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Basic idea

After the nice_violin() function, here’s how to make nice scatter plots easily!

Let’s first load the demo data. This data set comes with base R (meaning you have it too and can directly type this command into your R console).

data("mtcars")
head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Getting started

Load the rempsyc package:

Note: If you haven’t installed this package yet, you will need to install it via the following command: devtools::install_github("rempsyc/rempsyc"). Package devtools is necessary for this command. If you haven’t installed devtools yet, install it via install.packages("devtools").

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg")

### Save a high-resolution image file to specified directory
ggsave('nice_scatterplothere.tiff', width = 7, height = 7, unit = 'in', dpi = 300,
       path = "D:/R treasures/")
# Change the path to where you would like to save it.
# If you copy-paste your path name, remember to use "R" slashes ('/' rather than '\').
# Also remember to specify the .tiff extension of the file.

Pro tip: Change .tiff for .pdf or .eps for scalable vector graphics for high-resolution submissions to scientific journals!

Customization

Change x- and y- axis labels

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             ytitle = "Miles/(US) gallon",
             xtitle = "Weight (1000 lbs)")

Have points “jittered”

Meaning randomly moved around a bit to prevent overplotting (when two or more points overlap, thus hiding information).

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             has.jitter = TRUE)

Change the transparency of the points

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             alpha = 1) # default is 0.7

Remove points

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             has.points = FALSE,
             has.jitter = FALSE)

Add confidence band

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             has.confband = TRUE)

Set x- and y- scales manually

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             xmin = 1,
             xmax = 6,
             xby = 1,
             ymin = 10,
             ymax = 35,
             yby = 5)

Change plot colour

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             colours = "blueviolet")

Add correlation coefficient to plot and p-value

nice_scatter(data = mtcars,            
             predictor = "wt",
             response = "mpg",
             has.r = TRUE,
             has.p = TRUE)

Change location of correlation coefficient or p-value

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             has.r = TRUE,
             r.x = 4,
             r.y = 25,
             has.p = TRUE,
             p.x = 5,
             p.y = 20)

Plot by group

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             group = "cyl")

Use full range on the slope/confidence band

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             group = "cyl",
             has.fullrange = TRUE)

Add a legend

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             group = "cyl",
             has.legend = TRUE)

Change order of labels on the legend

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             group = "cyl",
             has.legend = TRUE,
             groups.order = c(8,4,6)) # These are the levels of 'mtcars$cyl', so we place lvl 8 first, then lvl 4, etc.

Change legend labels

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             group = "cyl",
             has.legend = TRUE,
             groups.labels = c("Weak","Average","Powerful")) # Warning: This applies after changing order of level

**Warning**: This only changes labels and applies after changing order of level!
Always use `groups.order` first if you also need to use `groups.labels`!
This is to make sure to have the right labels for the right groups!

Add a title to legend

nice_scatter(data = mtcars,            
             predictor = "wt",
             response = "mpg",
             group = "cyl",
             has.legend = TRUE,
             legend.title = "Cylinders")

Plot by group + manually specify colours

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             group = "cyl",
             colours = c("burlywood", "darkgoldenrod", "chocolate"))

Plot by group + use different line types for each group

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             group = "cyl",
             has.linetype = TRUE)

Plot by group + use different point shapes for each group

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             group = "cyl",
             has.shape = TRUE)

Plot by group, point shapes, line types, legend + no colours (black and white)

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             group = "cyl",
             has.legend = TRUE,
             legend.title = "Cylinders",
             has.linetype = TRUE,
             has.shape = TRUE,
             colours = rep("black", 3))

Putting it all together

If you’d like to see all available options at once (a bit long):

nice_scatter(data = mtcars,
             predictor = "wt",
             response = "mpg",
             ytitle = "Miles/(US) gallon",
             xtitle = "Weight (1000 lbs)",
             has.points = FALSE,
             has.jitter = TRUE,
             alpha = 1,
             has.confband = TRUE,
             has.fullrange = FALSE,
             group = "cyl",
             has.linetype = TRUE,
             has.shape = TRUE,
             xmin = 1,
             xmax = 6,
             xby = 1,
             ymin = 10,
             ymax = 35,
             yby = 5,
             has.r = TRUE,
             has.p = TRUE,
             r.x = 5.5,
             r.y = 25,
             colours = c("burlywood", "darkgoldenrod", "chocolate"),
             has.legend = TRUE,
             legend.title = "Cylinders",
             groups.labels = c("Weak", "Average", "Powerful"))

Special situation: Add group average

There’s no straightforward way to add group average, so here’s a hack to do it. We first have to create a second data set with another “group” that will be used as the average.

Black line + coloured lines

new.Data <- mtcars # This simply copies the 'mtcars' dataset
new.Data$cyl <- "Average" # That would be your "Group" variable normally
# And this operation fills all cells of that column with the word "Average" to identify our new 'group'
XData <- rbind(mtcars,new.Data) # This adds the new "Average" group rows to the original data rows

Then we need to create a FIRST layer of just the slopes. We add transparency to the group lines except the group average to emphasize the group average (with the new argument groups.alpha).

(p <- nice_scatter(data = XData,
                   predictor = "wt",
                   response = "mpg",
                   has.points = FALSE,
                   has.legend = TRUE,
                   group = "cyl",
                   colours = c("black", "#00BA38", "#619CFF", "#F8766D"), # We add colours manually because we want average to be black to stand out
                   groups.order = c("Average","4","6","8"), # We do this to have average on top since it's the most important
                   groups.alpha = c(1,0.5,0.5,0.5))) # This adds 50% transparency to all lines except the first one (Average) which is 100%

Finally we are ready to add a SECOND layer of just the points on top of our previous layer. We use standard ggplot syntax for this.

library(ggplot2)
p + geom_point(data = mtcars,
               size = 2, 
               alpha = 0.5,
               shape = 16, # We use shape 16 because the default shape 19 sometimes causes problems when exporting to PDF
               mapping = aes(x = wt, 
                             y = mpg, 
                             colour = factor(cyl), 
                             fill = factor(cyl)))

Black line, coloured dots

If you’d like instead to still show the group points but only the black average line, you can do the following as first layer:

(p <- nice_scatter(data = mtcars,
                   predictor = "wt",
                   response = "mpg",
                   has.points = FALSE,
                   has.legend = TRUE, # This argument is important else the next legend won't appear on the second layer!
                   colours = "black"))

Then to add the points as second layer we do the same as before:

p + geom_point(data = mtcars, 
               size = 2, 
               alpha = 0.5,
               shape = 16,
               mapping = aes(x = wt, 
                             y = mpg, 
                             colour = factor(cyl)))

Thanks for checking in

Make sure to check out this page again if you use the code after a time or if you encounter errors, as I periodically update or improve the code. Feel free to contact me for comments, questions, or requests to improve this function at https://github.com/rempsyc/rempsyc/issues. See all tutorials here: https://remi-theriault.com/tutorials.