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Look at your data!
Yes, that means look at your raw data by producing subsets, printing to the console, and all the other things we’ve been doing. But it also means that we should visualize our data.
The potential purposes for doing so are legion, but we can name a few for starters:
Before we continue, though, we should keep one thing in mind: data visualization is not about making pretty pictures. Saying so does not discount the importance of aesthetics in your visualizations. However, the aesthetics you employ should be chosen to make the message of the figure clear to your audience. In some cases, we might want to highlight a particular contrast between two variables. In others, we might want to visualize how a particular variable trends over time. In yet others, we may simply want to visualize features in our raw dataset.
Each of these objectives implies the usefulness of certain visualizations and the impropriety of others. In other words, we want the purpose of the visualization to dictate the means by which we display relevant data.
In this introduction, we will focus on a couple of broad concepts and code examples in order to get you up and running as quickly as possible. However, I highly recommend two (freely available) resources in particular:
Data Visualization: A Practical Introduction by Kieran Healy
Top 50 ggplot2 Visualizations - The Master List (with Full R Code) by Selva Prabhakaran
Keeping these bookmarked will probably cover about 90%4 Source: Thin air of your data visualization needs.
All the examples below use the NHEFS data we used in the chapter on statistical analysis, so let’s remind ourselves what the data look like.
nhefs <- readr::read_csv("data/nhefs.csv")Rows: 1629 Columns: 64
── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
dbl (64): seqn, qsmk, death, yrdth, modth, dadth, sbp, dbp, sex, age, race, ...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(nhefs)spec_tbl_df [1,629 × 64] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ seqn : num [1:1629] 233 235 244 245 252 257 262 266 419 420 ...
$ qsmk : num [1:1629] 0 0 0 0 0 0 0 0 0 0 ...
$ death : num [1:1629] 0 0 0 1 0 0 0 0 1 1 ...
$ yrdth : num [1:1629] NA NA NA 85 NA NA NA NA 84 86 ...
$ modth : num [1:1629] NA NA NA 2 NA NA NA NA 10 10 ...
$ dadth : num [1:1629] NA NA NA 14 NA NA NA NA 13 17 ...
$ sbp : num [1:1629] 175 123 115 148 118 141 132 100 163 184 ...
$ dbp : num [1:1629] 96 80 75 78 77 83 69 53 79 106 ...
$ sex : num [1:1629] 0 0 1 0 0 1 1 1 0 0 ...
$ age : num [1:1629] 42 36 56 68 40 43 56 29 51 43 ...
$ race : num [1:1629] 1 0 1 1 0 1 0 0 0 0 ...
$ income : num [1:1629] 19 18 15 15 18 11 19 22 18 16 ...
$ marital : num [1:1629] 2 2 3 3 2 4 2 2 2 2 ...
$ school : num [1:1629] 7 9 11 5 11 9 12 12 10 11 ...
$ education : num [1:1629] 1 2 2 1 2 2 3 3 2 2 ...
$ ht : num [1:1629] 174 159 168 170 182 ...
$ wt71 : num [1:1629] 79 58.6 56.8 59.4 87.1 ...
$ wt82 : num [1:1629] 68.9 61.2 66.2 64.4 92.1 ...
$ wt82_71 : num [1:1629] -10.09 2.6 9.41 4.99 4.99 ...
$ birthplace : num [1:1629] 47 42 51 37 42 34 NA NA 42 42 ...
$ smokeintensity : num [1:1629] 30 20 20 3 20 10 20 2 25 20 ...
$ smkintensity82_71: num [1:1629] -10 -10 -14 4 0 10 0 1 -10 -20 ...
$ smokeyrs : num [1:1629] 29 24 26 53 19 21 39 9 37 25 ...
$ asthma : num [1:1629] 0 0 0 0 0 0 0 0 0 0 ...
$ bronch : num [1:1629] 0 0 0 0 0 0 0 0 0 0 ...
$ tb : num [1:1629] 0 0 0 0 0 0 0 0 0 0 ...
$ hf : num [1:1629] 0 0 0 0 0 0 0 0 0 0 ...
$ hbp : num [1:1629] 1 0 0 1 0 0 0 0 0 0 ...
$ pepticulcer : num [1:1629] 1 0 0 0 0 0 0 0 0 0 ...
