Publications by Dell
Final Project Proposal D606
For this project, the New York City Leading Causes of Death dataset was utilized. This dataset is available on the City of New York’s official website. The following sections outline the reasons for selecting this particular dataset for our analysis. Data Preparation In this section we prepare the data: # Load necessary libraries library(dpl...
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Assignment 9D607 Web APIs
R Markdown 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. For this assignment the ...
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Lab 08 - Introduction to Linear regression
The Human Freedom Index is a report that attempts to summarize the idea of “freedom” through a bunch of different variables for many countries around the globe. It serves as a rough objective measure for the relationships between the different types of freedom - whether it’s political, religious, economical or personal freedom - and other...
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Project2 D607
Project 2 DATA 607: Preparing Dataset for Analysis # Load necessary libraries library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(tidyr) library(readr) ...
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lab7D606
Getting Started Load packages In this lab, we will explore and visualize the data using the tidyverse suite of packages, and perform statistical inference using infer. The data can be found in the companion package for OpenIntro resources, openintro. Let’s load the packages. library(tidyverse) library(openintro) library(infer) The data Ev...
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Assignment7D607
# Load necessary library library(stringi) # Read raw JSON data raw_json <- readLines("C:/Users/Dell/Downloads/jsonformatA7.js", warn = FALSE) # Remove non-UTF-8 characters cleaned_json <- stri_replace_all_fixed(raw_json, "\u0097", "", vectorize = TRUE) # Removing specific invalid character cleaned_json <- iconv(cleaned_json, from = "UTF-8",...
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Lab 06 - Inference for categorical data
Getting Started Load packages In this lab, we will explore and visualize the data using the tidyverse suite of packages, and perform statistical inference using infer. The data can be found in the companion package for OpenIntro resources, openintro. Let’s load the packages. library(tidyverse) library(openintro) library(infer) The data Yo...
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Project2 D607
Project 2 DATA 607: Analysis of HIV/AIDS Diagnoses by Neighborhood, Sex, and Race/Ethnicity in NYC #Introduction This is the analysis of dataset on HIV and AIds diagnoses in New York City, covering 2010 to 2021 that reveals critical trends and insights showing key years peaks and fluctuations, the data provides valuable context for understandin...
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Lab 05 part B Confidence intervals
If you have access to data on an entire population, say the opinion of every adult in the United States on whether or not they think climate change is affecting their local community, it’s straightforward to answer questions like, “What percent of US adults think climate change is affecting their local community?”. Similarly, if you had d...
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Lab 05 part A Sampling Distributions
In this lab, you will investigate the ways in which the statistics from a random sample of data can serve as point estimates for population parameters. We’re interested in formulating a sampling distribution of our estimate in order to learn about the properties of the estimate, such as its distribution. Setting a seed: We will take some ran...
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