Advanced Data Visualization in R

# Advanced Data Visualization in R
## Epi Doctoral seminar
### Sara E. Moore
### 16 March 2017

---

# How to access these slides
## (and associated code)

### Via git:

```bash
git clone https://github.com/saraemoore/Rdataviz2017.git
```

### Download directly:
https://github.com/saraemoore/Rdataviz2017/archive/master.zip

### View online:
http://saraemoore.github.io/Rdataviz2017

???

* I won't be showing all the code on the slides today, but it's all available in the R Markdown document used to create these slides in this repository on github, or in the corresponding R script that's also in the github repository.

---

# Prerequisites

You have:
* a working knowledge of R,
* some familiarity with the usage of `ggplot2` (such as what was presented during the [2016 UC Berkeley SCF/D-Lab R Bootcamp](https://github.com/berkeley-scf/r-bootcamp-2016)),
* an interest in creating data visualizations in R, both **static** (mostly using *ggplot2*) and **interactive** (using a variety of packages).

---
class: inverse, middle, center

# `ggplot2` and the Grammar of Graphics

---

# Why `ggplot`?

* It's pretty.
* Its commands are intuitive and "human-readable."
* Nearly any graphic can be created, so you can use it for everything and maintain a consistent style.
* It has (sort of) built-in support for maps.

???

* From what I understand, a lot of you are probably already using ggplot or are interested in learning to use it. But, I'm still going to tell you why it's a good thing to use.
* Of course, it's pretty. Graphics made with ggplot are eye-catching.  This is actually pretty important -- it goes a long way when you want anyone to look at your graphs.
* The code you use to call ggplot and create a graphic is fairly intuitive.  This is because of the "grammar of graphics" that it adheres to, and we'll get back to that in a bit. This is what the "gg" in "ggplot" stands for -- "grammar of graphics" -- so the philosophy is pretty central to the package.
* If you learn how to use it well, you can make almost any visualization in it, and your reports, presentations, papers, and so on will look more cohesive because you stuck to a particular style throughout.
* Finally, with a little help from other R packages, ggplot is able to interface with geographical maps, if that's important to you.

---

# Why not `ggplot`?

* It's slow.
* It won't do some things.
* There's a steep learning curve.
* ~~`lattice` is better at trellis graphs?~~ [Faceting](https://learnr.wordpress.com/2009/08/26/ggplot2-version-of-figures-in-lattice-multivariate-data-visualization-with-r-final-part/) is just as powerful.

???

* So, what are the arguments against ggplot?
* It's slower than other R graphics systems. This is a fair point. However, it's probably not something you'll notice under everyday use.
* It also won't draw some graphics that you might want it to.
    * One example that's often brought up is 3D surface plots. But, do you really want to make a static 3D perspective plot? There are other, arguably better, ways to represent three-dimensional data in 2D, like contour plots and heatmaps. This is kind of the story with most things that ggplot supposedly "can't" do -- it's a principled decision by a designer to limit the use of his product -- artistic license.
    * For example, having two y-axes, each using a different scale for a different variable, was almost impossible to do in ggplot until recently -- which may have been for the best because having two y-axes can be misleading.
    * Moving x-axis labels to the top, rather than the bottom, is another thing that was difficult until recently.
    * ggplot also refuses to use more than six shapes -- citing difficultly in determining which is which -- unless you manually override this by specifying your own shapes.
* ggplot can be difficult to break into. If you force yourself to use it, it will become natural fairly quickly, though. You can also start with `qplot()` aka "quick plot," but I won't be going over that here.
* Some people argue that lattice is better at trellis graphs, which were made popular by Bill Cleveland's 1993 book "Visualizing Data." However, I disagree. I've included a link here to a compilation of a series of blog posts from 2009 in which nearly every graphic in the entire Lattice book by Deepayan Sarkar was recreated in ggplot. You can tweak these examples to make them look even more like lattice output, if you want, but the point is that faceting works just as well.

---

# Tidy data 1

1. Each variable forms a column.
2. Each observation forms a row.
3. Each type of observational unit forms a table.

![](assets/img/tidydata_tab1and2.png)

]

![](assets/img/tidydata_tab3.png)

]

???

* One stumbling block when getting started with ggplot is that your data needs to be in a certain format before you can use it in ggplot effectively.  One name for this format is "tidy data." It can also be called long or tall, as opposed to wide, data, but tidy data is a particular type of long data.  The general idea is that there should be one row per observation, whatever you're calling a single observation for your purposes, and one column per variable.  This means that you're typically going to want to collapse indicators into factors, for example, or you may need to think carefully about what your observational unit is.
* Here, the first two tables display data that is not tidy. The third table displays the same data, but made tidy.

---

# The (Layered) Grammar of Graphics 1

* Move away from using "names" and "chart typologies."
* Instead, use "statements" constructed via grammar
* Why?
    + An infinite number of unique graphics can be created.
    + The implementation is **DRY** ("don't repeat yourself") not **WET** ("write everything twice" or "we enjoy typing").

> "Good grammar is just the first step in creating a good sentence." &nbsp;2

.footnote[1: <a name=cite-Wilkinson_2005></a><a name=cite-Wickham_2009></a><a name=cite-Wickham_2010></a>[Wilkinson (2009)](http://dx.doi.org/10.1007/0-387-28695-0); [Wickham (2010)](http://dx.doi.org/10.1007/978-0-387-98141-3); [Wickham (2009)](http://dx.doi.org/10.1198/jcgs.2009.07098) 2: [Wickham (2010)](http://dx.doi.org/10.1198/jcgs.2009.07098)]

???

* As I mentioned previously, one of the best qualities of ggplot is that a series of commands used to generate a graphic are human-readable.  This is because ggplot adheres to the so-called "grammar of graphics," first laid out by Leland Wilkinson in 1999. The general idea is, instead of using a "name," "chart typology," or drawing from what Hadley Wickham calls a "big collection of special cases," think more abstractly and use a "statement" to describe a graphic -- and we need a grammar to construct statements. This infinitely expands the number of unique graphics that can be created and it adheres to the **DRY** ("don't repeat yourself") programming principle (as opposed to WET, "write everything twice" or "we enjoy typing")
* Hadley Wickham's quote from his 2010 paper on his take on the grammar of graphics is just pointing out that this is not a recipe for a perfect graphic -- you can still make some pretty poor visualizations with ggplot -- but it is the first step.

---

# Components of the Grammar

Specify a statistical graphic using components of statements:
+ **Data** (`data`),
+ **Stat**istical transformations (`stat_*`: identity, count, mean, etc.),
+ **Geom**etric elements/objects (`geom_*`: points, lines, etc.),
+ **Aes**thetic mappings (`aes` and `aes_*`: color, shape, size, transparency, etc.),
+ **Coord**inate systems (`coord_*`: cartesian, polar, map, etc.),
+ **Guide**s/Legends (`guide_*`, `guides`),
+ **Scale**s/Axes and transformations thereof (`scale_*`),
+ **Facet**ing/conditioning/latticing/trellising (`facet_*`),
+ Tweaking graphical positioning and visual elements (`position_*`, `annotation_*`, `theme`, `element_*`, etc.), and
+ Layering.

???

* The general concepts or classes, which are kind of a mash-up of Wilkinson's and Wickham's philosophies, are listed here. The realizations of these concepts in `ggplot` are in parentheses so we can connect the ideas to the R commands. Also, layering is not a statement itself, but is implied by the order of the other components.

---

# The anatomy of a `ggplot` call

* All arguments to the first function called, `ggplot`, set graph defaults.
* These defaults can be changed for an individual layer (even `data`).

```r
ggplot(data=, aes(x=, y=, ...)) +
	geom_????(...) +
	...
```

```r
ggplot() +
	geom_????(data=, aes(x=, y=, ...), ...) +
	...
```

???

* Some guides, including the ggplot book, start you off with the qplot or "quick plot" command. That's nice if you're coming from another graphics framework like base graphics in R. But, in the interest of time, because I'm assuming a little bit of familiarity with ggplot with this group, and because the ggplot command is more powerful than qplot, I'm going to skip right to using the ggplot command here.
* These are some simple examples of a couple of ways in which you could create a very simple plot using ggplot. Any aesthetics or data you provide to the ggplot command, which always gets called first in the "statement," set defaults for the entire graph. You can instead choose to leave the ggplot command empty -- without arguments -- if you'd like to specify individual data and aesthetics for each geometric object. You can even specify a defaults AND specify different settings for individual geometric elements. Note that it is best practice to not repeat yourself, so typically you'll set some defaults up front and only change later in the statement any individual elements that you want to change.

---

# When geoms transform

geom                | stat                |  notable default settings
------------------- | ------------------- | -------------------
`geom_boxplot()`    | `stat_boxplot()`    | max length of whiskers (beyond hinges) = 1.5*IQR
`geom_count()`      | `stat_sum()`        |
`geom_bar()`        | `stat_count()`      |
`geom_histogram()`  | `stat_bin()`        | 30 bins: binwidth = [range of x]/30
`geom_freqpoly()`   | `stat_bin()`        | 30 bins: binwidth = [range of x]/30
`geom_dotplot()`    |                     | 30 bins: binwidth = [range of x]/30; Wilkinson's "dot-density" binning method
`geom_bin2d()`      | `stat_bin_2d()`     | 30 bins for each of x and y
`geom_hex()`        | `stat_bin_hex()`    | 30 bins for each of x and y (calls `hexbin::hexbin()`)

???

* Some geometric objects are straightforward. Others involve statistical transformations.
* On this slide and the next two is a reference list of the geometric elements in ggplot that "silently" transform data by default. In other words, their default stat is not "identity." Also listed here are the defaults of those transformations. You can certainly always call a "stat" function that creates a "geom", but it's much more common in practice to just call a "geom" and add arguments specifying the "stat".
* The boxplot is a special case where many elements cannot be set by the user, but that's alright because they're what you'd expect them to be. The middle line is the median and the lower and upper hinges represent the lower and upper quartiles, respectively. The one thing you can specify is how far - at a maximum - the whiskers extend beyond the hinges, but the default (1.5 x inter-quartile range) is consistent with John Tukey's boxplot. You can also make a notched boxplot with geom_boxplot, and the notch locations are not specifiable, but I won't go into the settings for that here -- you can find it in the documentation if you're interested.

---

# When geoms transform

geom                | stat                |  notable default settings
------------------- | ------------------- | -------------------
`geom_density_2d()` | `stat_density_2d()` | bivariate Gaussian kernel; bandwidths (x and y) estimated by `MASS::bandwidth.nrd()` using Scott's "rule of thumb"; 100 grid points for x and y (calls `MASS::kde2d()`)
`geom_contour()`    | `stat_contour()`    | 10 `pretty()` breakpoints covering [range of z]
`geom_density()`    | `stat_density()`    | Gaussian kernel; bandwidth estimated by `stats::bw.nrd0()` using Silverman's "rule of thumb" (calls `stats::density()`)
`geom_violin()`     | `stat_ydensity()`   | Gaussian kernel; bandwidth estimated by `stats::bw.nrd0()` using Silverman's "rule of thumb" (calls `stats::density()`); all violins have same area before trimming tails, tails are trimmed to [range of y]

???

* For one and two dimensional kernel density estimates, by default, the bandwidth is chosen automatically via "rules of thumb," which I won't go into here, but the formulas are available in the R help via the functions I've listed here. For two dimensional density estimates, the kernel cannot be changed to anything other than Gaussian.
* Whenever you allow ggplot to transform your data, you should always know what it's doing -- here I'm careful to state the defaults and changed them just to demonstrate how it can be done.  This is one tricky part about using a "smart" graphics package -- you need to be sure to keep up with what it's doing to your data. There was a biostat job talk a couple of years ago that was really amazing for the most part -- really advanced theory and good discussion -- but it was completely derailed for a good 5 minutes by discussion over a single graphic -- a ggplot violin plot -- and which wasn't even demonstrating a main point of the talk.  As you can see, violin plots are a bit tricky. The big issues were that there were several geometric objects drawn on the graph, and a couple of them performed transformations on the data, but it wasn't made clear exactly what those transformations were.  It's easy to use some of these functions without thinking very hard about them, particularly if you leave them at their default settings, but you really need to be careful.

---

# When geoms transform

geom | stat | notable default settings
------------------- | ------------------- | -------------------
`geom_smooth()` | `stat_smooth()` | if `$n<1000$`, `stats::loess()` with polynomial degree 2, `$\alpha=0.75$`, etc.; else, `gam::gam()` with penalized cubic regression splines, etc.; 80 evaluation points 
`geom_quantile()` | `stat_quantile()` | 3 quartiles; modified Barrodale & Roberts quantile regression method (calls `quantreg::rq()`)

???

* For loess and gam I only listed the most interesting parameters -- there are more that you can set. Just know that the "et cetera" indicates that any defaults not listed here for these functions are not modified in ggplot's call.
* Note that if a geom function has a default statistical transformation and that stat function has the same geometric object as its default -- which is often the case -- you'll get the same plot no matter which you decide to use (the geom or the stat).

---

# Other `ggplot` transformations

These `stat_*` functions do not have assumed `geom_*` mappings. See their documentation for details of the transformations applied.

* `stat_ecdf`: Empirical Cumulative Density Function
* `stat_ellipse`: Plot data ellipses.
* `stat_function`: Superimpose a function.
* `stat_qq`: Calculation for quantile-quantile plot.
* `stat_spoke`: Convert angle and radius to xend and yend.
* `stat_summary`: Summarise y values at every unique x.
* `stat_summary_bin`: Summarise y values at unique/binned x.
* `stat_summary_hex`: Apply function for 2D hexagonal bins.
* `stat_summary_2d`: Apply function for 2D rectangular bins.
* `stat_unique`: Remove duplicates.
* `stat_identity`

???

* There are many more stat functions that aren't the default stat for any geometric object.  However, they can still be called directly and the majority of them perform transformations on your data. Check the documentation for details.

---

# Where to go for help with `ggplot2`

* [RStudio's ggplot2 cheat sheet](https://www.rstudio.com/wp-content/uploads/2016/11/ggplot2-cheatsheet-2.1.pdf) (updated for ggplot2 v2.1.0, Nov 2016)
* [Hadley Wickham's ggplot2 book (2009)](http://link.springer.com/book/10.1007%2F978-0-387-98141-3), the book's [github repo](https://github.com/hadley/ggplot2-book), or the [companion website](http://ggplot2.org/book/) to the book
* [official documentation](http://docs.ggplot2.org/current/)
* [google group](https://groups.google.com/forum/#!forum/ggplot2)
* [ggplot2 on stackoverflow](http://stackoverflow.com/tags/ggplot2/info)
* The [data visualisation](http://r4ds.had.co.nz/data-visualisation.html) and [graphics for communication](http://r4ds.had.co.nz/graphics-for-communication.html) chapters in Garrett Grolemund and Hadley Wickham's [R for data science](http://r4ds.had.co.nz/).
* Winston Chang's R Graphics Cookbook, [1st edition (2012)](http://shop.oreilly.com/product/0636920023135.do) or forthcoming 2nd edition (2017)

???

Here are a few resources I've found helpful when learning how to do certain tasks in ggplot.

---
class: inverse, middle, center

# `ggplot2` Examples

???

OK, enough setup -- let's run through an example of using ggplot.

---

# The data

* [WHO Global Health Observatory](www.who.int/gho/en/)
* Example:

```r
library(WHO)
codes <- get_codes()
who_data <- get_data("WHS9_CBR")
```

* Alternatively, use data that ships with `ggplot2`:
    + [diamonds](http://docs.ggplot2.org/current/diamonds.html),
    + [mpg](http://docs.ggplot2.org/current/mpg.html),
    + movies (now available as a separate R package, [ggplot2movies](https://CRAN.R-project.org/package=ggplot2movies)),
    + etc.

???

* For the majority of the examples in this presentation, I'm going to use data acquired via the WHO R package. This package just downloads data via the World Health Organization's (WHO) Global Health Observatory (GHO) public API.
* If you would rather not go to this trouble, ggplot2 ships with several good datasets that you can use instead -- a few of them are listed here. You can try your hand with one of those if you want some practice.

---

# A simple scatterplot

```r
library(ggplot2)
p = ggplot(data = subset(who_cbdr, !is.na(country)),
		aes(x = value.birthsper1000,
			y = value.deathsper1000,
			color = worldbankincomegroup)) +
	geom_point()
p
```

???

* This is a simple example to get us started. If you've used ggplot before, this plot will probably be pretty boring for you, but we'll start here and make things more complicated in a moment.
* Here we have DATA which is the result of merging two datasets downloaded from the World Health Organization's (WHO) Global Health Observatory (GHO).
* For 193 countries, we have both crude birth rate per 1,000 people and crude mortality rate per 1,000 people in a single year -- 2013.
* We also have the World Bank income group and global region for each of these countries.
* Each row in the data is an observation, as we''ve defined it for this problem -- a single country. Each column is a variable. This data is tidy, at least for how we choose to represent it now -- but there are other ways to structure it.

---

???

* The crude birth and death rates are ratios. The numerator is the number of live births (or deaths) observed in a population during a reference period and the denominator is the number of person-years lived by the population during the same period. It is expressed as events per 1,000 population.
* For the World Bank income grouping, income is measured using gross national income per capita. Economies are then divided into four income groupings: low, lower-middle, upper-middle, and high, as you can see here.
* There are no implicit or explicit transformations of the data going on, and the only aesthetics are the position of each point in two dimensional space, defined by x and y, and the color of the points, which represent the income group. The geometric objects here are points, which are displayed in ggplot by default as circles.
* All that said, this is not a finished product.  We can tell that there is a pattern in the data, but to me, this plot raises more questions than it answers.
* I'm not going to dwell too much on what a good versus bad plot is -- that's a whole separate topic that others can cover better than I can -- but I just wanted to point that out here as a segue to the next few graphics.

---

# Reorder a group aesthetic

```r
levels(who_cbdr$worldbankincomegroup)
```

```
## [1] "Global"              "High-income"         "Low-income"         
## [4] "Lower-middle-income" "Upper-middle-income"
```

```r
# reorder factor levels
who_cbdr$worldbankincomegroup = factor(
	who_cbdr$worldbankincomegroup,
	levels = levels(who_cbdr$worldbankincomegroup)[c(2, 5:3, 1)],
	labels = c("High", "Upper-middle", "Lower-middle", "Low", "Global"))

# recreate plot with modifed data
p = p %+% subset(who_cbdr, !is.na(country))
p
```

???

