使用projmgr项目管理系统实现KPI自动化追踪-优快云博客

本文链接：https://blog.youkuaiyun.com/gitblog_01052/article/details/148664993

使用projmgr项目管理系统实现KPI自动化追踪

projmgr R-based project management tools 项目地址: https://gitcode.com/gh_mirrors/pr/projmgr

项目背景与概述

projmgr是一个强大的项目管理工具，它允许用户通过编程方式与项目管理平台进行交互。本文将重点介绍如何使用projmgr实现关键绩效指标(KPI)的自动化追踪和异常报告功能。

KPI追踪的基本原理

在业务分析场景中，我们经常需要监控各种KPI指标的实际表现与预期目标的差异。传统的手动检查方式效率低下，而projmgr提供了一种自动化解决方案：

通过数据分析识别异常指标
自动创建问题工单
标准化问题描述格式
添加分类标签便于管理

基础案例：销售数据监控

数据准备

假设我们有以下季度销售数据：

head(sales_data)
#      region   month sales_amt sales_expected
# 1 Northeast October      3144           5000
# 2 Southeast October      5394           5000
# 3   Midwest October      4204           4000
# 4 Southwest October      7442           4000
# 5 Northwest October      2470           2000

异常检测

设定25%的偏差阈值，筛选需要关注的区域：

deviations <- dplyr::filter(sales_data, 
                          abs(sales_amt - sales_expected) >= 0.25 * sales_expected)
deviations
#      region   month sales_amt sales_expected
# 1 Northeast October      3144           5000
# 2 Southwest October      7442           4000

自动化问题创建

编写自定义函数将数据框转换为可读的问题描述：

post_kpis <- function(ref, region, month, sales_amt, sales_expected){
  
  post_issue(ref,
             title = paste(region,
                          ifelse(sales_amt < sales_expected, "below", "above"),
                          "sales expectations in",
                          month),
             body = paste(
               "**Region**: ", region, "\n",
               "**Month**: ", month, "\n",
               "**Actual**: ", sales_amt, "\n",
               "**Expected**: ", sales_expected, "\n"
             ),
             labels = c(
               paste0("region:",region),
               paste0("month:",month),
               paste0("dir:", ifelse(sales_amt < sales_expected, "below", "above"))
             )
}

执行批量问题创建：

experigit <- create_repo_ref('emilyriederer', 'experigit')
pmap_chr(deviations, post_kpis, ref = experigit)
# [1] 158 159

进阶案例：多维度绩效监控

数据结构扩展

当需要监控多个KPI时，数据结构会更加复杂：

head(performance_data)
#      region   month sales_actual returns_actual visitors_actual sales_expected
# 1 Northeast October         3023            310              64           5000
# 2 Southeast October         5264            505             101           5000
# 3   Midwest October         4446            407              80           4000
# 4 Southwest October         7276            706             142           4000
# 5 Northwest October         2228            201              45           2000
#   returns_expected visitors_expected
# 1              300                60
# 2              200               100
# 3              400               160
# 4              700               140
# 5              200                40

数据重塑

使用tidyr进行数据转换，创建标准化的长格式数据：

performance_pivoted <-
  performance_data %>%
  tidyr::gather(metric, value, -region, -month) %>%
  tidyr::separate(metric, into = c('metric', 'type'), sep = "_") %>%
  tidyr::spread(type, value)

print(performance_pivoted)
#       region   month   metric actual expected
# 1    Midwest October  returns    407      400
# 2    Midwest October    sales   4446     4000
# 3    Midwest October visitors     80      160
# ...