R Programming Skill Assessment

Written by Coursera • Updated on

Test your R programming skills with this quick assessment. Answer 10 multiple-choice questions to get your score, then check the Scoring Guide to find your level and recommended learning path.

R Programming Skill Assessment

R Programming Quiz

1. What is the correct way to create a vector in R?

array(1, 2, 3)
c(1, 2, 3)
vector(1, 2, 3)
list(1, 2, 3)

2. Which function would you use to remove missing values from a dataset?

remove.na()
delete.na()
na.omit()
clear.na()

3. What does the pipe operator %>% do in R?

Concatenates strings
Passes the output of one function as input to another
Creates a logical OR condition
Performs matrix multiplication

4. Which package would you use for data visualization?

dataviz
ggplot2
visualize
plot

5. What is the difference between a list and a data frame?

Lists can contain different data types, data frames must have same type per column
Lists are one-dimensional, data frames are two-dimensional
There is no difference
Lists can only contain numbers

6. Which function would you use for linear regression?

regression()
linear.model()
lm()
linreg()

7. What does this code do?

mtcars %>%
  group_by(cyl) %>%
  summarise(mean_mpg = mean(mpg))
Calculates total MPG by cylinder
Calculates average MPG for each cylinder group
Counts cars by cylinder
Lists all cars with their MPG

8. What is the purpose of lapply()?

To apply a function to each element of a list
To create a list
To combine lists
To sort a list

9. Which statement about factors in R is correct?

They can only contain numbers
They are used to represent categorical data
They cannot be ordered
They are the same as characters

10. What does the tidyr package primarily do?

Cleans data
Reshapes data between wide and long formats
Creates plots
Performs statistical tests

Scoring Guide & Course Recommendations

0-30: Beginner Level

40-70: Intermediate Level

80-100: Advanced Level

Skills Covered by Level

Beginner Level

  • Basic syntax

  • Data types and structures

  • Basic functions

  • Simple data manipulation

  • Basic plotting

  • Reading/writing data

Intermediate Level

  • Data wrangling with tidyverse

  • Statistical analysis

  • Advanced visualization

  • Package usage

  • Basic modeling

  • Data cleaning

Advanced Level

  • Machine learning

  • Advanced statistics

  • Package development

  • Production deployment

  • Performance optimization

  • Complex data analysis

Career Paths

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