https://github.com/lren-chuv/sample-data-db-setup
Create a set of tables containing sample data in a database
https://github.com/lren-chuv/sample-data-db-setup
database-migrations docker-image reference-data
Last synced: 7 months ago
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Create a set of tables containing sample data in a database
- Host: GitHub
- URL: https://github.com/lren-chuv/sample-data-db-setup
- Owner: LREN-CHUV
- License: apache-2.0
- Created: 2017-07-18T18:07:19.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2019-05-17T09:06:33.000Z (over 6 years ago)
- Last Synced: 2025-01-19T15:34:53.754Z (9 months ago)
- Topics: database-migrations, docker-image, reference-data
- Language: Shell
- Homepage:
- Size: 1010 KB
- Stars: 0
- Watchers: 5
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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# Sample database setup
Setup a database with one table containing sample data.
## How to build the Docker image
Run: `./build.sh`
# Setup for database 'sample-data-db' with the selection of variables for sample
## Introduction
This Docker image uses Flyway to manage the database migration scripts for the 'sample-data-db' database used by MIP.
This database contains the data used for testing and debugging purposes.
## Usage
Run:
```console
$ docker run -i -t --rm -e FLYWAY_HOST=`hostname` hbpmip/sample-data-db-setup:0.7.1 migrate
```
where the environment variables are:
* FLYWAY_HOST: database host, default to 'db'.
* FLYWAY_PORT: database port, default to 5432.
* FLYWAY_DATABASE_NAME: name of the database or schema, default to 'data'
* FLYWAY_URL: JDBC url to the database, constructed by default from FLYWAY_DBMS, FLYWAY_HOST, FLYWAY_PORT and FLYWAY_DATABASE_NAME
* FLYWAY_DRIVER: Fully qualified classname of the jdbc driver (autodetected by default based on flyway.url)
* FLYWAY_USER: database user, default to 'data'.
* FLYWAY_PASSWORD: database password, default to 'data'.
* FLYWAY_SCHEMAS: Optional, comma-separated list of schemas managed by Flyway
* FLYWAY_TABLE: Optional, name of Flyway's metadata table (default: schema_version)
After execution, you should have:
* A table named **SAMPLE_DATA** containing the values of dataset linreg_sample coming from file linreg_sample.csv
* A table named **CHURN** containing the values of dataset churn coming from file churn.csv
* A table named **IRIS** containing the values of dataset iris coming from file iris.csv
* A table named **SAMPLE_DATA** containing the values of dataset linreg_sample coming from file linreg_sample.csv
* A table named **cde_features_A** containing the values of datasets desd-synthdata coming from file desd-synthdata.csv
* A table named **cde_features_B** containing the values of datasets nida-synthdata coming from file nida-synthdata.csv
* A table named **cde_features_C** containing the values of datasets qqni-synthdata coming from file qqni-synthdata.csv
* A table named **mip_cde_features** containing the values of datasets desd-synthdata, nida-synthdata, qqni-synthdata coming from file desd-synthdata.csv, nida-synthdata.csv and qqni-synthdata.csv respectively
## Build
Run: `./build.sh`
## Publish on Docker Hub
Run: `./publish.sh`
## Contents of dataset linreg_sample.csv
Dataset generated from https://richarddmorey.shinyapps.io/test/solve.Rmd using ID number 2049324 and secret 's3Cr34'
First perform the linear regression analysis with only score.math.course1 as the independent variable, then perform the linear regression analysis with both high school math courses as predictors.
```
Solution
term estimate std.error statistic p.value
(Intercept) 1.1577 1.7023 0.6801 0.4975
score.math.course1 1.0253 0.0288 35.5961 0.0000
Report (p) values for both slopes.
Solution
term estimate std.error statistic p.value
(Intercept) -2.2264 2.0310 -1.0962 0.2748
score.math.course1 0.2020 0.2856 0.7072 0.4806
score.math.course2 0.8098 0.2796 2.8964 0.0044
Report (R^2) and adjusted (R^2).
Solution
Dependent variable:
college.math
score.math.course1 0.202
(0.286)
score.math.course2 0.810***
(0.280)
Constant -2.226
(2.031)
Observations 150
R2 0.901
Adjusted R2 0.900
Residual Std. Error 9.551 (df = 147)
F Statistic 669.368*** (df = 2; 147)
Note: p<0.1; p<0.05; p<0.01
```
## License
Copyright (C) 2017 [LREN CHUV](https://www.unil.ch/lren/en/home.html)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
# Acknowledgements
This work has been funded by the European Union Seventh Framework Program (FP7/20072013) under grant agreement no. 604102 (HBP)
This work is part of SP8 of the Human Brain Project (SGA1).