The first in a series of introductory R sessions by the Central New York Community Foundation, "Directories & Reading Text Data" explores some fundamental base R functions for manipulating directories, including automatically creating them to unzip and read in large data files, as well as base R, package "readr", and package "data.table" functions for reading in text data with specificity and precision. Barring a machine learning practice set from UC Irvine's Machine Learning Repository, this work exclusively uses local-, regional-, and state-level Open Data for the aspiring social sector data scientist. It concludes with applied practice exercises relevant to Syracuse and Central New York.
This is Part II of the final course project for "Statistical Inference", Course 6 in the Coursera Data Science Specialization by Johns Hopkins University.
This is Part I of the final course project for "Statistical Inference", Course 6 in the Coursera Data Science Specialization by Johns Hopkins University.
This report leverages catastrophic weather data from the National Weather Service to analyze the most physically hazardous and economically detrimental types of catastrophic weather. The following code, and its accompanying narrative, perform the following: (1) Create a directory to where the weather data are downloaded and read into R; (2) Process the data via dimension reduction, formatting, and filtering; (3) Justify and transform variables to more accurately depict key data; (4) Aggregate and arrange weather events in order of health and economic impacts; (5) Report and visualize the top 10 most catastrophic events by health and economy