Re: [O] Trouble evaluating R source code blocks with C-c C-c

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sit-for(0.25)

org-babel-comint-eval-invisibly-and-wait-for-file("type2" "/var/folders/hj/hqfjch716qg5php160jbtfgh0000gn/T/babel-53134TSq/R-53134vJF" "{\n function(object,transfer.file) {\n object\n invisible(\n if (\n inherits(\n try(\n {\n tfile<-tempfile()\n write.table(object, file=tfile, sep=\"\\t\",\n na=\"nil\",row.names=FALSE,col.names=TRUE,\n quote=FALSE)\n file.rename(tfile,transfer.file)\n },\n silent=TRUE),\n \"try-error\"))\n {\n if(!file.exists(transfer.file))\n file.create(transfer.file)\n }\n )\n }\n}(object=.Last.value,transfer.file=\"/var/folders/hj/hqfjch716qg5php160jbtfgh0000gn/T/babel-53134TSq/R-53134vJF\")")

org-babel-R-evaluate-session("type2" "library(plyr)\nlibrary(Hmisc)\ng->tempg\n\n # CV_untreated\nas.integer(tempg$state_region/10)->tempg$State.code.68\nfactor(tempg$State.code.68)->tempg$State.code.68\ndata.frame(State=0,adjusted_cv=0)->e\n\nfor (i in c(1:35)) {\n subset(tempg,as.numeric(tempg$State.code.68)==i)->dd\n wtd.var(dd$adj_cal,weight=dd$weight)^0.5/wtd.mean(dd$adj_cal,weight=dd$weight)->cvs\n data.frame(State=i,adjusted_cv=cvs)->e1\n rbind(e,e1)->e\n}\n\nddply(tempg,.(State.code.68),summarise,value=wtd.mean(adj_cal,weight))->s1\n\ndata.frame(State=99,adjusted_cv=0)->f2\nwtd.var(tempg$adj_cal,weight=tempg$weight)^0.5/wtd.mean(tempg$adj_cal,weight=tempg$weight)->f2[1,2]\nrbind(e,f2)->cv1\n\n # CV_grouped data\nddply(tempg,.(sex,agegroup,fractile_adj),summarise,calories=wtd.mean(adj_cal,weight))->l1\nddply(tempg,.(sex,agegroup,fractile_adj),summarise,weight=sum(weight))->w\n\nmerge(w,l1,by=c(\"sex\",\"agegroup\",\"fractile_adj\"))->l1\n\nddply(tempg,.(fractile_adj_state,State.code.68,agegroup,sex),summarise,value=wtd.mean(adj_cal,weight))->s3\n\nddply(tempg,.(fractile_adj_state,State.code.68,agegroup,sex),summarise,sum_weight=sum(weight))->sw\n\nmerge(s3,sw,by=c(\"fractile_adj_state\",\"State.code.68\",\"agegroup\",\"sex\"))->s3\n\nfactor(s3$State.code.68)->s3$State.code.68\n\ndata.frame(State=99,grouped_cv=wtd.var(l1$calories,weight=l1$weight)^0.5/wtd.mean(l1$calories,weight=l1$weight))->cv3\n\nfor (i in c(1:35)) {\n subset(s3,as.numeric(s3$State.code.68)==i)->s3sub\n data.frame(State=i,grouped_cv=wtd.var(s3sub$value,s3sub$sum_weight)^0.5/wtd.mean(s3sub$value,s3sub$sum_weight))->t1\n rbind(cv3,t1)->cv3\n}\n\n# CV_from regression model\nregdata->p\nexp(predict.lm(reg))->p$predicted_cal\n\ndata.frame(State=99,predicted_cv=wtd.var(p$predicted_cal,weight=p$weight)^0.5/wtd.mean(p$predicted_cal,weight=p$weight),adjr2=summary(reg)$adj.r.squared)->cv2\n\n\n#data.frame(State=0,predicted_cv=0,adjr2=0)->e\n\nfor (i in c(1:35)) {\n subset(regdata,as.numeric(p$State.code.68)==i)->dd\n factor(dd$state_region)->dd$state_region\nfmla <- as.formula(\n ifelse(length(levels(dd$state_region))==1,\"log_cal~sector+sex+AgeChild+AgeAdult+foodprice+log(MPCE)\",\"log_cal~sector+sex+AgeChild+AgeAdult+foodprice+log(MPCE)+state_region\"))\n\n\n lm(fmla,data="" exp(predict.lm(regstate))->dd$predicted_cal\n wtd.var(dd$predicted_cal,weight=dd$weight)^0.5/wtd.mean(dd$predicted_cal,weight=dd$weight)->cvs\n data.frame(State=i,predicted_cv=cvs,adjr2=summary(regstate)$adj.r.squared)->e1\n rbind(cv2,e1)->cv2\n}\n\nsubset(cv2,select=-adjr2)->cv2\n\nmerge(cv1,cv3,by=\"State\")->t\nmerge(t,cv2,by=\"State\")->t\nmerge(t,statecode,by.x=\"State\",by.y=\"State.code.68\",all.x=TRUE)->t\nt$State.68[t$State==99]<-\"India\"\nround(t$grouped_cv,4)->t$grouped_cv\nround(t$adjusted_cv,4)->t$adjusted_cv\nround(t$predicted_cv,4)->t$predicted_cv\nnames(t)<-c(\"State.code.68\",\"CV (unit-level data)\",\"CV (grouped data)\",\"CV (based on regression model)\",\"State\")\nt->finvar\nt[order(t$State),c(5,2,3,4)]" value ("replace" "value") t nil)

