In DennisH3/jira-metrics: Jira R Package

library(jiraR)
library(janitor)
library(htmlwidgets)
library(plotly)

# Number of rows to generate
numIssues <- 100

# Note: Probabilities were rounded from JIRA's Pie Chart Report. These probabilities will be outdated as more
#       issues are added

# Initialize empty lists to be columns in the data frame
summaries <- list()
dateStart <- list()
dateResolved <- list()

# Generate random Issue key
keys <- paste("INNOSF-", sample.int(n = 200, size = numIssues, replace = FALSE), sep = "")

# Randomly choose an issue type
issueType <- sample(c("Epic", "Task", "Sub-task"), size = numIssues, replace = TRUE, 
                    prob = c(0.15, 0.55, 0.30))

# Randomly choose a status
status <- sample(c("To Do", "In Progress", "Done"), size = numIssues, replace = TRUE)

# Fill Project with "INNOSF"
project <- rep("INNOSF", numIssues)

# Randomly choose an assignee
assignee <- sample(c("SC", "KB", "DH", "Unassigned", "Other"), size = numIssues, replace = TRUE,
                  prob = c(0.50, 0.33, 0.15, 0.02, 0.05))

# Randomly choose a reporter
reporter <- sample(c("SC", "KB", "DH"), size = numIssues, replace = TRUE, prob = c(0.60, 0.40, 0.02))

# Randomly choose a creator
creator <- sample(c("SC", "KB", "DH"), size = numIssues, replace = TRUE, prob = c(0.60, 0.40, 0.02))

# Randomly choose a component
# Probabilities were calculated with (# of categorized issues/total # of known categorized issues
# multiplied by 62 (current number of issues uncategorized) + # of categorized issues)/100
component <- sample(c("Client Consultations", "Client Projects", "Outreach", "Jira Setup", 
                    "Surge Team Research"), size = numIssues, replace = TRUE,
                    prob = c(0.70, 0.15, 0.10, 0.05, 0.03))

# Randomly choose a label
label <- sample(c("QA", "Visualizations", "Background", "Capacity", "Connection"), size = numIssues,
                replace = TRUE, prob = c(0.50, 0.80, 0.10, 0.10, 0.10))

# Randomly choose a division (Full list of divisions can be added later)
division <- sample(c("PPD", "CPD", "AGRI", "OID", "RSID", "DIID", "DEM", "ICMIC", "CSDID", "DSS", "IATD",
                     "CELISD", "ITSLM"), size = numIssues, replace = TRUE)

# Randomly choose a field
field <- sample(3:9, size = numIssues, replace = TRUE)

# Randomly choose a workspace environment
workspace <- sample(c("AAW", "On-premise", "Off-premise", "VDI"), size = numIssues, replace = TRUE,
                    prob = c(0.30, 0.45, 0.20, 0.05))

# Fill the lists with synthetic data
for(i in 1:numIssues) {

  # Generate random summaries
  summaries[i] <- paste(sample(c(letters, LETTERS), size = 3, replace = TRUE), collapse = "")

  # Check if status is done
  if (status[i] == "Done"){

    # Generate a random date resolved
    dateResolved[[i]] <- sample(seq(from = as.Date('2020-10-15'), to = as.Date('2021-01-01'), by = "day"), 
                                size = 1)

    # Generate a random start date before but up to resolve date (can complete task on same day)
    dateStart[[i]] <- sample(seq(from = as.Date("2020-03-10"), to = dateResolved[[i]], by = "day"), 
                             size = 1)
  } else {

    # Otherwise, dateResolved is NA
    dateResolved[[i]] <- NA

    # Otherwise, Generate a random start date
    dateStart[[i]] <- sample(seq(from = as.Date("2020-03-10"), to = as.Date("2021-01-01"), by = "day"), 
                             size = 1)
  }
}

# Create the data frame from the lists and reorder the columns
df <- data.frame(unlist(issueType), unlist(summaries), unlist(project), unlist(keys), unlist(status),
                 unlist(division), unlist(field), unlist(label), unlist(workspace), unlist(component),
                 unlist(creator), unlist(reporter), unlist(assignee))

# Name the columns
names(df) <- c("Issue Type", "Summary", "Project", "Issue key", "Status", "Division", "Field", "Label",
               "Workspace", "Component", "Creator", "Reporter", "Assignee")

# Create a data frame for date started
dateStart <- select(pivot_longer(as.data.frame(dateStart), cols = 1:numIssues, values_to = "Date Started"),
                    "Date Started")

# Createa data frame for date resolved
dateResolved <- select(pivot_longer(as.data.frame(dateResolved), cols = 1:numIssues, 
                                    values_to = "Date Resolved"), "Date Resolved")

# Bind the data frames
df <- cbind(df, dateStart, dateResolved)

# Display
df

# R is reading the Date columns as string. Need to convert the last 2 rows to be date type
test <- read.csv("~/jiraR/tests/testData.csv", check.names = FALSE)
test

# Read actual metrics
# Load the data, clean the names and select columns to keep
df <- select(clean_names(read.csv("~/JIRA Metrics/JIRA_data_2021-01-13.csv")),
            c("issue_type", "summary", "project_key", "issue_key", "status", "custom_field_division_2", 
              "custom_field_field", "labels", "custom_field_workspace", "component_s", 
              "creator", "reporter", "assignee", "custom_field_start_date", "resolved"))

# Rename the columns
names(df) <- c("Issue Type", "Summary", "Project key", "Issue key", "Status", "Division", "Field", 
                     "Job Type", "Workspace", "Component", "Creator", "Reporter", "Assignee", "Date Started", 
                     "Date Resolved")

df

# Convert Start Date and Resolved columns to type Date
# Remove the hour information in date time
df[["Date Started"]] <- as.Date(df[["Date Started"]], format = '%Y-%m-%d')
df[["Date Resolved"]] <- as.Date(df[["Date Resolved"]], format = '%Y-%m-%d')

# Display
df

closedEpics(df)
newIssues(df)
sumTable(df)

activeIssues(df)

divIssues(df)

wsIssues(df)

cePlot(df)

niPlot(df)

aiPlot(df)

divPlot(df)

wsPlot(df)

library(shiny)
library(tidyverse)
library(data.table)
jiraApp()

DennisH3/jira-metrics documentation built on Dec. 17, 2021, 4:12 p.m.