After a lot of documentation, a lot of R CMD checks and a lot of patience from CRAN
people I'm happy to anonounce highcharter v0.1.0:
A(nother) wrapper for Highcharts charting library.
Now it's easy make a chart like this. Do you want to know how?
I like Highcharts. It was the
first charting javascript library what I used in a long time and have
very a mature API to plot a lot of types of charts. Obviously there are some R
packages to plot data using this library:
- Ramnath Vaidyanathan's rCharts.
What a library. This was the beginning to the R & JS romance. TherChartsapproach to plot data
is object oriented; here we used a lot ofchart$Key(arguments, ...). - highchartR package from jcizel.
This package we use thehighchartsfunction and give some parameters, like the variable's names
to get the chart.
With these package you can plot almost anything, so why another wrapper/package/ for highcharts?
The main reasons were/are:
- Write/code highcharts plots using the piping style and get similar results like
dygraphs, metricsgraphics,
taucharts, leaflet
packages. - Get all the funcionalities from the highcharts' API. This means
make a not so useR-friendly wrapper in the sense that you maybe need to make/construct
the chart specifying paramter by parameter (just like highcharts). But don't worry, there are
some shortcuts functions to plot some R objects on the fly (see the examples below). - Include and create themes :D.
- Put all my love for highcharts in somewhere.
Some Q&A
When use this package? I recommend use this when you have fihinsh your analysis and you want
to show your result with some interactivity. So, before use experimental plot to visualize, explore
the data with ggplot2 then use highcharter as one of the various alternatives what we have
in ouR community like ggivs, dygraphs, taucharts, metricsgraphics, plotly among others
(please check hafen's htmlwidgets gallery)
What are the advantages of using this package? Basically the advantages are inherited from
highcharts: have a numerous chart types with the same format, style, flavour. I think in a
situation when you use a treemap and a scatter chart from differents packages in a shiny app.
Other advantage is the posibility to create or modify themes (I like this a lot!) and
customize in every way your chart: beatiful tooltips, titles, credits, legends, add plotlines or
plotbands.
What are the disadvantages of this package and highcharts? One thing I miss is the facet
implementation like in taucharts and hrbrmstr's taucharts.
This is not really necesary but it's really good when a visualization library has it. Maybe
other disadvantage of this implmentation is the functions use standar evaluation
plot(data$x, data$y) instead something more direct like plot(data, ~x, ~y). That's why
I recommed this package to make the final chart instead use the package to explorer visually
the data.
The Hellow World chart
Let's see a simple chart.
library("highcharter")
library("magrittr")
library("dplyr")
data("citytemp")
citytemp
| month | tokyo | new_york | berlin | london |
|---|---|---|---|---|
| Jan | 7.0 | -0.2 | -0.9 | 3.9 |
| Feb | 6.9 | 0.8 | 0.6 | 4.2 |
| Mar | 9.5 | 5.7 | 3.5 | 5.7 |
| Apr | 14.5 | 11.3 | 8.4 | 8.5 |
| May | 18.2 | 17.0 | 13.5 | 11.9 |
| Jun | 21.5 | 22.0 | 17.0 | 15.2 |
| Jul | 25.2 | 24.8 | 18.6 | 17.0 |
| Aug | 26.5 | 24.1 | 17.9 | 16.6 |
| Sep | 23.3 | 20.1 | 14.3 | 14.2 |
| Oct | 18.3 | 14.1 | 9.0 | 10.3 |
| Nov | 13.9 | 8.6 | 3.9 | 6.6 |
| Dec | 9.6 | 2.5 | 1.0 | 4.8 |
hc <- highchart() %>%
hc_add_serie(name = "tokyo", data = citytemp$tokyo)
hc
Very simple chart. Here comes the powerful highchart API: Adding more series
data and adding themes.
hc <- hc %>%
hc_title(text = "Temperatures for some cities") %>%
hc_xAxis(categories = citytemp$month) %>%
hc_add_serie(name = "London", data = citytemp$london,
dataLabels = list(enabled = TRUE)) %>%
hc_add_serie(name = "New York", data = citytemp$new_york,
type = "spline") %>%
hc_yAxis(title = list(text = "Temperature"),
labels = list(format = "{value}? C")) %>%
hc_add_theme(hc_theme_sandsignika())
hc
Now, what we can do with a little extra effort:
library("httr")
library("purrr")
# get some data
swmovies <- content(GET("http://swapi.co/api/films/?format=json"))
swdata <- map_df(swmovies$results, function(x){
data_frame(title = x$title,
species = length(x$species),
planets = length(x$planets),
release = x$release_date)
}) %>% arrange(release)
swdata
| title | species | planets | release |
|---|---|---|---|
| A New Hope | 5 | 3 | 1977-05-25 |
| The Empire Strikes Back | 5 | 4 | 1980-05-17 |
| Return of the Jedi | 9 | 5 | 1983-05-25 |
| The Phantom Menace | 20 | 3 | 1999-05-19 |
| Attack of the Clones | 14 | 5 | 2002-05-16 |
| Revenge of the Sith | 20 | 13 | 2005-05-19 |
| The Force Awakens | 3 | 1 | 2015-12-11 |
# made a theme
swthm <- hc_theme_merge(
hc_theme_darkunica(),
hc_theme(
credits = list(
style = list(
color = "#4bd5ee"
)
),
title = list(
style = list(
color = "#4bd5ee"
)
),
chart = list(
backgroundColor = "transparent",
divBackgroundImage = "http://www.wired.com/images_blogs/underwire/2013/02/xwing-bg.gif",
style = list(fontFamily = "Lato")
)
)
)
# chart
highchart() %>%
hc_add_theme(swthm) %>%
hc_xAxis(categories = swdata$title,
title = list(text = "Movie")) %>%
hc_yAxis(title = list(text = "Number")) %>%
hc_add_serie(data = swdata$species, name = "Species",
type = "column", color = "#e5b13a") %>%
hc_add_serie(data = swdata$planets, name = "Planets",
type = "column", color = "#4bd5ee") %>%
hc_title(text = "Diversity in <span style="color:#e5b13a">
STAR WARS</span> movies",
useHTML = TRUE) %>%
hc_credits(enabled = TRUE, text = "Source: SWAPI",
href = "https://swapi.co/",
style = list(fontSize = "12px"))
More Examples
For ts objects. Compare this example with the dygrapths
one
highchart() %>%
hc_title(text = "Monthly Deaths from Lung Diseases in the UK") %>%
hc_add_serie_ts2(fdeaths, name = "Female") %>%
hc_add_serie_ts2(mdeaths, name = "Male")
A more elaborated example using the mtcars data. And it's nice like
juba's scatterD3.
