jknowles

############################################################
## Title: Make an Animated Heart in R
## Author: Jared Knowles
## Date:   May 5th, 2012
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# Make heart curve
t<-seq(-100,100,length.out=1000) # Order
x<-16*sin(t)^3 # Create the Xs from a formula
y<-(13*cos(t))-(5*cos(2*t))-(2*cos(3*t))-(cos(4*t)) # Create Ys from a formula

jknowles

library(shiny)
library(ggplot2)
library(eeptools)
library(plyr)
# Sampling examples
probs<-c(.05,.15,.4,.4)
samprobs<-c(.4,.4,.15,.05)

###############
# Convenience functions

jknowles

# 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)) + 

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library(shiny)
# data from : http://is-r.tumblr.com/post/35266021903/five-thirty-hate
election.data <- read.csv("http://www.oberlin.edu/faculty/cdesante/assets/downloads/election2012.csv")
#election.data <- read.csv("electiondata.csv")

shinyServer(function(input,output){
  
  output$voteplot<-reactivePlot(function(){
    
    Mode <- function(X) {

jknowles

library(lavaan)
model <- ' 
  # latent variable definitions
     ind60 =~ x1 + x2 + x3
     dem60 =~ y1 + a*y2 + b*y3 + c*y4
     dem65 =~ y5 + a*y6 + b*y7 + c*y8

  # regressions
    dem60 ~ ind60
    dem65 ~ ind60 + dem60

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

jknowles

How to Ask for Help using R
========================================================

The key to getting good help with an R problem is to provide a minimally working 
reproducible example (MWRE). Making an MWRE is really easy with R, and it will 
help ensure that those helping you can identify the source of the error, and 
ideally submit to you back the corrected code to fix the error instead of sending
you hunting for code that works. To have an MWRE you need the following items:

- a minimal dataset that produces the error

jknowles

# Generate lotto numbers
# Avoid a split pot
# Author: Jared Knowles
# Date: 11/26/2012

# All numbers are equally likely to be selected to win in PowerBall, 
# but what fun is winning if we have to share the pot?
# Therefore, we will only select numbers that minimize our 
# likelihood of sharing the pot. 

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

###############################################################################
## 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. 
################################################################################