4. Glossary

Here’s a quick reference to key terms we’ve covered in this session:

Interfaces

  • Graphical User Interface (GUI): Interaction with the computer using visual elements like buttons and menus.
    • Example: Clicking “File > Open File” in RStudio.
  • Command-Line Interface (CLI): Interaction with the computer by typing commands instead of using graphical elements.
    • Example: Typing getwd() in RStudio to print the current working directory.

R Programming Basics

  • Variable: A named container that stores data in R.
    • Example: x <- 10
  • Function: A block of reusable code that performs a specific task. Functions take inputs (arguments) and return outputs.
    • Example: sqrt(16) calculates the square root of 16.
  • Parameter: A placeholder in a function definition that accepts input values. Parameters are defined in the function header.
    • Example: In the function mean(x, na.rm = FALSE), x and na.rm are parameters.
  • Argument: The actual values provided to a function when it is called. Arguments are passed to the parameters.
    • Example: In mean(x = c(1, 2, 3), na.rm = TRUE), c(1, 2, 3) is the argument for the parameter x, and TRUE is the argument for the parameter na.rm.
  • Package: A collection of pre-built R functions, datasets, and documentation designed for specific tasks. Packages extend R’s capabilities.
    • Example: ggplot2 for advanced data visualization.

Directories

  • Home Directory: The default starting location on your computer for files.
    • Example: C:/Users/YourName/Documents
  • Working Directory: The folder R is currently using to read or save files.
    • Example: Set with setwd("path/to/folder").

Data Types

  • Numeric: Represents numbers (e.g., integers or doubles).
  • Character: Text or strings enclosed in quotes.
    • Example: "Hello, World!"
  • Logical: Two-valued/binary/Boolean variables - either TRUE or FALSE, often used in conditions or comparisons
  • Factor: Categorical data with fixed levels.
    • Example: factor(c("Male", "Female"))

Data Structures

  • Vector: A one-dimensional collection of data of the same type.
    • Example: c(1, 2, 3)
  • Dataframe: A two-dimensional structure with rows (observations) and columns (variables).
    • Example:
    data.frame(Name = c("Alice", "Bob"), Age = c(25, 30))
  • List: A flexible collection that can store data of different types.
    • Example: list(Name = "Alice", Scores = c(90, 85))

Dataframe Terminology

  • Observation: A row in a dataframe representing an individual entry.
  • Variable/Feature: A column in a dataframe representing a specific characteristic.

Operators

  • <-: Used to assign values to variables (preferred over =).
  • =: Also used for assignment or to specify arguments in functions.
  • ==: Comparison operator to check equality.

Exercise

1. You downloaded a file called data.csv from the internet and ran the following code:

read.csv("data.csv")

However, you get an error saying file not found. What might be causing this issue, and how can you fix it?

Click for Answer

Possible causes:

  • The file data.csv is not in your current working directory. Check with getwd() and move the file there or specify the full path.
  • The filename may have a typo or incorrect capitalization.
  • The file extension .csv might be missing or incorrect.

Solutions:

  • Use getwd() to confirm the working directory.
  • Verify the filename and path.
  • If the file is in another location, use setwd() or provide the full path, e.g., read.csv("C:/Users/YourName/Documents/data.csv").


2. In the function mean(x = c(1, 3, 5), na.rm = TRUE):

  • Identify the parameters.
  • Identify the arguments.
Click for Answer
  • Parameters: x, na.rm
  • Arguments: c(1, 3, 5) is the argument for x; TRUE is the argument for na.rm.


3. What is the data structure of c(12.5, 15.0, 20.0) and what will be the output of the following code?

typeof(c(12.5, 15.0, 20.0))
Click for Answer
  • The data structure is a numeric vector.
  • The output of typeof(c(12.5, 15.0, 20.0)) will be "double".


4. What will be the output of the following code?

fruits <- c("apple", "banana", "cherry")
fruits[4] <- "date"
print(fruits)
Click for Answer

The output will be:

[1] "apple" "banana" "cherry" "date"
Explanation: The fruits vector was extended to include “date” at index 4.


5. What’s the output of the following code and what are the differences of x as variables in these two different contexts?

x <- c(1, 2, 3)
df <- data.frame(x = c(4, 5, 6))
print(x)
print(df$x)
Click for Answer

The output will be:

print(x)

[1] 1 2 3

print(df$x)

[1] 4 5 6

Explanation:

  • x as a variable in R:

    • A named container that stores data in R. Here, x is a standalone vector holding three numeric values: 1, 2, and 3.

  • x as a variable/feature in a dataframe:

    • A column in the dataframe df that represents a specific characteristic. It is tied to the dataframe and must be accessed with df$x.

  • Key Difference:

    • x as a standalone variable and x as a column in a dataframe are both referred to as variables but differ in context.
    • In a dataframe, variables or features are stored in columns, which collectively describe given characteristics in a dataset.


6. What will the following code output and what’s the difference between = and == here?

x = 3
print(x)
print(x = 4)
print(x == 3)
Click for Answer

Output:

  • print(x):
[1] 3
  • print(x = 4):
[1] 4
  • print(x == 3):
[1] TRUE

Explanation:

  • = for assignment (though <- is preferred in R):

    • x = 3 assigns the value 3 to the variable x.
    • x = 4 inside print() temporarily assigns 4 to x and returns it, but this does not persist. After this line, x still holds the value 3.

  • == for comparison:

    • x == 3 checks whether the value of x is equal to 3 and returns TRUE since x was assigned 3 earlier.

  • Key Difference:

    • = is used to assign a value to a variable.
    • == is used to compare values, returning TRUE if they are equal or FALSE otherwise.

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