R vs Python for data analysis in R Programming - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a vector of numbers from 1 to 5 in R.

R Programming

numbers <- [1](1, 2, 3, 4, 5)

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Ac

Bmatrix

Cdata.frame

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using list() creates a list, not a numeric vector.

Using data.frame() creates a data frame, which is a table-like structure.

Using matrix() creates a matrix, which is two-dimensional.

In R, c() combines values into a vector, which is the basic data structure for numbers.

2fill in blank

medium

Complete the code to calculate the mean of a numeric vector named 'data' in R.

R Programming

average <- [1](data)

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Amean

Bsum

Cmedian

Dmode

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() returns the total, not the average.

Using median() returns the middle value, not the average.

Using mode() is not a base R function for numeric vectors.

The mean() function calculates the average value of numeric data in R.

3fill in blank

hard

Fix the error in the code to subset rows where the 'age' column is greater than 30 in a data frame 'df'.

R Programming

subset_df <- df[df$[1] > 30, ]

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AAge

Bage

Cage()

Dage[]

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Age' instead of 'age' if the column name is lowercase.

Using parentheses like 'age()' which is incorrect for column access.

Using brackets like 'age[]' which is not valid syntax here.

To access a column in a data frame by name, use $ followed by the exact column name without parentheses or brackets.

4fill in blank

hard

Fill both blanks to create a new data frame with only rows where 'score' is greater than 80 and select the 'name' and 'score' columns.

R Programming

high_scores <- df[df$[1] [2] 80, c('name', 'score')]

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Ascore

B>

C<

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' or '==' instead of '>' for filtering scores above 80.

Using wrong column names or misspelling 'score'.

To filter rows where 'score' is greater than 80, use df$score > 80. Then select columns by name.

5fill in blank

hard

Fill all three blanks to create a summary table with the mean of 'height' grouped by 'gender' using the dplyr package.

R Programming

library(dplyr)
summary <- df %>% group_by([1]) %>% summarise([2] = mean([3]))

Drag options to blanks, or click blank then click option'

Agender

Bavg_height

Cheight

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong column names for grouping or summarising.

Not using the pipe operator %>% correctly.

Use group_by(gender) to group data by gender, then summarise(avg_height = mean(height)) to calculate the average height per group.