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some examples with rbokeh inspired by ggplot2 and lattice

lattice

examples inspired by this book

ggplot2

examples inspired by this book

# examples from ggplot2 book
# http://www.amazon.com/ggplot2-Elegant-Graphics-Data-Analysis/dp/0387981403/ref=sr_1_1?ie=UTF8&qid=1423697224&sr=8-1&keywords=ggplot2
library(rbokeh)
library(ggplot2)
data(diamonds)
bp <- figure( height = 400, width = 700 )
bp %>% ly_points(carat,price,diamonds,color=cut,size=5)
# add hover
bp %>%
ly_points(carat,price,diamonds,color=cut,size=1,hover=list(cut,clarity,color))
# make it a hexbin
bp %>%
ly_hexbin( carat, price, diamonds )
# make it a hexbin with facet by color
lapply(
levels(unique(diamonds$color))
,function(c){
figure( height = 300, width = 300, title = paste0("Color: ",c) ) %>%
ly_hexbin( carat, log(price), diamonds[which(diamonds$color==c),] )
}
) %>%
grid_plot( nrow = 3, ncol = 3, same_axes = T )
# histogram on diamonds
bp %>% ly_hist( x = carat, data = diamonds, breaks = 2 )
# density on diamonds
bp %>% ly_density( x = carat, data = diamonds )
# quantile on diamonds
bp %>% ly_quantile(price,group = "color", diamonds)
bp %>% ly_quantile(price,group = "color", diamonds, distn=qnorm)
#demo a transform
bp %>%
ly_points( cyl, mpg^2, mtcars ) %>%
# not transformed
ly_points( cyl, mpg, mtcars, color = "red" ) %>%
# axis need to come after layers specified
y_axis( log = T )
# set vs map color
bp %>%
ly_points( mpg, wt, mtcars, color = "purple")
bp %>%
ly_points( mpg, wt, data.frame(name=rownames(mtcars),mtcars), color = cyl, hover = list(name))
# boxplot
data("Oxboys", package = "nlme")
bp %>%
ly_boxplot( Occasion, height, Oxboys )
# http://www.amazon.com/Lattice-Multivariate-Data-Visualization-Use/dp/0387759689/ref=cm_cr_pr_product_top
library(rbokeh)
library(dplyr)
library(pipeR)
bp <- figure( height = 400, width = 700 )
data(Chem97,package = "mlmRev")
##### figure 1_01 #####
bp %>%
ly_hist( gcsescore, Chem97, breaks = seq(0,8,0.5) )
# now do the facetted version
lapply(
as.character(sort(unique(Chem97$score)))
, function(s){
figure( 300, 300, title = paste0("Score: ",s) ) %>%
ly_hist(
gcsescore
,filter(Chem97, score == as.numeric(s))
,breaks = seq(0,8,0.5)
)
}
) %>%
grid_plot( nrow = 2, ncol = 3, same_axes=T )
##### figure 1_02 #####
bp %>%
ly_hist( gcsescore, Chem97, breaks = seq(0,8,0.5) )
# now do the facetted version
lapply(
as.character(sort(unique(Chem97$score)))
, function(s){
figure( 300, 300, title = paste0("Score: ",s) ) %>%
ly_density(
gcsescore
, data = filter(Chem97, score == as.numeric(s))
# get error with color mapping
, color = substr(RColorBrewer::brewer.pal(name="Dark2",n=6)[as.numeric(s)/2+1],2,7)
)
}
) %>>%
(~show(grid_plot( ., nrow = 2, ncol = 3, same_axes=T ))) %>>%
(~show(grid_plot( ., nrow = 2, ncol = 3, same_axes=T, byrow = F ))) %>>%
(~show(grid_plot( ., nrow = 3, ncol = 2, same_axes=T ))) %>>%
(grid_plot( ., nrow = 3, ncol = 2, same_axes=T, byrow = F ))
##### figure 1_03 #####
local({
bp2<- bp
lapply(
as.character(sort(unique(Chem97$score)))
, function(s){
bp2 <<- bp2 %>%
ly_density(
gcsescore
, data = filter(Chem97, score == as.numeric(s))
# get error with color mapping
, color = substr(RColorBrewer::brewer.pal(name="Dark2",n=6)[as.numeric(s)/2+1],2,7)
