genzplyr Cheat Sheet 📋💅

Quick reference guide

Data filtering and selection

# Filter rows (remove what you don't want)
df |> yeet(age > 18, score > 70)

# Select columns (vibe check which ones matter)
df |> vibe_check(name, age, score)
df |> vibe_check(starts_with("test"))
df |> vibe_check(-id)  # everything except id

Data transformation

# Create/modify columns (give your data a glow up)
df |> glow_up(
  age_squared = age^2,
  is_adult = age >= 18,
  score_scaled = scale(score)
)

# Rename columns (lowkey change names)
df |> lowkey(
  years_old = age,
  test_score = score
)

Sorting and arrangement

# Sort data (by slay factor)
df |> slay(score)           # ascending
df |> slay(desc(score))     # descending
df |> slay(age, desc(score)) # multiple columns

Grouping and summarizing

# Group data (squad up)
df |> squad_up(category)

# Summarize (no cap, real stats only)
df |> no_cap(
  avg_score = mean(score),
  total = n(),
  max_age = max(age)
)

# Combined
df |> 
  squad_up(category) |>
  no_cap(avg_score = mean(score)) |>
  disband()  # ungroup when done

Data inspection

# Get unique values (periodt, no duplicates)
df |> periodt(category)

# Count occurrences (it's giving frequency table)
df |> its_giving(category)
df |> its_giving(category, status)

# Take first n rows (send it)
df |> send_it(10)  # top 10 rows

# Extract a column (main character energy)
ages <- df |> main_character(age)

Common patterns that go hard

Pattern 1: Filter, transform, sort

data |>
  yeet(condition) |>
  glow_up(new_var = calculation) |>
  slay(desc(new_var))

Pattern 2: Group analysis

data |>
  squad_up(group_var) |>
  no_cap(
    mean_val = mean(value),
    count = n()
  ) |>
  slay(desc(mean_val))

Pattern 3: Data cleaning

data |>
  periodt() |>                    # remove duplicates
  yeet(!is.na(important_col)) |> # remove missing
  vibe_check(needed_cols) |>     # keep only what matters
  lowkey(better_name = old_name) # rename

Pattern 4: Top N by group

data |>
  squad_up(category) |>
  slay(desc(score)) |>
  send_it(3) |>      # top 3 per group
  disband()

Combining with regular dplyr

Mix and match! It’s all compatible:

mtcars |>
  yeet(mpg > 20) |>
  mutate(kpg = mpg * 1.6) |>  # regular dplyr
  vibe_check(mpg, kpg, hp) |>
  filter(hp > 100) |>          # regular dplyr
  slay(desc(kpg))

Pro tips

1. Pipe game strong: Use |> or %>% to chain operations
2. Vibe check early: Select columns you need first to speed things up
3. Squad up strategically: Group before summarizing
4. Don’t forget to disband: Ungroup when you’re done with grouped operations
5. Main character usage: Use main_character() when you need a vector, not a data frame

Debugging tips

If something’s not working: - Check your parentheses and commas - Make sure you’re piping (|>) between functions - Verify column names (they’re case-sensitive) - Use glimpse() or head() between steps to check your data

Most common combos

# The "I need quick stats" combo
df |> 
  squad_up(group) |> 
  no_cap(avg = mean(value), count = n())

# The "show me the best" combo  
df |> 
  yeet(condition) |> 
  slay(desc(score)) |> 
  send_it(5)

# The "clean this mess" combo
df |>
  periodt() |>
  yeet(!is.na(key_column)) |>
  vibe_check(needed_columns)

# The "make it pretty" combo
df |>
  lowkey(nice_name = ugly_name) |>
  glow_up(formatted = format(value, digits = 2)) |>
  slay(nice_name)

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Remember: This is just dplyr with a personality. Everything that works in dplyr works here too!

Keep slaying your data analysis fr fr 🔥