One reason for the popularity of the R environment is its plotting capacities. However, this comes at the price of specifying a lot of parameters and arguments. In this section we learn about plotting with base R. As this topic is huge, we will only scratch the surface and review those functions which we believe are useful for a day-to-day workflow. If you are interested in learning more on plotting with R, we highly recommend Paul Ross Murrell’s book R Graphics (2011). Further, we found the well structured Quick-R online tutorial by Robert I. Kabacoff very helpful and follow his structural approach.

In R we customize graphical features of the graphs (fonts, colors,
axes, titles) through graphic options. One way is to specify these
options through the `par()`

function. Setting parameter
values via the `par()`

function affects all plots for the
rest of the session or until we change them again. The format is:

`par(optionname = value, optionname = value, ...)`

Some useful cases are given below:

```
par() # view current settings
opar <- par() # make a copy of current settings
par(col.lab = "red") # red x and y labels
par(opar) # restore original settings
```

The `par()`

function is very powerful and exhaustive. Type
`help(par)`

into your console for further information. In
addition, we recommend to bookmark the compilation of important
arguments for the `par()`

function provided by Paul Ross Murrell.

Another way to specify graphical parameters is by providing the
`optionname = value`

pairs directly to the plotting function.
In this case, the options are only in effect for that specific graph.
Always remember to check out the help page for a specific plotting
function to determine which particular graphical parameters apply.

**Text and Symbol Size**

The following options can be used to control text and symbol size in graphs.

option | description |
---|---|

cex | number indicating the amount by which plotting text and symbols should be scaled relative to the default (=1) |

cex.axis | magnification of axis annotations relative to cex |

cex.lab | magnification of x and y labels relative to cex |

cex.main | magnification of titles relative to cex |

cex.sub | magnification of subtitles relative to cex |

**Plotting Symbols and Lines**

Plotting symbols are specified using the `pch`

argument.
Lines can be changed using the `lty`

and `lwd`

argument.

option | description |
---|---|

pch | specify symbols to use when plotting points |

lty | line type (number 1-6) |

lwd | line width relative to the default (default=1). 2 is twice as wide |

**Colors**

In R colors are specified either by index, name, hexadecimal, or RGB.
Type `colors()`

into the R console to return all available
color names.

`length(colors())`

`## [1] 657`

```
## print the first 25 color names
head(colors(), 25)
```

```
## [1] "white" "aliceblue" "antiquewhite" "antiquewhite1"
## [5] "antiquewhite2" "antiquewhite3" "antiquewhite4" "aquamarine"
## [9] "aquamarine1" "aquamarine2" "aquamarine3" "aquamarine4"
## [13] "azure" "azure1" "azure2" "azure3"
## [17] "azure4" "beige" "bisque" "bisque1"
## [21] "bisque2" "bisque3" "bisque4" "black"
## [25] "blanchedalmond"
```

Further we can create a vector of `n`

contiguous colors
using the functions:

`rainbow(n)`

`heat.colors(n)`

`terrain.colors(n)`

`topo.colors(n)`

`cm.colors(n)`

Another highly recommended package for color schemes, in particular
for maps, is the `RColorBrewer`

package. Go to the project webpage for
further information.

Options that specify colors include the following:

option | description |
---|---|

col | default plotting color |

col.axis | color for axis annotations |

col.lab | color for x and y labels |

col.main | color for titles |

col.sub | color for subtitles |

fg | plot foreground color (axes, boxes) |

bg | plot background color |

**Fonts**

We can specify font size and style with the following arguments:

option | description |
---|---|

font | integer specifying font to use for text (1=plain, 2=bold, 3=italic, 4=bold italic, 5=symbol) |

font.axis | font for axis annotations |

font.lab | font for x and y labels |

font.main | font for titles |

font.sub | font for subtitles |

**Margins and Graph Size**

Margin size is controlled using the following parameters:

option | description |
---|---|

mar | numerical vector indicating margin size in lines |

mai | numerical vector indicating margin size in inches |

pin | plot dimensions (width, height) in inches |

To showcase the different parameters above, we enhance the visualization of a simple sine plot with different amplitudes.

This is our baseline plot, without any modifications:

```
x <- seq(-2 * pi, 2 * pi, length.out = 100)
y1 <- sin(x)
y2 <- sin(x) + 0.5
y3 <- sin(x) - 0.5
y4 <- 2 * sin(x)
y5 <- 0.5 * sin(x)
plot(x, y1)
points(x, y2)
points(x, y2)
points(x, y3)
points(x, y4)
points(x, y5)
```

Quite a mess!

Let us improve the plot.

```
# start with the first plot
plot(x, y1,
type = "o", # overplotted points and lines
pch = 20, # specify symbol
col = 1, # line color
cex.axis = 0.75, # magnification of axis annotation relative to 1
col.axis = "blue", # color for axis annotation
col.lab = "blue", # color for x and y labels
fg = "blue", # axis color
font.axis = 3 # italic font style
)
# for the sake of writing less code we construct a list object
# with the different y values as members
y <- list(y2, y3, y4, y5)
## loop through the list y
for (i in 1:4) {
lines(x, y[[i]], # pick one y after the other
lty = i + 1, # set line type to 2,3,4,5
lwd = i + 1, # set line width to 2,3,4,5
col = i + 1 # set line color to 2,3,4,5
)
}
```

Well, much better, but not yet ready for publication. In the subsequent sections we learn how to further improve the plot above.

