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#' ## Save a plot: Example 1
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#' ## Save a plot: Example 2
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#' ## Save a plot: Example 2
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#' Download the PDF of the presentation
#' #' #' [ The R Script associated with this page is available here](`r output`). Download this file and open it (or copy-paste into a new script) with RStudio so you can follow along. #' #' #' ## Data #' In this module, we'll primarily use the `mtcars` data object. The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models). #' #' A data frame with 32 observations on 11 variables. #' #' | Column name | Description | #' |:--------------|:------------------------------------------| #' | mpg | Miles/(US) gallon | #' | cyl | Number of cylinders | #' | disp | Displacement (cu.in.) | #' | hp | Gross horsepower | #' | drat | Rear axle ratio | #' | wt | Weight (lb/1000) | #' | qsec | 1/4 mile time | #' | vs | V/S | #' | am | Transmission (0 = automatic, 1 = manual) | #' | gear | Number of forward gears | #' | carb | Number of carburetors | #' #' ``` #' #' Here's what the data look like: ## ----warning=F----------------------------------------------------------- library(ggplot2);library(knitr) kable(head(mtcars)) #' #' #' # Base graphics #' #' #' ## Base `plot()` #' #' R has a set of 'base graphics' that can do many plotting tasks (scatterplots, line plots, histograms, etc.) #' ## ------------------------------------------------------------------------ plot(y=mtcars$mpg,x=mtcars$wt) #' #' Or you can use the more common *formula* notation: #' ## ------------------------------------------------------------------------ plot(mpg~wt,data=mtcars) #' #' And you can customize with various parameters: #' ## ------------------------------------------------------------------------ plot(mpg~wt,data=mtcars, ylab="Miles per gallon (mpg)", xlab="Weight (1000 pounds)", main="Fuel Efficiency vs. Weight", col="red" ) #' #' #' Or switch to a line plot: #' ## ------------------------------------------------------------------------ plot(mpg~wt,data=mtcars, type="l", ylab="Miles per gallon (mpg)", xlab="Weight (1000 pounds)", main="Fuel Efficiency vs. Weight", col="blue" ) #' #' #' See `?plot` for details. #' #' ## Histograms #' #' Check out the help for basic histograms. ## ----results='hide'------------------------------------------------------ ?hist #' #' Plot a histogram of the fuel efficiencies in the `mtcars` dataset. #' ## ------------------------------------------------------------------------ hist(mtcars$mpg) #' #' #' # [`ggplot2`](http://ggplot2.org) #' The _grammar of graphics_: consistent aesthetics, multidimensional conditioning, and step-by-step plot building. #' #' #' 1. Data: The raw data #' 2. `geom_`: The geometric shapes representing data #' 3. `aes()`: Aesthetics of the geometric and statistical objects (color, size, shape, and position) #' 4. `scale_`: Maps between the data and the aesthetic dimensions #' #' ``` #' data #' + geometry, #' + aesthetic mappings like position, color and size #' + scaling of ranges of the data to ranges of the aesthetics #' ``` #' #' #' ### Additional settings #' #' 5. `stat_`: Statistical summaries of the data that can be plotted, such as quantiles, fitted curves (loess, linear models), etc. #' 6. `coord_`: Transformation for mapping data coordinates into the plane of the data rectangle #' 7. `facet_`: Arrangement of data into grid of plots #' 8. `theme`: Visual defaults (background, grids, axes, typeface, colors, etc.) #' #' For example, a simple scatterplot: #'
#'
#' Add variable colors and sizes:
#' 
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#' ## Simple scatterplot
#'
#' First, create a *blank* ggplot object with the data and x-y geometry set up.
