## 1.2 How do I code in R?

Now that you’re set up with R and RStudio, you are probably asking yourself, “OK. Now how do I use R?”. The first thing to note is that unlike other statistical software programs like Excel, SPSS, or Minitab that provide point-and-click interfaces, R is an interpreted language. This means you have to type in commands written in R code. In other words, you have to code/program in R. Note that we’ll use the terms “coding” and “programming” interchangeably in this book.

While it is not required to be a seasoned coder/computer programmer to use R, there is still a set of basic programming concepts that new R users need to understand. Consequently, while this book is not a book on programming, you will still learn just enough of these basic programming concepts needed to explore and analyze data effectively.

### 1.2.1 Basic programming concepts and terminology

We now introduce some basic programming concepts and terminology. Instead of asking you to memorize all these concepts and terminology right now, we’ll guide you so that you’ll “learn by doing.” To help you learn, we will always use a different font to distinguish regular text from computer_code. The best way to master these topics is, in our opinions, through deliberate practice with R and lots of repetition.

• Basics:
• Console pane: where you enter in commands.
• Running code: the act of telling R to perform an act by giving it commands in the console.
• Objects: where values are saved in R. We’ll show you how to assign values to objects and how to display the contents of objects.
• Data types: integers, doubles/numerics, logicals, and characters. Integers are values like -1, 0, 2, 4092. Doubles or numerics are a larger set of values containing both the integers but also fractions and decimal values like -24.932 and 0.8. Logicals are either TRUE or FALSE while characters are text such as “cabbage”, “Hamilton”, “The Wire is the greatest TV show ever”, and “This ramen is delicious.” Note that characters are often denoted with the quotation marks around them.
• Vectors: a series of values. These are created using the c() function, where c() stands for “combine” or “concatenate.” For example, c(6, 11, 13, 31, 90, 92) creates a six element series of positive integer values .
• Factors: categorical data are commonly represented in R as factors. Categorical data can also be represented as strings. We’ll study this difference as we progress through the book.
• Data frames: rectangular spreadsheets. They are representations of datasets in R where the rows correspond to observations and the columns correspond to variables that describe the observations. We’ll cover data frames later in Section 1.4.
• Conditionals:
• Testing for equality in R using == (and not =, which is typically used for assignment). For example, 2 + 1 == 3 compares 2 + 1 to 3 and is correct R code, while 2 + 1 = 3 will return an error.
• Boolean algebra: TRUE/FALSE statements and mathematical operators such as < (less than), <= (less than or equal), and != (not equal to). For example, 4 + 2 >= 3 will return TRUE, but 3 + 5 <= 1 will return FALSE.
• Logical operators: & representing “and” as well as | representing “or.” For example, (2 + 1 == 3) & (2 + 1 == 4) returns FALSE since both clauses are not TRUE (only the first clause is TRUE). On the other hand, (2 + 1 == 3) | (2 + 1 == 4) returns TRUE since at least one of the two clauses is TRUE.
• Functions, also called commands: Functions perform tasks in R. They take in inputs called arguments and return outputs. You can either manually specify a function’s arguments or use the function’s default values.
• For example, the function seq() in R generates a sequence of numbers. If you just run seq() it will return the value 1. That doesn’t seem very useful! This is because the default arguments are set as seq(from = 1, to = 1). Thus, if you don’t pass in different values for from and to to change this behavior, R just assumes all you want is the number 1. You can change the argument values by updating the values after the = sign. If we try out seq(from = 2, to = 5) we get the result 2 3 4 5 that we might expect.
• We’ll work with functions a lot throughout this book and you’ll get lots of practice in understanding their behaviors. To further assist you in understanding when a function is mentioned in the book, we’ll also include the () after them as we did with seq() above.

This list is by no means an exhaustive list of all the programming concepts and terminology needed to become a savvy R user; such a list would be so large it wouldn’t be very useful, especially for novices. Rather, we feel this is a minimally viable list of programming concepts and terminology you need to know before getting started. We feel that you can learn the rest as you go. Remember that your mastery of all of these concepts and terminology will build as you practice more and more.

