---
title: "Intro to RMarkdown"
author: "Leigh Phan"
date: "4/23/2020"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.

**To create an R Markdown document in RStudio, select `File` > `New File` > `R Markdown`.


### Headers

In Markdown, create headers using a hierachy, from H1, represented by `#` to H6, represented by `######`.



### Text


It's very easy to make some words **bold** and other words _italic_ with Markdown. 


### Lists
You can create unordered lists:


You can also create ordered lists:




### Adding Code to Your R Markdown File
When adding code (or a "code chunk") to your .Rmd file, the code is enclosed within the following:
```{r}

```


* Remember that you can use the keyboard shortcut **Ctrl + Alt + I** (_*OS X*_: **Cmd + Option + I**)

  
## Exporting Rmarkdown

When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document.

In order to export in RStudio, select the **Knit** button in the upper-left corner; in the drop-down, select a format and location to save the file. R Markdown files can be exported to a wide range of document types, including HTML, PDF, and MS Word. 

Now you have the tools to create and produce a report including your text, data, code, figures, and more! We'll cover creating reports in more detail in our next workshop on May 1, 2020.


Until then, enjoy exploring and creating with Markdown!

_The following are supplemental guides_:

* [RStudio Guide to Markdown](https://github.com/rstudio/cheatsheets/blob/master/rmarkdown-2.0.pdf)
  (Right-click the image to view larger version, or download a copy)
* [GitHub Guide to Mastering Markdown](https://guides.github.com/features/mastering-markdown/)



## Creating Plots Using ggplot2

**ggplot2** is a library used in R to creative graphics and charts. The library is based on concepts of [The Grammar of Graphics by Leland Wilkinson](https://link.springer.com/book/10.1007%2F978-1-4757-3100-2), the idea that you can build every graph from same components:

* a **data** set
* a **coordinate system**
* **geoms** -- visual marks that represent data points (short for "geometries")



Basics of Grammar of Graphicsf01_ggplot-grammar.png

("Grammar of Graphics" - hence the "GG" in "ggplot2"!)


#### Let's get started with ggplot2 by loading the R Library, `ggplot2`:
```{r}
library(gapminder)
library(ggplot2)

```

We'll be using the dataset from our previous lessons, **gapminder**, which is readily available with R. Let's take a look at the dataset using **`View`**:

```{r}

```

We can see that the variables (column headers) in the gapminder dataset include the following:

  * country
  * continent
  * year
  * lifeExp (life expectancy)
  * pop
  * gdpPercap (Gross Domestic product)

This is important to remember, so that when we begin creating graphics, we'll reference these in our code.

Example: let's say, using gapminder data, we want to create a graph displaying the life expectancy of each country in relation to GDP per capita.

Life Expectancy based on GDP per capitaf02_01_lifeExp-gdp.png
**Observations**
* What do you see when you look at this graphic?
* How would you describe it?


### Elements of a Chart

```{r}



```
* `ggplot` knows that in order to create a chart, it requires `data` and a way to "map" the variables of that data to some aesthetics, or how we want it to look, which is specified by the argument `mapping` and the `aes` function.

* By itself, the call to `ggplot` isn't enough to draw a figure:

```{r}


```

We need to tell `ggplot` how we want to _visually represent the data_, which we do by adding a new **geom** (or 'geometry') layer. In this example, we use `geom_point`, which tells `ggplot` we want to visually represent the relationship between **x** and **y** as a scatterplot of points:
```{r}


```

#### Challenge Set 1
1. Modify the example so that the figure shows how life expectancy has changed over time.
2. In the previous examples and challenge we used the `aes` function to tell the scatterplot **geom** about the **x** and **y** locations of each point. Another _aesthetic_ property we can modify is the point _color_. Modify the code from the previous challenge to **color** the points by the "continent" column. What trends do you see in the data? Are they what you expected?


## Layers
* Using a scatterplot probably isn’t the best for visualizing change over time. Instead, let’s tell ggplot to visualize the data as a line plot.

Gapminder: Life Expectancy Over Time(f03_lifeExp-year.png)

* To our current chart, we'll add another aesthetic, called `**by**` and set it to one of the data variables, `country`
* From our previous challenge, let's continue to differentiate each continent by color

```{r}



```

* What do you think we need to change in our code in order to create a line graph (instead of a scatterplot)?
* `by` aesthetic tells `ggplot` to draw a line for each country


What if we want to visualize both **lines** and **points** on the plot?
```{r}


```


It's important to note that each layer is drawn on top of previous layer. In this example, the points have been drawn _on top_ of the lines. For example:

```{r}



```

In this example, the _aesthetic_ mapping of **color** was moved from the global plot options in `ggplot` to the `**geom_line**` layer, so it no longer applies to the points. Now we can clearly see taht points are drawn on top of the lines.