## ------------------------------------------------------------------------
p <- ggplot(mtcars, aes(x=wt, y=mpg))
summary(p)
p
#'
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## ------------------------------------------------------------------------
p + geom_point()
#'
#' Or you can do both at the same time:
## ------------------------------------------------------------------------
ggplot(mtcars, aes(x=wt, y=mpg)) +
geom_point()
#'
#'
#' ### Aesthetic map: color by # of cylinders
#'
## ------------------------------------------------------------------------
p +
geom_point(aes(colour = factor(cyl)))
#'
#' ### Set shape using # of cylinders
## ------------------------------------------------------------------------
p +
geom_point(aes(shape = factor(cyl)))
#'
#' ### Adjust size by `qsec`
## ------------------------------------------------------------------------
p +
geom_point(aes(size = qsec))
#'
#' ### Color by cylinders and size by `qsec`
## ------------------------------------------------------------------------
p +
geom_point(aes(colour = factor(cyl),size = qsec))
#'
#' ### Multiple aesthetics
## ----fig.height=4--------------------------------------------------------
p +
geom_point(aes(colour = factor(cyl),size = qsec,shape=factor(gear)))
#'
#' ### Add a linear model
## ------------------------------------------------------------------------
p + geom_point() +
geom_smooth(method="lm")
#'
#' ### Add a LOESS smooth
## ------------------------------------------------------------------------
p + geom_point() +
geom_smooth(method="loess")
#'
#'
#' ### Change scale color
#'
## ------------------------------------------------------------------------
p + geom_point(aes(colour = cyl)) +
scale_colour_gradient(low = "blue")
#'
#' ### Change scale shapes
#'
## ------------------------------------------------------------------------
p + geom_point(aes(shape = factor(cyl))) +
scale_shape(solid = FALSE)
#'
#' ### Set aesthetics to fixed value
## ------------------------------------------------------------------------
ggplot(mtcars, aes(wt, mpg)) +
geom_point(colour = "red", size = 3)
#'
#' ### Transparancy: alpha=0.2
## ------------------------------------------------------------------------
d <- ggplot(diamonds, aes(carat, price))
d + geom_point(alpha = 0.2)
#'
#' Varying alpha useful for large data sets
#'
#' ### Transparancy: alpha=0.1
#'
## ------------------------------------------------------------------------
d +
geom_point(alpha = 0.1)
#'
#' ### Transparancy: alpha=0.01
#'
## ------------------------------------------------------------------------
d +
geom_point(alpha = 0.01)
#'
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#' ## Building ggplots
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#' 
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#' ## Other Plot types
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#' ### Discrete X, Continuous Y
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## ------------------------------------------------------------------------
p <- ggplot(mtcars, aes(factor(cyl), mpg))
p + geom_point()
#'
#' ### Discrete X, Continuous Y + geom_jitter()
#'
## ------------------------------------------------------------------------
p +
geom_jitter()
#'
#' ### Discrete X, Continuous Y + geom_violin()
#'
## ------------------------------------------------------------------------
p +
geom_violin()
#'
#' ### Discrete X, Continuous Y + geom_violin()
#'
## ------------------------------------------------------------------------
p +
geom_violin() + geom_jitter(position = position_jitter(width = .1))
#'
#' 
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#' ### Three Variables
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#'
#' Will return to this when we start working with raster maps.
#'
#' ### Stats
#' Visualize a data transformation
#'
#' 
#'
#' * Each stat creates additional variables with a common ``..name..`` syntax
#' * Often two ways: `stat_bin(geom="bar")` OR `geom_bar(stat="bin")`
#'
#' 
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#' ### 2D kernel density estimation
#'
#' Old Faithful Geyser Data on duration and waiting times.
#'
## ------------------------------------------------------------------------
library("MASS")
data(geyser)
m <- ggplot(geyser, aes(x = duration, y = waiting))
#'
#' 
[photo: Greg Willis](https://commons.wikimedia.org/wiki/File:Old_Faithful_(3679482556).jpg)
#'
#' See `?geyser` for details.
#'
## ------------------------------------------------------------------------
m +
geom_point()
#'
## ------------------------------------------------------------------------
m +
geom_point() + stat_density2d(geom="contour")
#'
#' Check `?geom_density2d()` for details
#'
## ------------------------------------------------------------------------
m +
geom_point() + stat_density2d(geom="contour") +
xlim(0.5, 6) + ylim(40, 110)
#'
#' Update limits to show full contours. Check `?geom_density2d()` for details
#'
#'
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#'
## ------------------------------------------------------------------------
m + stat_density2d(aes(fill = ..level..), geom="polygon") +
geom_point(col="red")
#'
#' Check `?geom_density2d()` for details
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#' ### Discrete color: default
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## ------------------------------------------------------------------------
b=ggplot(mpg,aes(fl))+
geom_bar( aes(fill = fl)); b
#'
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#' ### Discrete color: greys
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## ------------------------------------------------------------------------
b + scale_fill_grey( start = 0.2, end = 0.8,
na.value = "red")
#'
#'
#'
#' ### Continuous color: defaults
#'
## ---- message=F----------------------------------------------------------
a <- ggplot(mpg, aes(x=hwy,y=cty,col=displ)) +
geom_point(); a
#'
#'
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#' ### Continuous color: `gradient`
#'
## ---- message=F----------------------------------------------------------
a + scale_color_gradient( low = "red",
high = "yellow")
#'
#'
#'
#' ### Continuous color: `gradient2`
#'
## ---- message=F----------------------------------------------------------
a + scale_color_gradient2(low = "red", high = "blue",
mid = "white", midpoint = 4)
#'
#'
#'
#' ### Continuous color: `gradientn`
#'
## ---- message=F----------------------------------------------------------
a + scale_color_gradientn(
colours = rainbow(10))
#'
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#' ### Discrete color: brewer
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## ------------------------------------------------------------------------
b +
scale_fill_brewer( palette = "Blues")
#'
#' ## [colorbrewer2.org](http://colorbrewer2.org)
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#' ## ColorBrewer: Diverging
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#' ## ColorBrewer: Filtered
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#' ## Position
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#' ### Stacked bars
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## ------------------------------------------------------------------------
ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar()
#'
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#' ### Dodged bars
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#'
## ------------------------------------------------------------------------
ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar(position="dodge")
#'
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#' # Facets
#'
#' Use facets to divide graphic into *small multiples* based on a categorical variable.
