AH-ANTQ103: Workshop 1

Spring 2024

1. Navigating R studio

At this point, we have installed R and RStudio, opened this file, and are now met by four panels within the R interface.

The panel in the upper left corner, in which you read this file, is the source panel, where you write your code. Beneath that is the console, where the code is executed. We can also write code in this panel, but for convenience we will always use R Scripts placed in the source panel. At the top right, there is the environment panel. It contains an overview of all data that R has stored in its memory. While we work, this panel will fill up will all kinds of data that you create and save. Lastly, in the lower right corner we have a panel for navigating files, plots, help documents, and more useful things.

R can be used for many things, for example as a basic calculator. Try the following operations. The output will be printed in the console (below). Please note that adding spaces between the numbers and the operators is not necessary, but it makes the code more readable.

1 + 1
1 - 1
2 * 3
4 / 2
2 ^ 3

2. Data types

In R we have different types of data that we should be aware of. The most important ones for archaeologists are numbers, typically called a double (dbl) and text data, represented by a so called string of characters (chr). Whilst there are others, we will only focus on these two for now.

3. Variables

Oftentimes we want to use a specific value of data later on again, in which case you can easily assign variables to values in R. Try the following operations. After running the code by clicking the green triangle in the upper right corner of the grey box below, these variables should be added to the environment panel. Now try assigning a value to a variable yourself.

Note that the arrow can be typed by pressing Alt + -, as well as by typing <- manually.

a <- 1
b <- 5
c <- "Word"
d <- "This is a whole sentence"

We can now also use our variables in the calculations instead of the numbers. Try the following operations.

a + b
a - b
a * b
a / b
a ^ b

However, we should not be able to add these two variables together, since we defined them as text data, and R does not know how to add two strings together

c + d

4. Vectors

Next to variables, we will often encounter vectors, which can be described as groups of values and which can be created with c().

x <- c(21, 23, 20, 23)
y <- c("Paul", "John", "George", "Ringo")

5. Functions

We can now do small calculations with numbers in R, but there is a lot more to do once we make use of functions! Functions can automate standard tasks such as calculating an average and thus spare you a lot of time, but they can also take over more complex tasks that we can only do thanks to the function and someone who took the time to code them.

Try out the functions below on the vector we have just defined and find out what they do.

mean(x)
length(x)
unique(c(x, x))

These are examples of very easy functions in R. Oftentimes functions require more input and information about their arguments, which are parts of a function that further change and define the output.

6. Packages

Up to this point, we have only installed base R, which includes basic features and functions but remains fairly limited. R packages, which are collections of elaborate functions and datasets, expand the capabilities of base R. There are packages for all kinds of disciplines, also for specific archaeological purposes like the tesselle packages that aim at making archaeological data science more accessible. For most purposes however, the tidyverse package stands out as the most essential, as it equips R for modern data science tasks. Execute the code below to install the tidyverse and rio packages, but do not worry about the latter one for now, we will come back to it later.

install.packages("tidyverse")
install.packages("rio")

We only have to install packages once, but we have to load them using the library function each time we start a new R session.

library(tidyverse)
library(rio)

With new packages come new functions and we can easily loose track of what each function can do. Luckily we have some guidance built right into R in the form of helpfiles. By typing in a question mark followed by a function, a window with useful information opens up.

?sum

We can now use data structures from the tidyverse package. For example, we can create a tibble, a specialized type of data frame that you can view as a table for simplicity.

tibble <- tibble(
  name = c("Paul", "John", "George", "Ringo"),
  age = c(21, 23, 20, 23)
)

We then have several ways to look at the data. Try them out!

print(tibble)
View(tibble)
glimpse(tibble)

If we have a data set focussed on numeric variables, we can use the summary function to get a quick overview of the data.

summary(tibble)

Alternatively, we can also try and click on our tibble in the environment panel, which automatically opens the tibble up and spares you from writing some additional code.

7. The pipe operator

The pipe operator is a very useful operator in R. It allows us to chain together multiple operations. This makes our code more readable. It can be typed by pressing Ctrl + Shift + M, as well as by typing |> manually.

tibble |> 
  View()

Using the pipe may seem a bit counter-intuitive at the beginning. If you want to better understand how it works, think of it as putting what is in front of the pipe as the first argument into whatever follows the pipe.

8. The filter() function

The filter() function allows us to filter rows in a tibble. This is useful for selecting a subset of the data.

tibble |> 
  filter(age > 20)

The filter() function is a typical example of a function that has more than just one input:

The first argument is the tibble (Inserted here by the pipe operator). The second argument is the condition (the age must be larger than 20). The condition is a logical vector, which means that it is a vector of TRUE and FALSE values. TRUE means that the row is kept, and FALSE means that the row is removed.

9. Comparison operators

We just used the comparison operator >, but there are also <, ==, <=, >=, != which can be used in any logical condition. Try the different operators out to see what they do!

x <  21
x == 21
x <= 21
x >= 21
x != 21

10. The select() function

The select() function allows us to select columns in a tibble. This is useful for selecting a subset of the data.

tibble |> 
  select(name)

11. The mutate() function

The mutate() function allows us to create new columns in a tibble. This is very useful for creating new variables.

tibble |>
  mutate(age_squared = age ^ 2)

Note that we have seen the use of both == and = in different contexts. The simple = is often used to assign or define, like here in the mutate function, whereas == is strictly used as a comparison operator.

12. NA values

NA values are missing values. These are values that are not defined. When you want to enter a NA value into your data, simply type it like any other value.

tibble <- tibble(
  name = c("Paul", "John", "George", "Ringo"),
  age = c(21, NA, 20, 23)
)

If we want to know what rows contain NA values, we can use the is.na() function inside the filter().

tibble |>
  filter(is.na(age))

This is a good example how we can combine different functions. The is.na() function checks the age column (a vector) for NA values and returns TRUE if it finds one, and FALSE if not. The filter() function automatically filters out all values that are not TRUE, in our example every row that does not contain a NA value for age.

We do not require an operational operator here, because the is.na() function already returns the logical condition the filter() requires.

Using the “not” operator (!) we could also filter rows that do not contain NA values.

tibble |> 
  filter(!is.na(age)) |> 
  print()

13. Loading external data

Data link here.

There are two main ways of loading in external data in R, either directly through a link from the internet or by storing the data in the same place as your code and then specifying the path to the file.

In both cases, we need to use the appropriate R function for the type of file that we will be loading in, so that R knows how the rows and columns are separated in the original file. In our case we want to load an Excel file from a link, so we will use the import function from the rio package we installed earlier. Try loading the data from the link using the code below. We assign our new data set the name data.

data <- import("https://bit.ly/j_data_xlsx", setclass = "tbl_df")

This first workshop has been quite heavy on basic Data Science in order for you to understand the basics, but you should always keep in mind how you as researchers in archaeology can use these tools to derive information useful to you.

Assignment 1

1) Based on the tibble we created in step 6, create a new column called age_in_20_years. This should contain the age in 20 years.

2) Filter the tibble to only contain rows where the age in 20 years is greater than 40.

3) Print the tibble.

Assignment 2

1) Using the data we loaded in in step 13 about lithics from the Jerimalai rockshelter in East-Timor, create a data set that only contains rows where the Material is Chert and give it a descriptive name.

2) Create a second data set that should only contain rows using Chalcedony as a Material, also give this data set a descriptive name.

3) Which data set has more rows?