JirAgileR

Objective

The JirAgileR R package has the mission to bring the power of the project management tool 🔧 JIRA to R. By doing so, users benefit from the best capabilities of both platforms. More specifically, the package is a wrapper around JIRA’s REST API, allowing users to easily analyze JIRA projects and issues from within R. The underlying powertrain of the API is the Jira Query Language (JQL). You can find more information about it here. You can find a cheatsheet here.

The focus of this package lies in the following workflow aspects:

• Import
• Tidy

Hence, for easy transformation and manipulation, each function returns a data.frame with tidy data, following main rules where each row is a single observation of an issue or a project, each column is a variable and each value must have its own cell. Thus, it integrates well with both the dplyr and data.table R libraries. This also allows for an easy integration with tabular data.

More information about the package can be found at the following link: https://matbmeijer.github.io/JirAgileR/.

Functionalities as of 07 of June, 2021

1. Extract all project names with their basic information (e.g. Name, ID, Key, Type, Category etc.).
2. Retrieve all issues specific to a user defined JIRA query with hand-picked fields and all the associated information. Currently, the package supports the following JIRA fields:
   • aggregateprogress
   • aggregatetimeestimate
   • aggregatetimespent
   • assignee
   • comment
   • components
   • created
   • creator
   • description
   • duedate
   • environment
   • fixVersions
   • issuelinks
   • issuetype
   • labels
   • lastViewed
   • priority
   • progress
   • project
   • reporter
   • resolution
   • resolutiondate
   • status
   • summary
   • timeestimate
   • timespent
   • updated
   • versions
   • votes
   • watches
   • workratio
   • parent

Note

• To get all the information about the supported JQL fields visit the following link. The package supports extracting comments, yet as one issue may contain multiple comments, the data.frame is flattened to a combination of issues and comments. Thus, the information of an issue may be repeated the number of comments each issue has.

Roadmap

• 🔲 Define integrated Reference Classes within the package
• 🔲 Include plotting graphs 📊
• 🔲 Ability to obtain all available JIRA fields of a project
• ✅ Added get_jira_dashboards() function to retrieve JIRA dashboards
• ✅ Added get_jira_permissions() function to retrieve JIRA user permissions
• ✅ Added get_jira_groups() function to retrieve JIRA groups
• ✅ Added get_jira_server_info() function to retrieve JIRA server information
• ✅ Remove data.table dependency
• ✅ Ability to save domain, username & password as secret tokens in environment 🔐
• ✅ Include pipes to facilitate analysis
• ✅ Improve package robustness
• ✅ Include http status error codes
• ✅ Give user visibility of supported fields

Installation

You can install the latest release of this package from Github with the following commands in R:

if (!require("devtools")) install.packages("devtools")
devtools::install_github("matbmeijer/JirAgileR")

Examples

This is a basic example which shows you how to obtain a simple table of issues of a project and create a tabular report. Most of the times, you will need a username and your password to authenticate in your domain. Possible fields to obtain (which will populate the data.frame columns) can be found here.

library(JirAgileR, quietly = T)
library(knitr, quietly = T)
library(dplyr, quietly = T)

# Save credentials to pass them only one time
save_jira_credentials(domain = "https://bugreports.qt.io")

# Get full list of projects in domain
get_jira_projects() %>% 
  select(key, name)  %>% 
  kable(row.names = F, padding = 0)

# Retrieve the issues from a single project - in this case the project QTWB from bugreports.qt.io. See documentation to define which fields to see
get_jira_issues(jql_query = "project='QTWB'",
                fields = c("summary","created", "status")) %>% 
  select(key, summary, created, status_name, status_description, status_statuscategory_name) %>%
  head(2) %>%
  kable(row.names = F, padding = 0)