MetricsGraphics.js is a library built on top of D3 that is optimized for visualizing and laying out time-series data. It provides a simple way to produce common types of graphics in a principled, consistent and responsive way. The library currently supports line charts, scatterplots and histograms as well as features like rug plots and basic linear regression.
The metricsgraphics R package wraps most of the functionality of MetricsGraphics.js into an htmlwidget. This package will replicate many of the MetricsGraphics.js examples to demonstrate the functionality of the package.
library(metricsgraphics)
library(jsonlite)
library(RColorBrewer)
library(htmltools)
library(dplyr)
# this lets us add a title to the plot since the package follows the guidance
# of the htmlwidgets authors and does not include the MetricsGraphics.js title
# option to ensure consistent div sizing.
show_plot <- function(plot_object, title) {
div(style="margin:auto;text-align:center", strong(title), br(), plot_object)
}This is a simple line chart. You can remove the area portion by changing area=TRUE to area=FALSE. NOTE that you do not have to use show_plot for normal metricsgraphics usage. It’s used here for example purposes only.
fake_users_1 <- fromJSON("http://metricsgraphicsjs.org/data/fake_users1.json")
fake_users_1$date <- as.Date(fake_users_1$date)
fake_users_1 %>%
mjs_plot(x=date, y=value, width=600, height=200) %>%
mjs_axis_x(xax_format="date") %>%
mjs_line(area=TRUE) %>%
show_plot("Line Chart")This is an example of a graphic with a confidence band and extended x-axis ticks enabled. NOTE that by specifying the xaxformat="date" we do not, then, have to pre-convert date with as.Date()
confidence_band <- fromJSON("http://metricsgraphicsjs.org/data/confidence_band.json")
confidence_band %>%
mjs_plot(x=date, y=value, format="percentage", width=600, height=200) %>%
mjs_axis_x(xax_format="date",
show_secondary_x_label=FALSE,
extended_ticks=TRUE) %>%
mjs_line() %>%
mjs_add_confidence_band() %>%
show_plot("Confidence Band")When we have a data object of integers and a small range of values, the auto-generated set of y-axis ticks are filtered so that we don’t include fractional values.
small_range <- fromJSON("http://metricsgraphicsjs.org/data/small-range.json")
small_range %>%
mjs_plot(x=date, y=value, width=600, height=200) %>%
mjs_axis_x(xax_format="date") %>%
mjs_line(interpolate="basic", area=TRUE) %>%
show_plot("Small Range of Integers")The two graphics in this section are linked together. A rollover in one causes a rollover in the other. NOTE that setting linked=TRUE links all the charts on a given page/document that have that setting. This is a limitation of the MetricsGraphics.js library, not the package.
brief_1 <- fromJSON("http://metricsgraphicsjs.org/data/brief-1.json")
brief_2 <- fromJSON("http://metricsgraphicsjs.org/data/brief-2.json")
brief_1 %>%
mjs_plot(x=date, y=value, width=600, height=200, linked=TRUE) %>%
mjs_axis_x(xax_format="date", xax_count=4) %>%
mjs_line(area=TRUE) -> mjs_brief_1
brief_2 %>%
mjs_plot(x=date, y=value, width=600, height=200, linked=TRUE) %>%
mjs_axis_x(xax_format="date", xax_count=4) %>%
mjs_line() -> mjs_brief_2
div(style="margin:auto;text-align:center",
strong("Linked Graphic"), br(), mjs_brief_1,
strong("Other Linked Graphic"), br(), mjs_brief_2)Handling a solitary data point. NOTE that date+time objects (e.g. POSIXct) are a TODO for metricsgraphics.
solitary <- data.frame(
date=as.Date("2015-03-05"),
value=12000
)
solitary %>%
mjs_plot(x=date, y=value, width=600, height=200) %>%
mjs_axis_x(xax_format="date") %>%
mjs_point() %>%
show_plot("Singleton")This line chart contains multiple lines. NOTE that the example build a data frame from the MetricsGraphics.js example JSON data. Unlike icky straight JavaScript constructs, we get to work with our own sane, native data types.
