Grammar of Graphics theory has been applied to web visualization libraries. Here the height is based on the number of observations in the dataset for each possible number of cylinders. Bar plots frequently stack or dodge the bars to avoid overlap: Sometimes scatterplots with few unique $x$ and $y$ values are jittered (random noise is added) to reduce overplotting. A recently introduced package for interactive graphics also makes use of this concept.)) Remove the tick marks and labels (this can also be done with theme settings): The Scales section in R for Data Science provides some more details. Specifying a new default is very different from specifying a constant value as an aesthetic, which is rarely what you want: Additional geoms are available in packages like ggforce, ggridges, and others described on the ggplot2 extensions site. Continuous values are transformed with a linear scaling. For instance, multiple layers can be built using the same underlying data. For geom_point the default stat is stat_identity. Polygon vertices are encoded by longitude and latitude. In a strong sense, this book is best seen as a collection of several dozen short pieces. A recent blog post explains how the BBC Visual and Data Journalism team creates their graphics. A coordinate system (coord) maps the position of objects onto the plane of the plot, and controls how the axes and grid lines are drawn. ggplot2 represents an … A statistical transformation (stat) transforms the data, generally by summarizing the information. Adding a text aesthetic allows the tooltip display to be customized: There have been several efforts to develop a grammar of interactive graphics, including ggvis and animint; neither seems to be under active development at this time. For geom_histogram the default is stat_bin. In brief, the grammar reduces a statistical graph to a simple mapping: from data to geometric objects (points, lines or bars) with aesthetic attributes (color, shape, and size). A grammar of graphics is a tool that enables us to concisely describe the components of a graphic. Wickham, Hadley. We only need to specify one geom and stat, since each geom has a default stat. Instead, you might summarize the data by graphing the total number of observations within a set of categories. Nicholas J. Cox for the Journal of … position_jitter can be used with geom_point to avoid overplotting or break up rounding artifacts. Facet arrangement can also be used to convey other information, such as geographic location. We always start by loading up and looking at the dataset we want to analyze and visualize. ggplot2 can serve as a replacement for the base graphics in R and contains a number of defaults for web a… You can think of a ‘grammar of graphics’ as a bit like the ultimate DSL for creating charts and visualisations. The mapping is accomplished via a set of rules known as the grammar of graphics. The mtcars dataset consists of data that was extracted from the 1974 Motor TrendUS magazine, and depicts fuel consumption and 10 other attributes of a… Color assignment can also be controlled by scale functions. Aesthetics can be mapped to a variable or set to a fixed common value. The grammar of graphics has served as the foundation for the graphics frameworks in SPSS, Vega-Lite and several other systems. The structure of the program … Proper map projections are non-linear; this is easier to see with an Albers projection: Scales are used for controlling the mapping of values to physical representations such as colors, shapes, and positions. We start with a discussion of a theoretical framework for data visualization known as “the grammar of graphics.” This framework serves as the foundation for the ggplot2 package which we’ll use extensively in this chapter. The geofacet package allows facets to be placed in approximate locations of different geographic regions. Grammar of Graphics is a layered object-oriented graphics generation framework that is proposed by Leland Wilkinson. The summaries are written by students themselves, which gives you the best possible insight into what is important to study about this book. A stat is a function that takes in a dataset as the input and returns a dataset as the output; a stat can add new variables to the original dataset, or create an entirely new dataset. Read 5 reviews from the world's largest community for readers. 2 Applied to visualizations, a grammar of graphics is a grammar used to describe and create a wide range of statistical graphics. Published with The grammar is also useful because it suggests the high-level aspects of a plot that can be changed, giving you a framework to think about graphics, and hopefully shortening the distance from mind to paper. The grammarspeaks in terms of data as “tidy” rows of individual observations. 