15 Questions All R Users Have About Plots

Name Axes (With Up- Or Subscripts) and Put a Title to an R Plot?

You can easily name the axes and put a title in place to make your R plot more specific and understandable for your audience.

This can be easily done by adding the arguments main for the main title, sub for the subtitle, xlab for the label of the x-axis and ylab for the label of the y-axis.

Note that if you want to have a title with up-or subscripts, you can easily add these in the main argument of the plot() function. Make sure to use an expression() and use the ^ symbol for upscripts or square brackets for a subscript.

Try it out in the DataCamp Light chunk below:

You can also make sure that you have Greek letters in your axis labels by using code which combines the expression() and paste() functions. Execute the following piece of code:

Adjust the Appearance of the Axes’ Labels

For more information on these arguments, go to this page.

Remove a Plot’s Axis Labels and Annotations

If you want to get rid of the axis values of a plot, you can first add the arguments xaxt and yaxt, set as "n". These arguments are assigned a character which specifies the x-axis type. If you put in an "n", like in the command below, you can suppress the plotting of the axis.

Note that by giving any other character to the xaxt and yaxt arguments, the x-and y-axes are plotted.

Tip: not the information you are looking for? Go to this page.

Rotate a Plot’s Axis Labels

Note how this actually requires you to add an argument to the plot() function that basically says that there are no axes to be plotted (yet): this is the task of the two axis() function that come next.

The las argument can have three values attributed to it. According to whichever option you choose, the placement of the label will differ: if you choose 0, the label will always be parallel to the axis (which is the default); If you choose 1, the label will be put horizontally. Pick 2 if you want it to be perpendicular to the axis and 3 if you want it to be placed vertically.

But there is more. If you want to know more about the possibilities of the axis() function, keep on reading!

Move the Axis Labels of your R Plot

So, you want to move your axes’ labels around?

No worries, you can do this with the axis() function; As you may have noticed before in this tutorial, this function allows you to first specify where you want to draw the axes. Let’s say you want to draw the x-axis above the plot area and the y-axis to the right of it.

Remember that if you pass 1 or 2 to the axis() function, your axis will be drawn on the bottom and on the left of the plot area. Scroll a bit up to see an example of this in the previous piece of code!

As you can see, at first, you basically plot x and y, but you leave out the axes and annotations. Then, you add the axes that you want to see and specify their location with respect to the plot.

The flexibility that the axis() function creates for you keeps on growing! Check out the next frequently asked question to see what else you can solve by using this basic R function.

Tip: go to the last question to see more on how to move around text in the axis labels with hjust and vjust!

How to Change the Spacing of the Tick Marks of your R Plot

Letting R determine the tick marks of your plot can be quite annoying and there might come a time when you will want to adjust these.

1. Using the axis() Function to Determine the Tick Marks of your Plot

As you can see, you first define the position of the tick marks and their labels. Then, you draw your plot, specifying the x axis type as "n", suppressing the plotting of the axis.

Then, the real work starts:

• The axis() function allows you to specify the side of the plot on which the axis is to be drawn. In this case, the argument is completed with 1, which means that the axis is to be drawn below. If the value was 2, the axis would be drawn on the left and if the value was 3 or 4, the axis would be drawn above or to the right, respectively;
• The at argument allows you to indicate the points at which tick marks are to be drawn. In this case, you use the positions that were defined in V1;
• Likewise, the labels that you want to use are the ones that were specified in V2;
• You adjust the direction of the ticks through tck: by giving this argument a negative value, you specify that the ticks should appear below the axis.

Tip: try passing a positive value to the tck argument and see what happens!

2. Using other Functions to Determine the Tick Marks of your R Plot

You can also use the par() and plot() functions to define the positions of tickmarks and the number of intervals between them.

You can also run the following code to basically get the same result:

Note that this works only if you use no logarithmic scale. If the log coordinates set as true, or, in other words, if par(xlog=T), the three values that you pass to xaxp have a different meaning: for a small range, n is negative. Otherwise, n is in 1:3, specifying a case number, and x1 and x2 are the lowest and highest power of 10 inside the user coordinates, 10 ^ par(“usr”)[1:2].

