Clone repository & create a vesion control project

Log onto GitHub and create a new repository by cloning today’s lab template project. To remind you of the step:

• Go to Your repositories in your GitHub account and then click on the green New button.
• Click on Import a repository and type/copy the URL of today’s lab template project: https://github.com/uoeIDS/lab-01-template
• Add an appropriate name to your repository, say lab-01, and click on Begin import.

Open RStudio and create a new version control project using the GitHub repository you have just made. To remind you of the steps:

• Open RStudio and go to File > New Project…
• Select Version Control and then Git. Type/paste the URL of the repository you have just created.
• Browse an appropriate location for the project and then click on Create Project.

Open the R Markdown document lab-01.Rmd and change the author to your name. Knit the document and make sure that it complies correctly without any errors.

✅ ⬆️ Commit and push your changes to GitHub with an appropriate commit message. As a reminder of the steps:

• Go to the Git tab (top right panel) and click on Commit.
• In the new window, click the tick box next to each file that you want to stage and add an informative Commit message (eg, changed author text).
• Click the Commit button.
• Click on the Push button to send your changes to GitHub.

❗ Make sure you are familiar with the above steps as you will need to do this for you homework and future labs. Furthermore, remember do good version control practices by periodically keeping your repository on GitHub up-to-date by staging and committing any substantial changes, and then push them to GitHub.

Packages

In today’s lab we will using the tidyverse package and its dependencies. At the top of your template you will find the following code that will load this library whenever you knit your document.

library(tidyverse)

If you do not have this library installed, then your document may fail to knit. To install any R packages:

• Go do the Packages tab (bottom right panel)
• Click on the Install button
• Type the name of the package you wish to install (in this case tidyverse), and ensure that the option Install dependencies is ticked.
• Click on the Install button.
• You may be asked to select a CRAN mirror. It does not matter which server you use, but it is recommended to choose one that is close to your location geographically.

Alternatively, you can install the tidyverse package by running the command install.packages("tidyverse") once on the console (bottom left panel). Do not write this command in your Rmd file, otherwise as RStudio will attempt to re-install the package each you knit your document!

RStudio will then download and install the tidyverse package and any other dependent packages. Read the messages that are printed to the console and check that there are no installation errors.

Data

The dataset for this assignment can be found as a comma separated value (.csv) file in the data folder of your repository. You can read it into your work using the following code. (We will discuss more about importing and exporting data next week.)

visitors <- read_csv("data/UK-visitor-numbers.csv")

Use the head() command as demonstrated below to view the first 10 rows of the data set.

visitors %>% head(n = 10)

The variable in the visitors data set are:

Question 1

How many tourist attractions are there in the data set?

Each row in the data set corresponds to a different visitor attraction, so to answer this question we need to do is to count how many rows there are. The simplest solution is to run the command nrow(visitors) that calculates the number of rows there are in the visitors data set (similarly ncol(visitors) calculates the number of columns, i.e., the number of variables).

Alternatively, we can use the count() command using the %>% pipework as follows:

visitors %>% count()

The count() command can also be used for creating frequency tables by putting the name of a specific variable into the command. For example, the following creates a frequency table for the different admission types:

visitors %>% count(admission)

Question 2

What are the variable data types?

On viewing a printout of the data set either on the console or on the knitted document, the line immediately under the variables names indicate the data type of that variable. A detailed list of variable types can be found here.

To find this information using R code, we can use the class() command. For example, showing that the data type of the n_2022 variable is numerical can be achieved using the following code.

class(visitors$n_2022)

Applying the class command to each individual variable can be very tedious, especially if the data set contains many variables. Alternatively, we can use the summarise_all() command that applies a specified function to all of the columns of the data set. Therefore, we can extract the data type for all of the variables as follows:

visitors %>% summarise_all(class)

Understanding the data type of each variable is important to understand whether the R code we write will work as expected and to determine which commands will work for the data types in the data set. For example, the code "1" + "2" will create an error as the addition operator expects two numerical values, but the data type of "1" is character.

Question 3

Which attraction had the most number of visitors in 2022?

This question can be addressed by ordering the data based on the n_2022 variable. This can be performed using the arrange() command. Note however that by default the arrange() command orders the rows into increasing numerical order, so that the first row corresponds to the attraction with the least number of visits. The command desc() sorts the data in descending order, so that now the first row corresponds to the most visited attraction. For example:

For example:

visitors %>% arrange(desc(n_2022))

Question 4

What is the admission charge for the "National Museum of Scotland"?

