum over a column by group

ggplot(df, aes(x=g, weight=y) )+
geom_bar() +
labs(y="Sum of y")

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If you already have counts, specify stat=identity

df_ <- tibble(x=c('A', 'B', 'C'), y=c(10, 14, 18))

head(df_)

x	y
A	10
B	14
C	18

ggplot(df_, aes(x=x, y=y, fill=x)) +
geom_bar(stat='identity') +
guides(fill=F)

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ggplot(df, aes(x=g, y=z, fill=g)) +
geom_boxplot() +
geom_jitter(width=0.2)  +
guides(fill=FALSE)

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ggplot(df, aes(x=g, y=z, fill=g)) +
geom_boxplot() +
geom_jitter(width=0.2) +
coord_flip() +
guides(fill=FALSE)

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Saving plots

ggsave('figs/box.png')

Saving 6.67 x 6.67 in image

ggsave('figs/g3.pdf', g3)

Saving 6.67 x 6.67 in image

Retrieve saved files