# 4 Visualization

In this section we’ll explore visualization methods in R. Visualization has been a key element of R since its inception, since visualization is central to the exploratory philosophy of the language. The base plot system generally does a good job in coming up with the most likely graphical output based on the data you provide.

plot(penguins$body_mass_g, penguins$flipper_length_mm) (#fig:vis.penguinPlots-1)Flipper length by species

plot(penguins$species, penguins$flipper_length_mm) (#fig:vis.penguinPlots-2)Flipper length by species

Antarctic peninsula penguin data is from .

## 4.1 ggplot2

We’ll mostly focus however on gpplot2, based on the Grammar of Graphics because it provides considerable control over your graphics while remaining fairly easily readable, as long as you buy into its grammar.

ggplot2 looks at three aspects of a graph:

• data : where are the data coming from?
• geometry : what type of graph are we creating?
• aesthetics : what choices can we make about symbology and how do we connect symbology to data?

As with other tidyverse and RStudio packages, find the ggplot2 cheat sheet at https://www.rstudio.com/resources/cheatsheets/

The ggplot2 system provides plots of single and multiple variables, using various coordinate systems (including geographic).

## 4.2 Plotting one variable

• continuous
• histograms
• density plots
• dot plots
• discrete
• bar

[Create a new NDVI project]

We’ll look at a study of Normalized Difference Vegetation Index from a transect across a montane meadow in the northern Sierra Nevada, derived from multispectral drone imagery .

library(iGIScData)
library(tidyverse)
summary(XSptsNDVI)
##     DistNtoS       elevation     vegetation          geometry          NDVIgrowing     NDVIsenescent
##  Min.   :  0.0   Min.   :1510   Length:29          Length:29          Min.   :0.3255   Min.   :0.1402
##  1st Qu.: 37.0   1st Qu.:1510   Class :character   Class :character   1st Qu.:0.5052   1st Qu.:0.2418
##  Median :175.0   Median :1511   Mode  :character   Mode  :character   Median :0.6169   Median :0.2817
##  Mean   :164.7   Mean   :1511                                         Mean   :0.5901   Mean   :0.3662
##  3rd Qu.:275.5   3rd Qu.:1511                                         3rd Qu.:0.6768   3rd Qu.:0.5407
##  Max.   :298.8   Max.   :1511                                         Max.   :0.7683   Max.   :0.7578
ggplot(XSptsNDVI, aes(vegetation)) +
geom_bar() ### 4.2.1 Histogram

First, to prepare the data, we need to use a pivot_longer on XSptsNDVI:

XSptsPheno <- XSptsNDVI %>%
filter(vegetation != "pine") %>%
pivot_longer(cols = starts_with("NDVI"), names_to = "phenology", values_to = "NDVI") %>%
mutate(phenology = str_sub(phenology, 5, str_length(phenology)))
XSptsPheno <- read_csv("data/XSptsPheno.csv")
XSptsPheno %>%
ggplot(aes(NDVI)) +
geom_histogram(binwidth=0.05) Normal histogram: easier to visualize the distribution, see modes [sierra]

sierraData %>%
ggplot(aes(TEMPERATURE)) +
geom_histogram(fill="dark green") (#fig:vis.histogram2)Distribution of Average Monthly Temperatures, Sierra Nevada