)
}
)
bp2
})
##### figure 2_01 #####
data(Oats, package = "MEMSS")
apply(
unique(expand.grid(
levels(Oats$Block)
, levels(Oats$Variety)
, stringsAsFactors = F )
)
, MARGIN = 1
, function(tuple){
figure( width = 400, height = 400, title = paste0(tuple) ) %>>%
ly_lines(
nitro, yield
, data =
Oats %>>%
filter( Variety == tuple[[2]] & Block == tuple[[1]] ) %>>%
group_by ( nitro ) %>>%
summarise ( yield = mean( yield ) )
, line_color = substr(
RColorBrewer::brewer.pal(n=3,name="Set1")[which(tuple[[2]]==levels(Oats$Variety))]
,2,7
)
)
}
) -> bp2
bp2 %>%
grid_plot( nrow = 6, ncol = 3, same_axes = T, byrow = F)
##### figure 2_02 #####
grid_plot(bp2[seq(1,18,6)], nrow = 1, ncol = 3, same_axes = T )
##### figure 2_06 #####
data(barley,package="lattice")
# no facet to get started
bp %>%
ly_points( yield, variety, barley, color = year, hover = list( variety, yield ) )
# now show facet
lapply(
levels( barley$site )
,function(s){
figure( height = 200, width = 700, title = s ) %>%
ly_points(
yield, variety
# for fun do without dplyr
, data = subset(barley, site == s)
, color = year
, hover = list( variety, yield )
, size = 6
)
}
) %>%
grid_plot( nrow = length(.), ncol = 1, same_axes = T )
##### figure 2_07 #####
# no facet first
bp %>%
ly_lines(
nitro, yield
,data = Oats
,color = Variety
,group = Variety
)
# now with facets
lapply(
levels(Oats$Block)
,function(b){
figure( height = 400, width = 200, title = b ) %>>%
ly_lines(
nitro, yield
,data = subset( Oats, Block == b )
,color = Variety
,group = Variety
)
}
) %>>%
grid_plot( nrow = 1, ncol = length(.), same_axes = T )
##### figure 2_08 - 2_11 #####
# bar charts not yet supported but support is in process
##### figure 3_01 #####
data(faithful)
bp %>>%
# draw the density line using parameters in example
ly_density( eruptions, faithful, bw = 0.2, n = 200, kernel = "rect" ) %>>%
# draw density with defaults
ly_density( eruptions, faithful, color = "blue" ) %>>%
#add random y to jitter for rugplot of points at bottom
ly_points(
eruptions
# note use of inline expression
, runif(n = nrow(faithful), min = 0, max = 0.025)
, faithful
, size = 5
)
##### figure 3_02 #####
# like above but a rug plot
bp %>>%
# draw the density line using parameters in example
ly_density( eruptions, faithful, bw = 0.2, n = 200, kernel = "rect" ) %>>%
# sort of a hack but hey it works
ly_multi_line(
xs = cbind(faithful$eruptions,faithful$eruptions)
# note use of inline expression
, ys = cbind(rep(0,nrow(faithful)),rep(0.025,nrow(faithful)))
)
##### figure 3_03 ########
data(gvhd10, package = "latticeExtra")
#get density data for the plot
lapply(
levels(gvhd10$Days)
,function(d){
figure( height = 200, width = 400, title = paste0("Days: ", d) ) %>>%
ly_density(
log(FSC.H)
,data = subset(gvhd10, Days == d)
)
}
) %>>%
grid_plot( nrow = 4, ncol = 2, same_axes = T )
##### figure 3_05 ########
data(Chem97, package = "mlmRev")
# no facet first for simplicity
# since first example of ly_quantile
bp %>>%
ly_quantile(
x = gcsescore
,group = score
,data = Chem97
,distn = qnorm
)
lapply(
sort(unique(Chem97$score))
,function(d){
figure( width = 300 , height = 300, title = paste0("Score: ", d) ) %>>%
ly_quantile(
x = gcsescore
