Background
This is an unusual post for me, I have avoided writing about R Markdown because there are so many resources already available on the topic (e.g., here, here, and here). However, recently I ran a session on using RMarkdown for my colleagues in the Centre for Social Issues Research. The aim of this was to demonstrate the usefulness of R Markdown (and hopefully convert a few people). For this session I created a set of resources^{1} aimed at making the transition from SPSS to R Markdown a bit easier. The statistics content of these resources is mainly just some of the simpler standard tests taught to psychology undergraduate students.
The complete resources are available on this project page on the OSF. The main purpose of the exercise was to provide people with the tools to create this pdf using this R Markdown template. My hope is that by using this template, SPSS users might make the tranistion to R, and R Markdown (with the help of the wonderful papaja package Aust (2017)).
R Markdown Basics
I started off the workshop by going through some of the basics of R Markdown. When working with R Markdown, there are three types of text to be concerned with.
 Plain text: this is what you will use to write your manuscript. The formatting of this plain text is fairly straight forward (see this cheatsheet)^{2}
 Code chunks: these are denoted by three back ticks
```
at the top and the bottom of a chunk with commands in curly brackets in the title (on the same line as the firs set of back ticks). Code chunks allow you to run analyses without leaving your document. Output from code chunks can be included or omitted from your main output document.  Inline code: this is denoted by a single back tick to begin a piece of code and a single back tick to close it
code goes here
.
Each time you open a piece of code (a chunk or inline code) you have to identify what language you want to code in. This is done by including the letter “r” in the curly brackets or immediately after the opening back tick.
Working with R
In order to show some of the basic functionality of R, I ran some analyses using the data sets that are built into R. This means that anyone should be able to reproduce the analyses conducted using the template I provided (without needing to worry about loading data from other files). I also provided another document with an accompanying template detailing the steps for inputting data into R. But this process is more prone to errors and if you are not familiar with R it can be a bit unintuitive.
Working with dataframes
A dataframe is structured much like an SPSS file. There are rows and columns, the columns are named and generally represent variables. The rows (can also be named) generally represent cases. You can have multiple data frames loaded with different names, although they are commonly saved as df
(and these can be numbered df1 df2 df3
. If your document/code is well organised, it can be useful have a generic name for dataframes that you are working with. This means that much of your code can be recycled (particularly if the variable names are the same – if you run repeated studies, or studies with only minor changes, you will find that there is massive scope for recycling code – both chunks and inline)
Some basics:
 The entire dataframe can be printed to the console by running the name of the data frame
 The dollar sign can be used to call specific variables from the data frame i.e.,
df$variable_name
 A function has the form “function name” followed by parenthesis:
function_name()
.  The object that you want to run the function on goes in the parenthesis. e.g., if our dataframe was called
df
, and age was calledage
and we wanted to get the mean age we would runmean(df$age)
.  Sometimes missing data denoted by
NA
can mess with some functions, to account for this it is helpful to include the argumentna.rm = TRUE
in the function, e.g.,mean(df$age, na.rm = TRUE)
The mtcars
dataset comes with R. For information on it simply type help(mtcars)
. The variables of interest here are am
(transmission; 0 = automatic, 1 = manual), mpg
(miles per gallon), qsec
(1/4 mile time). Below we practice a few simple functions to find out information about the dataset.
Example code and output:
 Load the
mtcars
dataset into an object calleddf
using the commanddf < mtcars
 View the variable names associated with
df
by runningvariable.names(df)
## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
## [11] "carb"
 The mean miles per gallon can be calculated using
mean(df$mpg)
## [1] 20.09062
 The standard deviation of the same variable is calculated using
sd(df$mpg)
## [1] 6.026948
 Or if you want to see basic descriptives use
descriptives(df$mpg)
^{3}
## mean sd min max len
## 1 20.09062 6.026948 10.4 33.9 32
 To index by a variable we use square brackets
[]
and thewhich()
function. The following command gets the mean miles per gallon for all cars with manual transmission:
mean(df$mpg[which(df$am==1)])
 The following command gets the mean miles per gallon for all cars with manual transmission:
## [1] 24.39231
Statistical tests
Now that we know some of the basics, we’ll try running some statistical tests.
Running a ttest
I’m going to use the mtcars
dataset to illustrate how to run and write up a ttest, in such a way that:
 there is no copy/paste error in reporting values; and
 you can recycle code and text.
In addition to mpg
(miles per gallon), the other variable I am interested in is am
(transmission; 0 = automatic, 1 = manual).^{4}
The question I’m going to look at is:
 Is there a difference in miles per gallon depending on transmission?
