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SQL for Data Science - Part 2

Introduction This is the fourth post in the series R & Databases. You can find the links to the other two posts of this series below: Quick Guide: R & SQLite Data Wrangling with dbplyr SQL for Data Science - Part 1 In this post, we will learn to aggregate data order data and group data Libraries, Code & Data We will use the following libraries in this post:

SQL for Data Science - Part 1

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 Data Wrangling with dbplyr SQL for Data Science - Part 2 In this post, we will learn to: Libraries, Code & Data We will use the following libraries in this post: DBI RSQLite dbplyr All the data sets used in this post can be found here and code can be downloaded from here.

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.

Quick Guide: R & SQLite

Introduction This is the first post in the series R & Databases. You can find the links to the other two posts of this series below: Data Wrangling with dbplyr SQL for Data Science - Part 1 SQL for Data Science - Part 2 In this post, we will learn to: connect to a SQLite database from R display database information list tables in the database query data read entire table read few rows read data in batches create table in database overwrite table in database append data to table in database remove table from database generate SQL query close database connection Libraries, Code & Data We will use the following libraries in this post:

Working with Categorical Data using forcats

Introduction In this post, we will learn to work with categorical/qualitative data in R using forcats. Let us begin by installing and loading forcats and a set of other pacakges we will be using. Libraries & Code We will use the following packages: forcats dplyr magrittr ggplot2 tibbe purrr and readr The codes from here. library(forcats) library(tibble) library(magrittr) library(purrr) library(dplyr) library(ggplot2) library(readr) Case Study We will use a case study to explore the various features of the forcats package.

Working with Dates in R

Introduction In this post, we will learn to work with date/time data in R using lubridate, an R package that makes it easy to work with dates and time. Let us begin by installing and loading the pacakge. Libraries, Code & Data We will use the following packages: lubridate dplyr magrittr readr The data sets can be downloaded from here and the codes from here. library(lubridate) library(dplyr) library(magrittr) library(readr) Quic Intro Origin Let us look at the origin for the numbering system used for date and time calculations in R.

Hacking strings with stringr

Introduction In this post, we will learn to work with string data in R using stringr. As we did in the other posts, we will use a case study to explore the various features of the stringr package. Let us begin by installing and loading stringr and a set of other pacakges we will be using. Libraries, Code & Data We will use the following libraries: stringr dplyr magrittr tibble purrr and readr The data sets can be downloaded from here and the codes 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.