arrange multiple graphs using a for loop in ggplot2

Question

I want to produce a pdf which shows multiple graphs, one for each NetworkTrackingPixelId. I have a data frame similar to this:

> head(data)
  Net         


        

Answer 1

Unless I'm missing something, generating plots by a subsetting variable is very simple. You can use split(...) to split the original data into a list of data frames by NetworkTrackingPixelId, and then pass those to ggplot using lapply(...). Most of the code below is just to crate a sample dataset.

# create sample data
set.seed(1)
names <- c("Rubicon","Google","OpenX","AppNexus","Pubmatic")
dates <- as.Date("2014-02-16")+1:10
df <- data.frame(NetworkTrackingPixelId=rep(1:5,each=10),
                 Name=sample(names,50,replace=T),
                 Date=dates,
                 Impressions=sample(1000:10000,50))
# end create sample data

pdf("plots.pdf")
lapply(split(df,df$NetworkTrackingPixelId),
       function(gg) ggplot(gg,aes(x = Date, y = Impressions)) + 
          geom_point() + geom_line()+
          ggtitle(paste("NetworkTrackingPixelId:",gg$NetworkTrackingPixelId)))
dev.off()

This generates a pdf containing 5 plots, one for each NetworkTrackingPixelId.

Answer 2

I recently had a project that required producing a lot of individual pngs for each record. I found I got a huge speed up doing some pretty simple parallelization. I am not sure if this is more performant than the dplyr or data.table technique but it may be worth trying. I saw a huge speed bump:

require(foreach)
require(doParallel)
workers <- makeCluster(4)
registerDoParallel(workers) 
foreach(i = seq(1, length(mtcars$gear)), .packages=c('ggplot2')) %dopar% {
  j <- qplot(wt, mpg, data = mtcars[i,])
  png(file=paste(getwd(), '/images/',mtcars[i, c('gear')],'.png', sep=''))
  print(j)
  dev.off()
}

Answer 3

I think you would be better off writing a function for plotting, then using lapply for every Network Tracking Pixel.

For example, your function might look like:

    plot.function <- function(ntpid){
    sub = subset(dataset, dataset$networktrackingpixelid == ntpid)
    ggobj = ggplot(data=sub, aes(...)) + geom...
    ggsave(filename=sprintf("%s.pdf", ntpid))
    }

It would be helpful for you to put a reproducible example, but I hope this works! Not sure about the vector issue though..

Cheers!

Answer 4

Since I don't have your dataset, I will use the mtcars dataset to illustrate how to do this using dplyr and data.table. Both packages are the finest examples of the split-apply-combine paradigm in rstats. Let me explain:

Step 1 Split data by gear

• dplyr uses the function group_by
• data.table uses argument by

Step 2: Apply a function

• dplyr uses do to which you can pass a function that uses the pieces x.
• data.table interprets the variables to the function in context of each piece.

Step 3: Combine

There is no combine step here, since we are saving the charts created to file.

library(dplyr)
mtcars %.%
  group_by(gear) %.%
  do(function(x){ggsave(
    filename = sprintf("gear_%s.pdf", unique(x$gear)), qplot(wt, mpg, data = x)
  )})

library(data.table)
mtcars_dt = data.table(mtcars)
mtcars_dt[,ggsave(
  filename = sprintf("gear_%s.pdf", unique(gear)), qplot(wt, mpg)),
  by = gear
]

UPDATE: To save all files into one pdf, here is a quick solution.

plots = mtcars %.%
  group_by(gear) %.%
  do(function(x) {
    qplot(wt, mpg, data = x)
  })

pdf('all.pdf')
invisible(lapply(plots, print))
dev.off()