day4_ggplotSlides.Rmd

---
title: "`ggplot` graphics" 
author: "ECON 122"
date: "Day 4"
output: 
  ioslides_presentation:
    incremental: true
    widescreen: true
    keep_md: false
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(collapse=TRUE, prompt=TRUE, eval=TRUE, message=F, include=T,comment=NULL, warning=FALSE, error=TRUE)
```

```{r packageCheck, include=FALSE}
mypacks <- c("ggplot2")  # what packages are needed?
packs <- installed.packages()   # find installed package list
install.me <- mypacks[!(mypacks %in% packs[,"Package"])]  #what needs to be installed?
if (length(install.me) >= 1) install.packages(install.me, repos = "http://cran.us.r-project.org")   # install (if needed)
lapply(mypacks, library, character.only=TRUE)  # load all packages
```


## `ggplot2`: a grammar for graphics {.build}

- created by Hadley Wickham
- purposefully written to follow good graphing taxonomy
- allows you to create a graph in small, *readable* code chunks
- install the `ggplot2` package on your computer (if needed)
```{r, message = FALSE}
library(ggplot2)
packageVersion("ggplot2")
```

## `ggplot2` grammar {.build}

- Basic syntac is
```
ggplot(data) +    # data
  <geometry_funs>(aes(<variables>)) + # aesthetic variable mapping
  <label_funs> +  # add context
  <facet_funs> +  # add facets (optional)
  <coordinate_funs> +  # play with coords (optional)
  <scale_funs> + # play with scales (optional)
  <theme_funs> # play with axes, colors, etc (optional)
```
- can **layer** geometry functions
- can plot **stats** from raw data


## `Survey` data {.build}

- the dataset `Survey` provides student data from a survey:
```{r}
survey <- read.csv("http://raw.githubusercontent.com/mgelman/data/master/Survey.csv")
```
- Look first at GPA (Q15) and possible sci or non-sci major (Q16) 



## Data

- The first layer sets the graphical environment
```{r, fig.height=3.5}
ggplot(data=survey) 
```


## Aesthetics  {.build}

- Aesthetics describes the mapping of variables to your graph
    - for numeric, default scale is Cartesian 
- `aes` aesthetics can also be given in the `ggplot` command
```{r, fig.height=3.5}
ggplot(data=survey, aes(x=Question.15)) 
```

## Geometry  {.build}

- The geometry determines what form the plot has.
    + Here a `histogram` makes sense:
    
```{r, fig.height=3.5}
ggplot(data=survey, aes(x=Question.15)) + geom_histogram()
```

## EDA for data clean up  {.build}

- Obviously there were some typos in the GPA data. 
- Subset the data so only cases with a reasonable GPA are included.
```{r, fig.height=3.5}
survey <- survey[survey$Question.15 <=4.0 & survey$Question.15 >0,]
ggplot(data=survey, aes(x=Question.15)) + geom_histogram()
```

## Faceting  {.build}

- Can add a third variable, science or non-sci major (Q16), with a facet:
```{r, fig.height=3.5}
ggplot(data=survey, aes(x=Question.15)) + geom_histogram() + facet_wrap(~Question.16)
```

## Another option  {.build}

- Could also use side-by-side boxplots:
```{r, fig.height=3.5}
ggplot(data=survey, aes(y=Question.15, x=Question.16)) + geom_boxplot() 
```

## `ggplot` and `NA`'s  {.build}

- Need to omit `NA` rows from data if you want them omitted from your graphics
```{r, fig.height=3.5}
survey <- survey[!is.na(survey$Question.16),]
ggplot(data=survey, aes(y=Question.15, x=Question.16)) + geom_boxplot() 
```

## Adjusting coordinates  {.build}

- We can flip the x/y coordinates (`boxplot` always wants `x` to be categorical and `y` numeric)
```{r, fig.height=3.5}
ggplot(data=survey, aes(y=Question.15, x=Question.16)) + geom_boxplot() + coord_flip()
```

## Context! Add labels {.build}

- Options include `ggtitle` and `labs`, change text size too
```{r, fig.height=3.5}
ggplot(data=survey, aes(y=Question.15, x=Question.16)) + geom_boxplot() + coord_flip() + 
  labs(title="GPA by major", x="Major Area", y="GPA") + 
  theme(text=element_text(size=18))
```

## How to proceed {.build}

- Get to know basic command structure (`ggplot + geom`)
    - Use cheat sheet to see `aes` options for each `geom`
- Then add context layers: labels, font sizes, etc
- `?theme`: non-data features (fonts, legends, axes)
-  `?scale_`: scale (x,y,fill, shape) data features


## Scatterplot

- relationship between number of facebook friends (Q12) and GPA (Q15) 
```{r, fig.height=3.5}
ggplot(survey, aes(x=Question.12,y=Question.15)) + geom_point() 
```

## Scatterplot

Change symbol size and shape
```{r, fig.height=3.5}
ggplot(survey, aes(x=Question.12,y=Question.15)) + geom_point(size=3,shape=2) 
```

## Scatterplot

Change symbol shape by `major` (Q16): requires `aes` argument!
```{r, fig.height=3.5}
ggplot(survey, aes(x=Question.12,y=Question.15)) + 
  geom_point(aes(shape=Question.16), size=3) 
```

## Scatterplot

Change symbol color by `major` (Q16): requires `aes` argument!

- the `color` is the `discrete` variable "major", that is why `scale_color_discrete` is used
```{r, fig.height=3.5}
ggplot(survey, aes(x=Question.12,y=Question.15)) + 
  geom_point(aes(color=Question.16), size=3) + scale_color_discrete(name="major")
```

## Scatterplot

Change symbol size by number of tv hours/week (Q14): requires `aes` argument!
```{r, fig.height=3.5}
ggplot(survey, aes(x=Question.12,y=Question.15)) + 
  geom_point(aes(color=Question.16, size=Question.14)) + scale_color_discrete(name="major")
```

## Scatterplot

The `size` is the `continuous` variable tv, that is why `scale_size_continous` is used
```{r, fig.height=3.5}
ggplot(survey, aes(x=Question.12,y=Question.15)) + 
  geom_point(aes(color=Question.16, size=Question.14)) + scale_color_discrete(name="major") + 
  scale_size_continuous(name="tv hours/week")
```

## Scatterplot

What `scale` commands change legend names?
```{r, fig.height=3.5}
ggplot(survey, aes(x=Question.12,y=Question.15)) + 
  geom_point(aes(shape=Question.16, color=Question.14)) 
```

## Bar graphs

How does political comfort level (Q9) vary by religious group (Q8)?
```{r, fig.height=3.5, fig.width=8}
levels(survey$Question.8) <- c("not religious","religious, active", "religious, not active")
ggplot(survey, aes(x=Question.8, fill=Question.9)) + geom_bar(position="fill")
```