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I’m pleased to announce the release of the dbplyr package, which now contains all dplyr code related to connecting to databases. This shouldn’t affect you-as-a-user much, but it makes dplyr simpler, and makes it easier to release improvements just for database related code.

You can install the latest version of dbplyr with:

install.packages("dbplyr")

DBI and dplyr alignment

The biggest change in this release is that dplyr/dbplyr works much more directly with DBI database connections. This makes it much easier to switch between low-level queries written in SQL, and high-level data manipulation functions written with dplyr verbs.

To connect to a database, first use DBI::dbConnect() to create a database connection. For example, the following code connects to a temporary, in-memory, SQLite database, then uses DBI to copy over some data.

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

With this connection in hand, you can execute hand-written SQL queries:

DBI::dbGetQuery(con, "SELECT count() FROM iris")
#>   count()
#> 1     150

Or you can let dplyr generate the SQL for you:

iris2 <- tbl(con, "iris")

species_mean <- iris2 %>%
  group_by(Species) %>%
  summarise_all(mean)

species_mean %>% show_query()
#> <SQL>
#> SELECT `Species`, AVG(`Sepal.Length`) AS `Sepal.Length`, AVG(`Sepal.Width`) AS `Sepal.Width`, AVG(`Petal.Length`) AS `Petal.Length`, AVG(`Petal.Width`) AS `Petal.Width`
#> FROM `iris`
#> GROUP BY `Species`
species_mean
#> # Source:   lazy query [?? x 5]
#> # Database: sqlite 3.11.1 [:memory:]
#>      Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#>        <chr>        <dbl>       <dbl>        <dbl>       <dbl>
#> 1     setosa        5.006       3.428        1.462       0.246
#> 2 versicolor        5.936       2.770        4.260       1.326
#> 3  virginica        6.588       2.974        5.552       2.026

This alignment is made possible thanks to the hard work of Kirill Muller who has been working to make DBI backends more consistent, comprehensive, and easier to use. This work has been funded by the R Consortium and will continue this year with improvements to backends for the two major open source databases MySQL/MariaDB and PostgreSQL.

(You can continue to the old style src_mysql(), src_postgres(), and src_sqlite() functions, which still live in dplyr, but I recommend that you switch to the new style for new code)

SQL translation

We’ve also worked to improve the translation of R code to SQL. Thanks to @hhoeflin, dbplyr now has a basic SQL optimiser that considerably reduces the number of subqueries needed in many expressions. For example, the following code used to generate three subqueries, but now generates idiomatic SQL:

con %>% 
  tbl("mtcars") %>%
  filter(cyl > 2) %>%
  select(mpg:hp) %>%
  head(10) %>%
  show_query()
#> <SQL>
#> SELECT `mpg` AS `mpg`, `cyl` AS `cyl`, `disp` AS `disp`, `hp` AS `hp`
#> FROM `mtcars`
#> WHERE (`cyl` > 2.0)
#> LIMIT 10

At a lower-level, dplyr now:

  • Can translate case_when():
    library(dbplyr)
    translate_sql(case_when(x > 1 ~ "big", y < 2 ~ "small"), con = con)
    #> <SQL> CASE
    #> WHEN (`x` > 1.0) THEN ('big')
    #> WHEN (`y` < 2.0) THEN ('small')
    #> END
  • Has better support for type coercions:
    translate_sql(as.character(cyl), con = con)
    #> <SQL> CAST(`cyl` AS TEXT)
    translate_sql(as.integer(cyl), con = con)
    #> <SQL> CAST(`cyl` AS INTEGER)
    translate_sql(as.double(cyl), con = con)
    #> <SQL> CAST(`cyl` AS NUMERIC)
  • Can more reliably translate %IN%:
    translate_sql(x %in% 1:5, con = con)
    #> <SQL> `x` IN (1, 2, 3, 4, 5)
    translate_sql(x %in% 1L, con = con)
    #> <SQL> `x` IN (1)
    translate_sql(x %in% c(1L), con = con)
    #> <SQL> `x` IN (1)

You can now use in_schema() to refer to tables in schema: in_schema("my_schema_name", "my_table_name"). You can use the result of this function anywhere you could previously use a table name.

We’ve also included better translations for Oracle, MS SQL Server, Hive and Impala. We’re working to add support for more databases over time, but adding support on your own is surprisingly easy. Submit an issue to dplyr and we’ll help you get started.

