In this activity, we will review a scenario, and focus on cleaning real data in R. We will learn more about data cleaning functions and perform basic calculations to gain initial insights into our data.
Throughout this activity, we will also have the opportunity to practice writing our own code by making changes to the code chunks ourselves. If we encounter an error or get stuck, we can always check the Lesson2_Clean_Solutions .rmd file in the Solutions folder under Week 3 for the complete, correct code.
In this scenario, we are a junior data analyst working for a hotel booking company. We have been asked to clean a .csv file that was created after querying a database to combine two different tables from different hotels. In order to learn more about this data, we are going to need to use functions to preview the data’s structure, including its columns and rows. We will also need to use basic cleaning functions to prepare this data for analysis.
In order to start cleaning our data, we will need to by install the
required packages. If we have already installed and loaded
tidyverse, skimr, and janitor in
this session, feel free to skip the code chunks in this step.
install.packages("tidyverse")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)install.packages("skimr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)install.packages("janitor")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)Once a package is installed, we can load it by running the
library() function with the package name inside the
parentheses:
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(skimr)
library(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testThe data we have been asked to clean is currently an external .csv
file. In order to view and clean it in R, we will need to
import it. The tidyverse library readr package
has a number of functions for “reading in” or importing data, including
.csv files.
In the chunk below, we will use the read_csv() function
to import data from a .csv file in the project folder called
“hotel_bookings.csv” and save it as a data frame called
bookings_df:
If this line causes an error, copy in the line setwd(“projects/Course 7/Week 3”) before it.
bookings_df <- read_csv("hotel_bookings.csv")## Rows: 119390 Columns: 32
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (13): hotel, arrival_date_month, meal, country, market_segment, distrib...
## dbl (18): is_canceled, lead_time, arrival_date_year, arrival_date_week_numb...
## date (1): reservation_status_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Before we start cleaning our data, take some time to explore it. We
can use several functions that we are already familiar with to preview
our data, including the head() function in the code chunk
below:
head(bookings_df)## # A tibble: 6 × 32
## hotel is_canceled lead_time arrival_date_year arrival_date_month
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 Resort Hotel 0 342 2015 July
## 2 Resort Hotel 0 737 2015 July
## 3 Resort Hotel 0 7 2015 July
## 4 Resort Hotel 0 13 2015 July
## 5 Resort Hotel 0 14 2015 July
## 6 Resort Hotel 0 14 2015 July
## # ℹ 27 more variables: arrival_date_week_number <dbl>,
## # arrival_date_day_of_month <dbl>, stays_in_weekend_nights <dbl>,
## # stays_in_week_nights <dbl>, adults <dbl>, children <dbl>, babies <dbl>,
## # meal <chr>, country <chr>, market_segment <chr>,
## # distribution_channel <chr>, is_repeated_guest <dbl>,
## # previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
## # reserved_room_type <chr>, assigned_room_type <chr>, …We can also summarize or preview the data with the str()
and glimpse() functions to get a better understanding of
the data by running the code chunks below:
str(bookings_df)## spc_tbl_ [119,390 × 32] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ hotel : chr [1:119390] "Resort Hotel" "Resort Hotel" "Resort Hotel" "Resort Hotel" ...
## $ is_canceled : num [1:119390] 0 0 0 0 0 0 0 0 1 1 ...
## $ lead_time : num [1:119390] 342 737 7 13 14 14 0 9 85 75 ...
## $ arrival_date_year : num [1:119390] 2015 2015 2015 2015 2015 ...
## $ arrival_date_month : chr [1:119390] "July" "July" "July" "July" ...
## $ arrival_date_week_number : num [1:119390] 27 27 27 27 27 27 27 27 27 27 ...
## $ arrival_date_day_of_month : num [1:119390] 1 1 1 1 1 1 1 1 1 1 ...
## $ stays_in_weekend_nights : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ stays_in_week_nights : num [1:119390] 0 0 1 1 2 2 2 2 3 3 ...
## $ adults : num [1:119390] 2 2 1 1 2 2 2 2 2 2 ...
## $ children : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ babies : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ meal : chr [1:119390] "BB" "BB" "BB" "BB" ...
## $ country : chr [1:119390] "PRT" "PRT" "GBR" "GBR" ...
## $ market_segment : chr [1:119390] "Direct" "Direct" "Direct" "Corporate" ...
## $ distribution_channel : chr [1:119390] "Direct" "Direct" "Direct" "Corporate" ...
## $ is_repeated_guest : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_cancellations : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_bookings_not_canceled: num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ reserved_room_type : chr [1:119390] "C" "C" "A" "A" ...
