I’m pleased to announce that readxl 1.0.0 is available on CRAN. readxl makes it easy to bring tabular data out of Excel and into R, for modern .xlsx files and the legacy .xls format. readxl does not have any tricky external dependencies, such as Java or Perl, and is easy to install and use on Mac, Windows, and Linux.

You can install it with:

install.packages("readxl")

As well as fixing many bugs, this release:

  • Allows you to target specific cells for reading, in a variety of ways
  • Adds two new column types: "logical" and "list", for data of disparate type
  • Is more resilient to the wondrous diversity in spreadsheets, e.g., those written by 3rd party tools

You can see a full list of changes in the release notes. This is the first release maintained by Jenny Bryan.

Specifying the data rectangle

In an ideal world, data would live in a neat rectangle in the upper left corner of a spreadsheet. But spreadsheets often serve multiple purposes for users with different priorities. It is common to encounter several rows of notes above or below the data, for example. The new range argument provides a flexible interface for describing the data rectangle, including Excel-style ranges and row- or column-only ranges.

library(readxl)
read_excel(
  readxl_example("deaths.xlsx"),
  range = "arts!A5:F15"
)
#> # A tibble: 10 × 6
#>            Name Profession   Age `Has kids` `Date of birth`
#>                                  
#> 1   David Bowie   musician    69       TRUE      1947-01-08
#> 2 Carrie Fisher      actor    60       TRUE      1956-10-21
#> 3   Chuck Berry   musician    90       TRUE      1926-10-18
#> 4   Bill Paxton      actor    61       TRUE      1955-05-17
#> # ... with 6 more rows, and 1 more variables: `Date of death` 

read_excel(
  readxl_example("deaths.xlsx"),
  sheet = "other",
  range = cell_rows(5:15)
)
#> # A tibble: 10 × 6
#>           Name Profession   Age `Has kids` `Date of birth`
#>                                           
#> 1   Vera Rubin  scientist    88       TRUE      1928-07-23
#> 2  Mohamed Ali    athlete    74       TRUE      1942-01-17
#> 3 Morley Safer journalist    84       TRUE      1931-11-08
#> 4 Fidel Castro politician    90       TRUE      1926-08-13
#> # ... with 6 more rows, and 1 more variables: `Date of death`

There is also a new argument n_max that limits the number of data rows read from the sheet. It is an example of readxl’s evolution towards a readr-like interface. The Sheet Geometry vignette goes over all the options.

Column typing

The new ability to target cells for reading means that readxl’s automatic column typing will “just work” for most sheets, most of the time. Above, the Has kids column is automatically detected as logical, which is a new column type for readxl.

You can still specify column type explicitly via col_types, which gets a couple new features. If you provide exactly one type, it is recycled to the necessary length. The new type "guess" can be mixed with explicit types to specify some types, while leaving others to be guessed.

read_excel(
  readxl_example("deaths.xlsx"),
  range = "arts!A5:C15",
  col_types = c("guess", "skip", "numeric")
)
#> # A tibble: 10 × 2
#>            Name   Age
#>            
#> 1   David Bowie    69
#> 2 Carrie Fisher    60
#> 3   Chuck Berry    90
#> 4   Bill Paxton    61
#> # ... with 6 more rows

The new argument guess_max limits the rows used for type guessing. Leading and trailing whitespace is trimmed when the new trim_ws argument is TRUE, which is the default. Finally, thanks to Jonathan Marshall, multiple na values are accepted. The Cell and Column Types vignette has more detail.

"list" columns

Thanks to Greg Freedman Ellis we now have a "list" column type. This is useful if you want to bring truly disparate data into R without the coercion required by atomic vector types.