* One of the first things that sticks out at me in the previous plot is the ordering of the groups in the legend. The groups are in alphabetical order, which is the default for ggplot given no other information.
* Let's change the ordering to something more sensible: high, upper-middle, lower-middle, and then low.  There's an additional level in the data, Global, which represents summaries and isn't used in this scatterplot. We can put it last.
* Then we'll use this special addition operator provided by ggplot2 that allows us to switch out the original data for the plot with new data. Plot objects store the data, so even though we've changed the data frame in our R session, ggplot won't know about those changes unless we tell it to use the new data.

---

???

* As you can see, the legend is now ordered differently, and so the colors assigned to each group have changed, as well. Now the pattern in the data is a little easier to read, since, from left to right, the colors of the majority of the points generally go in the same order as they're displayed in the legend, from high income to low income.

---

# Add labels

```r
p = p +
	xlab("Crude birth rate") +
	ylab("Crude death rate") +
	labs(title = "Global population rates",
		 subtitle = "2013; per 1,000 population",
		 caption = "Source: UN World Population Prospects (https://esa.un.org/unpd/wpp/)") +
	theme(plot.title = element_text(hjust = 0.5),
		  plot.subtitle = element_text(hjust = 0.5),
		  plot.caption = element_text(size = 8))
p
```

* `ggtitle()` can instead be used to set the title and subtitle
* `scale_x_*()` and `scale_y_*()` can instead be used to set the x and y axis labels, respectively.
* As of ggplot2 v2.2.0, title(s) are left-aligned by default.

???

* Because it''s just good practice and is pretty critical to understanding a data visualization, let's add some labels to each axis and a title, subtitle, and data source caption to the graph.
* There are multiple ways to specify most labels in ggplot. This is just one option that seemed most efficient for this situation. Since we aren't changing other aspects of the x and y axis, I used `xlab()` and `ylab()` instead of the `scale_x_continuous()` and `scale_y_continuous()` functions. Likewise, because we're setting a caption here as well as a title and subtitle, I used `labs()` instead of `ggtitle()`, since `ggtitle()` can't set captions.
* Since newer versions of ggplot left-align titles, we're manually centering them via the `theme()` function.
* Also, subtitles and captions are new as of ggplot2 v2.2.0.

---

???

* OK, so now it's a lot clearer what's going on here, but the label on the legend sticks out like a sore thumb. Let's fix that.

---

# Improve legend and colors

```r
p = p +
	scale_colour_brewer("World Bank Income Group",
						palette = "Dark2") +
	theme(legend.position = "bottom")
p
```

* For discrete/qualitative color schemes: `scale_colour_hue()`, `scale_colour_discrete()`, `scale_colour_brewer()`, `scale_colour_grey()`, `scale_colour_manual()`, `scale_colour_identity()`
* For continuous (sequential or diverging) color schemes: `scale_colour_continuous()`, `scale_colour_distiller()`, `scale_colour_gradient()`, `scale_colour_gradient2()`, `scale_color_gradientn()`, `scale_colour_date()`, `scale_colour_datetime()`

???

* Because the aesthetic that we want to rename is color, we'll call some `scale_colour_*()` function to rename it.  Here, I'm using `scale_colour_brewer()` where the first argument is the new name. I've listed the other `scale_colour_*()` functions here. There are similar functions for each type of aesthetic, like shape, linetype, and so on.
* While we're at it, we can also tweak some visual elements. This isn't really required, but the default colors provided by ggplot aren't necessarily the best ones. For example, you might want to use colors that are more accessible for viewers who are color-blind.
* We can also move the legend around so it doesn't waste so much space where we could instead be displaying data.

---

???

* OK, so our legend is now a little less conspicuous down here at the bottom, it has a sensible title, and we've got a color scheme that's maybe a little easier to see and doesn't scream "ggplot defaults."

---

# R color palette packages

- [RColorBrewer](https://CRAN.R-project.org/package=RColorBrewer) ([palette browser](http://colorbrewer2.org)): custom color palettes with colorblind friendly, print-friendly, and photocopy-friendly options
- [munsell](https://github.com/cwickham/munsell): called by `scales` to generate continuous/gradient palettes in `ggplot2`
- [colorspace](https://CRAN.R-project.org/package=colorspace): color palettes based on the HCL (Hue-Chroma-Luminance) and HSV (Hue-Saturation-Value) systems
- [viridis](https://github.com/sjmgarnier/viridis): color schemes from Matplotlib (Python plotting library)
- [dichromat](https://CRAN.R-project.org/package=dichromat) ([palette browser](http://geography.uoregon.edu/datagraphics/color_scales.htm)): color palettes for color-impaired viewers
- [pals](https://github.com/kwstat/pals): color palettes and palette evaluation tools
- [ggsci](https://github.com/road2stat/ggsci/): color palettes inspired by scientific journals, dataviz libraries, scifi movies, TV shows
- [wesanderson](https://github.com/karthik/wesanderson): self-explanatory ([tumblr inspiration](http://wesandersonpalettes.tumblr.com/))
- [scales](http://cran.r-project.org/web/packages/scales/index.html): additional functions to deal with ggplot `scale`s, including color palettes
- `$\ldots$`

???

* If you'd like to explore some other color schemes, here are a few packages that can help you do that.
* The RColorBrewer package is actually what's driving the `scale_colour_brewer()` function that I used here.
* The munsell package was created by Hadley Wickham's sister and is what is used to create the default color schemes in ggplot.
* The dichromat package is pretty cool because it not only provides some color palettes, but it can also show you what any color palette would look like to a person with one of several types of color-blindness.

---

# Add a summary (or two)

```r
p = p +
	stat_ellipse(type = "t", level = 0.9,
				 segments = 80, alpha = 0.5) +
	geom_smooth(aes(color = NULL), method = "loess",
	 			span = 0.6, se = FALSE,
	 			color = "grey40", linetype = "longdash")
p
```

* An alternative way to map an aesthetic to only some layers: specify the mapping in each layer but not in the call to `ggplot`.

???

* OK, back to our scatterplot. Another common thing we might want to do with data we've plotted is to summarize it and display that summary over the raw data.
* Here, we're adding two summaries. The first is a 90% confidence ellipse, which is really just an ad hoc way of showing where we'd expect most of our data points to lie. The second is a locally weighted scatterplot smoother, or loess regression, with a span of 60%. I've also turned off the confidence band for the regression line because it doesn't seem to add much here and detracts from the rest of the plot.

---

???

* At the risk of making the plot a little "busy," these summaries seem to elucidate the relationship between these variables in this sample.
* The dashed line is our non-parametric regression fit and sheds some light on the pattern across groups.
* The ellipses give us an idea of what's going on within each group, on the whole. They've ordered themselves very nicely along the x-axis in the order of their income group.

---

# Tweak the theme and text

```r
old_theme = theme_set(theme_minimal(base_size = 14))
p
```

* Theme options: `theme_gray()`, `theme_bw()`, `theme_linedraw()`, `theme_light()`, `theme_dark()`, `theme_minimal()`, `theme_classic()`, `theme_void()`
* Alternatively, the call to `theme_minimal()` above could be "added" to the plot `p`. However, this would wipe out previous changes made to `p` via `theme()`.

???

* Now, we might like to change the theme to something a little cleaner. `theme_grey()` is the default ggplot2 theme, with its recognizable grey background, but not everyone loves it. Luckily, there are lots of built-in options to choose from.
* Here, we'll set the theme to something much cleaner since there's already a lot going on in our plot. We can also bump up the font size while we're at it to make the text more readable.
* Like everything in ggplot, there are multiple ways to accomplish this. Here, I'm using `theme_set()` because we've already modified parts of the theme (like the alignment of the title and subtitle and position of the legend). If we were to "add" `theme_minimal()` to our plot object, it would obliterate those theme changes and we'd have to redo them. However, `theme_set()` keeps them intact.

---

???

* And here we have a cleaner plot with text that's more legible.

---

# `ggplot2` theme packages

- [ggthemes](https://github.com/jrnold/ggthemes): extra `geom`s, `scale`s, and `theme`s for use with `ggplot2`
- [hrbrthemes](https://github.com/hrbrmstr/hrbrthemes): typography-centric themes for `ggplot2`
- [xkcd](https://CRAN.R-project.org/package=xkcd): `ggplot2` plot theme in the style of [XKCD comics](https://xkcd.com/)
- [ggplot2bdc](https://github.com/briandconnelly/ggplot2bdc): 'clean' and specialized themes + additional useful functions
- [ggthemr](https://github.com/cttobin/ggthemr): themes with predefined color palettes and options to modify other elements
- `$\ldots$`

???

* Here are a handful of packages that provide alternative ggplot2 themes.
* XKCD is more of a proof of concept than anything, I guess, but if you ever need it, it's there.

---

# Tweak the theme and text

```r
library(showtext)
sysfonts::font.add.google("Open Sans", "open_sans")
showtext::showtext.auto()
p = p + theme(text = element_text(family = "open_sans"))
p
```

* To use a font installed on the local system, call `font.add()` instead.
* Can instead set font via `base_family` argument of any `theme_*()` function.
* Turn off use of `showtext`: `showtext.auto(FALSE)`
* Alternatively, use `showtext.begin()` and `showtext.end()` to only turn on `showtext` as desired
* See [this blog post](http://statr.me/2014/07/showtext-with-knitr/) for details on using `showtext` with `knitr`

???

* At the risk of getting a little obsessive, let's try changing the font in our plot. This is something that may come up when you're preparing plots for publication, as some journals may want you to use Arial or some other specific font in everything you submit, including plots. You can accomplish this via other means, like Adobe Illustrator, but to me, this is much easier.
* Here, I'm using a newer package called `showtext` to apply the font to the plot and a package called `sysfonts` to grab the font I want from Google fonts.

---

???

* The difference here is subtle but the font has been switched out.
* You can also use fonts installed on your local system, and this package will convert them into shapes so that, if you save your plot as a pdf or something, the fonts don't have to be embedded to display properly on someone else's computer.
* The author of showtext recommends [`Cairo`](https://CRAN.R-project.org/package=Cairo) graphics devices for raster output due to sub-par antialiasing with default devices. I didn't have problems with the default graphics device, but your mileage may vary.

---

# Font packages

**Access system/Google/etc. fonts:**

- [showtext](https://github.com/yixuan/showtext): Easily use alternative fonts in R plots. Allows resulting (vector) graphics files to be font-independent.
- [sysfonts](https://github.com/yixuan/sysfonts): Companion package to [showtext](https://github.com/yixuan/showtext). Loads system and Google fonts.
- [extrafont](https://github.com/wch/extrafont): Use system TrueType fonts in R plots. Embeds fonts in resulting (vector) graphics files. A little more work to use but resulting files should retain text editability.
- [tikzDevice](https://github.com/yihui/tikzDevice): `$\LaTeX$`-friendly R graphics output

]

**Emoji/icons/custom images:**

- [emojifont](https://github.com/GuangchuangYu/emojifont)
- [emoGG](https://github.com/dill/emoGG)
- [ggimage](https://github.com/GuangchuangYu/ggimage)
- [ggflags](https://github.com/baptiste/ggflags)
- [rphylopic](https://github.com/sckott/rphylopic)

]

???

* The packages listed here on the left are for changing fonts on regular text and/or making sure that text renders the way you want it to in your output file. `extrafont` is kind of the original package in this arena, and it works great, but it requires a little more work to get it going, so I went with the simpler example for this presentation.
* On the right, you'll see some packages that allow you to use emoji and custom icons or images, mostly as plot markers rather than in place of text. For example, the ggflags package allows you to use country flags as markers in a scatterplot.

---

# Label observations of interest

```r
library(dplyr)
library(ggrepel)
p = p + geom_label_repel(data = subset(who_cbdr,
									   !is.na(country)) %>%
			group_by(worldbankincomegroup) %>%
			top_n(3, abs(scale(value.birthsper1000)) +
			  		 abs(scale(value.deathsper1000))),
		aes(label = country),
		show.legend = FALSE,
		size = 3.5,
		alpha = 0.65,
		box.padding = unit(0.6, "lines"),
    	point.padding = unit(0.4, "lines"),
    	segment.color = "grey50",
    	segment.alpha = 0.65)
p
```

* For a pure `ggplot2` solution, try `geom_label()` or `geom_text()` with `nudge_x` and/or `nudge_y` arguments

---

---

# Packages for labeling & orientation

- [ggrepel](https://github.com/slowkow/ggrepel): prevent plot labels from overlapping
- [directlabels](https://github.com/tdhock/directlabels) ([docs](http://directlabels.r-forge.r-project.org/)): directly label multicolor plots
- [ggstance](https://github.com/lionel-/ggstance): horizontal versions of common ggplots

???

---

# A heatmap with `ggplot2`

```r
wrap_labels = function(x, width = 15) {
	wrapped = strwrap(x, width = width, simplify = FALSE)
	lapply(wrapped, paste, collapse = "\n")
}

p1 = ggplot(who_yllmajorcause,
			aes(x = ghecauses, y = region, fill = med.pct)) +
	geom_tile() +
	scale_fill_distiller("Percent of total",
						 palette = "Blues",
						 direction = 1,
						 breaks = seq(0, 100, 25),
						 limits = c(0, 100),
						 guide = guide_colorbar(barwidth = 15,
						 						barheight = 1)) +
	scale_x_discrete("Cause Group", expand = c(0, 0),
					 labels = wrap_labels) +
	scale_y_discrete("Region", expand = c(0, 0),
					 labels = wrap_labels) +
	ggtitle("Median percent of years of life lost",
			subtitle = "in 2012 by cause and region") +
	theme_minimal(base_size = 16) +
	theme(legend.position = "bottom",
		  plot.title = element_text(hjust = 0.5),
		  plot.subtitle = element_text(hjust = 0.5))
p1
```

???

* Note that there is a package called `gplots` with a function called `heatmap.2`: this is commonly used to make heatmaps with dendrograms but is not the same as `ggplot`.
* expand argument on scale_x_discrete and scale_y_discrete ensures that there is no margin inside the plot - the tiles run all the way to the edge
* the labels argument accepts a mapping between new labels and old labels or a function

---

---

# Creating a dendrogram with `ggdendro`

```r
library(tidyr)
library(ggdendro)

who_yllmajorcause_mat = who_yllmajorcause %>%
						spread(region, med.pct) # long --> wide

rownames(who_yllmajorcause_mat) = who_yllmajorcause_mat[,"ghecauses"]
col_idx = which(colnames(who_yllmajorcause_mat)=="ghecauses")
who_yllmajorcause_mat = as.matrix(who_yllmajorcause_mat[, -col_idx])

yll_hc = hclust(dist(t(who_yllmajorcause_mat)), "average")
# plot the raw dendrogram
ggdendrogram(yll_hc, rotate = TRUE)
```

???

* transform the data frame from long to wide using tidyr::spread
* do a little housekeeping to get a matrix that the dist() function will like
* hierarchical clustering (hclust()) on the distances using average or UPGMA agglomeration method
* create a dendrogram and rotate it so that the branches are horizontal, not vertical (corresponding with rows of heatmap)

---

---

# Simplifying the dendrogram

```r
library(grid) # unit

yll_dendro = as.dendrogram(yll_hc)
yll_ddata = dendro_data(yll_dendro)

p2 = ggplot(segment(yll_ddata)) +
		geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) +
		coord_flip() +
		theme_dendro()
p2
```

---

---

# Heatmap, reordered

```r
# just in case there are extra factor levels, etc., un-factor
old_levels = levels(who_yllmajorcause$region)
who_yllmajorcause$region = as.character(who_yllmajorcause$region)

# re-factor with clustering/dendrogram ordering
new_order = order.dendrogram(yll_dendro)
who_yllmajorcause$region = factor(who_yllmajorcause$region,
								  old_levels[new_order])

# recreate heatmap with modifed data
p1 = p1 %+% who_yllmajorcause
p1
```

---

---

# Putting it all together

```r
library(gtable)

### dendrogram grob
# tweak these if the dendrogram doesn't line up:
dendro_margin = grid::unit(c(15, 0, -5, -10), "points")
p2_grob = ggplotGrob(p2 + theme(plot.margin = dendro_margin))

### heatmap grob
p1_grob = ggplotGrob(p1)

# add some space for the dendrogram
g1 = gtable_add_cols(p1_grob, grid::unit(4, "cm"))
# adjust top ("t") and bottom ("b") if no title/subtitle:
g = gtable_add_grob(g1,
					p2_grob,
	t = 5, b = 6,
	l = ncol(g1), r = ncol(g1))
grid.newpage()
grid.draw(g)
```

---

---

# Packages to pair with `ggplot2`

**Plot wrangling:**

- [grid](https://stat.ethz.ch/R-manual/R-devel/library/grid/html/00Index.html): now a 'base' package (installed with vanilla R). `grid` graphics are the foundation on which `ggplot2` and `lattice` build.
- [gridExtra](http://cran.r-project.org/web/packages/gridExtra/index.html): additional functions to tweak/manipulate grid graphics
- [scales](http://cran.r-project.org/web/packages/scales/index.html): additional functions to deal with `scale`s
- [gtable](http://cran.r-project.org/web/packages/gtable/index.html): use to dismantle/hack underlying table of Grid Graphical Objects (grobs) that make up a ggplot
- [ggsubplot](http://cran.r-project.org/web/packages/ggsubplot/index.html): embed smaller subplots within larger plots
- [cowplot](https://github.com/wilkelab/cowplot): arrange and label multiple plots on a grid, add overlays, etc.