## $ assigned_room_type : chr [1:119390] "C" "C" "C" "A" ...
## $ booking_changes : num [1:119390] 3 4 0 0 0 0 0 0 0 0 ...
## $ deposit_type : chr [1:119390] "No Deposit" "No Deposit" "No Deposit" "No Deposit" ...
## $ agent : chr [1:119390] "NULL" "NULL" "NULL" "304" ...
## $ company : chr [1:119390] "NULL" "NULL" "NULL" "NULL" ...
## $ days_in_waiting_list : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ customer_type : chr [1:119390] "Transient" "Transient" "Transient" "Transient" ...
## $ adr : num [1:119390] 0 0 75 75 98 ...
## $ required_car_parking_spaces : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ total_of_special_requests : num [1:119390] 0 0 0 0 1 1 0 1 1 0 ...
## $ reservation_status : chr [1:119390] "Check-Out" "Check-Out" "Check-Out" "Check-Out" ...
## $ reservation_status_date : Date[1:119390], format: "2015-07-01" "2015-07-01" ...
## - attr(*, "spec")=
## .. cols(
## .. hotel = col_character(),
## .. is_canceled = col_double(),
## .. lead_time = col_double(),
## .. arrival_date_year = col_double(),
## .. arrival_date_month = col_character(),
## .. arrival_date_week_number = col_double(),
## .. arrival_date_day_of_month = col_double(),
## .. stays_in_weekend_nights = col_double(),
## .. stays_in_week_nights = col_double(),
## .. adults = col_double(),
## .. children = col_double(),
## .. babies = col_double(),
## .. meal = col_character(),
## .. country = col_character(),
## .. market_segment = col_character(),
## .. distribution_channel = col_character(),
## .. is_repeated_guest = col_double(),
## .. previous_cancellations = col_double(),
## .. previous_bookings_not_canceled = col_double(),
## .. reserved_room_type = col_character(),
## .. assigned_room_type = col_character(),
## .. booking_changes = col_double(),
## .. deposit_type = col_character(),
## .. agent = col_character(),
## .. company = col_character(),
## .. days_in_waiting_list = col_double(),
## .. customer_type = col_character(),
## .. adr = col_double(),
## .. required_car_parking_spaces = col_double(),
## .. total_of_special_requests = col_double(),
## .. reservation_status = col_character(),
## .. reservation_status_date = col_date(format = "")
## .. )
## - attr(*, "problems")=<externalptr>glimpse(bookings_df)## Rows: 119,390
## Columns: 32
## $ hotel <chr> "Resort Hotel", "Resort Hotel", "Resort…
## $ is_canceled <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, …
## $ lead_time <dbl> 342, 737, 7, 13, 14, 14, 0, 9, 85, 75, …
## $ arrival_date_year <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 201…
## $ arrival_date_month <chr> "July", "July", "July", "July", "July",…
## $ arrival_date_week_number <dbl> 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,…
## $ arrival_date_day_of_month <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ stays_in_weekend_nights <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ stays_in_week_nights <dbl> 0, 0, 1, 1, 2, 2, 2, 2, 3, 3, 4, 4, 4, …
## $ adults <dbl> 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
## $ children <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ babies <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ meal <chr> "BB", "BB", "BB", "BB", "BB", "BB", "BB…
## $ country <chr> "PRT", "PRT", "GBR", "GBR", "GBR", "GBR…
## $ market_segment <chr> "Direct", "Direct", "Direct", "Corporat…
## $ distribution_channel <chr> "Direct", "Direct", "Direct", "Corporat…
## $ is_repeated_guest <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ previous_cancellations <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ previous_bookings_not_canceled <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ reserved_room_type <chr> "C", "C", "A", "A", "A", "A", "C", "C",…
## $ assigned_room_type <chr> "C", "C", "C", "A", "A", "A", "C", "C",…
## $ booking_changes <dbl> 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ deposit_type <chr> "No Deposit", "No Deposit", "No Deposit…
## $ agent <chr> "NULL", "NULL", "NULL", "304", "240", "…
## $ company <chr> "NULL", "NULL", "NULL", "NULL", "NULL",…
## $ days_in_waiting_list <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ customer_type <chr> "Transient", "Transient", "Transient", …
## $ adr <dbl> 0.00, 0.00, 75.00, 75.00, 98.00, 98.00,…
## $ required_car_parking_spaces <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ total_of_special_requests <dbl> 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 3, …
## $ reservation_status <chr> "Check-Out", "Check-Out", "Check-Out", …
## $ reservation_status_date <date> 2015-07-01, 2015-07-01, 2015-07-02, 20…We can also use colnames() to check the names of the
columns in our data set. Run the code chunk below to find out the column
