The Title

class: left, bottom
background-image: url("images/contour.png")
background-position: right
background-size: auto

# Visusalization with GGPlot

### Environmental Data Literacy

&nbsp;

---

# Learning Objectives

- Set up online repositories for data such as Google Drive and retrieve it directly into RStudio for utilization.

- Describe how the Grammar of Graphics deviates from built-in `plot`ting commands.

- Use an `aes`-thetic to define which data components of the data will be used in constructing the graphic.

- Apply `geom`metric data layers to existing plots.

---

# Online Repositories

---

# Data Integrity

Where data lives is an indication of its integrity.  The following characteristics are indications of

- Central location

- Easily accessible

- Described (metadata)

- Versioning

---

background-image: url("https://live.staticflickr.com/65535/50294277523_f97d3cfd61_c_d.jpg")
background-position: center
background-size: auto

# GitHub/GitLab

[Dyerlab GitHub Repository](https://github.com/dyerlab)

---

# Loading Directly

## GitHub or other Repository

```r
library( readr )
url <- "https://raw.githubusercontent.com/dyerlab/ENVS-Lectures/master/data/arapat.csv"
data <- read_csv(url)
```

---

# Loading Directly

```r
head( data )
```

```
## # A tibble: 6 x 3
## Stratum Longitude Latitude
## <chr> <dbl> <dbl>
## 1 88 -114. 29.3
## 2 9 -114. 29.0
## 3 84 -114. 29.0
## 4 175 -113. 28.7
## 5 177 -114. 28.7
## 6 173 -113. 28.4
```

---

# Loading From Google Drive

You can keep you data in a spreadsheet on Google Docs and then configure your repostitory to make it available for download to anyone who has a

[Here](https://docs.google.com/spreadsheets/d/1Mk1YGH9LqjF7drJE-td1G_JkdADOU0eMlrP01WFBT8s) is a data set from the Rice Rivers Center with the following characteristics:

- 8200 records

- The following variables: `DateTime, RecordID, PAR, WindSpeed_mph, WindDir, AirTempF, RelHumidity, BP_HG, Rain_in, H2O_TempC, SpCond_mScm, Salinity_ppt, PH, PH_mv, Turbidity_ntu, Chla_ugl, BGAPC_CML, BGAPC_rfu, ODO_sat, ODO_mgl, Depth_ft, Depth_m, SurfaceWaterElev_m_levelNad83m`

---

# Making Data Available

- Select `Publish to the Web`

- Select `Link`, which sheet to publish, and `Comma-separated values (csv)`

- Retrieve very long URL.
]

---

# Making Data Available

- Select `Publish to the Web`

- Select `Link`, which sheet to publish, and `Comma-separated values (csv)`

- Retrieve very long URL.
]

---

# Load in the Data

```r
## 
## NOTICE I SPLIT THIS SO IT FITS ON ONE LINE IN THE SLIDES
##

url_parts <- c( "https://docs.google.com/spreadsheets/d/e", 
 "/2PACX-1vRm_v8JJPSipkDywT354v2owDBWa1j82",
 "_OhvdQmSBRztSv8YuWZBYe73T3jiY6suQhYoGCiV",
 "Y3gu9jW/pub?gid=0&single=true&output=csv" )

rice <- read_csv( paste(url_parts, collapse="") )
```

---

# The Rice Data

```
## # A tibble: 6 x 23
## DateTime RecordID PAR WindSpeed_mph WindDir AirTempF RelHumidity BP_HG
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1/1/201… 43816 0 3.87 14.6 31.0 80.5 30.3
## 2 1/1/201… 43817 0 4.79 18.5 30.7 82.1 30.3
## 3 1/1/201… 43818 0 3.61 16.2 31.2 81.9 30.3
## 4 1/1/201… 43819 0 2.99 11.5 30.5 83 30.3
## 5 1/1/201… 43820 0 3.52 11.3 30.9 81.8 30.3
## 6 1/1/201… 43821 0 3.83 20.0 30.6 82.8 30.3
## # … with 15 more variables: Rain_in <dbl>, H2O_TempC <dbl>, SpCond_mScm <dbl>,
## # Salinity_ppt <dbl>, PH <dbl>, PH_mv <dbl>, Turbidity_ntu <dbl>,
## # Chla_ugl <dbl>, BGAPC_CML <dbl>, BGAPC_rfu <dbl>, ODO_sat <dbl>,
## # ODO_mgl <dbl>, Depth_ft <dbl>, Depth_m <dbl>,
## # SurfaceWaterElev_m_levelNad83m <dbl>
```

---

# The Grammar of Graphics

---

class: middle
background-image: url("https://live.staticflickr.com/65535/50295214407_bc0f4d10b6_c_d.jpg")
background-position: center
background-size: auto

---

class: middle
background-image: url("https://live.staticflickr.com/65535/50294412713_25bbd52230_c_d.jpg")
background-position: center
background-size: auto

---

# Components of Graphical Objects

- A aesthetic statement indicating which columns of data to use and how to use them in the plot (designating x-axis vs color, etc.).

