Publications by Diego Correa
DATA607 - Assignment 9
Introduction The New York Times web site provides a rich set of APIs, as described here: https://developer.nytimes.com/apis You’ll need to start by signing up for an API key. 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 into an R DataFrame. I started by creating an...
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speed reducers
Introduction Speed humps are traffic calming devices intended to slow traffic speeds on low volume, low speed roads. Speed humps are generally installed on local residential (non-truck route, non-bus route locations), while speed cushions are generally installed on designated truck route locations and bus route locations. Intuitively, I suspect t...
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Star Wars Fan
Star Wars FansDiego Correa12/3/2020 Introduction FiveThirtyEight ran a poll, surveying 1,186, respondents from June 3 to 6 in 2014. The survey included the following questions: Are you a fan?, Which movies have you watched? Age Group, Sex, Household Income, and Education. Research questions: Does this data provide convincing evidence of an in...
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DATA 607 - Assignment 10
Introduction In this assignment, you should start by getting the primary example code from chapter 2 working in an R Markdown document. You should provide a citation to this base code. You’re then asked to extend the code in two ways: 1. Work with a different corpus of your choosing, and 2. Incorporate at least one additional sentiment lexicon ...
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DATA605 - Discussion 11
Discussion Using R, create a simple linear regression model and test its assumptions. # Loading data url <- 'https://github.com/dcorrea614/MSDS/raw/master/cereal.csv' cereal <- read.csv(url) # linear model lm_cereal <- lm(cereal$rating ~ cereal$calories) summary(lm_cereal) ## ## Call: ## lm(formula = cereal$rating ~ cereal$calories) ##...
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DATA605 - Final Exam
youtube video Problem 1 Using R, generate a random variable X that has 10,000 random uniform numbers from 1 to N, where N can be any number of your choosing greater than or equal to 6. Then generate a random variable Y that has 10,000 random normal numbers with a mean of \(\mu = \sigma = (N+1)/2\) set.seed(123) N <- 10 sigma <- (N + 1)/2 mu ...
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DATA624 - Final Project
Libraries library(kableExtra) library(tidyverse) library(ggplot2) library(dplyr) library(psych) library(caret) library(mice) library(randomForest) library(caTools) library(corrplot) library(class) library(rpart) library(AppliedPredictiveModeling) library(naniar) library(xgboost) library(DiagrammeR) library(readxl) library(writexl...
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DATA624 - HW7
6.2 Developing a model to predict permeability (see Sect. 1.4) could save significant resources for a pharmaceutical company, while at the same time more rapidly identifying molecules that have a sufficient permeability to become a drug: Start R and use these commands to load the data: library(AppliedPredictiveModeling) data(permeability) The ...
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DATA622 - HW3
Libraries library(kableExtra) library(tidyverse) library(ggplot2) library(dplyr) library(psych) library(caret) library(mice) library(randomForest) library(caTools) library(corrplot) library(class) library(rpart) library(rpart.plot) library(naniar) Background For this assignment, we will be working with a dataset on loan approval sta...
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DATA624 - HW4
3.1 The UC Irvine Machine Learning Repository6 contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index and percentages of eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe. Using visualizations, explore the predi...
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