Grouping and summarizing data by column or row - R Tutorial

Contents

• Introduction Introduction

  • Getting started in the R tidyverse

    56s
  • (Locked)
    How to use the exercise files

    1m 56s
• 1. Tidyverse Fundamentals 1. Tidyverse Fundamentals

  • What is the tidyverse?

    2m 22s
  • Installing, loading, and working with the tidyverse packages

    5m 1s
  • (Locked)
    Introducing data.frame and tibbles

    8m 34s
  • What are %>% and |> for in the tidyverse

    3m 46s
  • (Locked)
    Using the %>% pipe in your code

    5m
  • (Locked)
    Using the |> pipe in your code

    6m 18s
  • (Locked)
    Datasets built into the tidyverse packages

    3m 15s
  • (Locked)
    Using the select() function to obtain columns from data

    4m 23s
  • (Locked)
    Using the filter() function to filter data by conditions

    6m 10s
  • (Locked)
    Using the mutate() function to modify and add columns

    4m
  • (Locked)
    Challenge: Rewrite this code to use the pipe of your choice

    2m 38s
  • (Locked)
    Solution: Rewrite this code to use the pipe of your choice

    3m 19s
• 2. Tidy Data: The Fundamental Idea behind the Tidyverse 2. Tidy Data: The Fundamental Idea behind the Tidyverse

  • What is tidy data?

    3m 2s
  • (Locked)
    Why does ggplot2 want tidy data?

    4m 22s
  • (Locked)
    Using pivot_longer() to tidy data into a long format

    4m 26s
  • (Locked)
    Cleaning column names with the janitor package

    3m 21s
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    Tidying columns containing multiple values with separate_*()

    4m 44s
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    List columns and nested tibbles

    5m 3s
• 3. Reading Data In and Out of the Tidyverse 3. Reading Data In and Out of the Tidyverse

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    Using projects to simplify file paths

    4m 57s
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    Using read_csv() to read CSV files

    4m 52s
  • (Locked)
    Using read_excel() to read data from Excel files

    2m 15s
  • (Locked)
    Using haven to import from SPSS and other formats

    4m 56s
• 4. Grouping and Summarizing Data with the Tidyverse 4. Grouping and Summarizing Data with the Tidyverse

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    Grouping and summarizing data by column or row

    2m 22s
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    Cross tabulations with count()

    3m 21s
  • (Locked)
    Column-wise groups: group_by() and mutate()

    3m 38s
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    Column-wise groups: group_by() and summarize()

    3m 10s
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    Column-wise groups: group_by() and reframe()

    3m 8s
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    Column-wise groups: Using the .by argument instead of group_by()

    2m 40s
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    Row-wise groups: rowwise() and c_across()

    4m 12s
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    Remember to ungroup()

    1m 50s
  • (Locked)
    Challenge: Find maximum penguin dimension by island

    1m 3s
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    Solution: Find maximum penguin dimension by island

    3m 40s
• 5. Important Packages and Functions in the Tidyverse 5. Important Packages and Functions in the Tidyverse

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    ggplot2 for beautiful data storytelling

    6m 57s
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    stringr for friendly string manipulation

    8m 2s
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    lubridate for manipulating dates and times

    4m 28s
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    forcats for manipulating factors

    7m 27s
  • (Locked)
    purrr for doing many things like iteration

    8m 41s
• 6. Working Smart with the Tidyverse 6. Working Smart with the Tidyverse

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    Handling NAs in the tidyverse with drop_na() and replace_na()

    4m 12s
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    Use case_when() instead of nested if or ifelse()

    6m 36s
  • (Locked)
    Use tidy-select functions to work with many columns at once

    4m 4s
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    Using across() in mutate() to modify multiple columns at once

    6m 52s
  • (Locked)
    Filtering many columns at once with if_any() and if_all()

    3m 37s
  • (Locked)
    Understanding how the tidyverse evolves and deprecates

    3m
  • (Locked)
    Challenge: Find all love songs remaining below position 80 in the top 10

    1m 3s
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    Solution: Find all love songs remaining below position 80 in the top 10

    3m 38s
• Conclusion Conclusion

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    Next steps

    1m 34s