org-babel-R-evaluate("type2" "library(plyr)\nlibrary(Hmisc)\ng->tempg\n\n # CV_untreated\nas.integer(tempg$state_region/10)->tempg$State.code.68\nfactor(tempg$State.code.68)->tempg$State.code.68\ndata.frame(State=0,adjusted_cv=0)->e\n\nfor (i in c(1:35)) {\n subset(tempg,as.numeric(tempg$State.code.68)==i)->dd\n wtd.var(dd$adj_cal,weight=dd$weight)^0.5/wtd.mean(dd$adj_cal,weight=dd$weight)->cvs\n data.frame(State=i,adjusted_cv=cvs)->e1\n rbind(e,e1)->e\n}\n\nddply(tempg,.(State.code.68),summarise,value=wtd.mean(adj_cal,weight))->s1\n\ndata.frame(State=99,adjusted_cv=0)->f2\nwtd.var(tempg$adj_cal,weight=tempg$weight)^0.5/wtd.mean(tempg$adj_cal,weight=tempg$weight)->f2[1,2]\nrbind(e,f2)->cv1\n\n # CV_grouped data\nddply(tempg,.(sex,agegroup,fractile_adj),summarise,calories=wtd.mean(adj_cal,weight))->l1\nddply(tempg,.(sex,agegroup,fractile_adj),summarise,weight=sum(weight))->w\n\nmerge(w,l1,by=c(\"sex\",\"agegroup\",\"fractile_adj\"))->l1\n\nddply(tempg,.(fractile_adj_state,State.code.68,agegroup,sex),summarise,value=wtd.mean(adj_cal,weight))->s3\n\nddply(tempg,.(fractile_adj_state,State.code.68,agegroup,sex),summarise,sum_weight=sum(weight))->sw\n\nmerge(s3,sw,by=c(\"fractile_adj_state\",\"State.code.68\",\"agegroup\",\"sex\"))->s3\n\nfactor(s3$State.code.68)->s3$State.code.68\n\ndata.frame(State=99,grouped_cv=wtd.var(l1$calories,weight=l1$weight)^0.5/wtd.mean(l1$calories,weight=l1$weight))->cv3\n\nfor (i in c(1:35)) {\n subset(s3,as.numeric(s3$State.code.68)==i)->s3sub\n data.frame(State=i,grouped_cv=wtd.var(s3sub$value,s3sub$sum_weight)^0.5/wtd.mean(s3sub$value,s3sub$sum_weight))->t1\n rbind(cv3,t1)->cv3\n}\n\n# CV_from regression model\nregdata->p\nexp(predict.lm(reg))->p$predicted_cal\n\ndata.frame(State=99,predicted_cv=wtd.var(p$predicted_cal,weight=p$weight)^0.5/wtd.mean(p$predicted_cal,weight=p$weight),adjr2=summary(reg)$adj.r.squared)->cv2\n\n\n#data.frame(State=0,predicted_cv=0,adjr2=0)->e\n\nfor (i in c(1:35)) {\n subset(regdata,as.numeric(p$State.code.68)==i)->dd\n factor(dd$state_region)->dd$state_region\nfmla <- as.formula(\n ifelse(length(levels(dd$state_region))==1,\"log_cal~sector+sex+AgeChild+AgeAdult+foodprice+log(MPCE)\",\"log_cal~sector+sex+AgeChild+AgeAdult+foodprice+log(MPCE)+state_region\"))\n\n\n lm(fmla,data="" exp(predict.lm(regstate))->dd$predicted_cal\n wtd.var(dd$predicted_cal,weight=dd$weight)^0.5/wtd.mean(dd$predicted_cal,weight=dd$weight)->cvs\n data.frame(State=i,predicted_cv=cvs,adjr2=summary(regstate)$adj.r.squared)->e1\n rbind(cv2,e1)->cv2\n}\n\nsubset(cv2,select=-adjr2)->cv2\n\nmerge(cv1,cv3,by=\"State\")->t\nmerge(t,cv2,by=\"State\")->t\nmerge(t,statecode,by.x=\"State\",by.y=\"State.code.68\",all.x=TRUE)->t\nt$State.68[t$State==99]<-\"India\"\nround(t$grouped_cv,4)->t$grouped_cv\nround(t$adjusted_cv,4)->t$adjusted_cv\nround(t$predicted_cv,4)->t$predicted_cv\nnames(t)<-c(\"State.code.68\",\"CV (unit-level data)\",\"CV (grouped data)\",\"CV (based on regression model)\",\"State\")\nt->finvar\nt[order(t$State),c(5,2,3,4)]" value ("replace" "value") t nil)

