Appendix C: Appendix: Tidyverse and Tibbles

C.1 Overview

The Tidyverse is a collection of R packages designed for data science.
They share a common design philosophy and work seamlessly together.

Core packages include:
- ggplot2: data visualization
- dplyr: data manipulation
- tidyr: data tidying
- readr: data import
- purrr: functional programming
- tibble: modern data frames
- stringr: string manipulation
- forcats: working with factors

You load them all with:

library(tidyverse)

D 1. What Are Tibbles?

Tibbles are modern replacements for base R data frames.

D.0.1 Key Features:

Don’t convert strings to factors automatically
Never change variable names
Print in a cleaner, more readable way
Show only the first 10 rows and as many columns as fit on screen

Example:

library(tibble)

tb <- tibble(
  x = 1:5,
  y = x^2,
  z = c("a", "b", "c", "d", "e")
)

tb

# A tibble: 5 × 3
      x     y z    
  <int> <dbl> <chr>
1     1     1 a    
2     2     4 b    
3     3     9 c    
4     4    16 d    
5     5    25 e

E 2. Differences from Data Frames

Subsetting with $ works the same, but [[ is stricter
Tibbles don’t do partial matching
Printing is truncated by default (no flooding the console)

tb$y

[1]  1  4  9 16 25

tb[["z"]]

[1] "a" "b" "c" "d" "e"

F 3. Creating Tibbles

You can create tibbles manually with tibble() or convert data frames with as_tibble().

df <- data.frame(a = 1:3, b = letters[1:3])
tb2 <- as_tibble(df)

G 4. Working with Tibbles

Tibbles work seamlessly with all dplyr verbs:

tb3 <- tibble(
  x = 1:6,
  y = c("a", "a", "b", "b", "c", "c")
)

tb3 |>
  dplyr::group_by(y) |>
  dplyr::summarize(mean_x = mean(x))

# A tibble: 3 × 2
  y     mean_x
  <chr>  <dbl>
1 a        1.5
2 b        3.5
3 c        5.5

H 5. Best Practices with Tibbles

Always use tibble() for clean, predictable data structures
Avoid row names; instead, use an explicit column
Use glimpse() for quick inspection
Use print(n = Inf) to see all rows when needed

I 6. When to Convert Back to Data Frames

Some base R functions don’t work with tibbles.
Use as.data.frame() if you need to revert:

df_back <- as.data.frame(tb)

I.1 In-Class Exercise

Create a tibble with three columns: name, age, and score.
Use mutate() to add a new column grade based on score.
Group by grade and calculate the average age.

J Conclusion

Tibbles are at the heart of the Tidyverse workflow, offering: - Clean printing - Safer subsetting - Compatibility with the pipe operator and dplyr verbs

Use them as your default data structure in this course.

--- title: "Appendix: Tidyverse and Tibbles" --- ## Overview The **Tidyverse** is a collection of R packages designed for **data science**.\ They share a common design philosophy and work seamlessly together. Core packages include:\ - `ggplot2`: data visualization\ - `dplyr`: data manipulation\ - `tidyr`: data tidying\ - `readr`: data import\ - `purrr`: functional programming\ - `tibble`: modern data frames\ - `stringr`: string manipulation\ - `forcats`: working with factors\ You load them all with: ```{r eval=FALSE} library(tidyverse) ``` ------------------------------------------------------------------------ # 1. What Are Tibbles? Tibbles are **modern replacements** for base R data frames. ### Key Features: - Don’t convert strings to factors automatically - Never change variable names - Print in a cleaner, more readable way - Show only the first 10 rows and as many columns as fit on screen Example: ```{r} library(tibble) tb <- tibble( x = 1:5, y = x^2, z = c("a", "b", "c", "d", "e") ) tb ``` ------------------------------------------------------------------------ # 2. Differences from Data Frames - Subsetting with `$` works the same, but `[[` is stricter - Tibbles don’t do **partial matching** - Printing is **truncated** by default (no flooding the console) ```{r} tb$y tb[["z"]] ``` ------------------------------------------------------------------------ # 3. Creating Tibbles You can create tibbles manually with `tibble()` or convert data frames with `as_tibble()`. ```{r} df <- data.frame(a = 1:3, b = letters[1:3]) tb2 <- as_tibble(df) ``` ------------------------------------------------------------------------ # 4. Working with Tibbles Tibbles work seamlessly with all **dplyr** verbs: ```{r} tb3 <- tibble( x = 1:6, y = c("a", "a", "b", "b", "c", "c") ) tb3 |> dplyr::group_by(y) |> dplyr::summarize(mean_x = mean(x)) ``` ------------------------------------------------------------------------ # 5. Best Practices with Tibbles - Always use `tibble()` for clean, predictable data structures - Avoid row names; instead, use an explicit column - Use `glimpse()` for quick inspection - Use `print(n = Inf)` to see all rows when needed ------------------------------------------------------------------------ # 6. When to Convert Back to Data Frames Some base R functions don’t work with tibbles.\ Use `as.data.frame()` if you need to revert: ```{r} df_back <- as.data.frame(tb) ``` ------------------------------------------------------------------------ ## In-Class Exercise 1. Create a tibble with three columns: name, age, and score.\ 2. Use `mutate()` to add a new column `grade` based on `score`.\ 3. Group by grade and calculate the average age. ------------------------------------------------------------------------ # Conclusion Tibbles are at the heart of the Tidyverse workflow, offering: - Clean printing - Safer subsetting - Compatibility with the pipe operator and dplyr verbs Use them as your **default** data structure in this course.