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A tutorial on tidy cross-validation with R Analyzing NetHack data, part 1: What kills the players Analyzing NetHack data, part 2: What players kill the most Building a shiny app to explore historical newspapers: a step-by-step guide Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 1 Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 2 Curly-Curly, the successor of Bang-Bang Dealing with heteroskedasticity; regression with robust standard errors using R Easy time-series prediction with R: a tutorial with air traffic data from Lux Airport Exporting editable plots from R to Powerpoint: making ggplot2 purrr with officer Fast food, causality and R packages, part 1 Fast food, causality and R packages, part 2 For posterity: install {xml2} on GNU/Linux distros Forecasting my weight with R From webscraping data to releasing it as an R package to share with the world: a full tutorial with data from NetHack Get text from pdfs or images using OCR: a tutorial with {tesseract} and {magick} Getting data from pdfs using the pdftools package Getting the data from the Luxembourguish elections out of Excel Going from a human readable Excel file to a machine-readable csv with {tidyxl} Historical newspaper scraping with {tesseract} and R How Luxembourguish residents spend their time: a small {flexdashboard} demo using the Time use survey data Imputing missing values in parallel using {furrr} Intermittent demand, Croston and Die Hard Looking into 19th century ads from a Luxembourguish newspaper with R Making sense of the METS and ALTO XML standards Manipulate dates easily with {lubridate} Manipulating strings with the {stringr} package Maps with pie charts on top of each administrative division: an example with Luxembourg's elections data Missing data imputation and instrumental variables regression: the tidy approach Modern R with the tidyverse is available on Leanpub Objects types and some useful R functions for beginners Pivoting data frames just got easier thanks to `pivot_wide()` and `pivot_long()` R or Python? Why not both? Using Anaconda Python within R with {reticulate} Searching for the optimal hyper-parameters of an ARIMA model in parallel: the tidy gridsearch approach Some fun with {gganimate} Split-apply-combine for Maximum Likelihood Estimation of a linear model Statistical matching, or when one single data source is not enough The best way to visit Luxembourguish castles is doing data science + combinatorial optimization The never-ending editor war (?) The year of the GNU+Linux desktop is upon us: using user ratings of Steam Play compatibility to play around with regex and the tidyverse Using Data Science to read 10 years of Luxembourguish newspapers from the 19th century Using a genetic algorithm for the hyperparameter optimization of a SARIMA model Using cosine similarity to find matching documents: a tutorial using Seneca's letters to his friend Lucilius Using linear models with binary dependent variables, a simulation study Using the tidyverse for more than data manipulation: estimating pi with Monte Carlo methods What hyper-parameters are, and what to do with them; an illustration with ridge regression {disk.frame} is epic {pmice}, an experimental package for missing data imputation in parallel using {mice} and {furrr} Building formulae Functional peace of mind Get basic summary statistics for all the variables in a data frame Getting {sparklyr}, {h2o}, {rsparkling} to work together and some fun with bash Importing 30GB of data into R with sparklyr Introducing brotools It's lists all the way down It's lists all the way down, part 2: We need to go deeper Keep trying that api call with purrr::possibly() Lesser known dplyr 0.7* tricks Lesser known dplyr tricks Lesser known purrr tricks Make ggplot2 purrr Mapping a list of functions to a list of datasets with a list of columns as arguments Predicting job search by training a random forest on an unbalanced dataset Teaching the tidyverse to beginners Why I find tidyeval useful tidyr::spread() and dplyr::rename_at() in action Easy peasy STATA-like marginal effects with R Functional programming and unit testing for data munging with R available on Leanpub How to use jailbreakr My free book has a cover! Work on lists of datasets instead of individual datasets by using functional programming Method of Simulated Moments with R New website! Nonlinear Gmm with R - Example with a logistic regression Simulated Maximum Likelihood with R Bootstrapping standard errors for difference-in-differences estimation with R Careful with tryCatch Data frame columns as arguments to dplyr functions Export R output to a file I've started writing a 'book': Functional programming and unit testing for data munging with R Introduction to programming econometrics with R Merge a list of datasets together Object Oriented Programming with R: An example with a Cournot duopoly R, R with Atlas, R with OpenBLAS and Revolution R Open: which is fastest? Read a lot of datasets at once with R Unit testing with R Update to Introduction to programming econometrics with R Using R as a Computer Algebra System with Ryacas

{disk.frame} is epic

Note: When I started writing this blog post, I encountered a bug and filed a bug report that I encourage you to read. The responsiveness of the developer was exemplary. Not only did Zhuo solve the issue in record time, he provided ample code snippets to illustrate the solutions. Hats off to him!