]

**Data wrangling:**

- [dplyr](http://cran.r-project.org/web/packages/dplyr/index.html): manipulate data
- [tidyr](http://cran.r-project.org/web/packages/tidyr/index.html): restructure data (esp. wide `$\leftrightarrow$` long)
- [lubridate](http://cran.r-project.org/web/packages/lubridate/index.html): "makes working with dates fun instead of frustrating"
- and other [tidyverse](https://github.com/tidyverse/) packages

]

---

# Packages to pair with `ggplot2`
## Specialized Geoms/Stats

- [ggdendro](https://github.com/andrie/ggdendro), [dendextend](https://github.com/talgalili/dendextend): dendrograms and tree diagrams with `ggplot2`
- [ggtern](http://www.ggtern.com/): ternary diagrams (as in `vcd::ternaryplot`) and other additional *geom*s for `ggplot2`.
- [GGally](https://github.com/ggobi/ggally): scatterplot plot matrices (as in `graphics::pairs`), pairwise plot matrices, parallel coordinates plots, survival plots, network plots, etc. with `ggplot2`
- [ggHorizon](https://github.com/thomaskern/ggHorizon): horizon graphs with `ggplot2` ([example 1](http://www.perceptualedge.com/articles/visual_business_intelligence/time_on_the_horizon.pdf), [example 2](http://vis.berkeley.edu/papers/horizon/2009-TimeSeries-CHI.pdf))
- [ggmosaic](https://github.com/haleyjeppson/ggmosaic): Mosaic plots
- [survminer](https://github.com/kassambara/survminer): Survival curves
- [waffle](https://github.com/hrbrmstr/waffle): waffle charts, sometimes referred to as square pie charts
- [slopegraph](https://github.com/leeper/slopegraph): visualization created by Edward Tufte for plotting timeseries data.
- [ggradar](https://github.com/ricardo-bion/ggradar): radar charts
- [ggbio](http://bioconductor.org/packages/release/bioc/html/ggbio.html): `ggplot2` extensions for the visualization of genomic data

---

# Packages to pair with `ggplot2`
## Specialized Geoms/Stats

- [ggstraw](https://github.com/nacnudus/ggstraw): visualize the difference between two events related to one object
- [ggraph](https://github.com/thomasp85/ggraph): `geom`s, `facet`s, and layouts for networks, graphs, trees, etc.
- [geomnet](https://github.com/sctyner/geomnet): `geom`s and `stat`s for network visuaslization
- [ggtree](https://guangchuangyu.github.io/ggtree/): visualize and annotate phylogenetic trees
- [ggnetwork](https://github.com/briatte/ggnetwork) ([docs](https://briatte.github.io/ggnetwork/)): `geom`s for network plots
- [ggTimeSeries](https://github.com/Ather-Energy/ggTimeSeries): time series visualizations
- [ggseas](https://github.com/ellisp/ggseas): seasonal adjustment tools
- [plotROC](https://github.com/sachsmc/plotROC): ROC plots using ggplot2. Some interactive functionality.
- [classifierplots](https://github.com/ambiata/classifierplots): visualize classifier performance as grid of diagnostic plots
- [ggExtra](https://github.com/daattali/ggExtra): marginal histograms, etc.
- [ggpmisc](https://bitbucket.org/aphalo/ggpmisc): add equations and parameters from model fits as text or labels, label peaks, valleys, or observations in low density regions, etc.
- [WVPlots](https://github.com/WinVector/WVPlots): "pre-packaged" ggplots
- [ggforce](https://github.com/thomasp85/ggforce), [ggalt](https://github.com/hrbrmstr/ggalt): additional coordinate systems, `geom`s, etc.

---

# Packages to pair with `ggplot2`
## Maps

- [ggmap](https://github.com/dkahle/ggmap): allows visualization of spatial data and models on top of Google Maps, OpenStreetMaps, or Stamen Maps using ggplot2
- [maps](http://cran.r-project.org/web/packages/maps/index.html): maps with `ggplot2`
- [maptools](http://cran.r-project.org/web/packages/maptools/index.html)
- [sp](http://cran.r-project.org/web/packages/sp/index.html)
- [rgdal](http://cran.r-project.org/web/packages/rgdal/index.html)
- [rworldmap](https://github.com/AndySouth/rworldmap/)
- [RgoogleMaps](http://cran.r-project.org/web/packages/RgoogleMaps/index.html)
- [statebins](https://github.com/hrbrmstr/statebins)

## Et Cetera

#### [Gallery of ggplot2 extensions](http://www.ggplot2-exts.org/gallery/)

???

* The examples for ggsubplot are a little "busy" but it actually can be useful in practice.  For example, if you want to zoom in on a particular part of a plot and show it as an inset, or show an inset of what a particular graph would look like under conditions like the null, and so on.
* Note that the munsell color system is what is used by ggplot by default, but you may want to use the same color system to make your own palettes.

---
class: inverse, middle, center

# Interactive graphics in R

---

# Why interactive?

![](assets/img/economist_quickanddead.png)

]

- They're pretty, fun, and they engage audiences (see [Hans Rosling's TED talks](https://www.ted.com/speakers/hans_rosling)).
- They allow you to connect with, explore, and discover more about your data -- visually.
- Static graphics are "dead" (according to The Economist).

]

---

# Interactive and animated `ggplot2` graphics

- [ggiraph](https://github.com/davidgohel/ggiraph): make components of ggplot2 graphics **interactive** via additional aesthetics and geoms
- [gganimate](https://github.com/dgrtwo/gganimate): create animated ggplot2 plots
- [plotly](https://github.com/ropensci/plotly) ([docs](https://plot.ly/r/): Convert `ggplot2` graphics `$\rightarrow$` interactive graphics via `ggplotly()` OR create interactive graphics directly with `plot_ly()`, which function via the plot.ly [ggplot2 library](https://plot.ly/ggplot2/) and [R library](https://plot.ly/r/), respectively. Can operate entirely locally (meaning your plot doesn't have to be uploaded to plot.ly's servers and shared with the world).

---

# `ggplot2` + `ggiraph`

```r
p_basic = ggplot(data = subset(who_cbdr, !is.na(country)),
			 aes(x = value.birthsper1000,
				 y = value.deathsper1000,
				 color = worldbankincomegroup)) +
	scale_colour_brewer("World Bank Income Group",
				palette = "Dark2") +
	xlab("Crude birth rate") +
	ylab("Crude death rate") +
	labs(title = "Global population rates",
		 subtitle = "2013; per 1,000 population",
		 caption = "Source: UN World Population Prospects (https://esa.un.org/unpd/wpp/)") +
	theme_minimal(base_size = 14) +
	theme(plot.title = element_text(hjust = 0.5),
		  plot.subtitle = element_text(hjust = 0.5),
		  plot.caption = element_text(size = 8),
		  legend.position = "bottom")

p_basic +
	geom_point()
```

---

---

# `ggplot2` + `ggiraph`

```r
# devtools::install_github("davidgohel/ggiraph")
library(ggiraph)

p_ggiraph = p_basic +
	geom_point_interactive(
		aes(tooltip = paste(gsub("'", "&#39;", country),
							paste(value.birthsper1000,
								  value.deathsper1000,
						  		  sep = ", "),
							sep = ": ")))
```

* Use `geom_*_interactive()` in place of `geom_*()`
* Use `tooltip`, `onclick` and `data_id` aesthetics to specify interactivity
    * `tooltip`: displayed when mouse is over an element.
	* `onclick`: javascript function to be executed when an element is clicked, such as `paste0("alert(\"", data$x, "\")" )`
    * `data_id`: id to be associated with elements.
* `gsub()` is used to substitute the ASCII equivalent for any apostrophes

---

# `ggplot2` + `ggiraph`

```r
# in an R session, opens in browser
ggiraph(ggobj = p_ggiraph,
		width_svg = 8,
		height_svg = 6,
		tooltip_opacity = 0.7)
```

### <a href="ggiraph_scatter_plot.html" target="_blank">View visualization in new tab</a>

---

# `ggplot` + `plotly`

```r
library(plotly)

p_plotly = p_basic +
	geom_point(aes(text = country))

ggplotly(p_plotly)
```

* Use the `text` aesthetic to add additional information to the tooltips. `ggplot()` may complain about this but `ggplotly()` understands it.

???

* Start with the static ggplot
* Note that graphics are NO LONGER uploaded to plot.ly and are **public** by default.
* Also, the conversion does not always go as planned.  Sometimes the ggplotly function will reject the ggplot object with a cryptic error message. Sometimes it will convert it, but it will not look sensible. The tooltips are also not as helpful as they could be -- but there are apparently cumbersome ways to hack them. Your mileage may vary.

