Data visualization using R

Tips & tricks

Stefan Hartmann

Overview

  1. Choosing the "right" plot
  2. Best practices and deadly sins of data visualization
  3. How to construct plots in (base) R
  4. Tips & tricks for efficient visualization

Before we start...

Before we start...

  • Feel free to interrupt me at any time!
  • There's A LOT of code in this presentation...

Before we start...

  • Feel free to interrupt me at any time!
  • There's A LOT of code in this presentation...
this <- is(what, code) {
  looks, like
}

Before we start...

  • Feel free to interrupt me at any time!
  • There's A LOT of code in this presentation...
this <- is(what, code) {
  looks, like
}
  • You don't have to type all of the code, it's more important that you understand the conceptual background first.
  • There will be some hands-on exercises.

Before we start...

  • Feel free to interrupt me at any time!
  • There's A LOT of code in this presentation...
this <- is(what, code) {
  looks, like
}
  • You don't have to type all of the code, it's more important that you understand the conceptual background first.
  • There will be some hands-on exercises.
  • Also, there will be some slides with more advanced stuff (and yellow background).

Why visualize?

  • For yourself

    • Exploring your data
    • detecting outliers
    • checking assumptions of statistical tests or models (e.g. are the data normally distributed?)
    • etc.
  • For others

    • Showing your findings in a clear and efficient way
    • Graphs tend to be more reader-friendly than tables...
    • and much more reader-friendly than long inline lists!

Choosing the "right" plot

  • What kind of data are you dealing with?
  • What is your research question?

Types of data: Levels of measurement

  • categorical variables:
    • nominal variable: e.g. married, not married, divorced; Swiss, German, French...
      • Subtype: binary variable, e.g. living/dead
    • ordinal variable: e.g. gold medal, silver medal, bronze medal
  • metric variables:
    • interval variable: e.g. temperature (Celsius, Fahreneit)
    • ratio variable: e.g. weight, temperature (Kelvin)
    • (absolute / count variable: natural unit, e.g. number of students, age)

Alternative visualizations

  • What kind of variable are we dealing with here?
  • Which visualization seems most appropriate to you?

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Alternative visualizations

  • What kind of variable are we dealing with here?
  • Which visualization seems most appropriate to you?

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Alternative visualizations

  • What kind of variable are we dealing with here?
  • Which visualization seems most appropriate to you?

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Alternative Visualizations

Alternative Visualizations

The plot as a metaphor

"The essence of a graphic display is that a set of numbers having both magnitudes and an order are represented by an appropriate visual metaphor - the magnitude and order of the metaphorical representation match the numbers." (Wainer 1984: 139)

Best practice for reporting and displaying data

  • Most importantly: Know your data!
  • When reporting percentages, also report the denominator (i.e. the size of your sample)
  • Example: "50% of academics are alcoholics" - it makes a difference whether your sample size is 2 or 2,000.
  • When reporting comparisons of absolute frequencies, double-check if your samples are comparable.
  • Example: "255 women agree that cats are adorable, but only 5 men." - it makes a difference whether your sample consists of 300 women and 300 men or of 300 women and 10 men.
  • When reporting means, also report standard deviations.
  • Example: [5,5,5,5,995] has the same mean as [1,180,300,223,146].

Best practice (Tufte 2001, Freeman et al. 2009)

  • Show the data
  • Avoid distorting the data
  • Keep "Ink-to-data ratio" as low as possible
  • Use meaningful x and y labels
  • Avoid overplotting (e.g. 3-dimensional plots when only 2 dimensions are displayed)

Beware of overplotting!

Further tips

  • If there is no natural order in your data, order them by value

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Further tips

  • Don't cut the y axis unless there are good conceptual reasons to do so.

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From data to plot


Preparing data for visualization

  • Use "tidy data":
    • One variable per column
    • One observation per row


"Long" vs. "wide" format

  • wide format: repeated responses in a single row
  • long format: repeated responses in different rows

Preparing data for visualization

  • Golden rule: Don't be sloppy with your data!

Preparing data for visualization

  • Data are often messy: What's wrong here?

