PhD Student in Psychology, Umeå University. Main interest is experimental and cognitive psychology. Enjoy programming in Python and R.
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 tutorial, we will learn the basics of installing, working and updating packages in Python. First, we will learn how to install Python packages, then how to use them, and finally, how to update Python packages when needed. More specifically, we are going to learn how to install and upgrade packages using pip, conda, and Anaconda Navigator.
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:
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.
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 short post, we are going to learn how to turn the code from blog posts to Jupyter notebooks.
In this post, we are going to use the Python packages BeautifulSoup4, json, and urllib. We are going to use these packages to scrape the code from webpages putting their code within <code></code>.
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 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.
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:
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 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.
