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Load the mtcars dataset into an object called df using the command df Below we practice a few simple functions to find out information about the dataset. The variables of interest here are am (transmission 0 = automatic, 1 = manual), mpg (miles per gallon), qsec (1/4 mile time). For information on it simply type help(mtcars).

Sometimes missing data denoted by NA can mess with some functions, to account for this it is helpful to include the argument na.rm = TRUE in the function, e.g., mean(df$age, na.rm = TRUE).

e.g., if our dataframe was called df, and age was called age and we wanted to get the mean age we would run mean(df$age).

The object that you want to run the function on goes in the parenthesis.

A function has the form “function name” followed by parenthesis: function_name().

The dollar sign can be used to call specific variables from the data frame i.e., df$variable_name.

The entire dataframe can be printed to the console by running the name of the data frame.

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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 in-line) Some basics: If your document/code is well organised, it can be useful have a generic name for dataframes that you are working with.

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. The rows (can also be named) generally represent cases. There are rows and columns, the columns are named and generally represent variables. Working with dataframesĪ dataframe is structured much like an SPSS file. But this process is more prone to errors and if you are not familiar with R it can be a bit unintuitive. I also provided another document with an accompanying template detailing the steps for inputting data 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). 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 is done by including the letter “r” in the curly brackets or immediately after the opening back tick.

In-line 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.Įach time you open a piece of code (a chunk or in-line code) you have to identify what language you want to code in.

Output from code chunks can be included or omitted from your main output document.

Code chunks allow you to run analyses without leaving your document. 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).The formatting of this plain text is fairly straight forward ( see this cheatsheet) 2 Plain text: this is what you will use to write your manuscript.When working with R Markdown, there are three types of text to be concerned with. I started off the workshop by going through some of the basics of R Markdown. 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)).

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The main purpose of the exercise was to provide people with the tools to create this pdf using this R Markdown template.

The complete resources are available on this project page on the OSF. The statistics content of these resources is mainly just some of the simpler standard tests taught to psychology undergraduate students. For this session I created a set of resources 1 aimed at making the transition from SPSS to R Markdown a bit easier.

The aim of this was to demonstrate the usefulness of R Markdown (and hopefully convert a few people). However, recently I ran a session on using RMarkdown for my colleagues in the Centre for Social Issues Research. 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).

Make anova table rmarkdown