More lines of code to help understand TOWT

understand/understand_model.R

@@ -167,6 +167,42 @@ print(regOut$bmod)
 # * Also, model improvement suggestion: choose tempKnots via k-means cluster analysis.
 
 
+
+## New stuff
+# Dump all the regression coefficients to a CSV file.
+
+#library(plyr)
+library(stringr)
+rol=Project[["model_obj_list"]][["models_list"]][["Data_pre_2_2.csv"]][["towt_model"]][["regOutList"]]
+mylist=list()
+for (irun in 1:length(rol)) {
+  cat(paste(rep("=",80),collapse=""),"\n")
+  cat(paste("Model run ",irun,"\n"))
+  cat(paste(rep("=",80),collapse=""),"\n")
+  print(summary.lm(rol[[irun]]$amod))
+
+
+  x=data.frame(t(rol[[irun]]$amod$coefficients))
+  x["_occ"]="occ"
+  y=data.frame(t(rol[[irun]]$bmod$coefficients))
+  y["_occ"]="unocc"
+  x["_irun"]=irun
+  y["_irun"]=irun
+  #newcoeffs=merge(x,y,all=T)
+  mylist=rbind(mylist,list(x),list(y))
+
+
+}
+newcoeffs=Reduce(function(x,y){merge(x,y,all=T)},mylist)
+mycoeffs=newcoeffs[order(newcoeffs$`_irun`,newcoeffs$`_occ`),str_sort(names(newcoeffs),numeric=T)]
+View(mycoeffs)
+write.table(mycoeffs,file="mycoeffsfile.csv",sep=",",row.names=F)
+
+
+
+
+
+
 ## Clean up: remove global variables from workspace and detach scope
 ## Comment out these lines if you want to inspect further
 rm(rds_file, isSavings, projectType, i, df, df2, tCenters, nModelRuns, irun, regOut)

understand/understand_occupancy.Rmd

@@ -225,7 +225,10 @@ TempF <- tempVecF[okload]
 # Figure out which times of week a building is in one of two modes
 #  (called 'occupied' or 'unoccupied')
 
-uTOW = unique(intervalOfWeek2)
+# RMV2.0 does not sort this vector. Although that doesn't matter,
+# I prefer to have it sorted now.
+#uTOW = unique(intervalOfWeek2)
+uTOW = sort(unique(intervalOfWeek2))
 nTOW = length(uTOW)
 
 # Define 'occupied' and 'unoccupied' based on a regression
@@ -289,7 +292,7 @@ ggplot() +
 ```{r}
 # To repeat the same data in every panel, simply construct a data frame
 # that does not contain the faceting variable.
-mydata=data.frame(x=TempF,y=loadVec2,ypred=predict(amod),tow=intervalOfWeek2)
+mydata=data.frame(x=TempF,y=loadVec2,ypred=predict(amod),tow=intervalOfWeek2,timeVec=timeVec)
 mydata[mydata$tow<10,]
 ```
 
@@ -375,15 +378,23 @@ Here is how I export the data to the widget.
 ```{r}
 # Convert to JSON for Chart.js
 dataByTOW=uTOW %>% purrr::map(~ mydata[mydata$tow==.x,])
-dataByTOWjson = jsonlite::toJSON(dataByTOW, pretty=F)
+
 #fitLine = unique(mydata[with(mydata,order(x)),c('x','ypred')])
 fitLine = data.frame(x=TempF,y=predict(amod)) %>% unique() %>% arrange(x)
+
+# Here's how to save the data directly as a block of javascript code.
+# This is a temporary hack to pass the data to a widget that is just hand-coded HTML.
+dataByTOWjson = jsonlite::toJSON(dataByTOW, pretty=F)
 fitLinejson = jsonlite::toJSON(fitLine, pretty=F)
 sink('datafile.js')
 cat(glue('const dataByTOW = {dataByTOWjson};
 const fitLine = {fitLinejson};'))
 sink()
 #mydata %>% mutate(x=x,y=ypred)
+
+# Here's how to store the data in native R objects.
+# This is for testing the next version of the widget, using htmlwidgets package.
+saveRDS(list(dataByTOW=dataByTOW, fitLine=fitLine, mydata=mydata), file="mydatafile.RDS")
 ```
 
 Here is the widget. Try pointing the mouse over the time-of-week grid:
@@ -392,7 +403,18 @@ Here is the widget. Try pointing the mouse over the time-of-week grid:
 * Horizontal: from midnight to midnight
 * Collapsed on: week of the year
 
-<iframe width="900" height="1200" src="./mychart3.html"></iframe>
+Here is the old code to insert an iframe. It should not actually render one.
+
+```
+iframe width="900" height="1200" src="./mychart3.html" /iframe
+```
+
+```{r}
+library(mywidget)
+#occupancy_widget("Hello, globals!", dataByTOWjson, fitLinejson, 'json')
+occupancy_widget("hello, solar system!",dataByTOW, fitLine, 'r', width='auto', height='auto')
+```
+
 
 That is all. Thank you for reading.
 Find more from [my fork of RMV2.0](https://github.com/simularis/RMV2.0/tree/dev/demo)