1 Introduction

In R, data are often stored in data frames which are tables in which each row represents a record and each column a variable. Because data frames are highly used, they have been improved in different objects such as tibble, data.table or AnnotatedDataFrame.

However, in many projects, the data do not fit in only one table but they are organized in different data frames, each of them dealing with a specific concept. These tables are often related to each other by some variables. Depending on the diversity of the concepts, it can be difficult to remember what these tables represent and how they are connected. Fortunately, because they are tables, a set of data frames can be directly documented using a relational data model.

The datamodelr R package provides tools to document relational data. The generate data models are leveraged by the dm R package to interact more easily with relational data.

Here we present the ReDaMoR package which also allows the manipulation of relational data models in R but with an approach quite different from the one implemented in datamodelr. It provides functions to create, import and save relational data models. These functions are accessible through a graphical user interface made with Shiny.

The main features of ReDaMoR are the following:

Create data model from scratch with a graphical user interface
Save data model in a json format
Import SQL data model generated with MySQL Workbench
Document data types as R types (character, numeric, logical, …)
Add comments to tables and fields to improve model documentation
Check and auto-correct model constraints (types, keys, uniqueness, mandatory fields)
Confront data to the model to check their compatibility

The ReDaMoR R package is licensed under GPL-3 and its source code is available on github.

2 Installation

2.1 From CRAN

install.packages("ReDaMoR")

2.2 Dependencies

The following R packages available on CRAN are required:

dplyr: A Grammar of Data Manipulation
magrittr: A Forward-Pipe Operator for R
visNetwork: Network Visualization using ‘vis.js’ Library
readr: Read Rectangular Text Data
shiny: Web Application Framework for R
shinyjs: Easily Improve the User Experience of Your Shiny Apps in Seconds
DT: A Wrapper of the JavaScript Library ‘DataTables’
rintrojs: Wrapper for the ‘Intro.js’ Library
colourpicker: A Colour Picker Tool for Shiny and for Selecting Colours in Plots
jsonlite: A Simple and Robust JSON Parser and Generator for R
markdown: Render Markdown with the C Library ‘Sundown’
rstudioapi: Safely Access the RStudio API
crayon: Colored Terminal Output

2.3 From github

devtools::install_github("patzaw/ReDaMoR")

3 Creating and modifying relational data using the graphical user interface

3.1 Running the app

The Shiny app is launched with the following command:

library(ReDaMoR)
m <- model_relational_data()

When the Done button is clicked, the model is returned in R environment. Because the interface can be closed accidentally, the model is also autosaved and can be recovered using the recover_RelDataModel() function. The recovered model can be provided as modelInput when calling model_relational_data(). For example:

m <- model_relational_data(recover_RelDataModel())

3.2 Model formalism

3.2.1 Data model example: the Human Phenotype Ontology

A data model example is provided within the package. It represents data extracted from the Human Phenotype Ontology (HPO)⁽¹⁾ and for which a subset is also provided within the ReDaMoR package (more details provided in the Confronting data section).

This example can be imported from the Shiny app when clicking on the Import button and then on the Try an example link. It can be also be loaded and displayed (and edited) using the following commands:

hpo_model <- read_json_data_model(
  system.file("examples/HPO-model.json", package="ReDaMoR")
)
plot(hpo_model)

## Edit the model # m <- model_relational_data(hpo_model)

The view is rendered by the visNetwork package. It means that it can take advantages of all the functions provided by the visNetwork package including the use of the model in Shiny apps.

3.2.2 Tables

Each box represents a table. The header of the box corresponds to the name of the table and the following lines document each field:

The type of the field is given between curly brackets
A star indicates that the field is unique
Field surrounded by brackets are optional
Field in bold are involved in the primary key
Indexes are also indicated between the curly brackets when they exist:
- idx. correspond to regular indexes
- uidx. correspond to unique indexes

When the cursor is over a box, table and field comments are displayed.

3.2.3 Foreign keys

Each arrow represent a foreign key:

The start of the arrow correspond to the origin table
The end of the arrow correspond to the target table
When the mouse is over the arrow, the fields involved in the foreign key are displayed along with the minimum and maximum cardinalities of the key on both sides (between brackets below the table name).

3.3 App organization

The app is divided in three main parts:

The main menu at the top of the page to execute general actions on the model
The model view on the left of the page to interact with the model
The edition view on the right of the page to execute contextual edition of the model

3.3.2 The model view

The model view provides a view of the data model rendered by the visNetwork package. Tables can be selected by clicking on it or by searching them using the box just above the model view. Autodraw and autofit capabilities are also provided in this area.

