This package allow you to use billboard.js, a re-usable easy interface JavaScript chart library, based on D3 v4+.
Supported chart types:
The main function is billboarder, all charts begin with.
You can add layer to your charts with function bb_*, these
functions correspond to a billboard option define in the API
docs. There are helpers functions to quickly create a type of chart
(bb_barchart, bb_linechart,
bb_piechart, bb_donutchart,
bb_gauge, bb_scatterplot), they have to be
called after billboarder.
You can create a simple bar chart by passing a
data.frame to bb_barchart, the first column
will be used as the x-axis, and the second one as the y-axis :
library("billboarder")
df <- as.data.frame(table(sample(letters[1:5], 50, TRUE)))
df
#> Var1 Freq
#> 1 a 5
#> 2 b 13
#> 3 c 13
#> 4 d 10
#> 5 e 9
billboarder() %>%
bb_barchart(data = df)If you want to create a grouped bar chart, first option is to put
your data in a “wide” format. Here we use stats::reshape,
but I recommend to use tidyr::spread or
data.table::dcast.
df <- as.data.frame(table(
sample(letters[1:5], 50, TRUE),
sample(LETTERS[1:5], 50, TRUE)
))
df.r <- reshape(data = df, idvar = "Var1", timevar = "Var2", direction = "wide")
df.r
#> Var1 Freq.A Freq.B Freq.C Freq.D Freq.E
#> 1 a 4 2 2 0 3
#> 2 b 1 3 2 1 0
#> 3 c 0 1 1 2 1
#> 4 d 4 3 3 2 3
#> 5 e 2 3 1 2 4
billboarder() %>%
bb_barchart(data = df.r)Second option is to define a mapping of your variable with function
bbaes (for more example of mapping, see vignette
billboarder-mapping).
billboarder() %>%
bb_barchart(
data = df,
mapping = bbaes(x = Var1, y = Freq, group = Var2)
)You can pass to the function bb_linechart a vector, in
that case x-axis will be the index of that vector :
billboarder() %>%
bb_linechart(data = sin(seq(-pi, pi, length.out = 10)))You can change the type of line with argument type, for
example an area-step :
billboarder() %>%
bb_linechart(data = sin(seq(-pi, pi, length.out = 10)), type = "area-step")If want to specify a variable to map to the x-axis, you had to pass a
data.frame to the function :
df <- data.frame(
var_x = seq(-pi, pi, length.out = 10),
sin = sin(seq(-pi, pi, length.out = 10))
)
df
#> var_x sin
#> 1 -3.1415927 -1.224647e-16
#> 2 -2.4434610 -6.427876e-01
#> 3 -1.7453293 -9.848078e-01
#> 4 -1.0471976 -8.660254e-01
#> 5 -0.3490659 -3.420201e-01
#> 6 0.3490659 3.420201e-01
#> 7 1.0471976 8.660254e-01
#> 8 1.7453293 9.848078e-01
#> 9 2.4434610 6.427876e-01
#> 10 3.1415927 1.224647e-16
billboarder() %>%
bb_linechart(data = df, x = "var_x")If the first variable of the data.frame is a
Date or a POSIX, it will be automatically
mapped to the x-axis :
df <- data.frame(
date = seq.Date(from = as.Date("2017-06-12"), by = "day", length.out = 10),
var = rnorm(10)
)
df
#> date var
#> 1 2017-06-12 1.16311621
#> 2 2017-06-13 0.38416993
#> 3 2017-06-14 1.42777510
#> 4 2017-06-15 -1.11532782
#> 5 2017-06-16 1.70008758
#> 6 2017-06-17 1.15593785
#> 7 2017-06-18 -0.17164490
#> 8 2017-06-19 -0.12505876
#> 9 2017-06-20 0.50649355
#> 10 2017-06-21 -0.05996544
billboarder() %>%
bb_linechart(data = df)For scatter plot, use a two column data.frame with
function bb_scatterplot, or specify the x variable and the
y variable (you can also specify a grouping variable) :
billboarder() %>%
bb_scatterplot(data = iris[, 1:2])For pie chart, use bb_piechart with a two column
data.frame :
df <- data.frame(
var = c("A", "B"),
count = c(457, 987)
)
billboarder() %>%
bb_piechart(data = df)Donut charts works the same as pie charts :
df <- data.frame(
var = c("A", "B"),
count = c(687, 246)
)
billboarder() %>%
bb_donutchart(data = df)Note : pie and donut are automatically sorted, you can change that
with bb_data(order = NULL).
Gauge only need one value :
billboarder() %>%
bb_gaugechart(value = 64)