Data Wrangling with dplyr

library(dplyr)

students <- tibble(
  name    = c('Aarav','Kavya','Rohan','Ananya','Vivaan'),
  subject = c('Math','Math','Science','Math','Science'),
  score   = c(92, 88, 75, 95, 82),
  year    = c(11, 11, 10, 11, 10)
)

# filter() — multiple conditions use & (AND) or | (OR)
students |> filter(score >= 90)
students |> filter(subject == 'Math', year == 11)   # AND
students |> filter(score > 80 | year == 10)          # OR
students |> filter(name %in% c('Aarav','Kavya'))     # %in%
students |> filter(!is.na(score))                    # not NA

# select() — choose, drop, or rename columns
students |> select(name, score)
students |> select(-year)                  # drop year
students |> select(starts_with('s'))       # helper: starts_with
students |> select(where(is.numeric))      # all numeric cols
students |> select(student=name, marks=score)  # rename while selecting

# filter() — keep rows matching a condition
library(dplyr)
high_mpg <- filter(mpg, hwy > 30, cyl == 4)
select(high_mpg, manufacturer, model, hwy, cyl)

# mutate() — add or transform columns
students |> mutate(
  grade     = if_else(score >= 90, 'A', if_else(score >= 80, 'B', 'C')),
  score_pct = score / 100,
  rank      = rank(desc(score))   # rank within mutate
)

# arrange() — sort ascending; desc() for descending
students |> arrange(desc(score), name)   # score descending, name alphabetical

# summarise() — aggregate to one row
students |> summarise(
  n          = n(),
  mean_score = mean(score),
  max_score  = max(score),
  pass_rate  = mean(score >= 80)
)

# group_by() + summarise() — aggregate per group
students |>
  group_by(subject, year) |>
  summarise(
    mean_score = mean(score),
    n          = n(),
    .groups    = 'drop'   # always add this to avoid warnings
  )

# group_by() + mutate() — per-group transform (keeps all rows!)
students |>
  group_by(subject) |>
  mutate(subject_rank = rank(desc(score)))

# group_by + summarise — mean MPG per car class
mpg %>%
  group_by(class) %>%
  summarise(
    avg_hwy  = round(mean(hwy), 1),
    avg_cty  = round(mean(cty), 1),
    count    = n()
  ) %>%
  arrange(desc(avg_hwy))

# mutate() — add derived columns
mpg %>%
  mutate(
    efficiency_ratio = round(hwy / cty, 2),
    size_class       = if_else(displ > 3.5, "Large", "Small")
  ) %>%
  select(model, displ, cty, hwy, efficiency_ratio, size_class) %>%
  head(6)

students <- tibble(id=1:4, name=c('Aarav','Kavya','Rohan','Ananya'))
enrolled <- tibble(id=c(1,2,4,5), course=c('R','Python','SQL','Julia'))

# inner_join — only rows with match in BOTH tables
inner_join(students, enrolled, by='id')
# Aarav-R, Kavya-Python, Ananya-SQL (Rohan dropped, 5 dropped)

# left_join — ALL rows from left; NA if no match on right
left_join(students, enrolled, by='id')
# Aarav-R, Kavya-Python, Rohan-NA, Ananya-SQL

# right_join — ALL rows from right
right_join(students, enrolled, by='id')

# full_join — ALL rows from both
full_join(students, enrolled, by='id')

# anti_join — rows in left NOT in right (non-enrolled students)
anti_join(students, enrolled, by='id')
# Rohan only

# semi_join — rows in left that HAVE a match in right (no extra cols)
semi_join(students, enrolled, by='id')
# Aarav, Kavya, Ananya (no extra columns from enrolled)

# Different column names
left_join(students, enrolled, by=c('id'='student_id'))

# inner_join — combine orders with customer info
orders     <- data.frame(id=c(1,2,3,4), cust_id=c(10,11,10,12), amount=c(250,180,320,95))
customers  <- data.frame(cust_id=c(10,11,13), name=c("Aarav","Kavya","Rohan"), city=c("Mumbai","Delhi","Pune"))
inner_join(orders, customers, by="cust_id")

library(dplyr)

# Standardise ALL numeric columns
df |> mutate(across(where(is.numeric), scale))

# Round specific columns to 2 decimal places
df |> mutate(across(c(math, english, science), round, digits=2))

# Multiple functions at once
df |> summarise(across(
  c(math, english, science),
  list(mean=mean, sd=sd, max=max),
  na.rm=TRUE
))
# Creates: math_mean, math_sd, math_max, english_mean, etc.

# Custom anonymous function
df |> mutate(across(where(is.character), ~ toupper(trimws(.))))

# rename_with() — transform column names consistently
df |> rename_with(tolower)              # all to lowercase
df |> rename_with(~ gsub(' ','_',.))