COVID-19 Estimated ICU Beds Occupied by State Time Series

Assignment Task

Part

1. Data Structure Introduction

Dataframe 1: ICU Beds Occupied By State Time Series

• This dataset provided the total number of ICU beds in each state
• Combining with the impatient bed statistics, the two metrics could be viewed as an indicator of local general and intensive care medical resources sufficiency level
• Time period spent from June 28, 2021 until July 28, 2021

2: Inpatient Beds Occupied By COVID-19 Patients Percentage By State Time Series

• This dataset provided the total number of impatient beds in each state and how many of those were occupied by COVID-19 patients each day
• Time period spent from June 28, 2021 until July 28, 2021

2. Data Cleaning and Merge

Data Cleaning: Grouping and Deleting Data

Data Cleaning: Duplicated Columns

1. Transposing the data frame using t(df) and applying the duplicated function to it. The output of duplicated will be a logical vector indicating which columns are duplicated. The function is used to extract the indices of the duplicated columns. Then returns the indices of the duplicated columns in the original data frame.

2. Finally, remove the duplicated columns from merged_df12. The -duplicated_columns argument specifies that the columns to be removed are those indexed by duplicated_columns. The resulting data frame will have only unique columns

Data Merging

1. Merge data frames 1 and 2 into merged_df12 that contain all rows where there is a match between the "state" and "collection_date" columns in both df1 and df2.

2.Join the merged_df12 and df3 data frames by matching their state and formatted_date columns, and assigns the result to merged_df123

3. Join the merged_df123 and df4 data frames by matching their state columns, and assigns the result to merged_df

4. The resulting merged_df data frame contains all rows from merged_df12 and df3 where the state and formatted_date columns match, as well as all columns from df4 where the state column matches.

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