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<!doctype HTML> | |
<meta charset = 'utf-8'> | |
<html> | |
<head> | |
<script src='//d3js.org/d3.v3.min.js' type='text/javascript'></script> | |
<script src='//dimplejs.org/dist/dimple.v2.1.0.min.js' type='text/javascript'></script> | |
<style> | |
.rChart { |
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<!doctype HTML> | |
<meta charset = 'utf-8'> | |
<html> | |
<head> | |
<script src='//d3js.org/d3.v3.min.js' type='text/javascript'></script> | |
<script src='//dimplejs.org/dist/dimple.v2.1.0.min.js' type='text/javascript'></script> | |
<style> | |
.rChart { |
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library(ggplot2) | |
library(ggthemes) | |
library(scales) | |
# Grab data from here: https://data.stackexchange.com/stackoverflow/query/596780/language-trends-questions-per-tag-per-month | |
# setwd() <-- You may need this | |
df <- read.csv('LanguageComparisonOverTime.csv') | |
str(df) |
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removeCommasInNumber <- function(column) { | |
column <- as.numeric(gsub(",", "", column)) | |
column | |
} | |
createChoropleth <- function(df, | |
colToPlot, | |
title, | |
legend, | |
numColors=1, |
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--- | |
title: "Intro to health data analysis in R" | |
output: html_notebook | |
--- | |
## Background | |
Today we want to demonstrate how R can provide a one-stop-shop for health data analysis. | |
We'll start with base R today, and show how [dplyr](https://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html) makes life easier. |
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<!DOCTYPE html> | |
<html xmlns="http://www.w3.org/1999/xhtml"> | |
<head> | |
<meta charset="utf-8"> | |
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> | |
<meta name="generator" content="pandoc" /> |