gower 0.2.0 is on CRAN

A new version of R package gower has just been released on CRAN.

Thanks to our new contributor David Turner who was kind enough to provide a pull request, gower now also computes weighted gower distances.

From the NEWS file:

  • gower_dist and gower_topn gain weight argument for weighted matching (thanks to David Turner)
  • gower_dist and gower_topn gain ignore_case argument for automatic column matching.
  • gower_dist now returns numeric(0) invisibly when there are no columns to compare.
  • gower_topn now returns a list with empty matrices when there are no columns to compare.
  • gower_topn now warns when n>nrow(y) and sets n=nrow(y)
  • bugfix: comparing factors with characters would cause a crash (thanks to max Kuhn)

Compute Gower's distance (or similarity) coefficient between records. Compute the top-n matches between records. Core algorithms are executed in parallel on systems supporting OpenMP.

Posted in programming, R | Tagged , , | 1 Comment

uRos2019: tutorials, keynote speakers, registration and call for papers!

The 7th use of R in Official Statistics conference is the event for all things R in the production and use of government statistics. The 7th installment of this conference will take place from 20 to 21 May 2019 at the National Institute of Statistics in Bucharest, Romania.

Keynote Speakers

We are very proud to announce that we have two excellent keynote speakers.

  • Julie Josse will talk about her work on theory and tools related to imputation and inference in the presence of missing data.
  • Giulio Barcaroli will talk about 12 years of using R at ISTAT, the Italian Statistical Office.

Full abstracts can be found here


The conference is preceded by three tutorials on Data Cleaning, Statistical Disclosure Control and Optimal Sampling Stratification.

Call for papers

Yes, abstracts and papers are welcomed until 12 April 2019! You can contribute by sending an abstract in any of the following topics (relating to official statistics):

Sampling and estimation | R in organization | Data cleaning | R in production: data analysis | Methods for official statistics | Shiny applications | Time series | Report and GUI programming | R in production: automation | Big data | Dissemination and visualization

Registration is open

You can now register by following instructions here.

Posted in R | Tagged , , , | Leave a comment

Add a static pdf vignette to an R package

Most vignettes are built when a package is built, but there are occasions where you just want to include a pdf. For example when you want to include a paper. Of course there is a package supporting this, but in this post I will show you how to do it yourself with ease.

The idea is very simple: vignettes can be in LaTeX, and it is possible to include pdf documents in LaTeX using the pdfpages package. So here's the step-by-step recipe:

  1. If you do not already have it, create the vignettes folder in your package directory.
  2. Put your static pdf there. Let's call it mypaper.pdf for now.
  3. Create a .Rnw file with the following content.

\includepdf[pages=-, fitpaper=true]{mypaper.pdf}

That's it.

Some notes.

  1. This repo contains an example.
  2. The option fitpaper=true is necessary because the Sweave package that is included when the vignette is built somehow causes the pages to rescale if it is not included.
  3. If you post your package to CRAN, myfile.pdf will be deleted from the directory so it is not part of a binary download.
  4. You can include errata or other notes, for example as follows:

\includepdf[pages=-, fitpaper=true]{mypaper.pdf}


A few things were borked in the original publication, here
is a list of sto0pid things I did:

\item{fubar 1}
\item{fubar 2}

Posted in programming, R | Tagged , , | 3 Comments

The program for uRos2018 is online

The uRos2018 conference is aimed at professionals and academics who are involved in producing or consuming official (government) statistics.

We are happy to announce that we recently posted the full program of the 6th international conference on the use of R in official Statistics (uRos2018) on our website.

In summary:

  • Six tutorials in the areas of
    • Data Cleaning
    • Network Analyses
    • Survey Estimation
    • Data manipulation with data.table
    • Analyzing spatial data
    • Visualizing spatial data.
  • Two keynote speakers:
    • Alina Matei, professor of statistics at the University of Neuchatel and maintainer of the sampling package.
    • Jeroen Ooms, R superprogrammer and maintainer of R and Rtools for Windows (UC Berkeley)
  • Eleven sessions with contributed talks with five presentations from
    all over the world.
  • One session devoted to the results of a two-day unconf that is held prior to the conference.
  • One social dinner 🙂
  • Two journals will devote a special topic to the conference.

All the abstracts will be published online soon.

Registration is still open

  • You are welcome to register
  • Follow us on twitter for the latest news and updates!
Posted in programming, R | Tagged | Leave a comment

stringdist now with C API

Version of stringdist is on CRAN. The main new feature, with a huge thanks to our awesome new contributor Chris Muir, is that we made it easy to call stringdist functionality from your package's C or C++ code.

The main steps to get it done are:

  1. Make sure to add stringdist to the Imports: and LinkingTo: fields in your DESRIPTION file
  2. Add the #include <stringdist_api> to your C/C++ source file.
  3. Start using stringdist from C!

