library(tidyverse)
library(knitr)
library(plotly)
library(readxl)
library(scales)
library(kableExtra)Create Interactive Data Visualization with Plotly in R
Abstract
This working document is a user guide on how to create interactive graphs with R via a package called plotly
Overview
- This is an RMarkdown explaining how we use the
Plotlypackage to more effectivley communicate clinical and translational research via interactive graphs - Plotly is a computing company that develops online data analytics and visualization tools
- It has open sourced many useful interactive visualization products.
- Plotly can be used in several programming languages (e.g.
Python,RandJavaScript)- This tutorial will be written with instruction for
Rusers - Although https://plot.ly/ is fabulous resource, we found that there were elements from those tutorials that were missing and we hope that this post will provide a useful resource to get started in
PlotlyinR
- This tutorial will be written with instruction for
- Why use Plotly?
- Plotly allows us to create visually appealing interactive plots.
- The ability to export to
htmland retain all interactive functionality is easily accomplished with Plotly. - It is focused around the ability to generate interactive plots with a few lines of code.
- Finally, all interactive features are compatible with modern web browsers.
Step 1, Download Plotly from CRAN
- Use the install.package() function to install the plotly R package from CRAN
install.packages("plotly")
Load Relevant Packages
Load dataset
- For the first example we will use mtcars (Motor Trend Car Road Tests), which is built into base
R
mtcars <- mtcarsView the mtcars Data Frame
mtcars %>%
kbl(align = "c") %>%
kable_styling(
bootstrap_options = c("striped","hover","condensed"),
full_width = F,
font_size = 16,
fixed_thead = T
) %>%
scroll_box(
height = "500px"
)| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
| Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
| Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
| Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
| Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
| Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |
| Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0 | 0 | 3 | 4 |
| Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1 | 0 | 4 | 2 |
| Merc 230 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.150 | 22.90 | 1 | 0 | 4 | 2 |
| Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.30 | 1 | 0 | 4 | 4 |
| Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.90 | 1 | 0 | 4 | 4 |
| Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.070 | 17.40 | 0 | 0 | 3 | 3 |
| Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.730 | 17.60 | 0 | 0 | 3 | 3 |
| Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.780 | 18.00 | 0 | 0 | 3 | 3 |
| Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.250 | 17.98 | 0 | 0 | 3 | 4 |
| Lincoln Continental | 10.4 | 8 | 460.0 | 215 | 3.00 | 5.424 | 17.82 | 0 | 0 | 3 | 4 |
| Chrysler Imperial | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.345 | 17.42 | 0 | 0 | 3 | 4 |
| Fiat 128 | 32.4 | 4 | 78.7 | 66 | 4.08 | 2.200 | 19.47 | 1 | 1 | 4 | 1 |
| Honda Civic | 30.4 | 4 | 75.7 | 52 | 4.93 | 1.615 | 18.52 | 1 | 1 | 4 | 2 |
| Toyota Corolla | 33.9 | 4 | 71.1 | 65 | 4.22 | 1.835 | 19.90 | 1 | 1 | 4 | 1 |
| Toyota Corona | 21.5 | 4 | 120.1 | 97 | 3.70 | 2.465 | 20.01 | 1 | 0 | 3 | 1 |
| Dodge Challenger | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.520 | 16.87 | 0 | 0 | 3 | 2 |
| AMC Javelin | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.435 | 17.30 | 0 | 0 | 3 | 2 |
| Camaro Z28 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.840 | 15.41 | 0 | 0 | 3 | 4 |
| Pontiac Firebird | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.845 | 17.05 | 0 | 0 | 3 | 2 |
| Fiat X1-9 | 27.3 | 4 | 79.0 | 66 | 4.08 | 1.935 | 18.90 | 1 | 1 | 4 | 1 |
| Porsche 914-2 | 26.0 | 4 | 120.3 | 91 | 4.43 | 2.140 | 16.70 | 0 | 1 | 5 | 2 |
| Lotus Europa | 30.4 | 4 | 95.1 | 113 | 3.77 | 1.513 | 16.90 | 1 | 1 | 5 | 2 |
| Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.50 | 0 | 1 | 5 | 4 |
| Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.50 | 0 | 1 | 5 | 6 |
| Maserati Bora | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.570 | 14.60 | 0 | 1 | 5 | 8 |
| Volvo 142E | 21.4 | 4 | 121.0 | 109 | 4.11 | 2.780 | 18.60 | 1 | 1 | 4 | 2 |
Make an Interactive Bar Chart in Plotly
- For this first example, we will make a bar chart of the number of vehicles with “4”, “6” and “8” cylinders
- One approach is to create a new data frame with two vectors (columns)
- one that we create with the String “Four Cylinders”, “Six Cylinders”, and “Eight Cylinders” and
- another Vector that is the sum of all of the rows in mtcars that have a “4”, “6” or “8” in them.
