install.packages(“tidyverse”)

library(tidyverse)

## ── Attaching packages ─────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.0
## ✓ tidyr   1.1.0     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## ── Conflicts ────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

# install.packages("nycflights13")
library(nycflights13)

View(flights)

Tip 1 of 18: Create new columns in a count or group_by

flights %>%
  mutate(long_flight = (air_time >= 6 * 60)) %>%
  View()

flights %>%
  mutate(long_flight = (air_time >= 6 * 60)) %>%
  count(long_flight)

## # A tibble: 3 x 2
##   long_flight      n
##   <lgl>        <int>
## 1 FALSE       322630
## 2 TRUE          4716
## 3 NA            9430

flights %>%
  count(long_flight = air_time >= 6 * 60)

## # A tibble: 3 x 2
##   long_flight      n
##   <lgl>        <int>
## 1 FALSE       322630
## 2 TRUE          4716
## 3 NA            9430

flights %>%
  count(flight_path = str_c(origin, " -> ", dest), sort = TRUE)

## # A tibble: 224 x 2
##    flight_path     n
##    <chr>       <int>
##  1 JFK -> LAX  11262
##  2 LGA -> ATL  10263
##  3 LGA -> ORD   8857
##  4 JFK -> SFO   8204
##  5 LGA -> CLT   6168
##  6 EWR -> ORD   6100
##  7 JFK -> BOS   5898
##  8 LGA -> MIA   5781
##  9 JFK -> MCO   5464
## 10 EWR -> BOS   5327
## # … with 214 more rows

flights %>%
  group_by(date = make_date(year, month, day)) %>%
  summarise(flights_n = n(), air_time_median = median(air_time, na.rm = TRUE)) %>%
  ungroup()

## `summarise()` ungrouping output (override with `.groups` argument)

## # A tibble: 365 x 3
##    date       flights_n air_time_median
##    <date>         <int>           <dbl>
##  1 2013-01-01       842            149 
##  2 2013-01-02       943            148 
##  3 2013-01-03       914            148 
##  4 2013-01-04       915            140 
##  5 2013-01-05       720            147 
##  6 2013-01-06       832            147 
##  7 2013-01-07       933            126.
##  8 2013-01-08       899            126.
##  9 2013-01-09       902            135 
## 10 2013-01-10       932            126 
## # … with 355 more rows

Tip 2 of 18: Sample and randomly shuffle data with slice_sample()

flights %>% slice_sample(n = 10)

flights %>% slice_sample(prop = 0.01)

flights %>% slice_sample(prop = 1)

flights %>% group_by(origin) %>% slice_sample(n = 3) %>% ungroup()

Tip 3 of 18: Create a date column specifying year, month, and day

flights %>% select(year, month, day) %>% mutate(date = make_date(year, month, day))

Tip 4 of 18: Parse numbers with parse_number()

numbers_1 <- tibble(number_col = c(“#1”, “#2”, “#3”)) numbers_2 <- tibble(number_col = c(“Number 5”, “#6”, “7”)) numbers_3 <- tibble(number_col = c(“1.2%”, “2.5%”, “50.9%”))

numbers_1 numbers_1 %>% mutate(number_col = parse_number(number_col))

numbers_2 numbers_2 %>% mutate(number_col = parse_number(number_col))

numbers_3 numbers_3 %>% mutate(number_col = parse_number(number_col))

Tip 5 of 18: Select columns with starts_with, ends_with, etc.

flights %>% select(starts_with(“dep_”))

flights %>% select(starts_with(“dep_”), everything())

flights %>% select(ends_with(“hour”))

flights %>% select(contains(“dep”))

Tip 6 of 18: case_when to create or change a column when conditions are met

flights %>% select(origin)

flights %>% mutate(origin = case_when( (origin == “EWR”) & dep_delay > 20 ~ “Newark International Airport - DELAYED”, (origin == “EWR”) & dep_delay <= 20 ~ “Newark International Airport - ON TIME DEPARTURE”, origin == “JFK” ~ “John F. Kennedy International Airport”, origin == “LGA” ~ “LaGuardia Airport” )) %>% count(origin)