These are just the highlights: you can see the full set of improvements and bug fixes in the release notes

Contributors

As with all R packages, this is truly a community effort. A big thanks goes to all those who contributed code or documentation to this release: Austen Head, Edgar Ruiz, Greg Freedman Ellis, Hannes Mühleisen, Ian Cook, Karl Dunkle Werner, Michael Sumner, Mine Cetinkaya-Rundel, @shabbybanks and Sergio Oller

Vision

Since you’ve read this far, I also wanted to touch on RStudio’s vision for databases. Many analysts have most of their data in databases, and making it as easy as possible to get data out of the database and into R makes a huge difference. Thanks to the community, R already has strong tools for talking to the popular open source databases. But support for connecting to enterprise databases and solving enterprise challenges has lagged somewhat. At RStudio we are actively working to solve these problems.

As well as dbplyr and DBI, we are working on many other pain points in the database ecosystem. You’ll hear much more about these packages in the future, but I wanted to touch on the highlights so you can see where we are heading. These pieces are not yet as integrated as they should be, but they are valuable by themselves, and we will continue to work to make a seamless database experience, that is as good as (or better than!) any other environment.

  • The odbc package provides a DBI compliant backend for any database with an ODBC driver. Compared to the existing RODBC package, odbc is faster (~3x for reading, ~2x for writing), translates date/time data types, and is under active development.RStudio is also planning on providing best-of-breed ODBC drivers for the most important enterprise databases to our Pro customers. If you’ve felt the pain of connecting to your enterprise database and would like to learn more, please schedule a meeting with our sales team.
  • You should never record database credentials in your R scripts, so we are working on safer ways to store them that don’t add a lot of extra hassle. One piece of the puzzle is the keyring package, which allows you to securely store information in your system keychain, and only decrypt it when needed.Another piece of the puzzle is the config package, which makes it easy to parameterise your database connection credentials so that you can connect to your testing database when experimenting locally, and your production database when you deploy your code.
  • Connecting to databases from Shiny can be challenging because you don’t want a fresh connection every for every user action (because that’s slow), and you don’t want one connection per app (because that’s unreliable). The pool package allows you to manage a shared pool of connections for your app, giving you both speed and reliability.
  • We’re also working to make sure all of these pieces are easily used from the IDE and inside R Markdown. One neat feature that you might not have heard about is support for SQL chunks in R Markdown.

If any of these pieces sound interesting, please stay tuned to the blog for more upcoming announcements. Please also check out out new database website: https://db.rstudio.com. Over time, this website will expand to document all database best practices, so you can find everything you need in one place.

I’m pleased to announce that bigrquery 0.4.0 is now on CRAN. bigrquery makes it possible to talk to Google’s BigQuery cloud database. It provides both DBI and dplyr backends so you can interact with BigQuery using either low-level SQL or high-level dplyr verbs.

Install the latest version of bigrquery with:

install.packages("bigrquery")

Basic usage

Connect to a bigquery database using DBI:

library(dplyr)

con <- DBI::dbConnect(dbi_driver(),
  project = "publicdata",
  dataset = "samples",
  billing = "887175176791"
)
DBI::dbListTables(con)
#> [1] "github_nested"   "github_timeline" "gsod"            "natality"       
#> [5] "shakespeare"     "trigrams"        "wikipedia"

(You’ll be prompted to authenticate interactively, or you can use a service token with set_service_token().)

Then you can either submit your own SQL queries or use dplyr to write them for you:

shakespeare <- con %>% tbl("shakespeare")
shakespeare %>%
  group_by(word) %>%
  summarise(n = sum(word_count))
#> 0 bytes processed
#> # Source:   lazy query [?? x 2]
#> # Database: BigQueryConnection
#>            word     n
#>           <chr> <int>
#>  1   profession    20
#>  2       augury     2
#>  3 undertakings     3
#>  4      surmise     8
#>  5     religion    14
#>  6     advanced    16
#>  7     Wormwood     1
#>  8    parchment     8
#>  9      villany    49
#> 10         digs     3
#> # ... with more rows

New features

  • dplyr support has been updated to require dplyr 0.7.0 and use dbplyr. This means that you can now more naturally work directly with DBI connections. dplyr now translates to modern BigQuery SQL which supports a broader set of translations. Along the way I also fixed a vareity of SQL generation bugs.
  • New functions insert_extract_job() makes it possible to extract data and save in google storage, and insert_table() allows you to insert empty tables into a dataset.
  • All POST requests (inserts, updates, copies and query_exec) now take .... This allows you to add arbitrary additional data to the request body making it possible to use parts of the BigQuery API that are otherwise not exposed. snake_case argument names are automatically converted to camelCase so you can stick consistently to snake case in your R code.
  • Full support for DATE, TIME, and DATETIME types (#128).

There were a variety of bug fixes and other minor improvements: see the release notes for full details.

Contributors

bigrquery a community effort: a big thanks go to Christofer Bäcklin, Jarod G.R. Meng and Akhmed Umyarov for their pull requests. Thank you all for your contributions!