Here's an example source file

#include <R.h>
#include <Rdefines.h>
#include <stringdist_api.h>

SEXP my_soundex(SEXP strings, SEXP useBytes){
  Rprintf("\nWow, using 'stringdist' soundex encoding, from my own C code!\n");
  return sd_soundex(strings, useBytes);

Great! how can I learn more?

  • The full API is desribed in a pdf file that is generated from doxygen that comes with the package. You can find it by typing ?stringdist_api on the R command line.
  • A minimal example package that links to stringdist is available on GitHub
  • A more sophisticated package with more elaborate examples can be found here: refinr (By Chris)

Any other news?

A few fixes, and a couple of long-deprecated function arguments have finally been removed. Check out the NEWS file on CRAN for a complete overview.

Happy coding!

Posted in programming, R | Tagged , | 2 Comments

The use of R in official statistics conference 2018

On September 12-14 the 6th international conference on the use of R in official statistics (#uRos2018) will take place at the Dutch National Statistical Office in Den Haag, the Netherlands. The conference is aimed at producers and users of official statistics from government, academia, and industry. The conference is modeled after the useR! conference and will consist of one day of tutorials (12th September 2018) followed by two days of conference (13, 14 September 2018). Topics include:

  • Examples of applying R in statistical production.
  • Examples of applying R in dissemination of statistics (visualisation, apps, reporting).
  • Analyses of big data and/or application of machine learning for official statistics.
  • Implementations of statistical methodology in the areas of sampling, editing, modelling and estimation, or disclosure control.
  • R packages connecting R to other standard tools/technical standards
  • Organisational and technical aspects of introducing R to the statistical office.
  • Teaching R to users in the office
  • Examples of accessing or using official statistics publications with R in other fields

    Keynote speakers
    We are very happy to announce that we confirmed two fantastic keynote speakers.

  • Alina Matei is a professor of statistics at the University of Neuchatel and maintainer of the important sampling package.
  • Jeroen Ooms is a postdoc at UC Berkeley, author of many infrastructural R packages and maintainer of R and Rtools for Windows.

    Call for abstracts

    The call for abstracts is open until 31 May. You can contribute to the conference by proposing a 20-minute talk, or a 3-hour tutorial. Also, authors have the opportunity to submit a paper for one of the two journals that will devote a special issue to the conference. Read all about it over here.


  • conference website
  • Follow uRos2018 on twitter

  • Posted in R | Tagged | Leave a comment

    Track changes in data with the lumberjack %>>%

    So you are using this pipeline to have data treated by different functions in R. For example, you may be imputing some missing values using the simputation package. Let us first load the only realistic dataset in R

    > data(retailers, package="validate")
    > head(retailers, 3)
      size incl.prob staff turnover other.rev total.rev staff.costs total.costs profit vat
    1  sc0      0.02    75       NA        NA      1130          NA       18915  20045  NA
    2  sc3      0.14     9     1607        NA      1607         131        1544     63  NA
    3  sc3      0.14    NA     6886       -33      6919         324        6493    426  NA

    This data is dirty with missings and full of errors. Let us do some imputations with simputation.

    > out <- retailers %>% 
    +   impute_lm(other.rev ~ turnover) %>%
    +   impute_median(other.rev ~ size)
    > head(out,3)
      size incl.prob staff turnover other.rev total.rev staff.costs total.costs profit vat
    1  sc0      0.02    75       NA  6114.775      1130          NA       18915  20045  NA
    2  sc3      0.14     9     1607  5427.113      1607         131        1544     63  NA
    3  sc3      0.14    NA     6886   -33.000      6919         324        6493    426  NA

    Ok, cool, we know all that. But what if you'd like to know what value was imputed with which method? That's where the lumberjack comes in.

    The lumberjack operator is a `pipe'[1] operator that allows you to track changes in data.

    > library(lumberjack)
    > retailers$id <- seq_len(nrow(retailers))
    > out <- retailers %>>% 
    +   start_log(log=cellwise$new(key="id")) %>>%
    +   impute_lm(other.rev ~ turnover) %>>%
    +   impute_median(other.rev ~ size) %>>%
    +   dump_log(stop=TRUE)
    Dumped a log at cellwise.csv
    > read.csv("cellwise.csv") %>>% dplyr::arrange(key) %>>% head(3)
      step                     time                      expression key  variable old      new
    1    2 2017-06-23 21:11:05 CEST impute_median(other.rev ~ size)   1 other.rev  NA 6114.775
    2    1 2017-06-23 21:11:05 CEST impute_lm(other.rev ~ turnover)   2 other.rev  NA 5427.113
    3    1 2017-06-23 21:11:05 CEST impute_lm(other.rev ~ turnover)   6 other.rev  NA 6341.683

    So, to track changes we only need to switch from %>% to %>>% and add the start_log() and dump_log() function calls in the data pipeline. (to be sure: it works with any function, not only with simputation). The package is on CRAN now, and please see the introductory vignette for more examples and ways to customize it.

