In this remove a column in R tutorial, we are going to work with dplyr to delete a column. Here, we are going to learn how to remove columns in R using the select() function. Specifically, we are going to remove columns by name and by index.
Finally, we will also learn how to remove columns from R dataframes that start with a letter, or a word ends with a letter, or word, or contains a character (like the underscore).
In this post, we will learn how to use Pandas get_dummies() method to create dummy variables in Python. Dummy variables (or binary/indicator variables) are often used in statistical analyses as well as in more simple descriptive statistics. Towards the end of the post, there’s a link to a Jupyter Notebook containing all Pandas get_dummies() examples.
One statistical analysis in which we may need to create dummy variables in regression analysis. In fact, regression analysis requires numerical variables and this means that when we, whether doing research or just analyzing data, wishes to include a categorical variable in a regression model, supplementary steps are required to make the results interpretable.
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 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 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 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 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.
In this post, we are going to learn 1) how to read SPSS (.sav) files in Python, and 2) how to write to SPSS (.sav) files using Python.
Python is a great general-purpose language as well as for carrying out statistical analysis and data visualization. However, Python is not really user-friendly when it comes to data storage. Thus, often our data will be archived using Excel, SPSS or similar software.
For example, learn how to import data from other file types, such as Excel, SPSS, and Stata in the following two posts:
If we ever need to learn how to read a file in Python in other formats, such a text file, it is doable. To read a file in Python without any libraries we just use the open() method.
In this Pandas groupby tutorial, we are going to learn how to organize Pandas dataframes by groups. More specifically, we are going to learn how to group by one and multiple columns.
Furthermore, we are going to learn how to calculate some basics summary statistics (e.g., mean, median), convert Pandas groupby to dataframe, calculate the percentage of observations in each group, and many more useful things.
First of all we are going to import pandas as pd, and read a CSV file, using the read_csv method, to a dataframe. In the example below, we use index_col=0 because the first row in the dataset is the index column.
Learn three data manipulation techniques with Pandas in this guest post by Harish Garg, a software developer and data analyst, and the author of Mastering Exploratory Analysis with pandas.
In this section, you’ll learn how to modify a DataFrame using the inplace parameter. You’ll first read a real dataset into Pandas. You’ll then see how the inplace parameter impacts a method execution’s end result. You’ll also execute methods with and without the inplace parameter to demonstrate the effect of inplace.
