There are three parts to this assignment: Part 1 -- Import data with readr, Part 2 -- Tidy data with tidyr, and Part 3 -- Manipulate data with dplyr. The instructions for each part are outlined below. Throughout this assignment, you may need to perform some of these methods/functions "out of order" as you could use the output from one method as the input of another method. Also, feel free to save instances of your dataframes as new variables, as needed. Part 1 -- Import data with readr In this part, you will import your datasets using the appropriate reader method(s). Therefore, whenever you need to import a dataset for this assignment, you will use this section for the code block. Review the readr library here: https://readr.tidyverse.org/ (Harici bir siteye bağlantılar.) Part 2 -- Tidy data with tidyr For this part, you will demonstrate your mastery of the tidyr library, which contains these basic use cases: Pivoting which converts between long and wide forms. Rectangling which turns deeply nested lists into tidy tibbles. Nesting which converts grouped data to a form where each group becomes a single row containing a nested data frame, and unnesting does the opposite. You will need to demonstrate at least two (2) methods/functions within each of these use cases. Additionally, you will conclude this part by using at least two (2) methods/functions which are not a part of pivoting, rectangling, or nesting (e.g., methods/functions which address missing values or can be used to split values across different columns). Therefore, in total, you will perform eight (8) methods/functions from the tidyr library. Review the tidyr library here: https://tidyr.tidyverse.org/ (Harici bir siteye bağlantılar.) Part 3 -- Manipulate data with dplyr For this part, you will demonstrate your mastery of the dplyr library by using the methods/functions that pertain to the following key use cases: Basic Single Table Verbs: Show your ability to manipulate data by using the filter(), slice(), and arrange() methods for rows and the select(), rename(), mutate(), and relocate() methods for columns. Data Summary Methods: Describe your dataset by using the following methods which provide summary information--including general methods such as summarize() [also known as summarise()] and count(); and at least one (1) specific method for each of the following: count, position, logical, order, rank, and spread of your data. Subsetting Rows: Apply at least one (1) method (not previously used) that returns a subset of rows. Subsetting Columns: Apply at least one (1) method (not previously used) that returns a subset of columns. Grouping Methods: Apply the group_by() method for rows and the group_cols() method for columns. New Row(s) (cases): Use the appropriate method(s) to add a new case (row). New Column(s) (variables): Use the appropriate methods to create a new column--by calculating a value based upon other columns (e.g., using 'height' and 'weight' columns to calculate BMI and saving 'bmi' as a new variable in the dataframe). Change Rows to Columns: Transpose a dataframe so that the rows and columns have been switched. Table Joins: Combine two tables by using an appropriate join method. Review the dplyr library here: https://dplyr.tidyverse.org/ (Harici bir siteye bağlantılar.) Template & Checklist Tools Please complete this assignment using Rmarkdown (html_document output). Please download the following files, open in R Studio, and then 'knit' these documents to view the output rendering. Data Wrangling Template: original Rmd file - Data Wrangling Assignment Template.Rmd Download Data Wrangling Assignment Template.Rmd html_document rendering - Data-Wrangling-Assignment-Template.html Download Data-Wrangling-Assignment-Template.html Note: The template provides some examples to serve as inspiration for you to organize your coding assignment. Checklist for the Week 3 Assignment original Rmd file - PUBH6680DW_Checklist.Rmd Download PUBH6680DW_Checklist.Rmd html_document rendering - PUBH6680DW_Checklist.html Download PUBH6680DW_Checklist.html Note: Please read the requirements carefully. Some items ask for "two" or "one" or "one each from a list." Submitting Before submitting your assignment, knit/render your finalized html_document within R Studio's Rmd file. Then, locate the filepaths/folders for (1) your Rmd file, (2) the html_document output file (i.e,. .html file with the same filename as your Rmd file), and (3) all dataset files. Submit your work for grading by the specified deadline by attaching the following files Your finalized Rmd file Your rendered HTML document file All dataset files (i.e., .CSV etc.) used during import. Please do NOT zip your files.
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