Save the transformed dataset and inspect it with glimpse() and summary().
Render to PDF and submit on Canvas.
4.7 Next Steps
Next week, we will extend these skills with group_by() and summarize() to calculate grouped summaries.
Source Code
---title: "Data Transformation with dplyr (Part 1)"---## Learning ObjectivesBy the end of this chapter, you should be able to:- Filter rows using `filter()`- Sort rows using `arrange()`- Select columns using `select()`- Create or modify columns using `mutate()`- Combine multiple transformations using the base R pipe `|>`------------------------------------------------------------------------## IntroductionThis chapter follows [*R for Data Science (Ch. 3)*](https://r4ds.hadley.nz/data-transform.html){target="_blank"} and introduces `dplyr`, a tidyverse package for data transformation.\We will use the `nycflights13::flights` dataset for examples.------------------------------------------------------------------------## Working with Rows### `filter()``filter()` keeps rows that match given conditions.```{r}library(tidyverse)library(nycflights13)flights |>filter(month ==1, day ==1)```> What's a **tibble**? See [Appendix C: Tidyverse and Tibbles](https://marctollis.github.io/cs506-book/tibbles.html)### `arrange()``arrange()` orders rows by a column.```{r}flights |>arrange(desc(dep_delay))```------------------------------------------------------------------------### In-Class Exercise 1 – RowsUsing the `flights` dataset:1. Filter for flights departing from **JFK** in **July**.\2. Arrange by **arrival delay** (largest to smallest).\3. Identify the flight with the worst delay.------------------------------------------------------------------------## Working with Columns### `select()``select()` chooses columns.```{r}flights |>select(year, month, day, dep_delay, arr_delay)```### `mutate()``mutate()` creates or modifies columns.```{r}flights |>mutate(speed = distance / air_time *60) |>select(tailnum, distance, air_time, speed)```------------------------------------------------------------------------### In-Class Exercise 2 – Columns1. Select `carrier`, `flight`, `dep_delay`, and `arr_delay`.\2. Create a column `gain = arr_delay - dep_delay`.\3. Display the first 10 rows.------------------------------------------------------------------------## Using Pipes to Combine StepsThe base R pipe `|>` passes results from one function to the next, making code easier to read.```{r}flights |>filter(month ==6, origin =="JFK") |>select(carrier, flight, dep_delay, arr_delay) |>mutate(gain = arr_delay - dep_delay) |>arrange(desc(gain)) |>head()```------------------------------------------------------------------------### In-Class Exercise 3 – PipesChain these steps using `|>`:1. Filter flights from JFK in June.\2. Select `carrier`, `flight`, `dep_delay`, `arr_delay`.\3. Create a column `gain`.\4. Arrange by largest gain and show the top 5.------------------------------------------------------------------------## Homework PreviewFor Homework, you will:- Use `flights` or another dataset.\- Filter for a subset of interest.\- Create at least two new variables with `mutate()`.\- Sort using `arrange()`.\- Save the transformed dataset and inspect it with `glimpse()` and `summary()`.Render to PDF and submit on Canvas.------------------------------------------------------------------------## Next StepsNext week, we will extend these skills with `group_by()` and `summarize()` to calculate grouped summaries.