- An estimate of a trendline through the data (the red one), which displays a statistical summary of the raw data.

- A set of geometric overlays for the points which include size and shape configurations.

- Specified color scheme for the regions.

- Labeling of a subset of the data (which is done using a separate data.frame derived from the first).

- Labels on axes.

- A legend positioned in a specific fashion.

- A title over the whole thing.

- A theme for the rest of the coloring and customized lines and grids.

---

class: middle
background-image: url("https://live.staticflickr.com/65535/50295214407_bc0f4d10b6_c_d.jpg")
background-position: center
background-size: auto

---

# The Grammar of Graphics

- Data

- Aesthetics

- Transformations

- Partitions

- Auxillary Text

- Overlays
]

---

# The `ggplot2` Library

.pull-right[
  ### R Packages for Data Science
  
  - RStudio + Hadley Wickham
  
  - Collection of Packages
  
  - Makes you .red[AWESOME]

```r
library( ggplot2 )
```

]

---

# The Aesthetics

An *aesthetic* is a function that allows you to tell the graphics which columns of data are to be used in the creation of graph features.

```r
aes( x = Sepal.Length, y = Sepal.Width )
```

```
## Aesthetic mapping: 
## * `x` -> `Sepal.Length`
## * `y` -> `Sepal.Width`
```

Commonly included **within** the initial call to `ggplot()`

```r
ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width ) )
```

---

# Stepwise Creation of a Plot

```r
ggplot( iris ) 
```

---

```r
ggplot( iris, aes( x = Sepal.Length) ) 
```

---

# Adding Geometry Layer

```r
ggplot( iris, aes( x = Sepal.Length) ) + geom_histogram()
```

---

# Simple Density Plot

```r
ggplot( iris, aes( x = Sepal.Length) ) + geom_density() 
```

---

# A Scatter Plot

```r
ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width) ) + geom_point() 
```

---

# Scatterplot with Colors

*Aesthetics* also contribute to symbologies and colors

```r
ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width, color=Species) ) + 
  geom_point() 
```

---

# In & Out of `aes()`

```r
ggplot( iris ) + 
  geom_point(aes( x = Sepal.Length, y = Sepal.Width, col=Species), shape=5)
```

---

# Iterative Building of Graphics

```r
p <- ggplot( iris )
p <- p + geom_point( aes( x = Sepal.Length, 
 y = Sepal.Width, 
 col=Species, 
 shape=Species), 
 size=3, 
 alpha=0.75 ) 
p <- p + xlab("Sepal Length")
p <- p + ylab("Sepal Length")
class(p)
```

```
## [1] "gg"     "ggplot"
```

---

# Printing out `p`

```r
p
```

---

# Scope of Visibility

Only things in `ggplot()` apply to *all* following components.  Placing `aes()` or `data=` parts in later components *only* make them visible to that particular component.

```r
ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width) ) + 
  geom_point() + 
  stat_smooth() 
```

```r
ggplot( iris ) + 
  geom_point( aes( x = Sepal.Length, y = Sepal.Width) ) + 
  stat_smooth(  aes( x = Sepal.Length, y = Sepal.Width) )
```

```r
ggplot() + 
  geom_point( aes( x = Sepal.Length, y = Sepal.Width), data = iris )  + 
  stat_smooth( aes( x = Sepal.Length, y = Sepal.Width), data = iris )  
```

---

# Themes

## Additive Components to the Plot

---

class: middle
background-image: url("https://media.giphy.com/media/dWEk3w1Uo97qw/giphy.gif")
background-position: center
background-size: cover

&nbsp;

