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# Using the dataUSA.io API for Census Data in R

This gist contains some notes on constructing a query for census and economic data from the [DataUSA.io](http://datausa.io/) site. This is a quick-start guide to their API; for in-depth documentation check out their [API documentation](https://github.com/DataUSA/datausa-api/wiki/Overview).

A great way to learn how to structure a query is to visit a specific datausa.io page and click on the "Options" button on top of any graph, then select "API" to see the query syntax that created the graph.


![Analytics](https://ga-beacon.appspot.com/UA-27835807-2/gist-id?pixel)

## Example Use

jknowles

// Additional tools for machine learning and predictive analytics in stata
/*
Author: Jared Knowles
Date: 09/12/2018
Purpose: Survey of some additional code helpful in conducting and explaining
or demonstrating predictive analytics to stakeholders.

You do not need to run all of this code - this is a survey of commands that
tackle different techniques. Pick and choose what might be most useful to you.
*/

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###############################################################################
## SDP Fall Workshop Predictive Analytics
## Advanced / Additional Code Snippets for Working with PA Data and Models
## Author: Jared E. Knowles
## Date: 09/14/2018
## You do not need to use all or even any of this code. The code does not need to 
## be run together. This is just a survey of some additional techniques/tricks you 
## can do in R to make explaining predictive models and complex data easier. 
## As always - your needs and approaches may different. 
################################################################################

jknowles

################################################################################
# Functions to find the data
################################################################################

# Finders
# Simple functions that take the ID code from CPE (e.g. 49-00039) and look up 
# the respective data for it in the structure provided in teh competition
find_police_shape <- function(dept_id, kaggle_kernel = FALSE) {
  if(kaggle_kernel == TRUE) {
    prefix = "../input/cpe-data/"

jknowles

###################################################################################################
## Title: Regression Module Assignment 1
## Exploring Student Composition Effects on Test Score Growth
## Author: Jared E. Knowles, Civilytics Consulting
## Date: 6/12/2019
## Last Updated: 6/17/2019
###################################################################################################

# ----------------------------------------------------------------------------
# Load the data

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# Load ggplot2
library(ggplot2)
# Load example data
data(mtcars)

# Create a character vector of car names
mtcars$name <- row.names(mtcars)

# Plot car names by mpg
ggplot(mtcars, aes(x = name, y = mpg)) +