---

<div id="htmlwidget-2fd3abcb1bb969d2a5c8" style="width:100%;height:100%;" class="plotly html-widget"></div>
<script type="application/json" data-for="htmlwidget-2fd3abcb1bb969d2a5c8">{"x":{"data":[{"x":[8.9,16.5,13.2,9.5,15.3,15.2,12.7,11.7,15.5,11.3,13.9,9.5,11.5,11.1,11.3,35.4,10.9,11.2,12.3,8.5,9.7,14.5,15.4,20.3,9.2,8.4,20.6,11,11.3,11.6,9.2,6.8,10.7,13.8,12.4,20.9,10.8,8.7,11,9.6,11.8,13.8,8.8,19.4,9.9,10.7,10,10.4,12,10.4,14.5,14.5,12.2,13.2,14.5],"y":[8.4,6.8,6.4,9,6.1,2.2,6.4,9.9,3.6,7.2,5.4,11.1,5.5,10,9.6,11.5,11.3,9.4,8.7,10.6,9.7,6.2,5.9,5.1,9.5,9.5,1.9,14.2,12,6.8,7.5,8.7,8.3,6.3,8.1,2.8,10,9.1,1.3,5.5,14.8,8.5,10.2,3.2,4.5,9.4,9.1,8.2,9.6,7.7,9.6,1.1,8.9,8.4,9.1],"text":["Andorra value.birthsper1000: 8.9 value.deathsper1000: 8.4 worldbankincomegroup: High","Antigua and Barbuda value.birthsper1000: 16.5 value.deathsper1000: 6.8 worldbankincomegroup: High","Australia value.birthsper1000: 13.2 value.deathsper1000: 6.4 worldbankincomegroup: High","Austria value.birthsper1000: 9.5 value.deathsper1000: 9 worldbankincomegroup: High","Bahamas value.birthsper1000: 15.3 value.deathsper1000: 6.1 worldbankincomegroup: High","Bahrain value.birthsper1000: 15.2 value.deathsper1000: 2.2 worldbankincomegroup: High","Barbados value.birthsper1000: 12.7 value.deathsper1000: 6.4 worldbankincomegroup: High","Belgium value.birthsper1000: 11.7 value.deathsper1000: 9.9 worldbankincomegroup: High","Brunei Darussalam value.birthsper1000: 15.5 value.deathsper1000: 3.6 worldbankincomegroup: High","Canada value.birthsper1000: 11.3 value.deathsper1000: 7.2 worldbankincomegroup: High","Chile value.birthsper1000: 13.9 value.deathsper1000: 5.4 worldbankincomegroup: High","Croatia value.birthsper1000: 9.5 value.deathsper1000: 11.1 worldbankincomegroup: High","Cyprus value.birthsper1000: 11.5 value.deathsper1000: 5.5 worldbankincomegroup: High","Czechia value.birthsper1000: 11.1 value.deathsper1000: 10 worldbankincomegroup: High","Denmark value.birthsper1000: 11.3 value.deathsper1000: 9.6 worldbankincomegroup: High","Equatorial Guinea value.birthsper1000: 35.4 value.deathsper1000: 11.5 worldbankincomegroup: High","Estonia value.birthsper1000: 10.9 value.deathsper1000: 11.3 worldbankincomegroup: High","Finland value.birthsper1000: 11.2 value.deathsper1000: 9.4 worldbankincomegroup: High","France value.birthsper1000: 12.3 value.deathsper1000: 8.7 worldbankincomegroup: High","Germany value.birthsper1000: 8.5 value.deathsper1000: 10.6 worldbankincomegroup: High","Greece value.birthsper1000: 9.7 value.deathsper1000: 9.7 worldbankincomegroup: High","Iceland value.birthsper1000: 14.5 value.deathsper1000: 6.2 worldbankincomegroup: High","Ireland value.birthsper1000: 15.4 value.deathsper1000: 5.9 worldbankincomegroup: High","Israel value.birthsper1000: 20.3 value.deathsper1000: 5.1 worldbankincomegroup: High","Italy value.birthsper1000: 9.2 value.deathsper1000: 9.5 worldbankincomegroup: High","Japan value.birthsper1000: 8.4 value.deathsper1000: 9.5 worldbankincomegroup: High","Kuwait value.birthsper1000: 20.6 value.deathsper1000: 1.9 worldbankincomegroup: High","Latvia value.birthsper1000: 11 value.deathsper1000: 14.2 worldbankincomegroup: High","Lithuania value.birthsper1000: 11.3 value.deathsper1000: 12 worldbankincomegroup: High","Luxembourg value.birthsper1000: 11.6 value.deathsper1000: 6.8 worldbankincomegroup: High","Malta value.birthsper1000: 9.2 value.deathsper1000: 7.5 worldbankincomegroup: High","Monaco value.birthsper1000: 6.8 value.deathsper1000: 8.7 worldbankincomegroup: High","Netherlands value.birthsper1000: 10.7 value.deathsper1000: 8.3 worldbankincomegroup: High","New Zealand value.birthsper1000: 13.8 value.deathsper1000: 6.3 worldbankincomegroup: High","Norway value.birthsper1000: 12.4 value.deathsper1000: 8.1 worldbankincomegroup: High","Oman value.birthsper1000: 20.9 value.deathsper1000: 2.8 worldbankincomegroup: High","Poland value.birthsper1000: 10.8 value.deathsper1000: 10 worldbankincomegroup: High","Portugal value.birthsper1000: 8.7 value.deathsper1000: 9.1 worldbankincomegroup: High","Qatar value.birthsper1000: 11 value.deathsper1000: 1.3 worldbankincomegroup: High","Republic of Korea value.birthsper1000: 9.6 value.deathsper1000: 5.5 worldbankincomegroup: High","Russian Federation value.birthsper1000: 11.8 value.deathsper1000: 14.8 worldbankincomegroup: High","Saint Kitts and Nevis value.birthsper1000: 13.8 value.deathsper1000: 8.5 worldbankincomegroup: High","San Marino value.birthsper1000: 8.8 value.deathsper1000: 10.2 worldbankincomegroup: High","Saudi Arabia value.birthsper1000: 19.4 value.deathsper1000: 3.2 worldbankincomegroup: High","Singapore value.birthsper1000: 9.9 value.deathsper1000: 4.5 worldbankincomegroup: High","Slovakia value.birthsper1000: 10.7 value.deathsper1000: 9.4 worldbankincomegroup: High","Slovenia value.birthsper1000: 10 value.deathsper1000: 9.1 worldbankincomegroup: High","Spain value.birthsper1000: 10.4 value.deathsper1000: 8.2 worldbankincomegroup: High","Sweden value.birthsper1000: 12 value.deathsper1000: 9.6 worldbankincomegroup: High","Switzerland value.birthsper1000: 10.4 value.deathsper1000: 7.7 worldbankincomegroup: High","Trinidad and Tobago value.birthsper1000: 14.5 value.deathsper1000: 9.6 worldbankincomegroup: High","United Arab Emirates value.birthsper1000: 14.5 value.deathsper1000: 1.1 worldbankincomegroup: High","United Kingdom of Great Britain and Northern Ireland value.birthsper1000: 12.2 value.deathsper1000: 8.9 worldbankincomegroup: High","United States of America value.birthsper1000: 13.2 value.deathsper1000: 8.4 worldbankincomegroup: High","Uruguay value.birthsper1000: 14.5 value.deathsper1000: 9.1 worldbankincomegroup: High"],"key":null,"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(27,158,119,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(27,158,119,1)"}},"hoveron":"points","name":"High","legendgroup":"High","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text"},{"x":[12.9,24.3,44.1,16.8,17.8,11,23.4,9,23.6,14.9,9.6,13.3,18.8,14.9,15.1,9.5,15.6,20.8,20.8,20.4,31.9,19.3,9.9,18.8,31.1,18,27,20.5,13.4,20.7,17.7,22,27.2,11.5,18.4,11.6,26,26.4,10.9,19.4,19.7,10.3,15.4,16.3,9.7,16.6,20.9,17.7,10.2,10.7,25.5,17.2,16.8,21.3,23.6,19.8,null],"y":[9.4,5.7,13.9,7.6,6.3,13.5,4,9.4,7.2,6.1,14.2,7.2,4.2,5,4.4,8.5,7.2,5.9,5.3,6.9,9,6.9,13,5.1,4.8,7.1,3.7,9.7,4.4,4.2,5,3.5,6.6,7.4,5.3,9.5,5.7,3.5,5.9,5,4.4,11.8,7,6.5,11.9,6.7,11.1,4.6,7.6,9.4,6.7,5.5,5.7,8.8,8.7,4.8,5.9],"text":["Albania value.birthsper1000: 12.9 value.deathsper1000: 9.4 worldbankincomegroup: Upper-middle","Algeria value.birthsper1000: 24.3 value.deathsper1000: 5.7 worldbankincomegroup: Upper-middle","Angola value.birthsper1000: 44.1 value.deathsper1000: 13.9 worldbankincomegroup: Upper-middle","Argentina value.birthsper1000: 16.8 value.deathsper1000: 7.6 worldbankincomegroup: Upper-middle","Azerbaijan value.birthsper1000: 17.8 value.deathsper1000: 6.3 worldbankincomegroup: Upper-middle","Belarus value.birthsper1000: 11 value.deathsper1000: 13.5 worldbankincomegroup: Upper-middle","Belize value.birthsper1000: 23.4 value.deathsper1000: 4 worldbankincomegroup: Upper-middle","Bosnia and Herzegovina value.birthsper1000: 9 value.deathsper1000: 9.4 worldbankincomegroup: Upper-middle","Botswana value.birthsper1000: 23.6 value.deathsper1000: 7.2 worldbankincomegroup: Upper-middle","Brazil value.birthsper1000: 14.9 value.deathsper1000: 6.1 worldbankincomegroup: Upper-middle","Bulgaria value.birthsper1000: 9.6 value.deathsper1000: 14.2 worldbankincomegroup: Upper-middle","China value.birthsper1000: 13.3 value.deathsper1000: 7.2 worldbankincomegroup: Upper-middle","Colombia value.birthsper1000: 18.8 value.deathsper1000: 4.2 worldbankincomegroup: Upper-middle","Cook Islands value.birthsper1000: 14.9 value.deathsper1000: 5 worldbankincomegroup: Upper-middle","Costa Rica value.birthsper1000: 15.1 value.deathsper1000: 4.4 worldbankincomegroup: Upper-middle","Cuba value.birthsper1000: 9.5 value.deathsper1000: 8.5 worldbankincomegroup: Upper-middle","Dominica value.birthsper1000: 15.6 value.deathsper1000: 7.2 worldbankincomegroup: Upper-middle","Dominican Republic value.birthsper1000: 20.8 value.deathsper1000: 5.9 worldbankincomegroup: Upper-middle","Ecuador value.birthsper1000: 20.8 value.deathsper1000: 5.3 worldbankincomegroup: Upper-middle","Fiji value.birthsper1000: 20.4 value.deathsper1000: 6.9 worldbankincomegroup: Upper-middle","Gabon value.birthsper1000: 31.9 value.deathsper1000: 9 worldbankincomegroup: Upper-middle","Grenada value.birthsper1000: 19.3 value.deathsper1000: 6.9 worldbankincomegroup: Upper-middle","Hungary value.birthsper1000: 9.9 value.deathsper1000: 13 worldbankincomegroup: Upper-middle","Iran (Islamic Republic of) value.birthsper1000: 18.8 value.deathsper1000: 5.1 worldbankincomegroup: Upper-middle","Iraq value.birthsper1000: 31.1 value.deathsper1000: 4.8 worldbankincomegroup: Upper-middle","Jamaica value.birthsper1000: 18 value.deathsper1000: 7.1 worldbankincomegroup: Upper-middle","Jordan value.birthsper1000: 27 value.deathsper1000: 3.7 worldbankincomegroup: Upper-middle","Kazakhstan value.birthsper1000: 20.5 value.deathsper1000: 9.7 worldbankincomegroup: Upper-middle","Lebanon value.birthsper1000: 13.4 value.deathsper1000: 4.4 worldbankincomegroup: Upper-middle","Libya value.birthsper1000: 20.7 value.deathsper1000: 4.2 worldbankincomegroup: Upper-middle","Malaysia value.birthsper1000: 17.7 value.deathsper1000: 5 worldbankincomegroup: Upper-middle","Maldives value.birthsper1000: 22 value.deathsper1000: 3.5 worldbankincomegroup: Upper-middle","Marshall Islands value.birthsper1000: 27.2 value.deathsper1000: 6.6 worldbankincomegroup: Upper-middle","Mauritius value.birthsper1000: 11.5 value.deathsper1000: 7.4 worldbankincomegroup: Upper-middle","Mexico value.birthsper1000: 18.4 value.deathsper1000: 5.3 worldbankincomegroup: Upper-middle","Montenegro value.birthsper1000: 11.6 value.deathsper1000: 9.5 worldbankincomegroup: Upper-middle","Namibia value.birthsper1000: 26 value.deathsper1000: 5.7 worldbankincomegroup: Upper-middle","Nauru value.birthsper1000: 26.4 value.deathsper1000: 3.5 worldbankincomegroup: Upper-middle","Palau value.birthsper1000: 10.9 value.deathsper1000: 5.9 worldbankincomegroup: Upper-middle","Panama value.birthsper1000: 19.4 value.deathsper1000: 5 worldbankincomegroup: Upper-middle","Peru value.birthsper1000: 19.7 value.deathsper1000: 4.4 worldbankincomegroup: Upper-middle","Romania value.birthsper1000: 10.3 value.deathsper1000: 11.8 worldbankincomegroup: Upper-middle","Saint Lucia value.birthsper1000: 15.4 value.deathsper1000: 7 worldbankincomegroup: Upper-middle","Saint Vincent and the Grenadines value.birthsper1000: 16.3 value.deathsper1000: 6.5 worldbankincomegroup: Upper-middle","Serbia value.birthsper1000: 9.7 value.deathsper1000: 11.9 worldbankincomegroup: Upper-middle","Seychelles value.birthsper1000: 16.6 value.deathsper1000: 6.7 worldbankincomegroup: Upper-middle","South Africa value.birthsper1000: 20.9 value.deathsper1000: 11.1 worldbankincomegroup: Upper-middle","Suriname value.birthsper1000: 17.7 value.deathsper1000: 4.6 worldbankincomegroup: Upper-middle","Thailand value.birthsper1000: 10.2 value.deathsper1000: 7.6 worldbankincomegroup: Upper-middle","The former Yugoslav republic of Macedonia value.birthsper1000: 10.7 value.deathsper1000: 9.4 worldbankincomegroup: Upper-middle","Tonga value.birthsper1000: 25.5 value.deathsper1000: 6.7 worldbankincomegroup: Upper-middle","Tunisia value.birthsper1000: 17.2 value.deathsper1000: 5.5 worldbankincomegroup: Upper-middle","Turkey value.birthsper1000: 16.8 value.deathsper1000: 5.7 worldbankincomegroup: Upper-middle","Turkmenistan value.birthsper1000: 21.3 value.deathsper1000: 8.8 worldbankincomegroup: Upper-middle","Tuvalu value.birthsper1000: 23.6 value.deathsper1000: 8.7 worldbankincomegroup: Upper-middle","Venezuela (Bolivarian Republic of) value.birthsper1000: 19.8 value.deathsper1000: 4.8 worldbankincomegroup: Upper-middle","Niue value.birthsper1000: NA value.deathsper1000: 5.9 worldbankincomegroup: Upper-middle"],"key":null,"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(217,95,2,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(217,95,2,1)"}},"hoveron":"points","name":"Upper-middle","legendgroup":"Upper-middle","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text"},{"x":[13.7,19.6,25.7,20.1,37.3,37.6,36.6,27.5,23.2,20.1,13.3,30.9,31,20.3,25.8,20.4,18.8,23.3,27.1,26.8,27.5,34.1,23.5,22.7,22.7,22.7,41.2,25.2,28.9,23.7,24.4,12.1,26.2,33.9,37.7,30.8,36.1,17.9,33.5,29.9,24,35.8,10.8,21.5,26.6,15.5,31.1,42.8],"y":[12.6,6.5,7,5.2,10.8,10.1,12.6,8.8,6.4,6.6,11.5,8.1,5.2,7.9,4.5,7.9,6.2,8.2,6.6,6.7,14.1,7.8,6.1,6.7,6.4,4.8,11.9,7.3,7.7,4.8,5.9,12.4,5.2,6.5,7,5.4,11.1,6.6,7.8,11.8,3.5,6,15.1,6.4,4.5,5.8,6.7,9.7],"text":["Armenia value.birthsper1000: 13.7 value.deathsper1000: 12.6 worldbankincomegroup: Lower-middle","Bhutan value.birthsper1000: 19.6 value.deathsper1000: 6.5 worldbankincomegroup: Lower-middle","Bolivia (Plurinational State of) value.birthsper1000: 25.7 value.deathsper1000: 7 worldbankincomegroup: Lower-middle","Cabo Verde value.birthsper1000: 20.1 value.deathsper1000: 5.2 worldbankincomegroup: Lower-middle","Cameroon value.birthsper1000: 37.3 value.deathsper1000: 10.8 worldbankincomegroup: Lower-middle","Congo value.birthsper1000: 37.6 value.deathsper1000: 10.1 worldbankincomegroup: Lower-middle","Côte d'Ivoire value.birthsper1000: 36.6 value.deathsper1000: 12.6 worldbankincomegroup: Lower-middle","Djibouti value.birthsper1000: 27.5 value.deathsper1000: 8.8 worldbankincomegroup: Lower-middle","Egypt value.birthsper1000: 23.2 value.deathsper1000: 6.4 worldbankincomegroup: Lower-middle","El Salvador value.birthsper1000: 20.1 value.deathsper1000: 6.6 worldbankincomegroup: Lower-middle","Georgia value.birthsper1000: 13.3 value.deathsper1000: 11.5 worldbankincomegroup: Lower-middle","Ghana value.birthsper1000: 30.9 value.deathsper1000: 8.1 worldbankincomegroup: Lower-middle","Guatemala value.birthsper1000: 31 value.deathsper1000: 5.2 worldbankincomegroup: Lower-middle","Guyana value.birthsper1000: 20.3 value.deathsper1000: 7.9 worldbankincomegroup: Lower-middle","Honduras value.birthsper1000: 25.8 value.deathsper1000: 4.5 worldbankincomegroup: Lower-middle","India value.birthsper1000: 20.4 value.deathsper1000: 7.9 worldbankincomegroup: Lower-middle","Indonesia value.birthsper1000: 18.8 value.deathsper1000: 6.2 worldbankincomegroup: Lower-middle","Kiribati value.birthsper1000: 23.3 value.deathsper1000: 8.2 worldbankincomegroup: Lower-middle","Kyrgyzstan value.birthsper1000: 27.1 value.deathsper1000: 6.6 worldbankincomegroup: Lower-middle","Lao People's Democratic Republic value.birthsper1000: 26.8 value.deathsper1000: 6.7 worldbankincomegroup: Lower-middle","Lesotho value.birthsper1000: 27.5 value.deathsper1000: 14.1 worldbankincomegroup: Lower-middle","Mauritania value.birthsper1000: 34.1 value.deathsper1000: 7.8 worldbankincomegroup: Lower-middle","Micronesia (Federated States of) value.birthsper1000: 23.5 value.deathsper1000: 6.1 worldbankincomegroup: Lower-middle","Mongolia value.birthsper1000: 22.7 value.deathsper1000: 6.7 worldbankincomegroup: Lower-middle","Morocco value.birthsper1000: 22.7 value.deathsper1000: 6.4 worldbankincomegroup: Lower-middle","Nicaragua value.birthsper1000: 22.7 value.deathsper1000: 4.8 worldbankincomegroup: Lower-middle","Nigeria value.birthsper1000: 41.2 value.deathsper1000: 11.9 worldbankincomegroup: Lower-middle","Pakistan value.birthsper1000: 25.2 value.deathsper1000: 7.3 worldbankincomegroup: Lower-middle","Papua New Guinea value.birthsper1000: 28.9 value.deathsper1000: 7.7 worldbankincomegroup: Lower-middle","Paraguay value.birthsper1000: 23.7 value.deathsper1000: 4.8 worldbankincomegroup: Lower-middle","Philippines value.birthsper1000: 24.4 value.deathsper1000: 5.9 worldbankincomegroup: Lower-middle","Republic of Moldova value.birthsper1000: 12.1 value.deathsper1000: 12.4 worldbankincomegroup: Lower-middle","Samoa value.birthsper1000: 26.2 value.deathsper1000: 5.2 worldbankincomegroup: Lower-middle","Sao Tome and Principe value.birthsper1000: 33.9 value.deathsper1000: 6.5 worldbankincomegroup: Lower-middle","Senegal value.birthsper1000: 37.7 value.deathsper1000: 7 worldbankincomegroup: Lower-middle","Solomon Islands value.birthsper1000: 30.8 value.deathsper1000: 5.4 worldbankincomegroup: Lower-middle","South Sudan value.birthsper1000: 36.1 value.deathsper1000: 11.1 worldbankincomegroup: Lower-middle","Sri Lanka value.birthsper1000: 17.9 value.deathsper1000: 6.6 worldbankincomegroup: Lower-middle","Sudan value.birthsper1000: 33.5 value.deathsper1000: 7.8 worldbankincomegroup: Lower-middle","Swaziland value.birthsper1000: 29.9 value.deathsper1000: 11.8 worldbankincomegroup: Lower-middle","Syrian Arab Republic value.birthsper1000: 24 value.deathsper1000: 3.5 worldbankincomegroup: Lower-middle","Timor-Leste value.birthsper1000: 35.8 value.deathsper1000: 6 worldbankincomegroup: Lower-middle","Ukraine value.birthsper1000: 10.8 value.deathsper1000: 15.1 worldbankincomegroup: Lower-middle","Uzbekistan value.birthsper1000: 21.5 value.deathsper1000: 6.4 worldbankincomegroup: Lower-middle","Vanuatu value.birthsper1000: 26.6 value.deathsper1000: 4.5 worldbankincomegroup: Lower-middle","Viet Nam value.birthsper1000: 15.5 value.deathsper1000: 5.8 worldbankincomegroup: Lower-middle","Yemen value.birthsper1000: 31.1 value.deathsper1000: 6.7 worldbankincomegroup: Lower-middle","Zambia value.birthsper1000: 42.8 value.deathsper1000: 9.7 worldbankincomegroup: Lower-middle"],"key":null,"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(117,112,179,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(117,112,179,1)"}},"hoveron":"points","name":"Lower-middle","legendgroup":"Lower-middle","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text"},{"x":[34.1,20,36.4,40.9,44.7,25.7,34.2,45.9,35.2,14.4,42.7,36.7,33,42.7,36.9,37.5,25.6,34.9,35.5,34.7,39.8,47.1,38.9,17.2,21,49.7,35.2,36.6,43.8,33,36.4,43.2,39.2,31.3],"y":[7.7,5.7,9.6,9.5,11.1,5.7,14,13.6,8.2,9.2,13.7,6.3,7.2,8.2,10.1,12.3,8.6,8.3,8,7,9,11.2,11.8,8.4,6.6,10.2,6.4,16.8,12.7,6,9.6,9.2,7.8,9.8],"text":["Afghanistan value.birthsper1000: 34.1 value.deathsper1000: 7.7 worldbankincomegroup: Low","Bangladesh value.birthsper1000: 20 value.deathsper1000: 5.7 worldbankincomegroup: Low","Benin value.birthsper1000: 36.4 value.deathsper1000: 9.6 worldbankincomegroup: Low","Burkina Faso value.birthsper1000: 40.9 value.deathsper1000: 9.5 worldbankincomegroup: Low","Burundi value.birthsper1000: 44.7 value.deathsper1000: 11.1 worldbankincomegroup: Low","Cambodia value.birthsper1000: 25.7 value.deathsper1000: 5.7 worldbankincomegroup: Low","Central African Republic value.birthsper1000: 34.2 value.deathsper1000: 14 worldbankincomegroup: Low","Chad value.birthsper1000: 45.9 value.deathsper1000: 13.6 worldbankincomegroup: Low","Comoros value.birthsper1000: 35.2 value.deathsper1000: 8.2 worldbankincomegroup: Low","Democratic People's Republic of Korea value.birthsper1000: 14.4 value.deathsper1000: 9.2 worldbankincomegroup: Low","Democratic Republic of the Congo value.birthsper1000: 42.7 value.deathsper1000: 13.7 worldbankincomegroup: Low","Eritrea value.birthsper1000: 36.7 value.deathsper1000: 6.3 worldbankincomegroup: Low","Ethiopia value.birthsper1000: 33 value.deathsper1000: 7.2 worldbankincomegroup: Low","Gambia value.birthsper1000: 42.7 value.deathsper1000: 8.2 worldbankincomegroup: Low","Guinea value.birthsper1000: 36.9 value.deathsper1000: 10.1 worldbankincomegroup: Low","Guinea-Bissau value.birthsper1000: 37.5 value.deathsper1000: 12.3 worldbankincomegroup: Low","Haiti value.birthsper1000: 25.6 value.deathsper1000: 8.6 worldbankincomegroup: Low","Kenya value.birthsper1000: 34.9 value.deathsper1000: 8.3 worldbankincomegroup: Low","Liberia value.birthsper1000: 35.5 value.deathsper1000: 8 worldbankincomegroup: Low","Madagascar value.birthsper1000: 34.7 value.deathsper1000: 7 worldbankincomegroup: Low","Malawi value.birthsper1000: 39.8 value.deathsper1000: 9 worldbankincomegroup: Low","Mali value.birthsper1000: 47.1 value.deathsper1000: 11.2 worldbankincomegroup: Low","Mozambique value.birthsper1000: 38.9 value.deathsper1000: 11.8 worldbankincomegroup: Low","Myanmar value.birthsper1000: 17.2 value.deathsper1000: 8.4 worldbankincomegroup: Low","Nepal value.birthsper1000: 21 value.deathsper1000: 6.6 worldbankincomegroup: Low","Niger value.birthsper1000: 49.7 value.deathsper1000: 10.2 worldbankincomegroup: Low","Rwanda value.birthsper1000: 35.2 value.deathsper1000: 6.4 worldbankincomegroup: Low","Sierra Leone value.birthsper1000: 36.6 value.deathsper1000: 16.8 worldbankincomegroup: Low","Somalia value.birthsper1000: 43.8 value.deathsper1000: 12.7 worldbankincomegroup: Low","Tajikistan value.birthsper1000: 33 value.deathsper1000: 6 worldbankincomegroup: Low","Togo value.birthsper1000: 36.4 value.deathsper1000: 9.6 worldbankincomegroup: Low","Uganda value.birthsper1000: 43.2 value.deathsper1000: 9.2 worldbankincomegroup: Low","United Republic of Tanzania value.birthsper1000: 39.2 value.deathsper1000: 7.8 worldbankincomegroup: Low","Zimbabwe value.birthsper1000: 31.3 value.deathsper1000: 9.8 worldbankincomegroup: Low"],"key":null,"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(231,41,138,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(231,41,138,1)"}},"hoveron":"points","name":"Low","legendgroup":"Low","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text"}],"layout":{"margin":{"t":50.7142659471426,"r":9.29846409298464,"b":50.5216549052165,"l":47.4221668742217},"font":{"color":"rgba(0,0,0,1)","family":"","size":18.5969281859693},"title":"Global population rates","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":22.3163138231631},"xaxis":{"domain":[0,1],"type":"linear","autorange":false,"tickmode":"array","range":[4.655,51.845],"ticktext":["10","20","30","40","50"],"tickvals":[10,20,30,40,50],"ticks":"","tickcolor":null,"ticklen":4.64923204649232,"tickwidth":0,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":14.8775425487754},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(235,235,235,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"y","title":"Crude birth rate","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":18.5969281859693},"hoverformat":".2f"},"yaxis":{"domain":[0,1],"type":"linear","autorange":false,"tickmode":"array","range":[0.315,17.585],"ticktext":["5","10","15"],"tickvals":[5,10,15],"ticks":"","tickcolor":null,"ticklen":4.64923204649232,"tickwidth":0,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":14.8775425487754},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(235,235,235,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"x","title":"Crude death rate","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":18.5969281859693},"hoverformat":".2f"},"shapes":[{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":1,"y0":0,"y1":1}],"showlegend":true,"legend":{"bgcolor":null,"bordercolor":null,"borderwidth":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.8775425487754},"y":0.931102362204724},"annotations":[{"text":"World Bank Income Group","x":1.02,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":18.5969281859693},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"left","yanchor":"bottom","legendTitle":true}],"hovermode":"closest"},"source":"A","config":{"modeBarButtonsToAdd":[{"name":"Collaborate","icon":{"width":1000,"ascent":500,"descent":-50,"path":"M487 375c7-10 9-23 5-36l-79-259c-3-12-11-23-22-31-11-8-22-12-35-12l-263 0c-15 0-29 5-43 15-13 10-23 23-28 37-5 13-5 25-1 37 0 0 0 3 1 7 1 5 1 8 1 11 0 2 0 4-1 6 0 3-1 5-1 6 1 2 2 4 3 6 1 2 2 4 4 6 2 3 4 5 5 7 5 7 9 16 13 26 4 10 7 19 9 26 0 2 0 5 0 9-1 4-1 6 0 8 0 2 2 5 4 8 3 3 5 5 5 7 4 6 8 15 12 26 4 11 7 19 7 26 1 1 0 4 0 9-1 4-1 7 0 8 1 2 3 5 6 8 4 4 6 6 6 7 4 5 8 13 13 24 4 11 7 20 7 28 1 1 0 4 0 7-1 3-1 6-1 7 0 2 1 4 3 6 1 1 3 4 5 6 2 3 3 5 5 6 1 2 3 5 4 9 2 3 3 7 5 10 1 3 2 6 4 10 2 4 4 7 6 9 2 3 4 5 7 7 3 2 7 3 11 3 3 0 8 0 13-1l0-1c7 2 12 2 14 2l218 0c14 0 25-5 32-16 8-10 10-23 6-37l-79-259c-7-22-13-37-20-43-7-7-19-10-37-10l-248 0c-5 0-9-2-11-5-2-3-2-7 0-12 4-13 18-20 41-20l264 0c5 0 10 2 16 5 5 3 8 6 10 11l85 282c2 5 2 10 2 17 7-3 13-7 17-13z m-304 0c-1-3-1-5 0-7 1-1 3-2 6-2l174 0c2 0 4 1 7 2 2 2 4 4 5 7l6 18c0 3 0 5-1 7-1 1-3 2-6 2l-173 0c-3 0-5-1-8-2-2-2-4-4-4-7z m-24-73c-1-3-1-5 0-7 2-2 3-2 6-2l174 0c2 0 5 0 7 2 3 2 4 4 5 7l6 18c1 2 0 5-1 6-1 2-3 3-5 3l-174 0c-3 0-5-1-7-3-3-1-4-4-5-6z"},"click":"function(gd) { \n // is this being viewed in RStudio?\n if (location.search == '?viewer_pane=1') {\n alert('To learn about plotly for collaboration, visit:\\n https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html');\n } else {\n window.open('https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html', '_blank');\n }\n }"}],"modeBarButtonsToRemove":["sendDataToCloud"]},"base_url":"https://plot.ly"},"evals":["config.modeBarButtonsToAdd.0.click"],"jsHooks":[]}</script>

---
class: inverse, middle, center

# `htmlwidgets`: Interactive data visualization framework in R

---

# `htmlwidgets` for interactive graphics in R

- `htmlwidgets` provides the framework on which most of the interactive plotting packages in R rely (if they use JavaScript plotting libraries on the backend).
- [package source](https://github.com/ramnathv/htmlwidgets), [docs](http://www.htmlwidgets.org/), [gallery](http://gallery.htmlwidgets.org/)
- Advanced usage may require knowledge/use of JavaScript.