- We’ll call the first vector “vehicles” and the second “cylinders”
- one that we create with the String “Four Cylinders”, “Six Cylinders”, and “Eight Cylinders” and
- This allows us to make a table that has a very defined x-axis and y-axis to make construtcting a bar graph very straight forward
vehicles <- c("Four Cylinders","Six Cylinders","Eight Cylinders")
cylinders <- c(sum(mtcars$cyl==4), sum(mtcars$cyl==6), sum(mtcars$cyl==8)) # of note, the sum() function will allow us to add up all the observations with either a "4" or a "6" or a "8" View these two vectors
vehicles %>%
kbl(align = "c") %>%
kable_styling(
bootstrap_options = c("striped","hover","condensed"),
full_width = F,
font_size = 16,
fixed_thead = T
) | x |
|---|
| Four Cylinders |
| Six Cylinders |
| Eight Cylinders |
cylinders %>%
kbl(align = "c") %>%
kable_styling(
bootstrap_options = c("striped","hover","condensed"),
full_width = F,
font_size = 16,
fixed_thead = T
) | x |
|---|
| 11 |
| 7 |
| 14 |
- As you can see, there are 11 vehicles with “4” contained in the columns’ cells, 7 with “6” and 14 with “8”
- Now combine these two vectors into a tibble, we’ll call it “veh_cyl”
- Of note, a
tibbleis a modern rework of the standarddata.frame, with some internal improvements to make code more reliable.- They are data frames, but do not follow all of the same rules
- For example, tibbles can have column names that are not normally allowed, such as numbers/symbols.
- They are data frames, but do not follow all of the same rules
- Of note, a
veh_cyl <- tibble(vehicles, cylinders)View new Tibble
veh_cyl %>%
kbl(align = "c") %>%
kable_styling(
bootstrap_options = c("striped","hover","condensed"),
full_width = F,
font_size = 16,
fixed_thead = T
)| vehicles | cylinders |
|---|---|
| Four Cylinders | 11 |
| Six Cylinders | 7 |
| Eight Cylinders | 14 |
Now that we have a tibble that can be easily turned into a bar graph, let’s use Plotly to make an interactive graph
plot_ly(data = veh_cyl, x = vehicles, y = cylinders, type = "bar", text = cylinders, textposition = "auto") %>%
layout(title = "Number of Vehicles in mtcars with 4, 6, and 8 Cylinders",
titlefont = list(size = 28, color = "orange", family = "Calibri"),
yaxis = list(title = "Number of Vehicles",
titlefont = list(color = "black", family = "Arial", size = 26),
tickfont = list(color = "black", family = "Arial", size = 20)),
xaxis = list(title = "Number of Cylinders",
titlefont = list(color = "red", family = "Times New Roman", size = 22),
tickfont = list(color = "green", family = "Cambria", size = 18)))%>%
layout(margin = list(
l = 10,
r = 10,
b = 0,
t = 40)) # Use the layout(margin) function to adjust the margins of the graphMake a Time Series in Plolty
- We will use a data set downloaded from GitHub that has dates and corresponding data
- This will also highlight how to download data directly from GitHub into your R Studio
- you will need the packages
httrandRCurl
library(httr)
library(RCurl)Download List of publications from Github
- We are going to uses a dataset from Reproducible Research in Ecology, Evolution, Behaviour, and Environmental Studies https://github.com/opetchey/RREEBES/tree/master/Beninca_etal_2008_Nature
no2 <- read.csv(text=getURL("https://raw.githubusercontent.com/opetchey/RREEBES/master/Beninca_etal_2008_Nature/data/nutrients_original.csv"), skip=7, header=T)Convert first column into dates
no2$Date <- as.Date(no2$Date, "%d/%m/%y") # of note, this %d/%m/%y format is critical, and is specific to the way the data is put into the data frame; b/c here the date in the original df is d/m/2 digit year, you need a lower case "y" to indicate that year is only two digits in the data frameGraph a Timeseries in Plotly
plot_ly( data = no2, x = no2$Date, y = no2$NO2) %>%
add_trace(type = "scatter" ,mode = "lines+markers") %>%
layout(
title = "Time Series of NO2",
xaxis = list(
title = "Year"),
yaxis = list(
title = "NO2"))Make a Chloropleth Map in Plotly
- A Chloropleth Map is a map that uses differences in shading, coloring, or the placing of symbols within predefined areas to indicate the average values of a property or quantity in those areas (https://en.wikipedia.org/wiki/Choropleth_map)
- For example let’s look at the rate of new cancers in the US according to the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute (NCI), 2016
- Visit “https://gis.cdc.gov/Cancer/USCS/DataViz.html” and click “export” to download a csv file.
