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根据数据表格,以及对研究问题的描述"How much of your paycheck comes from overtime? For many public...

Oct 15, 2024

根据数据表格,以及对研究问题的描述"How much of your paycheck comes from overtime? For many public employees in New York City, overtime is not some sort of occasional bonus but can make up a large portion of total compensation. This is most the case in places like Brooklyn, where certain public service roles depend more on overtime. But what does this reliance on overtime reveal about base salaries, staffing levels, and budget efficiency? Let's dive into the data and uncover what's really happening in New York City's workforce. Visualizing the Data Here are three key charts that break down the numbers: 1. Overtime by Job Title: A bar chart showing the percentage of total compensation that comes from overtime for different job titles. This highlights which roles rely most on overtime. A scatter plot showing the relationship between base salary and total compensation (including overtime), with different colors for job titles. This would help illustrate the impact of overtime on total compensation for each role. 2. Borough Breakdown: A scatter plot comparing total compensation (including overtime) across boroughs, showing how different areas like Brooklyn may have higher overtime reliance."利用R语言在Rstudio中写出完整的代码(要求英文,并且代码要是完整的带有标题和解释的)

Explanation

The code loads employee data, calculates total compensation, and creates visualizations to analyze overtime and compensation across job titles and boroughs.

Step-by-step Instruction

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Load the dataset: Read the employee data from a CSV file into a data frame

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Calculate total compensation: Create a new column that sums base salary and overtime

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Create visualizations: Generate bar charts and scatter plots to analyze the data

Time Complexity

The time complexity is O(n) for data processing where n is the number of rows in the dataset.

Space Complexity

The space complexity is O(n) as it stores the entire dataset in memory.

根据这个R语言模板"#Select relevant variables, place in new dataframes, and clean up values even_cleaner_housing_net_change <- cleaned_data %>% select(commntydst, netchange2010, netchange2011, netchange2012, netchange2013, netchange2014, netchange2015, netchange2016, netchange2017, netchange2018, netchange2019) %>% mutate(Total_Compensation = `Base Salary` + `Total OT Paid` + `Total Other Pay`, OT_Percentage = (`Total OT Paid` / Total_Compensation) * 100) %>% rename(community_district = commntydst) %>% arrange(even_cleaner_housing_net_change,community_district)"针对我的研究问题"How much of your paycheck comes from overtime? For many public employees in New York City, overtime is not some sort of occasional bonus but can make up a large portion of total compensation. This is most the case in places like Brooklyn, where certain public service roles depend more on overtime. But what does this reliance on overtime reveal about base salaries, staffing levels, and budget efficiency? Let's dive into the data and uncover what's really happening in New York City's workforce. Visualizing the Data Here are three key charts that break down the numbers: 1. Overtime by Job Title: A bar chart showing the percentage of total compensation that comes from overtime for different job titles. This highlights which roles rely most on overtime. A scatter plot showing the relationship between base salary and total compensation (including overtime), with different colors for job titles. This would help illustrate the impact of overtime on total compensation for each role. 2. Borough Breakdown: A scatter plot comparing total compensation (including overtime) across boroughs, showing how different areas like Brooklyn may have higher overtime reliance. 和数据包进行编码编写

Explanation

The R code processes a dataframe to select relevant columns, calculate total compensation, and arrange the data for analysis.

Step-by-step Instruction

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Calculate new metrics: Use the `mutate` function to create new columns for total compensation and overtime percentage

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Rename columns for clarity: Use the `rename` function to make column names more understandable

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Arrange the dataframe: Use the `arrange` function to sort the dataframe by community district

Time Complexity

The time complexity is O(n) where n is the number of rows in the dataframe, as each operation processes each row once.

Space Complexity

The space complexity is O(m) where m is the number of columns selected, as it creates a new dataframe with selected columns.

根据这个R语言模板"#Select relevant variables, place in new dataframes, and clean up values new_data_name <- cleaned_data %>% select() %>% mutate(Total_Compensation = `Base Salary` + `Total OT Paid` + `Total Other Pay`, OT_Percentage = (`Total OT Paid` / Total_Compensation) * 100) %>% rename() %>% arrange()"针对我的研究问题"How much of your paycheck comes from overtime? For many public employees in New York City, overtime is not some sort of occasional bonus but can make up a large portion of total compensation. This is most the case in places like Brooklyn, where certain public service roles depend more on overtime. But what does this reliance on overtime reveal about base salaries, staffing levels, and budget efficiency? Let's dive into the data and uncover what's really happening in New York City's workforce. Visualizing the DataHere are three key charts that break down the numbers: 1. Overtime by Job Title: A bar chart showing the percentage of total compensation that comes from overtime for different job titles. This highlights which roles rely most on overtime. A scatter plot showing the relationship between base salary and total compensation (including overtime), with different colors for job titles. This would help illustrate the impact of overtime on total compensation for each role. 2. Borough Breakdown: A scatter plot comparing total compensation (including overtime) across boroughs, showing how different areas like Brooklyn may have higher overtime reliance. 和数据包进行编码编写

Explanation

The R code processes a dataframe to analyze employee compensation, focusing on overtime. It selects relevant columns, calculates total compensation and overtime percentage, renames a column, and sorts the results.

Step-by-step Instruction

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Select relevant columns to focus on the necessary data for analysis

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Calculate total compensation and overtime percentage: Use the mutate function to derive new metrics from existing data

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Rename columns for clarity and arrange the data to highlight the most relevant information

Time Complexity

The time complexity is O(n) where n is the number of rows in the dataframe, as each row is processed to calculate new values.

Space Complexity

The space complexity is O(m) where m is the number of columns in the new dataframe, as new columns are created based on calculations.