timeseriesdb
{timeseriesdb} Manage Time Series with R and PostgreSQL
Science Score: 23.0%
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Low similarity (12.9%) to scientific vocabulary
Repository
{timeseriesdb} Manage Time Series with R and PostgreSQL
Basic Info
Statistics
- Stars: 24
- Watchers: 4
- Forks: 3
- Open Issues: 30
- Releases: 0
Metadata Files
README.md
{timeseriesdb}: A Time Series Database for Official Statistics
-> GitHub Pages Documentation Site <-
{timeseriesdb} maps R time series objects to PostgreSQL database relations for permanent storage. Instead of writing time series to spreadsheet files or .RData files on disk, {timeseriesdb} uses a set of PostgreSQL relations which allows to store data alongside extensive, multi-lingual meta information in context aware fashion. {timeseriesdb} was designed with official statistics in mind: It can keep track of various versions of the same time series to handle data revisions, e.g., in the case of GDP data.
Why {timeseriesdb} ?
{timeseriesdb} ...
- is lite weight but powerful: multi-language meta information, versioning of time series, ...
- built entirely based on license cost free open source components.
- tailored to the needs of Official and Economic Statistics
- administration friendly, extendable access rights management
- well documented, developer friendly.
- API ready: {timeseriesdb} can easily be extended to expose data through a REST API to allow for language agnostic access to your time series.
What Does {timeseriesdb} NOT DO ?
{timeseriesdb} is not built to incrementally append new observations as fast as possible. {timeseriesdb} does not try to compete with the amazing speed of InfluxDB. It's not a server log or IoT minded time series storage.
Quick Start Guide
Make sure you followed the installation notes to make sure all components of the {timeseriesdb} were installed properly: PostgreSQL, necessary PostgreSQL extension, R as well as the {timeseriesdb} R package.
Example Use (Basic Usage)
The following examples illustrate basic use in a nutshell. The learn more about the use of {timeseriesdb}, read the vignette articles.
Store a List of R Time Series Objects to the Database
```
Create DB connection.
In this case connect to a local db running on port 1111
/w lame passwords -- strongly discouraged for production.
con <- dbConnect(Postgres(), "devwriter", "localhost", 1111, "devwriter", "postgres") tsl <- list(ts1 = ts(rnorm(100), frequency = 12, start = 2002), ts2 = ts(rnorm(100), frequency = 12, start = 2001)) dbtsstore(connection, tsl) dbDisconnect(con) ```
Read Data into a list of R time Series object
``` con <- dbconnectioncreate( dbname = "postgres", user = "devadmin", host = "localhost", passwd = "devadmin", port = 1111 )
tsl <- dbreadts(connection, c("sometsid","anothertsid")) dbconnectionclose(con) ```
Advanced Features
{timeseriesdb} offers a plethora of features beyond just mere storage of time series themselves:
- store vintages (versions) of time series
- datasets to group time series
- store extensive, multi-lingual, versioned meta information at dataset and time series level
- individual, user specific collections of time series similar to bookmark or playlist functionality
- administration friendly access management with reasonable defaults (public, internal, restricted)
- release calendar functionality to facilitate publishing
Owner
- Name: Matt Bannert
- Login: mbannert
- Kind: user
- Location: Zurich, Switzerland
- Company: @KOF-ch @cynkra @banboo-data
- Website: https://whatsgood.io
- Twitter: whatsgoodio
- Repositories: 13
- Profile: https://github.com/mbannert
Analytics & Data Engineering @cynkra, lecturer @ ETH. 2021 Global coordinator for @_useRconf. Creator of devOps Carpentry.
GitHub Events
Total
- Watch event: 2
Last Year
- Watch event: 2
Committers
Last synced: over 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| Matthias Bannert | m****t@g****m | 359 |
| thoenis | s****i@k****h | 328 |
| HomoCodens | 3****s | 221 |
| angelicambg | a****g@g****m | 18 |
| maja | m****k | 6 |
| igbucur | i****r@g****m | 2 |
| Kirill Müller | k****r | 1 |
| Severin Thoeni | t****s@r****f | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 12 months ago
All Time
- Total issues: 58
- Total pull requests: 44
- Average time to close issues: 5 months
- Average time to close pull requests: about 1 month
- Total issue authors: 7
- Total pull request authors: 5
- Average comments per issue: 0.88
- Average comments per pull request: 0.41
- Merged pull requests: 37
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 1
- Pull request authors: 0
- Average comments per issue: 2.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- HomoCodens (33)
- mbannert (15)
- omuelle (6)
- GigiHadid97 (1)
- Kjir (1)
- majazaloznik (1)
- sboriss (1)
Pull Request Authors
- HomoCodens (30)
- majazaloznik (6)
- angelicambg (4)
- mbannert (3)
- krlmlr (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- cran 284 last-month
- Total docker downloads: 42,005
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 6
- Total maintainers: 1
cran.r-project.org: timeseriesdb
A Time Series Database for Official Statistics with R and PostgreSQL
- Homepage: https://github.com/mbannert/timeseriesdb
- Documentation: http://cran.r-project.org/web/packages/timeseriesdb/timeseriesdb.pdf
- License: GPL-3
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Latest release: 0.4.1
published almost 8 years ago
Rankings
Maintainers (1)
Dependencies
- R >= 3.0.0 depends
- DBI * imports
- RPostgres >= 1.2.0 imports
- data.table >= 1.9.4 imports
- jsonlite >= 1.1 imports
- utils * imports
- xts * imports
- dygraphs * suggests
- knitr * suggests
- openxlsx * suggests
- rmarkdown * suggests
- rstudioapi * suggests