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Month: October 2019

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:

Converting HTML to a Jupyter Notebook

In this short post, we are going to learn how to turn the code from blog posts to Jupyter notebooks. Specifically, we will learn how to convert HTML to Jupyter Notebooks (.ipynb).

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>.

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: