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ProjectHierarchy.Rmd
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ProjectHierarchy.Rmd
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---
title: "2018 BAM Workplan"
author: "Table: BAM's Project Structure"
---
**Last run: `r format(Sys.Date(), "%B %e, %Y")`**
* BAM pursues **projects** in four parallel but inter-connected **domains**.
* Within domains, projects are organized by overarching **themes**
* Projects working towards the same end goal are clustered into a single **cluster**.
* A **Project** it typically discrete, and results in an end result such as a paper or report.
# Plot in summarized, hierarchical format.
## import projects in flat database format
``` {r, echo=F}
projs <- read.csv("dataTables/qry_bamPROJECTSflat.csv", header=T)
projs$clusterNAME <- as.character(projs$clusterNAME)
projs$clusterNAME[projs$clusterNAME %in% c("Policies_Protocols")] <- "Policies & Protocols"
projs$clusterNAME[projs$clusterNAME %in% c("Operations_Functioning")] <- "Operations & Functioning"
```
### Add back in lookup tables and corresponding colours
``` {r, echo=F}
## Project Status
status <- read.csv("dataTables/lookup_projectSTATUS.CSV", header=T)
colnames(projs)[which(colnames(projs)=="projectSTATUS")] <- "ID"
projs <- merge(projs, status, by="ID", all.x=T)
projs <- projs[-1]
## Project proximity
proximity <- read.csv("dataTables/lookup_projectPROXIMITY.csv", header=T)
projs <- merge(projs, proximity, by="Proximity", all.x=T)
## Leads
leads <- read.csv("dataTables/bamPEOPLE.csv", header=T)
colnames(projs)[which(colnames(projs)=="LEAD")] <- "fullNAME"
projs <- merge(projs, leads[c("fullNAME", "personColour")], by="fullNAME", all.x=T)
```
``` {r, echo=F}
projs1 <- projs[c("domainNAME", "themeNAME", "clusterNAME", "PROJECTname", "PROJECTpurpose", "nature", "projectSTATUS", "Proximity")]
projs1$size <- 1
```
## Tree map
``` {r, fig.height=11, fig.width=8.5, eval=T, echo=F}
require(treemap)
treemap(projs1, index=colnames(projs1[1:5]), vSize="size", title="")
trash <- lapply(1:4, function(i) {
treemap(projs1[projs1$domainNAME %in% unique(projs1$domainNAME)[i],], index=colnames(projs1[2:5]), vSize="size", title="")
})
```
Pretty, but not information rich enough for my purposes. So let's try something else.
# Dynamic split screen based on data.frame contents
``` {r, echo=F}
F.hierarcplot <- function(dat,
col1L = 0,
col2L = 0.1,
col3L = 0.2,
col4L = 0.35,
col5L = 0.6,
col5R = 1,
wrapsize1=9,
wrapsize2=12,
wrapsize3=15,
wrapsize4=35,
wrapsize5=45,
fontsize1=1,
fontsize2=1,
fontsize3=1,
fontsize4=0.75,
fontsize5=0.5,
domcol="DomainColour",
thmcol="DomainColour",
cluscol="white",
projcol="ProximityColour",
detcol="StatusColour",
numcols=5,
statustoinclude=c("STALLED", "COMPLETE", "ABANDONED", "IDEA", "ACTIVE", "MERGED"))
{
# dat is a data frame containing the hierarchically-organized information.
# column coordinates are set based on expected content. Default values are what last worked for mine.
# note this function is written for 4 or 5 columns and isn't flexible to other options.
### Subset for status I care about
dat <- dat[dat$projectSTATUS %in% statustoinclude,]
#PROJECT COLUMN
# determine number of rows and sizes for project column
projcoor <- data.frame(L=rep(col4L, times=nrow(dat)),
R=rep(col5L, times=nrow(dat)),
B=rep(NA, times=nrow(dat)),
T=rep(NA, times=nrow(dat)))
for (i in 1:nrow(projcoor)) {
projcoor[i,3] <- 1 - (i / nrow(projcoor)) # bottom coordinate
projcoor[i,4] <- 1 - ((i-1)/nrow(projcoor)) # top coordinate
}
# determine number of rows and sizes for project detail column
detcoor <- data.frame(L=rep(col5L, times=nrow(dat)),
R=rep(col5R, times=nrow(dat)),
B=rep(NA, times=nrow(dat)),
T=rep(NA, times=nrow(dat)))
for (i in 1:nrow(detcoor)) {
detcoor[i,3] <- 1 - (i / nrow(detcoor)) # bottom coordinate
detcoor[i,4] <- 1 - ((i-1)/nrow(detcoor)) # top coordinate
}
# determine number of rows and sizes for cluster column
uniqueclus <- length(unique(dat$clusterNAME))
cluscoor <- data.frame(L=rep(col3L, times=uniqueclus),
R=rep(col4L, times=uniqueclus),
B=rep(NA, times=uniqueclus),
T=rep(NA, times=uniqueclus))
for (i in 1:nrow(cluscoor)) {
if (i == 1) {
nproj.in.prevclus <- 0
ithclus <- unique(dat$clus)[i]
nproj.in.ithclus <- length(dat$PROJECTname[dat$clusterNAME %in% ithclus])
} else {
prevclus <- unique(dat$clus)[1:(i-1)] # clusters previously populated
nproj.in.prevclus <- length(dat$PROJECTname[dat$clusterNAME %in% prevclus]) # number of projects within previously populated clusters
ithclus <- unique(dat$clus)[i] # cluster to populate on ith iteration
nproj.in.ithclus <- length(dat$PROJECTname[dat$clusterNAME %in% ithclus]) # number of projects within this cluster
}
cluscoor[i,3] <- 1 - ((nproj.in.ithclus + nproj.in.prevclus) / nrow(dat)) # bottom coordinate
cluscoor[i,4] <- 1 - (nproj.in.prevclus/nrow(dat)) # top coordinate
}
# determine number of rows and sizes for theme column
uniquetheme <- length(unique(dat$themeNAME))
themecoor <- data.frame(L=rep(col2L, times=uniquetheme),
R=rep(col3L, times=uniquetheme),
B=rep(NA, times=uniquetheme),
T=rep(NA, times=uniquetheme))
for (i in 1:nrow(themecoor)) {
if (i == 1) {
nproj.in.prevtheme <- 0
iththeme <- unique(dat$themeNAME)[i]
nproj.in.iththeme <- length(dat$PROJECTname[dat$themeNAME %in% iththeme])
} else {
prevtheme <- unique(dat$themeNAME)[1:(i-1)] # themes previously populated
nproj.in.prevtheme <- length(dat$PROJECTname[dat$themeNAME %in% prevtheme]) # number of projects within previously populated themes
iththeme <- unique(dat$themeNAME)[i] # cluster to populate on ith iteration
nproj.in.iththeme <- length(dat$PROJECTname[dat$themeNAME %in% iththeme]) # number of projects within this theme
}
themecoor[i,3] <- 1 - ((nproj.in.iththeme + nproj.in.prevtheme) / nrow(dat)) # bottom coordinate
themecoor[i,4] <- 1 - (nproj.in.prevtheme/nrow(dat)) # top coordinate
}
# determine number of rows and sizes for domain column
uniquedomain <- length(unique(dat$domainNAME))
domaincoor <- data.frame(L=rep(col1L, times=uniquedomain),
R=rep(col2L, times=uniquedomain),
B=rep(NA, times=uniquedomain),
T=rep(NA, times=uniquedomain))
for (i in 1:nrow(domaincoor)) {
if (i == 1) {
nproj.in.prevdomain <- 0
ithdomain <- unique(dat$domainNAME)[i]
nproj.in.ithdomain <- length(dat$PROJECTname[dat$domainNAME %in% ithdomain])
} else {
prevdomain <- unique(dat$domainNAME)[1:(i-1)] # themes previously populated
nproj.in.prevdomain <- length(dat$PROJECTname[dat$domainNAME %in% prevdomain]) # number of projects within previously populated domains
ithdomain <- unique(dat$domainNAME)[i] # cluster to populate on ith iteration
nproj.in.ithdomain <- length(dat$PROJECTname[dat$domainNAME %in% ithdomain]) # number of projects within this domain
}
domaincoor[i,3] <- 1 - ((nproj.in.ithdomain + nproj.in.prevdomain) / nrow(dat)) # bottom coordinate
domaincoor[i,4] <- 1 - (nproj.in.prevdomain/nrow(dat)) # top coordinate
}
# COMBINE ALL COORDINATES INTO ONE BIG MATRIX.
if (numcols == 4) {
allcoor <- rbind(themecoor, cluscoor, projcoor, detcoor)
texttoprint <- c(as.character(dat$themeNAME[!duplicated(dat$themeNAME)]),
as.character(dat$clusterNAME[!duplicated(dat$clusterNAME)]),
as.character(dat$PROJECTname),
as.character(dat$PROJECTpurpose))
wraplength <- c(rep(wrapsize2, times=length(unique(dat$themeNAME))), #theme
rep(wrapsize3, times=length(unique(dat$clusterNAME))), #cluster
rep(wrapsize4, times=length(dat$PROJECTname)), #project
rep(wrapsize5, times=length(dat$PROJECTpurpose))) #purpose
fontsize <- c(rep(fontsize2, times=length(unique(dat$themeNAME))), #theme
rep(fontsize3, times=length(unique(dat$clusterNAME))), #cluster
rep(fontsize4, times=length(dat$PROJECTname)), #project
rep(fontsize5, times=length(dat$PROJECTpurpose))) #purpose
thmcoldf <- data.frame(Theme=dat$themeNAME, ThemeColour=dat[[thmcol]])
thmcoldf <- thmcoldf[!duplicated(thmcoldf$Theme),]
bgcolour <- c(as.character(thmcoldf$ThemeColour), #theme
rep(cluscol, times=length(unique(dat$clusterNAME))), #cluster
as.character(dat[[projcol]]), #project
as.character(dat[[detcol]])) #purpose
}
if (numcols == 5){
allcoor <- rbind(domaincoor, themecoor, cluscoor, projcoor, detcoor)
texttoprint <- c(as.character(dat$domainNAME[!duplicated(dat$domainNAME)]),
as.character(dat$themeNAME[!duplicated(dat$themeNAME)]),
as.character(dat$clusterNAME[!duplicated(dat$clusterNAME)]),
as.character(dat$PROJECTname),
as.character(dat$PROJECTpurpose))
wraplength <- c(rep(wrapsize1, times=length(unique(dat$domainNAME))), # domain
rep(wrapsize2, times=length(unique(dat$themeNAME))), #theme
rep(wrapsize3, times=length(unique(dat$clusterNAME))), #cluster
rep(wrapsize4, times=length(dat$PROJECTname)), #project
rep(wrapsize5, times=length(dat$PROJECTpurpose))) #purpose
fontsize <- c(rep(fontsize1, times=length(unique(dat$domainNAME))), # domain
rep(fontsize2, times=length(unique(dat$themeNAME))), #theme
rep(fontsize3, times=length(unique(dat$clusterNAME))), #cluster
rep(fontsize4, times=length(dat$PROJECTname)), #project
rep(fontsize5, times=length(dat$PROJECTpurpose))) #purpose
domcoldf <- data.frame(Domain=dat$domainNAME, DomainColour=dat[[domcol]])
domcoldf <- domcoldf[!duplicated(domcoldf$Domain),]
thmcoldf <- data.frame(Theme=dat$themeNAME, ThemeColour=dat[[thmcol]])
thmcoldf <- thmcoldf[!duplicated(thmcoldf$Theme),]
bgcolour <- c(as.character(domcoldf$DomainColour), #domain
as.character(thmcoldf$ThemeColour), #theme
rep(cluscol, times=length(unique(dat$clusterNAME))), #cluster
as.character(dat[[projcol]]), #project
as.character(dat[[detcol]])) #purpose
}
split.screen(as.matrix(allcoor))
for(i in 1:nrow(allcoor)) {
par(bg=bgcolour[i])
screen(i)
par(mar = c(0, 0, 0, 0))
text(.5,.5,paste(strwrap(texttoprint[i],wraplength[i]), collapse="\n"), cex=fontsize[i])
box()
par(bg="white")
}
close.screen(all.screens = TRUE)
}
```
## All projects in all themes at once
Note, if you run this chunk the whole script won't read to markdown output. Fails at pandoc
``` {r, fig.height=11, fig.width=8.5, dpi=100, dev='tiff', fig.path="figures/", eval=F, echo=F}
trash <- lapply(1:length(unique(projs$domainNAME)), function(i){
tmp <- projs[projs$domainNAME == unique(projs$domainNAME)[i],]
lapply(1:length(unique(tmp$themeNAME)), function(j){
tmp2 <- tmp[tmp$themeNAME == unique(tmp$themeNAME)[j],]
F.hierarcplot(col2L = 0, col3L = 0.12, col4L = 0.27, col5L = 0.52, col5R = 1,
wrapsize1=9, wrapsize2=12, wrapsize3=15, wrapsize4=32, wrapsize5=84,
fontsize1=1, fontsize2=1, fontsize3=1, fontsize4=0.75, fontsize5=0.7,
dat=tmp2, numcols=4, domcol="DomainColour", thmcol="DomainColour", cluscol="white",
projcol="ProximityColour", detcol="StatusColour", statustoinclude = c("ACTIVE", "IDEA", "STALLED"))
})
})
```
## One domain at a time
### `r unique(projs$domainNAME)[1]`
``` {r Fig.Domain1, fig.height=6, fig.width=8.5, dpi=100, dev='jpeg', fig.path="figures/", eval=T, echo=F}
tmp <- projs[projs$domainNAME %in% unique(projs$domainNAME)[1],]
trash <- lapply(1:length(unique(tmp$themeNAME)), function(j){
tmp2 <- tmp[tmp$themeNAME == unique(tmp$themeNAME)[j],]
F.hierarcplot(col2L = 0, col3L = 0.15, col4L = 0.30, col5L = 0.52, col5R = 1,
wrapsize1=9, wrapsize2=12, wrapsize3=15, wrapsize4=28, wrapsize5=74,
fontsize1=1, fontsize2=1, fontsize3=1, fontsize4=0.75, fontsize5=0.7,
dat=tmp2, numcols=4, domcol="DomainColour", thmcol="DomainColour", cluscol="white",
projcol="ProximityColour", detcol="StatusColour", statustoinclude = c("ACTIVE", "IDEA", "STALLED"))
})
```
### `r unique(projs$domainNAME)[2]`
``` {r Fig.Domain2, fig.height=11, fig.width=8.5, dpi=100, dev='jpeg', fig.path="figures/", eval=T, echo=F}
tmp <- projs[projs$domainNAME %in% unique(projs$domainNAME)[2],]
trash <- lapply(1:length(unique(tmp$themeNAME)), function(j){
tmp2 <- tmp[tmp$themeNAME == unique(tmp$themeNAME)[j],]
F.hierarcplot(col2L = 0, col3L = 0.15, col4L = 0.32, col5L = 0.50, col5R = 1,
wrapsize1=9, wrapsize2=12, wrapsize3=15, wrapsize4=25, wrapsize5=74,
fontsize1=1, fontsize2=1, fontsize3=1, fontsize4=0.75, fontsize5=0.7,
dat=tmp2, numcols=4, domcol="DomainColour", thmcol="DomainColour", cluscol="white",
projcol="ProximityColour", detcol="StatusColour", statustoinclude = c("ACTIVE", "IDEA", "STALLED"))
})
```
### `r unique(projs$domainNAME)[3]`
``` {r Fig.Domain3, fig.height=8, fig.width=8.5, dpi=100, dev='tiff', fig.path="figures/", eval=T, echo=F}
tmp <- projs[projs$domainNAME %in% unique(projs$domainNAME)[3],]
trash <- lapply(1:length(unique(tmp$themeNAME)), function(j){
tmp2 <- tmp[tmp$themeNAME == unique(tmp$themeNAME)[j],]
F.hierarcplot(col2L = 0, col3L = 0.15, col4L = 0.32, col5L = 0.50, col5R = 1,
wrapsize1=9, wrapsize2=12, wrapsize3=15, wrapsize4=22, wrapsize5=74,
fontsize1=1, fontsize2=1, fontsize3=1, fontsize4=0.75, fontsize5=0.7,
dat=tmp2, numcols=4, domcol="DomainColour", thmcol="DomainColour", cluscol="white",