---

# `DT`

```r
library(DT)
dont_show_cols = which(grepl("^gho|^publish",
							 colnames(who_cbdr)))
datatable(who_cbdr[,-dont_show_cols],
		  caption = "Global population rates in 2013 per 1,000 population",
		  rownames = FALSE,
		  colnames = c("World Bank Income Group" = 2,
		  			   "Crude birth rate" = 5,
		  			   "Crude death rate" = 6),
		  options = list(pageLength = 5,
		  				 lengthMenu = c(5, 10, 15)))
```

* Uses the [DataTables](https://datatables.net/) JavaScript library

---

<div id="htmlwidget-8d87ca057abbb17adc70" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-8d87ca057abbb17adc70">{"x":{"filter":"none","caption":"<caption>Global population rates in 2013 per 1,000 population<\/caption>","data":[[2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013],["Global","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","High","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Low","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Lower-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle","Upper-middle",null,null,null,null,null,null],[null,"Andorra","Antigua and Barbuda","Australia","Austria","Bahamas","Bahrain","Barbados","Belgium","Brunei Darussalam","Canada","Chile","Croatia","Cyprus","Czechia","Denmark","Equatorial Guinea","Estonia","Finland","France","Germany","Greece","Iceland","Ireland","Israel","Italy","Japan","Kuwait","Latvia","Lithuania","Luxembourg","Malta","Monaco","Netherlands","New Zealand","Norway","Oman","Poland","Portugal","Qatar","Republic of Korea","Russian Federation","Saint Kitts and Nevis","San Marino","Saudi Arabia","Singapore","Slovakia","Slovenia","Spain","Sweden","Switzerland","Trinidad and Tobago","United Arab Emirates","United Kingdom of Great Britain and Northern Ireland","United States of America","Uruguay",null,"Afghanistan","Bangladesh","Benin","Burkina Faso","Burundi","Cambodia","Central African Republic","Chad","Comoros","Democratic People's Republic of Korea","Democratic Republic of the Congo","Eritrea","Ethiopia","Gambia","Guinea","Guinea-Bissau","Haiti","Kenya","Liberia","Madagascar","Malawi","Mali","Mozambique","Myanmar","Nepal","Niger","Rwanda","Sierra Leone","Somalia","Tajikistan","Togo","Uganda","United Republic of Tanzania","Zimbabwe",null,"Armenia","Bhutan","Bolivia (Plurinational State of)","Cabo Verde","Cameroon","Congo","Côte d'Ivoire","Djibouti","Egypt","El Salvador","Georgia","Ghana","Guatemala","Guyana","Honduras","India","Indonesia","Kiribati","Kyrgyzstan","Lao People's Democratic Republic","Lesotho","Mauritania","Micronesia (Federated States of)","Mongolia","Morocco","Nicaragua","Nigeria","Pakistan","Papua New Guinea","Paraguay","Philippines","Republic of Moldova","Samoa","Sao Tome and Principe","Senegal","Solomon Islands","South Sudan","Sri Lanka","Sudan","Swaziland","Syrian Arab Republic","Timor-Leste","Ukraine","Uzbekistan","Vanuatu","Viet Nam","Yemen","Zambia",null,"Albania","Algeria","Angola","Argentina","Azerbaijan","Belarus","Belize","Bosnia and Herzegovina","Botswana","Brazil","Bulgaria","China","Colombia","Cook Islands","Costa Rica","Cuba","Dominica","Dominican Republic","Ecuador","Fiji","Gabon","Grenada","Hungary","Iran (Islamic Republic of)","Iraq","Jamaica","Jordan","Kazakhstan","Lebanon","Libya","Malaysia","Maldives","Marshall Islands","Mauritius","Mexico","Montenegro","Namibia","Nauru","Palau","Panama","Peru","Romania","Saint Lucia","Saint Vincent and the Grenadines","Serbia","Seychelles","South Africa","Suriname","Thailand","The former Yugoslav republic of Macedonia","Tonga","Tunisia","Turkey","Turkmenistan","Tuvalu","Venezuela (Bolivarian Republic of)","Niue",null,null,null,null,null,null,null],["Global","Europe","Americas","Western Pacific","Europe","Americas","Eastern Mediterranean","Americas","Europe","Western Pacific","Americas","Americas","Europe","Europe","Europe","Europe","Africa","Europe","Europe","Europe","Europe","Europe","Europe","Europe","Europe","Europe","Western Pacific","Eastern Mediterranean","Europe","Europe","Europe","Europe","Europe","Europe","Western Pacific","Europe","Eastern Mediterranean","Europe","Europe","Eastern Mediterranean","Western Pacific","Europe","Americas","Europe","Eastern Mediterranean","Western Pacific","Europe","Europe","Europe","Europe","Europe","Americas","Eastern Mediterranean","Europe","Americas","Americas",null,"Eastern Mediterranean","South-East Asia","Africa","Africa","Africa","Western Pacific","Africa","Africa","Africa","South-East Asia","Africa","Africa","Africa","Africa","Africa","Africa","Americas","Africa","Africa","Africa","Africa","Africa","Africa","South-East Asia","South-East Asia","Africa","Africa","Africa","Eastern Mediterranean","Europe","Africa","Africa","Africa","Africa",null,"Europe","South-East Asia","Americas","Africa","Africa","Africa","Africa","Eastern Mediterranean","Eastern Mediterranean","Americas","Europe","Africa","Americas","Americas","Americas","South-East Asia","South-East Asia","Western Pacific","Europe","Western Pacific","Africa","Africa","Western Pacific","Western Pacific","Eastern Mediterranean","Americas","Africa","Eastern Mediterranean","Western Pacific","Americas","Western Pacific","Europe","Western Pacific","Africa","Africa","Western Pacific","Africa","South-East Asia","Eastern Mediterranean","Africa","Eastern Mediterranean","South-East Asia","Europe","Europe","Western Pacific","Western Pacific","Eastern Mediterranean","Africa",null,"Europe","Africa","Africa","Americas","Europe","Europe","Americas","Europe","Africa","Americas","Europe","Western Pacific","Americas","Western Pacific","Americas","Americas","Americas","Americas","Americas","Western Pacific","Africa","Americas","Europe","Eastern Mediterranean","Eastern Mediterranean","Americas","Eastern Mediterranean","Europe","Eastern Mediterranean","Eastern Mediterranean","Western Pacific","South-East Asia","Western Pacific","Africa","Americas","Europe","Africa","Western Pacific","Western Pacific","Americas","Americas","Europe","Americas","Americas","Europe","Africa","Africa","Americas","South-East Asia","Europe","Western Pacific","Eastern Mediterranean","Europe","Europe","Western Pacific","Americas","Western Pacific",null,"Africa","Americas","Eastern Mediterranean","Europe","South-East Asia","Western Pacific"],[23.4,8.9,16.5,13.2,9.5,15.3,15.2,12.7,11.7,15.5,11.3,13.9,9.5,11.5,11.1,11.3,35.4,10.9,11.2,12.3,8.5,9.7,14.5,15.4,20.3,9.2,8.4,20.6,11,11.3,11.6,9.2,6.8,10.7,13.8,12.4,20.9,10.8,8.7,11,9.6,11.8,13.8,8.8,19.4,9.9,10.7,10,10.4,12,10.4,14.5,14.5,12.2,13.2,14.5,12.1,34.1,20,36.4,40.9,44.7,25.7,34.2,45.9,35.2,14.4,42.7,36.7,33,42.7,36.9,37.5,25.6,34.9,35.5,34.7,39.8,47.1,38.9,17.2,21,49.7,35.2,36.6,43.8,33,36.4,43.2,39.2,31.3,35,13.7,19.6,25.7,20.1,37.3,37.6,36.6,27.5,23.2,20.1,13.3,30.9,31,20.3,25.8,20.4,18.8,23.3,27.1,26.8,27.5,34.1,23.5,22.7,22.7,22.7,41.2,25.2,28.9,23.7,24.4,12.1,26.2,33.9,37.7,30.8,36.1,17.9,33.5,29.9,24,35.8,10.8,21.5,26.6,15.5,31.1,42.8,25.1,12.9,24.3,44.1,16.8,17.8,11,23.4,9,23.6,14.9,9.6,13.3,18.8,14.9,15.1,9.5,15.6,20.8,20.8,20.4,31.9,19.3,9.9,18.8,31.1,18,27,20.5,13.4,20.7,17.7,22,27.2,11.5,18.4,11.6,26,26.4,10.9,19.4,19.7,10.3,15.4,16.3,9.7,16.6,20.9,17.7,10.2,10.7,25.5,17.2,16.8,21.3,23.6,19.8,null,16.6,38.3,17,26.2,13.6,19.9,14.7],[7.8,8.4,6.8,6.4,9,6.1,2.2,6.4,9.9,3.6,7.2,5.4,11.1,5.5,10,9.6,11.5,11.3,9.4,8.7,10.6,9.7,6.2,5.9,5.1,9.5,9.5,1.9,14.2,12,6.8,7.5,8.7,8.3,6.3,8.1,2.8,10,9.1,1.3,5.5,14.8,8.5,10.2,3.2,4.5,9.4,9.1,8.2,9.6,7.7,9.6,1.1,8.9,8.4,9.1,9.1,7.7,5.7,9.6,9.5,11.1,5.7,14,13.6,8.2,9.2,13.7,6.3,7.2,8.2,10.1,12.3,8.6,8.3,8,7,9,11.2,11.8,8.4,6.6,10.2,6.4,16.8,12.7,6,9.6,9.2,7.8,9.8,8.5,12.6,6.5,7,5.2,10.8,10.1,12.6,8.8,6.4,6.6,11.5,8.1,5.2,7.9,4.5,7.9,6.2,8.2,6.6,6.7,14.1,7.8,6.1,6.7,6.4,4.8,11.9,7.3,7.7,4.8,5.9,12.4,5.2,6.5,7,5.4,11.1,6.6,7.8,11.8,3.5,6,15.1,6.4,4.5,5.8,6.7,9.7,7.9,9.4,5.7,13.9,7.6,6.3,13.5,4,9.4,7.2,6.1,14.2,7.2,4.2,5,4.4,8.5,7.2,5.9,5.3,6.9,9,6.9,13,5.1,4.8,7.1,3.7,9.7,4.4,4.2,5,3.5,6.6,7.4,5.3,9.5,5.7,3.5,5.9,5,4.4,11.8,7,6.5,11.9,6.7,11.1,4.6,7.6,9.4,6.7,5.5,5.7,8.8,8.7,4.8,5.9,6.9,10.1,6.7,6.2,10.2,7.5,7.1]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th>year<\/th>\n <th>World Bank Income Group<\/th>\n <th>country<\/th>\n <th>region<\/th>\n <th>Crude birth rate<\/th>\n <th>Crude death rate<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":5,"lengthMenu":[5,10,15],"columnDefs":[{"className":"dt-right","targets":[0,4,5]}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>

---

# `leaflet`

```r
library(leaflet)

df = data.frame(name = "University Hall",
				lat = 37.8719032,
				long = -122.2664164)

leaflet(df) %>%
	addProviderTiles(providers$OpenStreetMap.Mapnik) %>%
	setView(-122.2585399, 37.8718992, zoom = 16) %>%
	addCircleMarkers(~long, ~lat, popup = ~name)
```

* Uses the [Leaflet](http://leafletjs.com/) JavaScript library
* Typically just `addTiles()` could be used instead of `addProviderTiles()`, but the default map background was not loading as of this writing.

---

<div id="htmlwidget-3e12195084a968e94b75" style="width:100%;height:100%;" class="leaflet html-widget"></div>
<script type="application/json" data-for="htmlwidget-3e12195084a968e94b75">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addProviderTiles","args":["OpenStreetMap.Mapnik",null,null,{"errorTileUrl":"","noWrap":false,"zIndex":null,"unloadInvisibleTiles":null,"updateWhenIdle":null,"detectRetina":false,"reuseTiles":false}]},{"method":"addCircleMarkers","args":[37.8719032,-122.2664164,10,null,null,{"lineCap":null,"lineJoin":null,"clickable":true,"pointerEvents":null,"className":"","stroke":true,"color":"#03F","weight":5,"opacity":0.5,"fill":true,"fillColor":"#03F","fillOpacity":0.2,"dashArray":null},null,null,"University Hall",null,null,null,null]}],"setView":[[37.8718992,-122.2585399],16,[]],"limits":{"lat":[37.8719032,37.8719032],"lng":[-122.2664164,-122.2664164]}},"evals":[],"jsHooks":[]}</script>

---

# `leaflet`

```r
head(subset(yll_bycondition,
            select = c(country, longitude, latitude, value)), 2)
```

```
##             country longitude latitude value
## 4             China  105.0000     35.0    11
## 7 Brunei Darussalam  114.6667      4.5    12
```

```r
pal = colorNumeric(palette = "plasma",
                   domain = yll_bycondition$value)

yll_bycondition$popup_label = paste0(yll_bycondition$country, ": ",
                                     yll_bycondition$value, "%")

leaflet(yll_bycondition) %>%
 addProviderTiles(providers$CartoDB.DarkMatter) %>%
 addCircleMarkers(lng = ~longitude, lat = ~latitude,
 popup = ~popup_label,
 color = ~pal(value), fillColor = ~pal(value)) %>%
 addLegend("bottomright", pal = pal, values = ~value,
 title = "Population's years of life lost to communicable & other group I conditions",
 labFormat = labelFormat(suffix = "%"), opacity = 0.8)
```

* Even better: use polygons to outline the countries instead of circle markers at their centers. See `addPolygons()`, `addGeoJSON()`, etc.

