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# Week 4 (March 25-31)

## 1. Data Transformation with dplyr

## 2. Joining relational data

## Class

## Mid-term Project Due!

This week builds onto our data wrangling skills by focusing on transforming and joining data. Common transformation procedures include filtering observations by their values, reordering the rows, selecting variables, creating new variables with functions of existing variables, and collapsing values down to single summary summary statistics (i.e. mean, max, variance).

Furthermore, it’s rare that a data analysis involves only a single table of data. Typically you have many tables of data, and you must combine them to answer the questions that you’re interested in. Collectively, multiple tables of data are called *relational* data because it is the relations, not just the individual datasets, that are important.

This module covers these basic capabilities by teaching you how to use the `dplyr`

package to perform common data transformation and joining tasks.

Although many fundamental data manipulation functions exist in R, they have been a bit convoluted to date and have lacked consistent coding and the ability to easily flow together. dplyr is one such package which was built for the sole purpose of simplifying the process of manipulating, sorting, summarizing, and joining data frames.

- Read and work through Chapter 5: Data Transformation in R for Data Science.
- Now that you’ve learned the basics, complete the Data Manipulation in R with dplyr DataCamp assignment to hone your skills.

It’s rare that a data analysis involves only a single data set. Typically you have many sets of data and you need to join them to perform your analysis and answer the questions that you’re interested in. This module will teach you to work with relational data sets.

- Read and work through Chapter 13: Relational Data in R for Data Science.
- Now that you’ve learned the basics, complete the Joining Data in R with dplyr DataCamp assignment to hone your skills.

Please download this material for Saturday’s:

In addition, be sure to have identified which data you are going to use for your final project. Be sure to have access to this data because you will work on it during class. Furthermore, identify at least 10 specific questions you want to ask of your project data. Using what you learned this week, what type of data transformations do you need to make to help answer these questions? Be ready to use dplyr to answer these questions in class.

See you in class on Saturday!

Your mid-term project is due by the end of class this week. Be sure to refer to the grading rubric so you understand what is expected. Create an HTML R markdown document titled “Project Proposal” and be sure to include your name in the YAML.