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Update Nov 2 2015: Wow, that was fast. Registration is full. If you add yourself to the waitlist, we’ll contact you first if/when we do this conference again.

In the three years since we launched Shiny, our focus has been on helping people get started with Shiny. But there’s a huge difference between using Shiny and using it well, and we want to start getting serious about helping people use Shiny most effectively. It’s the difference between having apps that merely work, and apps that are performant, robust, and maintainable.

That’s why RStudio is thrilled to announce the first ever Shiny Developer Conference, to be held at Stanford University on January 30-31, 2016, three months from today. We’ll skip past the basics, and dig into principles and practices that will simultaneously simplify and improve the robustness of your code. We’ll introduce you to some brand new tools we’ve created to help you build ever larger and more complex apps. And we’ll show you what to do if things go wrong.

Check out the agenda to see the complete lineup of speakers and talks.

We’re capping the conference at just 90 people, so if you’d like to level up your Shiny skills, register now at

Hope to see you there!

Note that this conference is intended for R users who are already comfortable writing Shiny apps. We won’t cover the basics of Shiny app creation at all. If you’re looking to get started with Shiny, please see our tutorial.

roxygen2 5.0.0 is now available on CRAN. roxygen2 helps you document your packages by turning specially formatted inline comments in R’s standard Rd format. Learn more at

In this release:

  • Roxygen records its version in a single place: the RoxygenNote field in your DESCRIPTION. This should make it easier to see what’s changed when you upgrade roxygen2, because only files with differences will be modified. Previously every Rd file was modified to update the version number.
  • You can now easily document functions that you’ve imported from another package:
    #' @importFrom magrittr %>%
    #' @export

    All imported-and-re-exported functions will be documented in the same file (rexports.Rd), with a brief descrption and links to the original documentation.

  • You can more easily generate package documentation by documenting the special string “_PACKAGE“:
    #' @details Details

    The title and description will be automatically filled in from the DESCRIPTION.

  • New tags @rawRd and @rawNamespace allow you to insert raw (unescaped) text in Rd and the NAMESPACE. @evalRd() is similar, but instead of literal Rd, you give it R code that produces literal Rd code when run. This should make it easier to experiment with new types of output.
  • Roxygen2 now parses the source code files in the order specified in the Collate field in DESCRIPTION. This improves the ordering of the generated documentation when using @describeIn and/or @rdname split across several .R files, as often happens when working with S4.
  • The parser has been completely rewritten in C++. This gives a nice performance boost and adds improves the error messages: now get the line number of the tag, not the start of the block.
  • @family now cross-links each manual page only once, instread of linking to all aliases.

There were many other minor improvements and bug fixes; please see the release notes for a complete list. A bug thanks goes to all the contributors who made this release possible.

readr 0.2.0 is now available on CRAN. readr makes it easy to read many types of tabular data, including csv, tsv and fixed width. Compared to base equivalents like read.csv(), readr is much faster and gives more convenient output: it never converts strings to factors, can parse date/times, and it doesn’t munge the column names.

This is a big release, so below I describe the new features divided into four main categories:

  • Improved support for international data.
  • Column parsing improvements.
  • File parsing improvements, including support for comments.
  • Improved writers.

There were too many minor improvements and bug fixes to describe in detail here. See the release notes for a complete list.


readr now has a strategy for dealing with settings that vary across languages and localities: locales. A locale, created with locale(), includes:

  • The names of months and days, used when parsing dates.
  • The default time zone, used when parsing datetimes.
  • The character encoding, used when reading non-ASCII strings.
  • Default date format, used when guessing column types.
  • The decimal and grouping marks, used when reading numbers.

I’ll cover the most important of these parameters below. For more details, see vignette("locales").
To override the default US-centric locale, you pass a custom locale to read_csv(), read_tsv(), or read_fwf(). Rather than showing those funtions here, I’ll use the parse_*() functions because they work with character vectors instead of a files, but are otherwise identical.

