Rsquared Academy Blog

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Categorical Data Analysis 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 Date and Time 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) Quick 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.

Readable Code with Pipes

Introduction R code contain a lot of parentheses in case of a sequence of multiple operations. When you are dealing with complex code, it results in nested function calls which are hard to read and maintain. The magrittr package by Stefan Milton Bache provides pipes enabling us to write R code that is readable. Pipes allow us to clearly express a sequence of multiple operations by: structuring operations from left to right avoiding nested function calls intermediate steps overwriting of original data minimizing creation of local variables Pipes If you are using tidyverse, magrittr will be automatically loaded.

Introduction to tibbles

Introduction A tibble, or tbl_df, is a modern reimagining of the data.frame, keeping what time has proven to be effective, and throwing out what is not. Tibbles are data.frames that are lazy and surly: they do less (i.e. they don’t change variable names or types, and don’t do partial matching) and complain more (e.g. when a variable does not exist). This forces you to confront problems earlier, typically leading to cleaner, more expressive code.

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.

Import Data into R - Part 2

Introduction This is the second post in the series Importing Data into R. In the previous post, we explored reading data from flat/delimited files. In this post, we will: list sheets in an excel file read data from an excel sheet read specific cells from an excel sheet read specific rows read specific columns read data from - SAS - SPSS - STATA Libraries, Data & Code We will use the readxl package.

Import Data into R - Part 1

Introduction In this post, we will learn to: read data from flat or delimited files handle column names/header skip text/info present before data specify column/variable types read specific columns/variables Libraries, Data & Code We will use the readr package. The data sets can be downloaded from here and the codes from here. library(readr) Types of Delimiters Before we start reading data from files, let us take a quick look at the different types of delimiters we have to deal with while reading or importing data.