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 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 create a binder so that our data analysis, for instance, can be fully reproduced by other researchers. That is, here we will learn how to use a tool called Binder for reproducible research.
In previous posts, we have learned how to carry out data analysis (e.g., ANOVA) and data visualization (e.g., Raincloud plots) using Python. The code we have used have been uploaded in the forms of Jupyter Notebooks.
For users of R Statistical Environment;
Although this is great, we also need to make sure that we share our computational environment so that our code can be re-run and produce the same output. That is, to have a fully reproducible example, we need a way to capture the different versions of the Python packages we’re using.
In this OpenSesame tutoria,l we will learn how to use images as stimuli and how to load the trials; including filenames, correct responses, and conditions from a pre-generated CSV file. To follow this tutorial you don’t need to know Python programming. However, we are going to generate the CSV file using a short Python script. This can be done manually, of course. See also this OpenSesame tutorial.
In this post you are going to learn how to create a simple experiment using the free experiment building software OpenSesame. As I have previously written about, OpenSesame, is an application, based on Python, for creating Psychology, Neuroscience, and Economics experiments. It offers a nice and easy to use interface. In this interface you can drag-and-drop different objects. This means that you don’t have to know any Python programming at all to create an experiment. If you need to know how to use images as stimuli you can see this OpenSesame Tutorial.
PsychoPy, as I have previously written about (e.g., Free and Useful Software and PsychoPy tutorial) is really a great Python tool for creating Psychology experiments. You can write Python code by either using “code view” or import the package in your favorite IDE. Furthermore, you can use the builder mode and just drag and drop different items and PsychoPy will create a Python Script for you.
If need inline scripts (in Python, of course) can be inserted. That is, you can combine drag-and-drop building with some coding.
In this post, I have collected some tutorial videos that can be useful for someone unfamiliar with PsychoPy.
Descriptive Statistics After data collection, most Psychology researchers use different ways to summarise the data. In this tutorial, we will learn how to do descriptive…
The aim of this post is to show you why you, as a psychology student or researcher (or any other kind researcher or student) should learn to program. The post is structured as follows. First I start by discussing why you should learn to program and then give some examples when programming skills are useful. I continue to suggest two programming languages that I think all Psychology students and researchers should learn.
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).
I recently wrote a post on the RStudio like Python IDE Rodeo (RStudio-like Python IDEs – Rodeo and Spyder). In that post, I installed and…