(df <- read_excel(
  readxl_example("clippy.xlsx"),
  col_types = c("text", "list")
))
#> # A tibble: 4 × 2
#>                   name      value
#>                  <chr>     <list>
#> 1                 Name  <chr [1]>
#> 2              Species  <chr [1]>
#> 3 Approx date of death <dttm [1]>
#> 4      Weight in grams  <dbl [1]>

tibble::deframe(df)
#> $Name
#> [1] "Clippy"
#> 
#> $Species
#> [1] "paperclip"
#> 
#> $`Approx date of death`
#> [1] "2007-01-01 UTC"
#> 
#> $`Weight in grams`
#> [1] 0.9

Everything else

To learn more, read the vignettes and articles or release notes. Highlights include:

  • General rationalization of sheet geometry, including detection and treatment of empty rows and columns.
  • Improved behavior and messaging around coercion and mismatched cell and column types.
  • Improved handling of datetimes with respect to 3rd party software, rounding, and the Lotus 1-2-3 leap year bug.
  • read_xls() and read_xlsx() are now exposed, so that files without an .xls or .xlsx extension can be read. Thanks Jirka Lewandowski!
  • readxl Workflows showcases patterns that reduce tedium and increase reproducibility when raw data arrives in a spreadsheet.

I’m planning to submit dplyr 0.6.0 to CRAN on May 11 (in four weeks time). In preparation, I’d like to announce that the release candidate, dplyr 0.5.0.9002 is now available. I would really appreciate it if you’d try it out and report any problems. This will ensure that the official release has as few bugs as possible.

Installation

Install the pre-release version with:

# install.packages("devtools")
devtools::install_github("tidyverse/dplyr")

If you discover any problems, please file a minimal reprex on GitHub. You can roll back to the released version with:

install.packages("dplyr")

Features

dplyr 0.6.0 is a major release including over 100 bug fixes and improvements. There are three big changes that I want to touch on here:

  • Databases
  • Improved encoding support (particularly for CJK on windows)
  • Tidyeval, a new framework for programming with dplyr

You can see a complete list of changes in the draft release notes.

Databases

Almost all database related code has been moved out of dplyr and into a new package, dbplyr. This makes dplyr simpler, and will make it easier to release fixes for bugs that only affect databases.

To install the development version of dbplyr so you can try it out, run:

devtools::install_github("hadley/dbplyr")

There’s one major change, as well as a whole heap of bug fixes and minor improvements. It is now no longer necessary to create a remote “src”. Instead you can work directly with the database connection returned by DBI, reflecting the robustness of the DBI ecosystem. Thanks largely to the work of Kirill Muller (funded by the R Consortium) DBI backends are now much more consistent, comprehensive, and easier to use. That means that there’s no longer a need for a layer between you and DBI.

You can continue to use src_mysql(), src_postgres(), and src_sqlite() (which still live in dplyr), but I recommend a new style that makes the connection to DBI more clear:

con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
DBI::dbWriteTable(con, "iris", iris)
#> [1] TRUE

iris2 <- tbl(con, "iris")
iris2
#> Source:     table<iris> [?? x 5]
#> Database:   sqlite 3.11.1 [:memory:]
#> 
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl>   <chr>
#> 1           5.1         3.5          1.4         0.2  setosa
#> 2           4.9         3.0          1.4         0.2  setosa
#> 3           4.7         3.2          1.3         0.2  setosa
#> 4           4.6         3.1          1.5         0.2  setosa
#> 5           5.0         3.6          1.4         0.2  setosa
#> 6           5.4         3.9          1.7         0.4  setosa
#> 7           4.6         3.4          1.4         0.3  setosa
#> 8           5.0         3.4          1.5         0.2  setosa
#> 9           4.4         2.9          1.4         0.2  setosa
#> 10          4.9         3.1          1.5         0.1  setosa
#> # ... with more rows

This is particularly useful if you want to perform non-SELECT queries as you can do whatever you want with DBI::dbGetQuery() and DBI::dbExecute().

If you’ve implemented a database backend for dplyr, please read the backend news to see what’s changed from your perspective (not much). If you want to ensure your package works with both the current and previous version of dplyr, see wrap_dbplyr_obj() for helpers.

Character encoding

We have done a lot of work to ensure that dplyr works with encodings other that Latin1 on Windows. This is most likely to affect you if you work with data that contains Chinese, Japanese, or Korean (CJK) characters. dplyr should now just work with such data.