projcol="ProximityColour", detcol="StatusColour", statustoinclude = c("ACTIVE", "IDEA", "STALLED"))
})
```
### `r unique(projs$domainNAME)[4]`
``` {r Fig.Domain4, fig.height=4, fig.width=8.5, dpi=100, dev='jpeg', fig.path="figures/", eval=T, echo=F}
tmp <- projs[projs$domainNAME %in% unique(projs$domainNAME)[4],]
trash <- lapply(1:length(unique(tmp$themeNAME)), function(j){
tmp2 <- tmp[tmp$themeNAME == unique(tmp$themeNAME)[j],]
F.hierarcplot(col2L = 0, col3L = 0.15, col4L = 0.32, col5L = 0.54, col5R = 1,
wrapsize1=9, wrapsize2=12, wrapsize3=15, wrapsize4=25, wrapsize5=74,
fontsize1=1, fontsize2=1, fontsize3=1, fontsize4=0.75, fontsize5=0.7,
dat=tmp2, numcols=4, domcol="DomainColour", thmcol="DomainColour", cluscol="white",
projcol="ProximityColour", detcol="StatusColour", statustoinclude = c("ACTIVE", "IDEA", "STALLED"))
})
```
### Legends
#### Project Status Legend
``` {r, Fig.StatusLegend, fig.height=7, fig.width=8.5, dpi=100, dev="jpeg", echo=F, eval=T}
colsplit <- 0.2
statcoor <- data.frame(L=rep(0, times=length(unique(status$projectSTATUS))),
R=rep(colsplit, times=length(unique(status$projectSTATUS))),
B=rep(NA, times=length(unique(status$projectSTATUS))),
T=rep(NA, times=length(unique(status$projectSTATUS))))
desccoor <- data.frame(L=rep(colsplit, times=length(unique(status$projectSTATUS))),
R=rep(1, times=length(unique(status$projectSTATUS))),
B=rep(NA, times=length(unique(status$projectSTATUS))),
T=rep(NA, times=length(unique(status$projectSTATUS))))
for (i in 1:nrow(statcoor)) {
statcoor[i,3] <- 1 - (i / nrow(statcoor)) # bottom coordinate
statcoor[i,4] <- 1 - ((i-1)/nrow(statcoor)) # top coordinate
desccoor[i,3] <- 1 - (i / nrow(statcoor)) # bottom coordinate
desccoor[i,4] <- 1 - ((i-1)/nrow(statcoor)) # top coordinate
}
allcoor <- rbind(statcoor, desccoor)
alltext <- c(as.character(status$projectSTATUS), as.character(status$description))
statcol <- c(as.character(status$StatusColour), rep("white", times=length(unique(status$projectSTATUS))))
stattext <- c(as.character(status$Status), as.character(status$description))
wraplength <- rep(90, each=nrow(allcoor))
split.screen(as.matrix(rbind(statcoor, desccoor)))
for(i in 1:length(alltext)) {
par(bg=statcol[i])
screen(i)
par(mar = c(0, 0, 0, 0))
text(.5,.5,paste(strwrap(alltext[i], wraplength[i]), collapse="\n"))
box()
par(bg="white")
}
close.screen(all.screens = TRUE)
```
#### Project Proximity Legend
``` {r, Fig.ProximityLegend, fig.height=4, fig.width=4, dpi=100, dev="jpeg", echo=F, eval=T}
colsplit <- 0.35
proxcoor <- data.frame(L=rep(0, times=length(unique(proximity$Proximity))),
R=rep(colsplit, times=length(unique(proximity$Proximity))),
B=rep(NA, times=length(unique(proximity$Proximity))),
T=rep(NA, times=length(unique(proximity$Proximity))))
desccoor <- data.frame(L=rep(colsplit, times=length(unique(proximity$Proximity))),
R=rep(1, times=length(unique(proximity$Proximity))),
B=rep(NA, times=length(unique(proximity$Proximity))),
T=rep(NA, times=length(unique(proximity$Proximity))))