Plot types

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Scatterplots: When to use a scatterplot

  • show / explore correlations between two variables
  • metric data on both the x- and the y-axis

Creating a scatterplot

  • First, let's create some data:
x <- c(1,3,3,4,7,8)
y <- c(1,1,3,9,8,5)
  • and create a simple plot:
plot(x,y)

Creating a scatterplot

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Creating a scatterplot

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Creating a scatterplot

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Creating a scatterplot

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Customizing a scatterplot: Labels

plot(x, y, xlab = "xlab", ylab = "ylab", main = "main")

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Customizing a scatterplot: Colors

plot(x, y, xlab = "xlab", ylab = "ylab", main = "main", col = "red")

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Customizing a scatterplot: Colors

plot(x, y, xlab = "xlab", ylab = "ylab", main = "main", col = rgb(1, 0, 0, alpha = 0.5))

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Some notes on color

  • keep in mind that what you see on your screen is not always what you get on a printer or projector
  • → make sure that your colors are not too similar!
  • use color-blind friendly color schemes
  • avoid red-green contrasts
  • in many cases, it makes sense to combine color with other aesthetics like shape or line type

Some notes on color

  • If you work with many different colors in a plot, check out R's color palettes, e.g. rainbow, heat.colors, terrain.colors
  • Another useful resource is the RColorBrewer package with a number of color-blind friendly palettes (argument colorblindFriendly = TRUE)

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Customizing a scatterplot: Text

plot(x, y, xlab = "xlab", ylab = "ylab", main = "main", col = "red")
text(x = 5, y = 5,"Note the the added text! \n In a different color!", col = "blue")

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Customizing a scatterplot: Shapes

plot(x, y, xlab = "xlab", ylab = "ylab", main = "main", col = "red",
     pch = 20)

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Customizing a scatterplot: Shapes - and sizes

plot(x, y, xlab = "xlab", ylab = "ylab", main = "main", col = "red",
     pch = "\u263A", cex = 2)

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  • Any single character can be used.
par(mar = c(5, 4, 4, 2) + 0.1)

Customizing a scatterplot: Shapes

par_cur <- par() # save default graphics parameters
par(mar = c(1,1,1,1)) # change margins
plot(1:20, rep(10,20), pch = c(1:20), cex=1.5, ylab="", xlab="", yaxt="n", xaxt="n")
text(1:20, rep(8.5,20), labels = 1:20)

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par(par_cur) # restore default graphics parameters

Customizing a scatterplot: x and y limits

plot(x, y, xlab = "xlab", ylab = "ylab", main = "main", col = "red",
     xlim = c(0, max(x)),
     ylim = c(0, max(y)))

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Customizing a scatterplot: grid

plot(x, y, xlab = "xlab", ylab = "ylab", main = "main", col = "red", 
     xlim = c(0, max(x)), ylim = c(0, max(y)))
grid(nx = 0, ny=10)

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  • Note: default color is "lightgray", which is often invisible in print
  • In many cases grids are a waste of ink.

Customizing a scatterplot: cex parameters

plot(x, y, 
     # cex = 2,
     # cex.axis = 2,
     xlab = "xlab", ylab = "ylab", # cex.lab = 2,
     main = "main", # cex.main = 2,
     xlim = c(0, max(x)), ylim = c(0, max(y)))

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Customizing a scatterplot: cex parameters

plot(x, y, 
     cex = 2,
     # cex.axis = 2,
     xlab = "xlab", ylab = "ylab", # cex.lab = 2,
     main = "main", # cex.main = 2,
     xlim = c(0, max(x)), ylim = c(0, max(y)))

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Customizing a scatterplot: cex parameters

plot(x, y, 
     cex = 2,
     cex.axis = 2,
     xlab = "xlab", ylab = "ylab", # cex.lab = 2,
     main = "main", # cex.main = 2,
     xlim = c(0, max(x)), ylim = c(0, max(y)))

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Customizing a scatterplot: cex parameters

plot(x, y, 
     cex = 2,
     cex.axis = 2,
     xlab = "xlab", ylab = "ylab", cex.lab = 2,
     main = "main", cex.main = 2,
     xlim = c(0, max(x)), ylim = c(0, max(y)))

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Adding datapoints from another dataframe

plot(x, y, col = "red", pch = 20)
points(x = c(4, 5, 6), y = c(2,6,8), col = "green", pch = 2)