$ colitis : num [1:1629] 0 0 0 0 0 0 0 0 0 0 ...
$ hepatitis : num [1:1629] 0 0 0 0 0 0 0 0 0 1 ...
$ chroniccough : num [1:1629] 0 0 0 0 0 0 0 0 0 0 ...
$ hayfever : num [1:1629] 0 0 1 0 0 0 0 0 0 0 ...
$ diabetes : num [1:1629] 1 0 0 0 0 0 1 0 0 0 ...
$ polio : num [1:1629] 0 0 0 0 0 0 0 0 0 0 ...
$ tumor : num [1:1629] 0 0 1 0 0 0 0 0 0 0 ...
$ nervousbreak : num [1:1629] 0 0 0 0 0 0 0 0 0 0 ...
$ alcoholpy : num [1:1629] 1 1 1 1 1 1 1 1 1 1 ...
$ alcoholfreq : num [1:1629] 1 0 3 2 2 3 1 0 1 0 ...
$ alcoholtype : num [1:1629] 3 1 4 3 1 2 3 2 3 1 ...
$ alcoholhowmuch : num [1:1629] 7 4 NA 4 2 1 4 1 2 6 ...
$ pica : num [1:1629] 0 0 0 0 0 0 0 0 0 0 ...
$ headache : num [1:1629] 1 1 1 0 1 1 1 1 1 1 ...
$ otherpain : num [1:1629] 0 0 1 1 0 0 0 0 1 0 ...
$ weakheart : num [1:1629] 0 0 0 1 0 0 0 0 0 0 ...
$ allergies : num [1:1629] 0 0 0 0 0 0 0 0 0 0 ...
$ nerves : num [1:1629] 0 0 1 0 0 0 1 1 0 0 ...
$ lackpep : num [1:1629] 0 0 0 0 0 0 0 0 0 0 ...
$ hbpmed : num [1:1629] 1 0 0 0 0 0 0 0 0 0 ...
$ boweltrouble : num [1:1629] 0 0 0 0 1 0 0 0 0 0 ...
$ wtloss : num [1:1629] 0 0 0 0 0 0 0 0 0 0 ...
$ infection : num [1:1629] 0 1 0 0 0 0 0 0 0 0 ...
$ active : num [1:1629] 0 0 0 1 1 1 0 0 2 1 ...
$ exercise : num [1:1629] 2 0 2 2 1 1 1 2 2 2 ...
$ birthcontrol : num [1:1629] 2 2 0 2 2 0 0 0 2 2 ...
$ pregnancies : num [1:1629] NA NA 2 NA NA 1 1 2 NA NA ...
$ cholesterol : num [1:1629] 197 301 157 174 216 212 205 166 337 279 ...
$ hightax82 : num [1:1629] 0 0 0 0 0 1 NA NA 0 0 ...
$ price71 : num [1:1629] 2.18 2.35 1.57 1.51 2.35 ...
$ price82 : num [1:1629] 1.74 1.8 1.51 1.45 1.8 ...
$ tax71 : num [1:1629] 1.102 1.365 0.551 0.525 1.365 ...
$ tax82 : num [1:1629] 0.462 0.572 0.231 0.22 0.572 ...
$ price71_82 : num [1:1629] 0.4438 0.5493 0.0562 0.0548 0.5493 ...
$ tax71_82 : num [1:1629] 0.64 0.793 0.32 0.305 0.793 ...
