Assignment-2--Loading,-Saving-and-describing-data

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Feb 20, 2024

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ANA 515 Assignment 2 - Loading, Saving and Describing data Srilekha Gurrapu 2023-06-15 Section 1: Description of the data The dataset I am using is the COVID-19 dataset, which provides comprehensive information about the COVID-19 pandemic across multiple countries. The data measures various aspects of the pandemic, including the number of cases, deaths, and recoveries. The dataset was collected from reliable sources such as national health agencies and international organizations tracking the pandemic. The data is saved in a CSV (Comma-Separated Values) format. It is a flat file that uses commas as the delimiter to separate the values in each row. The dataset is organized in a tabular format, where each row represents a specific observation (e.g., a specific country and date), and each column represents a variable or attribute related to that observation (e.g., cases, deaths, recoveries). The CSV format is a widely used and easily accessible format for storing structured data. It can be opened and processed using various tools and programming languages, including R, Python, and spreadsheet software. To read this data into R, I will use the read.csv function, which is a base R function specifically designed for reading CSV files. This function automatically handles the parsing of the CSV format, including the detection of the delimiter and the conversion of the data into appropriate data types. The resulting data will be stored in a dataframe, which is a common data structure in R for handling tabular data. Section 2: Reading the data into R In this code, I used the read_csv() function to read the data from the CSV file and assign it to the data dataframe object. The read_csv() function from the readr package is used to read the data from the zip file. The read_csv() function is designed to read CSV files and is a part of the readr package. The unzip() function is used to extract the file from the zip archive, and the extracted data is directly passed to the read_csv() function to read it as a CSV file. ## Warning: package 'readr' was built under R version 4.2.3 ## Rows: 35156 Columns: 10 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "," ## chr (2): Country/Region, WHO Region ## dbl (7): Confirmed, Deaths, Recovered, Active, New cases, New deaths, New r...

## date (1): Date ## ## Use `spec()` to retrieve the full column specification for this ℹ data. ## Specify the column types or set `show_col_types = FALSE` to quiet ℹ this message. Section 3: Cleaning the data In the code chunk, I performed some basic data cleaning operations on the dataset. First, I renamed the columns of the dataframe using the colnames function. I provided a vector of new column names that correspond to the desired names for each column. Next, I converted the “Date” column to the “Date” format using the as.Date function. This ensures that the date values are treated as dates in R, allowing for easier manipulation and analysis. Finally, I subsetted the dataset to keep only the “Country”, “Date”, “Total_Cases”, and “Total_Deaths” columns using indexing with square brackets ([]). This creates a new dataframe called data_subset that contains the selected columns. To verify the changes and check the cleaned dataset, I used the head function to display the first few rows of the data_subset dataframe. ## Warning: package 'dplyr' was built under R version 4.2.3 ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union ## # A tibble: 6 × 4 ## Date Country Total_Cases Total_Deaths ## <date> <chr> <dbl> <dbl> ## 1 2020-01-22 Afghanistan 0 0 ## 2 2020-01-22 Albania 0 0 ## 3 2020-01-22 Algeria 0 0 ## 4 2020-01-22 Andorra 0 0 ## 5 2020-01-22 Angola 0 0 ## 6 2020-01-22 Antigua and Barbuda 0 0 Section 4: Characteristics of the data. In the code, num_rows and num_cols variables are assigned the number of rows and columns in the dataframe data . The column_descriptions variable contains the

descriptions for each column. The paste() function to insert the column names and descriptions. The resulting table will have two columns: “Column Name” and “Description”. created a table using the kable function from the knitr package in R Markdown. This code will create a table using the kable function and format it as Markdown. The table will have two columns: “Column Name” and “Description”. The “column_table” data frame contains the column names and descriptions, which are then passed to the kable function for formatting. ## Warning: package 'knitr' was built under R version 4.2.3 Column Name Description Date The date of the recorded data for COVID-19 cases. Country The name of the country where the COVID-19 cases were reported. Total_Case s The total number of confirmed COVID-19 cases in a specific country or region. Total_Deat hs The total number of deaths caused by COVID-19 in a specific country or region. Total_Reco veries The total number of individuals who have recovered from COVID-19 in a specific country or region. Active_Cas es The number of active COVID-19 cases in a specific country or region. New cases The number of new COVID-19 cases reported on a specific date. New deaths The number of new deaths due to COVID-19 reported on a specific date. New recovered The number of new recoveries from COVID-19 reported on a specific date. WHO Region The World Health Organization (WHO) region to which the country or region belongs. #Inline code “This data set has 35156 rows and, 10, columns., The names of the columns and a brief description of each are in the table above” Section 5: Summary statistics. In this code, we first define the “selected_columns” variable to contain the names of the three columns you want to analyze. We then create a subset of the dataframe “data” by selecting only those columns using “subset_data <- data[selected_columns]”. Next, we use the apply() function to calculate the minimum, maximum, and mean values for each column in the subset_data dataframe. The na.rm = TRUE argument is used to exclude missing values from the calculations.

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We also use the colSums() and is.na() functions to count the number of missing values in each column. Finally, we create a new dataframe summary_data to store the summary statistics, with columns for the column names, minimum values, maximum values, mean values, and number of missing values. The summary_data dataframe is then printed to display the summary statistics in a tabular format. ## Column Minimum Maximum Mean Missing_Values ## Total_Cases Total_Cases 0 4290259 23566.631 0 ## Total_Deaths Total_Deaths 0 148011 1234.068 0 ## Total_Recoveries Total_Recoveries 0 1846641 11048.135 0 Histogram of Confirmed cases ## Warning: package 'ggplot2' was built under R version 4.2.3

Scatterplot of Confirmed cases and Deaths