- Make sure to save it in your project folder
- Also, please create a column,”state”, and enter the state abbreviations next to the corresponding full state name in “Area”.
- This is needed to add values to each state
df <- read.csv("USCS_OverviewMap.csv")View the Data Fram df
df %>%
kbl(align = "c") %>%
kable_styling(
bootstrap_options = c("striped","hover","condensed"),
full_width = F,
font_size = 16,
fixed_thead = T
) %>%
scroll_box(
height = "500px"
)| Code | Area | CancerType | Year | Sex | AgeAdjustedRate | CaseCount | Population | X |
|---|---|---|---|---|---|---|---|---|
| NM | 'New Mexico' | 'All Types of Cancer' | '2016' | 'Male and Female' | 359.4 | 9075 | 2085432 | 0.0043516 |
| AZ | 'Arizona' | 'All Types of Cancer' | '2016' | 'Male and Female' | 376.3 | 31443 | 6908642 | 0.0045513 |
| CA | 'California' | 'All Types of Cancer' | '2016' | 'Male and Female' | 385.6 | 164887 | 39296476 | 0.0041960 |
| CO | 'Colorado' | 'All Types of Cancer' | '2016' | 'Male and Female' | 388.8 | 23244 | 5530105 | 0.0042032 |
| DC | 'District of Columbia' | 'All Types of Cancer' | '2016' | 'Male and Female' | 378.3 | 2566 | 684336 | 0.0037496 |
| NV | 'Nevada' | 'All Types of Cancer' | '2016' | 'Male and Female' | 385.0 | 13054 | 2939254 | 0.0044413 |
| AK | 'Alaska' | 'All Types of Cancer' | '2016' | 'Male and Female' | 405.8 | 2882 | 741522 | 0.0038866 |
| HI | 'Hawaii' | 'All Types of Cancer' | '2016' | 'Male and Female' | 414.2 | 7395 | 1428683 | 0.0051761 |
| MA | 'Massachusetts' | 'All Types of Cancer' | '2016' | 'Male and Female' | 404.2 | 33626 | 6823721 | 0.0049278 |
| OR | 'Oregon' | 'All Types of Cancer' | '2016' | 'Male and Female' | 405.3 | 20596 | 4085989 | 0.0050406 |
| TX | 'Texas' | 'All Types of Cancer' | '2016' | 'Male and Female' | 391.8 | 109083 | 27904862 | 0.0039091 |
| UT | 'Utah' | 'All Types of Cancer' | '2016' | 'Male and Female' | 390.6 | 10494 | 3044321 | 0.0034471 |
| VA | 'Virginia' | 'All Types of Cancer' | '2016' | 'Male and Female' | 413.3 | 40322 | 8414380 | 0.0047920 |
| WY | 'Wyoming' | 'All Types of Cancer' | '2016' | 'Male and Female' | 402.3 | 2775 | 584910 | 0.0047443 |
| FL | 'Florida' | 'All Types of Cancer' | '2016' | 'Male and Female' | 419.3 | 119408 | 20656589 | 0.0057806 |
| ID | 'Idaho' | 'All Types of Cancer' | '2016' | 'Male and Female' | 439.2 | 8354 | 1680026 | 0.0049725 |
| IN | 'Indiana' | 'All Types of Cancer' | '2016' | 'Male and Female' | 443.0 | 34260 | 6634007 | 0.0051643 |
| MD | 'Maryland' | 'All Types of Cancer' | '2016' | 'Male and Female' | 440.9 | 30942 | 6024752 | 0.0051358 |
| MI | 'Michigan' | 'All Types of Cancer' | '2016' | 'Male and Female' | 436.8 | 53911 | 9933445 | 0.0054272 |
| MN | 'Minnesota' | 'All Types of Cancer' | '2016' | 'Male and Female' | 455.7 | 29619 | 5525050 | 0.0053609 |
| MO | 'Missouri' | 'All Types of Cancer' | '2016' | 'Male and Female' | 446.3 | 33171 | 6091176 | 0.0054457 |
| NE | 'Nebraska' | 'All Types of Cancer' | '2016' | 'Male and Female' | 446.4 | 9838 | 1907603 | 0.0051573 |
| ND | 'North Dakota' | 'All Types of Cancer' | '2016' | 'Male and Female' | 448.7 | 3765 | 755548 | 0.0049831 |
| OK | 'Oklahoma' | 'All Types of Cancer' | '2016' | 'Male and Female' | 447.9 | 20167 | 3921207 | 0.0051431 |