- attr(*, "spec")=
.. cols(
.. seqn = col_double(),
.. qsmk = col_double(),
.. death = col_double(),
.. yrdth = col_double(),
.. modth = col_double(),
.. dadth = col_double(),
.. sbp = col_double(),
.. dbp = col_double(),
.. sex = col_double(),
.. age = col_double(),
.. race = col_double(),
.. income = col_double(),
.. marital = col_double(),
.. school = col_double(),
.. education = col_double(),
.. ht = col_double(),
.. wt71 = col_double(),
.. wt82 = col_double(),
.. wt82_71 = col_double(),
.. birthplace = col_double(),
.. smokeintensity = col_double(),
.. smkintensity82_71 = col_double(),
.. smokeyrs = col_double(),
.. asthma = col_double(),
.. bronch = col_double(),
.. tb = col_double(),
.. hf = col_double(),
.. hbp = col_double(),
.. pepticulcer = col_double(),
.. colitis = col_double(),
.. hepatitis = col_double(),
.. chroniccough = col_double(),
.. hayfever = col_double(),
.. diabetes = col_double(),
.. polio = col_double(),
.. tumor = col_double(),
.. nervousbreak = col_double(),
.. alcoholpy = col_double(),
.. alcoholfreq = col_double(),
.. alcoholtype = col_double(),
.. alcoholhowmuch = col_double(),
.. pica = col_double(),
.. headache = col_double(),
.. otherpain = col_double(),
.. weakheart = col_double(),
.. allergies = col_double(),
.. nerves = col_double(),
.. lackpep = col_double(),
.. hbpmed = col_double(),
.. boweltrouble = col_double(),
.. wtloss = col_double(),
.. infection = col_double(),
.. active = col_double(),
.. exercise = col_double(),
.. birthcontrol = col_double(),
.. pregnancies = col_double(),
.. cholesterol = col_double(),
.. hightax82 = col_double(),
.. price71 = col_double(),
.. price82 = col_double(),
.. tax71 = col_double(),
.. tax82 = col_double(),
.. price71_82 = col_double(),
.. tax71_82 = col_double()
.. )
- attr(*, "problems")=<externalptr> You may find that working with plotting in base R is more efficient when you want quick-and-dirty visualizations—for instance, while you’re conducting exploratory data analysis.
# using built-in iris dataset
boxplot(nhefs$sbp)
boxplot(sbp ~ diabetes, data = nhefs)
## use na.omit() to drop missing values in the `sbp` variable
## R will throw an error otherwise
plot(density(na.omit(nhefs$sbp)))
plot(nhefs$sbp)
plot(nhefs$sbp ~ nhefs$age)
matrix_vars <- c("qsmk", "sbp", "dbp", "age", "smokeyrs", "wt71")
pairs(nhefs[, matrix_vars])
hist(nhefs$smokeyrs)
Base R and some packages provide built-in plotting methods for certain types of objects. Here, feeding an object of the class lm will return a series of linear regression diagnostics.
## run a regression on built-in dataset ChickWeight
fit <- lm(wt82_71 ~ smokeyrs, data = nhefs)
class(fit)[1] "lm"plot(fit)
If you run plot(fit) as above, you might find R irritating in that it will print these plots to your image device (image pane in RStudio) in succession, leaving you to flip between images. For a nicer output, you can output a grid of regression diagnostics.
par(mfrow = c(2, 2))
plot(fit)
Before we start, let’s load a few of the packages we’ll be using.
library(ggplot2) # plotting package
library(ggthemes) # a package that adds plot themes to ggplot2
library(magrittr) # use pipes "%>%"Before, we get into the examples, I’m going to apply a universal theme for the subsequent plots:
theme_set(theme_few(base_size = 16))The ggplot2 package in R is an excellent package for data visualization based on the grammar of graphics concept.5 Leland Wilkinson. The Grammar of Graphics. Springer, 2005. The basic idea is to divide plot features into conceptual layers defined by their function in the visualization.
Here’s a general schema for how this concept maps to plot components in ggplot2:
Note that I’ve assigned names to each element for the sake of introduction, but the descriptions themselves follow closely from Hadley Wickham’s (ggplot2 creator) own in the paper referenced here.6 Hadley Wickham (2010) A Layered Grammar of Graphics, Journal of Computational and Graphical Statistics, 19:1, 3-28, DOI: 10.1198/jcgs.2009.07098
In the following examples I will focus on longform syntax with the ggplot function. Learning the ins and outs of ggplot2 will give you more flexibility and control over your images, particularly when you need to produce publication-quality graphics. However, ggplot2 provides the qplot function for quick visualizations, akin to the base R graphics depicted above. One nice thing about qplot the its default setting to choose a geometric mapping automatically (hereafter, “geom”). Below, the function automatically selects a scatterplot.
qplot(x = age, y = sbp, data = nhefs)Warning: Removed 77 rows containing missing values (geom_point).