Ttest: Transmission and MPG
 Load mtcars and save it in your environment using
df < mtcars
 Create a new dataframe with a generic name e.g.,
x
using the command:x < df
 This command runs the ttest and you can see the output in the console
t.test(x$mpg~x$am)
##
## Welch Two Sample ttest
##
## data: x$mpg by x$am
## t = 3.7671, df = 18.332, pvalue = 0.001374
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 11.280194 3.209684
## sample estimates:
## mean in group 0 mean in group 1
## 17.14737 24.39231

The following code runs the ttest but saves the output as a list
t
that can be called later:t < t.test(x$mpg~x$am)
 As with dataframes, specific variables within a list can be called using the dollar sign

To call the p value simply type
t$p.value
## [1] 0.001373638
 To call the t statistic, type
t$statistic
## t
## 3.767123
 And to call the degrees of freedom, type
t$parameter
## df
## 18.33225
 Finally, to calculate the effect size and save it to an object type
td < cohensD(mpg~am, data=x)
From the above we can call each value we need using inline code to write up our results section as follows
This is what the paragraph will look like in your Rmd document:
An independent samples ttest revealed a significant difference in miles per gallon between cars with automatic transmission
(*M*
= `r mean(x$mpg[which(x$am==0)])`
, *SD*
= `r sd(x$mpg[which(x$am==0)])`
), and cars with manual transmission, (*M*
= `r mean(x$mpg[which(x$am==1)])`
, *SD*
= `r sd(x$mpg[which(x$am==1)])`
), *t*
(`r t$parameter`
) = `r t$statistic`
, *p*
`r paste(p_report(t$p.value))`
, *d*
= `r td
.
The above syntax will return the following:
An independent samples ttest revealed a significant difference in miles per gallon between cars with automatic transmission (M = 17.15, SD = 3.83), and cars with manual transmission, (M = 24.39, SD = 3.83), t(18.33) = 3.767, p = .001, d = 1.48.
If you want to run another ttest later on in your document you simply run it in a code chunk and create new objects (t
and td
) with the same names as before and you can use the same write up as above to report it.
Chisquare
To illustrate a chisquared test I will test if there is an association between engine type vs
(0 = Vshaped, 1 = straight) and transmission type (0 = automatic, 1 = manual). Again this is to illustrate the code/write up – not a real test.
 First create a table using the command
table(x$am,x$vs)
 Running a chisquared test using
chisq.test(table(x$am,x$vs))
returns:
##
## Pearson's Chisquared test with Yates' continuity correction
##
## data: table(x$am, x$vs)
## Xsquared = 0.34754, df = 1, pvalue = 0.5555
 As with the ttest, in order to report it using inline code you need to save the test as an object e.g.,
c
usingc < chisq.test(table(x$am,x$vs))
 Save the effect size using
w < sqrt(c$statistic/(length(x$mpg)*c$parameter))
 And calculate the observed power using
pw < pwr.chisq.test(w=w,N=length(x$mpg),df=(c$parameter),sig.level = .05)
Report using the following
A chisquared test for independence revealed no significant association between engine type and transmission type, χ^2^
(`r c$parameter`
, *N*
= `r length(x$mpg)`
) = `r c$statistic`
, *p*
`r paste(p_report(t$p.value))`
, *V*
= `r w
, the observed power was `r pw$power`
.
The above returns the following:
A chisquared test for independence revealed no significant association between engine type and transmission type, χ^{2}(1, N = 32) = 0.348, p = .556 V = 0.1, the observed power was 0.09. (see this resource for effect size calculations for chisquared tests).
ANOVA and Correlation
For details on the ANOVA check out the pdf using the R Markdown template on the OSF page.
Tables
Again using the mtcars
dataset, I’m going to make a few tables. Bear in mind that the code for these tables is designed to be used along with the papaja
package and rendered to pdf, so the html tables in this post will not be formatted as correctly as they should be (refer to the resources on the OSF to see what it should look like).