| RI | 'Rhode Island' | 'All Types of Cancer' | '2016' | 'Male and Female' | 450.6 | 5972 | 1057566 | 0.0056469 |
| SC | 'South Carolina' | 'All Types of Cancer' | '2016' | 'Male and Female' | 440.9 | 27313 | 4959822 | 0.0055069 |
| SD | 'South Dakota' | 'All Types of Cancer' | '2016' | 'Male and Female' | 449.5 | 4612 | 861542 | 0.0053532 |
| TN | 'Tennessee' | 'All Types of Cancer' | '2016' | 'Male and Female' | 455.7 | 36598 | 6649404 | 0.0055040 |
| VT | 'Vermont' | 'All Types of Cancer' | '2016' | 'Male and Female' | 442.4 | 3681 | 623354 | 0.0059052 |
| WA | 'Washington' | 'All Types of Cancer' | '2016' | 'Male and Female' | 445.1 | 37378 | 7280934 | 0.0051337 |
| AL | 'Alabama' | 'All Types of Cancer' | '2016' | 'Male and Female' | 457.8 | 27195 | 4860545 | 0.0055951 |
| AR | 'Arkansas' | 'All Types of Cancer' | '2016' | 'Male and Female' | 469.6 | 17053 | 2988231 | 0.0057067 |
| CT | 'Connecticut' | 'All Types of Cancer' | '2016' | 'Male and Female' | 465.5 | 21117 | 3587685 | 0.0058860 |
| DE | 'Delaware' | 'All Types of Cancer' | '2016' | 'Male and Female' | 487.2 | 6001 | 952698 | 0.0062990 |
| GA | 'Georgia' | 'All Types of Cancer' | '2016' | 'Male and Female' | 468.8 | 52056 | 10313620 | 0.0050473 |
| IL | 'Illinois' | 'All Types of Cancer' | '2016' | 'Male and Female' | 462.8 | 68954 | 12835726 | 0.0053720 |
| IA | 'Iowa' | 'All Types of Cancer' | '2016' | 'Male and Female' | 473.6 | 18146 | 3130869 | 0.0057958 |
| KS | 'Kansas' | 'All Types of Cancer' | '2016' | 'Male and Female' | 457.3 | 15312 | 2907731 | 0.0052660 |
| KY | 'Kentucky' | 'All Types of Cancer' | '2016' | 'Male and Female' | 509.7 | 27137 | 4436113 | 0.0061173 |
| LA | 'Louisiana' | 'All Types of Cancer' | '2016' | 'Male and Female' | 473.1 | 25451 | 4686157 | 0.0054311 |
| ME | 'Maine' | 'All Types of Cancer' | '2016' | 'Male and Female' | 473.4 | 8901 | 1330232 | 0.0066913 |
| MS | 'Mississippi' | 'All Types of Cancer' | '2016' | 'Male and Female' | 465.8 | 16265 | 2985415 | 0.0054482 |
| MT | 'Montana' | 'All Types of Cancer' | '2016' | 'Male and Female' | 456.6 | 6194 | 1038656 | 0.0059635 |
| NH | 'New Hampshire' | 'All Types of Cancer' | '2016' | 'Male and Female' | 480.9 | 8442 | 1335015 | 0.0063235 |
| NJ | 'New Jersey' | 'All Types of Cancer' | '2016' | 'Male and Female' | 474.8 | 51521 | 8978416 | 0.0057383 |
| NY | 'New York' | 'All Types of Cancer' | '2016' | 'Male and Female' | 474.8 | 113026 | 19836286 | 0.0056979 |
| NC | 'North Carolina' | 'All Types of Cancer' | '2016' | 'Male and Female' | 458.4 | 55394 | 10156689 | 0.0054539 |
| OH | 'Ohio' | 'All Types of Cancer' | '2016' | 'Male and Female' | 456.1 | 65645 | 11622554 | 0.0056481 |
| PA | 'Pennsylvania' | 'All Types of Cancer' | '2016' | 'Male and Female' | 482.5 | 80089 | 12787085 | 0.0062633 |
| WV | 'West Virginia' | 'All Types of Cancer' | '2016' | 'Male and Female' | 472.0 | 11698 | 1828637 | 0.0063971 |
| WI | 'Wisconsin' | 'All Types of Cancer' | '2016' | 'Male and Female' | 458.6 | 32688 | 5772917 | 0.0056623 |
Add a vector to the Data Frame specifying what will be revealed via hovering
df$hover <- with(df, paste(df$Area, df$Year,"Sex:", df$Sex, "Case count:", df$CaseCount, sep = "<br>"))- This is a very nice option.