#### Challenge Set 2
1. Switch the order of the point and line layers from the previous example. What happened?

### Setting an aesthetic to a value instead of mapping
So far we've seen how to use an aes (such as color) as a mapping to a variable in the data.

```{r}
# geom_line(mapping = aes(color=continent))
```

But what if we want to change the color of all lines to blue?
```{r}
# geom_line(mapping=aes(color="blue"))
```
Since we don't want to create a mapping to a specific variable, we move the color specification outside of the `aes()` function. e.g.:
```{r}
# geom_line(color="blue")
```


## Transformations & Statistics
```{r}
# ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) + geom_point()
```


##### Changing scale of a graph
* Currently hard to see relationship between points due to outliers
* Can change **scale** of units on x-axis using `scale` functions
* Modify transparency of points using `alpha` function
* `log10` function applied transformation to values before plotting, so each multiple of 10 corresponds to an increase in 1 on the transformed scale (e.g. a GDP per capita of 1,000 is now 3 on the x-axis)

```{r}


```

#### Line Fitting

We can fit a simple relationship to the data by adding another layer, `geom_smooth`:

```{r}


```

```{r}


```

### Challenge Set 3
1. Modify the color and size of the points on the point layer in the previous example.
  Hint: do not use the `aes` function.

2. Modify your solution to the previous challenge so that the points are now a different shape and are colored by continent with new trendlines. Hint: The color argument can be used inside the aesthetic.


## Multi-panel figures
We start by making a subset of data including only countries located in the Americas. This includes 25 countries, which will begin to clutter the figure. Note that we apply a “theme” definition to rotate the x-axis labels to maintain readability. Nearly everything in ggplot2 is customizable.

```{r}
americas <- gapminder[gapminder$continent == "Americas",]
ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
  geom_line() + 
  facet_wrap( ~ country) + 
  theme(axis.text.x = element_text(angle = 45))
```


## Modifying text

To clean this figure up for publication, we need to change some text elements.

We can do this by adding some different layers.

* **theme** layer - controls the axis text and overall text size.
* **labs** function - Sets the labels for axes, plot title, and any legend
* **aes** - Legend titles are set using same names used in `aes`
* Below the color legend title is set using `color = "Continent"`, while the title of a fill legend would be set using `fill = "MyTitle"`.



```{r}
ggplot(data = americas, mapping = aes(x = year, y = lifeExp, color=continent)) +
  geom_line() + facet_wrap( ~ country) +
  labs(
    x = "Year",              # x axis title
    y = "Life expectancy",   # y axis title
    title = "Figure 1",      # main title of figure
    color = "Continent"      # title of legend
  ) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))
```


## Exporting the plot

Export a plot using `**ggsave()**` - 

```{r}
americas <- gapminder[gapminder$continent == "Americas",]
ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
  geom_line() + 
  facet_wrap( ~ country) + 
  theme(axis.text.x = element_text(angle = 45))
```
```{r}
ggplot(data = americas, mapping = aes(x = year, y = lifeExp, color=continent)) +
  geom_line() + facet_wrap( ~ country) +
  labs(
    x = "Year",              # x axis title
    y = "Life expectancy",   # y axis title
    title = "Figure 1",      # main title of figure
    color = "Continent"      # title of legend
  ) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))
```

```{r}
lifeExp_plot <- ggplot(data = americas, mapping = aes(x = year, y = lifeExp, color=continent)) +
  geom_line() + facet_wrap( ~ country) +
  labs(
    x = "Year",              # x axis title
    y = "Life expectancy",   # y axis title
    title = "Figure 1",      # main title of figure
    color = "Continent"      # title of legend
  ) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

ggsave(filename = "results/lifeExp.png", plot = lifeExp_plot, width = 12, height = 10, dpi = 300, units = "cm")
```

```{r}
packageVersion("ggplot2")
```


#### Challenge Set 4
Generate boxplots to compare life expectancy between the different continents during the available years.

Advanced:
    * Rename y axis as Life Expectancy.
    * Remove x axis labels.
    
```{r}
americas <- gapminder[gapminder$continent == "Americas",]
ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
  geom_line() + 
  facet_wrap( ~ country) + 
  theme(axis.text.x = element_text(angle = 45))
```