Tidyeval

dplyr has a new approach to non-standard evaluation (NSE) called tidyeval. Tidyeval is described in detail in a new vignette about programming with dplyr but, in brief, it gives you the ability to interpolate values in contexts where dplyr usually works with expressions:

my_var <- quo(homeworld)

starwars %>%
  group_by(!!my_var) %>%
  summarise_at(vars(height:mass), mean, na.rm = TRUE)
#> # A tibble: 49 × 3
#>         homeworld   height  mass
#>             <chr>    <dbl> <dbl>
#> 1        Alderaan 176.3333  64.0
#> 2     Aleen Minor  79.0000  15.0
#> 3          Bespin 175.0000  79.0
#> 4      Bestine IV 180.0000 110.0
#> 5  Cato Neimoidia 191.0000  90.0
#> 6           Cerea 198.0000  82.0
#> 7        Champala 196.0000   NaN
#> 8       Chandrila 150.0000   NaN
#> 9    Concord Dawn 183.0000  79.0
#> 10       Corellia 175.0000  78.5
#> # ... with 39 more rows

This will make it much easier to eliminate copy-and-pasted dplyr code by extracting repeated code into a function.

This also means that the underscored version of each main verb (filter_(), select_() etc). is no longer needed, and so these functions have been deprecated (but remain around for backward compatibility).

Over the couple of months there have been a bunch of smaller releases to packages in the tidyverse. This includes:

  • forcats 0.2.0, for working with factors.
  • readr 1.1.0, for reading flat-files from disk.
  • stringr 1.2.0, for manipulating strings.
  • tibble 1.3.0, a modern re-imagining of the data frame.

This blog post summarises the most important new features, and points to the full release notes where you can learn more.

(If you’ve never heard of the tidyverse before, it’s an set of packages that are designed to work together to help you do data science. The best place to learn all about it is R for Data Science.)

forcats 0.2.0

forcats has three new functions:

  • as_factor() is a generic version of as.factor(), which creates factors from character vectors ordered by appearance, rather than alphabetically. This ensures means that as_factor(x) will always return the same result, regardless of the current locale.
  • fct_other() makes it easier to convert selected levels to “other”:
    x <- factor(rep(LETTERS[1:6], times = c(10, 5, 1, 1, 1, 1)))
    
    x %>% 
      fct_other(keep = c("A", "B")) %>% 
      fct_count()
    #> # A tibble: 3 × 2
    #>        f     n
    #>    
    #> 1      A    10
    #> 2      B     5
    #> 3  Other     4
    
    x %>% 
      fct_other(drop = c("A", "B")) %>% 
      fct_count()
    #> # A tibble: 5 × 2
    #>        f     n
    #>    
    #> 1      C     1
    #> 2      D     1
    #> 3      E     1
    #> 4      F     1
    #> 5  Other    15
  • fct_relabel() allows programmatic relabeling of levels:
    x <- factor(letters[1:3])
    x
    #> [1] a b c
    #> Levels: a b c
    
    x %>% fct_relabel(function(x) paste0("-", x, "-"))
    #> [1] -a- -b- -c-
    #> Levels: -a- -b- -c-

See the full list of other changes in the release notes.

stringr 1.2.0

This release includes a change to the API: str_match_all() now returns NA if an optional group doesn’t match (previously it returned “”). This is more consistent with str_match() and other match failures.

x <- c("a=1,b=2", "c=3", "d=")

x %>% str_match("(.)=(\\d)?")
#>      [,1]  [,2] [,3]
#> [1,] "a=1" "a"  "1" 
#> [2,] "c=3" "c"  "3" 
#> [3,] "d="  "d"  NA
x %>% str_match_all("(.)=(\\d)?,?")
#> [[1]]
#>      [,1]   [,2] [,3]
#> [1,] "a=1," "a"  "1" 
#> [2,] "b=2"  "b"  "2" 
#> 
#> [[2]]
#>      [,1]  [,2] [,3]
#> [1,] "c=3" "c"  "3" 
#> 
#> [[3]]
#>      [,1] [,2] [,3]
#> [1,] "d=" "d"  NA