    There are many ways to track changes in data. That is why the lumberjack is completely extensible. The package comes with a few loggers, but users or package authors are invited to write their own. Please see the extending lumberjack vignette for instructions.

    If this post got you interested, please install the package using


    You can get started with the introductory vignette or even just use the lumberjack operator %>>% as a (close) replacement of the %>% operator.

    As always, I am open to suggestions and comments. Either through the packages github page.

    Also, I will be talking at useR2017 about the simputation package, but I will sneak in a bit of lumberjack as well :p.

    And finally, here's a picture of a lumberjack smoking a pipe.

    [1] It really should be called a function composition operator, but potetoes/potatoes.

    Posted in programming, R | Tagged , | Leave a comment

    Announcing the simputation package: make imputation simple

    I am happy to announce that my simputation package has appeared on CRAN this weekend. This package aims to simplify missing value imputation. In particular it offers standardized interfaces that

    • make it easy to define both imputation method and imputation model;
    • for multiple variables at once;
    • while grouping data by categorical variables;
    • all fitting in the magrittr not-a-pipeline.

    A few examples

    To start with an example, let us first create a data set with some missings.

    dat <- iris
    # empty a few fields
    dat[1:3,1] <- dat[3:7,2] <- dat[8:10,5] <- NA
    ##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    ## 1            NA         3.5          1.4         0.2  setosa
    ## 2            NA         3.0          1.4         0.2  setosa
    ## 3            NA          NA          1.3         0.2  setosa
    ## 4           4.6          NA          1.5         0.2  setosa
    ## 5           5.0          NA          1.4         0.2  setosa
    ## 6           5.4          NA          1.7         0.4  setosa
    ## 7           4.6          NA          1.4         0.3  setosa
    ## 8           5.0         3.4          1.5         0.2    <NA>
    ## 9           4.4         2.9          1.4         0.2    <NA>
    ## 10          4.9         3.1          1.5         0.1    <NA>

    Below, we first impute Sepal.Width and Sepal.Length by regression on Petal.Width and Species. After this we impute Species using a decision tree model (CART) using every other variable as a predictor (including the ones just imputed).

    library(magrittr)    # load the %>% operator
    imputed <- dat %>% 
      impute_lm(Sepal.Width + Sepal.Length ~ Petal.Width + Species) %>%
      impute_cart(Species ~ .)
    ##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    ## 1      4.979844    3.500000          1.4         0.2  setosa
    ## 2      4.979844    3.000000          1.4         0.2  setosa
    ## 3      4.979844    3.409547          1.3         0.2  setosa
    ## 4      4.600000    3.409547          1.5         0.2  setosa
    ## 5      5.000000    3.409547          1.4         0.2  setosa
    ## 6      5.400000    3.561835          1.7         0.4  setosa
    ## 7      4.600000    3.485691          1.4         0.3  setosa
    ## 8      5.000000    3.400000          1.5         0.2  setosa
    ## 9      4.400000    2.900000          1.4         0.2  setosa
    ## 10     4.900000    3.100000          1.5         0.1  setosa

    The package is pretty lenient against failure of imputation. For example, if one of the predictors is missing, fields just remain unimputed and if one of the models cannot be fitted, only a warning is issued (not shown here).

    dat %>% impute_lm(Sepal.Length ~ Sepal.Width + Species) %>% head(3)
    ##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    ## 1     5.076579         3.5          1.4         0.2  setosa
    ## 2     4.675654         3.0          1.4         0.2  setosa
    ## 3           NA          NA          1.3         0.2  setosa

    So here, the third Sepal.Length value could not be imputed since the predictor Sepal.Width is missing.

    It is possible to split data into groups before estimating the imputation model and predicting missing values. There are two ways. The first is to use the | operator to specify grouping variables.