2.1 The grammar of graphics. Demonstrate how to use layered grammar of graphics to build Minard’s graph of Napoleon’s invasion of Russia; Practice generating layered graphics using ggplot2; Before class. For geom_point the required aesthetics are. A blog post on use of R and ggplot by FiveThirtyEight. Stats can provide computed variables that can be referenced as ..... For stat_bin some of the computed variables are. Some alternate complete themes provided by ggplot2 are. May 20, 2014 - Explore Dear English Major's board "Grammar Graphics", followed by 712 people on Pinterest. A grammar of a language defines the rules of structuring words and phrases into meaningful expressions. Before writing the graphics for SYSTAT in the 1980's, I began by teaching a seminar in statistical graphics and collecting as many different quantitative graphics as I could find. Geoms are classified by their dimensionality: Each geom can only display certain aesthetics or visual attributes of the geom. Rather than explicitly declaring each component of a layered graphic (which will use more code and introduces opportunities for errors), we can establish intelligent defaults for specific geoms and scales. Here, each row represents observations of a single diamo… The plot may also contain statistical transformations of data and is drawn onto a … A recent project gganimate to add animation to ggplot looks very promising. Height determines how tall to draw the bar. There are many projections used in map making; the default projection used by coord_map is the Mercator projection. They are defined by five basic parts: Layers are typically related to one another and share many common features. For instance, whenever we want to use a bar geom, we can default to using a stat that counts the number of observations in each group of our variable in the $x$ position. 3 What is the grammar of graphics? Wilkinson, L. (2005), The Grammar of Graphics, 2nd ed., Springer. In these situations, the statistical transformation is an, How to build a complicated, layered graphic, Practice generating layered graphics using ggplot2, Computer programming as a form of problem solving, Practice transforming college education (data), Practice transforming and visualizing factors, Practice exploring college education (data), Drawing vector maps with simple features and ggplot2, Practice getting data from the Twitter API, Practicing sentiment analysis with Harry Potter, Components of the layered grammar of graphics. "Warts and all, The Grammar of Graphics is a richly rewarding work, an outstanding achievement by one of the leaders of statistical graphics. I was determined to produce a package that could draw every statistical graphic I had ever seen. Sometimes you don’t need to make a statistical transformation. For example, for some presidential approval ratings data, the common assignment of red for republican and blue for democrat can be obtained by, ggplot2 supports the notion of themes for adjusting non-data appearance aspects of a plot, such as. For example, Figure 8.4 is similar to a meme circulating on Facebook that shows how English grammar, in this case spacing and the use of a hyphen, … Each geom has some required and some optional aesthetics. In an Rmarkdown document the interactive plot is embedded in the. this theory is proposed by Leland Wilkinson in the book The Grammar of Graphics. After all, itcontains all of the information you’re trying to convey. You can think of a ‘grammar of graphics’ as a bit like the ultimate DSL for creating charts and visualisations. Rather than describing the theory behind the grammar, let me explain it by deconstructing the plot you see below. This paper builds on Wilkinson (2006), describing … pre-defined theme functions allow consistent style changes. So, the grammar of graphics is an attempt to formalise or create the vocabulary and the language and the building blocks of understanding to allow us to understand: what makes a good visualisation? Another version of the Old Faithful data available as geyser in package MASS has some rounding in the duration variable: The default amount of jittering isn’t quite enough in this case: To jitter only horizontally and by a larger amount you can use. Read chapters 1-4 from R for Data Science. Arrangement according to a calendar is also useful. For shapes 21–25 the color aesthetic specifies the border color and fill specifies the interior color. The default coordinate system is coord_cartesian. The aspect ratio can be important for recognizing features and patterns. stat_function can be used to add a density curve specified as a mixture of two normal densities: For bar charts these allow choosing between stacked and side-by-side charts. This is a powerful tool when investigating whether patterns are the same or different across conditions, and allows the subsets to be visualized on the same plot (known as conditioned or trellis plots). In general, I try to avoid being too theoretical in my courses and workshops, but… ---
title: "The Grammar of Graphics"
output:
  html_document:
    toc: yes
    code_download: true
---

```{r global_options, include=FALSE}
knitr::opts_chunk$set(collapse=TRUE)
```

```{r, include = FALSE}
library(dplyr)
library(ggplot2)
library(lattice)
library(gridExtra)
set.seed(12345)
```

