Summary table

Let’s recap our data wrangling verbs in the table below. Using these verbs and the pipe %>% operator, we’ll be able to write easily legible code. This code will be used to perform almost all the data wrangling and data transformation necessary for the rest of this course.

Summary of Data Wrangling Verbs

Verb	Data Wrangling Operation
`filter()`	Picks out a subset of rows
`summarize()`	Summarizes many values to one using a summary statistic function like `mean()`, `median()`, etc.
`group_by()`	Adds grouping structure to rows in a data frame; note that this doesn’t change values in the data frame, but rather only the metadata
`mutate()`	Creates new variables by mutating existing ones
`arrange()`	Arranges rows of a data variable in ascending (default) or descending order
`inner_join()`	Join/merges two data frames, matching rows by a key variable

Getting Started with Data in R

Data Visualization

Data Wrangling

Data Importing and “Tidy” Data

Basic Regression

Multiple Regression

Statistical Inference with the infer Package

Bootstrapping and Confidence Intervals

Hypothesis Testing

Inference for Regression

Price Prediction With Regression Analysis in R

Tell a Story with Data

Appendix

Uber Data Analysis Using the R Language

Recap: Data Wrangling

Summary table

Summary of Data Wrangling Verbs