There are three new features:

  • In str_replace(), replacement can now be a function. The function is once for each match and its return value will be used as the replacement.
    redact <- function(x) {
      str_dup("-", str_length(x))
    }
    
    x <- c("It cost $500", "We spent $1,200 on stickers")
    x %>% str_replace_all("\\$[0-9,]+", redact)
    #> [1] "It cost ----"                "We spent ------ on stickers"
  • New str_which() mimics grep():
    fruit <- c("apple", "banana", "pear", "pinapple")
    
    # Matching positions    
    str_which(fruit, "p")
    #> [1] 1 3 4
    
    # Matching values
    str_subset(fruit, "p")
    #> [1] "apple"    "pear"     "pinapple"
  • A new vignette (vignette("regular-expressions")) describes the details of the regular expressions supported by stringr. The main vignette (vignette("stringr")) has been updated to give a high-level overview of the package.

See the full list of other changes in the release notes.

readr 1.1.0

readr gains two new features:

  • All write_*() functions now support connections. This means that that you can write directly to compressed formats such as .gz, bz2 or .xz (and readr will automatically do so if you use one of those suffixes).
    write_csv(iris, "iris.csv.bz2")
  • parse_factor(levels = NULL) and col_factor(levels = NULL) will produce a factor column based on the levels in the data, mimicing factor parsing in base R (with the exception that levels are created in the order seen).
    iris2 <- read_csv("iris.csv.bz2", col_types = cols(
      Species = col_factor(levels = NULL)
    ))

See the full list of other changes in the release notes.

tibble 1.3.0

tibble has one handy new function: deframe() is the opposite of enframe(): it turns a two-column data frame into a named vector.

df <- tibble(x = c("a", "b", "c"), y = 1:3)
deframe(df)
#> a b c 
#> 1 2 3

See the full list of other changes in the release notes.

We’re excited to announce the release of RStudio Connect: version 1.4.6. This is an incremental release which features significantly improved startup time and support for server-side Shiny bookmarks.

Creating a server-side Shiny bookmark in RStudio Connect

Improved Startup & Job Listing Time

We now track R process jobs in the database which allows us to list and query jobs much more quickly. This decreases the startup time of the RStudio Connect service — allowing even the busiest of servers to spin up in a matter of seconds. Additionally, operations that involve listing jobs such as viewing process logs for a particular application should be noticeably faster.

Server-Side Shiny Bookmarks

Shiny v0.14 introduced a feature by which users could bookmark the current state of the application by either encoding the state in the URL or saving the state to the server. As of this release, RStudio Connect now supports server-side bookmarking of Shiny applications.

Other notable changes this release:

  • BREAKING: Changed the default for Authorization.DefaultUserRole from publisher to viewer. New users will now be created with a viewer account until promoted. The user roles documentation explains the differences. To restore the previous behavior, set DefaultUserRole = publisher. Because viewer users cannot be added as collaborators on content, this means that in order to add a remote user as a collaborator on content you must first create their account, then promote them to a publisher account.
  • Fixed a bug in the previous release that had broken Applications.ViewerOnDemandReports and Applications.ViewerCustomizedReports. These settings are again functional and allow you to manage the capabilities of a viewer of a parameterized report on the server.
  • Tune the number of concurrent processes to use when building R packages. This is controlled with the Server.CompilationConcurrency setting and passed as the value to the make flag -jNUM. The default is to permit four concurrent processes. Decrease this setting in low memory environments.
  • The /etc/rstudio-connect/rstudio-connect.gcfg file is installed with more restrictive permissions.
  • Log file downloads include a more descriptive file name by default. Previously, we used the naming convention <jobId>.log, which resulted in file names like GBFCaiPE6tegbrEM.log. Now, we use the naming convention rstudio-connect.<appId>.<reportId>.<bundleId>.<jobType>.<jobId>.log, which results in file names like rstudio-connect.34.259.15.packrat_restore.GBFCaiPE6tegbrEM.log.
  • Bundle the admin guide and user guide in the product. You can access both from the Documentation tab.
  • Implemented improved, pop-out filtering panel when filtering content, which offers a better experience on small/mobile screens.
  • Improvements to the parameterized report pane when the viewer does not have the authority to render custom versions of the document.
  • Database performance improvements which should improve performance in high-traffic environments.

