Module 2 challenge :Data Analysis with R Programming (Google Data Analytics Professional Certificate) Answers 2025

Question 1

Which of the following are valid variable names in R?

✅ value_2
❌ value%2
❌ value(2)
❌ value-2

Explanation:
In R, variable names can include letters, numbers, and underscores (_) but cannot contain special characters (like %, -, or parentheses).

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Question 2

Which statements about vectors in R are correct?

✅ All data elements must have the same data type.
✅ Data elements are stored in a sequence.
❌ Data elements are defined using curly braces.
❌ All data must be stored in vectors.

Explanation:
R vectors hold homogeneous (same type) data in a sequence.
They’re defined using c(), not {}.
Not all data in R must be in vectors (you can have lists, data frames, etc.).

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Question 3

Which code could return “2020-07-10”?

✅ mdy(“July 10th, 2020”)
✅ ymd(20200710)
❌ myd(2020, July 10)
❌ dmy(“7-10-2020”)

Explanation:

• mdy() → month-day-year (valid for “July 10th, 2020”).

• ymd() → year-month-day (valid for “20200710”).

• myd() is not a valid function in lubridate.

• dmy("7-10-2020") returns October 7, 2020, not July 10.

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Question 4

Code: change_1 <- 70 — what kind of operator?

✅ Assignment
❌ Arithmetic
❌ Logical
❌ Relational

Explanation:
The <- operator in R assigns a value (here, 70) to an object (change_1).

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Question 5

Best practice for naming functions in R:

✅ Function names should be verbs
❌ Should start with a special character
❌ Should be very long
❌ Should be capitalized

Explanation:
Functions should describe an action (e.g., calculate_mean(), plot_graph()), making them clear and consistent with R style guides.

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Question 6

R packages include sample datasets, reusable functions, and documentation.

✅ True

Explanation:
Packages often include datasets, functions, and help documentation (accessible with ?function_name).

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Question 7

A system of packages for data manipulation, exploration, and visualization:

✅ tidyverse
❌ Base
❌ Recommended
❌ CRAN

Explanation:
Tidyverse includes packages like dplyr, ggplot2, tidyr, readr, etc.
All share a consistent grammar and design philosophy for modern data analysis.

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Question 8

Filter where age = 10 → arrange by height → group by gender

✅

❌ Other options

Explanation:
The question specifies the order:
1️⃣ Filter → 2️⃣ Arrange → 3️⃣ Group.
In nested form, that’s exactly what the correct code does.

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🧾 Summary Table

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