Going Deeper with R (v1)

42 lessons

Advanced Data Wrangling and Analysis
Overview
Importing Data
Tidy Data
Reshaping Data
Dealing with Missing Data
Changing Variable Types
Advanced Variable Creation
Advanced Summarizing
Binding Data Frames
Functions
Merging Data
Renaming Variables
Quick Interlude to Reorganize our Code
Exporting Data
Advanced Data Visualization
Data Visualization Best Practices
Tidy Data
Pipe Data Into ggplot
Reorder Plots to Highlight Findings
Line Charts
Use Color to Highlight Findings
Declutter
Use the scales Package for Nicely Formatted Values
Use Direct Labeling
Use Axis Text Wisely
Use Titles to Highlight Findings
Use Color in Titles to Highlight Findings
Use Annotations to Explain
Tweak Spacing
Customize Your Theme
Customize Your Fonts
Try New Plot Types
Advanced RMarkdown
Advanced Markdown Text Formatting
Tables
Advanced YAML
Inline R Code
Making Your Reports Shine: Word Edition
Making Your Reports Shine: HTML Edition
Making Your Reports Shine: PDF Edition
Presentations
Dashboards
Other Formats
Wrapping Up
You Did It!

Binding Data Frames

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This lesson is called Binding Data Frames, part of the Going Deeper with R (v1) course. This lesson is called Binding Data Frames, part of the Going Deeper with R (v1) course.

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Transcript

Click on the transcript to go to that point in the video. Please note that transcripts are auto generated and may contain minor inaccuracies.

Your Turn

Solution

Heads up: in the video I forgot to add two lines of code in the video to add the year. Lines 24 and 50 in the code below have the code that is missing in the video.

enrollment_by_race_ethnicity_17_18 <- enrollment_17_18 %>%
    select(-contains("grade")) %>% 
    select(-contains("kindergarten")) %>% 
    select(-contains("percent")) %>% 
    pivot_longer(cols = -district_id,
                 names_to = "race_ethnicity",
                 values_to = "number_of_students") %>%
    mutate(number_of_students = na_if(number_of_students, "-")) %>% 
    mutate(number_of_students = replace_na(number_of_students, 0)) %>% 
    mutate(number_of_students = as.numeric(number_of_students)) %>%
    mutate(race_ethnicity = str_remove(race_ethnicity, "x2017_18")) %>%
     mutate(race_ethnicity = case_when(
      race_ethnicity == "american_indian_alaska_native" ~ "American Indian Alaska Native",
      race_ethnicity == "asian" ~ "Asian",
      race_ethnicity == "black_african_american" ~ "Black/African American",
      race_ethnicity == "hispanic_latino" ~ "Hispanic/Latino",
      race_ethnicity == "multiracial" ~ "Multi-Racial",
      race_ethnicity == "native_hawaiian_pacific_islander" ~ "Pacific Islander",
      race_ethnicity == "white" ~ "White"
    )) %>% 
    group_by(district_id) %>% 
    mutate(pct = number_of_students / sum(number_of_students)) %>% 
    ungroup() %>%
    mutate(year = "2017-2018")
    
enrollment_by_race_ethnicity_18_19 <- enrollment_18_19 %>%
    select(-contains("grade")) %>% 
    select(-contains("kindergarten")) %>% 
    select(-contains("percent")) %>% 
    pivot_longer(cols = -district_id,
                 names_to = "race_ethnicity",
                 values_to = "number_of_students") %>%
    mutate(number_of_students = na_if(number_of_students, "-")) %>% 
    mutate(number_of_students = replace_na(number_of_students, 0)) %>% 
    mutate(number_of_students = as.numeric(number_of_students)) %>%
    mutate(number_of_students = as.numeric(number_of_students)) %>%
    mutate(race_ethnicity = str_remove(race_ethnicity, "x2018_19")) %>%
     mutate(race_ethnicity = case_when(
      race_ethnicity == "american_indian_alaska_native" ~ "American Indian Alaska Native",
      race_ethnicity == "asian" ~ "Asian",
      race_ethnicity == "black_african_american" ~ "Black/African American",
      race_ethnicity == "hispanic_latino" ~ "Hispanic/Latino",
      race_ethnicity == "multiracial" ~ "Multi-Racial",
      race_ethnicity == "native_hawaiian_pacific_islander" ~ "Pacific Islander",
      race_ethnicity == "white" ~ "White"
    )) %>% 
    group_by(district_id) %>% 
    mutate(pct = number_of_students / sum(number_of_students)) %>% 
    ungroup() %>%
    mutate(year = "2018-2019")

enrollment_by_race_ethnicity <- bind_rows(enrollment_by_race_ethnicity_17_18,
                                          enrollment_by_race_ethnicity_18_19)

Import 2017-2018 enrollment data into a data frame called enrollment_by_race_ethnicity_17_18 and clean it using the code you used for the 2018-2019 data
Use bind_rows() to make a enrollment_by_race_ethnicity data frame.

Hint: You’ll need to change some of your code from importing the 2018-2019 data to make the race_ethnicity variable get recoded correctly!

Learn More

The best place to learn about binding data is the dplyr website.

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

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Isaac Macha

April 22, 2021

when putting a new column, I keep getting this error. How do I solve this :

Error in FUN(left) : invalid argument to unary operator

See below code enrollment_by_ethnicity_18_19 %>% mutate(year = 2018)

tawheeda wahabzada

April 28, 2021

Hi David, in the solutions, you noted that lines 25 and 51 have the missing code. I only see blank spaces.

Atlang Mompe

May 2, 2021

Hi David, did you intentionally omit the binding code? In any case I figured it out, but just want to flag that it is not in the solutions. Thanks, Atty

Christine Vandenberghe

May 3, 2021

Sorry I don't see bind rows in the solution, did I miss it?

Maria Cristina Limlingan

November 15, 2021

HI David,

I'm still getting NAs for race_ethnicity even if I changed the code to this - not sure what else to try? mutate(race_ethnicity = str_remove(race_ethnicity, "x2017_2018_")) %>%

JULIO VERA DE LEON

April 30, 2022

Hi, I ran into a similar issue when we were using the replace_na with "0", in this case for the second dataset (17-18) I received this from the console:

Error in mutate(): ! Problem while computing number_of_students = replace_na(number_of_students, "0"). Caused by error in vec_assign(): ! Can't convert replace to match type of data .

So, I just had to remove the quotation marks from the 0, in order to get it work.

Andrew Paquin

April 24, 2023

Hi Dave and Charlie, I've run into a problem. I duplicated my code for the 2018-19 data and adapted it for the 2017-18 data. When I run it, however, I get an error message pertaining to line 73 - mutate(number_of_students = na_if(number_of_students, "-")). What puzzles me is that the exact same line works as expected in the original code chunk. Here's the error message: Error in mutate(): ℹ In argument: number_of_students = na_if(number_of_students, "-"). Caused by error in na_if(): ! Can't convert y to match type of x . Run rlang::last_trace() to see where the error occurred. This video (link below) provides a bit more detail: https://www.loom.com/share/4dffa13937994d65bb212ecd544e8e20

Going Deeper with R (v1)

Advanced Data Wrangling and Analysis

Advanced Data Visualization

Advanced RMarkdown

Wrapping Up

Going Deeper with R (v1)

Binding Data Frames

This lesson is locked

Transcript

Your Turn

Solution

Learn More

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

Isaac Macha

tawheeda wahabzada

Atlang Mompe

Christine Vandenberghe

Maria Cristina Limlingan

JULIO VERA DE LEON

Andrew Paquin