---

<div id="htmlwidget-55a6427ac415e056d658" style="width:100%;height:100%;" class="leaflet html-widget"></div>
<script type="application/json" data-for="htmlwidget-55a6427ac415e056d658">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addProviderTiles","args":["CartoDB.DarkMatter",null,null,{"errorTileUrl":"","noWrap":false,"zIndex":null,"unloadInvisibleTiles":null,"updateWhenIdle":null,"detectRetina":false,"reuseTiles":false}]},{"method":"addCircleMarkers","args":[[35,4.5,40,-17,24.25,16,-18,-2,59,35,56,13.46666666,15.5,20,6.5,25,56,3.25,46,44,46.11666666,35,39,60,-8,48.66666666,-20,13.16666666,-30,17.25,13,-3.5,40.5,41,0,42,8,65,31,35.83333333,-29.5,8.5,11,34,30,-6,46,15,14,15,8,24,-27,-10,53,6,-12.16666666,47,-5,48,18,11.5,54,19,40,8,49.75,41.83333333,-20.28333333,-22,16,-41,9,-10,8,15,-6,-33,16.16666666,7,60,-12.5,26,0,19,53,5,12,45.16666666,49.75,27,46,18.25,-13.5,2.5,10,23,22,-18.25,1,37,29.5,32,47,13,39.5,-23,62,28,41,15,7,1,38,-29,27.5,44,43,9.5,47.33333333,4,10,21.5,51,28,64,2,39,15,33,31.47,36,41,7,17,13,25,-2,1.36666666,52.5,21,4,39,-22,47,50.83333333,-34,24,33,-1,15,32,11,42.83333333,33.83333333,13,42.5,57,-20,20,13.83333333,25.5,52,40,-8.83333333,62,-26.5,40,10,49,8,-15],[105,114.66666666,45,-65,-76,-24,175,-77.5,26,33,10,-16.56666666,-90.25,77,-9.5,17,24,73,105,21,14.81666666,38,71,100,159,19.5,30,-59.53333333,-71,-88.75,-2,30,47.5,20,25,43.5,-2,-18,36,14.58333333,28.5,-11.5,-61,9,70,147,25,30,-14,48,-5,90,133,-55,28,12,44.25,20,120,68,105,43,-2,-70.66666666,-4,38,6.16666666,22,57.55,17,8,174,-80,-76,1.16666666,100,35,-56,107.83333333,21,-95,18.5,50.55,25,-72.41666666,-8,-59,-15,15.5,15.5,30,2,-77.5,34,112.5,8,-102,98,35,38,127.5,45.75,-5,29,122,-8,-58,10,84,64,19,30,32,-97,24,90.5,18,25,2.25,13.33333333,-72,-84,-80,9,3,26,10,22,-86.5,44,35.13,138,75,81,-4,-85,45,30,103.8,5.75,57,-56,35,24,8,4,-64,54,65,11.75,39,53,-10,12.83333333,35.83333333,105,19.3,25,47,-12,-88.91666666,51.25,20,127,125.91666666,15,31.5,60,49,32,-66,30],10,null,null,{"lineCap":null,"lineJoin":null,"clickable":true,"pointerEvents":null,"className":"","stroke":true,"color":["#48039F","#4D02A2","#38049A","#C23C81","#B7318A","#B02991","#7201A8","#930FA3","#1F068F","#1F068F","#2C0594","#FDB32F","#D14E72","#CE4B75","#FEBD2A","#6700A8","#1F068F","#7701A8","#7701A8","#17078B","#17078B","#43039E","#E26561","#43039E","#CC4779","#2C0594","#FBD724","#6300A7","#43039E","#9C179E","#FEBD2A","#FEBD2A","#8A09A5","#3E049C","#FCCD25","#3E049C","#FBA03A","#1F068F","#8E0CA4","#260591","#FBD724","#FCD225","#5302A3","#6D00A8","#EF7D51","#F48948","#320597","#FBA03A","#FDB32F","#F68E45","#FEBD2A","#D7566C","#260591","#6700A8","#260591","#FEBD2A","#FBA03A","#17078B","#BB3488","#4D02A2","#F79242","#FDAE32","#320597","#A41F99","#2C0594","#FEBD2A","#260591","#1F068F","#48039F","#F79242","#F6E826","#2C0594","#8E0CA4","#A01A9C","#FDC727","#8004A8","#FEB82C","#43039E","#8E0CA4","#F0F921","#320597","#FBD724","#6300A7","#F9DD25","#F3854B","#2C0594","#930FA3","#FBD724","#0D0887","#260591","#7701A8","#2C0594","#8E0CA4","#F7E225","#7701A8","#FCD225","#6300A7","#CE4B75","#FCD225","#FCCD25","#38049A","#7B02A8","#A41F99","#38049A","#B42D8D","#43039E","#8E0CA4","#2C0594","#D45270","#9C179E","#F0F921","#FCCD25","#FEB82C","#38049A","#FA9B3D","#B42D8D","#1F068F","#260591","#FDB32F","#17078B","#7701A8","#43039E","#320597","#260591","#8A09A5","#0D0887","#FEB82C","#2C0594","#B7318A","#B42D8D","#43039E","#43039E","#8506A6","#5801A4","#FCCD25","#930FA3","#6D00A8","#FA9B3D","#5D01A6","#2C0594","#8A09A5","#8E0CA4","#5801A4","#FDAE32","#260591","#320597","#6300A7","#6700A8","#F48948","#FEB82C","#FA9B3D","#6D00A8","#FDC727","#260591","#48039F","#DE6164","#1F068F","#1F068F","#FBA03A","#FDC229","#6D00A8","#48039F","#1F068F","#6700A8","#FA9B3D","#260591","#FCCD25","#8A09A5","#F7E225","#43039E","#6300A7","#F9DD25"],"weight":5,"opacity":0.5,"fill":true,"fillColor":["#48039F","#4D02A2","#38049A","#C23C81","#B7318A","#B02991","#7201A8","#930FA3","#1F068F","#1F068F","#2C0594","#FDB32F","#D14E72","#CE4B75","#FEBD2A","#6700A8","#1F068F","#7701A8","#7701A8","#17078B","#17078B","#43039E","#E26561","#43039E","#CC4779","#2C0594","#FBD724","#6300A7","#43039E","#9C179E","#FEBD2A","#FEBD2A","#8A09A5","#3E049C","#FCCD25","#3E049C","#FBA03A","#1F068F","#8E0CA4","#260591","#FBD724","#FCD225","#5302A3","#6D00A8","#EF7D51","#F48948","#320597","#FBA03A","#FDB32F","#F68E45","#FEBD2A","#D7566C","#260591","#6700A8","#260591","#FEBD2A","#FBA03A","#17078B","#BB3488","#4D02A2","#F79242","#FDAE32","#320597","#A41F99","#2C0594","#FEBD2A","#260591","#1F068F","#48039F","#F79242","#F6E826","#2C0594","#8E0CA4","#A01A9C","#FDC727","#8004A8","#FEB82C","#43039E","#8E0CA4","#F0F921","#320597","#FBD724","#6300A7","#F9DD25","#F3854B","#2C0594","#930FA3","#FBD724","#0D0887","#260591","#7701A8","#2C0594","#8E0CA4","#F7E225","#7701A8","#FCD225","#6300A7","#CE4B75","#FCD225","#FCCD25","#38049A","#7B02A8","#A41F99","#38049A","#B42D8D","#43039E","#8E0CA4","#2C0594","#D45270","#9C179E","#F0F921","#FCCD25","#FEB82C","#38049A","#FA9B3D","#B42D8D","#1F068F","#260591","#FDB32F","#17078B","#7701A8","#43039E","#320597","#260591","#8A09A5","#0D0887","#FEB82C","#2C0594","#B7318A","#B42D8D","#43039E","#43039E","#8506A6","#5801A4","#FCCD25","#930FA3","#6D00A8","#FA9B3D","#5D01A6","#2C0594","#8A09A5","#8E0CA4","#5801A4","#FDAE32","#260591","#320597","#6300A7","#6700A8","#F48948","#FEB82C","#FA9B3D","#6D00A8","#FDC727","#260591","#48039F","#DE6164","#1F068F","#1F068F","#FBA03A","#FDC229","#6D00A8","#48039F","#1F068F","#6700A8","#FA9B3D","#260591","#FCCD25","#8A09A5","#F7E225","#43039E","#6300A7","#F9DD25"],"fillOpacity":0.2,"dashArray":null},null,null,["China: 11%","Brunei Darussalam: 12%","Armenia: 8%","Bolivia (Plurinational State of): 38%","Bahamas: 35%","Cabo Verde: 33%","Fiji: 19%","Ecuador: 26%","Estonia: 4%","Cyprus: 4%","Denmark: 6%","Gambia: 67%","Guatemala: 43%","India: 42%","Liberia: 69%","Libya: 17%","Lithuania: 4%","Maldives: 20%","Mongolia: 20%","Serbia: 3%","Slovenia: 3%","Syrian Arab Republic: 10%","Tajikistan: 49%","Russian Federation: 10%","Solomon Islands: 41%","Slovakia: 6%","Zimbabwe: 74%","Barbados: 16%","Chile: 10%","Belize: 28%","Burkina Faso: 69%","Burundi: 69%","Azerbaijan: 24%","Albania: 9%","Congo: 72%","Georgia: 9%","Ghana: 63%","Iceland: 4%","Jordan: 25%","Malta: 5%","Lesotho: 74%","Sierra Leone: 73%","Trinidad and Tobago: 13%","Tunisia: 18%","Pakistan: 55%","Papua New Guinea: 58%","Romania: 7%","Sudan: 63%","Senegal: 67%","Yemen: 59%","Côte d'Ivoire: 69%","Bangladesh: 45%","Australia: 5%","Brazil: 17%","Belarus: 5%","Cameroon: 69%","Comoros: 63%","Hungary: 3%","Indonesia: 36%","Kazakhstan: 12%","Lao People's Democratic Republic: 60%","Djibouti: 66%","United Kingdom of Great Britain and Northern Ireland: 7%","Dominican Republic: 30%","Spain: 6%","Ethiopia: 69%","Luxembourg: 5%","The former Yugoslav republic of Macedonia: 4%","Mauritius: 11%","Namibia: 60%","Niger: 77%","New Zealand: 6%","Panama: 25%","Peru: 29%","Togo: 71%","Thailand: 22%","United Republic of Tanzania: 68%","Uruguay: 10%","Viet Nam: 25%","Central African Republic: 80%","Canada: 7%","Angola: 74%","Bahrain: 16%","Democratic Republic of the Congo: 75%","Haiti: 57%","Ireland: 6%","Guyana: 26%","Guinea-Bissau: 74%","Croatia: 2%","Czechia: 5%","Egypt: 20%","France: 6%","Jamaica: 25%","Malawi: 76%","Malaysia: 20%","Nigeria: 73%","Mexico: 16%","Myanmar: 42%","Mozambique: 73%","Kenya: 72%","Republic of Korea: 8%","Kuwait: 21%","Morocco: 30%","Republic of Moldova: 8%","Philippines: 34%","Portugal: 10%","Paraguay: 25%","Norway: 6%","Nepal: 44%","Uzbekistan: 28%","Chad: 80%","South Sudan: 72%","Uganda: 68%","United States of America: 8%","South Africa: 62%","Bhutan: 34%","Bosnia and Herzegovina: 4%","Bulgaria: 5%","Benin: 67%","Austria: 3%","Colombia: 20%","Costa Rica: 10%","Cuba: 7%","Germany: 5%","Algeria: 24%","Finland: 2%","Equatorial Guinea: 68%","Greece: 6%","Honduras: 35%","Iraq: 34%","Israel: 10%","Japan: 10%","Kyrgyzstan: 23%","Sri Lanka: 14%","Mali: 72%","Nicaragua: 26%","Saudi Arabia: 18%","Rwanda: 62%","Singapore: 15%","Netherlands: 6%","Oman: 24%","Suriname: 25%","Turkey: 14%","Botswana: 66%","Switzerland: 5%","Belgium: 7%","Argentina: 16%","United Arab Emirates: 17%","Afghanistan: 58%","Gabon: 68%","Eritrea: 62%","Iran (Islamic Republic of): 18%","Guinea: 71%","Italy: 5%","Lebanon: 11%","Cambodia: 48%","Montenegro: 4%","Latvia: 4%","Madagascar: 63%","Mauritania: 70%","El Salvador: 18%","Qatar: 11%","Poland: 4%","Democratic People's Republic of Korea: 17%","Timor-Leste: 62%","Sweden: 5%","Swaziland: 72%","Turkmenistan: 24%","Somalia: 76%","Ukraine: 10%","Venezuela (Bolivarian Republic of): 16%","Zambia: 75%"],null,null,null,null]},{"method":"addLegend","args":[{"colors":["#0D0887 , #43039E 10.2564102564103%, #7701A8 23.0769230769231%, #A41F99 35.8974358974359%, #C8437B 48.7179487179487%, #E4695E 61.5384615384615%, #F79242 74.3589743589744%, #FDC229 87.1794871794872%, #F0F921 100%, #F0F921 "],"labels":["10%","20%","30%","40%","50%","60%","70%","80%"],"na_color":null,"na_label":"NA","opacity":0.8,"position":"bottomright","type":"numeric","title":"Population's years of life lost to communicable & other group I conditions","extra":{"p_1":0.102564102564103,"p_n":1},"layerId":null,"className":"info legend"}]}],"limits":{"lat":[-41,65],"lng":[-102,175]}},"evals":[],"jsHooks":[]}</script>

---

# `rbokeh`

```r
library(rbokeh)
p <- figure(data = subset(who_cbdr, !is.na(country)),
 width = 800, height = 600,
 title = "Global population rates in 2013 per 1,000 population") %>%
 ly_points(value.birthsper1000, value.deathsper1000,
 color = worldbankincomegroup,
 hover = list(country,
 value.birthsper1000,
 value.deathsper1000)) %>%
 x_axis(label = "Crude Birth Rate") %>%
 y_axis(label = "Crude Death Rate")
p
```

