Data Manipulation with R Book Description: This book starts with the installation of R and how to go about using Rand its libraries.

The R language provides a rich environment for working with data, especially data to be used for statistical modeling or graphics. Learn with Alison in this free online Data Analysis course about manipulating and visualizing your data using the R programming language. Main data manipulation functions. In simple words, these... Use of ML algorithms for data manipulation. This course shows you how to create, subset, and manipulate data.tables. This book is a step-by step, example-oriented tutorial that will show both intermediate and advanced users how data manipulation is facilitated smoothly using R. This book is aimed at intermediate to advanced level users of R who want to perform data manipulation with R, and those who want to clean and aggregate data effectively. Re-computing the levels of all factor columns in a data frame; Restructuring data. select(): Select columns (variables) by their names. I assume you have installed R on your machine/laptop already. We then discuss the mode of R objects and its classes and then highlight different R data types with their basic operations. mydata$Ques1<- recode (mydata$Q1, "1:4=0; 5:6=1") filter(): Pick rows (observations/samples) based on their values.

This second book takes you through how to do manipulation of tabular data in R. Tabular data is the most commonly encountered data structure we encounter so being able to tidy up the data we receive, summarise it, and combine it with other datasets are vital skills that we all need to be effective at analysing data. Written for intermediate to advanced users of R, this tutorial will enhance your data manipulation capabilities considerably. arrange(): Reorder the rows. Renaming columns in a data frame; Adding and removing columns from a data frame; Reordering the columns in a data frame; Merging data frames; Comparing data frames - Search for duplicate or unique rows across multiple data frames. These functions process data faster than Base R functions and are known the best for data exploration and transformation, as well. It takes you step-by-step through the tools and techniques needed to enable analysis and visualization. series! This is a good... Use of packages for data manipulation . These functions are included in the dplyr package:. The data.table package provides a high-performance version of base R's data.frame with syntax and feature enhancements for ease of use, convenience and programming speed.

). Data Manipulation in R With dplyr Package There are different ways to perform data manipulation in R, such as using Base R functions like subset (), with (), within (), etc., Packages like data.table, ggplot2, reshape2, readr, etc., and different Machine Learning algorithms. distinct(): Remove duplicate rows.