Creating a New Data Frame in Descending Order: A Step-by-Step Guide

In this article, we will explore how to create a new data frame from the nycflights13 dataset using the tidyverse package. Specifically, we will focus on extracting the 5 days of the year with the highest mean distance when leaving from John F. Kennedy International Airport (JFK). We will also demonstrate how to sort this data frame in descending order.

Prerequisites: Installing Required Packages

Before we begin, ensure that you have installed the required packages. The nycflights13 dataset is a built-in dataset in R and can be loaded using the nycflights13 package. Additionally, the tidyverse package provides various functions for data manipulation and analysis.

To install the required packages, run the following command:

# Install required packages
pacman::p_load(nycflights13, tidyverse)

Loading the Data and Filtering by Origin

The first step is to load the nycflights13 dataset and filter it to include only flights departing from JFK.

# Load nycflights13 dataset
library(nycflights13)

# Filter flights departing from JFK
jfk_flights <- flights[origin == "JFK", ]

Calculating the Mean Distance for Each Day

Next, we will calculate the mean distance for each day using the summarise function. This function groups the data by month and day and calculates the mean distance.

# Calculate mean distance for each day
jfk_flights %>%
  summarise(mean_distance = mean(distance), .by = c(month, day))

Identifying the Top 5 Days with Highest Mean Distance

We can use the slice_max function to identify the top 5 days with the highest mean distance. This function returns the rows with the maximum value in each group.

# Identify top 5 days with highest mean distance
top_5_days <- jfk_flights %>%
  summarise(mean_distance = mean(distance), .by = c(month, day)) %>%
  slice_max(mean_distance, n = 5)

Shaping the Data Frame

Finally, we can reshape the data frame to have columns for month, day, and mean_distance using the select function.

# Shape data frame
data <- top_5_days %>%
  select(month, day, mean_distance)

Sorting the Data Frame in Descending Order

To sort the data frame in descending order by mean distance, we can use the arrange function.

# Sort data frame in descending order
sorted_data <- data %>%
  arrange(desc(mean_distance))

Combining the Code

Here is the complete code:

pacman::p_load(nycflights13, tidyverse)

# Load nycflights13 dataset
library(nycflights13)

# Filter flights departing from JFK
jfk_flights <- flights[origin == "JFK", ]

# Calculate mean distance for each day
top_5_days <- jfk_flights %>%
  summarise(mean_distance = mean(distance), .by = c(month, day)) %>%
  slice_max(mean_distance, n = 5)

# Shape data frame
data <- top_5_days %>%
  select(month, day, mean_distance)

# Sort data frame in descending order
sorted_data <- data %>%
  arrange(desc(mean_distance))

# Print sorted data frame
print(sorted_data)

Example Use Cases

Here are some example use cases for creating a new data frame in descending order:

• Analyzing Sales Data: Suppose you have a dataset of sales transactions and want to analyze the top 5 products with highest sales. You can create a data frame by filtering the sales data, calculating the total sales for each product, identifying the top 5 products with highest sales, shaping the data frame, and sorting it in descending order.
• Identifying Top-Performing Employees: Suppose you have a dataset of employee performance and want to identify the top 5 employees with highest performance ratings. You can create a data frame by filtering the employee data, calculating the average rating for each employee, identifying the top 5 employees with highest ratings, shaping the data frame, and sorting it in descending order.

Conclusion

Creating a new data frame from a dataset in descending order is an essential step in data analysis and visualization. By using functions like summarise, slice_max, select, and arrange, you can extract insights from your data and make informed decisions.

Last modified on 2023-10-01