#'
#' `facet_wrap()` for one variable:
#'
## ------------------------------------------------------------------------
ggplot(mpg, aes(x = cty, y = hwy, color = factor(cyl))) +
geom_point()+
facet_wrap(~year)
#'
#'
#'
#' `facet_grid()`: two variables
#'
## ------------------------------------------------------------------------
ggplot(mpg, aes(x = cty, y = hwy, color = factor(cyl))) +
geom_point()+
facet_grid(year~cyl)
#'
#' *Small multiples* (via facets) are very useful for visualization of timeseries (and especially timeseries of spatial data.)
#'
#'
#' # Themes
#' Set *default* display parameters (colors, font sizes, etc.) for different purposes (for example print vs. presentation) using themes.
#'
#' ## GGplot Themes
#'
#' 
#'
#' Quickly change plot appearance with themes.
#'
#' ### More options in the `ggthemes` package.
## ------------------------------------------------------------------------
library(ggthemes)
#'
#' Or build your own!
#'
#'
#'
#' ### Theme examples: default
## ------------------------------------------------------------------------
p=ggplot(mpg, aes(x = cty, y = hwy, color = factor(cyl))) +
geom_jitter() +
labs(
x = "City mileage/gallon",
y = "Highway mileage/gallon",
color = "Cylinders"
)
#'
#'
#'
#' ### Theme examples: default
## ------------------------------------------------------------------------
p
#'
#'
#'
#' ### Theme examples: Solarized
## ------------------------------------------------------------------------
p + theme_solarized()
#'
#'
#'
#' ### Theme examples: Solarized Dark
## ------------------------------------------------------------------------
p + theme_solarized(light=FALSE)
#'
#'
#'
#' ### Theme examples: Excel
## ------------------------------------------------------------------------
p + theme_excel()
#'
#'
#'
#' ### Theme examples: _The Economist_
## ------------------------------------------------------------------------
p + theme_economist()
#'
#' ## Theme examples: _XKCD_
#' XKCD: A webcomic of romance, sarcasm, math, and language.
#'
#' 
#' Note: the following code will only work if you have the xkcd font installed. See `xkcd::vignette("xkcd-intro")` for details.
#'
## ---- warning=FALSE, message=F-------------------------------------------
library(xkcd)
ggplot(mtcars, aes(mpg, wt)) +
geom_point() +
geom_smooth()+
xkcdaxis(xrange=c(10,35), yrange=c(0,7))+
ylab("Weight")+xlab("Miles per Gallon")+
theme_xkcd()
#'
#' # Saving/exporting
#'
#' ## Saving using the GUI
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#'
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#' ## Saving using `ggsave()`
#' Save a `ggplot` with sensible defaults:
## ----eval=F--------------------------------------------------------------
## ggsave(filename, plot = last_plot(), scale = 1, width, height)
#'
#'
#'
#' ## Saving using devices
#'
#' Save any plot with maximum flexibility:
#'
## ----eval=F--------------------------------------------------------------
## pdf(filename, width, height) # open device
## ggplot() # draw the plot(s)
## dev.off() # close the device
#'
#' **Formats**
#'
#' * pdf
#' * jpeg
#' * png
#' * tif
#'
#' and more...
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#' ## Save a plot: Example 2
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#' ## Colophon
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#' Sources:
#'
#' * [ggplot cheatsheet](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf)
#'
#' Licensing:
#'
#' * Presentation: [CC-BY-3.0 ](http://creativecommons.org/licenses/by/3.0/us/)
#' * Source code: [MIT](http://opensource.org/licenses/MIT)
#'