Upgrade Planning: The migration of jobs from disk to the database may take a few minutes. The server will be unavailable during this migration which will be performed the first time RStudio Connect v1.4.6 starts. Even on the busiest of servers we would expect this migration to complete in under 5 minutes.

If you haven’t yet had a chance to download and try RStudio Connect we encourage you to do so. RStudio Connect is the best way to share all the work that you do in R (Shiny apps, R Markdown documents, plots, dashboards, etc.) with collaborators, colleagues, or customers.

You can find more details or download a 45 day evaluation of the product at https://www.rstudio.com/products/connect/. Additional resources can be found below.

Shiny 1.0.1 is now available on CRAN! This release primarily includes bug fixes and minor new features.

The most notable additions in this version of Shiny are the introduction of the reactiveVal() function (like reactiveValues(), but it only stores a single value), and that the choices of radioButtons() and checkboxGroupInput() can now contain HTML content instead of just plain text. We’ve also added compatibility for the development version of ggplot2.

Breaking changes

We unintentionally introduced a minor breaking change in that checkboxGroupInput used to accept choices = NULL to create an empty input. With Shiny 1.0.1, this throws an error; using choices = character(0) works. We intend to eliminate this breakage in Shiny 1.0.2.

Update (4/20/2017): This has now been fixed in Shiny 1.0.2, currently available on CRAN.

Also, the selected argument for radioButtons, checkboxGroupInput, and selectInput once upon a time accepted the name of a choice, instead of the value of a choice; this behavior has been deprecated with a warning for several years now, and in Shiny 1.0.1 it is no longer supported at all.

Storing single reactive values with reactiveVal

The reactiveValues object has been a part of Shiny since the earliest betas. It acts like a reactive version of an environment or named list, in that you can store and retrieve values using names:

rv <- reactiveValues(clicks = 0)

observeEvent(input$button, {
 currentValue <- rv$clicks
 rv$clicks <- currentValue + 1
})

If you only have a single value to store, though, it’s a little awkward that you have to use a data structure designed for multiple values.

With the new reactiveVal function, you can now create a reactive object for a single variable:

clicks <- reactiveVal(0)

observeEvent(input$button, {
 currentValue <- clicks()
 clicks(currentValue + 1)
})

As you can see in this example, you can read the value by calling it like a function with no arguments; and you set the value by calling it with one argument.

This has the added benefit that you can easily pass the clicks object to another function or module (no need to wrap it in a reactive()).

More flexible radioButtons and checkboxGroupInput

It’s now possible to create radio button and checkbox inputs with arbitrary HTML as labels. To do so, however, you need to pass different arguments to the functions. Now, when creating (or updating) either of radioButtons() or checkboxGroupInput(), you can specify the options in one of two (mutually exclusive) ways:

  • What we’ve always had:
    Use the choices argument, which must be a vector or list. The names of each element are displayed in the app UI as labels (i.e. what the user sees in your app), and the values are used for computation (i.e. the value is what’s returned by input$rd, where rd is a radioButtons() input). If the vector (or list) is unnamed, the values provided are used for both the UI labels and the server values.
  • What’s new and allows HTML:
    Use both the choiceNames and the choiceValues arguments, each of which must be an unnamed vector or list (and both must have the same length). The elements in choiceValues must still be plain text (these are the values used for computation). But the elements in choiceNames (the UI labels) can be constructed out of HTML, either using the HTML() function, or an HTML tag generation function, like tags$img() and icon().

Here’s an example app that demos the new functionality (in this case, we have a checkboxGroupInput() whose labels include the flag of the country they correspond to):

ggplot2 > 2.2.1 compatibility

The development version of ggplot2 has some changes that break compatibility with earlier versions of Shiny. The fixes in Shiny 1.0.1 will allow it to work with any version of ggplot2.

