RSQLServer

An R package that provides a SQL Server R Database Interface (DBI), based on the cross-platform jTDS JDBC driver.

Installation

You can't install the package from CRAN yet. But you install the development version from GitHub:

# install.packages('devtools')
devtools::install_github('imanuelcostigan/RSQLServer')

Config file

We recommend that you store server details and credentials in ~/sql.yaml. This is partly so that you do not need to specify a username and password in calls to dbConnect(). But it is also because in testing, we've found that the jTDS single sign-on (SSO) library is a bit flaky. The contents of this file should look something like this:

SQL_PROD:
    server: 11.1.111.11
    type: &type sqlserver
    port: &port 1433
    domain: &domain companyname
    user: &user winusername
    password: &pass winpassword
    useNTLMv2: &ntlm true
SQL_DEV:
    server: 11.1.111.15
    type: *type
    port: *port
    domain: *domain
    user: *user
    password: *pass
    useNTLMv2: *ntlm

Usage

Ensure that your ~/sql.yaml file contains a valid SQL Server entry named TEST. In the following, the TEST server, generously provided by Microsoft for the purposes of this package's development, has a database containing the nycflights13 package data sets.

DBI usage

The following illustrates how you can make use of the DBI interface. Note that we do not attach the RSQLServer package.

library(DBI)
nycflights <- dbConnect(RSQLServer::SQLServer(), server = "TEST", database = 'DBItest')
# RSQLServer only returns tables with type TABLE and VIEW.
dbListTables(nycflights)
#> [1] "airlines"   "airports"   "cars"       "flights"    "planes"    
#> [6] "test_table" "weather"
dbListFields(nycflights, 'airlines')
#> [1] "carrier" "name"
dbReadTable(nycflights, 'airlines')
#>    carrier                        name
#> 1       9E           Endeavor Air Inc.
#> 2       AA      American Airlines Inc.
#> 3       AS        Alaska Airlines Inc.
#> 4       B6             JetBlue Airways
#> 5       DL        Delta Air Lines Inc.
#> 6       EV    ExpressJet Airlines Inc.
#> 7       F9      Frontier Airlines Inc.
#> 8       FL AirTran Airways Corporation
#> 9       HA      Hawaiian Airlines Inc.
#> 10      MQ                   Envoy Air
#> 11      OO       SkyWest Airlines Inc.
#> 12      UA       United Air Lines Inc.
#> 13      US             US Airways Inc.
#> 14      VX              Virgin America
#> 15      WN      Southwest Airlines Co.
#> 16      YV          Mesa Airlines Inc.

# Fetch all results
res <- dbSendQuery(nycflights, 'SELECT TOP 10 * FROM airlines')
dbFetch(res)
#>    carrier                        name
#> 1       9E           Endeavor Air Inc.
#> 2       AA      American Airlines Inc.
#> 3       AS        Alaska Airlines Inc.
#> 4       B6             JetBlue Airways
#> 5       DL        Delta Air Lines Inc.
#> 6       EV    ExpressJet Airlines Inc.
#> 7       F9      Frontier Airlines Inc.
#> 8       FL AirTran Airways Corporation
#> 9       HA      Hawaiian Airlines Inc.
#> 10      MQ                   Envoy Air
dbClearResult(res)
#> [1] TRUE

dplyr usage

The following illustrates how you can make use of the dplyr interface. Again, we do not attach the RSQLServer package.

library(dplyr, warn.conflicts = FALSE)
flights <- tbl(nycflights, "flights")
flights %>% 
  filter(carrier == "UA") %>% 
  arrange(-year, -month, -day, -dep_time)
#> Source:     lazy query [?? x 19]
#> Database:   SQLServerConnection
#> Ordered by: -year, -month, -day, -dep_time
#> 
#>     year month   day dep_time sched_dep_time dep_delay arr_time
#>    <int> <int> <int>    <int>          <int>     <dbl>    <int>
#> 1   2013    12    31       NA           1000        NA       NA
#> 2   2013    12    31       NA            840        NA       NA
#> 3   2013    12    31       NA            754        NA       NA
#> 4   2013    12    31       NA           2000        NA       NA
#> 5   2013    12    31       NA           1500        NA       NA
#> 6   2013    12    31       NA           1430        NA       NA
#> 7   2013    12    31       NA            855        NA       NA
#> 8   2013    12    31       NA            705        NA       NA
#> 9   2013    12    31       NA            600        NA       NA
#> 10  2013    12    31       NA            830        NA       NA
#> # ... with more rows, and 12 more variables: sched_arr_time <int>,
#> #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>
collect(flights)
#> # A tibble: 336,776 × 19
#>     year month   day dep_time sched_dep_time dep_delay arr_time
#> *  <int> <int> <int>    <int>          <int>     <dbl>    <int>
#> 1   2013     1     1      517            515         2      830
#> 2   2013     1     1      533            529         4      850
#> 3   2013     1     1      542            540         2      923
#> 4   2013     1     1      544            545        -1     1004
#> 5   2013     1     1      554            600        -6      812
#> 6   2013     1     1      554            558        -4      740
#> 7   2013     1     1      555            600        -5      913
#> 8   2013     1     1      557            600        -3      709
#> 9   2013     1     1      557            600        -3      838
#> 10  2013     1     1      558            600        -2      753
#> # ... with 336,766 more rows, and 12 more variables: sched_arr_time <int>,
#> #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>

Then close the connection