In this example, you use the par() function: you set xlog to TRUE and add the xaxp argument to give the coordinates of the extreme tick marks and the number of intervals between them. In this case, you set the minimum value to 1, the maximal value to 4 and you add that the number of intervals between each tick mark should be 3.

Then, you plot x and y, adding the log argument to specify whether to plot the x-or y-axis or both on a log scale. You can pass "x", "y", and "xy" as values to the log arguments to do this.

How to add Minor Tick Marks to an R Plot

• The nx argument allows you to specify the number of intervals in which you want to divide the area between the major tick marks on the axis. If you pass the value 1 to it, the minor tick marks will be suppressed;
• ny allows you to do the same as nx, but then for the y-axis;
• The tick.ratio indicates the ratio of lengths of minor tick marks to major tick marks. The length of the latter is retrieved from par(tck=x).

Adding and Changing an R Plot’s Legend with Basic R

Note that the arguments pt.cex and title.cex that are described in the documentation of legend() don’t really work. There are some workarounds:

1. Put the title or the labels of the legend in a different font by adding the text.font argument to legend():

2. Draw the legend twice with different cex argument values

Tip: if you’re interested in knowing more about the colors that you can use in R, check out this very helpful PDF document.

How to add and Change an R Plot’s Legend and Labels In ggplot2

And it gives you a default legend. But, in most cases, you will want to adjust the appearance of the legend some more.

There are two ways of changing the legend title and labels in ggplot2:

1. Tell the scale to use have a different title and labels

Note that the plot looks a bit squished in the Plot tab of the DataCamp Light chunk, but that this wouldn’t be a problem if you execute the code in RStudio.

Note that you can create two legends if you add the argument shape into the geom_point() function and into the labels arguments!

If you want to move the legend to the bottom of the plot, you can specify the legend.position as "bottom". The legend.justification argument, on the other hand, allows you to position the legend inside the plot.

Tip: check out all kinds of scales that could be used to let ggplot know that other names and labels should be used here.

2. Change the data frame so that the factor has the desired form

You can then use the new factor names to make your plot in ggplot2, avoiding the “hassle” of changing the names and labels with extra lines of code in your plotting.

Tip: for a complete cheat sheet on ggplot2, you can go here.

Drawing a Grid in your R Plot with Basic R

Drawing a Grid in an R Plot with ggplot2

The code chunk below shows you how you can make sure that your plot has a grid with the ggplot2 package. Note that the chol data has already been loaded in and that the data manipulation steps from above have been applied to it.

If you’re not sure what the data looks like, print out the first rows of the data frame in the R Console of the DataCamp Light chunk:

Tip: if you don’t want to have the minor grid lines, just pass element_blank() to panel.grid.minor.

If you want to fill the background up with a color, add the panel.background = element_rect(fill = "navy") to your code. You can see how this is done in the DataCamp Light block below. The chol data has been loaded and lightly manipulated, just like before. Try it out for yourself!

Adjusting the Size of Points in an R Plot with Basic R

The circles of this plot receive the values of df$x3 as the radii, while the argument inches controls the size of the symbols. When this argument receives a positive number as input, the symbols are scaled to make largest dimension this size in inches.

Adjusting the Size of Points in your R Plot with ggplot2

Note that you can also pass the size argument to the geom_point() function:

ggplot(chol, 
       aes(x=chol$WEIGHT, y=chol$HEIGHT)) + 
  geom_point(size = 2)

You’ll get the same result, but don’t take my word for it! Try replacing the last piece of code in the DataCamp Light chunk with this chunk.

Drawing Error Bars with Basic R

The bad news: R can’t draw error bars just like that. The good news: you can still draw the error bars without needing to install extra packages!

