Awesome

dm

Are you using multiple data frames or database tables in R? Organize them with dm.

Use it for data analysis today.

Build data models tomorrow.

Deploy the data models to your organization’s Relational Database Management System (RDBMS) the day after.

Overview

dm bridges the gap in the data pipeline between individual data frames and relational databases. It’s a grammar of joined tables that provides a consistent set of verbs for consuming, creating, and deploying relational data models. For individual researchers, it broadens the scope of datasets they can work with and how they work with them. For organizations, it enables teams to quickly and efficiently create and share large, complex datasets.

dm objects encapsulate relational data models constructed from local data frames or lazy tables connected to an RDBMS. dm objects support the full suite of dplyr data manipulation verbs along with additional methods for constructing and verifying relational data models, including key selection, key creation, and rigorous constraint checking. Once a data model is complete, dm provides methods for deploying it to an RDBMS. This allows it to scale from datasets that fit in memory to databases with billions of rows.

Features

dm makes it easy to bring an existing relational data model into your R session. As the dm object behaves like a named list of tables it requires little change to incorporate it within existing workflows. The dm interface and behavior is modeled after dplyr, so you may already be familiar with many of its verbs. dm also offers:

visualization to help you understand relationships between entities represented by the tables
simpler joins that “know” how tables are related, including a “flatten” operation that automatically follows keys and performs column name disambiguation
consistency and constraint checks to help you understand (and fix) the limitations of your data

That’s just the tip of the iceberg. See Getting started to hit the ground running and explore all the features.

Installation

The latest stable version of the {dm} package can be obtained from CRAN with the command

<pre class='chroma'> <a href='https://rdrr.io/r/utils/install.packages.html'>install.packages</a>("dm")</pre>

The latest development version of {dm} can be installed from R-universe:

<pre class='chroma'> # Enable repository from cynkra <a href='https://rdrr.io/r/base/options.html'>options</a>( repos = <a href='https://rdrr.io/r/base/c.html'>c</a>( cynkra = "https://cynkra.r-universe.dev", CRAN = "https://cloud.r-project.org" ) ) # Download and install dm in R <a href='https://rdrr.io/r/utils/install.packages.html'>install.packages</a>('dm')</pre>

or from GitHub:

<pre class='chroma'> # install.packages("devtools") devtools::<a href='https://remotes.r-lib.org/reference/install_github.html'>install_github</a>("cynkra/dm")</pre>

Usage

Create a dm object (see Getting started for details).

<pre class='chroma'> <a href='https://rdrr.io/r/base/library.html'>library</a>(<a href='https://dm.cynkra.com/'>dm</a>) dm <- <a href='https://dm.cynkra.com/reference/dm_nycflights13.html'>dm_nycflights13</a>(table_description = TRUE) dm #> ── Metadata ──────────────────────────────────────────────────────────────────── #> Tables: `airlines`, `airports`, `flights`, `planes`, `weather` #> Columns: 53 #> Primary keys: 4 #> Foreign keys: 4</pre>

dm is a named list of tables:

<pre class='chroma'> <a href='https://rdrr.io/r/base/names.html'>names</a>(dm) #> [1] "airlines" "airports" "flights" "planes" "weather" <a href='https://rdrr.io/r/base/nrow.html'>nrow</a>(dm$airports) #> [1] 86 dm$flights <a href='https://magrittr.tidyverse.org/reference/pipe.html'>%>%</a> count(origin) #> # A tibble: 3 × 2 #> origin n #> <chr> <int> #> 1 EWR 641 #> 2 JFK 602 #> 3 LGA 518</pre>

Visualize relationships at any time:

Simple joins:

<pre class='chroma'> dm <a href='https://magrittr.tidyverse.org/reference/pipe.html'>%>%</a> <a href='https://dm.cynkra.com/reference/dm_flatten_to_tbl.html'>dm_flatten_to_tbl</a>(flights) #> Renaming ambiguous columns: %>% #> dm_rename(flights, year.flights = year) %>% #> dm_rename(flights, month.flights = month) %>% #> dm_rename(flights, day.flights = day) %>% #> dm_rename(flights, hour.flights = hour) %>% #> dm_rename(airlines, name.airlines = name) %>% #> dm_rename(airports, name.airports = name) %>% #> dm_rename(planes, year.planes = year) %>% #> dm_rename(weather, year.weather = year) %>% #> dm_rename(weather, month.weather = month) %>% #> dm_rename(weather, day.weather = day) %>% #> dm_rename(weather, hour.weather = hour) #> # A tibble: 1,761 × 48 #> year.flights month.…¹ day.f…² dep_t…³ sched…⁴ dep_d…⁵ arr_t…⁶ sched…⁷ arr_d…⁸ #> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> #> 1 2013 1 10 3 2359 4 426 437 -11 #> 2 2013 1 10 16 2359 17 447 444 3 #> 3 2013 1 10 450 500 -10 634 648 -14 #> 4 2013 1 10 520 525 -5 813 820 -7 #> 5 2013 1 10 530 530 0 824 829 -5 #> 6 2013 1 10 531 540 -9 832 850 -18 #> 7 2013 1 10 535 540 -5 1015 1017 -2 #> 8 2013 1 10 546 600 -14 645 709 -24 #> 9 2013 1 10 549 600 -11 652 724 -32 #> 10 2013 1 10 550 600 -10 649 703 -14 #> # ℹ 1,751 more rows #> # ℹ abbreviated names: ¹month.flights, ²day.flights, ³dep_time, #> # ⁴sched_dep_time, ⁵dep_delay, ⁶arr_time, ⁷sched_arr_time, ⁸arr_delay #> # ℹ 39 more variables: carrier <chr>, flight <int>, tailnum <chr>, #> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, #> # hour.flights <dbl>, minute <dbl>, time_hour <dttm>, name.airlines <chr>, #> # name.airports <chr>, lat <dbl>, lon <dbl>, alt <dbl>, tz <dbl>, dst <chr>, #> # tzone <chr>, year.planes <int>, type <chr>, manufacturer <chr>, #> # model <chr>, engines <int>, seats <int>, speed <int>, engine <chr>, #> # year.weather <int>, month.weather <int>, day.weather <int>, #> # hour.weather <int>, temp <dbl>, dewp <dbl>, humid <dbl>, wind_dir <dbl>, #> # wind_speed <dbl>, wind_gust <dbl>, precip <dbl>, pressure <dbl>, …</pre>

Check consistency:

Learn more in the Getting started article.

Getting help

If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub. For questions and other discussion, please use community.rstudio.com.

Funded by:

Please note that the ‘dm’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.