A note on Shiny v1.0.0

In January of this year, we quietly released Shiny 1.0.0 to CRAN. A lot of work went into that release, but other than minor bug fixes and features, it was mostly laying the foundation for some important features that will arrive in the coming months. So if you’re wondering if you missed the blog post for Shiny 1.0.0, you didn’t.

Full changes

As always, you can view the full changelog for Shiny 1.0.1 (and 1.0.0!) in our NEWS.md file.

We’re excited to announce the release of RStudio Connect: version 1.4.4.1. This release includes the ability to manage different versions of your work on RStudio Connect.

Managing old versions of deployed content in RStudio Connect

Rollback / Roll Forward
The most notable feature of this release is the ability to “rollback” to a previously deployed version of your work or “roll forward” to a more recent version of your work.

You can also download a particular version, perhaps as a starting place for a new report or application, and delete old versions that you want to remove from the server.

Other important features allow you to:

  • Specify the number of versions to retain. You can alter the setting Applications.BundleRetentionLimit to specify how many versions of your applications you want to keep on disk. By default, we retain all bundles eternally.
  • Limit the number of scheduled reports that will be run concurrently using the Applications.ScheduleConcurrency setting. This setting will help ensure that your server isn’t overwhelmed by too many reports all scheduled to run at the same time of day. The default is set to 2.
  • Create a printable view of your content with a new “Print” menu option.
  • Notify users of unsaved changes before they take an action in parameterized reports.

The release also includes numerous security and stability improvements.

If you haven’t yet had a chance to download and try RStudio Connect we encourage you to do so. RStudio Connect is the best way to share all the work that you do in R (Shiny apps, R Markdown documents, plots, dashboards, etc.) with collaborators, colleagues, or customers.

You can find more details or download a 45 day evaluation of the product at https://www.rstudio.com/products/connect/. Additional resources can be found below.

Leaflet 1.1.0 is now available on CRAN! The Leaflet package is a tidy wrapper for the Leaflet.js mapping library, and makes it incredibly easy to generate interactive maps based on spatial data you have in R.

leaflet-choro

This release was nearly a year in the making, and includes many important new features.

  • Easily add textual labels on markers, polygons, etc., either on hover or statically
  • Highlight polygons, lines, circles, and rectangles on hover
  • Markers can now be configured with a variety of colors and icons, via integration with Leaflet.awesome-markers
  • Built-in support for many types of objects from sf, a new way of representing spatial data in R (all basic sf/sfc/sfg types except MULTIPOINT and GEOMETRYCOLLECTION are directly supported)
  • Projections other than Web Mercator are now supported via Proj4Leaflet
  • Color palette functions now natively support viridis palettes; use "viridis", "magma", "inferno", or "plasma" as the palette argument
  • Discrete color palette functions (colorBin, colorQuantile, and colorFactor) work much better with color brewer palettes
  • Integration with several Leaflet.js utility plugins
  • Data with NA points or zero rows no longer causes errors
  • Support for linked brushing and filtering, via Crosstalk (more about this to come in another blog post)

Many thanks to @bhaskarvk who contributed much of the code for this release.

Going forward, our intention is to prevent any more Leaflet.js plugins from accreting in the core leaflet package. Instead, we have made it possible to write 3rd party R packages that extend leaflet (though the process to do this is not documented yet). In the meantime, Bhaskar has started developing his own leaflet.extras package; it already supports several plugins, for everything from animated markers to heatmaps.

Couldn’t make it to Orlando in January?  We’re excited to bring you the next best thing.

Whether you missed the conference, missed a talk, or just want to refresh your memory, you can find all the recordings from the first ever conference about All Things R & RStudio at https://www.rstudio.com/resources/webinars/#rstudioconf. Just click on +rstudio::conf 2017 when you get there to expand the list.

Of course, the session recordings can’t capture the complete “in person” experience. You had to be there to immerse in the 2 day workshops led by Hadley Wickham, Garrett Grolemund, and Joe Cheng; join in cocktails and conversation at the Datacamp Happy Hour; attend the exclusive Friday evening access to Universal Studio’s Wizarding World of Harry Potter under a full moon; hang out at the RStudio Connect lounge and with our friends from Cloudera, Mango Solutions, MetrumRG, Quantide, and Supstat; or make and renew friendships with more than 400 attendees from every line of work and the entire RStudio team. We hope you’ll consider joining us in January 2018 – location and exact dates still to be announced.

