Assignment 1

My first assignment has three parts:

(a)The video I watched : RStudio Cloud Demo with Dr. Mine Çetinkaya-Rundel

Summary

RStudio Cloud demo presentation provides information about the usage and sharing of RStudio Cloud. RStudio Cloud is a cloud-based development environment designed primarily for educational purposes. Users can create projects, share them, and interact with students.

Highlights

-🏢 RStudio Cloud provides users with easy access to a specific educational environment.

-🎓 You can collaborate in short-term activities by sharing a single project.

-🔄 You can enable students to continuously work on their projects by copying them.

-👀 You can assist and provide feedback to students by reviewing their projects.

-💡 You can provide a starting point for students and standardize learning materials using basic projects.

(b)The differences between R and Python

1.Syntax and Data Structures

Creating a vector and performing vectorized operations in R

numbers <- c(1, 2, 3, 4, 5)

squared <- numbers^2

mean_value <- mean(numbers)

Creating a list and performing element-wise operations in Python

numbers = [1, 2, 3, 4, 5]

squared = [x**2 for x in numbers]

mean_value = sum(numbers) / len(numbers)

2.Libraries and Ecosystem:

Using the dplyr package for data manipulation in R

library(dplyr)

df <- data.frame(x = c(1, 2, 3, 4, 5), y = c(6, 7, 8, 9, 10))

result <- df %>% filter(x > 2) %>% select(y)

Using pandas for data manipulation in Python

import pandas as pd

df = pd.DataFrame({‘x’: [1, 2, 3, 4, 5], ‘y’: [6, 7, 8, 9, 10]})

result = df[df[‘x’] > 2][‘y’]

3.Function and Package Imports:

Loading the ggplot2 package in R

library(ggplot2)

ggplot(df, aes(x, y)) + geom_point()

# Importing the matplotlib library in Python

import matplotlib.pyplot as plt

plt.scatter(df[‘x’], df[‘y’])

plt.show()

(c) na_example Data Set

Install the “dslabs” package (only need to install once)

#install.packages(“dslabs”)

#Load the “dslabs” library

library(dslabs)

#Load the “na_example” data

data(“na_example”)

#Print the “na_example” data

print(na_example)

#Check for NAs and replace them with 1

na_check <- ifelse(is.na(na_example), 1, 0)

#Calculate the total number of NAs

sum_na <- sum(na_check)

sum_na # Total number of NAs

#Replace NAs with 0

without_na <- ifelse(is.na(na_example), 0, na_example)

#Calculate the updated number of NAs (should be 0)

updated_num_na <- sum(ifelse(is.na(without_na), 1, 0))

updated_num_na