Cumulative histogram with proportions: easier to see percentiles, median

n <- length(sierraData$TEMPERATURE) sierraData %>% ggplot(aes(TEMPERATURE)) + geom_histogram(aes(y=cumsum(..count..)/n), fill="dark goldenrod") (#fig:vis.histogram3)Cumulative Distribution of Average Monthly Temperatures, Sierra Nevada ### 4.2.2 Density Plot Density represents how much out of the total. The total area (sum of widths of bins times densities of that bin) adds up to 1. [NDVI] XSptsPheno %>% ggplot(aes(NDVI)) + geom_density() (#fig:vis.density)Density plot of NDVI, Knuthson Meadow Note that NDVI values are <1 so bins are very small numbers, so in this case densities can be >1. Using alpha and mapping phenology as fill color. This illustrates two useful ggplot methods: • “mapping” a variable (phenology) to an aesthetic property (fill color of the density polygon) • setting a a property (alpha = 0.2) to all polygons of the density plot. The alpha channel of colors defines its opacity, from invisible (0) to opaque (1) so is commonly used to set as its reverse, transparency. XSptsPheno %>% ggplot(aes(NDVI, fill=phenology)) + geom_density(alpha=0.2) [eucoak] tidy_eucoak %>% ggplot(aes(log(runoff_L),fill=tree)) + geom_density(alpha=0.2) (#fig:vis.density.eucoak)Runoff under Eucalyptus and Oak in Bay Area sites ### 4.2.3 boxplot ggplot(data = tidy_eucoak) + geom_boxplot(aes(x = site, y = runoff_L)) (#fig:vis.boxplot.eucoak)Runoff under Eucalyptus and Oak, Bay Area Sites Get color from tree within aes() ggplot(data = tidy_eucoak) + geom_boxplot(aes(x=site, y=runoff_L, color=tree)) (#fig:vis.boxplot.eucoak.col)Runoff at Bay Area Sites, colored as Eucalyptus and Oak Visualizing soil CO_2_ data with a Tukey box plot [soilCO2] In a study of soil CO_2_ in the Marble Mountains of California, we sampled extracted soil air in a 11-point transect across Marble Valley in 1997. soilCO2 <- soilCO2_97 soilCO2$SITE <- factor(soilCO2\$SITE)  # in order to make the numeric field a factor
ggplot(data = soilCO2, mapping = aes(x = SITE, y = CO2%)) +
geom_boxplot() (#fig:vis.marble.boxplot)Visualizing soil CO_2_ data with a Tukey box plot

## 4.3 Plotting two variables

### 4.3.1 Two continuous variables

[sierra] We’ve looked at this before – the scatterplot

ggplot(data=sierraFeb) +
geom_point(mapping = aes(TEMPERATURE, ELEVATION)) (#fig:vis.sierra.scatterplot)Scatter plot of February temperature vs elevation

• The aes (“aesthetics”) function specifies the variables to use as x and y coordinates
• geom_point creates a scatter plot of those coordinate points

Set color for all (not in aes())

ggplot(data=sierraFeb) +
geom_point(aes(TEMPERATURE, ELEVATION), color="blue") • color is defined outside of aes, so is applies to all points.
• mapping is first argument of geom_point, so mapping = is not needed.

### 4.3.2 Two variables, one discrete

[eucoak]

ggplot(tidy_eucoak) +
geom_bar(aes(site, runoff_L), stat="identity") (#fig:vis.eucoak.bar)Two variables, one discrete

## 4.4 Color systems

You can find a lot about color systems. See these sources:

### 4.4.1 Color from variable, in aesthetics

In this graph, color is defined inside aes, so is based on COUNTY [sierra]

ggplot(data=sierraFeb) +
geom_point(aes(TEMPERATURE, ELEVATION, color=COUNTY)) (#fig:vis.sierra.color)Color set within aes()

Plotting lines using the same x,y in aesthetics

sierraFeb %>%
ggplot(aes(TEMPERATURE,ELEVATION)) +
geom_point(color="blue") +
geom_line(color="red") (#fig:vis.sierra.color.line)Using aesthetics settings for both points and lines

Note the use of pipe to start with the data then apply ggplot. [generic_methods]

River map & profile

We’ll build a riverData dataframe with x & y location values and elevation. We’ll need to start by creating empty vectors we’ll populate with values in a loop: d, longd and s are assigned an empty value double(), then slope s (since it only occurs between two points) needs one NA value assigned for the last point s[length(x] to have the same length as other vectors.

