R statistical programming related stuff
In this remove a column in R tutorial, we are going to work with dplyr to delete a column. Here, we are going to learn how to remove columns in R using the select() function. Specifically, we are going to remove columns by name and by index.
Finally, we will also learn how to remove columns from R dataframes that start with a letter, or a word ends with a letter, or word, or contains a character (like the underscore).
In this post, we will learn how to carry out descriptive statistics in R. After we have learned how to do this, we will learn how to create a nice latex table and how to save the summary statistics to a .csv file.
Carrying out descriptive statistics, also known as summary statistics, is a very good starting point for most statistical analyses. It is, furthermore, a very good way to summarize and communicate information about the data we have collected.
In this post we are going to learn 1) how to read SPSS (.sav) files in R, and 2) how to write to SPSS (.sav) files using R. More specifically, here we are going to work with the following two R packages haven (from the Tidyverse) and foreign to:
In this post, we are going to learn how to read SAS (. sas7bdat) files in R. More specifically, we are going to use the packages haven, and sas7bdat. Furthermore, we will also learn how to load .sas7bdat files into R using RStudio.
If you are interested in other methods on how to import data in R:
In this post, we will learn how make scatter plots using R and the package ggplot2.
More specifically, we will learn how to make scatter plots, change the size of the dots, change the markers, the colors, and change the number of ticks.
Furthermore, we will learn how to plot a trend line, add text, plot a distribution on a scatter plot, among other things. In the final section of the scatter plot in R tutorial, we will learn how to save plots in high resolution.
In this tutorial, we will learn how to work with Excel files in R statistical programming environment. It will provide an overview of how to use R to load xlsx files and write spreadsheets to Excel.
In a previous post, we learned how to use Binder and Python for reproducible research. Now, we are going to learn how to create a Binder for our data analysis in R, so it can be fully reproduced by other researchers. More specifically, in this post, we will learn how to use Binder for reproducible research.
Many researchers upload their code for data analysis and visualization using git (e.g., GitHub, Gitlab).
No doubt, uploading your R scripts is great. However, we also need to make sure that we share the complete computational environment so that our code can be re-run and so that others can reproduce the results. That is, to make sure that other researchers really can reproduce our code, we need a way to capture the versions of the R packages we used when publishing our research.
In this post, we will learn how to carry out repeated measures Analysis of Variance (ANOVA) in R and Python. To be specific, we will use the R package afex and the Python package pingouin to carry out one-way and two-way ANOVA for within-subject’s design. The structure of the following data analysis tutorial is as follows; a brief introduction to (repeated measures) ANOVA, carrying out within-subjects ANOVA in R using afex and in Python using pingouin. In the end, there will be a comparison of the results and the pros and cons of using R or Python for data analysis (i.e., ANOVA).
Good resources for learning R as a Psychologist are hard to find. By that I mean that there are so many great sites and blogs around the internet to learn R. Thus, it may be hard to find learning resources that target Psychology researchers.
Recently I wrote about four good R books targeted for Psychology students and researchers (i.e., R books for Psychologists). There are, however, of course, other good resources for Psychological researchers to learn R programming.
Therefore, this post will list some of the best blogs and sites to learn R. The post will be divided into two categories; general and Psychology focused R sites and blogs. For those who are not familiar with R, I will start with a brief introduction on what R is (if you know R already; click here to skip to the links).
R is a free and open-source statistical programming environment. Being open-source and free it has a large and helpful online community (for instance, see StackOverflow). When I went from carrying out analysis in SPSS to do them in R, I searched for good books targeted to Psychologists. The following 4 R books are useful and good for Psychologists that want to learn R.
The first book, Discovering Statistics Using R, may be a really good start if you are an undergraduate and have no experience of programming or statistics. The next two books are from intermediate to advanced level. The last book is, at the moment, free and is also a great introduction to statistics.
