tidyr_practice.Rmd

---
title: "TidyR"
output: html_document
---

```{r}
library(tidyverse)
```

## Example datasets

```{r}
AirPassengers
```

```{r read_in_data}

gap_wide <- readr::read_csv('https://raw.githubusercontent.com/OHI-Science/data-science-training/master/data/gapminder_wide.csv')

## yesterdays data format
gapminder <- readr::read_csv('https://raw.githubusercontent.com/OHI-Science/data-science-training/master/data/gapminder.csv')

head(gap_wide)
head(gapminder)
```

## `gather()`

Use gather to turn `gap_wide` into a long format dataset

```{r}

head(gap_wide)

gap_long <- gap_wide %>%
  gather(key = obstype_year,
         value = obs_values)

head(gap_long)
```

```{r}
gap_long <- gap_wide %>%
  gather(key = obstype_year,
         value = obs_values,
         dplyr::starts_with("pop"),
         dplyr::starts_with("lifeExp"),
         dplyr::starts_with("gdpPercap")) 

## The :: indicates that starts_with comes from the dplyr package

head(gap_long)
```

## keep columns with `-` in gather()
```{r}
gap_long <- gap_wide %>%
  gather(key = obstype_year,
         value = obs_values,
         -continent, - country)
head(gap_long)
```

## separate out the year
```{r}
gap_long <- gap_wide %>%
  gather(key = obstype_year,
         value = obs_values,
         -continent, - country) %>%
  separate(obstype_year,
           into = c("obs_type", "year"),
           sep = "_",
           convert = T)

head(gap_long)
```

## plot long format data

```{r}
life_df <- gap_long %>%
  filter(obs_type == "lifeExp",
         continent == "Americas")

head(life_df)

ggplot(data = life_df, aes(x = year, y = obs_values, color = country)) +
  geom_line()
```


```{r}
meanLE_DF <- gap_long %>% 
  filter(obs_type =="lifeExp",
         year >= 1982,
         year <= 2007) %>% 
  group_by(continent, year) %>% 
  summarize(mean = mean(obs_values))

ggplot(data = meanLE_DF, aes(x=year,y=mean,color=continent)) +
  geom_line() +
  labs(title = "Life E")

```