Names of months and days

The first argument to locale() is date_names which controls what values are used for month and day names. The easiest way to specify them is with a ISO 639 language code:

locale("ko") # Korean
#> <locale>
#> Numbers:  123,456.78
#> Formats:  %Y%.%m%.%d / %H:%M
#> Timezone: UTC
#> Encoding: UTF-8
#> <date_names>
#> Days:   일요일 (일), 월요일 (월), 화요일 (화), 수요일 (수), 목요일 (목),
#>         금요일 (금), 토요일 (토)
#> Months: 1월, 2월, 3월, 4월, 5월, 6월, 7월, 8월, 9월, 10월, 11월, 12월
#> AM/PM:  오전/오후
locale("fr") # French
#> <locale>
#> Numbers:  123,456.78
#> Formats:  %Y%.%m%.%d / %H:%M
#> Timezone: UTC
#> Encoding: UTF-8
#> <date_names>
#> Days:   dimanche (dim.), lundi (lun.), mardi (mar.), mercredi (mer.),
#>         jeudi (jeu.), vendredi (ven.), samedi (sam.)
#> Months: janvier (janv.), février (févr.), mars (mars), avril (avr.), mai
#>         (mai), juin (juin), juillet (juil.), août (août),
#>         septembre (sept.), octobre (oct.), novembre (nov.),
#>         décembre (déc.)
#> AM/PM:  AM/PM

This allows you to parse dates in other languages:

parse_date("1 janvier 2015", "%d %B %Y", locale = locale("fr"))
#> [1] "2015-01-01"
parse_date("14 oct. 1979", "%d %b %Y", locale = locale("fr"))
#> [1] "1979-10-14"


readr assumes that times are in Coordinated Universal Time, aka UTC. UTC is the best timezone for data because it doesn’t have daylight savings. If your data isn’t already in UTC, you’ll need to supply a tz in the locale:

parse_datetime("2001-10-10 20:10")
#> [1] "2001-10-10 20:10:00 UTC"
parse_datetime("2001-10-10 20:10", 
  locale = locale(tz = "Pacific/Auckland"))
#> [1] "2001-10-10 20:10:00 NZDT"
parse_datetime("2001-10-10 20:10", 
  locale = locale(tz = "Europe/Dublin"))
#> [1] "2001-10-10 20:10:00 IST"

List all available times zones with OlsonNames(). If you’re American, note that “EST” is not Eastern Standard Time – it’s a Canadian time zone that doesn’t have DST! Instead of relying on ambiguous abbreivations, use:

  • PST/PDT = “US/Pacific”
  • CST/CDT = “US/Central”
  • MST/MDT = “US/Mountain”
  • EST/EDT = “US/Eastern”

Default formats

Locales also provide default date and time formats. The time format isn’t currently used for anything, but the date format is used when guessing column types. The default date format is %Y-%m-%d because that’s unambiguous:

#>  Date[1:1], format: "2010-10-10"

If you’re an American, you might want you use your illogical date sytem::

#>  chr "01/02/2013"
  locale = locale(date_format = "%d/%m/%Y")))
#>  Date[1:1], format: "2013-02-01"

Character encoding

All readr functions yield strings encoded in UTF-8. This encoding is the most likely to give good results in the widest variety of settings. By default, readr assumes that your input is also in UTF-8, which is less likely to be the case, especially when you’re working with older datasets. To parse a dataset that’s not in UTF-8, you need to a supply an encoding.
The following code creates a string encoded with latin1 (aka ISO-8859-1), and shows how it’s different from the string encoded as UTF-8, and how to parse it with readr:

x <- "Émigré cause célèbre déjà vu.\n"
y <- stringi::stri_conv(x, "UTF-8", "Latin1")

# These strings look like they're identical:
#> [1] "Émigré cause célèbre déjà vu.\n"
#> [1] "Émigré cause célèbre déjà vu.\n"
identical(x, y)
#> [1] TRUE

# But they have different encodings:
#> [1] "UTF-8"
#> [1] "latin1"

# That means while they print the same, their raw (binary)
# representation is actually rather different:
#>  [1] c3 89 6d 69 67 72 c3 a9 20 63 61 75 73 65 20 63 c3 a9 6c c3 a8 62 72
#> [24] 65 20 64 c3 a9 6a c3 a0 20 76 75 2e 0a
#>  [1] c9 6d 69 67 72 e9 20 63 61 75 73 65 20 63 e9 6c e8 62 72 65 20 64 e9
#> [24] 6a e0 20 76 75 2e 0a

# readr expects strings to be encoded as UTF-8. If they're
# not, you'll get weird characters
#> [1] "Émigré cause célèbre déjà vu.\n"
#> [1] "\xc9migr\xe9 cause c\xe9l\xe8bre d\xe9j\xe0 vu.\n"