## Background

The _Grammar of Graphics_ is a language proposed by Leland Wilkinson
for describing statistical graphs.

* Wilkinson, L. (2005), _The Grammar of Graphics_, 2nd ed., Springer.

The grammar of graphics has served as the foundation for the graphics
frameworks in [SPSS](https://www.ibm.com/products/spss-statistics),
[Vega-Lite](https://vega.github.io/vega-lite/) and several other
systems.

`ggplot2` represents an implementation and extension of the grammar
of graphics for R.

* Wickham, H. (2016), _ggplot2: Elegant Graphics for Data Analysis_,
  2nd ed., Springer.
* On line documentation: <http://docs.ggplot2.org/>.
* Wickham. H., and Grolemund, G. (2016),
  [_R for Data Science_](http://r4ds.had.co.nz/), O'Reilly.
* [Data visualization cheatsheet](https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf)
* Winston Chang (in preparation), [_R Graphics Cookbook_, 2nd
  edition](https://r-graphics.org/), O’Reilly. ([Book source on
  GitHub](https://github.com/wch/rgcookbook))
  
The idea is that any basic plot can be built out of a combination of

* a data set;
* one or more geometrical representation (_geoms_);
* mappings of values to _aesthetic_ features of the geom;
* a _stat_ to produce values to be mapped;
* position adjustments;
* a coordinate system;
* a scale specification;
* a faceting scheme.

`ggplot2` provides tools for specifying these components and adjusting
their features.

Many components and features are provided by default and do not need
to be specified explicitly unless the defaults are to be changed.


## A Basic Template

The simplest graph needs a data set, a geom, and a mapping:

```r
ggplot(data = <DATA>) + <GEOM>(mapping = aes(<MAPPINGS>))
```

The appearance of geom objects is controlled by _aesthetic_ features.

Each geom has some required and some optional aesthetics.

For `geom_point` the required aesthetics are

* `x` position
* `y` position.

Optional aesthetics include

* `color`
* `fill`
* `shape`
* `size`

```{r}
ggplot(mpg) + geom_point(aes(x = displ, y = hwy, color = class))
ggplot(mpg) + geom_point(aes(x = displ, y = hwy, color = class, shape = factor(cyl)))
```

Aesthetics can be mapped to a variable or set to a fixed common value.
This can be used to override default settings:

```{r}
ggplot(mpg) + geom_point(aes(x = displ, y = hwy), color = "blue", shape = 1)
```

Available point shapes are specified by number:

```{r, echo = FALSE, eval = FALSE}
generateRPointShapes<-function(){
  oldPar<-par()
  par(font=2, mar=c(0.5,0,0,0))
  y=rev(c(rep(1,6),rep(2,5), rep(3,5), rep(4,5), rep(5,5)))
  x=c(rep(1:5,5),6)
  plot(x, y, pch = 0:25, cex=1.5, ylim=c(1,5.5), xlim=c(1,6.5),
       axes=FALSE, xlab="", ylab="", bg="blue")
  text(x, y, labels=0:25, pos=3)
  par(mar=oldPar$mar,font=oldPar$font )
}
generateRPointShapes()
```
```{r, echo = FALSE}
ggplot(NULL, aes(x = rep(1:5, 5), y = rev(rep(1:5, each = 5)))) +
    geom_point(shape = 1:25, size = 5, fill = "blue") +
    geom_text(aes(label = 1 : 25), nudge_y = 0.25, size = 6) +
    theme_void()
```

Shapes 1--19 have their color set by the `color` aesthetic and ignore
the `fill` aesthetic.

For shapes 21--25 the `color` aesthetic specifies the border color and
`fill` specifies the interior color.

```{r}
ggplot(mutate(mpg, cyl = factor(cyl))) +
    geom_point(aes(x = displ, y = hwy, fill = cyl),
               shape = 21, size = 4)
```

Specifying a new default is very different from specifying a constant
value as an aesthetic, which is rarely what you want:

```{r}
ggplot(mpg) + geom_point(aes(x = displ, y = hwy, color = "blue"))
```


## Geometric Objects

`ggplot2` provides a number of geoms:

```{r, echo = FALSE, results = "asis"}
showList <- function(v, ncol = 4, pad = 2) {
    w <- max(nchar(v)) + pad
    nrow <- ceiling(length(v) / ncol)
    v <- c(v, character(ncol * nrow - length(v)))

    cat("```r\n")
    for (i in seq_len(nrow)) {
        line <- v[ncol * (i - 1) + (1 : ncol)]
        for (j in 1 : ncol)
            if (j < ncol)
                cat(sprintf("%-*s", w, line[j]))
            else
                cat(sprintf("%s\n", line[j]))
        ## cat(sprintf("%-*s%-*s%-*s%s\n",
        ##             w, line[1], w, line[2], w, line[3], line[4]))
    }
    cat("```\n")
}
showList(ls("package:ggplot2", pat = "^geom_"))
```

Additional geoms are available in packages like `ggforce`, `ggridges`,
and others described on the [`ggplot2` extensions
site](https://www.ggplot2-exts.org).

