dplyr, R package part of tidyverse, provides a great set of tools to manipulate datasets in the tabular form. dplyr has a set of core functions for “data munging”. Here is the list of core functions from dplyr select() picks variables based on their names. mutate() adds new variables that are functions of existing variables […]
R
dplyr count(): Explore Variables with count in dplyr
In this tutorial, we will see examples of using count() function from dplyr to explore variables in a dataframe. One of the first things to do after loading a data is to perform simple exploratory data analysis. One typically starts data exploration with a quick look at the data with functions like glimpse() or head(). […]
How To Get A Peek at Dataframe in R
Getting a quick look at the dataframe to understand the variables we have or data types is an important aspect of data analysis. If you are used to working with excel, your first impulse is to open the data in excel. However, getting a look at the data programmatically in R has many advantages including […]
PCA with tidymodels in R
tidymodels, is one of the new suite of packages for doing machine learning analysis in R with tidy principles from RStudio. Tidymodels, the metapackage, has a core set of packages for statistical/machine learning models like infer, parsnip, recipes, rsample, and dials in addition to the core tidyverse packages dplyr, ggplot2, purr, and broom. In addition […]
dplyr 1.0.0 is here: Quick fun with Summarise() and rowwise()
New version of dplyr, version 1.0.0 is here. It was originally supposed to be available in early May and finally out on CRAN now. One of the cool things with the new dplyr version 1.0.0 is its cool new logo. Jokes apart, dplyr 1.0.0 is loaded with new features and Hadley Wickham has started teasing […]