However, we can specify the geom explicitly. Here we get a smoothed line:
qplot(x = age, y = sbp, data = nhefs, geom = "smooth")`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'Warning: Removed 77 rows containing non-finite values (stat_smooth).
The ggplot2 website contains extensive documentation on all of the package’s functions, including many helpful worked examples.
Strange Symbols
Note the difference between the magrittr pipe %>%, which we use to feed our data set to the ggplot() function and the + symbol we use to bind together the lines describing plot elements. This distinction is mandatory, as ggplot2 will not allow you to use the magrittr pipe to link plot layers.
nhefs %>%
ggplot(aes(x = sbp)) +
geom_boxplot()Warning: Removed 77 rows containing non-finite values (stat_boxplot).
nhefs %>%
ggplot(aes(x = factor(diabetes), y = sbp)) +
geom_boxplot()Warning: Removed 77 rows containing non-finite values (stat_boxplot).
nhefs %>%
ggplot(aes(x = sbp, color = factor(diabetes))) +
geom_density()Warning: Removed 77 rows containing non-finite values (stat_density).
If we want to see the relationship between age and systolic blood pressure, we can make a scatterplot.
nhefs %>%
ggplot(aes(x = age, y = sbp)) +
geom_point()Warning: Removed 77 rows containing missing values (geom_point).
Perhaps this relationship varies by diabetes status. In that case we need to differentiate each point based on the diabetes variable. We have multiple options to do so. Two that make immediate sense would be to differentiate species by color or by shape.
nhefs %>%
ggplot(
aes(
x = age,
y = sbp,
color = factor(diabetes)
)
) +
geom_point(size = 3)Warning: Removed 77 rows containing missing values (geom_point).
nhefs %>%
ggplot(
aes(
x = age,
y = sbp,
shape = factor(diabetes)
)
) +
geom_point(size = 3) +
scale_shape_manual(values = c(21, 22, 23)) # choose some shapes (optional)Warning: Removed 77 rows containing missing values (geom_point).
Another option would have been to create a separate scatterplot for each species. In the plot below, each section of the grid is called a facet. We’ll also save an initial (unfaceted) plot so we can play with the facet settings a bit.
## labels to use for axis and facet plots
dblabels <- c("0" = "No", "1" = "Yes", "2" = "Missing")
sbplab <- "Systolic blood pressure"
agelab <- "Age"
sbp_facet <- nhefs %>%
ggplot(aes(x = age, y = sbp)) +
geom_point() +
labs(x = agelab, y = sbplab)
sbp_facet +
facet_wrap(
vars(diabetes),
labeller = labeller(diabetes = dblabels)
)Warning: Removed 77 rows containing missing values (geom_point).
If we wanted a single column, we could have asked facet_wrap() for one.
sbp_facet +
facet_wrap(
vars(diabetes),
labeller = labeller(diabetes = dblabels),
ncol = 1
)Warning: Removed 77 rows containing missing values (geom_point).
Finally, we also could have plotted the group-specific densities using facets instead of colors.
nhefs %>%
ggplot(aes(x = sbp)) +
geom_density() +
facet_wrap(
vars(diabetes),
labeller = labeller(diabetes = dblabels)
)Warning: Removed 77 rows containing non-finite values (stat_density).
In practice, you will choose particular plot designs to illuminate the aspects of your data you want to highlight.
To demonstrate the difference between two functions, geom_bar() and geom_col(), we’ll create a binary variable that encodes whether a given observation had a petal length less than 2 or at least 2.
library(dplyr)
### make discrete categories for Petal.Length
### only to demonstrate geom_bar and geom_col
nhefs_new <- nhefs %>%
mutate(
sbp.group = case_when(
sbp >= 140 ~ "High",
sbp < 140 ~ "Low",
TRUE ~ NA_character_
)
)
head(nhefs_new)# A tibble: 6 × 65
seqn qsmk death yrdth modth dadth sbp dbp sex age race income
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 233 0 0 NA NA NA 175 96 0 42 1 19
2 235 0 0 NA NA NA 123 80 0 36 0 18
3 244 0 0 NA NA NA 115 75 1 56 1 15
4 245 0 1 85 2 14 148 78 0 68 1 15
5 252 0 0 NA NA NA 118 77 0 40 0 18
6 257 0 0 NA NA NA 141 83 1 43 1 11
# … with 53 more variables: marital <dbl>, school <dbl>, education <dbl>,
# ht <dbl>, wt71 <dbl>, wt82 <dbl>, wt82_71 <dbl>, birthplace <dbl>,
# smokeintensity <dbl>, smkintensity82_71 <dbl>, smokeyrs <dbl>,
# asthma <dbl>, bronch <dbl>, tb <dbl>, hf <dbl>, hbp <dbl>,
# pepticulcer <dbl>, colitis <dbl>, hepatitis <dbl>, chroniccough <dbl>,
# hayfever <dbl>, diabetes <dbl>, polio <dbl>, tumor <dbl>,
# nervousbreak <dbl>, alcoholpy <dbl>, alcoholfreq <dbl>, …The geom_bar() function will count up the number of members in each group.
