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

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of data frame summary in R

What is the output of the following R code snippet?

R Programming

df <- data.frame(age = c(25, 30, 35, 40, 45), score = c(88, 92, 95, 85, 90))
summary(df)

A

age: 25 30 35 40 45
score: 88 92 95 85 90

B

age  score
25   88
30   92
35   95
40   85
45   90

C Error in summary(df): object 'df' not found

D

      age           score     
 Min.   :25.00   Min.   :85.00  
 1st Qu.:30.00   1st Qu.:88.00  
 Median :35.00   Median :90.00  
 Mean   :35.00   Mean   :90.00  
 3rd Qu.:40.00   3rd Qu.:92.00  
 Max.   :45.00   Max.   :95.00  

💡 Hint

summary() in R gives statistical summaries for each column in a data frame.

The summary() function in R returns minimum, 1st quartile, median, mean, 3rd quartile, and maximum for numeric columns in a data frame.

🧠 Conceptual

intermediate

Key difference in data handling between R and Python

Which statement best describes a key difference in how R and Python handle data analysis?

A R primarily uses data frames as a native data structure, while Python uses lists only.

B R has built-in support for statistical models, while Python requires external libraries for similar tasks.

C Python's pandas library provides data frames similar to R, but R's data frames are more integrated into the language.

D Python cannot handle large datasets efficiently, unlike R.

💡 Hint

Think about how each language treats data frames and libraries.

R has data frames as a core part of the language, while Python uses pandas library to provide similar functionality. R's data frames are more native and integrated.

🔧 Debug

advanced

Identify the error in R code for reading CSV

What error will this R code produce?

R Programming

data <- read.csv('datafile.csv', header=FALSE, sep=';')
head(data)

A Error: cannot open file 'datafile.csv': No such file or directory

B Error: unexpected symbol in 'read.csv('datafile.csv', header=FALSE, sep=';')'

C No error, outputs first 6 rows of data

D Error: unused argument (sep = ';')

💡 Hint

Check if the file exists in the working directory.

If the file 'datafile.csv' does not exist in the current directory, read.csv will throw a file not found error.

❓ data_output

advanced

Output of Python pandas groupby aggregation

What is the output of this Python code using pandas?

R Programming

import pandas as pd

df = pd.DataFrame({'team': ['A', 'A', 'B', 'B'], 'points': [10, 15, 10, 5]})
result = df.groupby('team').sum()
print(result)

A

      points
team         
A         25
B         15

B

team  points
A     10
A     15
B     10
B     5

C

points
A  10
B  5

D Error: 'groupby' is not a function

💡 Hint

groupby sums points for each team.

The groupby('team').sum() aggregates points by team, summing values for each group.

🚀 Application

expert

Choosing between R and Python for a project

You have a project requiring advanced statistical modeling and interactive visualizations. Which choice is best and why?

A Use R because Python lacks libraries for data visualization.

B Use R because it has strong statistical packages and interactive visualization libraries like Shiny.

C Use Python because R cannot create interactive visualizations.

D Use Python because it has better support for statistical modeling than R.

💡 Hint

Consider the strengths of each language in statistics and visualization.

R is well-known for advanced statistics and has Shiny for interactive web apps, making it ideal for this project.

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