Basic Data Visualization

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# Basic Data Visualization

### Environmental Data Literacy

---

background-image: url("images/throw_into_pool.gif")
background-position: center
background-size: cover

## .white[How most learn to make graphics]

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background-color: black

---
class: center, middle
background-image: url("images/DataWorkCycle.png")
background-size: contain

---

# The Example Data - Iris

There is a classic data set in statistics called *Fisher's Iris Data Set* (see more about [Ronald Fisher](https://en.wikipedia.org/wiki/Ronald_Fisher) ) looking at 50 measurements of *sepal* and *pedal* lengths amongst three species of *Iris*.<sup>1</sup>

![Iris species](https://live.staticflickr.com/65535/50163458792_2e3e877468_c_d.jpg)
.footnote[[1] These data were first published in Fisher RA (1936) The use of multiple measurements in taxonomic problems. *Annals of Eugenics*, **7**:  [doi](https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1469-1809.1936.tb02137.x)).]

---

# Anatomy of a Flower

--
![Iris morphology](https://live.staticflickr.com/65535/50274494323_4e215e571b_c_d.jpg)

---

# The Example Data - Iris

<div id="htmlwidget-560ed6e5f3e766e751d3" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-560ed6e5f3e766e751d3">{"x":{"filter":"none","fillContainer":false,"data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","45","46","47","48","49","50","51","52","53","54","55","56","57","58","59","60","61","62","63","64","65","66","67","68","69","70","71","72","73","74","75","76","77","78","79","80","81","82","83","84","85","86","87","88","89","90","91","92","93","94","95","96","97","98","99","100","101","102","103","104","105","106","107","108","109","110","111","112","113","114","115","116","117","118","119","120","121","122","123","124","125","126","127","128","129","130","131","132","133","134","135","136","137","138","139","140","141","142","143","144","145","146","147","148","149","150"],[5.1,4.9,4.7,4.6,5,5.4,4.6,5,4.4,4.9,5.4,4.8,4.8,4.3,5.8,5.7,5.4,5.1,5.7,5.1,5.4,5.1,4.6,5.1,4.8,5,5,5.2,5.2,4.7,4.8,5.4,5.2,5.5,4.9,5,5.5,4.9,4.4,5.1,5,4.5,4.4,5,5.1,4.8,5.1,4.6,5.3,5,7,6.4,6.9,5.5,6.5,5.7,6.3,4.9,6.6,5.2,5,5.9,6,6.1,5.6,6.7,5.6,5.8,6.2,5.6,5.9,6.1,6.3,6.1,6.4,6.6,6.8,6.7,6,5.7,5.5,5.5,5.8,6,5.4,6,6.7,6.3,5.6,5.5,5.5,6.1,5.8,5,5.6,5.7,5.7,6.2,5.1,5.7,6.3,5.8,7.1,6.3,6.5,7.6,4.9,7.3,6.7,7.2,6.5,6.4,6.8,5.7,5.8,6.4,6.5,7.7,7.7,6,6.9,5.6,7.7,6.3,6.7,7.2,6.2,6.1,6.4,7.2,7.4,7.9,6.4,6.3,6.1,7.7,6.3,6.4,6,6.9,6.7,6.9,5