---

```r
p + theme_bw()
```

---

```r
p + theme_gray()
```

---

```r
p + theme_linedraw()
```

---

```r
p + theme_dark()
```

---

```r
p + theme_minimal()
```

---

```r
p + theme_classic()
```

---

```r
p + theme_void()
```

---

# Customizing Theme Components

```r
p + theme_bw( base_size = 18 )
```

---
class: inverse

# Create Your Own Themes

```r
source("theme_dyerlab_grey.R")
p + theme_dyerlab_grey()
```

---

# Boxplot

```r
ggplot( iris, aes( x = Sepal.Length) ) + 
  geom_boxplot( notch=TRUE )
```

---

# Species Differences

```r
ggplot( iris, aes(x=Species, y=Sepal.Length) )  + 
  geom_boxplot( notch=TRUE )
```

---

# Species Differences Fill Colors

```r
ggplot( iris, aes(x=Species, y=Sepal.Length) )  + 
  geom_boxplot( notch=TRUE, fill=c("#002145") ) +
  ylab("Sepal Length")
```

---

# Species Differences Fill Colors

```r
ggplot( iris, aes(x=Species, y=Sepal.Length) )  + 
  geom_boxplot( notch=TRUE, fill=c("#002145", "#a5acaf","#66c010") ) +
  ylab("Sepal Length")
```

---

# Overlays

---

# Overlaying a Trendline

```r
ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width) ) + 
  geom_point()  + 
  stat_smooth()
```

---

# Overlaying a Trendline

```r
ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width) ) + 
  geom_point()  + 
  stat_smooth( method="lm", formula = "y ~ x")
```

---

# Stacking Order

```r
ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width) ) + 
  geom_point( color="red")  + 
  stat_smooth( fill="black", alpha=1)
```

---

# Stacking Order

```r
ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width) ) + 
  stat_smooth( fill="black", alpha=1) + 
  geom_point( color="red")  
```

---

# On-The-Fly Transformations

Customizing the y-axis data format...

```r
ggplot( iris, aes(x = Sepal.Length) ) + 
  geom_histogram( aes( y = ..density.. ), 
                  color="green", 
                  fill="orange", bins = 15 ) + 
  geom_density( color = "magenta", lwd=1.5 )  
```

---

# Textual Overlays

```r
cor_model <- cor.test( iris$Sepal.Length, iris$Sepal.Width)
cor_model
```

```
## 
## 	Pearson's product-moment correlation
## 
## data:  iris$Sepal.Length and iris$Sepal.Width
## t = -1.4403, df = 148, p-value = 0.1519
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.27269325  0.04351158
## sample estimates:
##        cor 
## -0.1175698
```

```r
names( cor_model ) 
```

```
## [1] "statistic"   "parameter"   "p.value"     "estimate"    "null.value" 
## [6] "alternative" "method"      "data.name"   "conf.int"
```

---

# Formatting as Text

```r
cor.text <- paste( "r = ", 
 format( cor_model$estimate, digits=4), 
 ";\n P = ", 
 format( cor_model$p.value, digits=4 ), 
 sep="" ) 
cor.text
```

```
## [1] "r = -0.1176;\n P = 0.1519"
```

---

```r
p + geom_text( aes(x=7.25, y=4.25, label=cor.text) )
```

---

# Labels

```r
sLength <- by( iris$Sepal.Length, iris$Species, mean )
sWidth <- by( iris$Sepal.Width, iris$Species, mean )

df <- data.frame( Sepal.Length = as.numeric( sLength ),
 Sepal.Width = as.numeric( sWidth ),
 Species = levels( iris$Species ) )
df
```

```
##   Sepal.Length Sepal.Width    Species
## 1        5.006       3.428     setosa
## 2        5.936       2.770 versicolor
## 3        6.588       2.974  virginica
```

---

# Labels

```r
ggplot( iris, aes(Sepal.Length, Sepal.Width) ) + 
  geom_point( aes(color=Species) ) + 
  geom_text( aes(label=Species), data=df) 
```

---

# Smart Labels

```r
library( ggrepel )
```

![ggrepel Help](https://live.staticflickr.com/65535/50305860422_f3cf5f5545_c_d.jpg)

---

```r
ggplot( iris, aes(Sepal.Length, Sepal.Width) ) + 
  geom_point( aes(color=Species) ) + 
  geom_label_repel( aes(label=Species), data=df )
```

---

# Remove Legend

```r
ggplot( iris, aes(Sepal.Length, Sepal.Width) ) + 
  geom_point( aes(color=Species) ) + 
  geom_label_repel( aes(label=Species), data=df ) + 
  guides( color = FALSE )
```

---

class: middle
background-image: url("images/contour.png")
background-position: right
background-size: auto

# Questions?

![Peter Sellers](images/peter_sellers.gif)
]

&nbsp;

.bottom[ If you have any questions for about the content presented herein, please feel free to send me  
an [email](mailto://rjdyer@vcu.edu) and I'll get back to you as soon as possible.]