First, you’ll need to define the following function to calculate some summary statistics of your data frame:

summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
                      conf.interval=.95, .drop=TRUE) {
    library(plyr)

    # New version of length which can handle NA's: if na.rm==T, don't count them
    length2 <- function (x, na.rm=FALSE) {
        if (na.rm) sum(!is.na(x))
        else       length(x)
    }

    # This does the summary. For each group's data frame, return a vector with
    # N, mean, and sd
    datac <- ddply(data, groupvars, .drop=.drop,
      .fun = function(xx, col) {
        c(N    = length2(xx[[col]], na.rm=na.rm),
          mean = mean   (xx[[col]], na.rm=na.rm),
          sd   = sd     (xx[[col]], na.rm=na.rm)
        )
      },
      measurevar
    )

    # Rename the "mean" column    
    datac <- rename(datac, c("mean" = measurevar))

    datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean

    # Confidence interval multiplier for standard error
    # Calculate t-statistic for confidence interval: 
    # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
    ciMult <- qt(conf.interval/2 + .5, datac$N-1)
    datac$ci <- datac$se * ciMult

    return(datac)
}

Note that the function definition comes from this source and that it is also available through the Rmisc library.

Next, you can use this to calculate some statistics of the chol data, which has already been loaded in for you. After, you can use these resulting numbers to make your plot.

Make sure to inspect the data before you start this exercise!

If you want to read up on all the arguments that arrows() can take, go here.

Drawing Error Bars With ggplot2

Plot Two Plots Side by Side Using Basic R

By adding the par() function with the mfrow argument, you specify a vector, which in this case contains 1 and 2: all figures will then be drawn in a 1-by-2 array on the device by rows (mfrow).

In other words, the boxplots from above will be printed in one row inside two columns.

Plot Two Plots Next to each other Using ggplot2

If you want to put plots side by side and if you don’t want to specify limits, you can consider using the ggplot2 package to draw your plots side-by-side.

Your data chol and the summarySE() function have been loaded in. Inspect them in the R Console if you’re not sure what they look like:

Note how you just add the facet_grid() function to indicate that you want two plots next to each other. The element that is used to determine how the plots are drawn, is MORT, as you can well see above!

Plot more Plots Side by Side Using gridExtra

Note how here again you determine how the two plots will appear to you thanks to the ncol argument.

Plot more Plots Side by Side Using lattice

Just like the solution with ggplot2 package, the lattice package also doesn’t require you to specify limits or the way you want your plots printed next to each other.

Instead, you use bwplot() to make trellis graphs with the graph type of a box plot. Trellis graphs display a variable or the relationship between variables, conditioned on one or more other variables.

In this case, if you’re using the chol data set (which you can find here or load in with the read.table() function, you display the variable CHOL separately for every combination of factor SMOKE and MORT levels.

Note that the data has been loaded in for you in the DataCamp Light chunk below!

Plotting Plots Next to each other with gridBase

Another way even to put two plots next to each other is by using the gridBase package, which takes care of the “integration of base and grid graphics”. This could be handy when you want to put a basic R plot and a ggplot next to each other.

You work as follows: first, you activate the necessary packages in your workspace. In this case, you want to have gridBase ready to put the two plots next to each other and grid and ggplot2 to actually make your plots:

library(grid)
library(gridBase)
library(ggplot2)

plot.new()

gl <- grid.layout(nrow=1, 
                  ncol=2)
vp.1 <- viewport(layout.pos.col=1, 
                 layout.pos.row=1) 
vp.2 <- viewport(layout.pos.col=2, 
                 layout.pos.row=1)
                 
pushViewport(viewport(layout=gl))
pushViewport(vp.1)

par(new=TRUE, 
    fig=gridFIG())
plot(x = 1:10, 
     y = 10:1)
     
popViewport()

pushViewport(vp.2)

ggplotted <- qplot(x=1:10,y=10:1, 'point')
print(ggplotted, newpage = FALSE)

popViewport(1)

If you want and need it, you can start an empty plot to then set up the layout. Note that since you want the two plots to be generated next to each other, this requires you to make a grid layout consisting of one row and two columns.

Now, you want to fill up the cells of the grid with viewports. These define rectangular regions on your graphics device with the help of coordinates within those regions. In this case, it’s much more handy to use the specifications of the grid that have just been described above rather than real x-or y-coordinates. That is why you should use the layout.pos.col and layout.pos.row arguments.

