PR#37 converted to slides per issue #40

Author: Sam Penrose

img/countries_and_colors.png

@@ -0,0 +1,104 @@
+---
+layout: slides
+title: R for reproducible scientific analysis
+subtitle: Why Use R?
+---
+
+```{r, include=FALSE}
+library(ggplot2)
+theme_set(theme_bw())
+source("tools/chunk-options.R")
+library(dplyr)
+gapminder <- tbl_df(read.csv("data/gapminder-FiveYearData.csv"))
+```
+
+## Why R?
+
+* Powerful statistical analysis
+* and powerful visualisation
+* integrated elegantly
+
+## What We'll Accomplish
+
+* Get to know R and RStudio
+* Analyze a meaningful data set
+* Extract insights and deliver them visually
+* Leave ready to learn more R independently
+
+## R loves ingesting data
+
+```
+gapminder <- read.csv(
+  "data/gapminder-FiveYearData.csv",
+  header=TRUE,
+  sep=',')
+```
+
+## Data w/ column names
+
+head(gapminder, 1) # Show me the first row
+
+country year     pop continent lifeExp gdpPercap
+1 Afghanistan 1952 8425333      Asia  28.801  779.4453
+
+## Quickly graph ...
+```
+ggplot(
+  data=gapminder,
+  aes(x=lifeExp, y=gdpPercap)
+) + geom_point()
+```
+
+## ... to see what we have
+![First plot](img/first_plot.png)
+
+## Let's graph more factors
+```
+ggplot(
+  data=gapminder,
+  aes(x=year, y=lifeExp, by=country, colour=continent)
+) + geom_line()
+  + geom_point()
+```
+
+## Pretty!
+![Countries and colors](img/countries_and_colors.png)
+
+## dyplr gives us ...
+```{r}
+library(dplyr)
+cors <- gapminder %>%
+  group_by(year) %>%
+  summarise(
+    gdpPercap.lifeExp = cor(gdpPercap, lifeExp),
+    gdpPercap.pop = cor(gdpPercap, pop),
+    pop.lifeExp = cor(pop, lifeExp))
+```
+
+## ... pairwise correlations
+```
+head(cors, 1)
+Source: local data frame [1 x 4]
+year gdpPercap.lifeExp gdpPercap.pop  pop.lifeExp
+1 1952         0.2780236   -0.02526041 -0.002724782
+```
+
+## Restructuring the table ...
+```{r}
+library(tidyr)
+tidy.cors <- cors %>% gather(
+  variables, correlation,
+  gdpPercap.lifeExp, gdpPercap.pop,
+  pop.lifeExp)
+```
+## ... a subtle art ...
+```
+head(tidy.cors, 1)
+
+Source: local data frame [1 x 3]
+  year         variables   correlation
+1 1952 gdpPercap.lifeExp   0.2780236
+```
+
+## ... produces great results
+![GDP and Life](img/gdp_and_life.png)