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Adding a legend

plot(x, y, col = "red", pch = 20)
points(x = c(4, 5, 6), y = c(2,6,8), col = "green", pch = 2)
legend ("topleft", 
        inset = c(0.01,0.01),    # distance from the margins
        pch = c(20,2),           # the two point characters we used
        col = c("red", "green"), # the two colors we used
        legend = c("red dots", "green triangles"))

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Adding a regression line

  • In scatterplots, you often don't see the wood for the trees
  • So you might want to visualize a general trend
  • To this end, you can add a regression line
  • i.e. the straight line that is closest to all points

Adding a regression line

  • We use the lm function for generating the model and the abline function, which adds straight lines to a plot
plot(x, y)
model <- lm(y ~ x)
abline(model)

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Adding a lo(w)ess curve

  • lowess/loess: locally weighted polynomial regression models
  • "The basic idea underlying smoothers is to use the observations in a given span (or bin) of values of X to calculate the average increase in Y . You then move this span from left to right along the horizontal axis, each time calculating the new increase in y." (Baayen 2008: 34)

Adding a lo(w)ess curve

plot(x,y, main = "lowess")
lines(lowess(x, y))
scatter.smooth(x,y, main = "loess")

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From scatterplot to lineplot

plot(x, y, type = "l")

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From scatterplot to lineplot

plot(x, y, type = "b")

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From scatterplot to lineplot: Line types

  • Line types can be customized using the lty parameter:
plot(x, y, type = "b", lty = 2)
lines(x = c(2:7), y = c(4:9), lty = 3, col = "darkgrey")

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When to use a lineplot

  • Lineplots are useful for showing e.g. change over time
  • Count variable on y-axis, (at least) ordinal variable on x-axis
  • Why should we avoid nominal variables on the x-axis?

When to use a lineplot

  • Lineplots are useful for showing e.g. change over time
  • Count variable on y-axis, (at least) ordinal variable on x-axis
  • Why should we avoid nominal variables on the x-axis?

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Hands-on task: Creating a scatterplot

  • Use read.csv to read in the dataframe height_weight.csv
  • Plot height against weight.

Hands-on task: Creating a scatterplot

hw <- read.csv("examples/height_weight.csv")
plot(hw$height, hw$weight, 
     xlab = "Height", ylab = "Weight", main = "Height~Weight")
model_hw <- lm(hw$weight~hw$height)
abline(model_hw, col = "darkgrey", lty = 2)

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Hands-on task: Creating a more complex scatterplot

  • Use read.csv to read in the dataframe Pokemon.csv
  • Plot height_m against weight_kg.
  • Use the col parameter to show the color of each Pokémon, as indicated in the "Color" column.
  • Use the pch parameter to show the form of each Pokémon, as indicated in the "Form" column.
  • Hint: For the pch part, first look what happens when you try as.numeric(pok$Form)
  • Finally, add a regression line to the plot.

Hands-on task: Creating a more complex scatterplot

# read data
pok <- read.csv("examples/Pokemon.csv")

# plot
plot(pok$Height_m, pok$Weight_kg, col = pok$Color, pch = as.numeric(pok$Form),
    xlab="Height", ylab="Weight", main = "Height~Weight, Pokémon")
model_pok <- lm(pok$Weight_kg~pok$Height_m)
abline(model_pok)

Hands-on task: Creating a more complex scatterplot

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Creating a barplot

  • Barplots are useful to show counts of categorical variables (e.g. number of men vs. number of women in parliament)...
  • summary statistics (usually: means) of metric variables across different categories (e.g. mean height of humans vs. Klingons)
  • But beware: Bar plots can hide information, cf. #barbarplots ("Friends don't let friends do bar charts")

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Creating a barplot

  • Main argument of the barplot() function is height
  • This can be a vector or a matrix
  • Let's try it out:
# define a vector
bar_heights <- c(50, 80)
barplot(bar_heights)

Creating a barplot

  • The labels of the bars can be specified using names.arg:
barplot(bar_heights, names.arg = c("stuff", "more\nstuff"))

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Creating a barplot

  • The other arguments are largely the same as in the case of scatterplots:
barplot(bar_heights, names.arg = c("stuff", "more\nstuff"),
        main = "I'm a barplot", xlab = "I'm the x label", ylab = "I'm the y label",
        cex.main = 2, cex.lab = 2, cex.axis = 2, cex.names = 2)

