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https://github.com/bergant/datamodelr
Data model diagrams in R
https://github.com/bergant/datamodelr
data-structures model-diagram r
Last synced: 3 days ago
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Data model diagrams in R
- Host: GitHub
- URL: https://github.com/bergant/datamodelr
- Owner: bergant
- License: other
- Created: 2015-10-05T23:14:45.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2021-03-02T16:25:40.000Z (almost 4 years ago)
- Last Synced: 2025-01-02T07:08:28.755Z (10 days ago)
- Topics: data-structures, model-diagram, r
- Language: R
- Homepage:
- Size: 2.08 MB
- Stars: 285
- Watchers: 12
- Forks: 28
- Open Issues: 16
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - bergant/datamodelr - Data model diagrams in R (R)
README
**This project is no longer actively maintained. Please take a look at [dm](https://krlmlr.github.io/dm/) package.**
# datamodelr
Define and display data model diagrams:
* __Data model definition__
+ Define data model manually with [YAML](#model-definition-in-yaml)
+ Extract data model from [R data frames](#model-diagram-of-interconnected-data-frames)
+ Reverse-engineer [SQL Server Database](#reverse-engineer-sql-server-database)
+ Reverse-engineer [PostgreSQL Database](#reverse-engineer-postgresql-database)
* __Rendering__
+ Define model [segments](#diagram-segments)
+ Display [focused sub-diagram ](#focused-data-model-diagram) or
+ [Hide columns](#hide-columns) to improve model diagram readability
+ Use [colors](#colors) to emphasize specific tables
+ Define [graph direction](#graph-direction) or
other [graphviz attributes](#graphviz-attributes)
+ Display [additional column attributes](#additional-column-attributes)
Use [shiny](#shiny-application) to implement interactive model definition and
rendering.
## Installation
```r
devtools::install_github("bergant/datamodelr")
```
## Usage
### Model Definition in YAML
Define a data model in YAML:
```yaml
# data model segments
- segment: &md Master data
- segment: &tran Transactions
# Tables and columns
- table: Person
segment: *md
columns:
Person ID: {key: yes}
Name:
E-mail:
Street:
Street number:
City:
ZIP:
- table: Order
segment: *tran
columns:
Order ID: {key: yes}
Customer: {ref: Person}
Sales person: {ref: Person}
Order date:
Requested ship date:
Status:
- table: Order Line
segment: *tran
columns:
Order ID: {key: yes, ref: Order}
Line number: {key: yes}
Order item: {ref: Item}
Quantity:
Price:
- table: Item
segment: *md
display: accent1
columns:
Item ID: {key: yes}
Item Name:
Description:
Category:
Size:
Color:
```
Create a data model object with `dm_read_yaml`:
```r
library(datamodelr)
file_path <- system.file("samples/example.yml", package = "datamodelr")
dm <- dm_read_yaml(file_path)
```
Create a graph object to plot the model:
```r
graph <- dm_create_graph(dm, rankdir = "BT")
dm_render_graph(graph)
```
### Model Diagram of Interconnected Data Frames
Attach flights database
([nycflights13](http://github.com/hadley/nycflights13#nycflights13) package)
and create a data model from data frames:
```r
library("nycflights13")
dm_f <- dm_from_data_frames(flights, airlines, weather, airports, planes)
```
Create plot:
```r
graph <- dm_create_graph(dm_f, rankdir = "BT", col_attr = c("column", "type"))
dm_render_graph(graph)
```
Add references and primary keys:
```r
dm_f <- dm_add_references(
dm_f,
flights$carrier == airlines$carrier,
flights$origin == airports$faa,
flights$dest == airports$faa,
flights$tailnum == planes$tailnum,
weather$origin == airports$faa
)
graph <- dm_create_graph(dm_f, rankdir = "BT", col_attr = c("column", "type"))
dm_render_graph(graph)
```
### Reverse-engineer SQL Server Database
This example uses [Northwind](https://northwinddatabase.codeplex.com/) sample
database and [RODBC](http://CRAN.R-project.org/package=RODBC)
package as an interface to SQL Server.
```r
library(RODBC)
con <- odbcConnect(dsn = "NW")
sQuery <- dm_re_query("sqlserver")
dm_northwind <- sqlQuery(con, sQuery, stringsAsFactors = FALSE, errors=TRUE)
odbcClose(con)
# convert to a data model
dm_northwind <- as.data_model(dm_northwind)
```
Plot the result:
```r
graph <- dm_create_graph(dm_northwind, rankdir = "BT")
dm_render_graph(graph)
```
### Reverse-engineer PostgreSQL Database
This example uses [DVD Rental](http://www.postgresqltutorial.com/postgresql-sample-database/)
sample database and [RPostgreSQL](https://cran.r-project.org/package=RPostgreSQL)
package as an interface to PostgreSQL database.