# If you know the encoding, supply it:
parse_character(y, locale = locale(encoding = "latin1"))
#> [1] "Émigré cause célèbre déjà vu.\n"

If you don’t know what encoding the file uses, try guess_encoding(). It’s not 100% perfect (as it’s fundamentally a heuristic), but should at least get you pointed in the right direction:

#>     encoding confidence
#> 1 ISO-8859-2        0.4
#> 2 ISO-8859-1        0.3

# Note that the first guess produces a valid string, 
# but isn't correct:
parse_character(y, locale = locale(encoding = "ISO-8859-2"))
#> [1] "Émigré cause célčbre déjŕ vu.\n"
# But ISO-8859-1 is another name for latin1
parse_character(y, locale = locale(encoding = "ISO-8859-1"))
#> [1] "Émigré cause célèbre déjà vu.\n"


Some countries use the decimal point, while others use the decimal comma. The decimal_mark option controls which readr uses when parsing doubles:

parse_double("1,23", locale = locale(decimal_mark = ","))
#> [1] 1.23

The big_mark option describes which character is used to space groups of digits. Do you write 1,000,000, 1.000.000, 1 000 000, or 1'000'000? Specifying the grouping mark allows parse_number() to parse large number as they’re commonly written:

#> [1] 1234.56

# dplyr is smart enough to guess that if you're using , for 
# decimals then you're probably using . for grouping:
parse_number("1.234,56", locale = locale(decimal_mark = ","))
#> [1] 1234.56

Column parsing improvements

One of the most useful parts of readr are the column parsers: the tools that turns character input into usefully typed data frame columns. This process is now described more fully in a new vignette: vignette("column-types").
By default, column types are guessed by looking at the data. I’ve made a number of tweaks to make it more likely that your code will load correctly the first time:

  • readr now looks at the first 1000 rows (instead of just the first 100) when guessing column types: this only takes a fraction more time, but should hopefully yield better guesses for more inputs.

  • col_date() and col_datetime() no longer recognise partial dates like 19, 1900, 1900-01. These triggered many false positives and after re-reading the ISO8601 spec, I believe they actually refer to periods of time, so should not be parsed into a specific instant.

  • col_integer() no longer recognises values started with zeros (e.g. 0001) as these are often used as identifiers.

  • col_number() will automatically recognise numbers containing the grouping mark (see below for more details).

But you can override these defaults with the col_types() argument. In this version, col_types gains some much needed flexibility:

  • New cols() function takes of assembling the list of column types, and with its .default argument, allows you to control the default column type:
    read_csv("x,y\n1,2", col_types = cols(.default = "c"))
    #> Source: local data frame [1 x 2]
    #>       x     y
    #>   (chr) (chr)
    #> 1     1     2

    You can refer to parsers with their full name (e.g. col_character()) or their one letter abbreviation (e.g. c). The default value of .default is “?”: guess the type of column from the data.

  • cols_only() allows you to load only the specified columns:

    read_csv("a,b,c\n1,2,3", col_types = cols_only("b" = "?"))
    #> Source: local data frame [1 x 1]
    #>       b
    #>   (int)
    #> 1     2

Many of the individual parsers have also been improved:

  • col_integer() and col_double() no longer silently ignore trailing characters after the number.

  • New col_number()/parse_number() replace the old col_numeric()/ parse_numeric(). This parser is less flexible, so it’s less likely to silently ignored bad input. It’s designed specifically to read currencies and percentages. It only reads the first number from a string, ignoring the grouping mark defined by the locale:

    #> [1] 1234566
    #> [1] 1234
    #> [1] 27
  • New parse_time() and col_time() allow you to parse times. They have an optional format argument, that uses the same components as parse_datetime(). If format is omitted, they use a flexible parser that looks for hours, then an optional colon, then minutes, then an optional colon, then optional seconds, then optional am/pm.
    parse_time(c("1:45 PM", "1345", "13:45:00"))
    #> [1] 13:45:00 13:45:00 13:45:00

    parse_time() returns the number of seconds since midnight as an integer with class “time”. readr includes a basic print method.

  • parse_date()/col_date() and parse_datetime()/col_datetime() gain two new format strings: “%+” skips one or more non-digits, and %p reads in AM/PM (and am/pm).

