Advanced Data Wrangling in R

Angela Qian

Advanced Data Wrangling in R

Joining • Reshaping • Aggregating

Angela Qian
M06 – Advanced Data Wrangling

Why Data Wrangling Matters

Data rarely arrives in a clean format.

  • Tables must be joined
  • Columns must be reshaped
  • Values must be summarized

Advanced wrangling enables meaningful analysis.

# A tibble: 3 × 3
     id name  amount
  <dbl> <chr>  <dbl>
1     1 Alice    100
2     2 Bob      200
3     3 Carol     NA
# A tibble: 4 × 3
  month product   sales
  <chr> <chr>     <dbl>
1 Jan   product_A   100
2 Jan   product_B    90
3 Feb   product_A   120
4 Feb   product_B   110
# A tibble: 2 × 2
  category mean_value
  <chr>         <dbl>
1 A                15
2 B                35

Slide 7 — What I Learned From Revealjis

Tip

Revealjs integrates narrative and code into one reproducible workflow.

Strengths

  • Reproducible
  • Version controlled
  • Supports live computation

Weaknesses

  • Requires Markdown knowledge
  • Less drag-and-drop flexibility than PowerPoint