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Creating a barplot

  • The space argument defines the space between bars, default is 0.2 if height is a vector

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Creating a barplot

  • The space argument defines the space between bars, default is 0.2 if height is a vector

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Creating a barplot

  • The space argument defines the space between bars, default is 0.2 if height is a vector

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Creating a barplot

  • The space argument defines the space between bars, default is 0.2 if height is a vector

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Creating a barplot

  • The space argument defines the space between bars, default is 0.2 if height is a vector

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Creating a barplot

  • Knowing the width of bars and the space between them is important if you want to add text
  • To simplify the task, you can set space = 0
barplot(bar_heights / sum(bar_heights), # get relative frequencies
        names.arg = c("stuff", "more\nstuff"), space = 0)
text(x = c(0:1)+0.5,
     y = (bar_heights / sum(bar_heights)) - 0.05,
     labels = bar_heights)

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Creating a barplot

  • Or you can use the magic of vector addition and multiplication
  • 0.5:1.5 yields {0.5,1.5}, 0.2 * 1:2 yields {0.2, 0.4} (= 0.2 * 1, 0.2 * 2)
barplot(bar_heights / sum(bar_heights), # get relative frequencies
        names.arg = c("stuff", "more\nstuff"))
text(x = (0.5:1.5)  + (0.2 * 1:2),
     y = (bar_heights / sum(bar_heights)) - 0.05,
     labels = bar_heights)

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Creating a barplot from a matrix

bar_matrix <- matrix(c(2,4,5,4,3,3,7,6), nrow = 2)
bar_matrix
##      [,1] [,2] [,3] [,4]
## [1,]    2    5    3    7
## [2,]    4    4    3    6

Creating a barplot from a matrix

barplot(bar_matrix)

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Generating a barplot from a matrix

barplot(bar_matrix, beside = T)

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Creating a barplot

Hands-on example: action-sentence compatibility task

Hands-on task: Creating a barplot

  • Task: Read in file actionsentence.csv with read.csv
  • Inspect the data using head, str, and View
  • Subset the data: Omit rows with direction == "distractor"

Hands-on task: Creating a barplot

rt <- read.csv("examples/actionsentence.csv", fileEncoding = "UTF8")
rt <- subset(rt, direction != "distractor")

Hands-on task: Creating a barplot

  • No we want to show the means for "toward" and "away" sentences using a barplot.
  • We want to abstract over the individual subjects, so it makes sense to transpose the table from wide to long format first.
library(reshape2)
rt2 <- melt(rt, id.vars = c("ID", "sentence", "direction"))

Hands-on task: Creating a barplot

  • No we want to show the means for "toward" and "away" sentences using a barplot.
  • We want to abstract over the individual subjects, so it makes sense to transpose the table from wide to long format first.
library(reshape2)
rt2 <- melt(rt, id.vars = c("ID", "sentence", "direction"))

# same result but with (imho) more complicated syntax: "gather" from tidyr package
library(tidyr)
rt3 <- gather(rt, variable, value, -ID, -sentence, -direction)
all(rt2==rt3) # checks if all values are identical

Hands-on task: Creating a barplot

  • No we want to show the means for "toward" and "away" sentences using a barplot.
  • We want to abstract over the individual subjects, so it makes sense to transpose the table from wide to long format first.
library(reshape2)
rt2 <- melt(rt, id.vars = c("ID", "sentence", "direction"))

# same result but with (imho) more complicated syntax: "gather" from tidyr package
library(tidyr)
rt3 <- gather(rt, variable, value, -ID, -sentence, -direction)
all(rt2==rt3) # cheks if all values are identical
## [1] TRUE

Hands-on task: Creating a barplot

# get mean values for "away" and "towards" subsets:
rt2_away <- subset(rt2, direction == "away")
rt2_toward <- subset(rt2, direction == "toward")

mean_away <- mean(rt2_away$value)
mean_toward <- mean(rt2_toward$value)

# combine both to one vector
rt_means <- c(mean_away, mean_toward)
rt_means
## [1] 1867.9 1789.5

Hands-on task: Creating a barplot

barplot(rt_means, names.arg = c("away", "toward"))

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Barplot: Adding confidence intervals

  • The mean alone doesn't say very much
  • As mentioned before, [5,5,5,5,995] has the same mean as [1,180,300,223,146]
  • This is why researchers tend to add error bars to barplots
  • in most cases, these error bars represent 95 % confidence intervals...
  • i.e. the interval where we can be 95% confident that it contains the true mean.