fake_users2_list <- fromJSON("http://metricsgraphicsjs.org/data/fake_users2.json")
fake_users2 <- data.frame(
date=fake_users2_list[[1]]$date,
value_1=fake_users2_list[[1]]$value,
value_2=fake_users2_list[[2]]$value,
value_3=fake_users2_list[[3]]$value
)
fake_users2 %>%
mjs_plot(x=date, y=value_1, width=600, height=200) %>%
mjs_axis_x(xax_format="date") %>%
mjs_line() %>%
mjs_add_line(value_2) %>%
mjs_add_line(value_3) %>%
mjs_add_legend(c("Line 1", "Line 2", "Line 3")) %>%
show_plot("Multi-Line Chart")fake_users2 %>%
mjs_plot(x=date, y=value_1, width=600, height=200) %>%
mjs_axis_x(xax_format="date") %>%
mjs_line(color="blue") %>%
mjs_add_line(value_2, color="rgb(255,100,43)") %>%
mjs_add_line(value_3, color="#ccccff") %>%
mjs_add_legend(c("Line 1", "Line 2", "Line 3")) %>%
show_plot("Multi-Line Char with Custom Colors")Since we pass in whole data frames, there is no “missing” data points or columns. Also, since a linked chart example was shown already, we can’t show that multi-line example, so all three will not be reproduced here.
We can show inline legend labels (placed to the right) vs bottom.
fake_users3_list <- fromJSON("http://metricsgraphicsjs.org/data/fake_users3.json")
fake_users3 <- data.frame(
date=fake_users3_list[[1]]$date,
value_1=fake_users3_list[[1]]$value,
value_2=fake_users3_list[[2]]$value,
value_3=fake_users3_list[[3]]$value
)
fake_users3 %>%
mjs_plot(x=date, y=value_1, width=600, height=200, right=40) %>%
mjs_axis_x(xax_format="date") %>%
mjs_line() %>%
mjs_add_line(value_2) %>%
mjs_add_line(value_3) %>%
mjs_add_legend(c('US', 'CA', 'DE'), inline=TRUE) %>%
show_plot("Labeling Lines")This is an example of a graphic where we’re not plotting dates on the x-axis and where the axes include labels and the line animates on load. We’ve also enabled extended ticks along the y-axis.
xnotondate <- fromJSON("http://metricsgraphicsjs.org/data/xnotdate.json")
xnotondate %>%
mjs_plot(x=males, y=females, width=600, height=240,
left=80, right=40, bottom=50) %>%
mjs_line(animate_on_load=TRUE, area=FALSE) %>%
mjs_labs("Males", "Females") %>%
mjs_axis_y(extended_ticks=TRUE) %>%
show_plot("Axis Labels")Here is an example that shows percentages.
some_percentages <- fromJSON("http://metricsgraphicsjs.org/data/some_percentage.json")
some_percentages[[1]] %>%
mjs_plot(x=date, y=value, format="percentage", width=600, height=200) %>%
mjs_axis_x(xax_format="date") %>%
mjs_line(area=TRUE) %>%
show_plot("Some Percentages")Here is an example that uses custom units for currency.
some_currency <- fromJSON("http://metricsgraphicsjs.org/data/some_currency.json")
some_currency %>%
mjs_plot(x=date, y=value, width=600, height=200) %>%
mjs_axis_x(xax_format="date") %>%
mjs_line() %>%
mjs_axis_y(yax_units="$") %>%
show_plot("Some Currency")You can change the y-axis’ scale to logarithmic by setting y_scale_type to "log".
log_scale <- fromJSON("http://metricsgraphicsjs.org/data/log.json")
log_scale %>%
mjs_plot(x=date, y=value, width=600, height=200) %>%
mjs_axis_x(xax_format="date") %>%
mjs_line(area=TRUE) %>%
mjs_axis_y(y_scale_type="log") %>%
show_plot("Log Scale")You can hide either axis by setting show=FALSE in mjs_axis_x or mjs_axis_y.