Geoms can be added as layers to a plot.

Mappings common to all, or most, geoms can be specified in the `ggplot` call:

```{r, message = FALSE}
ggplot(mpg, aes(x = displ, y = hwy)) +  geom_smooth() + geom_point()
```

Geoms can also use different data sets. This was used to show muted
views of the full data in faceted plots.

<!-- variant of code in prercep.Rmd -->
```{r, fig.width = 8}
library(gapminder)
gd <- filter(gapminder, year %in% c(1977, 1987, 1997, 2007))
ggplot(gd, aes(x = gdpPercap, y = lifeExp, color = continent)) +
    geom_point(data = mutate(gd, year = NULL), color = "grey80") +
    geom_point(size = 2.5) +
    scale_x_log10() +
    facet_wrap(~ year)
```


## Statistical Transformations

All geoms use a statistical transformation (_stat_) to convert raw
data to the values to be mapped to the object's features.

The available stats are

```{r, echo = FALSE, results = "asis"}
showList(ls("package:ggplot2", pat = "^stat_"), ncol = 3)
```

Each geom has a default stat, and each stat has a default geom.

* For `geom_point` the default stat is `stat_identity`.

* For `geom_bar` the default stat is `stat_count`.

* For `geom_histogram` the default is `stat_bin`.

Stats can provide _computed variables_ that can be referenced as
`..<variable>..`.

For `stat_bin` some of the computed variables are

* `count`: number of points in bin
* `density`: density of points in bin, scaled to integrate to 1

By default, `geom_histogram` uses `y = ..count..`.

```{r}
ggplot(faithful) +
    geom_histogram(aes(x = eruptions),
                   binwidth = 0.25, fill = "grey", color = "black")
```

Using `y = ..density..` produces a density scaled axis.

```{r}
p <- ggplot(faithful) +
    geom_histogram(aes(x = eruptions, y = ..density..),
                   binwidth = 0.25, fill = "grey", color = "black")
p
```

`stat_function` can be used to add a density curve specified as a
mixture of two normal densities:

```{r}
ms <- mutate(faithful, type = ifelse(eruptions < 3, "short", "long")) %>%
    group_by(type) %>%
    summarize(mean = mean(eruptions), sd = sd(eruptions), n = n()) %>%
    mutate(p = n / sum(n))
f <- function(x)
    ms$p[1] * dnorm(x, ms$mean[1], ms$sd[1]) +
    ms$p[2] * dnorm(x, ms$mean[2], ms$sd[2])
p + stat_function(fun = f, color = "red")
```


## Position Adjustments

The available position adjustments:

```{r, echo = FALSE, results = "asis"}
showList(ls("package:ggplot2", pat = "^position_"), ncol = 3)
```

For bar charts these allow choosing between stacked and side-by-side
charts.

The default is `position_stack`:

```{r}
ggplot(diamonds, aes(x = cut, fill = clarity)) + geom_bar(position = "stack")
```

`position_dodge` produces side-by-side bar charts:

```{r}
ggplot(diamonds, aes(x = cut, fill = clarity)) + geom_bar(position = "dodge")
```

`position_fill` rescales all bars to be equal height to help compare
proportions within bars.

Specifying `y = ..prop..` produces a better `y` axis label.

```{r}
ggplot(diamonds, aes(x = cut, fill = clarity)) + geom_bar(position = "fill")
```

Using the counts to scale the widths produces a _spine plot_, a
variant of a _mosaic plot_. This is easiest to do with the `ggmosaic`
package.

`position_jitter` can be used with `geom_point` to avoid overplotting
or break up rounding artifacts.