File parsing improvements

read_csv(), read_tsv(), and read_delim() gain extra arguments that allow you to parse more files:

  • Multiple NA values can be specified by passing a character vector to na. The default has been changed to na = c("", "NA").
    read_csv("a,b\n.,NA\n1,3", na = c(".", "NA"))
    #> Source: local data frame [2 x 2]
    #>       a     b
    #>   (int) (int)
    #> 1    NA    NA
    #> 2     1     3
  • New comment argument allows you to ignore all text after a string:
    "#This is a comment
    #This is another comment
    2,20", comment = "#")
    #> Source: local data frame [2 x 2]
    #>       a     b
    #>   (int) (int)
    #> 1     1    10
    #> 2     2    20
  • trim_ws argument controls whether leading and trailing whitespace is removed. It defaults to TRUE.
    read_csv("a,b\n     1,     2")
    #> Source: local data frame [1 x 2]
    #>       a     b
    #>   (int) (int)
    #> 1     1     2
    read_csv("a,b\n     1,     2", trim_ws = FALSE)
    #> Source: local data frame [1 x 2]
    #>        a      b
    #>    (chr)  (chr)
    #> 1      1      2

Specifying the wrong number of column names, or having rows with an unexpected number of columns, now gives a warning, rather than an error:

#> Warning: 2 parsing failures.
#> row col  expected    actual
#>   1  -- 3 columns 2 columns
#>   2  -- 3 columns 4 columns
#> Source: local data frame [2 x 3]
#>       a     b     c
#>   (int) (int) (int)
#> 1     1     2    NA
#> 2     1     2     3

Note that the warning message now also shows you the first five problems. I hope this will often allow you to iterate immediately, rather than having to look at the full problems().


Despite the name, readr also provides some tools for writing data frames to disk. In this version there are three output functions:

  • write_csv() and write_tsv() write tab and comma delimted files, and write_delim() writes with user specified delimiter.

  • write_rds() and read_rds() wrap around readRDS() and saveRDS(), defaulting to no compression, because you’re usually more interested in saving time (expensive) than disk space (cheap).

All these functions invisibly return their output so you can use them as part of a pipeline:

my_df %>%
  some_manipulation() %>%
  write_csv("interim-a.csv") %>%
  some_more_manipulation() %>%
  write_csv("interim-b.csv") %>%
  even_more_manipulation() %>%

You can now control how missing values are written with the na argument, and the quoting algorithm has been further refined to only add quotes when needed: when the string contains a quote, the delimiter, a new line or the same text as missing value.
Output for doubles now uses the same precision as R, and POSIXt vectors are saved in a ISO8601 compatible format.
For testing, you can use format_csv(), format_tsv(), and format_delim() to write csv to a string:

mtcars %>%
  head(4) %>%
  format_csv() %>%
#> mpg,cyl,disp,hp,drat,wt,qsec,vs,am,gear,carb
#> 21,6,160,110,3.9,2.62,16.46,0,1,4,4
#> 21,6,160,110,3.9,2.875,17.02,0,1,4,4
#> 22.8,4,108,93,3.85,2.32,18.61,1,1,4,1
#> 21.4,6,258,110,3.08,3.215,19.44,1,0,3,1

This is particularly useful for generating reprexes.

testthat 0.11.0 is now available on CRAN. Testthat makes it easy to turn your existing informal tests into formal automated tests that you can rerun quickly and easily. Learn more at Install the latest version with:


In this version:

  • New expect_silent() ensures that code produces no output, messages, or warnings. expect_output(), expect_message(), expect_warning(), and expect_error() now accept NA as the second argument to indicate that there shouldn’t be any output, messages, warnings, or errors (i.e. they should be missing)
    f <- function() {
    #> Error: f() produced output, warnings, messages
    expect_warning(log(-1), NA)
    #> Error: log(-1) expected no warnings:
    #> *  NaNs produced
  • Praise gets more diverse thanks to Gabor Csardi’s praise package, and you now also get random encouragment if your tests don’t pass.
  • testthat no longer muffles warning messages. This was a bug in the previous version, as warning messages are usually important and should be dealt with explicitly, either by resolving the problem or explicitly capturing them with expect_warning().
  • Two new skip functions make it easier to skip tests that don’t work in certain environments: skip_on_os() skips tests on the specified operating system, and skip_on_appveyor() skips tests on Appveyor.

There were a number of other minor improvements and bug fixes. See the release notes for a complete list.

A big thanks goes out to all the contributors who made this release happen. There’s no way I could be as productive without the fantastic commmunity of R developers who come up with thoughtful new features, and who discover and fix my bugs!