fake_users_1 <- fromJSON("http://metricsgraphicsjs.org/data/fake_users1.json")
brief_1 <- fromJSON("http://metricsgraphicsjs.org/data/brief-1.json")
fake_users_1 %>%
mjs_plot(x=date, y=value, width=600, height=200) %>%
mjs_axis_x(xax_format="date", show=FALSE) %>%
mjs_line() -> no_x
brief_1 %>%
mjs_plot(x=date, y=value, width=600, height=200) %>%
mjs_axis_x(xax_format="date") %>%
mjs_axis_y(show=FALSE) %>%
mjs_line() -> no_y
div(style="margin:auto;text-align:center",
strong("No X Axis"), br(), no_x,
strong("No Y Axis"), br(), no_y)You can now change line color without resorting to CSS:
fake_users_1 <- fromJSON("http://metricsgraphicsjs.org/data/fake_users1.json")
fake_users_1 %>%
mjs_plot(x=date, y=value, width=600, height=200) %>%
mjs_axis_x(xax_format="date", show=FALSE) %>%
mjs_line(color="#8c001a", area=TRUE) %>%
mjs_axis_y(rug=TRUE) %>%
show_plot("Colors!")You can set rug plots on either axis by setting rug=TRUE in mjs_axis_x or mjs_axis_y.
fake_users_1 <- fromJSON("http://metricsgraphicsjs.org/data/fake_users1.json")
fake_users_1 %>%
mjs_plot(x=date, y=value, width=600, height=200) %>%
mjs_axis_x(xax_format="date", show=FALSE) %>%
mjs_line() %>%
mjs_axis_y(rug=TRUE) %>%
show_plot("Rug Plots")Markers are vertical lines that can be added at arbitrary points. Markers that are close to each other won’t collide. NOTE that metricsgraphics will do date conversions from strings automagically.
some_percentages <- fromJSON("http://metricsgraphicsjs.org/data/some_percentage.json")
some_percentages[[1]] %>%
mjs_plot(x=date, y=value, format="percentage", width=600, height=200) %>%
mjs_axis_x(xax_format="date") %>%
mjs_line(area=TRUE) %>%
mjs_add_marker("2014-02-01", "1st Milestone") %>%
mjs_add_marker(as.Date("2014-03-15"), "2nd Milestone") %>%
show_plot("Markers")Baselines are horizontal lines that can added at arbitrary points.
fake_users_1 <- fromJSON("http://metricsgraphicsjs.org/data/fake_users1.json")
fake_users_1 %>%
mjs_plot(x=date, y=value, width=600, height=200) %>%
mjs_axis_x(xax_format="date", show=FALSE) %>%
mjs_add_baseline(160000000, "a baseline") %>%
mjs_line(area=TRUE) %>%
show_plot("Baselines")Scatterplots are first-class citizens as well
points_1 <- fromJSON("http://metricsgraphicsjs.org/data/points1.json")
points_1 %>%
mjs_plot(x=x, y=y, width=600, height=460) %>%
mjs_point(y_rug=TRUE) %>%
mjs_axis_x() %>%
show_plot("Simple Scatterplot")points_1 %>%
mjs_plot(x=x, y=y, width=600, height=460) %>%
mjs_point(y_rug=TRUE, color_accessor=v, color_type="category", color_range=c("green", "orange")) %>%
mjs_axis_x() %>%
show_plot("Color mapping")points_1 %>%
mjs_plot(x=x, y=y, width=600, height=460) %>%
mjs_point(y_rug=TRUE, x_rug=TRUE, color_accessor=z, size_accessor=w, color_type="category") %>%
mjs_axis_x(rug=TRUE) %>%
show_plot("Size Too!")This does not mimic the MetricsGraphics.js examples but shows off mjs_grid functionality:
moar_plots <- lapply(1:7, function(x) {
mjs_plot(rbeta(10000, x, x), width="250px", height="250px", linked=TRUE) %>%
mjs_histogram(bar_margin=2) %>%
mjs_labs(x_label=sprintf("Plot %d", x))
})
mjs_grid(moar_plots, nrow=4, ncol=3, widths=c(rep(0.33, 3)))