3.3.3 The edition view

In the edition view the user can:

Change the color of a table
Duplicate or remove tables
Add and remove foreign keys
Edit table information
- table name
- table comment
- fields
- primary key
- indexes

3.3.4 Contextual help tour

An help tour can be launched when clicking on the button in the main menu. This help tour is contextual: its content depends on the state of the app.

3.3.5 keyboard shortcuts

Some common keyboard shortcuts are implemented:

Ctrl+Z / Ctrl+Shift+Z for undo and redo
Enter for validating a new name
del for deleting tables or foreign keys
F2 for renaming tables or editing foreign key cardinalities

3.4 Public instance of the app

A public instance of the app is available here.

You can easily deploy your own instance by copying these two lines in an app.R file:

library(ReDaMoR)
model_relational_data()

Adding foreign keys

In the interface, when you select one or two tables (holding down the ctrl key) you can create a foreign key by clicking on the “Key” button (See in the figure below surrounded in blue).

The following interface should appear:

For each table you can select the fields participating to the relationship (surrounded in blue) and add them with the “+” button (surrounded in red). You can also change the cardinality of the 2 sides of the relationship (surrounded in yellow). You can also change the direction of the relationship with the arrow button (surrounded in red). When you’ve finished, you can click on the “Add” button (surrounded in purple) to create the foreign key.

After having created a relationship you can still change its cardinalities by selecting the relationship of interest and by clicking on the edit “keys” button. If you need to apply more changes, you’ll have to delete the key (“Discard keys” button) and recreate it.

4 Confronting data

Data can be confronted to the model using the confront_data() function. During this process the following checks are performed:

All the tables in the model and only those are available.
For each table:
- All the columns in the model and only those are available.
- The type of each column corresponds to the type in the model.
- Not any NA values are found in non-nullable columns.
- There is no duplicated value in unique columns.
- There is no duplicated value in unique indexes.
Foreign keys are tested according to their cardinality: all values in on table should be available in the other table if the corresponding minimum cardinality is greater than 0.

A subset (500 phenotypes among more than 14 000 in the original resource) of the HPO⁽¹⁾ is provided within the package and can be directly confronted to the data model. The function prints a report with messages about the global success of the data model confrontation and additional failure or warning messages for relevant tables.

confrontation_report <- confront_data(
  hpo_model,
  path=list.files(
    system.file("examples/HPO-subset", package="ReDaMoR"),
    full.names=TRUE
  ),
  returnData=TRUE
)
#> Processing "HPO_hp" (table 1 / 9)
#> Processing "HPO_altId" (table 2 / 9)
#> Processing "HPO_sourceFiles" (table 3 / 9)
#> Processing "HPO_diseases" (table 4 / 9)
#> Processing "HPO_diseaseHP" (table 5 / 9)
#> Processing "HPO_diseaseSynonyms" (table 6 / 9)
#> Processing "HPO_parents" (table 7 / 9)
#> Processing "HPO_descendants" (table 8 / 9)
#> Processing "HPO_synonyms" (table 9 / 9)
#> Model
#> SUCCESS
#> 
#> Check configuration
#>    - Optional checks: unique, not nullable, foreign keys
#>    - Maximum number of records: Inf
#> 
#> HPO_hp
#> SUCCESS
#> Field issues or warnings
#>    - description: SUCCESS Missing values 117/500 = 23%

This report can also be formatted using markdown and included in a document such as this one.

# view_confrontation_report(confrontation_report) # Use RStudio viewer
format_confrontation_report_md(
  confrontation_report,
  title="Example: Confrontation with original data",
  level=1, numbered=FALSE
) %>%
  cat()

Example: Confrontation with original data

SUCCESS

Check configuration

Optional checks: unique, not nullable, foreign keys
Maximum number of records: Inf

HPO_hp

SUCCESS

Field issues or warnings

description: SUCCESS Missing values 117/500 = 23%

The returnData argument is used to keep the data in the confrontation report.

hpo_tables <- confrontation_report$data

In the example below the data are altered and confronted again to the data model. The confrontation report shows the discrepancies between the model and the data in order to facilitate the correction of one or the other.

hpo_tables$HPO_diseases <- hpo_tables$HPO_diseases %>% slice(1:100)
hpo_tables$HPO_synonyms[1:10, "synonym"] <- NA
hpo_tables$HPO_hp <- hpo_tables$HPO_hp %>% mutate(level=as.character(level))
confront_data(hpo_model, hpo_tables, verbose=FALSE) %>%
  format_confrontation_report_md(
    title="Example: Confrontation with altered data",
    level=1, numbered=FALSE
  ) %>%
    cat()

Example: Confrontation with altered data

FAILURE

Check configuration

Optional checks: unique, not nullable, foreign keys
Maximum number of records: Inf

HPO_hp

FAILURE

Field issues or warnings

description: SUCCESS Missing values 117/500 = 23%
level: FAILURE Unexpected “character”

HPO_diseaseHP

FAILURE

Foreign keys issues or warnings

HPO_diseases [db->db + id->id]: FAILURE All keys of HPO_diseaseHP should be available in HPO_diseases.