nhefs_new %>%
ggplot(aes(x = sbp.group)) +
geom_bar()
Let’s make that a little prettier, shall we?
The NA you see represented in the plots indicates missing values of systolic blood pressure.
nhefs_new %>%
ggplot(aes(x = sbp.group)) +
geom_bar(
fill = "lightgray",
color = "black",
width = 0.5
)
Often you’ll find that you’ll be summarizing your data to make certain types of plots. Let’s say we had already counted the number of members per sbp.group level ourselves, as so:
nhefs_sum <- nhefs_new %>% count(sbp.group)
nhefs_sum# A tibble: 3 × 2
sbp.group n
<chr> <int>
1 High 390
2 Low 1162
3 <NA> 77To create the preceding plot, we would use geom_col() instead of geom_bar():
nhefs_sum %>%
ggplot(aes(x = sbp.group, y = n)) +
geom_col(
fill = "lightgray",
color = "black",
width = 0.5
)
In many cases, you’ll find more than one viable route to your chosen graphic.
Base R has a nice function to create scatterplot matrices. To do so with ggplot2 we need to install another package called GGally.
One wonderful thing about ggplot2 is that it boasts a universe of extensions written by R users to achieve specific plotting goals. While learning the ins and outs of ggplot2 is fun (for me, anyway), you may find extensions useful.
GGally::ggpairs(nhefs[, matrix_vars])
Missing Data
Note that I’ve suppressed a number of warnings produced by this ggpairs example. These warnings highlight that some of the variables used in the plot have varying numbers of missing values.
This visualization is nice because it gives you a lot of information in a small space. The bottom row even contains embedded facets!
Whether you’re writing a manuscript, submitting final materials for publication, compiling internal reports, or developing materials for the web, you will need to pick an appropriate image format for your output.
The table below summarizes some of the more common image formats you’ll come across and when they may be useful. Perhaps the most important distinction between formats is between raster and vector images. Raster formats represent images using pixels, while vector formats represent image components using paths. For practical purposes, the most important thing to remember is that vector formats are scalable without loss of resolution, while raster images aren’t.
Images in this Book The majority of images in this book are in SVG format, which means you can zoom in on them to your heart’s content without the image becoming blurry! Right-click on any of the prior images on this page, open it in a new tab, and zoom in to see what I mean.
I try to use vector formats whenever possible, as it is easy to convert a vector format to a raster format—i.e., to rasterize—but not the other way around. Therefore, I usually find myself saving images in PDF or SVG format, depending on the type of document I’m working on. In some cases, particularly for the web, using a raster format such as PNG may be the best choice to reduce file size and increase page load speeds.
|
Suitable for
|
||||
|---|---|---|---|---|
| Format | Web | Word | Publication | |
| Raster | ||||
| JPG | Yes | Yes | Yes | No |
| PNG | Yes | Yes | Yes | No |
| GIF | Yes | Yes | Yes | No |
| TIFF | No | Yes | Yes | Yes |
| Vector | ||||
| EPS | No | Yes | No | Yes |
| No | Yes | No | Yes | |
| WMF | No | No | Yes | No |
| SVG | Yes | No | No | No |
Let’s take a look at some examples to get an idea of the pros and cons of several formats.
