This is the code for the time tracking experiment that I did during the Fall 2020 semester. You can use the code to get JSON-formatted data of all of your Clockify time entries in your active workspace.
A writeup of my findings will be posted soon, and this README will be updated with its location.
This is really just something I did for fun, so I'm not planning on making any major updates to the code for the project after this, aside from possibly adding some visualization-related code.
One of the known limitations of the code is that it chooses whatever is designated as your active workspace by default. Since I am a first-time Clockify user and only have one active workspace, this was not an issue for me, but you may have to rework the code if you have more workspaces and this causes problems for you.
- Python 3.7+.
Pipenvtool. This tool makes installing the dependencies for this project and handling virtual environments straightforward. So, if you haven't installed that, do so using the steps detailed here.- You're currently logged into Clockify and have an active workspace.
-
Generate an API key by going to your user settings page and scrolling down to the API heading. Then click Generate to generate a personal API key. Store this API key in an environment variable named
CLOCKIFY_API_KEY. Alternatively, you can add the API key as a command-line argument using the-aor--api-keyoptions. -
Clone the project repo and enter it:
git clone https://github.com/zaataylor/time-tracking.git
cd time-tracking- Install the required project dependencies included in the
Pipfileand activate the created virtual environment using:
pipenv install
pipenv shell- Get time tracking data by invoking
python get_time_data.pywith the proper command line parameters. The simplest invocation to get started is:
python get_time_data.py -a {API_KEY} --num-entries {NUM_ENTRIES}where API_KEY is the value of the key obtained from step 1 and NUM_ENTRIES is a
positive integer representing some number of entries made in Clockify. Here is the full
usage information:
usage: get_time_data.py [-h] [-a API_KEY]
(--num-pages NUM_PAGES | --num-entries NUM_ENTRIES)
[-p PROJ_FILE] [-t TASKS_FILE] [-e ENTRIES_FILE]
[-d DELAY]
Get Clockify projects, tasks, and time entry data and write it to files.
optional arguments:
-h, --help show this help message and exit
-a API_KEY, --api-key API_KEY
Your personal Clockify API key. If not specified here,
then you must have the CLOCKIFY_API_KEY environment
variable set on your system.
--num-pages NUM_PAGES
The number of (contiguous) pages to request from the
Clockify API.
--num-entries NUM_ENTRIES
Lower bound on the number of entries to grab from the
Clockify API. If specified, this value will be used to
calculate how many pages to grab from the Clockify
API.
-p PROJ_FILE, --projects-file PROJ_FILE
The name of the file to write projects information to.
Defaults to 'projects.json' if not specified.
-t TASKS_FILE, --tasks-file TASKS_FILE
The name of the file to write tasks information to.
Defaults to 'tasks.json' if not specified.
-e ENTRIES_FILE, --entries-file ENTRIES_FILE
The name of the file to write time entry data to.
Defaults to 'entries.json' if not specified.
-d DELAY, --delay DELAY
Delay between subsequent calls to the Clockify API, in
seconds. Default value is 0.2. Using a value less than
this may cause the program to fail due to Clockify API
rate limiting.
Have fun tracking! :)- (Optional) Invoke the
preprocess_data.pyscript to process the time entry data retrieved from the Clockify API in an opinionated fashion. The simplest invocation to get started (assuming no file name parameters were specified in step 4) is:
python preprocess_data.pyHere's an example of what the preprocessor script does to a time entry:
Raw Clockify API data:
{
"id": "5fb02c5f94454c3d296af408",
"description": "Read PRIMES is in P",
"tagIds": null,
"userId": "some-user-id-here",
"billable": true,
"taskId": "5f30780549070a418a96891b",
"projectId": "5f306f4649070a418a968435",
"timeInterval": {
"start": "2020-11-14T19:13:35Z",
"end": "2020-11-14T19:20:31Z",
"duration": "PT6M56S"
},
"workspaceId": "some-workspace-id-here",
"isLocked": false,
"customFieldValues": null
}Preprocessed Data:
{
"task_id": "5f30780549070a418a96891b",
"task_name": "MA 522",
"project_id": "5f306f4649070a418a968435",
"project_name": "Classes",
"description": "read primes is in p",
"start_time_utc": "2020-11-14T19:13:35Z",
"start_date_utc": "2020-11-14",
"end_time_utc": "2020-11-14T19:20:31Z",
"end_date_utc": "2020-11-14",
"duration_seconds": 416
}You can see more detailed usage information here:
usage: preprocess_data.py [-h] [-p PROJ_FILE] [-t TASKS_FILE]
[-e ENTRIES_FILE] [-d PROC_DATA_FILE] [--csv]
Preprocess Clockify API data from files in an opinionated fashion.
optional arguments:
-h, --help show this help message and exit
-p PROJ_FILE, --projects-file PROJ_FILE
The name of the file to get projects information from.
Defaults to 'projects.json' if not specified.
-t TASKS_FILE, --tasks-file TASKS_FILE
The name of the file to get tasks information from.
Defaults to 'tasks.json' if not specified.
-e ENTRIES_FILE, --entries-file ENTRIES_FILE
The name of the file to get time entry data from.
Defaults to 'entries.json' if not specified.
-d PROC_DATA_FILE, --data-file PROC_DATA_FILE
The name of the file that will hold the preprocessed
data. Defaults to 'preprocessed_data.json'.
--csv Specify this flag to indicate that the data should
also be exported to CSV format. The CSV file will have
the same name as the file specified by the -d/--data-
file argument, or be named 'preprocessed_data.csv' if
that argument is not given.
Have fun preprocessing! :)The processing logic in preprocess_data.py is fairly extensible, so feel free to add logic that
makes more sense for your use case. :)
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