some examples with rbokeh inspired by ggplot2 and lattice

Readme.md

lattice

examples inspired by this book

ggplot2

examples inspired by this book

examples_ggplot2.R

#  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 )

examples_lattice.R

# 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) )
    )
  )