I’m pleased to announce that dplyr 0.7.0 is now on CRAN! (This was dplyr 0.6.0 previously; more on that below.) dplyr provides a “grammar” of data transformation, making it easy and elegant to solve the most common data manipulation challenges. dplyr supports multiple backends: as well as in-memory data frames, you can also use it with remote SQL databases. If you haven’t heard of dplyr before, the best place to start is the Data transformation chapter in R for Data Science.

You can install the latest version of dplyr with:

install.packages("dplyr")

Features

dplyr 0.7.0 is a major release including over 100 improvements and bug fixes, as described in the release notes. In this blog post, I want to discuss one big change and a handful of smaller updates. This version of dplyr also saw a major revamp of database connections. That’s a big topic, so it’ll get its own blog post next week.

Tidy evaluation

The biggest change is a new system for programming with dplyr, called tidy evaluation, or tidy eval for short. Tidy eval is a system for capturing expressions and later evaluating them in the correct context. It is is important because it allows you 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 x 3
#>         homeworld   height  mass
#>                  
#>  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 makes it possible to write your functions that work like dplyr functions, reducing the amount of copy-and-paste in your code:

starwars_mean <- function(my_var) {
  my_var <- enquo(my_var)
  
  starwars %>%
    group_by(!!my_var) %>%
    summarise_at(vars(height:mass), mean, na.rm = TRUE)
}
starwars_mean(homeworld)

You can also use the new .data pronoun to refer to variables with strings:

my_var <- "homeworld"

starwars %>%
  group_by(.data[[my_var]]) %>%
  summarise_at(vars(height:mass), mean, na.rm = TRUE)

This is useful when you’re writing packages that use dplyr code because it avoids an annoying note from R CMD check.

To learn more about how tidy eval helps solve data analysis challenge, please read the new programming with dplyr vignette. Tidy evaluation is implemented in the rlang package, which also provides a vignette on the theoretical underpinnings. Tidy eval is a rich system and takes a while to get your head around it, but we are confident that learning tidy eval will pay off, especially as it roles out to other packages in the tidyverse (tidyr and ggplot2 are next on the todo list).

The introduction of tidy evaluation means that the standard evaluation (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).

Character encoding

We have done a lot of work to ensure that dplyr works with encodings other than 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. Please let us know if you have problems!

New datasets

dplyr has some new datasets that will help write more interesting examples:

  • starwars, shown above, contains information about characters from the Star Wars movies, sourced from the Star Wars API. It contains a number of list-columns.
    starwars
    #> # A tibble: 87 x 13
    #>                  name height  mass    hair_color  skin_color eye_color
    #>                                         
    #>  1     Luke Skywalker    172    77         blond        fair      blue
    #>  2              C-3PO    167    75                  gold    yellow
    #>  3              R2-D2     96    32           white, blue       red
    #>  4        Darth Vader    202   136          none       white    yellow
    #>  5        Leia Organa    150    49         brown       light     brown
    #>  6          Owen Lars    178   120   brown, grey       light      blue
    #>  7 Beru Whitesun lars    165    75         brown       light      blue
    #>  8              R5-D4     97    32            white, red       red
    #>  9  Biggs Darklighter    183    84         black       light     brown
    #> 10     Obi-Wan Kenobi    182    77 auburn, white        fair blue-gray
    #> # ... with 77 more rows, and 7 more variables: birth_year ,
    #> #   gender , homeworld , species , films ,
    #> #   vehicles , starships 
  • storms has the trajectories of ~200 tropical storms. It contains a strong grouping structure.
    storms
    #> # A tibble: 10,010 x 13
    #>     name  year month   day  hour   lat  long              status category
    #>                             
    #>  1   Amy  1975     6    27     0  27.5 -79.0 tropical depression       -1
    #>  2   Amy  1975     6    27     6  28.5 -79.0 tropical depression       -1
    #>  3   Amy  1975     6    27    12  29.5 -79.0 tropical depression       -1
    #>  4   Amy  1975     6    27    18  30.5 -79.0 tropical depression       -1
    #>  5   Amy  1975     6    28     0  31.5 -78.8 tropical depression       -1
    #>  6   Amy  1975     6    28     6  32.4 -78.7 tropical depression       -1
    #>  7   Amy  1975     6    28    12  33.3 -78.0 tropical depression       -1
    #>  8   Amy  1975     6    28    18  34.0 -77.0 tropical depression       -1
    #>  9   Amy  1975     6    29     0  34.4 -75.8      tropical storm        0
    #> 10   Amy  1975     6    29     6  34.0 -74.8      tropical storm        0
    #> # ... with 10,000 more rows, and 4 more variables: wind ,
    #> #   pressure , ts_diameter , hu_diameter 
  • band_members, band_instruments and band_instruments2 has a tiny amount of data about bands. It’s designed to be very simple so you can illustrate how joins work without getting distracted by the details of the data.
    band_members
    #> # A tibble: 3 x 2
    #>    name    band
    #>      
    #> 1  Mick  Stones
    #> 2  John Beatles
    #> 3  Paul Beatles
    band_instruments
    #> # A tibble: 3 x 2
    #>    name  plays
    #>     
    #> 1  John guitar
    #> 2  Paul   bass
    #> 3 Keith guitar