---

<div id="htmlwidget-5579e4dd63c6f56fb666" style="width:800px;height:600px;" class="rbokeh html-widget"></div>
<script type="application/json" data-for="htmlwidget-5579e4dd63c6f56fb666">{"x":{"elementid":"07164e7ff5eb2797e6c079417a1f1f9e","modeltype":"Plot","modelid":"867cff8f8bff17555b0ae6f0a00d6ab7","docid":"460809a7c9fec1be70670174fdf903cf","docs_json":{"460809a7c9fec1be70670174fdf903cf":{"version":"0.12.2","title":"Bokeh Figure","roots":{"root_ids":["867cff8f8bff17555b0ae6f0a00d6ab7"],"references":[{"type":"Plot","id":"867cff8f8bff17555b0ae6f0a00d6ab7","attributes":{"id":"867cff8f8bff17555b0ae6f0a00d6ab7","plot_width":800,"plot_height":600,"sizing_mode":"scale_both","x_range":{"type":"Range1d","id":"97c263cb61d0a053b15693a3f2abc631"},"y_range":{"type":"Range1d","id":"c7a821bca304fb3a37ebaf4ab1cfcd44"},"left":[{"type":"LinearAxis","id":"abc2e133183da8dd658168f4ea68df97"}],"below":[{"type":"LinearAxis","id":"93f061eb8f6911c8d05555bfa89468d3"}],"right":[],"above":[],"renderers":[{"type":"BoxAnnotation","id":"f237490b0ffd0b3f58780451ac984aff"},{"type":"GlyphRenderer","id":"e9b18009d72a5c96f5e9f1ff6f416cb3"},{"type":"LinearAxis","id":"93f061eb8f6911c8d05555bfa89468d3"},{"type":"Grid","id":"57f5d43e50454a243e5eb36fa4305079"},{"type":"LinearAxis","id":"abc2e133183da8dd658168f4ea68df97"},{"type":"Grid","id":"ea117aa9f2a0236fae5887a5ad95f4c9"},{"type":"GlyphRenderer","id":"2b417ac3aaf885bf575503f751ba30b8"},{"type":"GlyphRenderer","id":"3faffe25431e99808116983ce195fb48"},{"type":"GlyphRenderer","id":"57e1fdab56ec77e64ae8091a80bc29ef"},{"type":"GlyphRenderer","id":"ea4cc9214babc59e33a9e0e535eb5313"},{"type":"Legend","id":"f86dee6a2037fe10b38a71858fd0850b"}],"extra_y_ranges":{},"extra_x_ranges":{},"tags":[],"min_border_left":4,"min_border_right":4,"min_border_top":4,"min_border_bottom":4,"lod_threshold":null,"toolbar":{"type":"Toolbar","id":"a497eda30248da26c1384cce92529b77"},"tool_events":{"type":"ToolEvents","id":"a078c5e2c452d049a88e6caef8353444"},"title":{"type":"Title","id":"9470e923e9439eb5dc50a440a13bef97"}},"subtype":"Figure"},{"type":"Toolbar","id":"a497eda30248da26c1384cce92529b77","attributes":{"id":"a497eda30248da26c1384cce92529b77","tags":[],"active_drag":"auto","active_scroll":"auto","active_tap":"auto","tools":[{"type":"PanTool","id":"97005ce1df0c756f70691a4e6676b507"},{"type":"WheelZoomTool","id":"059485f8aa2ff85f602df7b24f431c3f"},{"type":"BoxZoomTool","id":"d7523ba847c840c91f789f12e284678b"},{"type":"ResetTool","id":"274030f2ac7c485a5378c8bd4b2eb443"},{"type":"SaveTool","id":"1fbc81af62e4461bde5c780e953a313e"},{"type":"HelpTool","id":"c5803b5d101c6c753f6521a561ecc1e9"},{"type":"HoverTool","id":"221b26393c56feb0406a5d4704f8581a"}],"logo":null}},{"type":"PanTool","id":"97005ce1df0c756f70691a4e6676b507","attributes":{"id":"97005ce1df0c756f70691a4e6676b507","tags":[],"plot":{"type":"Plot","id":"867cff8f8bff17555b0ae6f0a00d6ab7","subtype":"Figure"},"dimensions":["width","height"]}},{"type":"ToolEvents","id":"a078c5e2c452d049a88e6caef8353444","attributes":{"id":"a078c5e2c452d049a88e6caef8353444","tags":[]},"geometries":[]},{"type":"WheelZoomTool","id":"059485f8aa2ff85f602df7b24f431c3f","attributes":{"id":"059485f8aa2ff85f602df7b24f431c3f","tags":[],"plot":{"type":"Plot","id":"867cff8f8bff17555b0ae6f0a00d6ab7","subtype":"Figure"},"dimensions":["width","height"]}},{"type":"BoxAnnotation","id":"f237490b0ffd0b3f58780451ac984aff","attributes":{"id":"f237490b0ffd0b3f58780451ac984aff","tags":[],"line_color":{"units":"data","value":"black"},"line_alpha":{"units":"data","value":1},"fill_color":{"units":"data","value":"lightgrey"},"fill_alpha":{"units":"data","value":0.5},"line_dash":[4,4],"line_width":{"units":"data","value":2},"level":"overlay","top_units":"screen","bottom_units":"screen","left_units":"screen","right_units":"screen","render_mode":"css"}},{"type":"BoxZoomTool","id":"d7523ba847c840c91f789f12e284678b","attributes":{"id":"d7523ba847c840c91f789f12e284678b","tags":[],"plot":{"type":"Plot","id":"867cff8f8bff17555b0ae6f0a00d6ab7","subtype":"Figure"},"overlay":{"type":"BoxAnnotation","id":"f237490b0ffd0b3f58780451ac984aff"}}},{"type":"ResetTool","id":"274030f2ac7c485a5378c8bd4b2eb443","attributes":{"id":"274030f2ac7c485a5378c8bd4b2eb443","tags":[],"plot":{"type":"Plot","id":"867cff8f8bff17555b0ae6f0a00d6ab7","subtype":"Figure"}}},{"type":"SaveTool","id":"1fbc81af62e4461bde5c780e953a313e","attributes":{"id":"1fbc81af62e4461bde5c780e953a313e","tags":[],"plot":{"type":"Plot","id":"867cff8f8bff17555b0ae6f0a00d6ab7","subtype":"Figure"}}},{"type":"HelpTool","id":"c5803b5d101c6c753f6521a561ecc1e9","attributes":{"id":"c5803b5d101c6c753f6521a561ecc1e9","tags":[],"plot":{"type":"Plot","id":"867cff8f8bff17555b0ae6f0a00d6ab7","subtype":"Figure"},"redirect":"http://hafen.github.io/rbokeh","help_tooltip":"Click to learn more about rbokeh."}},{"type":"Title","id":"9470e923e9439eb5dc50a440a13bef97","attributes":{"id":"9470e923e9439eb5dc50a440a13bef97","tags":[],"plot":null,"text":"Global population rates in 2013 per 1,000 population"}},{"type":"HoverTool","id":"221b26393c56feb0406a5d4704f8581a","attributes":{"id":"221b26393c56feb0406a5d4704f8581a","tags":[],"plot":{"type":"Plot","id":"867cff8f8bff17555b0ae6f0a00d6ab7","subtype":"Figure"},"renderers":[{"type":"GlyphRenderer","id":"e9b18009d72a5c96f5e9f1ff6f416cb3"}],"names":[],"anchor":"center","attachment":"horizontal","line_policy":"prev","mode":"mouse","point_policy":"snap_to_data","tooltips":[["country","@hover_col_1"],["value.birthsper1000","@hover_col_2"],["value.deathsper1000","@hover_col_3"]]}},{"type":"ColumnDataSource","id":"f6218510521d446a9dbd1b741e398c16","attributes":{"id":"f6218510521d446a9dbd1b741e398c16","tags":[],"column_names":["x","y","line_color","fill_color","hover_col_1","hover_col_2","hover_col_3"],"selected":[],"data":{"x":[8.9,16.5,13.2,9.5,15.3,15.2,12.7,11.7,15.5,11.3,13.9,9.5,11.5,11.1,11.3,35.4,10.9,11.2,12.3,8.5,9.7,14.5,15.4,20.3,9.2,8.4,20.6,11,11.3,11.6,9.2,6.8,10.7,13.8,12.4,20.9,10.8,8.7,11,9.6,11.8,13.8,8.8,19.4,9.9,10.7,10,10.4,12,10.4,14.5,14.5,12.2,13.2,14.5,34.1,20,36.4,40.9,44.7,25.7,34.2,45.9,35.2,14.4,42.7,36.7,33,42.7,36.9,37.5,25.6,34.9,35.5,34.7,39.8,47.1,38.9,17.2,21,49.7,35.2,36.6,43.8,33,36.4,43.2,39.2,31.3,13.7,19.6,25.7,20.1,37.3,37.6,36.6,27.5,23.2,20.1,13.3,30.9,31,20.3,25.8,20.4,18.8,23.3,27.1,26.8,27.5,34.1,23.5,22.7,22.7,22.7,41.2,25.2,28.9,23.7,24.4,12.1,26.2,33.9,37.7,30.8,36.1,17.9,33.5,29.9,24,35.8,10.8,21.5,26.6,15.5,31.1,42.8,12.9,24.3,44.1,16.8,17.8,11,23.4,9,23.6,14.9,9.6,13.3,18.8,14.9,15.1,9.5,15.6,20.8,20.8,20.4,31.9,19.3,9.9,18.8,31.1,18,27,20.5,13.4,20.7,17.7,22,27.2,11.5,18.4,11.6,26,26.4,10.9,19.4,19.7,10.3,15.4,16.3,9.7,16.6,20.9,17.7,10.2,10.7,25.5,17.2,16.8,21.3,23.6,19.8,null],"y":[8.4,6.8,6.4,9,6.1,2.2,6.4,9.9,3.6,7.2,5.4,11.1,5.5,10,9.6,11.5,11.3,9.4,8.7,10.6,9.7,6.2,5.9,5.1,9.5,9.5,1.9,14.2,12,6.8,7.5,8.7,8.3,6.3,8.1,2.8,10,9.1,1.3,5.5,14.8,8.5,10.2,3.2,4.5,9.4,9.1,8.2,9.6,7.7,9.6,1.1,8.9,8.4,9.1,7.7,5.7,9.6,9.5,11.1,5.7,14,13.6,8.2,9.2,13.7,6.3,7.2,8.2,10.1,12.3,8.6,8.3,8,7,9,11.2,11.8,8.4,6.6,10.2,6.4,16.8,12.7,6,9.6,9.2,7.8,9.8,12.6,6.5,7,5.2,10.8,10.1,12.6,8.8,6.4,6.6,11.5,8.1,5.2,7.9,4.5,7.9,6.2,8.2,6.6,6.7,14.1,7.8,6.1,6.7,6.4,4.8,11.9,7.3,7.7,4.8,5.9,12.4,5.2,6.5,7,5.4,11.1,6.6,7.8,11.8,3.5,6,15.1,6.4,4.5,5.8,6.7,9.7,9.4,5.7,13.9,7.6,6.3,13.5,4,9.4,7.2,6.1,14.2,7.2,4.2,5,4.4,8.5,7.2,5.9,5.3,6.9,9,6.9,13,5.1,4.8,7.1,3.7,9.7,4.4,4.2,5,3.5,6.6,7.4,5.3,9.5,5.7,3.5,5.9,5,4.4,11.8,7,6.5,11.9,6.7,11.1,4.6,7.6,9.4,6.7,5.5,5.7,8.8,8.7,4.8,5.9],"line_color":["#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728"],"fill_color":["#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#1F77B4","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#FF7F0E","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#2CA02C","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728","#D62728"],"hover_col_1":["Andorra ","Antigua and Barbuda ","Australia ","Austria ","Bahamas ","Bahrain ","Barbados ","Belgium ","Brunei Darussalam ","Canada ","Chile ","Croatia ","Cyprus ","Czechia ","Denmark ","Equatorial Guinea ","Estonia ","Finland ","France ","Germany ","Greece ","Iceland ","Ireland ","Israel ","Italy ","Japan ","Kuwait ","Latvia ","Lithuania ","Luxembourg ","Malta ","Monaco ","Netherlands ","New Zealand ","Norway ","Oman ","Poland ","Portugal ","Qatar ","Republic of Korea ","Russian Federation ","Saint Kitts and Nevis ","San Marino ","Saudi Arabia ","Singapore ","Slovakia ","Slovenia ","Spain ","Sweden ","Switzerland ","Trinidad and Tobago ","United Arab Emirates ","United Kingdom of Great Britain and Northern Ireland","United States of America ","Uruguay ","Afghanistan ","Bangladesh ","Benin ","Burkina Faso ","Burundi ","Cambodia ","Central African Republic ","Chad ","Comoros ","Democratic People's Republic of Korea ","Democratic Republic of the Congo ","Eritrea ","Ethiopia ","Gambia ","Guinea ","Guinea-Bissau ","Haiti ","Kenya ","Liberia ","Madagascar ","Malawi ","Mali ","Mozambique ","Myanmar ","Nepal ","Niger ","Rwanda ","Sierra Leone ","Somalia ","Tajikistan ","Togo ","Uganda ","United Republic of Tanzania ","Zimbabwe ","Armenia ","Bhutan ","Bolivia (Plurinational State of) ","Cabo Verde ","Cameroon ","Congo ","Côte d'Ivoire ","Djibouti ","Egypt ","El Salvador ","Georgia ","Ghana ","Guatemala ","Guyana ","Honduras ","India ","Indonesia ","Kiribati ","Kyrgyzstan ","Lao People's Democratic Republic ","Lesotho ","Mauritania ","Micronesia (Federated States of) ","Mongolia ","Morocco ","Nicaragua ","Nigeria ","Pakistan ","Papua New Guinea ","Paraguay ","Philippines ","Republic of Moldova ","Samoa ","Sao Tome and Principe ","Senegal ","Solomon Islands ","South Sudan ","Sri Lanka ","Sudan ","Swaziland ","Syrian Arab Republic ","Timor-Leste ","Ukraine ","Uzbekistan ","Vanuatu ","Viet Nam ","Yemen ","Zambia ","Albania ","Algeria ","Angola ","Argentina ","Azerbaijan ","Belarus ","Belize ","Bosnia and Herzegovina ","Botswana ","Brazil ","Bulgaria ","China ","Colombia ","Cook Islands ","Costa Rica ","Cuba ","Dominica ","Dominican Republic ","Ecuador ","Fiji ","Gabon ","Grenada ","Hungary ","Iran (Islamic Republic of) ","Iraq ","Jamaica ","Jordan ","Kazakhstan ","Lebanon ","Libya ","Malaysia ","Maldives ","Marshall Islands ","Mauritius ","Mexico ","Montenegro ","Namibia ","Nauru ","Palau ","Panama ","Peru ","Romania ","Saint Lucia ","Saint Vincent and the Grenadines ","Serbia ","Seychelles ","South Africa ","Suriname ","Thailand ","The former Yugoslav republic of Macedonia ","Tonga ","Tunisia ","Turkey ","Turkmenistan ","Tuvalu ","Venezuela (Bolivarian Republic of) ","Niue "],"hover_col_2":[" 8.9","16.5","13.2"," 9.5","15.3","15.2","12.7","11.7","15.5","11.3","13.9"," 9.5","11.5","11.1","11.3","35.4","10.9","11.2","12.3"," 8.5"," 9.7","14.5","15.4","20.3"," 9.2"," 8.4","20.6","11.0","11.3","11.6"," 9.2"," 6.8","10.7","13.8","12.4","20.9","10.8"," 8.7","11.0"," 9.6","11.8","13.8"," 8.8","19.4"," 9.9","10.7","10.0","10.4","12.0","10.4","14.5","14.5","12.2","13.2","14.5","34.1","20.0","36.4","40.9","44.7","25.7","34.2","45.9","35.2","14.4","42.7","36.7","33.0","42.7","36.9","37.5","25.6","34.9","35.5","34.7","39.8","47.1","38.9","17.2","21.0","49.7","35.2","36.6","43.8","33.0","36.4","43.2","39.2","31.3","13.7","19.6","25.7","20.1","37.3","37.6","36.6","27.5","23.2","20.1","13.3","30.9","31.0","20.3","25.8","20.4","18.8","23.3","27.1","26.8","27.5","34.1","23.5","22.7","22.7","22.7","41.2","25.2","28.9","23.7","24.4","12.1","26.2","33.9","37.7","30.8","36.1","17.9","33.5","29.9","24.0","35.8","10.8","21.5","26.6","15.5","31.1","42.8","12.9","24.3","44.1","16.8","17.8","11.0","23.4"," 9.0","23.6","14.9"," 9.6","13.3","18.8","14.9","15.1"," 9.5","15.6","20.8","20.8","20.4","31.9","19.3"," 9.9","18.8","31.1","18.0","27.0","20.5","13.4","20.7","17.7","22.0","27.2","11.5","18.4","11.6","26.0","26.4","10.9","19.4","19.7","10.3","15.4","16.3"," 9.7","16.6","20.9","17.7","10.2","10.7","25.5","17.2","16.8","21.3","23.6","19.8"," NA"],"hover_col_3":[" 8.4"," 6.8"," 6.4"," 9.0"," 6.1"," 2.2"," 6.4"," 9.9"," 3.6"," 7.2"," 5.4","11.1"," 5.5","10.0"," 9.6","11.5","11.3"," 9.4"," 8.7","10.6"," 9.7"," 6.2"," 5.9"," 5.1"," 9.5"," 9.5"," 1.9","14.2","12.0"," 6.8"," 7.5"," 8.7"," 8.3"," 6.3"," 8.1"," 2.8","10.0"," 9.1"," 1.3"," 5.5","14.8"," 8.5","10.2"," 3.2"," 4.5"," 9.4"," 9.1"," 8.2"," 9.6"," 7.7"," 9.6"," 1.1"," 8.9"," 8.4"," 9.1"," 7.7"," 5.7"," 9.6"," 9.5","11.1"," 5.7","14.0","13.6"," 8.2"," 9.2","13.7"," 6.3"," 7.2"," 8.2","10.1","12.3"," 8.6"," 8.3"," 8.0"," 7.0"," 9.0","11.2","11.8"," 8.4"," 6.6","10.2"," 6.4","16.8","12.7"," 6.0"," 9.6"," 9.2"," 7.8"," 9.8","12.6"," 6.5"," 7.0"," 5.2","10.8","10.1","12.6"," 8.8"," 6.4"," 6.6","11.5"," 8.1"," 5.2"," 7.9"," 4.5"," 7.9"," 6.2"," 8.2"," 6.6"," 6.7","14.1"," 7.8"," 6.1"," 6.7"," 6.4"," 4.8","11.9"," 7.3"," 7.7"," 4.8"," 5.9","12.4"," 5.2"," 6.5"," 7.0"," 5.4","11.1"," 6.6"," 7.8","11.8"," 3.5"," 6.0","15.1"," 6.4"," 4.5"," 5.8"," 6.7"," 9.7"," 9.4"," 5.7","13.9"," 7.6"," 6.3","13.5"," 4.0"," 9.4"," 7.2"," 6.1","14.2"," 7.2"," 4.2"," 5.0"," 4.4"," 8.5"," 7.2"," 5.9"," 5.3"," 6.9"," 9.0"," 6.9","13.0"," 5.1"," 4.8"," 7.1"," 3.7"," 9.7"," 4.4"," 4.2"," 5.0"," 3.5"," 6.6"," 7.4"," 5.3"," 9.5"," 5.7"," 3.5"," 5.9"," 5.0"," 4.4","11.8"," 7.0"," 6.5","11.9"," 6.7","11.1"," 4.6"," 7.6"," 9.4"," 6.7"," 5.5"," 5.7"," 8.8"," 8.7"," 4.8"," 5.9"]}}},{"type":"Circle","id":"33c558a48eb09ce5a9f96b8d3fb8c315","attributes":{"id":"33c558a48eb09ce5a9f96b8d3fb8c315","tags":[],"size":{"units":"screen","value":10},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":0.5},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"},"line_color":{"units":"data","field":"line_color"},"fill_color":{"units":"data","field":"fill_color"}}},{"type":"Circle","id":"4694a8892044365aba8d08beb80e51d3","attributes":{"id":"4694a8892044365aba8d08beb80e51d3","tags":[],"size":{"units":"screen","value":10},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":0.5},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"},"line_color":{"units":"data","value":"#e1e1e1"},"fill_color":{"units":"data","value":"#e1e1e1"}}},{"type":"Circle","id":"416f14e308994284aac7d1635e461d06","attributes":{"id":"416f14e308994284aac7d1635e461d06","tags":[],"size":{"units":"screen","value":10},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":1},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"},"line_color":{"units":"data","field":"line_color"},"fill_color":{"units":"data","field":"fill_color"}}},{"type":"GlyphRenderer","id":"e9b18009d72a5c96f5e9f1ff6f416cb3","attributes":{"id":"e9b18009d72a5c96f5e9f1ff6f416cb3","tags":[],"selection_glyph":null,"nonselection_glyph":{"type":"Circle","id":"4694a8892044365aba8d08beb80e51d3"},"hover_glyph":{"type":"Circle","id":"416f14e308994284aac7d1635e461d06"},"name":null,"data_source":{"type":"ColumnDataSource","id":"f6218510521d446a9dbd1b741e398c16"},"glyph":{"type":"Circle","id":"33c558a48eb09ce5a9f96b8d3fb8c315"}}},{"type":"LinearAxis","id":"93f061eb8f6911c8d05555bfa89468d3","attributes":{"id":"93f061eb8f6911c8d05555bfa89468d3","tags":[],"plot":{"type":"Plot","id":"867cff8f8bff17555b0ae6f0a00d6ab7","subtype":"Figure"},"axis_label":"Crude Birth Rate","formatter":{"type":"BasicTickFormatter","id":"4a42f24ddd3d969c47bf76e287ebabe2"},"ticker":{"type":"BasicTicker","id":"cf49ecb7ffc7c10444c6ba4174072361"},"visible":true,"axis_label_text_font_size":"12pt"}},{"type":"BasicTickFormatter","id":"4a42f24ddd3d969c47bf76e287ebabe2","attributes":{"id":"4a42f24ddd3d969c47bf76e287ebabe2","tags":[]}},{"type":"BasicTicker","id":"cf49ecb7ffc7c10444c6ba4174072361","attributes":{"id":"cf49ecb7ffc7c10444c6ba4174072361","tags":[],"num_minor_ticks":5}},{"type":"Grid","id":"57f5d43e50454a243e5eb36fa4305079","attributes":{"id":"57f5d43e50454a243e5eb36fa4305079","tags":[],"dimension":0,"plot":{"type":"Plot","id":"867cff8f8bff17555b0ae6f0a00d6ab7","subtype":"Figure"},"ticker":{"type":"BasicTicker","id":"cf49ecb7ffc7c10444c6ba4174072361"}}},{"type":"LinearAxis","id":"abc2e133183da8dd658168f4ea68df97","attributes":{"id":"abc2e133183da8dd658168f4ea68df97","tags":[],"plot":{"type":"Plot","id":"867cff8f8bff17555b0ae6f0a00d6ab7","subtype":"Figure"},"axis_label":"Crude Death Rate","formatter":{"type":"BasicTickFormatter","id":"e89bef5b829f4c30c30102e29cf0263e"},"ticker":{"type":"BasicTicker","id":"044b128504674869461ce664d30137b4"},"visible":true,"axis_label_text_font_size":"12pt"}},{"type":"BasicTickFormatter","id":"e89bef5b829f4c30c30102e29cf0263e","attributes":{"id":"e89bef5b829f4c30c30102e29cf0263e","tags":[]}},{"type":"BasicTicker","id":"044b128504674869461ce664d30137b4","attributes":{"id":"044b128504674869461ce664d30137b4","tags":[],"num_minor_ticks":5}},{"type":"Grid","id":"ea117aa9f2a0236fae5887a5ad95f4c9","attributes":{"id":"ea117aa9f2a0236fae5887a5ad95f4c9","tags":[],"dimension":1,"plot":{"type":"Plot","id":"867cff8f8bff17555b0ae6f0a00d6ab7","subtype":"Figure"},"ticker":{"type":"BasicTicker","id":"044b128504674869461ce664d30137b4"}}},{"type":"ColumnDataSource","id":"33e1d1a27ad9cb6d07d1e3c25442bfca","attributes":{"id":"33e1d1a27ad9cb6d07d1e3c25442bfca","tags":[],"column_names":["x","y"],"selected":[],"data":{"x":[null,null],"y":[null,null]}}},{"type":"Circle","id":"4b3c4cc368b2733dfd6e1588fe19bb98","attributes":{"id":"4b3c4cc368b2733dfd6e1588fe19bb98","tags":[],"size":{"units":"screen","value":0},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":0.5},"line_color":{"units":"data","value":"#1F77B4"},"fill_color":{"units":"data","value":"#1F77B4"},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"}}},{"type":"Circle","id":"d21c9e8560ea5d344f93aea6c754fdad","attributes":{"id":"d21c9e8560ea5d344f93aea6c754fdad","tags":[],"size":{"units":"screen","value":0},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":0.5},"line_color":{"units":"data","value":"#e1e1e1"},"fill_color":{"units":"data","value":"#e1e1e1"},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"}}},{"type":"Circle","id":"3da1d95fabe2085910b12fca01de0ce4","attributes":{"id":"3da1d95fabe2085910b12fca01de0ce4","tags":[],"size":{"units":"screen","value":0},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":1},"line_color":{"units":"data","value":"#1F77B4"},"fill_color":{"units":"data","value":"#1F77B4"},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"}}},{"type":"GlyphRenderer","id":"2b417ac3aaf885bf575503f751ba30b8","attributes":{"id":"2b417ac3aaf885bf575503f751ba30b8","tags":[],"selection_glyph":null,"nonselection_glyph":{"type":"Circle","id":"d21c9e8560ea5d344f93aea6c754fdad"},"hover_glyph":{"type":"Circle","id":"3da1d95fabe2085910b12fca01de0ce4"},"name":null,"data_source":{"type":"ColumnDataSource","id":"33e1d1a27ad9cb6d07d1e3c25442bfca"},"glyph":{"type":"Circle","id":"4b3c4cc368b2733dfd6e1588fe19bb98"}}},{"type":"Circle","id":"48369d2d62486ce719da8774ee7b0071","attributes":{"id":"48369d2d62486ce719da8774ee7b0071","tags":[],"size":{"units":"screen","value":0},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":0.5},"line_color":{"units":"data","value":"#FF7F0E"},"fill_color":{"units":"data","value":"#FF7F0E"},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"}}},{"type":"Circle","id":"5f1b391a5e817ed15be7e94c52f87f16","attributes":{"id":"5f1b391a5e817ed15be7e94c52f87f16","tags":[],"size":{"units":"screen","value":0},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":0.5},"line_color":{"units":"data","value":"#e1e1e1"},"fill_color":{"units":"data","value":"#e1e1e1"},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"}}},{"type":"Circle","id":"eaf1ebe8ad51a92421ca66a5aa07fd4b","attributes":{"id":"eaf1ebe8ad51a92421ca66a5aa07fd4b","tags":[],"size":{"units":"screen","value":0},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":1},"line_color":{"units":"data","value":"#FF7F0E"},"fill_color":{"units":"data","value":"#FF7F0E"},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"}}},{"type":"GlyphRenderer","id":"3faffe25431e99808116983ce195fb48","attributes":{"id":"3faffe25431e99808116983ce195fb48","tags":[],"selection_glyph":null,"nonselection_glyph":{"type":"Circle","id":"5f1b391a5e817ed15be7e94c52f87f16"},"hover_glyph":{"type":"Circle","id":"eaf1ebe8ad51a92421ca66a5aa07fd4b"},"name":null,"data_source":{"type":"ColumnDataSource","id":"33e1d1a27ad9cb6d07d1e3c25442bfca"},"glyph":{"type":"Circle","id":"48369d2d62486ce719da8774ee7b0071"}}},{"type":"Circle","id":"9a9364821681329a6a41875cbded5569","attributes":{"id":"9a9364821681329a6a41875cbded5569","tags":[],"size":{"units":"screen","value":0},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":0.5},"line_color":{"units":"data","value":"#2CA02C"},"fill_color":{"units":"data","value":"#2CA02C"},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"}}},{"type":"Circle","id":"fffeaa1ca5a84ad57faad202b2ef9fb0","attributes":{"id":"fffeaa1ca5a84ad57faad202b2ef9fb0","tags":[],"size":{"units":"screen","value":0},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":0.5},"line_color":{"units":"data","value":"#e1e1e1"},"fill_color":{"units":"data","value":"#e1e1e1"},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"}}},{"type":"Circle","id":"70947e5a0e62fe70954c27af2d1c1125","attributes":{"id":"70947e5a0e62fe70954c27af2d1c1125","tags":[],"size":{"units":"screen","value":0},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":1},"line_color":{"units":"data","value":"#2CA02C"},"fill_color":{"units":"data","value":"#2CA02C"},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"}}},{"type":"GlyphRenderer","id":"57e1fdab56ec77e64ae8091a80bc29ef","attributes":{"id":"57e1fdab56ec77e64ae8091a80bc29ef","tags":[],"selection_glyph":null,"nonselection_glyph":{"type":"Circle","id":"fffeaa1ca5a84ad57faad202b2ef9fb0"},"hover_glyph":{"type":"Circle","id":"70947e5a0e62fe70954c27af2d1c1125"},"name":null,"data_source":{"type":"ColumnDataSource","id":"33e1d1a27ad9cb6d07d1e3c25442bfca"},"glyph":{"type":"Circle","id":"9a9364821681329a6a41875cbded5569"}}},{"type":"Circle","id":"e6bf4d6ac6477b7cfc88a772b273f902","attributes":{"id":"e6bf4d6ac6477b7cfc88a772b273f902","tags":[],"size":{"units":"screen","value":0},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":0.5},"line_color":{"units":"data","value":"#D62728"},"fill_color":{"units":"data","value":"#D62728"},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"}}},{"type":"Circle","id":"429399aa2eaa8bffa9adf690832dc9bb","attributes":{"id":"429399aa2eaa8bffa9adf690832dc9bb","tags":[],"size":{"units":"screen","value":0},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":0.5},"line_color":{"units":"data","value":"#e1e1e1"},"fill_color":{"units":"data","value":"#e1e1e1"},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"}}},{"type":"Circle","id":"a8137ab2c42bec7b9cc10b2c4e7d3b22","attributes":{"id":"a8137ab2c42bec7b9cc10b2c4e7d3b22","tags":[],"size":{"units":"screen","value":0},"visible":true,"line_alpha":{"units":"data","value":1},"fill_alpha":{"units":"data","value":1},"line_color":{"units":"data","value":"#D62728"},"fill_color":{"units":"data","value":"#D62728"},"x":{"units":"data","field":"x"},"y":{"units":"data","field":"y"}}},{"type":"GlyphRenderer","id":"ea4cc9214babc59e33a9e0e535eb5313","attributes":{"id":"ea4cc9214babc59e33a9e0e535eb5313","tags":[],"selection_glyph":null,"nonselection_glyph":{"type":"Circle","id":"429399aa2eaa8bffa9adf690832dc9bb"},"hover_glyph":{"type":"Circle","id":"a8137ab2c42bec7b9cc10b2c4e7d3b22"},"name":null,"data_source":{"type":"ColumnDataSource","id":"33e1d1a27ad9cb6d07d1e3c25442bfca"},"glyph":{"type":"Circle","id":"e6bf4d6ac6477b7cfc88a772b273f902"}}},{"type":"Legend","id":"f86dee6a2037fe10b38a71858fd0850b","attributes":{"id":"f86dee6a2037fe10b38a71858fd0850b","tags":[],"plot":{"type":"Plot","id":"867cff8f8bff17555b0ae6f0a00d6ab7","subtype":"Figure"},"legends":[["worldbankincomegroup",[]],[" High",[{"type":"GlyphRenderer","id":"2b417ac3aaf885bf575503f751ba30b8"}]],[" Low",[{"type":"GlyphRenderer","id":"3faffe25431e99808116983ce195fb48"}]],[" Lower-middle",[{"type":"GlyphRenderer","id":"57e1fdab56ec77e64ae8091a80bc29ef"}]],[" Upper-middle",[{"type":"GlyphRenderer","id":"ea4cc9214babc59e33a9e0e535eb5313"}]]],"location":"top_right"}},{"type":"Range1d","id":"97c263cb61d0a053b15693a3f2abc631","attributes":{"id":"97c263cb61d0a053b15693a3f2abc631","tags":[],"start":3.797,"end":52.703}},{"type":"Range1d","id":"c7a821bca304fb3a37ebaf4ab1cfcd44","attributes":{"id":"c7a821bca304fb3a37ebaf4ab1cfcd44","tags":[],"start":0.00099999999999989,"end":17.899}}]}}},"debug":false},"evals":[],"jsHooks":[]}</script>