### 1.2.2 Errors, warnings, and messages

One thing that intimidates new R and RStudio users is how it reports errors, warnings, and messages. R reports errors, warnings, and messages in a glaring red font, which makes it seem like it is scolding you. However, seeing red text in the console is not always bad.

R will show red text in the console pane in three different situations:

• Errors: When the red text is a legitimate error, it will be prefaced with “Error in…” and will try to explain what went wrong. Generally when there’s an error, the code will not run. For example, we’ll see in Subsection 1.3.3 if you see Error in ggplot(...) : could not find function "ggplot", it means that the ggplot() function is not accessible because the package that contains the function (ggplot2) was not loaded with library(ggplot2). Thus you cannot use the ggplot() function without the ggplot2 package being loaded first.
• Warnings: When the red text is a warning, it will be prefaced with “Warning:” and R will try to explain why there’s a warning. Generally your code will still work, but with some caveats. For example, you will see in Chapter 2 if you create a scatterplot based on a dataset where two of the rows of data have missing entries that would be needed to create points in the scatterplot, you will see this warning: Warning: Removed 2 rows containing missing values (geom_point). R will still produce the scatterplot with all the remaining non-missing values, but it is warning you that two of the points aren’t there.
• Messages: When the red text doesn’t start with either “Error” or “Warning”, it’s just a friendly message. You’ll see these messages when you load R packages in the upcoming Subsection 1.3.2 or when you read data saved in spreadsheet files with the read_csv() function as you’ll see in Chapter 4. These are helpful diagnostic messages and they don’t stop your code from working. Additionally, you’ll see these messages when you install packages too using install.packages() as discussed in Subsection 1.3.1.

Remember, when you see red text in the console, don’t panic. It doesn’t necessarily mean anything is wrong. Rather:

• If the text starts with “Error”, figure out what’s causing it. Think of errors as a red traffic light: something is wrong!
• If the text starts with “Warning”, figure out if it’s something to worry about. For instance, if you get a warning about missing values in a scatterplot and you know there are missing values, you’re fine. If that’s surprising, look at your data and see what’s missing. Think of warnings as a yellow traffic light: everything is working fine, but watch out/pay attention.
• Otherwise, the text is just a message. Read it, wave back at R, and thank it for talking to you. Think of messages as a green traffic light: everything is working fine and keep on going!

### 1.2.3 Tips on learning to code

Learning to code/program is quite similar to learning a foreign language. It can be daunting and frustrating at first. Such frustrations are common and it is normal to feel discouraged as you learn. However, just as with learning a foreign language, if you put in the effort and are not afraid to make mistakes, anybody can learn and improve.

Here are a few useful tips to keep in mind as you learn to program:

• Remember that computers are not actually that smart: You may think your computer or smartphone is “smart,” but really people spent a lot of time and energy designing them to appear “smart.” In reality, you have to tell a computer everything it needs to do. Furthermore, the instructions you give your computer can’t have any mistakes in them, nor can they be ambiguous in any way.
• Take the “copy, paste, and tweak” approach: Especially when you learn your first programming language or you need to understand particularly complicated code, it is often much easier to take existing code that you know works and modify it to suit your ends. This is as opposed to trying to type out the code from scratch. We call this the “copy, paste, and tweak” approach. So early on, we suggest not trying to write code from memory, but rather take existing examples we have provided you, then copy, paste, and tweak them to suit your goals. After you start feeling more confident, you can slowly move away from this approach and write code from scratch. Think of the “copy, paste, and tweak” approach as training wheels for a child learning to ride a bike. After getting comfortable, they won’t need them anymore.
• The best way to learn to code is by doing: Rather than learning to code for its own sake, we find that learning to code goes much smoother when you have a goal in mind or when you are working on a particular project, like analyzing data that you are interested in and that is important to you.
• Practice is key: Just as the only method to improve your foreign language skills is through lots of practice and speaking, the only method to improving your coding skills is through lots of practice. Don’t worry, however, we’ll give you plenty of opportunities to do so!