Most plotting functions allow you to include axis and text options in the function call (similarly to other graphical parameters). For finer control, however, R offers some particularly useful features:

**Titles**

In order to add labels to a plot we apply the `title()`

function.

```
title(main = "main title", sub = "sub-title",
xlab = "x-axis label", ylab = "y-axis label")
```

We can add a main title (`main`

), subtitle
(`sub`

) and labels to annotate the axes of a plot
(`xlab`

and `ylab`

). Note that other graphical
parameters (such as text size, font, rotation, and color) can also be
specified in the `title()`

function.

**Text Annotations**

We add text to graphs by calling the `text()`

and
`mtext()`

functions. The `text()`

function places
text within the graph while the `mtext()`

function places
text in one of the four margins.

```
text(location, "text to place", pos, ...)
mtext("text to place", side, line = n, ...)
```

Common options are described below:

option | description |
---|---|

location | location can be an x,y coordinate |

pos | position relative to location: 1=below, 2=left, 3=above, 4=right |

side | which margin to place text: 1=bottom, 2=left, 3=top, 4=right |

Note that other common options are `cex`

, `col`

and `font`

.

**Math Annotations**

We can add mathematical formulas to a graph using TeX-like
rules. Type `help(plotmath)`

into your console for details
and examples.

**Axes**

We can specify the limits of the axes by providing a vector for the
lower and upper bound to the `xlim()`

and `ylim()`

arguments. In addition, we can create custom axes using the
`axis()`

function. Note that if we create a custom axis, we
have to suppress the axis automatically generated by a plotting
function. The option `axes = FALSE`

suppresses both x and y
axes. `xaxt = "n"`

and `yaxt = "n"`

suppress the x
and y axis, respectively.

`axis(side, at, labels, pos, lty, col, las, tck, ...)`

option | description |
---|---|

side | an integer indicating the side of the graph to draw the axis (1=bottom, 2=left, 3=top, 4=right) |

at | a numeric vector indicating where tick marks should be drawn |

labels | a character vector of labels to be placed at the tick marks |

pos | the coordinate at which the axis line is to be drawn |

lty | line type |

col | line and tick mark color |

las | labels are parallel (=0) or perpendicular (=2) to axis |

tck | length of tick mark as fraction of plotting region |

(…) | other graphical parameters |

**Legend**

A legend is added with the `legend()`

function.

`legend(location, title, legend, ...)`

Common options are described below. For more on legends, type
`help(legend)`

into your console.

option | description |
---|---|

location | There are several ways to indicate the location of the legend. You can give an x,y coordinate for the upper left hand corner of the legend. You can also use the keywords “bottom”, “bottomleft”, “left”, “topleft”, “top”, “topright”, “right”, “bottomright”, or “center”. |

title | a character string for the legend title (optional) |

legend | a character vector with the labels |

bty | box type |

bg | background color |

cex | size |

text.col | text color |

horiz | TRUE sets the legend horizontally rather than vertically. |

(…) | other graphical parameters |

In order to showcase our new skills we return to the plot of the previous section and improve its visual representation. Recall where we left:

```
## data generation
x <- seq(-2 * pi, 2 * pi, length.out = 100)
y1 <- sin(x)
y2 <- sin(x) + 0.5
y3 <- sin(x) - 0.5
y4 <- 2 * sin(x)
y5 <- 0.5 * sin(x)
## start with the first plot
plot(x, y1,
type = "o", # overplotted points and lines
pch = 20, # specify symbol
col = 1, # line color
cex.axis = 0.75, # magnification of axis annotation relative to 1
col.axis = "blue", # color for axis annotation
col.lab = "blue", # color for x and y labels
fg = "blue", # axis color
font.axis = 3 # italic font style
)
# for the sake of writing less code we construct a list object
# with the different y values as members
y <- list(y2, y3, y4, y5)
## loop through the list y
for (i in 1:4) {
lines(x, y[[i]], # pick one y after the other
lty = i + 1, # set line type to 2,3,4,5
lwd = i + 1, # set line width to 2,3,4,5
col = i + 1 # set line color to 2,3,4,5
)
}
```

