Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
Data Wrangling with dplyr - Part 3
# install
install.packages('dplyr')
install.packages('readr')
# library
library(dplyr)
library(readr)
# import data
ecom <- readr::read_csv('https://raw.githubusercontent.com/rsquaredacademy/datasets/master/web.csv')
ecom
# check the sources of traffic and device types.
ecom %>%
distinct(referrer)
ecom %>%
distinct(device)
# rename columns
ecom %>%
rename(time_on_site = duration)
# sampling data
ecom %>%
sample_n(700)
ecom %>%
group_by(referrer) %>%
sample_n(100)
ecom %>%
sample_frac(size = 0.7)
ecom %>%
group_by(referrer) %>%
sample_frac(0.3)
# extract the `device` column.
ecom %>%
pull(device)
# extract the first column.
ecom %>%
pull(1)
# extract the last column
ecom %>%
pull(-1)
# extract the first 20 rows
ecom %>%
slice(1:20)
# extract the last row
ecom %>%
slice(n())
# total number of observations in the data
ecom %>%
tally()
# observations of different types of referrers
ecom %>%
group_by(referrer) %>%
tally()
# observations of referrers and bouncers
ecom %>%
group_by(referrer, bouncers) %>%
tally()
# observations of referrers and purchasers
ecom %>%
group_by(referrer, purchase) %>%
tally()
ecom %>%
group_by(referrer, purchase) %>%
tally() %>%
filter(purchase == 'true')
# use `count()` instead of `tally()`
ecom %>%
count(referrer, purchase)
# top 2 referrers that bring orders
ecom %>%
count(referrer, purchase) %>%
filter(purchase == 'true') %>%
arrange(desc(n)) %>%
top_n(n = 2)
ecom %>%
pull(n_pages) %>%
between(5, 15)
mtcars %>%
select(mpg, disp, cyl, gear, carb) %>%
mutate(
type = case_when(
disp > 200 ~ 'High',
cyl == 8 ~ 'Eight',
TRUE ~ 'True'
)
)
ecom %>%
pull(referrer) %>%
nth(1)
ecom %>%
pull(referrer) %>%
nth(1000)
ecom %>%
pull(referrer) %>%
last()
ecom %>%
pull(referrer) %>%
first()
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
You can’t perform that action at this time.