 Firstly save
mtcars
as an object usingx < mtcars
 We will make a table of engine type (Vshaped vs Straight) agains number of gears (3, 4, or 5) using
table(x$vs,x$gear)
which returns:
##
## 3 4 5
## 0 12 2 4
## 1 3 10 1
 This table is useful to us, but it doesn’t really sit well in our document
 We can address this using the
apa_table()
function  Before using this function we need to create a matrix that can be passed through the function using the command
test < as.data.frame.matrix(table(x$vs,x$gear))
 We can give names to the rows using
test <
rownames<(test, c("Vshaped","Straight"))
 And we can give names to the columns using
test <
colnames<(test, c("3 gears","4 gears","5 gears"))
 Finally we pass our object (matrix)
test
through theapa_table()
function to render a table that will be embedded in our document  The code below will generate the Table 1:
apa_table(
test
, align = c("l", "c", "c", "c")
, caption = "Engine type and number of gears"
, added_stub_head = "Engine Type"
#, col_spanners = makespanners()
)
Table 1: Engine type and number of gears
Engine Type  3 gears  4 gears  5 gears 

Vshaped  12  2  4 
Straight  3  10  1 
For more complex tables and example figures refer to the relevant section of the pdf and R Markdown template on the OSF page.
Using Citations
The citr
can be used to cite while you write much like using endnote with Word. I have exported my Zotero Library (using BetterBibTex) to a .bib “My Library.bib” file in the directory I am working in. To cite I simply use @ and the citation key e.g., @haidt_emotional_2001
returns Haidt (2001). To enclose the entire citation in parenthesis use square brackets [@haidt_emotional_2001]
gives: (Haidt 2001). The full reference is automatically added to the reference list (see below).
It is also generally good practice to cite R and the R packages you have used in your analyses. In the current post I used R (Version 3.6.1; R Core Team 2017) and the Rpackages blogdown (Version 0.12; Xie, Hill, and Thomas 2017), bookdown (Version 0.10; Xie 2016), citr (Version 0.3.0; Aust 2016), desnum (Version 0.1.1; McHugh 2017), extrafont (Version 0.17; Chang 2014), ggplot2 (Version 3.2.0; Wickham 2009), knitr (Version 1.23; Xie 2015), lsr (Version 0.5; Navarro 2015), MASS (Version 7.3.51.4; Venables and Ripley 2002), papaja (Version 0.1.0.9842; Aust and Barth 2017), pwr (Version 1.2.2; Champely 2018), and scales (Version 1.0.0; Wickham 2016).
References
Aust, Frederik. 2016. Citr: ’RStudio’ Addin to Insert Markdown Citations. https://CRAN.Rproject.org/package=citr.
———. 2017. “Papaja (Preparing APA Journal Articles) Is an R Package That Provides Document Formats and Helper Functions to Produce Complete APA Manscripts from RMarkdownFiles (PDF and Word Documents).” https://github.com/crsh/papaja.
Aust, Frederik, and Marius Barth. 2017. Papaja: Create APA Manuscripts with R Markdown. https://github.com/crsh/papaja.
Champely, Stephane. 2018. Pwr: Basic Functions for Power Analysis. https://CRAN.Rproject.org/package=pwr.
Chang, Winston. 2014. Extrafont: Tools for Using Fonts. https://CRAN.Rproject.org/package=extrafont.
Haidt, Jonathan. 2001. “The Emotional Dog and Its Rational Tail: A Social Intuitionist Approach to Moral Judgment.” Psychological Review 108 (4): 814–34. https://doi.org/10.1037/0033295X.108.4.814.
McHugh, Cillian. 2017. “Desnum: Creates Some Useful Functions.” https://github.com/cillianmiltown/R_desnum.
Navarro, Daniel. 2015. Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners. (Version 0.5). Adelaide, Australia. http://ua.edu.au/ccs/teaching/lsr.
R Core Team. 2017. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.Rproject.org/.
Venables, W. N., and B. D. Ripley. 2002. Modern Applied Statistics with S. Fourth. New York: Springer. http://www.stats.ox.ac.uk/pub/MASS4.
Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. SpringerVerlag New York. http://ggplot2.org.
———. 2016. Scales: Scale Functions for Visualization. https://CRAN.Rproject.org/package=scales.
Xie, Yihui. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. http://yihui.name/knitr/.
———. 2016. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.
Xie, Yihui, Alison Presmanes Hill, and Amber Thomas. 2017. Blogdown: Creating Websites with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/blogdown.

The aim of this post is to help make these resources more accessible. As such, there will likely be a lot of duplication between this post and the resources on the OSF.↩

Italics are achieved by placing a star either side of the text you want italicised
*italics*
= italics; Bold is achieved by placing a double star either side of the text you want italicised**bold**
= bold↩ 
from the
desnum
package↩ 
This test, and all tests that follow are for illustration purposes only, I have not checked any assumptions to see if I can run the tests, I just want to provide sample code that you can use for your own analyses.↩
Rbloggers.com offers daily email updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...