First, let’s make a plot to use in the following examples.
imgfmt_plot <- nhefs %>%
ggplot(aes(x = sbp)) +
geom_density(
aes(color = factor(diabetes),
fill = factor(diabetes)),
alpha = 0.1
) +
scale_color_viridis_d(name = "Diabetes", labels = dblabels) +
scale_fill_viridis_d(name = "Diabetes", labels = dblabels) +
guides(
color = guide_legend(override.aes = list(linetype = 0))
) +
labs(x = "\nSystolic blood pressure", y = "Density\n") +
theme_few()We’ll proceed to save this plot in a few different formats and at different resolutions using the ggsave() function. You can open the images in a new tab to view them at their true size and resolution. After presenting to code to produce each one, I’ll provide a table so you can easily download the relevant files. One nice thing about ggsave() is that it will automatically detect the export format based on the file extension we specify.
Resolution Resolution refers either to print or screen resolution. DPI stands for “dots per inch”, the measure journals will use to request publication-quality graphics for their print editions. PPI stands for “pixels per inch”, a measure of the image’s resolution on screen.
ggsave(
filename = "images/fmt-example-png-72dpi.png",
plot = imgfmt_plot,
width = 6,
height = 4,
units = "in",
dpi = 72
)ggsave(
filename = "images/fmt-example-png-300dpi.png",
plot = imgfmt_plot,
width = 6,
height = 4,
units = "in",
dpi = 300
)ggsave(
filename = "images/fmt-example-tiff-1200dpi.tiff",
plot = imgfmt_plot,
width = 6,
height = 4,
units = "in",
dpi = 1200
)For the PDF, EPS, and SVG formats, we do not need to specify the image resolution (dpi) because these are vector formats.
ggsave(
filename = "images/fmt-example-pdf.pdf",
plot = imgfmt_plot,
width = 6,
height = 4,
units = "in"
)ggsave(
filename = "images/fmt-example-eps.eps",
plot = imgfmt_plot,
width = 6,
height = 4,
units = "in"
)ggsave(
filename = "images/fmt-example-svg.svg",
plot = imgfmt_plot,
width = 6,
height = 4,
units = "in"
)| Format | Resolution (dpi) | Download | Accepted by journals? |
|---|---|---|---|
| PNG | 72 (web standard) | PNG 72dpi | No |
| PNG | 300 | PNG 300dpi | No |
| TIFF | 1200 (print quality for plots) | TIFF 1200dpi | Yes |
| N/A | Yes (usually) | ||
| EPS | N/A | EPS | Yes |
| SVG | N/A | SVG | No |
Most of the time you’ll probably focus on creating static graphics for publication. However, you may find dynamic graphics fitting when sharing HTML notebooks or other web-based resources.
Luckily, the plotly package makes creating dynamic graphics from ggplot2 plots trivial.
Lets make an interactive graphic out of our static sbp_facet plot from earlier in this chapter. It’s literally this simple!
sbp_facet_plotly <- sbp_facet +
facet_wrap(
vars(diabetes),
labeller = labeller(diabetes = dblabels)
) +
ggtitle("Systolic blood pressure, by age and diabetes status") +
theme_few() +
theme(axis.title.x = element_text(margin = margin(t = 15)),
axis.title.y = element_text(margin = margin(r = 15)),
plot.margin = margin(t = 10, r = 10, b = 10, l = 20))
plotly::ggplotly(sbp_facet_plotly)If you mouse over the points, you should see information about each observation (i.e., x and y variable values).
You could also use the plot_ly() function to make the plotly figure directly.
plotly::plot_ly(
data = nhefs,
x = ~ age,
y = ~ sbp,
color = ~ factor(diabetes),
type = "scatter",
mode = "markers",
colors = viridisLite::cividis(n = length(unique(nhefs$diabetes))),
marker = list(colorscale = "Viridis"),
width = 700
) %>%
plotly::layout(
xaxis = list(title = agelab),
yaxis = list(title = sbplab),
legend = list(title = list(text = "<b>Diabetes</b>")),
margin = list(b = 75)
)If interactive graphics is an avenue you’d like to pursue further, look into the r2d3 library, which allows you to create beautiful visualizations using the powerful d3 Javascript library.
Plotly is actually powered by d3.
For R users, however, the plotly package provides a more immediate means to translate your data into interactive graphics. As we saw above, we can create a nice plot in ggplot2 and export it for the web with plotly::ggplotly!