Dplyr

Data Wrangling with dbplyr

Introduction This is the third post in the series R & Databases. You can find the links to the other two posts of this series below: Quick Guide: R & SQLite In this post, we will learn to query data from a database using dplyr. Libraries, Code & Data We will use the following libraries in this post: DBI RSQLite dbplyr dplyr All the data sets used in this post can be found here and code can be downloaded from here.

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.