Barplot: adding confidence intervals

  • We can obtain the confidence intervals for each of our two means using the t.test() function
t_away <- t.test(rt2_away$value)
str(t_away)
## List of 9
##  $ statistic  : Named num 14.8
##   ..- attr(*, "names")= chr "t"
##  $ parameter  : Named num 29
##   ..- attr(*, "names")= chr "df"
##  $ p.value    : num 4.65e-15
##  $ conf.int   : num [1:2] 1610 2126
##   ..- attr(*, "conf.level")= num 0.95
##  $ estimate   : Named num 1868
##   ..- attr(*, "names")= chr "mean of x"
##  $ null.value : Named num 0
##   ..- attr(*, "names")= chr "mean"
##  $ alternative: chr "two.sided"
##  $ method     : chr "One Sample t-test"
##  $ data.name  : chr "rt2_away$value"
##  - attr(*, "class")= chr "htest"

Barplot: adding confidence intervals

  • We can use the arrows function to plot confidence intervals
  • arrows is usually used to draw arrows (duh!)
  • But these arrows can be customized in very useful ways...
plot(c(1:10), c(1:10), type = "n")
arrows(x0 = 2, x1 = 4,y0 = 5, y1 = 5)
arrows(x0 = 8, x1 = 8, y0 = 5, y1 = 9, 
       angle = 90,  # set angle to 90 degrees = flat arrow head
       code = 3)    # draw arrow head on BOTH ends of the "arrow"

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Barplot: adding confidence intervals

ci_away <- t.test(rt2_away$value)$conf.int
ci_toward <- t.test(rt2_toward$value)$conf.int

barplot(rt_means, names.arg = c("away", "toward"))
par(xpd=T)
arrows(x0 = 0.7, x1 = 0.7, y0 = ci_away[1], y1 = ci_away[2], 
       angle = 90, code = 3, length = .2)
arrows(x0 = 1.9, x1 = 1.9, y0 = ci_toward[1], y1 = ci_toward[2], 
       angle = 90, code = 3, length = .2)

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Hands-on task: Creating a dodged barplot

  • Read in the file avengers.csv
  • Plot the screentimes of Thor and Iron Man in the three Avengers movies as a dodged barplot (i.e. a barplot with side-by-side bars).
  • Hint: For a dodged barplot, barplot() needs a matrix as input. This is a bit tricky - toy around with matrix() to create a matrix that looks like this (without the row and column names)
##          Avengers1 Avengers2 Avengers3
## Iron Man        37        45        18
## Thor            25        14        14
  • Another hint: It might help to reorder the data. Using avengers[order(avengers$character),] you can sort them by the "character" column.

Hands-on task: Creating a dodged barplot

# read in data
avengers <- read.csv("examples/avengers.csv")

# sort by "character" column
avengers <- avengers[order(avengers$Character),]
avengers_matrix <- matrix(avengers$Screentime, ncol = 3, byrow = T)

Hands-on task: Creating a dodged barplot

# plot
barplot(avengers_matrix, beside = T, names.arg = c("Avengers 1", "Avengers 2", "Avengers 3"),
        legend.text = c("Iron Man", "Thor"))

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Graphical parameters

With the help of graphical parameters, you can change the appearance of your plot. See ?par for more information. Some of the most important ones:

  • mar: margins.
  • xpd: If TRUE, you can plot outside the plot region. If FALSE (the default), plotting is confined to the plot region.
  • mfrow: numer of c(rows, columns)
  • bg: background color (or no color if you choose "transparent"; default is white)
  • Type par() to see the current settings (= the default values if you haven't changed them). This can come in handy if you want to change parameters and then restore the defaults afterwards.
  • You can even store the current values as an object by typing e.g. par_default <- par() and later on restore the current settings via par(par_default).