library(tidyverse)
x <- c(1000, 1100, 1300, 1500, 1600, 1800, 1900)
y <- c(500, 700, 800, 1000, 1200, 1300, 1500)
elev <- c(0, 1, 2, 5, 25, 75, 150)
d <- double()      # creates an empty numeric vector
longd <- double()  # ("double" means double-precision floating point)
s <- double()
for(i in 1:length(x)){
if(i==1){longd[i] <- 0; d[i] <-0}
else{
d[i] <- sqrt((x[i]-x[i-1])^2 + (y[i]-y[i-1])^2)
longd[i] <- longd[i-1] + d[i]
s[i-1] <- (elev[i]-elev[i-1])/d[i]}}
s[length(x)] <- NA  # make the last slope value NA since we have no data past it, and so the vector lengths are all the same
riverData <- bind_cols(x=x,y=y,elev=elev,d=d,longd=longd,s=s)
riverData
## # A tibble: 7 x 6
##       x     y  elev     d longd        s
##   <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
## 1  1000   500     0    0     0   0.00447
## 2  1100   700     1  224.  224.  0.00447
## 3  1300   800     2  224.  447.  0.0106
## 4  1500  1000     5  283.  730.  0.0894
## 5  1600  1200    25  224.  954.  0.224
## 6  1800  1300    75  224. 1177.  0.335
## 7  1900  1500   150  224. 1401. NA
ggplot(riverData, aes(x,y)) +
geom_line(mapping=aes(col=s), size=1.2) +
geom_point(mapping=aes(col=s, size=elev)) +
ggtitle("Simulated river path, elevations and slopes") (#fig:vis.longprof-1)Longitudinal Profiles

ggplot(riverData, aes(longd,elev)) + geom_line(aes(col=s), size=1.5) + geom_point()  +
ggtitle("Elevation over longitudinal distance upstream") (#fig:vis.longprof-2)Longitudinal Profiles

ggplot(riverData, aes(longd,s)) + geom_point(aes(col=s), size=3) +
ggtitle("Slope over longitudinal distance upstream") Figure 4.1: Slope by longitudinal distance

### 4.4.2 Trend line

[sierra]

sierraFeb %>%
ggplot(aes(TEMPERATURE,ELEVATION)) +
geom_point(color="blue") +
geom_smooth(color="red", method="lm") (#fig:vis.sierra.trendline)Trend line using geom_smooth with a linear model

### 4.4.3 General symbology

A useful vignette accessed by vignette("ggplot2-specs") lets you see aesthetic specifications for symbols, including:

• Color & fill
• Lines
• line type, size, ends
• Polygon
• border color, linetype, size
• fill
• Points
• shape
• size
• color & fill
• stroke
• Text
• font face & size
• justification

#### 4.4.3.1 Categorical symbology

One example of a “Big Data” resource is EPA’s Toxic Release Inventory [air_quality] that tracks releases from a wide array of sources, from oil refineries on down. One way of dealing with big data in terms of exploring meaning is to use symbology to try to make sense of it.

csvPath <- system.file("extdata","TRI_2017_CA.csv", package="iGIScData")
filter(5.1_FUGITIVE_AIR > 100 & 5.2_STACK_AIR > 100)
ggplot(data = TRI, aes(log(5.2_STACK_AIR), log(5.1_FUGITIVE_AIR),
color = INDUSTRY_SECTOR)) +
geom_point() (#fig:vis.TRI.col)EPA Toxic Release Inventory, as a big data set needing symbology clarification

#### 4.4.3.2 Graphs from grouped data

[NDVI]

XSptsPheno %>%
ggplot() +
geom_point(aes(elevation, NDVI, shape=vegetation,
color = phenology), size = 3) +
geom_smooth(aes(elevation, NDVI,
color = phenology), method="lm") (#fig:vis.phenogroup.col)NDVI symbolized by vegetation in two seasons