- You can create a vector (here we are calling it “hover”) that will be the contents displayed when you hover over the map
- This “hover” vector needs to be specified in the
text =part of the code below
- This “hover” vector needs to be specified in the
- You can create a vector (here we are calling it “hover”) that will be the contents displayed when you hover over the map
View the first 2 rows of the data frame df with hover vector
(head(df[1:2,])) %>%
kbl(align = "c") %>%
kable_styling(
bootstrap_options = c("striped","hover","condensed"),
full_width = F,
font_size = 16,
fixed_thead = T
) | Code | Area | CancerType | Year | Sex | AgeAdjustedRate | CaseCount | Population | X | hover |
|---|---|---|---|---|---|---|---|---|---|
| NM | 'New Mexico' | 'All Types of Cancer' | '2016' | 'Male and Female' | 359.4 | 9075 | 2085432 | 0.0043516 | 'New Mexico'<br>'2016'<br>Sex:<br>'Male and Female'<br>Case count:<br>9075 |
| AZ | 'Arizona' | 'All Types of Cancer' | '2016' | 'Male and Female' | 376.3 | 31443 | 6908642 | 0.0045513 | 'Arizona'<br>'2016'<br>Sex:<br>'Male and Female'<br>Case count:<br>31443 |
Plot the Chloropleth Map
# give state boundaries a white border
l <- list(color = toRGB("white"), width = 2)
# specify some map projection/options
g <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('steelblue')
)
p <- plot_geo(df, locationmode = 'USA-states') %>%
add_trace(
z = ~AgeAdjustedRate, text = ~hover, locations = df$Code,
color = ~AgeAdjustedRate, colors = 'Purples'
) %>%
colorbar(title = "Rate per 100,000 people") %>%
layout(
title = 'US Cancer Statistics Rate of New Cancers- 2016 (Source: CDC and NCI)',
geo = g
)
pSessionInfo
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] RCurl_1.98-1.7 httr_1.4.4 kableExtra_1.3.4 scales_1.2.1
[5] readxl_1.4.1 plotly_4.10.1.9000 knitr_1.41 forcats_0.5.2
[9] stringr_1.5.0 dplyr_1.0.10 purrr_0.3.5 readr_2.1.3
[13] tidyr_1.2.1 tibble_3.1.8 ggplot2_3.4.0 tidyverse_1.3.2
loaded via a namespace (and not attached):
[1] svglite_2.1.0 lubridate_1.9.0 assertthat_0.2.1
[4] digest_0.6.30 utf8_1.2.2 R6_2.5.1
[7] cellranger_1.1.0 backports_1.4.1 reprex_2.0.2
[10] evaluate_0.18 highr_0.9 pillar_1.8.1
[13] rlang_1.0.6 lazyeval_0.2.2 googlesheets4_1.0.1
[16] rstudioapi_0.14 data.table_1.14.6 rmarkdown_2.18
[19] webshot_0.5.3 googledrive_2.0.0 htmlwidgets_1.5.4
[22] munsell_0.5.0 broom_1.0.1 compiler_4.2.0
[25] modelr_0.1.10 xfun_0.35 systemfonts_1.0.4
[28] pkgconfig_2.0.3 htmltools_0.5.3 tidyselect_1.2.0
[31] fansi_1.0.3 viridisLite_0.4.1 crayon_1.5.2
[34] tzdb_0.3.0 dbplyr_2.2.1 withr_2.5.0
[37] bitops_1.0-7 grid_4.2.0 jsonlite_1.8.4
[40] gtable_0.3.1 lifecycle_1.0.3 DBI_1.1.3
[43] magrittr_2.0.3 cli_3.4.1 stringi_1.7.8
[46] fs_1.5.2 xml2_1.3.3 ellipsis_0.3.2
[49] generics_0.1.3 vctrs_0.5.1 tools_4.2.0
[52] glue_1.6.2 hms_1.1.2 fastmap_1.1.0
[55] yaml_2.3.6 timechange_0.1.1 colorspace_2.0-3
[58] gargle_1.2.1 rvest_1.0.3 haven_2.5.1
Comments about code for Titles and Axes
plotlyuses different code that perhaps the more popularggpolotpackage thatRusers are often very familiar withlayout()andlist()functions perform much of the work for these tasks, as seen abovetextposition = "auto"in the first ()