niccokunzmann/python-recurring-ical-events
Data sources
| Repository | Pull requests | Issues | Complete | Read |
|---|---|---|---|---|
| niccokunzmann/python-recurring-ical-events | 176 / 176 | 123 / 123 | 100% | 1 day ago |
Download the data behind this page. The ODS spreadsheets also have the data each table was made from, and what the download is.
- response times: ODS, CSV, Parquet, JSON
- pull requests and issues: ODS, CSV, Parquet
- comments and reviews: ODS, CSV, Parquet
- commits: ODS, CSV, Parquet
- contributors: ODS, CSV, Parquet
How to check the numbers with scipy.
Sources shown
Response times
Time until the first response of each class of people (edit classes). The author's own comments do not count. Measured, normal and exponential use only the items that got a response; Kaplan-Meier counts the others, too, as waiting at least until they were closed or until now, and is "-" where less than half (or 95 %) got a response.
| class | n | fit | average | std error | median | 95% |
|---|---|---|---|---|---|---|
| bot | 1 | measured | 1min | - | 1min | 1min |
| bot | 1 | normal | 1min | - | 1min | - |
| bot | 1 | exponential | 1min | 1min | 1min | 4min |
| bot | 176 | Kaplan-Meier | - | - | - | - |
| collaborator or higher | 50 | measured | 14d 4h | 3d 20h | 14h 28min | 61d 6h |
| collaborator or higher | 50 | normal | 14d 4h | 3d 19h | 14d 4h | 58d 9h |
| collaborator or higher | 50 | exponential | 14d 4h | 2d | 9d 20h | 42d 12h |
| collaborator or higher | 176 | Kaplan-Meier | - | - | 32d 22h | 132d 14h |
| external | 10 | measured | 5d 12h | 1d 20h | 2d 22h | 14d 11h |
| external | 10 | normal | 5d 12h | 1d 18h | 5d 12h | 14d 16h |
| external | 10 | exponential | 5d 12h | 1d 17h | 3d 19h | 16d 12h |
| external | 176 | Kaplan-Meier | - | - | - | - |
n counts the items with a response of the class. Kaplan-Meier counts all items, so its n is the same for every class: the items without a response of the class count as waiting, until they were closed or until now. How to read the response times
Trend: no clear change of the time until the first response over time (p = 0.92, 60 responses). How the trend is computed
| class | n | fit | average | std error | median | 95% |
|---|---|---|---|---|---|---|
| collaborator or higher | 58 | measured | 10d 10h | 5d 7h | 12h 27min | 29d 22h |
| collaborator or higher | 58 | normal | 10d 10h | 5d 6h | 10d 10h | 76d 7h |
| collaborator or higher | 58 | exponential | 10d 10h | 1d 8h | 7d 5h | 31d 5h |
| collaborator or higher | 123 | Kaplan-Meier | - | - | 55d 21h | - |
| external | 15 | measured | 161d 3h | 70d 7h | 48d 5h | 613d 16h |
| external | 15 | normal | 161d 3h | 67d 22h | 161d 3h | 593d 23h |
| external | 15 | exponential | 161d 3h | 41d 14h | 111d 16h | 482d 17h |
| external | 123 | Kaplan-Meier | - | - | - | - |
n counts the items with a response of the class. Kaplan-Meier counts all items, so its n is the same for every class: the items without a response of the class count as waiting, until they were closed or until now. How to read the response times
Trend: no clear change of the time until the first response over time (p = 0.59, 64 responses). How the trend is computed
Badges
Embed the response time in a README. The badge counts with the default classes of people. The badge shows what is in the database and never calls GitHub. Parameters: kind = pr or issue, percentile = 0-100, class = a class name (optional), model = measured, normal, exponential or kaplan-meier (optional), label = the text of the left side (optional).