```r
library(RPostgreSQL)
#> Loading required package: DBI
con <- dbConnect(dbDriver("PostgreSQL"), dbname="dvdrental", user ="postgres")
sQuery <- dm_re_query("postgres")
dm_dvdrental <- dbGetQuery(con, sQuery)
dbDisconnect(con)
#> [1] TRUE
dm_dvdrental <- as.data_model(dm_dvdrental)
```
Show model:
```r
graph <- dm_create_graph(dm_dvdrental, rankdir = "RL")
dm_render_graph(graph)
```
### Focused Data Model Diagram
To focus in on a few tables from your model use `focus` attribute in `dm_create_graph` function:
```r
focus <- list(tables = c(
"customer",
"payment",
"rental",
"inventory",
"film"
))
graph <- dm_create_graph( dm_dvdrental, rankdir = "RL", focus = focus)
dm_render_graph(graph)
```
### Hide columns
To emphasize table relations and hide the "non-key""
columns use `view_type = "keys_only"`:
```r
graph <- dm_create_graph(dm_dvdrental, view_type = "keys_only", rankdir = "RL")
dm_render_graph(graph)
```
### Diagram Segments
Arrange tables in clusters with `dm_set_segment` function:
```r
table_segments <- list(
Transactions = c("rental", "inventory", "payment"),
Party = c("customer", "staff", "address", "city", "country", "store"),
Film = c("film", "film_actor", "actor", "language", "film_category", "category") )
dm_dvdrental_seg <- dm_set_segment(dm_dvdrental, table_segments)
```
Render diagram with segments:
```r
graph <- dm_create_graph(dm_dvdrental_seg, rankdir = "RL", view_type = "keys_only")
dm_render_graph(graph)
```
### Graph Direction
Use `rankdir` to change the direction of graph:
```r
graph <- dm_create_graph(dm_dvdrental_seg, rankdir = "BT", view_type = "keys_only")
dm_render_graph(graph)
```
### Colors
To emphasise tables with colors use `dm_set_display` function:
```r
display <- list(
accent1 = c("rental", "payment"),
accent2 = c("customer"),
accent3 = c("staff", "store"),
accent4 = c("film", "actor") )
dm_dvdrental_col <- dm_set_display(dm_dvdrental_seg, display)
graph <- dm_create_graph(dm_dvdrental_col, rankdir = "BT", view_type = "keys_only")
dm_render_graph(graph)
```
Default color scheme includes:
### Custom Colors
Add your colors with `dm_add_colors` function:
```r
my_colors <-
dm_color_scheme(
purple = dm_palette(
line_color = "#8064A2",
header_bgcolor = "#B1A0C7",
header_font = "#FFFFFF",
bgcolor = "#E4DFEC"
),
red = dm_palette(
line_color = "#C0504D",
header_bgcolor = "#DA9694",
header_font = "#FFFFFF",
bgcolor = "#F2DCDB"
)
)
dm_add_colors(my_colors)
dm <- dm_set_display(dm, display = list(
red = c("Order", "Order Line"),
purple = "Item"
))
graph <- dm_create_graph(dm, rankdir = "RL")
dm_render_graph(graph)
```
### Graphviz Attributes
To change general graph, node or edge
[graphviz](http://www.graphviz.org/doc/info/attrs.html)
attributes use `graph_attrs`, `edge_attrs` and `node_attrs` arguments
when creating graph. This example changes
graph background,
arrow style (edge attribute) and
font (node attribute):
```r
graph <- dm_create_graph(
dm,
graph_attrs = "rankdir = RL, bgcolor = '#F4F0EF' ",
edge_attrs = "dir = both, arrowtail = crow, arrowhead = odiamond",
node_attrs = "fontname = 'Arial'")
dm_render_graph(graph)
```
### Additional Column Attributes
To include additional column attributes set `col_attr` when creating graph:
```r
focus <- list(tables = c(
"customer",
"rental",
"inventory",
"film"
))
graph <- dm_create_graph( dm_dvdrental, rankdir = "RL", focus = focus,
col_attr = c("column", "type"))
dm_render_graph(graph)
```
### Shiny Application
Try **datamodelr** Shiny application:
```r
shiny::runApp(system.file("shiny", package = "datamodelr"))
```
## Utilised Packages
datamodelr depends on:
* [DiagrammeR](http://rich-iannone.github.io/DiagrammeR/) for graph rendering
* [yaml](https://github.com/viking/r-yaml) for parsing YAML files in R
* RStudio [shiny](http://shiny.rstudio.com/) and
[shinyAce](http://trestletech.github.io/shinyAce/) for shiny application demo.