Tip 7 of 18: str_replace_all to find and replace multiple options at once

flights %>% mutate(origin = str_replace_all(origin, c( “^EWR\(" = "Newark International Airport", "^JFK\)” = “John F. Kennedy International Airport”, “^LGA$” = “LaGuardia Airport” ))) %>% count(origin)

Tip 8 of 18: Transmute to create or change columns and keep only those columns

flights %>% transmute(date = make_date(year, month, day), tailnum)

Tip 9 of 18: Use pipes %>% everywhere including inside mutates

airlines %>% mutate(name = name %>% str_to_upper() %>% str_replace_all(" (INC|CO)\.?\(", "") %>% str_replace_all(" AIR ?(LINES|WAYS)?( CORPORATION)?\)“,”“) %>% str_to_title() %>% str_replace_all(”\bUs\b“,”US")) %>% count(name)

airlines %>% mutate(name = str_replace_all(str_to_title(str_replace_all(str_replace_all(str_to_upper(name), " (INC|CO)\.?\(", ""), " AIR ?(LINES|WAYS)?( CORPORATION)?\)“,”“)),”\bUs\b“,”US"))

Tip 10 of 18: Filter groups without making a new column

flights %>% count(carrier, sort = TRUE)

flights_top_carriers <- flights %>% group_by(carrier) %>% filter(n() >= 10000) %>% ungroup()

flights_top_carriers %>% count(carrier, sort = TRUE)

Tip 11 of 18: Split a string into columns based on a regular expression

airlines %>% count(name)

airlines %>% extract( name, into = c(“short_name”, “remainder”), regex = "^([^\\s]+) (.*)$" )

airlines %>% extract( name, into = c(“short_name”, “remainder”), regex = "^([^\\s]+) (.*)$", remove = FALSE )

Tip 12 of 18: semi_join to pick only rows from the first table which are matched in the second table

airways_beginning_with_a <- airlines %>% filter(name %>% str_detect(“^A”))

flights %>% semi_join(airways_beginning_with_a, by = “carrier”) %>% count(carrier)

Tip 13 of 18: anti_join to pick only rows from the first table which are NOT matched in the second table

flights %>% anti_join(airways_beginning_with_a, by = “carrier”)

Tip 14 of 18: fct_reorder to sort bar charts

flights_with_airline_names <- flights %>% left_join(airlines, by = “carrier”)

flights_with_airline_names %>% count(name) %>% ggplot(aes(name, n)) + geom_col()

flights_with_airline_names %>% count(name) %>% mutate(name = fct_reorder(name, n)) %>% ggplot(aes(name, n)) + geom_col()

Tip 15 of 18: coord_flip to display counts more beautifully

flights_with_airline_names %>% count(name) %>% mutate(name = fct_reorder(name, n)) %>% ggplot(aes(name, n)) + geom_col() + coord_flip()

Tip 16 of 18: fct_lump to lump some factor levels into “Other”

flights_with_airline_names %>% mutate(name = fct_lump(name, n = 5)) %>% count(name) %>% mutate(name = fct_reorder(name, n)) %>% ggplot(aes(name, n)) + geom_col() + coord_flip()

Tip 17 of 18: Generate all combinations using crossing

crossing( customer_channel = c(“Online”, “Physical store”), customer_status = c(“New”, “Repeat”), spend_range = c(“$0-$100”, “$100-$200”, “$200-$500”, “$500+”) )

Tip 18 of 18: Create functions that take column names with double curly braces

col_summary <- function(data, col_names, na.rm = TRUE) { data %>% summarise(across({{ col_names }}, list( min = min, max = max, median = median, mean = mean ), na.rm = na.rm, .names = "{col}_{fn}" )) }

flights_with_airline_names %>% col_summary(c(air_time, arr_delay))

flights_with_airline_names %>% group_by(carrier) %>% col_summary(c(air_time, arr_delay))