Publications by Corey Sparks, PhD
Covid-19 Rate Analysis
library(dplyr) library(tigris) options(tigris_class = "sf") library(tidycensus) library(data.table) library(tidyquant) library(ggplot2) Spatial data cos<-counties(state="TX", cb=T) cos$fips<-as.numeric(cos$GEOID) Population estimates data coests<-get_estimates(geography="county", year=2018, product = "population", output = "wide") head(coests) ...
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Demography Informal Methods Seminar Series - Lasso and regularization
Regularization in Regression I would suggest you read Chapter 6 and especially section 6.2 of An Introduction to Statistical Learning to get a full treatment of this topic. If you want to read the sources for the Lasso, check out the paper by Friedman, Hastie and Tibshirani (2010) Why do I want to talk about this? The Lasso and regularization are...
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Demography Informal Methods Seminar Series – Introduction to R
Welcome to R. R is an interpreted languages, not a compiled one. This means, you type something into R and it does it. If you’re coming to R from SAS, there is no data step. There are no procs. The SAS and R book is very useful for going between the two programs. If you’re coming from SPSS and you’ve been using the button clicking method, b...
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Demography Informal Methods Seminar Series - Lasso and regularization
Regularization in Regression I would suggest you read Chapter 6 and especially section 6.2 of An Introduction to Statistical Learning to get a full treatment of this topic. If you want to read the sources for the Lasso, check out the paper by Friedman, Hastie and Tibshirani (2010) Why do I want to talk about this? The Lasso and regularization are...
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Demography Informal Methods Seminar - Introduction to Time Series Analysis
Time Series Analysis I would suggest you read chapters 1 through 3 of Time Series Analysis and Its Applications 4th ed to get a introductory treatment of this topic. I also strongly urge you to check out Rob Hyndman’s book on forecasting, which is free. In time series analysis, we are typically interested in describing the pattern of variation ...
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Demography Informal Methods Seminar - Classification Trees
Classification models I would suggest you read section 5.1 of Introduction to Statistical Learning to get a full treatment of this topic In classification methods, we are typically interested in using some observed characteristics of a case to predict a binary categorical outcome. This can be extended to a multi-category outcome, but the largest ...
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Demography Informal Methods Seminar - Population Projection and the Lee-Carter Model
Population Projection One of the most fundamental forms of demographic analysis Uses age-structured rates of fertility and mortality to project the population structure forward into time Shows patterns of population growth and age composition in future populations Further analysis can show population growth rates and sensitivity of the growth ra...
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DEM 5093/7093 Lab 1 using R - Basic Map making
Get a Census developer API Key Obtain one at http://api.census.gov/data/key_signup.html Save your API key to your working directory use tidycensus::census_api_key(key = "yourkeyhere", install = T) one time to install your key for use in tidycensus library(tidycensus) library(tidyverse) library(sf) library(ggplot2) v15_Profile <- load_variab...
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DEM 7093 H1 Example
Corey Sparks corey.sparks@utsa.edu Use a screen capture tool to take a capture of you R session showing version 4.0.3 ...
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DEM 5093/7093 Lab 2 using R - Map projections and transformations
library(tidycensus, quietly = T) library(sf, quietly = T) library(dplyr, quietly = T) Read in Bexar county tracts sa_acs<-get_acs(geography = "tract", state = "TX", county = "Bexar", year = 2017, variables = c("DP05_0001E", "DP03_0119PE"), geometry = T, ...
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