This blog post is a short presentation of {disk.frame} a package that makes it easy to work with data that is too large to fit on RAM, but not large enough that it could be called big data. Think data that is around 30GB for instance, or more, but nothing at the level of TBs.

I have already written a blog post about this topic, using Spark and the R library {sparklyr}, where I showed how to set up {sparklyr} to import 30GB of data. I will import the same file here, and run a very simple descriptive analysis. If you need context about the file I’ll be using, just read the previous blog post.

The first step, as usual, is to load the needed packages:


The next step is to specify how many cores you want to dedicate to {disk.frame}; of course, the more cores you use, the faster the operations:

setup_disk.frame(workers = 6)
options(future.globals.maxSize = Inf)

setup_disk.frame(workers = 6) means that 6 cpu threads will be dedicated to importing and working on the data. The second line, future.globals.maxSize = Inf means that an unlimited amount of data will be passed from worker to worker, as described in the documentation.

Now comes the interesting bit. If you followed the previous blog post, you should have a 30GB csv file. This file was obtained by merging a lot of smaller sized csv files. In practice, you should keep the files separated, and NOT merge them. This makes things much easier. However, as I said before, I want to be in the situation, which already happened to me in the past, where I get a big-sized csv file and I am to provide an analysis on that data. So, let’s try to read in that big file, which I called combined.csv:

path_to_data <- "path/to/data/"

flights.df <- csv_to_disk.frame(
  paste0(path_to_data, "combined.csv"), 
  outdir = paste0(path_to_data, "combined.df"),
  in_chunk_size = 2e6,
  backend = "LaF")

Let’s go through these lines, one at a time. In the first line, I simply define the path to the folder that contains the data. The next chunk is where I read in the data using the csv_to_disk_frame() function. The first option is simply the path to the csv file. The second option outdir = is where you need to define the directory that will hold the intermediary files, which are in the fst format. This folder, that contains these fst files, is the disk.frame. fst files are created by the {fst} package, which provides a fast, easy and flexible way to serialize data frames. This means that files that are in that format can be read and written much much faster than by other means (see a benchmark of {fst} here). The next time you want to import the data, you can use the disk.frame() function and point it to the combined.df folder. in_chunk_size = specifies how many lines are to be read in one swoop, and backend = is the underlying engine that reads in the data, in this case the {LaF} package. The default backend is {data.table} and there is also a {readr} backend. As written in the note at the beginning of the blog post, I encourage you to read the github issue to learn why I am using the LaF backend (the {data.table} and {readr} backend work as well).

Now, let’s try to replicate what I did in my previous blog post, namely, computing the average delay in departures per day. With {disk.frame}, one has to be very careful about something important however; all the group_by() operations are performed per chunk. This means that a second group_by() call might be needed. For more details, I encourage you to read the documentation.

The code below is what I want to perform; group by day, and compute the average daily flight delay:

mean_dep_delay <- flights.df %>%
  group_by(YEAR, MONTH, DAY_OF_MONTH) %>%
  summarise(mean_delay = mean(DEP_DELAY, na.rm = TRUE))

However, because with {disk.frame}, group_by() calls are performed per chunk, the code must now be changed. The first step is to compute the sum of delays within each chunk, and count the number of days within each chunk. This is the time consuming part:

tic <- Sys.time()
mean_dep_delay <- flights.df %>%
  group_by(YEAR, MONTH, DAY_OF_MONTH) %>%
  summarise(sum_delay = sum(DEP_DELAY, na.rm = TRUE), n = n()) %>%
(toc = Sys.time() - tic)
Time difference of 1.805515 mins

This is pretty impressive! It is much faster than with {sparklyr}. But we’re not done yet, we still need to compute the average:

mean_dep_delay <- mean_dep_delay %>%
  group_by(YEAR, MONTH, DAY_OF_MONTH) %>%
  summarise(mean_delay = sum(sum_delay)/sum(n))

It is important to keep in mind that group_by() works by chunks when dealing with disk.frame objects.

To conclude, we can plot the data:

dep_delay <- mean_dep_delay %>%
  arrange(YEAR, MONTH, DAY_OF_MONTH) %>%
  mutate(date = ymd(paste(YEAR, MONTH, DAY_OF_MONTH, sep = "-")))

ggplot(dep_delay, aes(date, mean_delay)) +
  geom_smooth(colour = "#82518c") + 
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

{disk.frame} is really promising, and I will monitor this package very closely. I might write another blog post about it, focusing this time on using machine learning with disk.frame objects.

Hope you enjoyed! If you found this blog post useful, you might want to follow me on twitter for blog post updates and buy me an espresso or paypal.me, or buy my ebook on Leanpub

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