.8,6.8,6.7,6.7,6.3,6.5,6.2,5.9],[3.5,3,3.2,3.1,3.6,3.9,3.4,3.4,2.9,3.1,3.7,3.4,3,3,4,4.4,3.9,3.5,3.8,3.8,3.4,3.7,3.6,3.3,3.4,3,3.4,3.5,3.4,3.2,3.1,3.4,4.1,4.2,3.1,3.2,3.5,3.6,3,3.4,3.5,2.3,3.2,3.5,3.8,3,3.8,3.2,3.7,3.3,3.2,3.2,3.1,2.3,2.8,2.8,3.3,2.4,2.9,2.7,2,3,2.2,2.9,2.9,3.1,3,2.7,2.2,2.5,3.2,2.8,2.5,2.8,2.9,3,2.8,3,2.9,2.6,2.4,2.4,2.7,2.7,3,3.4,3.1,2.3,3,2.5,2.6,3,2.6,2.3,2.7,3,2.9,2.9,2.5,2.8,3.3,2.7,3,2.9,3,3,2.5,2.9,2.5,3.6,3.2,2.7,3,2.5,2.8,3.2,3,3.8,2.6,2.2,3.2,2.8,2.8,2.7,3.3,3.2,2.8,3,2.8,3,2.8,3.8,2.8,2.8,2.6,3,3.4,3.1,3,3.1,3.1,3.1,2.7,3.2,3.3,3,2.5,3,3.4,3],[1.4,1.4,1.3,1.5,1.4,1.7,1.4,1.5,1.4,1.5,1.5,1.6,1.4,1.1,1.2,1.5,1.3,1.4,1.7,1.5,1.7,1.5,1,1.7,1.9,1.6,1.6,1.5,1.4,1.6,1.6,1.5,1.5,1.4,1.5,1.2,1.3,1.4,1.3,1.5,1.3,1.3,1.3,1.6,1.9,1.4,1.6,1.4,1.5,1.4,4.7,4.5,4.9,4,4.6,4.5,4.7,3.3,4.6,3.9,3.5,4.2,4,4.7,3.6,4.4,4.5,4.1,4.5,3.9,4.8,4,4.9,4.7,4.3,4.4,4.8,5,4.5,3.5,3.8,3.7,3.9,5.1,4.5,4.5,4.7,4.4,4.1,4,4.4,4.6,4,3.3,4.2,4.2,4.2,4.3,3,4.1,6,5.1,5.9,5.6,5.8,6.6,4.5,6.3,5.8,6.1,5.1,5.3,5.5,5,5.1,5.3,5.5,6.7,6.9,5,5.7,4.9,6.7,4.9,5.7,6,4.8,4.9,5.6,5.8,6.1,6.4,5.6,5.1,5.6,6.1,5.6,5.5,4.8,5.4,5.6,5.1,5.1,5.9,5.7,5.2,5,5.2,5.4,5.1],[0.2,0.2,0.2,0.2,0.2,0.4,0.3,0.2,0.2,0.1,0.2,0.2,0.1,0.1,0.2,0.4,0.4,0.3,0.3,0.3,0.2,0.4,0.2,0.5,0.2,0.2,0.4,0.2,0.2,0.2,0.2,0.4,0.1,0.2,0.2,0.2,0.2,0.1,0.2,0.2,0.3,0.3,0.2,0.6,0.4,0.3,0.2,0.2,0.2,0.2,1.4,1.5,1.5,1.3,1.5,1.3,1.6,1,1.3,1.4,1,1.5,1,1.4,1.3,1.4,1.5,1,1.5,1.1,1.8,1.3,1.5,1.2,1.3,1.4,1.4,1.7,1.5,1,1.1,1,1.2,1.6,1.5,1.6,1.5,1.3,1.3,1.3,1.2,1.4,1.2,1,1.3,1.2,1.3,1.3,1.1,1.3,2.5,1.9,2.1,1.8,2.2,2.1,1.7,1.8,1.8,2.5,2,1.9,2.1,2,2.4,2.3,1.8,2.2,2.3,1.5,2.3,2,2,1.8,2.1,1.8,1.8,1.8,2.1,1.6,1.9,2,2.2,1.5,1.4,2.3,2.4,1.8,1.8,2.1,2.4,2.3,1.9,2.3,2.5,2.3,1.9,2,2.3,1.8],["setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","versicolor","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica","virginica"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>Sepal.Length<\/th>\n      <th>Sepal.Width<\/th>\n      <th>Petal.Length<\/th>\n      <th>Petal.Width<\/th>\n      <th>Species<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":8,"columnDefs":[{"className":"dt-right","targets":[1,2,3,4]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[8,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>

---

# The Example Data - Iris

```r
summary( iris )
```

```
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
## 
```

---

# Learning Objectives

For this lecture, the learning objectives include:

- Create univariate and bivariate plots of data (continuous-continuous & continuous-categorical).

