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 selection of variables using a grid of graphic types and variations on these types.
matrixplot()graphics, a generalization of a matrix plot in the sense that the graphic that is replicated in each cell of the matrix can be selected from the catalogue of bivariate graphics.
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.
You can install the released version of brinton from CRAN with:
And then load the functions included in the package:
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:
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() 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:
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:
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
While the wideplot function displays a grid of univariate graphics, if a matrix of bivariate graphics is intended for variables of one particular type, the matrixplot() function is useful. Type for instance the following code:
Same thing if the desired matrix has to include graphics from variables of two different types (note that the graphic type has to be compatible):
If the user is interested in one particular graphic then the function
plotup() is useful.
Or, as example of a graphic that requires more than one input variable:
The default output of the
plotup() function is a
c("gg", "ggplot") object but the
output argument allows, as a side effect, to write and present the graphic in a html file or to print the ggplot2 function in the console:
plotup(infert, "pooled.stratum", "color binned stripe graph", output = "console") #> 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_minimal() + #> 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')