    # We first need to complete 'Species'. Here, we use sequential 
    # hot deck after sorting by Petal.Length
    dat %<>% impute_shd(Species ~ Petal.Length) 
    # Now impute Sepal.Length by regressing on 
    # Sepal.Width, computing a model for each Species.
    dat %>% impute_lm(Sepal.Length ~ Sepal.Width | Species) %>% head(3)
    ##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    ## 1     5.067813         3.5          1.4         0.2  setosa
    ## 2     4.725677         3.0          1.4         0.2  setosa
    ## 3           NA          NA          1.3         0.2  setosa

    The second way is to use the group_by command from dplyr

    dat %>% dplyr::group_by(Species) %>% 
        impute_lm(Sepal.Length ~ Sepal.Width) %>% 
    ## Source: local data frame [3 x 5]
    ## Groups: Species [1]
    ##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    ##          <dbl>       <dbl>        <dbl>       <dbl>  <fctr>
    ## 1     5.067813         3.5          1.4         0.2  setosa
    ## 2     4.725677         3.0          1.4         0.2  setosa
    ## 3           NA          NA          1.3         0.2  setosa

    Note: by using group_by, we also transformed the data.frame to a tibble, which not only sounds funny when you pronounce it (tibble, TIBBLE, tibble? tibbebbebbebble) but is also pretty useful.

    Supported methods and how to specify them

    Currently, the package supports the following methods:

    • Model based (optionally add [non-]parametric random residual)
      • linear regression
      • robust linear regression
      • CART models
      • Random forest
    • Donor imputation (including various donor pool specifications)
      • k-nearest neigbour (based on gower's distance)
      • sequential hotdeck (LOCF, NOCB)
      • random hotdeck
      • Predictive mean matching
    • Other
      • (groupwise) median imputation (optional random residual)
      • Proxy imputation (copy from other variable)

    Any call to one of the impute_ functions looks as follows:

    impute_<method>(data, formula [, <method-specific options>])

    and the formula always has the following form:

    <imputed variables> ~ <model specification> [|<grouping variables>]

    The parts in square brackets are optional.

    Please see the package vignette for more examples and details, or ?simputation::impute_ for an overview of all imputation functions.

    Happy imputing!

    Posted in programming, R | Tagged , , | 5 Comments

    stringdist released

    stringdist was accepted on CRAN at the end of last week.

    This release just fixes a few bugs affecting the stringdistmatrix function, when called with a single argument.

    From the NEWS file:

    • bugfix in stringdistmatrix(a): value of p, for jw-distance was ignored (thanks to Max Fritsche)
    • bugfix in stringdistmatrix(a): Would segfault on q-gram w/input > ~7k strings and q>1 (thanks to Connor McKay)
    • bugfix in jaccard distance: distance not always correct when passing multiple strings (thanks to Robert Carlson)

    Actually the last bug has not bitten anyone since it was masked by the second one 🙂 (it was reported and fixed a long time ago but popped up again after fixing the second bug -- hat tip to Hadley for testthat!). The second fix also ensures that stringdist's memory allocator for q-gram storage is called fewer times which yields a speed gain in computation of q-gram based distances.

    Posted in programming, R | Tagged | 2 Comments

    validate version 0.1.5 is out

    A new version of the validate package for data validation was just accepted on CRAN and will be available on all mirrors in a few days.

    The most important addition is that you can now reference the data set as a whole, using the "dot" syntax like so:

    iris %>% check_that(
      , "Sepal.Width" %in% names(.)) %>% 
      rule items passes fails nNA error warning                  expression
    1   V1     1      1     0   0 FALSE   FALSE               nrow(.) > 100
    2   V2     1      1     0   0 FALSE   FALSE "Sepal.Width" %in% names(.)

    Also, it is now possible to return a logical, even when the result is NA, by passing the na.value option.

    dat = data.frame(x=c(1,NA,-1))
    v = validator(x > 0)
    [1,]  TRUE
    [2,]    NA
    [3,] FALSE
    [1,]  TRUE
    [2,] FALSE
    [3,] FALSE

    A complete list of changes and bugfixes can be found in the NEWS file. Below I include changes in 1.4 since I did not write about it before.

    I will be talking about this package at the upcoming useR!2016 event, so join me if you're interested!

    version 0.1.5

    • The '.' is now used to reference the validated data set as whole.
    • Small change in output of 'compare' to match the table in van den Broek et al. (2013)

    version 0.1.4

    • 'confront' now emits a warining when variable name conflicts with name of a reference data set
    • Deprecated 'validate_reset', in favour of the shorter 'reset' (use 'validate::reset' in case of ambiguity)
    • Deprecated 'validate_options' in favour of the shorter 'voptions'
    • New option na.value with default value NA, controlling the output when a rule evaluates to NA.
    • Added rules from the ESSnet on validation (deliverable 17) to automated tests.
    • added 'grepl' to allowed validation syntax (suggested by Dusan Sovic)
    • exported a few functions w/ keywords internal for extensibility
    • Bugfix: blocks sometimes reported wrong nr of blocks (in case of a single connected block.)
    • Bugfix: macro expansion failed when macros were reused in other macros.
    • Bugfix: certain nonlinear relations were recognized as linear
    • Bugfix: rules that use (anonymous) function definitions raised error when printed.
    Posted in programming, R, Uncategorized | Tagged , , | Leave a comment