This project explores census data to see if people are migrating from areas of the country with a low income per capita to areas with a high income per capita.

Load a relational data structure into a NoSQL database. I chose to load data from Pittsburgh's bike share into Neo4j, a graph database.

Your task is to analyze an existing recommender system that you find interesting.

Classify emails from Apache Spam Assassin's public ccorpi as either spam or ham.

Your task is to choose one of the New York Times APIs, construct an interface in R to read in the JSON data, and transform it to an R dataframe.

Basic Titanic Decision Tree

The purpose of this assignment is to explore the difference between JSON, XML and HTML files. Create a file of each type to store information on 3 books. Then parse the files into a data frame. Are all three data frames the same?

In this assignement we need to take 3 untidy data sets that our classmates provided to use and perform the analysis requested

This is the answer to 4.23 in the Open Stats text book.

The purpose of the assignment is to read in a file with on time and delayed number of flights for 2 airlines and perform an analysis compaing arrival delays of the two airlines

In this project, you’re given a text file with chess tournament results where the information has some structure. Your job is to create an R Markdown file that generates a .CSV file (that could for example be imported into a SQL database) with the following information for all of the players: Player’s Name, Player’s State, Total Number of Points, Player’s Pre-Rating, and Average Pre Chess Rating of Opponents

Choose six recent popular movies. Ask at least five people that you know (friends, family, classmates, imaginary friends) to rate each of these movie that they have seen on a scale of 1 to 5. Take the results (observations) and store them in a mySQL database. Load the information into an R dataframe.

Your task is to study the dataset and the associated description of the data (i.e. “data dictionary”). You may need to look around a bit, but it’s there! You should take the data, and create a data frame with a subset of the columns (and if you like rows) in the dataset. You should include the column that indicates edible or poisonous and three or four other columns. You should also add meaningful column names and replace the abbreviations used in the data—for example, in the appropriate column, “e” might become “edible.” Your deliverable is the R code to perform these transformation tasks.