- Apply varying basic symbologies for representing data in plots.

- Use named and hex colors to better

---

# Univariate Data

---

# A Single Vector of Data

```r
sepal_length <- iris$Sepal.Length
head(sepal_length)
```

```
## [1] 5.1 4.9 4.7 4.6 5.0 5.4
```

---

# A Single Vector of Data - Histograms

```r
hist( sepal_length )
```

---

# Arguments to Customize Plots

- `xlab` & `ylab`: The names attached to both x- and y-axes.

- `main`: The title on top of the graph.

- `breaks`: This controls the way in which the original data are partitioned (e.g., the width of the bars along the x-axis).     
   - If you pass a single number, `n` to this option, the data will be partitioned into `n` bins.  
   - If you pass a sequence of values to this, it will use this sequence as the boundaries of bins.

- `col`: The color of the bar (not the border)

- `probability`: A flag as either `TRUE` or `FALSE` (the default) to have the y-axis scaled by total likelihood of each bins rather than a count of the numbrer of elements in that range.

---

# Density Plots

```r
d_sepal.length <- density( sepal_length )
d_sepal.length
```

```
## 
## Call:
## 	density.default(x = sepal_length)
## 
## Data: sepal_length (150 obs.);	Bandwidth 'bw' = 0.2736
## 
##        x               y            
##  Min.   :3.479   Min.   :0.0001495  
##  1st Qu.:4.790   1st Qu.:0.0341599  
##  Median :6.100   Median :0.1534105  
##  Mean   :6.100   Mean   :0.1905934  
##  3rd Qu.:7.410   3rd Qu.:0.3792237  
##  Max.   :8.721   Max.   :0.3968365
```

```r
class(d_sepal.length)
```

```
## [1] "density"
```

---

# Density Plots

```r
plot( d_sepal.length )
```

---

# Bivariate Data

---

# The Generality of `plot()`

In `R`, many objects understand how to `plot` themselves.

- Density objects

- Analyses (regression, ANOVA, etc)

- points, lines, polygons, & rasters

---

# A Scatter Plot

```r
plot( iris$Sepal.Length, iris$Sepal.Width )
```

---

# Functional Forms

### Listing Data as Separate Values

```r
plot( x, y )
```

&nbsp;

### List Functional Form of the Data

```r
plot( y ~ x)
```

---

# Customizing Plots

---

# `plot()` Options

Parameter | Description
----------|-----------------------------------------------------------------------------------------------------------
`type`    | The kind of plot to show ('p'oint, 'l'ine, 'b'oth, or 'o'ver).  A point plot is the default.
`pch`     | The character (or symbol) being used to plot.  There 26 recognized general characters to use for plotting.  The default is `pch=1`. 
`col`     | The color of the symbols/lines that are plot.
`cex`     | The magnification size of the character being plot.  The default is `cex=1` and deviation from that will increase (cex > 1) or decrease (0 < cex < 1) the scaling of the symbols.  Also works for `cex.lab` and `cex.axis`.
`lwd`     | The width of any lines in the plot.
`lty`     | The type of line to be plot (solid, dashed, etc.)
`bty`     | The 'Box' type around the plot ("o", "1", "7","c","u", "]", and my favorite "n")

---

---

# Species Differences in the `iris` dataset

```r
summary( iris )
```

---

# Symbology

```r
symbol <- as.numeric( iris$Species)
symbol
```

```
##   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
##  [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3
## [112] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [149] 3 3
```