Another version of the Old Faithful data available as `geyser` in
package `MASS` has some rounding in the `duration` variable:
	
```{r}
data(geyser, package = "MASS")

## Adjust for different meaning of `waiting` variable
geyser2 <- na.omit(mutate(geyser, duration = lag(duration)))

p <- ggplot(geyser2, aes(x = duration, y = waiting))
p + geom_point()
```

The default amount of jittering isn't quite enough in this case:

```{r}
p + geom_point(position = "jitter")
```

To jitter only horizontally and by a larger amount you can use

```{r}
p + geom_point(position = position_jitter(height = 0, width = 0.1))
```


## Coordinate Systems

Coordinate system functions include

```{r, echo = FALSE, results = "asis"}
showList(ls("package:ggplot2", pat = "^coord_"))
```


### Cartesian Coordinates

The default coordinate system is `coord_cartesian`.

`coord_cartesian` can be used to _zoom in_ on a particular regiion:

```{r}
p + geom_point() + coord_cartesian(xlim=c(3,4))
```

`coord_fixed` and `coord_equal` fix the _aspect ratio_ for a cartesian
coordinate system.

The aspect ratio is the ratio of the number physical display units per
`y` unit to the number of physical display units par `x` unit.

The aspect ratio can be important for recognizing features and patterns.

```{r, fig.height = 2}
river <- scan("https://www.stat.uiowa.edu/~luke/data/river.dat")
r <- data.frame(flow = river, month = seq_along(river))
ggplot(r, aes(x = month, y = flow)) +
    geom_point() +
    coord_fixed(ratio = 4)
```


### Polar Coordinates

A filled bar chart like

```{r}
p <- ggplot(diamonds) + geom_bar(aes(x = 1, fill = cut), position = "fill")
p
```

is turned into a pie chart by changing to polar coordinates:

```{r}
p + coord_polar(theta = "y")
```


### Coordinate Systems for Maps

Coordinate systems are particularly important for maps.

Polygons for many polotical and geographic boundaries are available
through the `map_data` function.

```{r}
usa <- map_data("state")
```

Polygon vertices are encoded by longitude and latitude. Plotting these
in the default cartesian coordinate system usually does not work well:

```{r}
m <- ggplot(usa, aes(x = long, y = lat, group = group)) +
    geom_polygon(fill = "white", color = "black")
m
```

Using a fixed aspect ratio is better, but an aspect ratio of 1 does
not work well:

```{r}
m + coord_equal()
```

The problem is that away from the equator a one degree change in
latitude corresponds to a larger distance than a one degree change in
longitude.

The ratio of one degree longitude separation to one degree latitude
separation for the latitude at the middle of iowa of 41 degrees is


```{r}
longlat <- cos(41/90 * pi /2)
longlat
```

A better map is obtained using the aspect ration `1 / longlat`:

```{r}
m + coord_fixed(1 / longlat)
```

The best approach to use a coordinate system designed specifically for
maps.

```{r}
m + coord_map()
```

There are many projections used in map making; the default projection
used by `coord_map` is the
[Mercator](https://en.wikipedia.org/wiki/Mercator_projection)
projection.

Proper map projections are non-linear; this is easier to see with an
Albers projection:

```{r}
m + coord_map("albers", 20, 50)
```


## Scales

Scales are used for controlling the mapping of values to physical
representations such as colors, shapes, and positions.

Scale functions are also responsible for producing _guides_ for
translating physical representations back to values, such as

* axis labels and marks;

* color or shape legends.

There are `r length(ls("package:ggplot2", pat = "scale_"))` scale
functions; some examples are

```r
scale_color_gradient      scale_shape_manual     scale_x_log10
scale_color_identity	  scale_size_area		 scale_y_log10
scale_fill_gradient                              scale_x_sqrt
scale_fill_manual                                scale_y_sqrt

```

Start with a basic plot:
```{r}
p <- ggplot(mpg, aes(x = displ, y = hwy)) + geom_point()
p
```

Remove the tick marks and labels (this can also be done with theme settings):
```{r}
p + scale_x_continuous(labels = NULL, breaks = NULL)
```

Change the tick locations and labels:
```{r}
p + scale_x_continuous(labels = paste(c(2, 4, 6), "ltr"), breaks = c(2, 4, 6))
```

Use a logarithmic axis:
```{r}
p + scale_x_log10(labels = paste(c(2, 4, 6), "ltr"),
                  breaks = c(2, 4, 6),
                  minor_breaks = c(3, 5, 7))
```

The
[Scales](http://r4ds.had.co.nz/graphics-for-communication.html#scales)
section in [R for Data Science](http://r4ds.had.co.nz/) provides some
more details.