In the meantime, the recordings showcase some of the most interesting uses of R and RStudio for everyone.

Enjoy and share!

If big data is your thing, you use R, and you’re headed to Strata + Hadoop World in San Jose March 13 & 14th, you can experience in person how easy and practical it is to analyze big data with R and Spark.

In a beginner level talk by RStudio’s Edgar Ruiz and an intermediate level  workshop by Win-Vector’s John Mount, we cover the spectrum: What R is, what Spark is, how Sparklyr works, and what is required to set up and tune a Spark cluster. You’ll also learn practical applications including: how to quickly set up a local Spark instance, store big data in Spark and then connect to the data with R, use R to apply machine-learning algorithms to big data stored in Spark, and filter and aggregate big data stored in Spark and then import the results into R for analysis and visualization.

2:40pm–3:20pm Wednesday, March 15, 2017
Sparklyr: An R interface for Apache Spark
Edgar Ruiz (RStudio)
Primary topic: Spark & beyond
Location: LL21 C/D
Level: Beginner
Secondary topics: R

1:30pm–5:00pm Tuesday, March 14, 2017
Modeling big data with R, sparklyr, and Apache Spark
John Mount (Win-Vector LLC)
Primary topic: Data science & advanced analytics
Location: LL21 C/D
Level: Intermediate
Secondary topics: R

While you’re  at the conference be sure to look us up in the Innovator’s Pavilion – booth number P8 during the Expo Hall hours. We’ll have the latest books from RStudio authors, t-shirts to win, demonstrations of RStudio Connect and RStudio Server Pro and, of course, stickers and cheatsheets. Share with us what you’re doing with RStudio and get your product and company questions answered by RStudio employees.

See you in San Jose! (https://conferences.oreilly.com/strata/strata-ca)

We’re excited to announce the latest release of RStudio Connect: version 1.4.2. This release includes a number of notable features including an overhauled interface for parameterized R Markdown reports.

Enhanced Parameterized R Markdown Reports

Enhanced Parameterized R Markdown Reports

The most notable feature in this release is the ability to publish parameterized R Markdown reports that are easier for anyone to customize. If you’re unfamiliar, parameterized R Markdown reports allow you to inject input parameters into your R Markdown document to alter what analysis the report performs. The parameters of your R Markdown report are now visible on the left-hand sidebar, allowing users to easily tweak the inputs to the document and quickly view the output in the browser.

Users even have the opportunity to create private versions of the report which they can schedule to run again, email, or save and revisit in the browser. Of course, you can continue to use the wide variety of output formats (notebooks, dashboards, books, and others) while using parameterized R Markdown.

In addition to the parameterized report overhaul, there are some other notable features included in this release.

  • Content private by default – Content is set to private (“Just Me”) by default. Users can still change the visibility of their content before publishing, as before.
  • Execute R as the authenticated viewer – You can now choose to have some applications execute their underlying R process as the authenticated viewer currently looking at the app. This allows applications to access any data or resource that the associated user has access to on the server. Requires PAM authentication. More details here.

Other important features include:

  • Show progress indicator when updating a report.
  • Users can now filter content to include only items that they can edit or view.
  • Users now only count against the named user license limit after they log in for the first time.
  • Added support for global “System Messages” that can display an HTML message to your users on the landing pages. Details here.
  • Updated packrat to gain more transparency on package build errors.
  • Updated the list of SSL ciphers to correspond with modern best-practices.

If you haven’t yet had a chance to download and try RStudio Connect we encourage you to do so. RStudio Connect is the best way to share all the work that you do in R (Shiny apps, R Markdown documents, plots, dashboards, etc.) with collaborators, colleagues, or customers.

You can find more details or download a 45 day evaluation of the product at https://www.rstudio.com/products/connect/. Additional resources can be found below.