[eucoak]

ggplot(data = tidy_eucoak) +
geom_point(mapping = aes(x = rain_mm, y = runoff_L, color = tree)) +
geom_smooth(mapping = aes(x = rain_mm, y= runoff_L, color = tree),
method = "lm") +
scale_color_manual(values = c("seagreen4", "orange3")) (#fig:vis.eucoak.rainrunoff.col)Eucalyptus and Oak: rainfall and runoff

#### 4.4.3.3 Faceted graphs

This is another option to displaying groups of data, with parallel graphs

ggplot(data = tidy_eucoak) +
geom_point(aes(x=rain_mm,y=runoff_L)) +
geom_smooth(aes(x=rain_mm,y=runoff_L), method="lm") +
facet_grid(tree ~ .) (#fig:vis.parallel.graphs)Faceted graph alternative

## 4.5 Titles and subtitles

ggplot(data = tidy_eucoak) +
geom_point(aes(x=rain_mm,y=runoff_L, color=tree)) +
geom_smooth(aes(x=rain_mm,y=runoff_L, color=tree), method="lm") +
scale_color_manual(values=c("seagreen4","orange3")) +
labs(title="rainfall ~ runoff",
subtitle="eucalyptus & oak sites, 2016") ## 4.6 Pairs Plot

[sierra] Pairs plots are an excellent exploratory tool to see which variables are correlated. Since only continuous data are useful for this, and since pairs plots can quickly get overly complex, it’s good to use dplyr::select to select the continuous variables, or maybe use a helper function like is.numeric with dplyr::select_if:

sierraFeb %>%
dplyr::select_if(is.numeric) %>%
pairs() Figure 4.2: Pairs plot for Sierra Nevada stations variables

The GGally package has a very nice looking pairs plot that goes far beyond that description. In addition to scatterplots it has boxplots, histograms, density plots, and correlations, all stratified and colored by species. The following code is mostly borrowed from :

library(palmerpenguins)
penguins %>%
dplyr::select(species, body_mass_g, ends_with("_mm")) %>%
GGally::ggpairs(aes(color = species, alpha = 0.8)) +
scale_colour_manual(values = c("darkorange","purple","cyan4")) +
scale_fill_manual(values = c("darkorange","purple","cyan4")) Figure 4.3: Enhanced GGally pairs plot for palmerpenguin data

## 4.7 Exercises

1. Create a bar graph of the counts of the species in the penguins data frame . What can you say about what it shows?

2. Use bind_cols in dplyr to create a tibble from built-in vectors state.abb and state.region, then use ggplot with geom_bar to create a bar graph of the four regions. [generic_methods]

3. Convert the built-in time series treering into a tibble tr using the tibble() function with the single variable assigned as treering = treering (or just specifying treering will also work for this simple example), then create a histogram, using that tibble and variable for the data and x settings needed. Attach a screen capture of the histogram. (Also, learn about the treering data by entering ?treering in the console and read the Help displayed.)

4. Start by clearing your environment with the broom icon in the Environment tab, then we’ll create two tibbles: Create a new tibble st using bind_cols with Name=state.name, Abb=state.abb, Region=state.region, and as_tibble(state.x77). Note that this works since all of the parts are sorted by state. Then use summary(st) and copy and paste its results for your answer.

5. From st, create a density plot from the variable Frost (number of days with frost for that state). Attach that plot, and answer: approximately what is the modal value?

6. From st create a a boxplot of Area by Region. Which region has the highest and which has the lowest median Area? Do the same for Frost.

7. From st, compare murder rate (y=Murder) to Frost (x) in a scatter plot, colored by Region.

8. Add a trend line (smooth) with method=“lm” to your scatterplot, not colored by Region (but keep the points colored by Region). What can you say about what this graph is showing you?

10. Change your scatterplot to place labels using the Abb variable (still colored by Region) using geom_label(aes(label=Abb, col=Region)). Any observations about outliers?