# Plotting with R (Part I)

*visibility*306

*event*2020-09-23

*access_time*2 years ago

*language*English

For data analyst, it is critical to use charts to tell data stories clearly. R has numerous libraries to create charts and graphs. This article summarizes the high-level R plotting APIs (incl. graphical parameters) and provides examples about plotting Pie Chart, Bar Chart, BoxPlot, Histogram, Line and Scatterplot using R.

### Device, screen and layout

Before plotting, it is important to understand R's

Category | Functions |

Graphical devices | These APIs provide controls over multiple graphics devices: dev.list(), dev.cur(), dev.set(number), dev.off() |

Screens | These APIs can be used to define a number of regions within the current device which can, to some extent, be treated as separate graphics devices. It is useful for generating multiple plots on a single device. * cannot work with multiple graphic device |

Layouts (not compatible with split.screen) | layout divides the device up into as many rows and columns as there are in matrix mat, with the column-widths and the row-heights specified in the respective arguments. layout(matrix), layout.show(n) |

#### Examples

The following are some code examples (script *R26.GraphicDevices.R*) using these APIs:

# list devices dev.list() dev.set(3) dev.cur() dev.off() dev.list() #window windows() png() dev.list() dev.set(3) dev.off(2) # split screen split.screen(c(1, 2)) screen(1) screen(2) # layout layout(matrix(1:4, 2, 2)) layout.show(4)

layout(matrix(1:4,2,2), widths=c(1, 3),heights=c(3, 1))

### Graphic functions

The following table summarizes R graphic functions that can be used in plotting:

Function | Description |

plot(x) | lot of the values of x (on the y-axis) ordered on the x-axis |

plot(x, y) | bivariate plot of x (on the x-axis) and y (on the y-axis) |

sunflowerplot(x, y) | the points with similar coordinates are drawn as a ﬂower which petal number represents the number of points |

pie(x) | circular pie-chart |

boxplot(x) | “box-and-whiskers” plot |

stripchart(x) | plot of the values of x on a line (an alternative to boxplot() for small sample sizes) |

coplot(x~y | z) | bivariate plot of x and y for each value (or interval of values) of z |

interaction.plot (f1, f2, y) | (f1, f2, y) |

if f1 and f2 are factors, plots the means of y (on the y-axis) with respect to the values of f1 (on the x-axis) and of f2 (diﬀerent curves); the option fun allows to choose the summary statistic of y (by default fun=mean) | |

matplot(x,y) | bivariate plot of the ﬁrst column of x vs. the ﬁrst one of y, the second one of x vs. the second one of y, etc. |

dotchart(x) | if x is a data frame, plots a Cleveland dot plot (stacked plots line-by-line and column-by-column) |

fourfoldplot(x) | visualizes, with quarters of circles, the association between two dichotomous variables for diﬀerent populations (x must be an array with dim=c(2, 2, k), or a matrix with dim=c(2, 2) if k = 1) |

assocplot(x) | Cohen–Friendly graph showing the deviations from independence of rows and columns in a two dimensional contingency table |

mosaicplot(x) | ‘mosaic’ graph of the residuals from a log-linear regression of a contingency table |

pairs(x) | if x is a matrix or a data frame, draws all possible bivariate plots between the columns of x |

plot.ts(x) | if x is an object of class "ts", plot of x with respect to time, x may be multivariate but the series must have the same frequency and dates |

ts.plot(x) | Similar as above but if x is multivariate the series may have diﬀerent dates and must have the same frequency |

hist(x) | histogram of the frequencies of x |

barplot(x) | histogram of the values of x |

qqnorm(x) | quantiles of x with respect to the values expected under a normal law |

qqplot(x, y) | quantiles of y with respect to the quantiles of x |

contour(x, y, z) | contour plot (data are interpolated to draw the curves), x and y must be vectors and z must be a matrix so that dim(z)=c(length(x), length(y)) (x and y may be omitted) |

filled.contour (x, y, z) | Similar as above but the areas between the contours are coloured, and a legend of the colours is drawn as well |

image(x, y, z) | Similar as above but the actual data are represented with colours |

persp(x, y, z) | Similar as above but in perspective |

stars(x) | if x is a matrix or a data frame, draws a graph with segments or a star where each row of x is represented by a star and the columns are the lengths of the segments |

symbols(x, y, ...) | draws, at the coordinates given by x and y, symbols (circles, squares, rectangles, stars, thermometers or “boxplots”) which sizes, colours, etc, are speciﬁed by supplementary arguments |

termplot(mod.obj) | plot of the (partial) eﬀects of a regression model (mod.obj) |

