Pipe Data in ggplot

This lesson is called Pipe Data in ggplot, part of the Going Deeper with R course. This lesson is called Pipe Data in ggplot, part of the Going Deeper with R course.

Transcript

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# Load Packages -----------------------------------------------------------

library(tidyverse)
library(fs)

# Create Directory --------------------------------------------------------

dir_create("data")

# Download Data -----------------------------------------------------------

download.file(
  "https://github.com/rfortherestofus/going-deeper-positron/raw/main/data/third_grade_math_proficiency.rds",
  mode = "wb",
  destfile = "data/third_grade_math_proficiency.rds"
)

# Import Data -------------------------------------------------------------

third_grade_math_proficiency <-
  read_rds("data/third_grade_math_proficiency.rds") |>
  select(
    academic_year,
    school,
    school_id,
    district,
    proficiency_level,
    number_of_students
  ) |>
  mutate(
    is_proficient = case_when(
      proficiency_level >= 3 ~ TRUE,
      .default = FALSE
    )
  ) |>
  group_by(academic_year, school, district, school_id, is_proficient) |>
  summarize(number_of_students = sum(number_of_students, na.rm = TRUE)) |>
  ungroup() |>
  group_by(academic_year, school, district, school_id) |>
  mutate(
    percent_proficient = number_of_students /
      sum(number_of_students, na.rm = TRUE)
  ) |>
  ungroup() |>
  filter(is_proficient == TRUE) |>
  select(academic_year, school, district, percent_proficient) |>
  rename(year = academic_year)


# Plot --------------------------------------------------------------------

third_grade_math_proficiency |>
  filter(year == "2018-2019") |>
  filter(district == "Portland SD 1J") |>
  ggplot(aes(x = percent_proficient, y = school)) +
  geom_col()

Your Turn

Create a new R script file.
Download the enrollment data by race/ethnicity and create a data frame called enrollment_by_race_ethnicity using the starter code below.
Pipe your data into a bar chart that shows the breakdown of race/ethnicity among students in Beaverton SD 48J in 2022-2023.

# Load Packages -----------------------------------------------------------

library(tidyverse)
library(fs)

# Create Directory --------------------------------------------------------

dir_create("data")

# Download Data -----------------------------------------------------------

download.file(
  "https://github.com/rfortherestofus/going-deeper-positron/raw/main/data/enrollment_by_race_ethnicity.rds",
  mode = "wb",
  destfile = "data/enrollment_by_race_ethnicity.rds"
)

# Import Data -------------------------------------------------------------

enrollment_by_race_ethnicity <-
  read_rds("data/enrollment_by_race_ethnicity.rds") |>
  select(-district_institution_id) |>
  select(year, district, everything())

# Plot --------------------------------------------------------------------

Have any questions? Put them below and we will help you out!

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Eda Akpek • May 6, 2026

I didn't understand the first part of the solution:

enrollment_by_race_ethnicity <- read_rds("data/enrollment_by_race_ethnicity.rds") |> select(-district_institution_id) |> select(year, district, everything())

How to know to do the second two lines of code in this code chunk? What is happening when you do it this way?

Gracielle Higino Coach • May 7, 2026

The first select() is removing the column district_institution_id by adding a minus sign before the name of the column, the second is reordering the columns to place variables year and district first (from left to right) before all other columns (defined by the everything() function). The select() function does a lot of things, more than just selecting variables! You can even rename columns with it. You can read more about it here: https://dplyr.tidyverse.org/reference/select.html