for (i in 1:nrow(proxcoor)) {
proxcoor[i,3] <- 1 - (i / nrow(proxcoor)) # bottom coordinate
proxcoor[i,4] <- 1 - ((i-1)/nrow(proxcoor)) # top coordinate
desccoor[i,3] <- 1 - (i / nrow(proxcoor)) # bottom coordinate
desccoor[i,4] <- 1 - ((i-1)/nrow(proxcoor)) # top coordinate
}
allcoor <- rbind(proxcoor, desccoor)
alltext <- c(as.character(proximity$Proximity), as.character(proximity$Description))
proxcol <- c(as.character(proximity$ProximityColour), rep("white", times=length(unique(proximity$ProximityColour))))
proxtext <- c(as.character(proximity$Proximity), as.character(proximity$Description))
wraplength <- rep(30, each=nrow(allcoor))
split.screen(as.matrix(rbind(proxcoor, desccoor)))
for(i in 1:length(alltext)) {
par(bg=proxcol[i])
screen(i)
par(mar = c(0, 0, 0, 0))
text(.5,.5,paste(strwrap(alltext[i], wraplength[i]), collapse="\n"))
box()
par(bg="white")
}
close.screen(all.screens = TRUE)
```
# Tabular format
``` {r setup, echo=F, message=F, warning=F, eval=F, echo=F}
source("1.SetupScript.R")
```
``` {r, eval=F, echo=F}
projs <- read.csv("dataTables/qry_bamPROJECTSflat.csv", header=T)
kable(head(projs), row.names=F)
```
# Plot in tabular format.
``` {r, clean.table, echo=F, eval=F}
allproj <- data.frame(Domain=projs$Domain, Theme=projs$Theme, Cluster=projs$Cluster, Project=paste(projs$Project, " [", projs$Lead, "]", sep=""), Status=projs$Project.Status)
allproj <- allproj[!is.na(allproj$Domain),]
allproj <- allproj[allproj$Domain != "",]
allproj <- allproj[!duplicated(allproj),]
allproj <- allproj[!allproj$Status %in% c("ABANDONED", "MERGED", "MORPHED"),]
allproj$Domain <- factor(allproj$Domain, levels=domains$Domain[domains$Domain.Number])
allproj$Theme <- factor(allproj$Theme, levels=themes$Theme[order(themes$Theme.Order)])
allproj$Cluster <- factor(allproj$Cluster, levels=clusters$Cluster[order(clusters$Order)])
allproj <- allproj[order(allproj$Domain, allproj$Theme, allproj$Cluster, allproj$Project),]
allproj$Domain <- as.character(allproj$Domain)
allproj$Theme <- as.character(allproj$Theme)
allproj$Cluster <- as.character(allproj$Cluster)
allproj$Project <- as.character(allproj$Project)
```
### All currently active, queued, or proposed projects, including those not led by BAM team members
``` {r, echo=F, eval=F}
subproj <- allproj[allproj$Status %in% c("ACTIVE", "IDEA", "ONGOING", "QUEUED", "STALLED", "NOT BAM"),]
subproj$Domain[duplicated(subproj$Domain)] <- " \" "
subproj$Theme[duplicated(subproj$Theme)] <- " \" "
subproj$Cluster[duplicated(subproj$Cluster)] <- " \" "
kable(subproj[c("Domain", "Theme", "Cluster", "Project")], row.names=F)
```
### Historical record of complete and active projects
Only includes those summarized on SharePoint
``` {r, echo=F, eval=F}
allproj$Domain[duplicated(allproj$Domain)] <- " \" "
allproj$Theme[duplicated(allproj$Theme)] <- " \" "
allproj$Cluster[duplicated(allproj$Cluster)] <- " \" "
kable(allproj[c("Domain", "Theme", "Cluster", "Project", "Status")], row.names=F)
```