---

# Species Differences by Symbol

```r
plot( iris$Sepal.Length, iris$Sepal.Width, pch=symbol )
```

---

# Additional Customizations

```r
plot( Sepal.Width ~ Sepal.Length, data = iris, 
      pch = symbol, bty="n", cex=1.5, cex.axis=1.5, cex.lab = 1.5, 
      xlab="Sepal Length", ylab="Sepal Width")
```

---

# Color Spaces

##  Named & Hexadecimal

---

# Named Colors

In `R`, there are 657 different *named* colors accessable through the function `colors()`.

```r
sample( colors(), size=5, replace = FALSE )
```

```
## [1] "red2"       "gray62"     "slateblue2" "gray83"     "pink3"
```

---

```r
raw_colors <- sample( colors(), size=3, replace=FALSE)
colors <- raw_colors[ symbol ]
colors
```

```
##   [1] "gold1"          "gold1"          "gold1"          "gold1"         
##   [5] "gold1"          "gold1"          "gold1"          "gold1"         
##   [9] "gold1"          "gold1"          "gold1"          "gold1"         
##  [13] "gold1"          "gold1"          "gold1"          "gold1"         
##  [17] "gold1"          "gold1"          "gold1"          "gold1"         
##  [21] "gold1"          "gold1"          "gold1"          "gold1"         
##  [25] "gold1"          "gold1"          "gold1"          "gold1"         
##  [29] "gold1"          "gold1"          "gold1"          "gold1"         
##  [33] "gold1"          "gold1"          "gold1"          "gold1"         
##  [37] "gold1"          "gold1"          "gold1"          "gold1"         
##  [41] "gold1"          "gold1"          "gold1"          "gold1"         
##  [45] "gold1"          "gold1"          "gold1"          "gold1"         
##  [49] "gold1"          "gold1"          "gray16"         "gray16"        
##  [53] "gray16"         "gray16"         "gray16"         "gray16"        
##  [57] "gray16"         "gray16"         "gray16"         "gray16"        
##  [61] "gray16"         "gray16"         "gray16"         "gray16"        
##  [65] "gray16"         "gray16"         "gray16"         "gray16"        
##  [69] "gray16"         "gray16"         "gray16"         "gray16"        
##  [73] "gray16"         "gray16"         "gray16"         "gray16"        
##  [77] "gray16"         "gray16"         "gray16"         "gray16"        
##  [81] "gray16"         "gray16"         "gray16"         "gray16"        
##  [85] "gray16"         "gray16"         "gray16"         "gray16"        
##  [89] "gray16"         "gray16"         "gray16"         "gray16"        
##  [93] "gray16"         "gray16"         "gray16"         "gray16"        
##  [97] "gray16"         "gray16"         "gray16"         "gray16"        
## [101] "palevioletred3" "palevioletred3" "palevioletred3" "palevioletred3"
## [105] "palevioletred3" "palevioletred3" "palevioletred3" "palevioletred3"
## [109] "palevioletred3" "palevioletred3" "palevioletred3" "palevioletred3"
## [113] "palevioletred3" "palevioletred3" "palevioletred3" "palevioletred3"
## [117] "palevioletred3" "palevioletred3" "palevioletred3" "palevioletred3"
## [121] "palevioletred3" "palevioletred3" "palevioletred3" "palevioletred3"
## [125] "palevioletred3" "palevioletred3" "palevioletred3" "palevioletred3"
## [129] "palevioletred3" "palevioletred3" "palevioletred3" "palevioletred3"
## [133] "palevioletred3" "palevioletred3" "palevioletred3" "palevioletred3"
## [137] "palevioletred3" "palevioletred3" "palevioletred3" "palevioletred3"
## [141] "palevioletred3" "palevioletred3" "palevioletred3" "palevioletred3"
## [145] "palevioletred3" "palevioletred3" "palevioletred3" "palevioletred3"
## [149] "palevioletred3" "palevioletred3"
```