hcmtcars <- highchart() %>%
hc_title(text = "Motor Trend Car Road Tests") %>%
hc_subtitle(text = "Source: 1974 Motor Trend US magazine") %>%
hc_xAxis(title = list(text = "Weight")) %>%
hc_yAxis(title = list(text = "Miles/gallon")) %>%
hc_chart(zoomType = "xy") %>%
hc_add_serie_scatter(mtcars$wt, mtcars$mpg,
mtcars$drat, mtcars$hp,
rownames(mtcars),
dataLabels = list(
enabled = TRUE,
format = "{point.label}"
)) %>%
hc_tooltip(useHTML = TRUE,
headerFormat = "<table>",
pointFormat = paste("<tr><th colspan="1"><b>{point.label}</b></th></tr>",
"<tr><th>Weight</th><td>{point.x} lb/1000</td></tr>",
"<tr><th>MPG</th><td>{point.y} mpg</td></tr>",
"<tr><th>Drat</th><td>{point.z} </td></tr>",
"<tr><th>HP</th><td>{point.valuecolor} hp</td></tr>"),
footerFormat = "</table>")
hcmtcars
Let's try treemaps
library("treemap")
library("viridisLite")
data(GNI2010)
tm <- treemap(GNI2010, index = c("continent", "iso3"),
vSize = "population", vColor = "GNI",
type = "value", palette = viridis(6))
hc_tm <- highchart() %>%
hc_add_serie_treemap(tm, allowDrillToNode = TRUE,
layoutAlgorithm = "squarified",
name = "tmdata") %>%
hc_title(text = "Gross National Income World Data") %>%
hc_tooltip(pointFormat = "<b>{point.name}</b>:<br>
Pop: {point.value:,.0f}<br>
GNI: {point.valuecolor:,.0f}")
hc_tm
You can do anything
As uncle Bem said some day:
You can use this pacakge for evil purposes so be good with the people who see
your charts. So, I will not be happy if I see one chart like this:
iriscount <- count(iris, Species)
iriscount
| Species | n |
|---|---|
| setosa | 50 |
| versicolor | 50 |
| virginica | 50 |
highchart(width = 400, height = 400) %>%
hc_title(text = "Nom! a delicious 3d pie!") %>%
hc_subtitle(text = "your eyes hurt?") %>%
hc_chart(type = "pie", options3d = list(enabled = TRUE, alpha = 70, beta = 0)) %>%
hc_plotOptions(pie = list(depth = 70)) %>%
hc_add_serie_labels_values(iriscount$Species, iriscount$n) %>%
hc_add_theme(hc_theme(
chart = list(
backgroundColor = NULL,
divBackgroundImage = "https://media.giphy.com/media/Yy26NRbpB9lDi/giphy.gif"
)
))
Other charts just for charting
data("favorite_bars")
data("favorite_pies")
highchart() %>%
hc_title(text = "This is a bar graph describing my favorite pies
including a pie chart describing my favorite bars") %>%
hc_subtitle(text = "In percentage of tastiness and awesomeness") %>%
hc_add_serie_labels_values(favorite_pies$pie, favorite_pies$percent, name = "Pie",
colorByPoint = TRUE, type = "column") %>%
hc_add_serie_labels_values(favorite_bars$bar, favorite_bars$percent, type = "pie",
name = "Bar", colorByPoint = TRUE, center = c('35%', '10%'),
size = 100, dataLabels = list(enabled = FALSE)) %>%
hc_yAxis(title = list(text = "percentage of tastiness"),
labels = list(format = "{value}%"), max = 100) %>%
hc_xAxis(categories = favorite_pies$pie) %>%
hc_credits(enabled = TRUE, text = "Source (plz click here!)",
href = "https://www.youtube.com/watch?v=f_J8QU1m0Ng",
style = list(fontSize = "12px")) %>%
hc_legend(enabled = FALSE) %>%
hc_tooltip(pointFormat = "{point.y}%")
Well, I hope you use, reuse and enjoy this package!
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