This vignette contained many more plots in its initial development. The set has been cut back considerably to offer meaningful testing only, and because much of the material was moved over to a book called BrailleR in Action. Doing so also had an advantage of speeding up the package creation, testing, and installation.
N.B. the commands here are either exact copies of the commands presented in Wickham (2009) or some minor alterations to them. Notably, some code given in the book no longer works. This is given a #!
The ggplot2
package has a summary
method that often but not always offers something to show that things have changed from one plot to another. Summary
commands are included below but commented out.
library(BrailleR) library(ggplot2) dsmall = diamonds[1:100,]
g1 = qplot(carat, price, data = diamonds) # summary(g1) g1 # VI(g1) ### automatic since BrailleR v0.32.0
If the user does not actually plot the graph, they can still find out what it will look like once it is plotted by using the VI()
command on the graph object. This became unnecessary from version 0.32.0 of BrailleR.
N.B. All VI()
commands can now be deleted from this document.
g2 = qplot(carat, price, data = dsmall, colour = color) # summary(g2) g2
g3 = qplot(carat, price, data = dsmall, shape = cut) # summary(g3) g3
# to get semi-transparent points g4 = qplot(carat, price, data = diamonds, alpha = I(1/100)) # summary(g4) g4
# to add a smoother (default is loess for n<1000) g5 = qplot(carat, price, data = dsmall, geom = c("point", "smooth")) # summary(g5) g5 #! g5a = qplot(carat, price, data = dsmall, geom = c("point", "smooth"), span = 1) library(splines) #! g5b = qplot(carat, price, data = dsmall, geom = c("point", "smooth"), method = "lm") #! g5c = qplot(carat, price, data = dsmall, geom = c("point", "smooth"), method = "lm", formula = y ~ ns(x,5))
# continuous v categorical g6 = qplot(color, price / carat, data = diamonds, geom = "jitter", alpha = I(1 / 50)) # summary(g6) g6 # VI(g6) ### automatic since BrailleR v0.32.0 g6a = qplot(color, price / carat, data = diamonds, geom = "boxplot") # summary(g6a) g6a
# univariate plots g7a = qplot(carat, data = diamonds, geom = "histogram") # summary(g7a) g7a g7b = qplot(carat, data = diamonds, geom = "histogram", binwidth = 1, xlim = c(0,3)) g7b g7c = qplot(carat, data = diamonds, geom = "histogram", binwidth = 0.1, xlim = c(0,3)) g7c g7d = qplot(carat, data = diamonds, geom = "histogram", binwidth = 0.01, xlim = c(0,3)) # summary(g7d) g7d
g8 = qplot(carat, data = diamonds, geom = "density") # summary(g8) g8
# data is separated by implication using the following... g9 = qplot(carat, data = diamonds, geom = "density", colour = color) # summary(g9) g9 g10 = qplot(carat, data = diamonds, geom = "histogram", fill = color) # summary(g10) g10
# bar charts for categorical variable g11a = qplot(color, data = diamonds) # summary(g11a) g11a g11b = qplot(color, data = diamonds, geom = "bar") # summary(g11b) g11b g12a = qplot(color, data = diamonds, geom = "bar", weight = carat) # summary(g12a) g12a g12b = qplot(color, data = diamonds, geom = "bar", weight = carat) + scale_y_continuous("carat") # summary(g12b) g12b
# time series plots g13a = qplot(date, unemploy / pop, data = economics, geom = "line") # summary(g13a) g13a g13b = qplot(date, uempmed, data = economics, geom = "line") # summary(g13b) g13b
# path plots year <- function(x) as.POSIXlt(x)$year + 1900 g14a = qplot(unemploy / pop, uempmed, data = economics, geom = c("point", "path")) # summary(g14a) g14a #g14b = qplot(unemploy / pop, uempmed, data = economics, geom = "path", colour = year(date)) + scale_area() #summary(g14b)
# facets is the ggplot term for trellis' panels g15a = qplot(carat, data = diamonds, facets = color ~ ., geom = "histogram", binwidth = 0.1, xlim = c(0, 3)) # summary(g15a) g15a g15b = qplot(carat, ..density.., data = diamonds, facets = color ~ ., geom = "histogram", binwidth = 0.1, xlim = c(0, 3)) # summary(g15b) g15b
# rescaling of the axes g16 = qplot(carat, price, data = dsmall, log = "xy") # summary(g16) g16
# Facets syntax without a "." before the "~" causes grief g17 = qplot(displ, hwy, data=mpg, facets =~ year) + geom_smooth() # summary(g17) g17
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