New and improved verbs

  • The pull() generic allows you to extract a single column either by name or position. It’s similar to select() but returns a vector, rather than a smaller tibble.
    mtcars %>% pull(-1) %>% str()
    #>  num [1:32] 4 4 1 1 2 1 4 2 2 4 ...
    mtcars %>% pull(cyl) %>% str()
    #>  num [1:32] 6 6 4 6 8 6 8 4 4 6 ...

    Thanks to Paul Poncet for the idea!

  • arrange() for grouped data frames gains a .by_group argument so you can choose to sort by groups if you want to (defaults to FALSE).
  • All single table verbs now have scoped variants suffixed with _if(), _at() and _all(). Use these if you want to do something to every variable (_all), variables selected by their names (_at), or variables that satisfy some predicate (_if).
    iris %>% summarise_if(is.numeric, mean)
    starwars %>% select_if(Negate(is.list))
    storms %>% group_by_at(vars(month:hour))

Other important changes

  • Local join functions can now control how missing values are matched. The default value is na_matches = "na", which treats two missing values as equal. To prevent missing values from matching, use na_matches = "never".

You can change the default behaviour by calling pkgconfig::set_config("dplyr::na_matches", "never").

  • bind_rows() and combine() are more strict when coercing. Logical values are no longer coerced to integer and numeric. Date, POSIXct and other integer or double-based classes are no longer coerced to integer or double to avoid dropping important metadata. We plan to continue improving this interface in the future.

Breaking changes

From time-to-time I discover that I made a mistake in an older version of dplyr and developed what is now a clearly suboptimal API. If the problem isn’t too big, I try to just leave it – the cost of making small improvements is not worth it when compared to the cost of breaking existing code. However, there are bigger improvements where I believe the short-term pain of breaking code is worth the long-term payoff of a better API.

Regardless, it’s still frustrating when an update to dplyr breaks your code. To minimise this pain, I plan to do two things going forward:

  • Adopt an odd-even release cycle so that API breaking changes only occur in odd numbered releases. Even numbered releases will only contain bug fixes and new features. This is why I’ve skipped dplyr 0.6.0 and gone directly to dplyr 0.7.0.
  • Invest time in developing better tools isolating packages across projects so that you can choose when to upgrade a package on a project-by-project basis, and if something goes wrong, easily roll back to a version that worked. Look for news about this later in the year.

Contributors

dplyr is truly a community effort. Apart from the dplyr team (myself, Kirill Müller, and Lionel Henry), this release wouldn’t have been possible without patches from Christophe Dervieux, Dean Attali, Ian Cook, Ian Lyttle, Jake Russ, Jay Hesselberth, Jennifer (Jenny) Bryan, @lindbrook, Mauro Lepore, Nicolas Coutin, Daniel, Tony Fischetti, Hiroaki Yutani and Sergio Oller. Thank you all for your contributions!

Shinydashboard 0.6.0 is now on CRAN! This release of shinydashboard was aimed at both fixing bugs and also bringing the package up to speed with users’ requests and Shiny itself (especially fully bringing bookmarkable state to shinydashboard’s sidebar). In addition to bug fixes and new features, we also added a new “Behavior” section to the shinydashboard website to explain this release’s two biggest new features, and also to provide users with more material about shinydashboard-specific behavior.

Sidebar

This release introduces two new sidebar inputs. One of these inputs reports whether the sidebar is collapsed or expanded, and the other input reports which (if any) menu item in the side bar is expanded. In the screenshot below, the Charts tab is expanded.

These inputs are unusual since they’re automatically available without you needing to declare them, and they have a fixed name. The first input is accessible via input$sidebarCollapsed and can have only two values: TRUE, which indicates that the sidebar is collapsed, and FALSE, which indicates that it is expanded (default).

The second input is accessible via input$sidebarItemExpanded. If no menuItem() in the sidebar is currently expanded, the value of this input is NULL. Otherwise, input$sidebarItemExpanded holds the value of the expandedName of whichever menuItem() is currently expanded (expandedName is a new argument to menuItem(); if none is provided, shinydashboard creates a sensible default).

Full changes

As usual, you can view the full changelog for shinydashboard in the NEWS file.

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.

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.

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.

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)