Dplyr

Data Wrangling with dplyr - Part 3

Introduction In the previous post, we learnt to combine tables using dplyr. In this post, we will explore a set of helper functions in order to: extract unique rows rename columns sample data extract columns slice rows arrange rows compare tables extract/mutate data using predicate functions count observations for different levels of a variable Libraries, Code & Data We will use the following packages: dplyr readr The data sets can be downloaded from here and the codes from here.

Data Wrangling with dplyr - Part 2

Introduction In the previous post we learnt about dplyr verbs and used them to compute average order value for an online retail company data. In this post, we will learn to combine tables using different *_join functions provided in dplyr. Libraries, Code & Data We will use the following packages: dplyr readr The data sets can be downloaded from here and the codes from here. library(dplyr) library(readr) options(tibble.

Data Wrangling with dplyr - Part 1

Introduction According to a survey by CrowdFlower, data scientists spend most of their time cleaning and manipulating data rather than mining or modeling them for insights. As such, it becomes important to have tools that make data manipulation faster and easier. In today’s post, we introduce you to dplyr, a grammar of data manipulation. Libraries, Code & Data We will use the following libraries: dplyr and readr The data sets can be downloaded from here and the codes from here.