Saving graphs

  • In RStudio, you can use the "Export" button in the plot window
  • However, the graphics files generated this way have low resolution (72dpi), unless you export an svg image
  • This is why you should use png(), tiff(), jpeg(), or bmp() instead.
png(filename = "myplot.jpg")
plot(x,y)
dev.off()

Saving graphs

  • Saving graphs as vector graphics (SVG) has its advantages...
  • but not all programmes can handle SVG files ☹
  • Most publishers request PNG or TIFF files (some are also ok with JPG or BMP)

Saving graphs: Layout

  • Often you'll want to arrange plots in rows and/or columns
  • You have already encountered par(mfrow=c(nrow,ncol))
  • More complex arrangements are possible with layout
  • To use this function, you first have to define a matrix
m <- matrix(c(1,2,2,
              1,2,2),
           nrow = 2, byrow = T)

Saving graphs: Layout

layout(m)
barplot(c(20, 40), names.arg = c("a", "b"))
plot(x = c(7,9,15,24), y = c(8,15, 40, 32), type = "l", ylab="y", xlab="x")

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par(par_cur)

Saving graphs: Layout

  • In order to export the plot, we just add the graphics device commands png() (or the like) and dev.off():
png("myfile.png", width = 7, height = 7, un = "in", res = 300)
layout(m)
barplot(c(20, 40), names.arg = c("a", "b"))
plot(x = c(7,9,15,24), y = c(8,15, 40, 32), type = "l", ylab="y", xlab="x")
dev.off()
## quartz_off_screen 
##                 2
par(par_cur)

Tips and tricks for efficient visualization

  • You don't have to start from scratch each time you create a plot.
  • You can re-use code that you already have...
  • and you don't even have to copy & paste!

Re-using code

  • R and RStudio offer multiple different ways to re-use code:
  • functions
  • packages (= collections of functions)
  • code snippets

  • Let's explore them in turn.

Functions

  • R makes it very easy to write your own functions:

Functions

  • R makes it very easy to write your own functions:
myfunction <- function(x) {
  return(x + 2)
}

myfunction(40)
## [1] 42

Functions

  • And functions can also be used to store chunks of code that you use for visualizations:
myplot <- function(x, y, ...) { #... for inheritance
  plot(x, y, pch = 20, type = "b", lty = 2, lwd = 2)
}
myplot(x = c(1,3,7), y = c(7,3,1))

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  • The "..." in the function definition makes sure that all other arguments not explicitly specified here are passed on to the "plot" function.

Packages

  • Packages are collections of functions
  • It's only a small step from writing your own functions to compiling your own package
  • For guidance on how to create your own package, see http://r-pkgs.had.co.nz/

Code snippets

  • Macros for inserting snippets of code that you use very often
  • for example, if you have a specific configuration for exporting plots that you use over and over again, you can type it every time...
  • or you can just use a code snippet:
  • Tools > Global Options > Code > Edit Snippets

Code snippets

  • Macros for inserting snippets of code that you use very often
  • for example, if you have a specific configuration for exporting plots that you use over and over again, you can type it every time...
  • or you can just use a code snippet:
  • Tools > Global Options > Code > Edit Snippets
snippet plotexp
    png("filename.png", width = 6.5, height = 5, un = "in", res = 300)

    dev.off()

snippet plot2pan
    par(mfrow = c(1,2))
    png("filename.png", width = 13, height = 5, un = "in", res = 300)

    dev.off()

Code snippets

More things to explore

  • lattice graphics
  • ggplot2
  • plotly / shiny for interactive visualizations
  • GoogleVis motion charts
  • and a lot more!

References

  • Baayen, R. Harald. 2008. Analyzing Linguistic Data: A Practical Introduction to Statistics Using R. Cambridge: Cambridge University Press.
  • Freeman, Jenny V., Stephen John Walters & Michael J. Campbell. 2008. How to display data. Malden, Mass: BMJ Books.
  • Rifkin, Erik & Edward Bouwer. 2007. The illusion of certainty: health benefits and risks. New York, NY: Springer.
  • Tufte, Edward R. 2001. The visual display of quantitative information. 2nd ed. Cheshire: Graphics Press.
  • Wainer, Howard. 1984. How to display data badly. The American Statistician 28(2). 137–147.