---

# `htmlwidgets` packages

- [plotly](https://github.com/ropensci/plotly) ([docs](https://plot.ly/r/)):
    - Convert `ggplot2` graphics `$\rightarrow$` interactive graphics via `ggplotly()` and the plot.ly [ggplot2 library](https://plot.ly/ggplot2/) OR
    - Create interactive graphics directly with `plot_ly()` and the plot.ly [R library](https://plot.ly/r/)
    - Can operate entirely locally (your plot isn't automatically uploaded to plot.ly's servers and shared with the world).
- [ggiraph](https://github.com/davidgohel/ggiraph); [ggiraphExtra](https://github.com/cardiomoon/ggiraphExtra): make `ggplot2` graphics interactive.
- [DT](https://github.com/rstudio/DT) ([docs](http://rstudio.github.io/DT/)): interactive tables
- [leaflet](https://github.com/rstudio/leaflet) ([docs](http://rstudio.github.io/leaflet)): interactive maps
- [dygraphs](https://github.com/rstudio/dygraphs) ([docs](http://rstudio.github.io/dygraphs)): interactive time series plots
- [d3heatmap](https://github.com/rstudio/d3heatmap) ([demo](http://rpubs.com/jcheng/mtcars-heatmap)): interactive heatmaps
- [sparkline](https://github.com/htmlwidgets/sparkline): interactive versions of very small graphics (Edward Tufte)
- [DiagrammeR](http://rich-iannone.github.io/DiagrammeR/): interactive diagrams

---

# `htmlwidgets` packages

- [Highcharter](http://jkunst.com/highcharter/): interface to the Highcharts JavaScript library
- [formattable](https://renkun.me/formattable/): visualize/format vectors and data frames
- [networkD3](http://christophergandrud.github.io/networkD3/): interactive [D3](http://d3js.org/) JavaScript network graphs
- [threejs](https://github.com/bwlewis/rthreejs): interactive 3d plots and globes
- [MetricsGraphics](http://hrbrmstr.github.io/metricsgraphics/): interface to the MetricsGraphics D3 JavaScript library
- [rbokeh](https://github.com/bokeh/rbokeh) ([docs](http://hafen.github.io/rbokeh/), [useR2016 slides](http://slides.com/hafen/rbokeh#/)): interface to the Bokeh Python library
- [streamgraph](http://hrbrmstr.github.io/streamgraph/): interactive streamgraphs
- [visNetwork](https://github.com/datastorm-open/visNetwork) ([docs](http://datastorm-open.github.io/visNetwork)): interactive network graphs
- `$\ldots$`

---

# `htmlwidgets` helper packages

- [crosstalk](https://github.com/rstudio/crosstalk) ([docs](http://rstudio.github.io/crosstalk/)): htmlwidgets that can interact with one another
- [flexdashboard](https://github.com/rstudio/flexdashboard) ([docs](http://rmarkdown.rstudio.com/flexdashboard)): interactive dashboards via R Markdown
- [shinyjs](https://github.com/daattali/shinyjs) ([demo](http://daattali.com/shiny/shinyjs-demo/)): helper functions that can be used with Shiny or interactive R Markdown documents.
- [manipulateWidget](https://github.com/rte-antares-rpackage/manipulateWidget): add more interactivity to htmlwidgets

---
class: inverse, middle, center

# Other R packages for interactive and animated graphics

---

# R packages for interactive graphics
## Other packages worth mentioning

- [googleVis](https://github.com/mages/googleVis): R interface to Google Charts API
- [ggvis](https://github.com/rstudio/ggvis): SVG and HTML5 Canvas graphics, rendered using [vega](http://trifacta.github.io/vega/), declared in a "grammar of graphics" style similar to `ggplot2` ([docs](http://ggvis.rstudio.com/))
- [animint](https://github.com/tdhock/animint) ([useR2016 tutorial](https://github.com/tdhock/interactive-tutorial)): animated interactive web graphics
- [rgl](https://r-forge.r-project.org/projects/rgl/): interactive 3D visualizations using OpenGL and other frameworks/formats ([vignette](https://cran.r-project.org/web/packages/rgl/vignettes/rgl.html))

???

* This is by no means an exhaustive list.
* I'm only listing R packages that are currently available to download and use, appear to be actively maintained and haven't been subsumed by other R packages, produce graphics that can viewed in a web browser locally or embedded in an html document like this one that was produced with knitr and rmarkdown. I'm not going to list anything that requires server-side support from interfaces like Shiny or Rook to be interactive, for example.
* Many of these produce graphics in SVG, or Scalable Vector Graphics, which is, according to wikipedia, an XML-based vector image format for 2D graphics with support for interactivity and animation.  Not all of them do, though -- for example, googleVis produces some output as Flash.

---

# MotionChart with `googleVis`

```r
library(googleVis)
# for knitr:
# data.frame with >=4 cols: x, y, id, time. color and size optional,
# but if you don't provide them,
# it will choose them for you (if there are columns left to use).
# Motion Chart only displayed when hosted on a web server
# or placed in a directory listed in Flash's local trusted sources.
mc = gvisMotionChart(as.data.frame(
		subset(who_mort,
			   !is.na(country)&!is.na(value.maternal),
			   select = c(country, year, region, worldbankincomegroup, value.infant, value.maternal))),
	idvar = "country", timevar = "year",
	xvar = "value.infant", yvar = "value.maternal",
	colorvar = "worldbankincomegroup",
	options = list(width = 750, height = 650))
```

```r
plot(mc) # in an R session, opens in browser
```

### <a href="gvis_motion_chart.html" target="_blank">View visualization in new tab</a> (Flash-based)

???

* "The googleVis package provides an interface between R and the Google Charts API. Google Charts offer interactive charts which can be embedded into web pages. The best known of these charts is probably the Motion Chart, popularised by Hans Rosling in his TED talks.
The functions of the googleVis package allow the user to visualise data stored in R data frames with Google Charts without uploading the data to Google. The output of a googleVis function is html code that contains the data and references to JavaScript functions hosted by Google."
* data.frame with >=4 cols: x, y, id, time. color and size optional, but if you don't provide them, it will choose them for you (if there are columns left to use)
* by default the html file is saved in a temporary folder. if you have problems with a motion chart not displaying, it may be because of Flash's security settings. You'll need to manually add the directory where the html file is stored to the list of trusted local directories.

---
background-image: url('assets/img/dogs.jpg')
background-position: 50% 50%
class: center, bottom, inverse

# Dog tax

---
class: inverse, middle, center

# Questions?

---

# References

<a name=bib-Wickham_2009></a>[[1]](#cite-Wickham_2009) H. Wickham.
_ggplot2_. Springer Science + Business Media, 2009. URL:
[http://dx.doi.org/10.1007/978-0-387-98141-3](http://dx.doi.org/10.1007/978-0-387-98141-3).

<a name=bib-Wickham_2010></a>[[2]](#cite-Wickham_2010) H. Wickham.
"A Layered Grammar of Graphics". In: _Journal of Computational and
Graphical Statistics_ 19.1 (2010), pp. 3-28. URL:
[http://dx.doi.org/10.1198/jcgs.2009.07098](http://dx.doi.org/10.1198/jcgs.2009.07098).

<a name=bib-Wickham_2014></a>[[3]](#cite-Wickham_2014) H. Wickham.
"Tidy Data". In: _Journal of Statistical Software_ 59.10 (2014).
URL:
[http://www.jstatsoft.org/v59/i10](http://www.jstatsoft.org/v59/i10).

<a name=bib-Wilkinson_2005></a>[[4]](#cite-Wilkinson_2005) L.
Wilkinson. _The Grammar of Graphics_. Springer-Verlag New York, .
URL:
[http://dx.doi.org/10.1007/0-387-28695-0](http://dx.doi.org/10.1007/0-387-28695-0).

---

# Credits

#### Built using [xaringan](https://github.com/yihui/xaringan) with
+ [knitr](http://yihui.name/knitr),
+ [R Markdown](https://rmarkdown.rstudio.com),
+ the [remark.js](https://github.com/gnab/remark/) framework,
+ the [RefManageR](http://cran.r-project.org/web/packages/RefManageR/index.html) bibliography manager, and
+ the [highlight.js](https://highlightjs.org/) syntax highlighter.

---

# Session Info

**R version 3.3.2 (2016-10-31)**

**Platform:** x86_64-apple-darwin13.4.0 (64-bit)

**attached base packages:** 
_grid_, _stats_, _graphics_, _grDevices_, _utils_, _datasets_ and _base_

**other attached packages:** 
_pander(v.0.6.0)_, _googleVis(v.0.6.2)_, _rbokeh(v.0.5.0)_, _leaflet(v.1.1.0)_, _DT(v.0.2)_, _plotly(v.4.5.6)_, _gdtools(v.0.1.3)_, _ggiraph(v.0.3.2.9000)_, _gtable(v.0.2.0)_, _ggdendro(v.0.1-20)_, _tidyr(v.0.6.1)_, _ggrepel(v.0.6.5)_, _dplyr(v.0.5.0)_, _showtext(v.0.4-6)_, _sysfonts(v.0.5)_, _ggplot2(v.2.2.1)_ and _RefManageR(v.0.13.1)_

**loaded via a namespace (and not attached):** 
_Rcpp(v.0.12.9)_, _lubridate(v.1.6.0)_, _lattice(v.0.20-34)_, _assertthat(v.0.1)_, _rprojroot(v.1.2)_, _digest(v.0.6.12)_, _mime(v.0.5)_, _R6(v.2.2.0)_, _plyr(v.1.8.4)_, _backports(v.1.0.5)_, _evaluate(v.0.10)_, _httr(v.1.2.1)_, _xaringan(v.0.2)_, _lazyeval(v.0.2.0)_, _hexbin(v.1.27.1)_, _R.utils(v.2.5.0)_, _R.oo(v.1.21.0)_, _rmarkdown(v.1.3)_, _labeling(v.0.3)_, _stringr(v.1.2.0)_, _htmlwidgets(v.0.8)_, _RCurl(v.1.95-4.8)_, _munsell(v.0.4.3)_, _shiny(v.1.0.0)_, _httpuv(v.1.3.3)_, _gistr(v.0.3.6)_, _base64enc(v.0.1-3)_, _rvg(v.0.1.3)_, _htmltools(v.0.3.5)_, _tibble(v.1.2)_, _gridExtra(v.2.2.1)_, _codetools(v.0.2-15)_, _XML(v.3.98-1.5)_, _viridisLite(v.0.1.3)_, _showtextdb(v.1.0)_, _MASS(v.7.3-45)_, _bitops(v.1.0-6)_, _R.methodsS3(v.1.7.1)_, _jsonlite(v.1.3)_, _xtable(v.1.8-2)_, _DBI(v.0.6)_, _magrittr(v.1.5)_, _scales(v.0.4.1)_, _bibtex(v.0.4.0)_, _stringi(v.1.1.2)_, _pryr(v.0.1.2)_, _viridis(v.0.3.4)_, _xml2(v.1.1.1)_, _RColorBrewer(v.1.1-2)_, _tools(v.3.3.2)_, _RJSONIO(v.1.3-0)_, _officer(v.0.1.1)_, _purrr(v.0.2.2)_, _maps(v.3.1.1)_, _crosstalk(v.1.0.0)_, _yaml(v.2.1.14)_, _colorspace(v.1.3-2)_, _knitr(v.1.15.1)_ and _methods(v.3.3.2)_