By now, you have some experience manually entering data in
R to create a data frame. As a data analyst, it will also
be common for you to import data from external files into your
R console and use it to create a data frame to analyze it.
In this activity you will learn how to import data from outside of
R using the read_csv() function. Then, once
you have imported a data file, you will use R functions to
manipulate and interact with that data.
You can start importing and exploring data with the code chunks in the RMD space. To interact with the code chunk, click the green arrow in the top-right corner of the chunk. The executed code will appear in the RMD space and your console.
Throughout this activity, you will also have the opportunity to practice writing your own code by making changes to the code chunks yourself. If you encounter an error or get stuck, you can always check the Lesson2_Import_Solutions .rmd file in the Solutions folder under Week 3 for the complete, correct code.
In this scenario, you are a junior data analyst working for a hotel booking company. You 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, you are going to need to use functions to preview the data’s structure, including its columns and rows. You will also need to use basic cleaning functions to prepare this data for analysis.
Start by installing your required package. If you have already
installed and loaded tidyverse in this session, feel free
to skip the code chunks in this step.
#install.packages("tidyverse")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 errorsOne of the most common file types data analysts import into
R is comma separated values files, or .csv files. The
tidyverse library package readr has a number
of functions for “reading in” or importing data, including .csv files
and other external sources.
In the chunk below, use the read_csv() function to
import data from a .csv 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(“/cloud/project/Course 7/Week 3”) before it.
The results will display as column specifications:
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.Now that you have the bookings_df, you can work with it
using all of the R functions you have learned so far.
One common function you can use to preview the data is the
head() function, which returns the columns and first
several rows of data. Check out the head() function by
running the 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>, …In addition to head() there are a number of other useful
functions to summarize or preview your data frame. For example, the
str() and function will provide summaries of each column in
your data arranged horizontally. Check out the str()
function by running the code chunk 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>To find out what columns you have in your data frame, try running the
the colnames() function in the code chunk below:
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"If you want to create another data frame using
bookings_df that focuses on the average daily rate, which
is referred to as adr in the data frame, and
adults, you can use the following code chunk to do
that:
new_df <- select(bookings_df, `adr`, adults)To create new variables in your data frame, you can use the
mutate() function. This will make changes to the data
frame, but not to the original data set you imported. That source data
will remain unchanged.
mutate(new_df, total = `adr` / adults)## # A tibble: 119,390 × 3
## adr adults total
## <dbl> <dbl> <dbl>
## 1 0 2 0
## 2 0 2 0
## 3 75 1 75
## 4 75 1 75
## 5 98 2 49
## 6 98 2 49
## 7 107 2 53.5
## 8 103 2 51.5
## 9 82 2 41
## 10 106. 2 52.8
## # ℹ 119,380 more rowsNow you can find your own .csv to import! Using the RStudio Cloud
interface, import and save the file in the same folder as this R
Markdown document. To do this, go to the Files tab in the lower-right
console. Then, click the Upload button next to the + New Folder button.
This will open a popup to let you browse your computer for a file.
Select any .csv file, then click Open. Now, write code in the chunk
below to read that data into R:
read_csv("Importar_csv.csv")## Rows: 4 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (4): Ana, Bea, Carlos, David
##
## 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.## # A tibble: 4 × 4
## Ana Bea Carlos David
## <dbl> <dbl> <dbl> <dbl>
## 1 1 1 0 4
## 2 2 2 9 5
## 3 3 3 9 6
## 4 4 4 8 3You can check the solutions document for this activity to check your work.
Now that you know how to import data using the
read_csv() function, you will be able to work with data
that has been stored externally right in your R console.
You can continue to practice these skills by modifying the code chunks
in the rmd file, or use this code as a starting point in your own
project console. As you become more familiar with the process of
importing data, consider how importing data from a .csv file changed the
way you accessed and interacted with the data. Did you do anything
differently? Being able to import data from external sources will allow
you to work with even more data, giving you even more options for
analyzing data in R.
Make sure to mark this activity as complete in Coursera.