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)

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

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)

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

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, ]

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

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')]

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

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.

Practice

1. Which language is known for having many built-in statistical functions and is often preferred by statisticians?

easy

A. R

B. Python

C. Java

D. SQL

Solution

1. Step 1: Understand language focus

   R is designed with statistics and data analysis in mind, offering many built-in statistical tools.

2. Step 2: Compare with Python

   Python is more general-purpose and flexible but requires additional libraries for statistics.

3. Final Answer:

   R -> Option A

4. Quick Check:

   Statistics focus = R [OK]

Hint: R has built-in stats tools; Python needs libraries [OK]

Common Mistakes:

• Confusing Python's flexibility with built-in stats
• Thinking Java or SQL are for stats analysis

2. Which of the following is the correct way to import the pandas library in Python for data analysis?

easy

A. library(pandas)

B. import pandas library

C. import pandas as pd

D. use pandas

Solution

1. Step 1: Recall Python import syntax

   Python uses 'import' keyword and 'as' to create an alias.

2. Step 2: Check each option

   import pandas as pd uses correct Python syntax. library(pandas) is R syntax. Options C and D are invalid.

3. Final Answer:

   import pandas as pd -> Option C

4. Quick Check:

   Python import syntax = import pandas as pd [OK]

Hint: Python imports use 'import ... as ...' [OK]

Common Mistakes:

• Using R syntax in Python
• Missing 'as' keyword
• Wrong import keywords

3. Consider this R code snippet for data analysis:

data <- data.frame(x = 1:3, y = c(4, 5, 6))
summary(data)

What will be the output of summary(data)?

medium

A. An error because summary() is not a valid function

B. A summary of statistics (min, max, mean) for columns x and y

C. Prints the data frame as is

D. Only the first row of data

Solution

1. Step 1: Understand summary() in R

   summary() gives basic statistics like min, max, mean for each column in a data frame.

2. Step 2: Apply to given data

   Columns x and y have numeric values, so summary() will show stats for both.

3. Final Answer:

   A summary of statistics (min, max, mean) for columns x and y -> Option B

4. Quick Check:

   summary() in R = stats summary [OK]

Hint: summary() in R shows stats for each column [OK]

Common Mistakes:

• Thinking summary() prints data
• Assuming summary() causes error
• Confusing with head() function

4. This Python code tries to create a DataFrame but has an error:

import pandas as pd
data = {'a': [1, 2], 'b': [3, 4, 5]}
df = pd.DataFrame(data)

What is the error and how to fix it?

medium

A. Missing import statement; add 'import numpy as np'

B. Syntax error in dictionary definition; fix quotes

C. No error; code runs fine

D. Error due to unequal list lengths; fix by making lists same length

Solution

1. Step 1: Identify cause of error

   DataFrame columns must have equal length lists; here 'a' has 2 elements, 'b' has 3.

2. Step 2: Fix the error

   Make both lists same length, e.g., 'b': [3, 4] or 'a': [1, 2, 3].

3. Final Answer:

   Error due to unequal list lengths; fix by making lists same length -> Option D

4. Quick Check:

   DataFrame columns equal length = fix error [OK]

Hint: DataFrame columns must have equal length lists [OK]

Common Mistakes:

• Ignoring list length mismatch
• Thinking import numpy needed
• Assuming syntax error in dict

5. You want to automate a data cleaning task that involves reading multiple CSV files, merging them, and applying machine learning models. Which language is generally better suited for this task and why?

hard

A. Python, because it is flexible and has many libraries for automation and ML

B. R, because it has built-in machine learning and automation tools

C. R, because it is easier to write loops and automation scripts

D. Python, because it has better built-in statistical functions

Solution

1. Step 1: Analyze task requirements

   Task needs automation, file handling, merging, and machine learning.

2. Step 2: Compare language strengths

   Python is widely used for automation and machine learning with many libraries like pandas, scikit-learn.

3. Step 3: Evaluate options

   R is strong in stats but less flexible for automation; Python fits better here.

4. Final Answer:

   Python, because it is flexible and has many libraries for automation and ML -> Option A

5. Quick Check:

   Automation + ML = Python [OK]

Hint: Python excels in automation and machine learning tasks [OK]

Common Mistakes:

• Choosing R for automation
• Confusing built-in stats with automation
• Ignoring Python's library ecosystem