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Category: Programming

Programming related posts

In this category you will find posts that are related to programming and should be interesting for psychologists, cognitive scientists, and neuroscientists. Well, almost every researcher would probably find some of the information useful at some time!

Every research psychologist, cognitive scientist, and neuroscientist, should know how to program.. Knowing how to program and write scripts will make many of a researchers everyday tasks much easier. For instance, instead of going through line after line of raw data you can write a Python script that runs through each cell in each column. Furthermore, you get the possibility to use more advanced, and cutting edge, statistical techniques by using R statistical programming environment.

Another example might be to create experiments using PsychoPy (either by coding using Python or using the drag-and-drop interface) and the cheap and open-source Arduino microcontroller. Also, coding is fun and relaxing!

Tutorial: How to Read Stata Files in Python with Pandas

In this post, we are going to learn how to read Stata (.dta) files in Python.

As previously described (in the read .sav files in Python post) Python is a general-purpose language that also can be used for doing data analysis and data visualization. One example of data visualization will be found in this post.

One potential downside, however, is that Python is not really user-friendly for data storage. This has, of course, lead to that our data many times are stored using Excel, SPSS, SAS, or similar software. See, for instance, the posts about reading .sav, and sas files in Python:

How to Handle Coroutines with asyncio in Python

When a program becomes very long and complex, it is convenient to divide it into subroutines, each of which implements a specific task. However, subroutines cannot be executed independently, but only at the request of the main program, which is responsible for coordinating the use of subroutines.

In this post, we introduce a generalization of the concept of subroutines, known as coroutines: just like subroutines, coroutines compute a single computational step, but unlike subroutines, there is no main program to coordinate the results. The coroutines link themselves together to form a pipeline without any supervising function responsible for calling them in a particular order. 

Three Easy Methods to Upgrade Pip to the Latest Version

In this tutorial, we will learn how to upgrade pip. This may be handy, if we, for instance, are working with old Python environments, This, in turn, may leed to that we need to upgrade pip to the latest version.

In this post, we will use pip, conda (Anacondas package manager), and Anaconda Navigator to upgrade pip.

How to Import Data: Reading SAS Files in R

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:

How to Make a Scatter Plot in R with Ggplot2

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.

How to Read SAS Files in Python with Pandas

In this post, we are going to learn how to read SAS (.sas7bdat) files in Python.

As previously described (in the read .sav files in Python post) Python is a general-purpose language that also can be used for doing data analysis and data visualization.

The example .sas7bdat file that we are going to load into a Pandas dataframe using Python.

One potential downside, however, is that Python is not really user-friendly for data storage. This has, of course, lead to that our data many times are stored using Excel, SPSS, SAS, or similar software. See, for instance, the posts about reading .sav, .dta, and .xlxs files in Python:

How to use iloc and loc for Indexing and Slicing Pandas Dataframes

In this post, we are going to work with Pandas iloc, and loc. More specifically, we are going to learn slicing and indexing by iloc and loc examples.

Once we have a dataset loaded as a Pandas dataframe, we often want to start accessing specific parts of the data based on some criteria. For instance, if our dataset contains the result of an experiment comparing different experimental groups, we may want to calculate descriptive statistics for each experimental group separately.

How to Use Binder and R for Reproducible Research

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.