Time to improve…

```
## colors from RColorBrewer package
colors <- RColorBrewer::brewer.pal(5, "Set1")
# for the sake of writing less code we construct a list object
# with the different y values as members
y <- list(y2, y3, y4, y5)
## start with the first plot
plot(x, y1,
type = "o", # overplotted points and lines
pch = 20, # specify symbol
col = colors[1], # line color, first from Set1 of RColorBrewer package
cex.axis = 0.75, # magnification of axis annotation relative to 1
col.axis = "blue", # color for axis annotation
col.lab = "blue", # color for x and y labels
fg = "blue", # axis color
font.axis = 3, # italic font style
ylab = "", # no y-label
xlab = "", # no x-label
xaxt = "n", # no x-axis, as we want to customize it later
ylim = c(-2, 2), # set y-axis limits
las = 2 # rotate tick labels
)
## add plot title
title(
main = "Sine curves for different amplitudes", # title
cex.main = 1.2, # title size
font.main = 3 # italic font style title
)
## add custom axis
x_axis <- c(
-2 * pi, -1.5 * pi, -pi,
-pi / 2, 0, pi / 2, pi,
1.5 * pi, 2 * pi
)
axis(1, # side,
at = c(
-2 * pi, -1.5 * pi, -pi,
-pi / 2, 0, pi / 2, pi,
1.5 * pi, 2 * pi
), # set ticks
labels = expression(
-2 * pi, -1.5 * pi, -pi,
-pi / 2, 0, pi / 2, pi,
1.5 * pi, 2 * pi
), # label ticks
col = "blue", # color ticks
col.axis = "blue", # color axis
cex.axis = 0.8, # tick label size
font.axis = 3 # tick label font style
)
## loop through the list y
for (i in 1:4) {
lines(x, y[[i]], # pick one y after the other
lwd = 2, # set line width
col = colors[i + 1] # set line color from Set1 of RColorBrewer package
)
}
## add horizontal and vertical lines
abline(h = 0, col = "blue", lty = 2) # horizontal line
abline(v = 0, col = "blue", lty = 2) # vertical line
## add legend
legend("topright", # position
legend = expression(
sin(x), # legend labels
sin(x) + 0.5,
sin(x) - 0.5,
2 * sin(x),
sin(x) / 2
),
pch = c(19, rep(NA, 4)), # legend point symbol
lty = rep(1, 5), # legend line type
col = colors, # legend line color
cex = 0.75, # legend label font size
bty = "n" # no box around legend items
)
```

Nice, what do you think? However, honestly we wrote quite some code
for this visualization. In the subsequent sections we will see that
there are contributed packages, such as `ggplot2`

, which simplify the production
of high quality plots.

Another nice feature is to combine multiple plots into one overall
graph. In order to achieve that with the R base plotting system we apply
either the `par()`

or the `layout()`

function.

Using the `par()`

function we have to include the argument
`mfrow = c(nrows, ncols)`

to create a matrix of
`nrows`

\(\times\)
`ncols`

plots that are **filled in by row**. The
argument `mfcol = c(nrows, ncols)`

fills in the matrix by
columns.

Let us reload the *student* data set to showcase this
functionality.

`students <- read.csv("https://userpage.fu-berlin.de/soga/data/raw-data/students.csv")`

In order to plot 4 figures arranged in 2 rows and 2 columns we set
`mfrow = c(2,2)`

.

```
par(mfrow = c(2, 2), cex.main = 0.75)
## plot 1
plot(students$height, students$weight, main = "Scatterplot of height vs. weight")
## plot 2
hist(students$nc.score, main = "Histogram of NC-score")
## plot 3
boxplot(students$salary, main = "Boxplot of salary")
## plot 4
barplot(table(students$religion),
las = 2,
cex.names = 0.65,
main = "Barplot of religious groups"
)
```

In order to plot two figures arranged in 2 rows and 1 column we set
`mfrow = c(2,1)`

.

```
par(mfrow = c(2, 1), cex.main = 0.75)
## plot 1
plot(students$height, students$weight, main = "Scatterplot of height vs. weight")
## plot 2
barplot(table(students$religion),
las = 2,
cex.names = 0.65,
main = "Barplot of religious groups"
)
```

Alternatively, we may apply the `layout()`

function. In
this case we feed a `matrix`

object to the function
specifying the location of the figures to plot.

In order to plot one figure in row 1 and two figures in row 2 we need to write:

```
layout(matrix(c(1, 1, 2, 3), 2, 2, byrow = TRUE))
hist(students$age, breaks = 50)
hist(students$height)
hist(students$weight)
```

Optionally, we may include the arguments `widths`

and
`heights`

in the `layout()`

function to control
the size of each figure more precisely. Visit the help page for the
`layout()`

function for more details
(`help(layout)`

).

```
par(cex.main = 0.7)
layout(matrix(c(1, 1, 2, 3), 2, 2, byrow = TRUE),
widths = c(3, 1), # column 2 is 1/3 the width of column 1
heights = c(2, 1)
) # row 2 is 1/2 the height of row 1
hist(students$age, breaks = 50)
hist(students$height)
hist(students$weight)
```

**Citation**

The E-Learning project SOGA-R was developed at the Department of Earth Sciences by Kai Hartmann, Joachim Krois and Annette Rudolph. You can reach us via mail by soga[at]zedat.fu-berlin.de.

You may use this project freely under the Creative Commons Attribution-ShareAlike 4.0 International License.

Please cite as follow: *Hartmann,
K., Krois, J., Rudolph, A. (2023): Statistics and Geodata Analysis
using R (SOGA-R). Department of Earth Sciences, Freie Universitaet Berlin.*