In this short post, we are going to learn how to turn the code from blog posts to Jupyter notebooks.
Month: October 2019
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 (.sas7dbat) 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 and .xlxs files in Python:
While there are many things important to businesses, few are more important than data. Data helps businesses make decisions, help improve conversations with customers, deliver…
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.