HPO_diseaseSynonyms

FAILURE

Foreign keys issues or warnings

HPO_diseases [db->db + id->id]: FAILURE All keys of HPO_diseaseSynonyms should be available in HPO_diseases.

HPO_synonyms

FAILURE

Field issues or warnings

synonym: FAILURE Missing values 10/730 = 1%

4.1 Drafting a data model from data frames

Beside confronting a model to data, a model can also be drafted from a set of tables. Only column names and types are documented during this process. Uniqueness, mandatory and key constraints still need to be documented by the user to complete the model. It can easily be done with model_relational_data().

hpo_tables <- confrontation_report$data
new_model <- df_to_model(
  list=names(hpo_tables), envir=as.environment(hpo_tables)
)
new_model %>%
  auto_layout(lengthMultiplier=250) %>%
  plot()

# model_relational_data(new_model)

5 Support of matrices

Matrices (2 dimensions) are supported by ReDaMoR (>= 0.5). A matrix is modeled as a table with 3 and only 3 fields: 2 of types row and column and the third of your choice. The row and column compose the primary index of this special table, but they cannot be unique independently (a cell in a matrix is always identified by its row and its column). The example below shows a model of a gene_expression matrix: the rows correspond to genes, the column to samples and the value to the gene expression value in each sample. Genes and samples are further described in the genes and samples tables.

ge_model <- read_json_data_model(
  system.file("examples/GE-model.json", package="ReDaMoR")
)
plot(ge_model)

6 TKCat: Tailored Knowledge Catalog

The aim of TKCat (Tailored Knowledge Catalog) is to facilitate the management of data from knowledge resources that are frequently used alone or together in research environments. In TKCat, knowledge resources are manipulated as modeled database (MDB) objects. These objects provide access to the data tables along with a general description of the resource and a detail data model generated with ReDaMoR documenting the tables, their fields and their relationships. These MDB are then gathered in catalogs that can be easily explored an shared. TKCat provides tools to easily subset, filter and combine MDBs and create new catalogs suited for specific needs.

A modeled database (MDB) in TKCat gathers the following information:

General database information including a mandatory name and optionally the following fields: title, description, url, version and maintainer.
A data model created using the [ReDaMoR][redamor] package.
A list of tables corresponding to reference concepts shared by different MDBs. The way these concepts are identified is defined in specific documents called collections.
The data themselves organized according to the data model.

MDB objects can be subsetted, combined, filtered, joined and merged. TKCat supports different implementations of MDB objects such as memoMDB (data in memory), fileMDB (data in files) and chMDB (data in ClickHouse database). More information is provided in the TKCat user guide.

7 Acknowledgments

This work was entirely supported by UCB Pharma. (Early Solutions department)

8 References

(1) Sebastian Köhler, Leigh Carmody, Nicole Vasilevsky, Julius O B Jacobsen, et al. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Research. (2018) doi: 10.1093/nar/gky1105

Modeling Relational Data in R with ReDaMoR

ReDaMoR version: 0.6.3

Patrice Godard

April 13 2022

1 Introduction

2 Installation

2.1 From CRAN

2.2 Dependencies

2.3 From github

3 Creating and modifying relational data using the graphical user interface

3.1 Running the app

3.2 Model formalism

3.2.1 Data model example: the Human Phenotype Ontology

3.2.2 Tables

3.2.3 Foreign keys

3.3 App organization

3.3.1 The main menu

3.3.2 The model view

3.3.3 The edition view

3.3.4 Contextual help tour

3.3.5 keyboard shortcuts

3.4 Public instance of the app

Adding foreign keys

4 Confronting data

Example: Confrontation with original data

Check configuration

HPO_hp

Field issues or warnings

Example: Confrontation with altered data

Check configuration

HPO_hp

Field issues or warnings

HPO_diseaseHP

Foreign keys issues or warnings

HPO_diseaseSynonyms

Foreign keys issues or warnings

HPO_synonyms

Field issues or warnings

4.1 Drafting a data model from data frames

5 Support of matrices

6 TKCat: Tailored Knowledge Catalog

7 Acknowledgments

8 References