gapminder_notebook.Rmd

---
title: "R Notebook"
output:
  html_document:
    toc: yes
    toc_float: true
---

# Title level 1

This is the second day of Software Carpentry!
I hope everyone is enjoying using git.

## Title level 2

Here is some text in the second level of the document.

Here is a line that will show up on the web.

## Add Section 3.

Add section 3.

**bold**

*italics*

Load packages
```{r}
library(tidyverse)
library(plotly)
source("functions.R")
```



I downloaded the file and loaded it into R
```{r}
download.file("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder-FiveYearData.csv", destfile = "data/gapminder-FiveYearData.csv")

gapminder <- read.csv("data/gapminder-FiveYearData.csv")

head(gapminder)
```

I wonder if rstats increases life expectancy over the years


```{r}
p <- ggplot(data=gapminder,aes(x=year,y=lifeExp)) +
    geom_point()

p
```

Let's see the interactive version

```{r}
ggplotly(p)
```

# Making your own functions

If you are repeating yourself in your code, you may be able to solve that problem by making your own function!


```{r}


cars <- c(3,4,5,6,7,10)

se(cars)
```


# Data manipulation with `dplyr`

You will likely want to get subsections of your dataframe and/or calculate means of a variable for a certain subsection, `dplyr` is your friend!

Explored select

```{r}
gapminder <- read.csv("data/gapminder-FiveYearData.csv")

year_country_gdp <- select(gapminder,year, country, gdpPercap)
year_country_gdp <- select(gapminder,-pop, -continent, -lifeExp)
names(year_country_gdp)

```

Explore filter

```{r}
euro <- filter(gapminder,continent=="Europe")
year_country_gdp_euro <- select(euro,year, country, gdpPercap)

year_country_gdp_euro <- gapminder  %>% 
    filter(continent=="Europe") %>% 
    select(year, country, gdpPercap)
```


exploring the amazing group_by and summarize functions

```{r}
mean_gdp_percountry <- gapminder %>% 
    group_by(country) %>% 
    summarise(mean_gdp=mean(gdpPercap),
              se_gdp=se(gdpPercap))

mean_gdp_percountry
```

Challenge: I want the mean, se, and sample size of life expetancy by continent
```{r}
mean_se_life_percontinent<-gapminder %>% 
  group_by(continent,country) %>% 
  summarise(mean_life=mean(lifeExp),
            se_life=se(lifeExp),
            samsize_life=n())

mean_se_life_percontinent
```

combining ggplot and dplyr

```{r}
euro_countries <- gapminder %>% 
    filter(continent=="Europe") %>% 
    ggplot(aes(x=year,y=lifeExp,color=country)) +
    geom_line()+
    facet_wrap(~country)

euro_countries
ggsave("euro.png")
write.csv(mean_gdp_percountry,"processed/mean_gdp_percountry.csv")
```


# Data manipulation with `tidyr`

```{r}
# command to download the 'wide' data
download.file("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/data/gapminder_wide.csv", destfile = "data/gapminder_wide.csv")

gapminder_wide <- read.csv("data/gapminder_wide.csv")

gap_long <- gapminder_wide %>% 
    gather(obstype_year,
           obs_values,
           3:38)

head(gap_long)
```

separate the obs_type column

```{r}
gap_normal <- gap_long %>% 
    separate(obstype_year,into=c("obs_type","year"),sep="_") %>% 
    spread(obs_type,obs_values)

head(gap_normal)

all.equal(gapminder,gap_normal)
```

```{r}
gap_normal <- gap_normal %>% 
    arrange(country,continent,year)
all.equal(gapminder,gap_normal)

```