Introduction

Data wrangling in R is like cooking: you have your ingredients (data), and you use tools (functions) to prepare them (clean, transform) for analysis (consumption!). One essential tool is adding an “index column” – a unique identifier for each row. This might seem simple, but there are several ways to do it in base R and tidyverse packages like dplyr and tibble. Let’s explore and spice up your data wrangling skills!

Examples

Adding Heat with Base R

Ex 1: The Sequencer:

Imagine lining up your rows. cbind(df, 1:nrow(df)) adds a new column with numbers 1 to n, where n is the number of rows in your data frame (df).

# Sample data
df <- data.frame(name = c("Alice", "Bob", "Charlie"), age = c(25, 30, 28))

# Add index using cbind
df_with_index <- cbind(index = 1:nrow(df), df)
df_with_index

  index    name age
1     1   Alice  25
2     2     Bob  30
3     3 Charlie  28

Ex 2: Row Name Shuffle:

Prefer names over numbers? rownames(df) <- 1:nrow(df) assigns row numbers as your index, replacing existing row names.

# Sample data
df <- data.frame(name = c("Alice", "Bob", "Charlie"), age = c(25, 30, 28))

df_with_index <- cbind(index = rownames(df), df)
df_with_index

  index    name age
1     1   Alice  25
2     2     Bob  30
3     3 Charlie  28

Ex 3: The All-Seeing Eye:

seq_len(nrow(df)) generates a sequence of numbers, perfect for adding as a new column named “index”.

# Sample data
df <- data.frame(name = c("Alice", "Bob", "Charlie"), age = c(25, 30, 28))

df_with_index <- cbind(index = seq_len(nrow(df)), df)
df_with_index

  index    name age
1     1   Alice  25
2     2     Bob  30
3     3 Charlie  28

The Tidyverse Twist:

The tidyverse offers unique approaches:

Ex 1: Tibble Magic:

tibble::rowid_to_column(df) adds a column named “row_id” with unique row identifiers.

library(tibble)

# Convert df to tibble
df_tib <- as_tibble(df)

# Add row_id
df_tib_indexed <- rowid_to_column(df_tib)
df_tib_indexed

# A tibble: 3 × 3
  rowid name      age
  <int> <chr>   <dbl>
1     1 Alice      25
2     2 Bob        30
3     3 Charlie    28

Ex 2: dplyr’s Ranking System:

dplyr::row_number() assigns ranks (starting from 1) based on the order of your data.

library(dplyr)
# Add row number
df_tib_ranked <- df_tib |>
  mutate(rowid = row_number()) |>
  select(rowid, everything())

df_tib_ranked

# A tibble: 3 × 3
  rowid name      age
  <int> <chr>   <dbl>
1     1 Alice      25
2     2 Bob        30
3     3 Charlie    28

Choose Your Champion:

Experiment and see what suits your workflow! Base R offers flexibility, while tidyverse provides concise and consistent syntax.

Now You Try!

1. Create your own data frame with different data types.
2. Apply the methods above to add index columns.
3. Explore customizing column names and data types.
4. Share your creations and challenges in the R community!

Remember, data wrangling is a journey, not a destination. Keep practicing, and you’ll be adding those index columns like a seasoned R pro in no time!