dplyr, R package that is at core of tidyverse suite of packages, provides a great set of tools to manipulate datasets in the tabular form. dplyr has a set of useful functions for “data munging”, including select(), mutate(), summarise(), and arrange() and filter(). And in this tidyverse tutorial, we will learn how to use dplyr’s […]
R
SVD: One Matrix Decomposition to Rule Them All
One of the nice things about twitter, when you follow awesome people, is that you will come across tweets that will just blow your mind. Last week is just one such week with some fantastic and funniest tweetorials. One of the tweetorials was from Prof. Daniela Witten for @WomenInStat. And it starts like this and […]
dplyr mutate(): Create New Variables with mutate
dplyr, R package part of tidyverse suite of packages, provides a great set of tools to manipulate datasets in the tabular form. dplyr has a set of core functions for “data munging”,including select(), mutate(), filter(), summarise(), and arrange(). And in this tidyverse tutorial, a part of tidyverse 101 series, we will learn how to use […]
dplyr select(): Select one or more variables from a dataframe
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 […]
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 […]