April 2, 2014

## Today

• Quick intro to dplyr
• Charlotte & Alix tag team programming

Charlotte's RStudio preferences tips:

• Uncheck Restore .Rdata into workspace at startup
• Save R workspace on exit: Never

If you have large objects in memory you don't want to be saving unneccessary copies and loading them each time you start R. It also forces you to write reproducible code.

## This intro

This intro is no substitute for reading the introduction vignette. Read it!

We'll probably also assign the databases and memory vignettes in future, but there's no harm in taking a look at them early.

## Why learn dplyr?

dplyr is a package for data manipulation and exploration

• It's designed to be fast and avoid unneccessary memory consumption.

• It's a useful mental model, which means it reduces cognitive effort: you expend some energy learning dplyr now, in exchange for less brain power required in future for routine data analysis tasks.

• Master dplyr on data.frames and move seamlessly to databases.

dplyr is not plyr!

To run code later:

library(dplyr)
library(hflights)
hflights_df <- tbl_df(hflights)

## Five data manipulation verbs

An action on a data.frame, results in a data.frame

First argument is always a data.frame, remaining arguments specify the action, (no need for \$):

verb(hflights_df, ...)

Verb Action Example
filter subset rows filter(hflights_df, Dest == "PDX")
select subset columns select(hflights_df, ArrDelay, UniqueCarrier)
arrange reorder rows arrange(hflights_df, desc(ArrDelay))
mutate add new columns mutate(hflights_df, more15 = ArrDelay > 15)
summarise reduce to a single row summarise(hflights_df, avg_delay = mean(ArrDelay, na.rm = TRUE))

## group_by

Apply to a data.frame to define a "grouping" of rows based on the levels of one or more columns. The verbs know about groups:

• summarise, mutate, filter - operate within each group
• arrange - orders first by grouping variable
• select - no effect

group_by first:

carriers <- group_by(hflights_df, UniqueCarrier)

then use a verb:

summarise(carriers,
median_delay = median(ArrDelay, na.rm = TRUE))

## Getting good

Just a matter of learning to translate questions into a sequence verbs and grouping operations. Then writing the code is easy.

Which day in 2011 had the most delays?

• group by day
• summarise by the proportion of delayed flights
• arrange by decreasing count

## There's more to learn

• the general purpose verb do, do some function within each group. For example, let's you do things like fit a regression model to each group and keep the results in a list.
• addtional useful dplyr functions: n(), n_distinct(), first(), last(), nth(),
• joins
• windowing functions: ?ranking, lag(), lead()
• using databases