brinton
This package introduces:
wideplot()
graphics for exploring the structure of a dataset through a grid of variables and graphic types.longplot()
graphics, which present the entire catalog of available graphics for representing one particular variable or a limited number of variables using a grid of graphic types and variations on these types.plotup()
function, which complements the previous two functions in that it presents a particular graphic for a specific variable or a limited number of variables of a dataset.
Future work will include the ability to draw longplot()
and plotup()
graphics from a selection of a wider number and combinations of types of
variables within a dataset.
Installation
You can install the released version of brinton from CRAN with:
install.packages("brinton")
And then load the functions included in the package:
library(brinton)
#> Loading required package: ggplot2
#> Loading required package: gridExtra
#> Loading required package: rmarkdown
#> Loading required package: glue
#> M a G i C i N G R a P H S
Example
When a new dataset comes up, R users use to call the str()
function
that describes textually the main characteristics of this dataset. The
new wideplot()
function does the same but graphically. As an example,
just run:
wideplot(infert)
This function includes the argument dataclass
that sets the types of
variables to be included in the grid as well as the order in which they
are listed. As an example, the following line will plot only the numeric
variables included in the iris dataset:
wideplot(infert, dataclass = c("numeric"))
Although, the wideplot()
function shows only a small set of the
graphics suitable for each type of data. If the user wants to see all
the available graphics for some specific variable included in the
dataset, then the longplot()
is useful. As an example, just run:
longplot(infert, "pooled.stratum")
Any of the graphics presented in the longplot()
output can also be
presented. If one wants to compare the output of some other specific
graphics one can, for instance, run:
wideplot(infert,
dataclass = c("numeric"),
numeric = c("point graph", "binned point graph", "binned heatmap"),
label = TRUE)
Sometimes, specially with character variables, there is not enought
space for the graphic area, and the user may want to increase the width
of the columns in the matrix. The user can then modify the defalut value
of the ncol
argument:
wideplot(infert,
dataclass = c("numeric"),
numeric = c("point graph", "binned point graph", "binned heatmap"),
label = TRUE,
ncol = 3)
If the user is interested in one particular graphic then the function
plotup()
is useful.
plotup(infert, "pooled.stratum", "color binned stripe graph")
Or, as example of a graphic that requires more than one input variable:
plotup(faithful, c("waiting", "eruptions"), "color scatter plot")
The default output is still a html file but the plotup()
function
allows to plot the graphic in the plots pane or the console:
plotup(infert, "pooled.stratum", "color binned stripe graph", output = "plots pane")
plotup(faithful, c("waiting", "eruptions"), "color scatter plot", output = "plots pane")
#> Warning: Use of `faithful$waiting` is discouraged. Use `waiting` instead.
#> Warning: Use of `faithful$eruptions` is discouraged. Use `eruptions` instead.
plotup(infert, "pooled.stratum", "color binned stripe graph", output = "console")
#> theme_set(theme_minimal())
#>
#> binwidth <- (max(infert['pooled.stratum'], na.rm=TRUE)-min(infert['pooled.stratum'], na.rm=TRUE))/20
#> ggplot(infert, aes(x=pooled.stratum)) +
#> geom_bin2d(aes(y=1), binwidth = c(binwidth, 1)) +
#> scale_fill_gradientn(colours = colorRampPalette(rev(RColorBrewer::brewer.pal(4, 'Spectral')))(3)) +
#> theme(panel.grid = element_line(colour = NA),
#> axis.text.y =element_text(color = NA),
#> axis.title.y =element_text(color = NA),
#> axis.ticks.x =element_line(color = 'black'),
#> legend.position='none')