Why Chart?

Formula versus aes()

Since the formula interface is used extensively in R to specify models, it is a little bit strange that corresponding plots with {ggplot2} must be specified in a totally different way. The {chart} package allows to specify the same “aesthetics” through the familiar formula interface. For instance, here is a simple model:

data(trees)
trees_lm <- lm(Volume ~ Girth, data = trees)
summary(trees_lm)
#> 
#> Call:
#> lm(formula = Volume ~ Girth, data = trees)
#> 
#> Residuals:
#>    Min     1Q Median     3Q    Max 
#> -8.065 -3.107  0.152  3.495  9.587 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -36.9435     3.3651  -10.98 7.62e-12 ***
#> Girth         5.0659     0.2474   20.48  < 2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 4.252 on 29 degrees of freedom
#> Multiple R-squared:  0.9353, Adjusted R-squared:  0.9331 
#> F-statistic: 419.4 on 1 and 29 DF,  p-value: < 2.2e-16

Our model is specified with Volume ~ Girth, which must be translated into aes(x = Girth, y = Volume) in {ggplot2}. A common pitfall for beginners is to invert X and Y here because they appear in a different order in both interfaces.

library(ggplot2)
ggplot(trees, aes(x = Girth, y = Volume)) +
  geom_point() +
  geom_smooth(method = "lm", formula = 'y ~ x')

With {chart}, we can use the formula interface directly:

library(chart)
chart(trees, Volume ~ Girth) +
  geom_point() +
  geom_smooth(method = "lm", formula = 'y ~ x')

Of course, it is not necessarily the same formula that is used for the model and the chart. The formula interface has different meaning, depending on the function that uses it. To specify more aesthetics in {chart} than just x and y, we use %arg=% terms. For instance, to specify the color of the points, we use %col=%:

chart(trees, Volume ~ Girth %col=% Height) +
  geom_point()

The generic function chart()

On the contrary to ggplot(), chart() is a generic. It means you do not have to specify a data frame for data =, but you can provide any object that has chart(), or autoplot() methods defined. There are not many methods defined in the {chart} package itself. Here is the method for function objects that create base R plots, converted into {chart} compatible objects:

chart(function() {
  plot(Volume ~ Girth, data = trees, pch = 19, col = "black")
  grid(lty = "solid")
})

The {chart} package is designed as a basic framework to implement several methods for different R objects. It uses the “subsettable type” semantic, that is, its type = argument can define different plots for the same object class, and then, calling chart$<type>(....) produces the given type of plot for your object.

TODO: make a short example here.

See packages like {modelit} or {exploreit} for several examples of such methods.

chart() produces near publication-ready plots

Another aspect of chart() is that it is tailored to produce plots that are as close as possible to be publication-ready (but they. remain fully editable with {ggplot2} functions, of course). Hence, the default theme, and the labels of axes or legends are set up accordingly. If the data frame has label and units attributes defined for the variables, they are used to construct the labels of the axes and legends. Here is an example with the trees dataset:

trees <- data.io::labelise(trees, label = list(
    Volume = "Volume of timber",
    Girth  = "Diameter at 1.4m",
    Height = "Height"
  ), units = list(
    Volume = "cubic feet",
    Girth  = "inches",
    Height = "feet"))
chart(trees, Volume ~ Girth %col=% Height) +
  geom_point()