Cleaning Data in R

R is a programming language that is essential for data science used for statistical computing and graphics. In this 13-video course learners explore essential methods for wrangling and cleaning data with R. Begin by recognizing types of unclean data and criteria for ensuring data quality. First learners see how to fetch a JSON (JavaScript Object Notation) document over HTTP and load data into a dplyr table. Learn how to load multiple sheets from an Excel document and how to handle common errors encountered when reading CSV (comma-separated values) data. Read data from a relational database with a SQL (structured query language) query. Explore joining tabular data by combining two related data sets by using a join operation and spreading data—reshaping tabular data by spreading values from rows to columns. Look at summarizing data applying a summary function using dplyr; imputing data using mean imputation to replace missing values; and extracting matches using a regular expression and data wrangling tools from the tidyverse package. The closing exercise practices data wrangling functions using R.