---

# Adding a Legend

```r
plot( Sepal.Width ~ Sepal.Length, data = iris,
      col = colors, pch=20, bty="n", cex=2,
      xlab="Sepal Length", ylab="Sepal Width")
legend(6.5,4.3, pch=20, cex=1.5, col=raw_colors,legend=levels(iris$Species) )
```

---

# Hex Colors

Color spaces defined by:

- Red 
 - Green 
 - Blue

In base-16 no less:

> 0 1 2 3 4 5 6 7 8 9 A B C D E F

So for 2-digits, that is 256 distinct values for each color

> 00 → FF

---

# Hex Colors

Represented triplets of RRGGBB preceded by hashtag

```r
raw_colors <- c("#86cb92", "#8e4162", "#260F26")
colors <- raw_colors[ symbol ]
```

---

---

# Color Theme Generators

Google up something like "Color Theme Generator" and see what you find.

One I use is: [coolors](https://coolors.co)

---

# Color Brewer

[Color Brewer](https://colorbrewer2.org)

---

# Color Brewer in R

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---

```r
library(RColorBrewer)
display.brewer.all()
```

---

# Categorical Data

---

# Categorical Dependent Data

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class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>Sepal.Length<\/th>\n      <th>Sepal.Width<\/th>\n      <th>Petal.Length<\/th>\n      <th>Petal.Width<\/th>\n      <th>Species<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":7,"columnDefs":[{"className":"dt-right","targets":[1,2,3,4]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[7,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>

---

# Mean Sepal Length, by Species

```r
mu.Setosa <- mean( iris$Sepal.Length[ iris$Species == "setosa" ])
mu.Versicolor <- mean( iris$Sepal.Length[ iris$Species == "versicolor" ])
mu.Virginica <- mean( iris$Sepal.Length[ iris$Species == "virginica" ])

meanSepalLength <- c( mu.Setosa, mu.Versicolor, mu.Virginica )
meanSepalLength
```

```
## [1] 5.006 5.936 6.588
```

---

# The BarPlot

```r
barplot( meanSepalLength, 
         names.arg = c("setosa","versicolor","virginica"), 
         xlab="Iris Species",
         ylab="Mean Sepal Length")
```

---

# A Shortcut

meanSepalLength <- c( mu.Setosa, mu.Versicolor, mu.Virginica )
meanSepalLength
```

```r
meanSepalLength <- by( iris$Sepal.Length, iris$Species, mean )
meanSepalLength
```

```
## iris$Species: setosa
## [1] 5.006
## ------------------------------------------------------------ 
## iris$Species: versicolor
## [1] 5.936
## ------------------------------------------------------------ 
## iris$Species: virginica
## [1] 6.588
```

---

```r
barplot( meanSepalLength, 
         xlab = "Iris Species",
         ylab = "Average Sepal Length")
```

---

# The Boxplot - High Density Information

A boxplot contains a high amount of information content and is appropriate when the groupings on the x-axis are categorical.  For each category, the graphical representation includes:

- The median value for the raw data

- A box indicating the area between the first and third quartile (e.g,. the values enclosing the 25% - 75% of the data).  The top and bottoms are often referred to as the *hinges* of the box.

- A notch (if requested), represents confidence around the estimate of the median.

- Whiskers extending out to shows `$\pm 1.5 * IQR$` (the Inner Quartile Range)

- Any points of the data that extend beyond the whiskers are plot as points.

---

```r
boxplot( Sepal.Length ~ Species, data=iris, 
         notch=TRUE, ylab="Sepal Length")
```

---

# Overrlaying Text

---

# Textifying Your Plot

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

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

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

---

```r
plot( Sepal.Width ~ Sepal.Length, data = iris, 
      col=colors, 
      pch=20, 
      bty="n", 
      xlab="Sepal Length", ylab="Sepal Width")
text( 6.5, 4.2, cor.text, cex=1.2 )
```

---

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

# Questions?

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

.bottom[ If you have any questions for about the content presented herein, please feel free to drop a comment on the class LMS and I'll get back to you as soon as possible.]