#### Commonalities of graphic functions

There are some common shared parameters for these plotting functions:

- add=FALSE: if TRUE superposes the plot on the previous one (if it exists)
- axes=TRUE: if FALSE does not draw the axes and the box
- type="p":

"p": points

"l": lines

"b": points connected by lines

"o": Similar as above but the lines are over the points

"h": vertical lines

"s": steps, the data are represented by the top of the vertical lines

"S": Similar as above but the data are represented by the bottom of the vertical lines

- xlim=, ylim= speciﬁes the lower and upper limits of the axes, for example with xlim=c(1, 10) or xlim=range(x)
- xlab=, ylab= annotates the axes (character vector)
- main= main title (character vector)
- sub= sub-title

#### Simple examples

The following code snippet shows some basic examples (script *R27.GraphicalFunctions.R*) using these common parameters:

# plot x <- rnorm(30,20,10) plot(x, type="p", main="Plot with Type p", ) plot(x, type="l", main="Plot with Type l", add=FALSE) plot(x, type="b", main="Plot with Type b", add=FALSE) plot(x, type="o", main="Plot with Type o", add=FALSE) plot(x, type="h", main="Plot with Type h", add=FALSE) plot(x, type="s", main="Plot with Type s", add=FALSE) par(bg="green") plot(x, type="S", main="Plot with Type S", add=FALSE)

### Low level plotting commands

Low level plotting commands are used to affect an existing graph. They can be used to add these items to the graph:

- data labels
- lines and points
- legends
- title, sub title
- …

The following table summarizes all the low-level plotting commands:

Commands | Description |

points(x, y) | adds points (the option type= can be used) |

lines(x, y) | Similar as above but with lines |

text(x, y, labels, ...) | adds text given by labels at coordinates (x,y); a typical use is: plot(x, y, type="n"); text(x, y, names) |

mtext(text, side=3, line=0, ...) | adds text given by text in the margin speciﬁed by side (see axis() below); line speciﬁes the line from the plotting area |

segments(x0, y0, x1, y1) | draws lines from points (x0,y0) to points (x1,y1) |

arrows(x0, y0, x1, y1, angle= 30, code=2) | Same as above with arrows at points (x0,y0) if code=2, at points (x1,y1) if code=1, or both if code=3; angle controls the angle from the shaft of the arrow to the edge of the arrow head |

abline(a,b) | draws a line of slope b and intercept a |

abline(h=y) | draws a horizontal line at ordinate y |

abline(v=x) | draws a vertical line at abcissa x |

abline(lm.obj) | draws the regression line given by lm.obj |

rect(x1, y1, x2, y2) | draws a rectangle which left, right, bottom, and top limits are x1, x2, y1, and y2, respectively |

polygon(x, y) | draws a polygon linking the points with coordinates given by x and y |

legend(x, y, legend) | adds the legend at the point (x,y) with the symbols given by legend |

title() | adds a title and optionally a sub-title |

axis(side, vect) | adds an axis at the bottom (side=1), on the left (2), at the top (3), or on the right (4); vect (optional) gives the abcissa (or ordinates) where tick-marks are drawn |

box() | adds a box around the current plot |

rug(x) | draws the data x on the x-axis as small vertical lines |

### Graphic parameters

Graphs can be improved using graphical parameters. They can be used either as options of graphical functions or with function **par**.

For example, the following code snippet will set the device background color as green for all the following plots:

par(bg="green")

In next part, I will show plotting examples of different chart types.

*info*Last modified by Raymond 2 years ago

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