,data = subset(Chem97,score == d)
,distn = qnorm
,color = substr(RColorBrewer::brewer.pal(name="Dark2",n=6)[as.numeric(d)/2+1],2,7)
)
}
) %>>%
grid_plot( nrow = 2, ncol = 3, same_axes = T )
##### figure 3_06 ########
local({
lapply(
levels(Chem97$gender)
,function(g){
bp <- figure( width = 300 , height = 300, title = paste0("Gender: ", g) )
Reduce(
function(x,y){
bp <<- ly_quantile(
bp
,x = gcsescore
,data = subset(Chem97, score == y & gender == g)
,distn = qnorm
,color = substr(RColorBrewer::brewer.pal(name="Dark2",n=6)[as.numeric(y)/2+1],2,7)
,size = 5
)
}
,sort(unique(Chem97$score))
)
return(bp)
}
)
}) %>>%
grid_plot( nrow = 1, ncol = 2, same_axes = T )
##### figure 3_08 #######
# use a less functional approach to get the data in ECDF form
data(Chem97, package = "mlmRev")
data <- data.frame()
gender <- unique(Chem97$gender)
scores <- unique(Chem97$score)
for (i in 1 : length(scores) ) {
for (j in 1 : length(gender) ) {
tempdata <- list()
#code primarily from lattice panel.ecdfplot
#note: example subset gcsescore > 0
n = sum(!is.na(Chem97[which( Chem97$gender == gender[j] & Chem97$score == scores[i] & Chem97$gcsescore > 0 ),]$gcsescore))
tempdata$x = sort(Chem97[which( Chem97$gender == gender[j] & Chem97$score == scores[i] & Chem97$gcsescore > 0 ),]$gcsescore)
tempdata$y = seq_len(n)/n
tempdata$gender = rep( gender[j], length(tempdata$x) )
tempdata$score = rep(scores[i], length(tempdata$x) )
data <- rbind( data, data.frame( tempdata ) )
}
}
colnames(data) <- c("x1", "y1", "gender", "score")
local({
lapply(
sort(unique(data$score))
,function(s){
bp <- figure( width = 300 , height = 300, title = paste0("Score: ", s) )
Reduce(
function(x,y){
print(y)
bp <<- ly_points(
bp
,x = x1
,y = y1
,data = subset(data, score == s & gender == y )
,color = gender
,size = 3
)
}
,levels(data$gender)
,init = levels(data$gender)[1]
)
return(bp)
}
)
}) %>>%
grid_plot( nrow = 2, ncol = 3, same_axes = T )
##### figure 3_09 #######
data(Chem97, package = "mlmRev")
data <- data.frame()
gender <- unique(Chem97$gender)
scores <- unique(Chem97$score)
for (i in 1 : length(scores) ) {
for (j in 1 : length(gender) ) {
tempdata <- list()
#code primarily from lattice panel.qqmath
n <- sum(!is.na( Chem97[which(Chem97$gcsescore > 0 & Chem97$score == scores[i] & Chem97$gender == gender[j] ),]$gcsescore))
tempdata$x = qunif(ppoints(n))
tempdata$y = quantile(
x = Chem97[which(Chem97$gcsescore > 0 & Chem97$score == scores[i] & Chem97$gender == gender[j] ),]$gcsescore,
ppoints(n),
names = FALSE,
type = 7,
na.rm = TRUE)
tempdata$gender = rep( gender[j], n )
tempdata$score = rep(scores[i], n )
data <- rbind( data, data.frame( tempdata ) )
}
}
colnames(data) <- c("x1", "y1", "gender", "score")
local({
lapply(
sort(unique(data$score))
,function(s){
bp <- figure( width = 200 , height = 500, title = paste0("Score: ", s) )
Reduce(
function(x,y){
print(y)
bp <<- ly_points(
bp
,x = x1
,y = y1
,data = subset(data, score == s & gender == y )
,color = gender
,size = 1
,hover = list(x1,y1)
) %>>%
x_axis( label = "Std Normal ") %>>%
y_axis( label = "GCSEScore")
}
,levels(data$gender)
,init = levels(data$gender)[1]
)
return(bp)
}
)
}) %>>%
grid_plot( nrow = 1, ncol = 6, same_axes = T )
##### figure 3_10 #######
data(Chem97, package = "mlmRev")