Color assignment can also be controlled by scale functions. For example,
for some presidential approval ratings data

```{r}
pr_appr <- data.frame(pres = c("Obama", "Carter", "Clinton",
                               "G.W. Bush", "Reagan", "G.H.W Bush", "Trump"),
                      appr = c(79, 78, 68, 65, 58, 56, 40),
                      party = c("D", "D", "D", "R", "R", "R", "R"),
                      year = c(2009, 1977, 1993, 2001, 1981, 1989, 2017))
pr_appr <- mutate(pr_appr, pres = reorder(pres, appr))
```

the common assignment of red for republican and blue for democrat can
be obtained by

```{r}
ggplot(pr_appr, aes(x = pres, y = appr, fill = party)) +
    geom_col() + coord_flip() +
    scale_fill_manual(values = c(R = "red", D = "blue")) 
```


## Themes

`ggplot2` supports the notion of _themes_ for adjusting non-data
appearance aspects of a plot, such as

* plot titles
* axis and legend placement and titles
* background colors
* guide line placement

Theme elements can be customized in several ways:

* `theme` can be used to adjust individual elements in a plot.
* `theme_set` adjusts default settings for a session;
* pre-defined theme functions allow consistent style changes.

The
[full documentation](http://ggplot2.tidyverse.org/reference/theme.html)
of the `theme` function lists many customizable elements.

One simple example:

```{r}
ggplot(mutate(mpg, cyl = factor(cyl))) +
    geom_point(aes(x = displ, y = hwy, fill = cyl),
               shape = 21, size = 3) +
    theme(legend.position = "top",
          axis.text = element_text(size = 12),
          axis.title = element_text(size = 14, face = "bold"))
```

Another example:

```{r}
gthm <- theme(plot.background = element_rect(fill = "lightblue", color = NA),
              panel.background = element_rect(fill = "lightblue2"))
p + gthm
```

Some alternate complete themes provided by `ggplot2` are

```r
theme_bw        theme_gray      theme_minimal   theme_void
theme_classic   theme_grey      theme_dark      theme_light
```

Some examples:

```{r}
p_bw <- p + theme_bw() + ggtitle("BW")
p_classic <- p + theme_classic() + ggtitle("Classic")
p_min <- p + theme_minimal() + ggtitle("Minimal")
p_void <- p + theme_void() + ggtitle("Void")
grid.arrange(p_bw, p_classic, p_min, p_void, nrow = 2)
```

The
[`ggthemes`](http://www.rpubs.com/Mentors_Ubiqum/ggthemes_1)
package provides some additional themes. Some examples:

```{r}
library(ggthemes)
p_econ <- p + theme_economist() + ggtitle("Economist")
p_wsj <- p + theme_wsj() + ggtitle("WSJ")
p_tufte <- p + theme_tufte() + ggtitle("Tufte")
p_few <- p + theme_few() + ggtitle("Few")
grid.arrange(p_econ, p_wsj, p_tufte, p_few, nrow = 2)
```	

`ggthemes` also provides `theme_map` that removes unnecessary elements
from maps:

```{r}
m + coord_map() + theme_map()
```

The
[Themes](http://r4ds.had.co.nz/graphics-for-communication.html#themes)
section in [R for Data Science](http://r4ds.had.co.nz/) provides some
more details.


## Facets

Faceting uses the _small multiples_ approach to introduce additional
variables.

For a single variable `facet_wrap` is usually used:

```{r}
p <- ggplot(mpg) + geom_point(aes(x = displ, y = hwy))
p + facet_wrap(~ class)
```

For two variables, each with a modest number of categories,
`facet_grid` can be effective:

```{r}
p + facet_grid(factor(cyl) ~ drv)
```

Usually facets use common axis scales, but one or both can be allowed
to vary.

A useful approach for showing time series data with a good aspect
ration can be to split the data into facets for non-overlapping
portions of the time axis.

```{r}
rd <- data.frame(month = seq_along(river),
                 flow = river,
                 panel = cut(seq_along(river), 4))
ggplot(rd, aes(x = month, y = flow)) +
    geom_point() +
    facet_wrap(~ panel, scale = "free_x", ncol = 1)
```

Facet arrangement can also be used to convey other information, such
as geographic location.

The [`geofacet` package](https://hafen.github.io/geofacet/) allows
facets to be placed in approximate locations of different geographic
regions.