names in this data set:
colnames(bookings_df)## [1] "hotel" "is_canceled"
## [3] "lead_time" "arrival_date_year"
## [5] "arrival_date_month" "arrival_date_week_number"
## [7] "arrival_date_day_of_month" "stays_in_weekend_nights"
## [9] "stays_in_week_nights" "adults"
## [11] "children" "babies"
## [13] "meal" "country"
## [15] "market_segment" "distribution_channel"
## [17] "is_repeated_guest" "previous_cancellations"
## [19] "previous_bookings_not_canceled" "reserved_room_type"
## [21] "assigned_room_type" "booking_changes"
## [23] "deposit_type" "agent"
## [25] "company" "days_in_waiting_list"
## [27] "customer_type" "adr"
## [29] "required_car_parking_spaces" "total_of_special_requests"
## [31] "reservation_status" "reservation_status_date"Some packages contain more advanced functions for summarizing and
exploring our data. One example is the skimr package, which
has a number of functions for this purpose. For example, the
skim_without_charts() function provides a detailed summary
of the data. Try running the code below:
skim_without_charts(bookings_df)| Name | bookings_df |
| Number of rows | 119390 |
| Number of columns | 32 |
| _______________________ | |
| Column type frequency: | |
| character | 13 |
| Date | 1 |
| numeric | 18 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| hotel | 0 | 1 | 10 | 12 | 0 | 2 | 0 |
| arrival_date_month | 0 | 1 | 3 | 9 | 0 | 12 | 0 |
| meal | 0 | 1 | 2 | 9 | 0 | 5 | 0 |
| country | 0 | 1 | 2 | 4 | 0 | 178 | 0 |
| market_segment | 0 | 1 | 6 | 13 | 0 | 8 | 0 |
| distribution_channel | 0 | 1 | 3 | 9 | 0 | 5 | 0 |
| reserved_room_type | 0 | 1 | 1 | 1 | 0 | 10 | 0 |
| assigned_room_type | 0 | 1 | 1 | 1 | 0 | 12 | 0 |
| deposit_type | 0 | 1 | 10 | 10 | 0 | 3 | 0 |
| agent | 0 | 1 | 1 | 4 | 0 | 334 | 0 |
| company | 0 | 1 | 1 | 4 | 0 | 353 | 0 |
| customer_type | 0 | 1 | 5 | 15 | 0 | 4 | 0 |
| reservation_status | 0 | 1 | 7 | 9 | 0 | 3 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| reservation_status_date | 0 | 1 | 2014-10-17 | 2017-09-14 | 2016-08-07 | 926 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 |
|---|---|---|---|---|---|---|---|---|---|
| is_canceled | 0 | 1 | 0.37 | 0.48 | 0.00 | 0.00 | 0.00 | 1 | 1 |
| lead_time | 0 | 1 | 104.01 | 106.86 | 0.00 | 18.00 | 69.00 | 160 | 737 |
| arrival_date_year | 0 | 1 | 2016.16 | 0.71 | 2015.00 | 2016.00 | 2016.00 | 2017 | 2017 |
| arrival_date_week_number | 0 | 1 | 27.17 | 13.61 | 1.00 | 16.00 | 28.00 | 38 | 53 |
| arrival_date_day_of_month | 0 | 1 | 15.80 | 8.78 | 1.00 | 8.00 | 16.00 | 23 | 31 |
| stays_in_weekend_nights | 0 | 1 | 0.93 | 1.00 | 0.00 | 0.00 | 1.00 | 2 | 19 |
| stays_in_week_nights | 0 | 1 | 2.50 | 1.91 | 0.00 | 1.00 | 2.00 | 3 | 50 |
| adults | 0 | 1 | 1.86 | 0.58 | 0.00 | 2.00 | 2.00 | 2 | 55 |
| children | 4 | 1 | 0.10 | 0.40 | 0.00 | 0.00 | 0.00 | 0 | 10 |
| babies | 0 | 1 | 0.01 | 0.10 | 0.00 | 0.00 | 0.00 | 0 | 10 |
| is_repeated_guest | 0 | 1 | 0.03 | 0.18 | 0.00 | 0.00 | 0.00 | 0 | 1 |
| previous_cancellations | 0 | 1 | 0.09 | 0.84 | 0.00 | 0.00 | 0.00 | 0 | 26 |
| previous_bookings_not_canceled | 0 | 1 | 0.14 | 1.50 | 0.00 | 0.00 | 0.00 | 0 | 72 |
| booking_changes | 0 | 1 | 0.22 | 0.65 | 0.00 | 0.00 | 0.00 | 0 | 21 |
| days_in_waiting_list | 0 | 1 | 2.32 | 17.59 | 0.00 | 0.00 | 0.00 | 0 | 391 |
| adr | 0 | 1 | 101.83 | 50.54 | -6.38 | 69.29 | 94.58 | 126 | 5400 |
| required_car_parking_spaces | 0 | 1 | 0.06 | 0.25 | 0.00 | 0.00 | 0.00 | 0 | 8 |
| total_of_special_requests | 0 | 1 | 0.57 | 0.79 | 0.00 | 0.00 | 0.00 | 1 | 5 |
Based on the functions we have used so far, how would we describe our
data in a brief to our stakeholder? Now, let’s say we are primarily
interested in the following variables: ‘hotel’, ‘is_canceled’, and
‘lead_time’. Create a new data frame with just those columns, calling it
trimmed_df by adding the variable names to this code
chunk:
trimmed_df <- bookings_df %>%
select(hotel, is_canceled, lead_time)Remember to check the solutions doc if we are having trouble filling out any of these code chunks.
