gapminder_analyses_demo.Rmd

---
title: "Gapminder_analysis_demo"
author: "Sarah Sutton"
date: "October 4, 2015"
output: html_document
---




```{r, echo=FALSE}
#install.packages("dplyr")
library("dplyr")
```


Load the data file, select three countries and make scatter plots of their GDP vs. year.

```{r read-in-data, echo=FALSE}
gap.in <- read.table("output/combined_gapMinder.tsv", sep = "\t", header=TRUE)

  filter(gap.in, country == "China") -> china
  plot(china$year,
       china$gdpPercap,
       xlab="Year",
       ylab="GDP",
       main="China")
  
  filter(gap.in, country == "Brazil") -> brazil
 plot(brazil$year,
       brazil$gdpPercap,
       xlab="Year",
       ylab="GDP",
       main="Brazil")

   filter(gap.in, country == "Zimbabwe") -> zim
 plot(zim$year,
       zim$gdpPercap,
       xlab="Year",
       ylab="GDP",
       main="Zimbabwe") 

```

*China's* GDP seems to take an exponential growth curve.

*Brazil's* GDP takes a steep upturn in the 1970's, and then the growth rate slows, but continues to increase. 

*Zimbabwe's* GDP rises sharply in the 70's, then is quite erratic before plummeting in the 90's.

```{r aggregate-data, echo=FALSE}

head(summarise(group_by(gap.in, continent),
     mean_pop = mean(pop),
     min_pop = min(pop),
     max_pop = max(pop)))

```

The above table shows that Africa's population grew a lot.

```{r life_expectancy, echo=FALSE}

hist(gap.in$lifeExp,
     xlab = "Age",
     main = "Life Expectancy")

hist(gap.in$lifeExp,
     xlab = "Age",
     breaks = 26,
     main = "Life Expectancy")


```


The narrower breaks for the histogram of life expectancy shows the stronger bimodal distribution with a peak at 44 years of age and a higher peak at 72.