This question asks you to search through, or filter, the data to find the row that satisfies a particular condition. The filter() command can be used to extract all of the rows in the data set that satisfies a specific logical test, retaining all rows where the expression is TRUE and removing those where it is FALSE.

There are 6 key operations when performing a logical test:

The logical test that is needed to filter the data set is to assess whether the content of the attraction variable exactly matches the text "National Museum of Scotland". Specifically:

visitors %>% filter(attraction == "National Museum of Scotland")

Question 5

How many "O"utside attractions are there in the "Yorkshire and the Humber" region that gives "Members" free admission, which had more than 100,000 visitors in 2022?

The previous question used the filter() command to extract the rows based on a single logical test, but here we need to apply 4 logical tests. The filter() command can still be applied by writing the R code for each test separated by a comma.

We can stop there and view the result of the filtering to see which attractions meet the 4 conditions, but the question asks for ‘How many’, not ‘Which’. Therefore, we can pipe (%>%) the result from the filer() command directly into the count() command:

visitors %>%
  filter(
    setting == "O",
    region == "Yorkshire and the Humber",
    admission == "Members",
    n_2022 >= 100000
    ) %>%
  count()

Coding Style: Writing all of the code for this example on a single line will work but doing so can make your code very difficult to read. This is particularly important when there is an error and you are trying to de-bug your code. Make use of ‘whitespace’ (spaces, new lines and tabbing) to aid the readability of your code.

Question 6

What are the mean and median visitor numbers in 2022 across all attractions?

R has many commands that can be used to compute a wide range of standard statistical values. These include the mean() command for computing the arithmetic mean or averaged value, and median() command for computing the median or mid-point in the data.

visitors %>% 
  summarise(
    mean_2022 = mean(n_2022),
    med_2022 = median(n_2022)
  )

Question 7

Which setting (inside, outside or mixed) has the largest mean visitor numbers in 2022?

This question is a simple extension of the previous question where we are now interested in computing the mean statistic per setting rather than across all settings. To implement this change, we need to group the data before calculating the summary statistics. This is achieved by using the group_by command as follows:

visitors %>% 
  group_by(setting) %>%
  summarise(
    mean_2022 = mean(n_2022),
    med_2022 = median(n_2022)
  )

From the output you can easily read which setting, whether "I"nside, "O"utside or "M"ixed, have the largest

If you have time, discuss in your group why the mean values across all settings are much larger than the median values.

Question 8

What is the interquartile range (the width of the middle 50% of data set between the lower and upper quartiles) the for each of the four nations of the UK?

This is a more complex question that requires some pre-processing of the data before doing any calculations.

First, look at the region variable. There are entries for "Northern Ireland", "Scotland" and "Wales", but England is split into 9 different regions. The first stage is to transform the data set by creating a new variable called nation that groups all of 9 English regions.

A transformation of the data can be performed using the mutate() command. For this question we need to consider how best to transform the region variable to the new nation variable. The transformation can be described by the following if-else function:

\[{nation} = \left\{ \begin{array}{ll}\text{Northern Ireland} & \text{if}~{region} = \text{Northern Ireland}, \text{else},\\\text{Scotland} & \text{if}~{region} = \text{Scotland},\text{else},\\\text{Wales} & \text{if}~{region} = \text{Wales},\text{else},\\\text{England} & \text{otherwise}.\\ \end{array}\right.\]

This function can be implemented using the case_when() function. The general structure of this command is as follows:

case_when(
  logical_test_1 ~ returned_value_1,
  logical_test_2 ~ returned_value_2,
  ...
  logical_test_n ~ returned_value_n,
  TRUE ~ return_value_default
)

On entering this command, the first logical test is performed and if the answer is TRUE then the command returns the value after the ~ symbol. If the result of the first test is FALSE then the next logical test is evaluated. The TRUE statement at the end acts as the ‘otherwise’ scenario to capture all remaining cases that did not satisfy any of the earlier tests.

The case_when command can be used to implement the transformation of the region variable to construct the new nation variable as follows:

visitors_with_nations <- visitors %>%
  mutate(
    nation = case_when(
      region == "Northern Ireland" ~ "Northern Ireland",
      region == "Scotland" ~ "Scotland",
      region == "Wales" ~ "Wales",
      TRUE ~ "England"
    )
  )

Here we have assigned the result of the transformation to a new variable in memory called visitors_with_nations so that we can use the result without the need to continuously re-evaluate the nation variable as part of a workflow pipework.

Finally, we can now summarise the data to compute the inter-quartile range for each nation using the IQR() command:

visitors_with_nations %>% 
  group_by(nation) %>%
  summarise(
    IQR_2022 = IQR(n_2022)
  )