We might notice that some of the column names aren’t very intuitive, so we will want to rename them to make them easier to understand. We might want to create the same exact data frame as above, but rename the variable ‘hotel’ to be named ‘hotel_type’ to be crystal clear on what the data is about
Fill in the space to the left of the ‘=’ symbol with the new variable name:
trimmed_df %>%
select(hotel, is_canceled, lead_time) %>%
rename(hotel_type = hotel)## # A tibble: 119,390 × 3
## hotel_type is_canceled lead_time
## <chr> <dbl> <dbl>
## 1 Resort Hotel 0 342
## 2 Resort Hotel 0 737
## 3 Resort Hotel 0 7
## 4 Resort Hotel 0 13
## 5 Resort Hotel 0 14
## 6 Resort Hotel 0 14
## 7 Resort Hotel 0 0
## 8 Resort Hotel 0 9
## 9 Resort Hotel 1 85
## 10 Resort Hotel 1 75
## # ℹ 119,380 more rowsAnother common task is to either split or combine data in different columns. In this example, we can combine the arrival month and year into one column using the unite() function:
example_df <- bookings_df %>%
select(arrival_date_year, arrival_date_month) %>%
unite(arrival_month_year, c("arrival_date_month", "arrival_date_year"), sep = " ")We can also use themutate() function to make changes to
our columns. Let’s say we wanted to create a new column that summed up
all the adults, children, and babies on a reservation for the total
number of people. Modify the code chunk below to create that new
column:
example_df <- bookings_df %>%
mutate(guests = adults + children + babies)
head(example_df)## # A tibble: 6 × 33
## hotel is_canceled lead_time arrival_date_year arrival_date_month
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 Resort Hotel 0 342 2015 July
## 2 Resort Hotel 0 737 2015 July
## 3 Resort Hotel 0 7 2015 July
## 4 Resort Hotel 0 13 2015 July
## 5 Resort Hotel 0 14 2015 July
## 6 Resort Hotel 0 14 2015 July
## # ℹ 28 more variables: arrival_date_week_number <dbl>,
## # arrival_date_day_of_month <dbl>, stays_in_weekend_nights <dbl>,
## # stays_in_week_nights <dbl>, adults <dbl>, children <dbl>, babies <dbl>,
## # meal <chr>, country <chr>, market_segment <chr>,
## # distribution_channel <chr>, is_repeated_guest <dbl>,
## # previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
## # reserved_room_type <chr>, assigned_room_type <chr>, …Great. Now it’s time to calculate some summary statistics! Calculate
the total number of canceled bookings and the average lead time for
booking - we’ll want to start our code after the %>% symbol. Make a
column called ‘number_canceled’ to represent the total number of
canceled bookings. Then, make a column called ‘average_lead_time’ to
represent the average lead time. Use the summarize()
function to do this in the code chunk below:
example_df <- bookings_df %>%
summarize(number_canceled = sum(is_canceled), average_lead_time = mean(lead_time))
head(example_df)## # A tibble: 1 × 2
## number_canceled average_lead_time
## <dbl> <dbl>
## 1 44224 104.If we are having trouble completing any of the code chunks in these activities, remember that we can reference the RMarkdown documents in the ‘Solutions’ for help.
Now we have some experience cleaning and analyzing data in
R; we used basic cleaning functions like
rename() and clean_names() and performed basic
calculations on real data. We can continue to practice these skills by
modifying the code chunks in the rmd file, or use this code as a
starting point in our own project console. One of the reasons
R is such a powerful tool for data analysis is because we
can perform so many different tasks in one place. With the functions we
have been learning in this course, we can import data, create and view
data frames, and even clean data without leaving our console.
Make sure to mark this activity as complete in Coursera.