Since the viewports are only descriptions or definitions, these kinds of objects need to be pushed onto the viewport tree before you can see any effect on the drawing. You want to use the pushViewport() function to accomplish this.

Now you can proceed to adding the first rectangular region vp.1 to the ViewPort tree, after which you tell R with gridFig() to draw a base plot within a grid viewport (vp.1, that is). The fig argument normally takes the coordinates of the figure region in the display region of the device. In this case, you use the fig argument to start a new plot, adding it to an existing plot use by adding new = TRUE in the par() function as well. You plot the base graphic and remove the viewport from the tree:

plot.new()

gl <- grid.layout(nrow=1, 
                  ncol=2)

vp.1 <- viewport(layout.pos.col=1, 
                 layout.pos.row=1) 
vp.2 <- viewport(layout.pos.col=2, 
                 layout.pos.row=1)

pushViewport(viewport(layout=gl))

pushViewport(vp.1)

par(new=TRUE, 
    fig=gridFIG())
plot(x = 1:10, 
     y = 10:1)
popViewport()

Since you want the two plots to be generated next to each other, you want to put one plot in the first column and the other in the second column, both located on the first row.

Note that the pushViewport() function takes the viewport() function, which in itself contains a layout argument. This last argument indicates “a grid layout object which splits the viewport into subregions”.

Note also that you can specify in the popViewport() function an argument to indicate how many viewports you want to remove from the tree. If this value is 0, this indicates that you want to remove the viewports right up to the root viewport. The default value of this argument is 1.

Go to add the second rectangular region vp.2 to the ViewPort tree. You can then make the ggplot and remove the viewport from the tree.

pushViewport(vp.2)
ggplotted <- qplot(x=1:10,
                   y=10:1, 
                   'point')
print(ggplotted, 
      newpage = FALSE)
popViewport(1)

Note that you need to print to print the graphics object made by qplot() in order to actually draw it and get it displayed. At the same time, you also want to specify newpage = FALSE, otherwise you’ll only see the qplot()…

Also, remember that the default value of viewports to remove in the function popViewport() is set at 1. This makes it kind of redundant to put popViewport(1) in the code.

Using Basic R to Plot Multiple Lines or Points in the same R Plot

Next, you see that these lines are plotted with the use of the lines() function.

This gives the following result:

Note that the lines() function takes in three arguments: the x-axis and the y-axis that you want to plot and the color (represented with the argument col) in which you want to plot them. You can also include the following features:

Here are some examples:

lines(x,y2,col="green", lty = 2, lwd = 3)
lines(x,y2,col="green", lty = 5, lwd = 2, pch = 2)
lines(x,y3,col="black", lty = 3, lwd = 5, pch = 3, lend = 0, ljoin = 2)
lines(x,y4,col="blue", lty = 1, lwd = 2, pch = 3, lend = 2, ljoin = 1, lmitre = 2)

Note that the pch argument does not function all that well with the lines() function and that it’s best to use it only with points().

You can add the same arguments to the points() function as you did with the lines() function and that are listed above. There are some additions, though:

Code examples of these arguments are the following:

points(x,y4,col="blue", pch=21, bg = "red") 
points(x, y5, col="yellow", pch = 5, bg = "blue") 

Using ggplot2 to Plot Multiple Lines or Points In One R Plot

Adjusting the Aspect Ratio with Basic R

Adjusting the Aspect Ratio for your Plots with ggplot2

To fix the aspect ratio for ggplot2 plots, you just add the function coord_fixed(), which provides a “fixed scale coordinate system [that] forces a specified ratio between the physical representation of data units on the axes”.

In other words, this function allows you to specify a number of units on the y-axis which is equivalent to one unit on the x-axis. The default is always set at 1, which means that one unit on the x-axis has the same length as one unit on the y-axis. If your ratio is set at a higher value, the units on the y-axis are longer than units on the x-axis and vice versa.

Adjusting the Aspect Ratio for your Plots with MASS

Tip: you might do well starting a new plot frame before executing the code above!

Note that you can give additional arguments to the eqscplot() function to customize the scatterplot’s look!