Google Data Analytics Professional Certificate Data Analysis with R Programming Syllabus

Data Analysis with R Programming syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for Data Analysis with R Programming in Google Data Analytics Professional Certificate, not a summary of it.

1. 1

   Programming and Data Analytics

   3 topics

   • Why use R for analytics
   • Programming fundamentals
   • The RStudio environment
2. 2

   Programming Using RStudio

   4 topics

   • R syntax and operators
   • Data types and structures in R
   • Functions and the pipe operator
   • Installing and loading packages
3. 3

   Working with Data in R

   3 topics

   • Importing and viewing data
   • Cleaning and transforming data
   • Organizing and summarizing data frames
4. 4

   Visualization, Aesthetics, and Annotations

   3 topics

   • Plotting with ggplot2
   • Customizing visualizations
   • Saving and exporting plots
5. 5

   Documentation and Reports

   3 topics

   • R Markdown documents
   • Creating reproducible reports
   • Exporting and sharing notebooks

Data Analysis with R Programming flashcards for Google Data Analytics Professional Certificate

24 of 50 cards from the Data Analysis with R Programming deck — real questions with worked answers.

1. Why is R a popular choice for data analytics compared with spreadsheets?

   R is free and open-source, handles large amounts of data quickly, produces reproducible analyses through saved scripts, and creates high-quality visualizations — while spreadsheets are better suited to smaller datasets and manual, point-and-click work.

2. What is R, in one sentence?

   R is an open-source programming language and environment designed for statistical analysis, data visualization, and data analysis.

3. What does 'open-source' mean in the context of R?

   The source code is freely available to everyone: anyone can use, modify, and distribute R and contribute packages, which drives a large, active community of users and developers.

4. Define 'programming language'.

   A system of words and symbols used to write instructions that a computer can execute.

5. What are the three core concepts of R programming fundamentals highlighted in the course?

   Functions (commands that perform tasks), comments (notes explaining code, marked with #), and variables (named storage for values or objects).

6. How do you write a comment in R, and why are comments important?

   Begin the line with the hash symbol #, e.g. # This calculates the mean. R ignores comments when running code; they document what the code does, making it easier for you and others to understand and reproduce.

7. What naming rules apply to variables in R?

   Names should start with a letter and can contain letters, numbers, dots, and underscores; they cannot start with a number or underscore, cannot contain spaces or special symbols like $ or %, and R is case-sensitive.

8. What is RStudio and how does it relate to R?

   RStudio is an integrated development environment (IDE) for R — a workspace that makes writing, running, and managing R code easier. R is the language; RStudio is the tool you use it in.

9. Name the four panes of the RStudio interface and their purposes.

   1) Source editor — write and save scripts; 2) Console — where code is executed and output appears; 3) Environment/History — shows loaded data and variables; 4) Files/Plots/Packages/Help — file browser, plot viewer, package manager, and documentation.

10. In RStudio, what is the difference between running code in the console and writing it in a script (source) file?

    Console code runs immediately but is not saved for reuse; a script in the source editor is a saved, editable file so the analysis can be rerun and reproduced. Ctrl+Enter (Cmd+Enter on Mac) sends a script line to the console.

11. What is the assignment operator in R and how is it used?

    The <- operator assigns a value to a variable, e.g. sales <- 100 stores 100 in the variable sales. (= also works, but <- is the R convention.)

12. List R's main arithmetic operators.

    + addition, - subtraction, * multiplication, / division, ^ exponentiation (e.g. 2^3 gives $2^{3} = 8$), %% modulus (remainder), and %/% integer division.

13. What are R's relational (comparison) operators?

    < less than, > greater than, <= less than or equal ($\leq$), >= greater than or equal ($\geq$), == equal to, != not equal ($\neq$). They return TRUE or FALSE.

14. What are R's main logical operators and what do they do?

    & (AND) is TRUE only if both conditions are TRUE; | (OR) is TRUE if at least one condition is TRUE; ! (NOT) negates a logical value.

15. What are the main data types in R?

    Numeric (decimals, e.g. 3.5), integer (e.g. 4L), character/string (text in quotes), logical (TRUE/FALSE), and complex; dates/times are also handled (via packages like lubridate).

16. What is a vector in R and how do you create one?

    A vector is a group of data elements of the same type stored in a one-dimensional sequence; create it with the combine function c(), e.g. c(2, 4, 6).

17. How does a list differ from a vector in R?

    A vector's elements must all be the same data type; a list can contain elements of different types — including other lists — making it a more flexible structure.

18. What is a data frame in R?

    The most common data structure for analysis: a two-dimensional, table-like collection of columns where each column can be a different data type, but every column must have the same number of rows, and each column has a unique name.

19. What is the difference between a matrix and a data frame in R?

    Both are two-dimensional, but a matrix must contain elements that are all the same data type, while a data frame allows different types across columns.

20. What is a factor in R?

    A data structure used to store categorical variables with a limited set of possible values (levels), e.g. survey responses like 'low', 'medium', 'high'.

21. What is a tibble and how does it differ from a standard data frame?

    A tibble is the tidyverse's streamlined data frame: it never changes input data types or variable names, never creates row names, and prints only the first 10 rows for easier viewing.

22. How does R represent missing values, and how can you handle them?

    Missing values appear as NA. Functions like is.na() detect them, many functions accept na.rm = TRUE to ignore them, and drop_na() removes rows containing them.

23. What is a function in R? Give an example.

    A body of reusable code that performs a specific task, called by name with arguments in parentheses — e.g. mean(c(2, 4, 6)) returns $\frac{2+4+6}{3} = 4$, and print("hello") displays text.

24. What are arguments in an R function?

    The information (values, variables, or datasets) supplied inside a function's parentheses that the function needs to run, e.g. in sqrt(16), the argument is 16 and the result is $\sqrt{16} = 4$.

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