An example for data from US states:

```{r, message = FALSE}
library(geofacet)
ggplot(state_unemp, aes(year, rate)) +
    geom_line() +
    facet_geo(~ state, grid = "us_state_grid2", label = "name") +
    scale_x_continuous(labels = function(x) paste0("'", substr(x, 3, 4))) +
    labs(title = "Seasonally Adjusted US Unemployment Rate 2000-2016",
       caption = "Data Source: bls.gov",
    x = "Year",
    y = "Unemployment Rate (%)") +
  theme(strip.text.x = element_text(size = 6))
```

Arrangement according to a calendar is also useful.


## A More Complete Template

```r
ggplot(data = <DATA>) +
    <GEOM>(mapping = aes(<MAPPINGS>),
           stat = <STAT>,
           position = <POSITION>) +
    < ... MORE GEOMS ... > +
    <COORDINATE_ADJUSTMENT> +
    <SCALE_ADJUSTMENT> +
    <FACETING> +
    <THEME_ADJUSTMENT>
```


## Interaction

The `ggplotly` function in the [`plotly` package](https://plot.ly/r/)
can be used to add some interactive features to a plot created with
`ggplot2`.

* In an R session a call to `ggplotly` opens a browser window with the
  interactive plot.
* In an Rmarkdown document the interactive plot is embedded in the
  `html` file.

```{r, message = FALSE}
library(plotly)
p <- ggplot(mutate(mpg, cyl = factor(cyl))) +
    geom_point(aes(x = displ, y = hwy, fill = cyl),
               shape = 21, size = 3)
ggplotly(p)
```

Adding a `text` aesthetic allows the tooltip display to be customized:

```{r, message = FALSE}
p <- ggplot(mutate(mpg, cyl = factor(cyl))) +
    geom_point(aes(x = displ, y = hwy, fill = cyl,
                   text = paste(year, manufacturer, model)),
               shape = 21, size = 3)
ggplotly(p, tooltip = "text") %>%
    style(hoverlabel = list(bgcolor = "white"))
```

## Notes

* There have been several efforts to develop a grammar of interactive
  graphics, including [`ggvis`](http://ggvis.rstudio.com/) and
  [`animint`](https://tdhock.github.io/animint/); neither seems to be
  under active development at this time.

* A promising approach is
  [Vega-Lite](https://vega.github.io/vega-lite), with a Python
  interface [Altair](https://altair-viz.github.io/) and an R interface
  [altair](https://vegawidget.github.io/altair/) to the Python
  interface.

* A recent project
  [`gganimate`](https://github.com/thomasp85/gganimate) to add
  animation to `ggplot` looks very promising.

* A number of other [`ggplot`
  extensions](https://exts.ggplot2.tidyverse.org/) are available.

* A [recent blog
  post](https://medium.com/bbc-visual-and-data-journalism/how-the-bbc-visual-and-data-journalism-team-works-with-graphics-in-r-ed0b35693535)
  explains how the [BBC Visual and Data
  Journalism](https://medium.com/bbc-visual-and-data-journalism) team
  creates their graphics. More details are provided in an [_R cook
  book_](https://bbc.github.io/rcookbook/).

* A [blog
  post](https://blog.revolutionanalytics.com/2016/07/data-journalism-with-r-at-538.html)
  on use of R and `ggplot` by
  [FiveThirtyEight](https://fivethirtyeight.com/).  The `ggthemes`
  packages includes `theme_fivethirtyeight` to emulate their style.

```{r, error = TRUE}
rub <- read.csv("rubber.csv")

library(altair)

chartTH <- alt$Chart(rub)$
    mark_point()$
    encode(
        x = alt$X("H:Q", scale = alt$Scale(domain = range(rub$H))),
        y = alt$Y("T:Q", scale = alt$Scale(domain = range(rub$T))))

brush <- alt$selection_interval()

chartTH_brush <- chartTH$add_selection(brush)

chartTH_selection <- 
  chartTH_brush$encode(
    color = alt$condition(brush, "Origin:N", alt$value("lightgray"))
  )

chartAT <- chartTH_selection$
    encode(
        x = alt$X("T:Q", scale = alt$Scale(domain = range(rub$T))),
        y = alt$Y("A:Q", scale = alt$Scale(domain = range(rub$A))))

chartAT | chartTH_selection
```


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