Chem97 %>>%
dplyr::group_by( gender, score ) %>%
do(
data.frame(
x = qnorm(ppoints(100))
,y = quantile(.$gcsescore, ppoints(100), names = F, type = 7, na.rm = F)
,score = unique(.$score)
,gender = unique(.$gender)
)
) %>>%
(reshape2::dcast(., x + score ~ gender, value.var = "y")) %>>%
(dat~
lapply(
sort(unique(dat$score))
,function(s){
figure( width = 200, height = 200, title = paste0("score: ", s) ) %>>%
ly_points(
M
,F
,data = filter( dat, score == s )
,size = 4
,hover = list(score,M,F)
) %>>%
ly_abline( a= 0, b = 1 )
}
)
) %>>%
grid_plot( nrow = 2, ncol = 3, same_axes = T )
##### figure 3_11 #######
data(Chem97, package = "mlmRev")
# non facet first since first boxplot
figure( width = 700, height = 400
# little clunky to force sort on categorical axis
, xlim = as.character(sort(unique(Chem97$score)))
) %>>%
ly_boxplot(
as.character(score)
,gcsescore
,data = Chem97
) %>>%
x_axis(label = "Average GCSE Score")
# now facet it
lapply(
levels(Chem97$gender)
,function(g){
# non facet first since first boxplot
figure( width = 500, height = 300
# little clunky to force sort on categorical axis
, xlim = as.character(sort(unique(Chem97$score)))
) %>>%
ly_boxplot(
as.character(score)
,gcsescore
,data = filter(Chem97, gender == g)
) %>>%
ly_lines(
as.character(score)
,mean_gcse
,data = filter(Chem97, gender == g) %>>%
group_by( score) %>>%
summarize( mean_gcse = mean(gcsescore) )
, color = "black"
) %>>%
x_axis(label = "Average GCSE Score")
}
) %>>%
grid_plot( nrow = 1, ncol = 2 )#, same_axes messes up sort )
##### figure 3_12 #######
data(Chem97, package = "mlmRev")
lapply(
sort(unique(Chem97$score))
,function(s){
# non facet first since first boxplot
figure( width = 200, height = 500, title = paste0("score: ", s) ) %>>%
ly_boxplot(
gender
,gcsescore
,data = filter(Chem97, score == s)
) %>>%
ly_points(
gender
,mean_gcse
,data = filter(Chem97, score == s) %>>%
group_by( gender ) %>>%
summarize( mean_gcse = mean(gcsescore) )
, color = "black"
, glyph = 9
)
}
) %>>%
grid_plot( nrow = 1, ncol = 2, same_axes = T )
##### figure 3_15 #######
data(quakes)
figure( height = 400, width = 700, ylim = as.character(seq(4,6.5,0.1)) ) %>>%
ly_points(
depth
,factor(mag)
,data = quakes
,size = 4
,color = "black"
,alpha = 1
)
##### figure 3_16 #######
# don't think the jitter is possible with a categorical scale/axis
data(quakes)
figure( height = 400, width = 700, xlim = as.character(seq(4,6.5,0.1)) ) %>>%
ly_points(
factor(mag)
,depth
,data = quakes
,size = 4
,color = "black"
,alpha = 1
)
##### figure 3_17 #######
# again, not sure jitter possible with categorical scale/axis
data(barley,package="lattice")
barley %>>%
(
data.frame(
.
,residuals = sqrt(abs(residuals(lm(yield~variety+year+site,.))))
)
) %>>%
(dat~
ly_points(
figure( width = 700, height = 400, xlim = levels(dat$site) )
,site
,residuals
,data = dat
,color = year
) %>>%
ly_points(
site
,median_residual
,color = year
,data = dat %>>% group_by( site, year ) %>>% summarize( median_residual = median(residuals) )
,glyph = 3
,size = 30
) %>>%
ly_lines(
site
,median_residual
,color = year
,data = dat %>>% group_by( site, year ) %>>% summarize( median_residual = median(residuals) )
)
)
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