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 Pandas Excel tutorial, we will learn how to work with Excel files in Python. It will provide an overview of how to use Pandas to load xlsx files and write spreadsheets to Excel.
In the first section, we will go through, with examples, how to use Pandas read_excel to; read an Excel file, read specific columns from a spreadsheet, read multiple spreadsheets and combine them to one dataframe. Furthermore, we are going to learn how to read many Excel files, and how to convert data according to specific datatypes (e.g., using Pandas dtypes).
When we have done this, we will continue by learning how to use Pandas to write Excel files; how to name the sheets and how to write to multiple sheets.
- Read the How to import Excel into R blog post if you need an overview on how to read xlsx files into R dataframes.
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.
Rpy2 Tutorial: How to Run R in Python
In this tutorial, we will learn how to use rpy2 to install r packages and run r functions to carry out data analysis and data visualization. More specifically, we will learn how to sue the r packages r-packages ‘afex‘ and ‘emmeans‘, using Python, and rpy2. Finally, we will also learn how to display R plots in Jupyter notebooks using rpy2, using two different methods.
Obviously; rpy2 requires that we have both R (version +3.2.x) and Python (versions 2.7 and 3.X) installed. There are pre-compiled binaries available for Linux and Windows (unsupported and unofficial, however).