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report.Rmd
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report.Rmd
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---
title: "Project Management"
date: "`r Sys.Date()`"
output:
html_document:
fig_caption: yes
fig_height: 6
fig_width: 7
highlight: tango
number_sections: yes
theme: cerulean
toc: yes
toc_depth: 2
toc_float: yes
pdf_document: default
word_document:
toc: yes
toc_depth: '2'
params:
n: NA
---
```{r setup, include=FALSE, message=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```
<!-- ```{r} -->
<!-- library(shiny) -->
<!-- library(shinydashboard) -->
<!-- library(dplyr) -->
<!-- library(leaflet) -->
<!-- library(ggplot2) -->
<!-- library(tidyverse) -->
<!-- library(DT) -->
<!-- library(plotly) -->
<!-- library(purrr) -->
<!-- library(glue) -->
<!-- library(rhandsontable) -->
<!-- library(tidyr) -->
<!-- library(shinyalert) -->
<!-- library(shinyjs) -->
<!-- library(supercaliheatmapwidget) -->
<!-- library(lubridate) -->
<!-- library(vistime) -->
<!-- library(timevis) -->
<!-- library(rmarkdown) -->
<!-- raw_data_tasks <- read.csv("./Data/task_tracker.csv") -->
<!-- raw_data_projects <- read.csv("./Data/project_tracker.csv") -->
<!-- raw_data_team <- read.csv("./Data/team_tracker.csv") -->
<!-- raw_data_time <- read.csv("./Data/time_tracker.csv") -->
<!-- raw_data_time$day <- dmy(raw_data_time$day) -->
<!-- ``` -->
# **project share of research area pie chart**
```{r message=FALSE, warning=FALSE}
##---------------Home page / project share of research area pie chart------------------------------------------
df_ProjectType <- raw_data_projects %>%
group_by(PROJECT.TYPE) %>%
summarise("#Projects" = n())
fig <- plot_ly(type='pie', labels=df_ProjectType$PROJECT.TYPE, values=df_ProjectType$`#Projects`,
textinfo='label+percent',insidetextorientation='radial')
fig <- fig %>% layout(legend = list(orientation = 'h'))
fig
```
# **Project status chart**
```{r message=FALSE, warning=FALSE}
##---------------Home page / Project status chart--------------------------------------------------------------
ggplot(raw_data_projects) +
aes(x = PROJECT.TYPE, fill = PROJECT.STATUS) +
geom_bar(position = "dodge") +
scale_fill_hue() +
coord_flip() +
theme_bw() +
theme(legend.position = "none") +
facet_wrap(vars(PROJECT.STATUS))
```
# **project complexity donut chart**
```{r message=FALSE, warning=FALSE}
##---------------Home page / project complexity donut chart-------------------------------------------------
p1 <- raw_data_projects %>%
group_by(COMPLEXITY) %>%
summarise("Projects" = n())
fig <- p1 %>% plot_ly(labels = ~COMPLEXITY, values = ~Projects)
fig <- fig %>% add_pie(hole = 0.6)
fig <- fig %>% layout(showlegend = T,xaxis = list(showgrid = T),yaxis = list(showgrid = T))
fig
```
# **Overview of projects**
```{r message=FALSE, warning=FALSE}
##---------------Home page / Overview of projects-------------------------------------------------
df_tmp_projects <-raw_data_projects
df_tmp_projects[,-1]
```
# **List of upcoming tasks for delivery**
```{r message=FALSE, warning=FALSE}
df_tmp_upcoming <- raw_data_tasks
df_tmp_upcoming$END.DATE <- dmy(df_tmp_upcoming$END.DATE)
df_tmp_upcoming$START.DATE <- dmy(df_tmp_upcoming$START.DATE)
df_tmp_upcoming <- df_tmp_upcoming%>%
filter((as.Date(END.DATE) > today()) & (STATUS != "Completed"))
df_tmp_upcoming[,c("PROJECT.NAME", "TASK.NAME", "START.DATE","END.DATE", "STATUS")]
```
# **List of overdew tasks**
```{r message=FALSE, warning=FALSE}
df_tmp_overdue <- raw_data_tasks
df_tmp_overdue$END.DATE <- dmy(df_tmp_overdue$END.DATE)
df_tmp_overdue$START.DATE <- dmy(df_tmp_overdue$START.DATE)
df_tmp_overdue <- df_tmp_overdue %>%
filter(as.Date(END.DATE) < today() & STATUS != "Completed")
df_tmp_overdue[,c("PROJECT.NAME", "TASK.NAME", "START.DATE","END.DATE", "STATUS") ]
```
# **Status of audit for each of the projects**
```{r message=FALSE, warning=FALSE}
p1 <- raw_data_projects %>%
group_by(AUDIT) %>%
summarise("count" = n())
fig <- p1 %>% plot_ly(labels = ~AUDIT, values = ~count)
fig <- fig %>% add_pie(hole = 0.6)
fig <- fig %>% layout(showlegend = T,xaxis = list(showgrid = T),yaxis = list(showgrid = T))
fig
```
# **List of all the tasks for each of the projects**
```{r message=FALSE, warning=FALSE}
df_proj_task <- raw_data_tasks %>%
group_by(PROJECT.NAME, TASK.NAME) %>%
summarise("cnt" = n()) %>%
arrange(desc(cnt))
ggplot(df_proj_task) +
aes(x = PROJECT.NAME) +
geom_bar(fill = "#6baed6") +
labs(y = "Number of Tasks") +
coord_flip() +
theme_bw()
```