org-babel-execute:R("library(plyr)\nlibrary(Hmisc)\ng->tempg\n\n # CV_untreated\nas.integer(tempg$state_region/10)->tempg$State.code.68\nfactor(tempg$State.code.68)->tempg$State.code.68\ndata.frame(State=0,adjusted_cv=0)->e\n\nfor (i in c(1:35)) {\n subset(tempg,as.numeric(tempg$State.code.68)==i)->dd\n wtd.var(dd$adj_cal,weight=dd$weight)^0.5/wtd.mean(dd$adj_cal,weight=dd$weight)->cvs\n data.frame(State=i,adjusted_cv=cvs)->e1\n rbind(e,e1)->e\n}\n\nddply(tempg,.(State.code.68),summarise,value=wtd.mean(adj_cal,weight))->s1\n\ndata.frame(State=99,adjusted_cv=0)->f2\nwtd.var(tempg$adj_cal,weight=tempg$weight)^0.5/wtd.mean(tempg$adj_cal,weight=tempg$weight)->f2[1,2]\nrbind(e,f2)->cv1\n\n # CV_grouped data\nddply(tempg,.(sex,agegroup,fractile_adj),summarise,calories=wtd.mean(adj_cal,weight))->l1\nddply(tempg,.(sex,agegroup,fractile_adj),summarise,weight=sum(weight))->w\n\nmerge(w,l1,by=c(\"sex\",\"agegroup\",\"fractile_adj\"))->l1\n\nddply(tempg,.(fractile_adj_state,State.code.68,agegroup,sex),summarise,value=wtd.mean(adj_cal,weight))->s3\n\nddply(tempg,.(fractile_adj_state,State.code.68,agegroup,sex),summarise,sum_weight=sum(weight))->sw\n\nmerge(s3,sw,by=c(\"fractile_adj_state\",\"State.code.68\",\"agegroup\",\"sex\"))->s3\n\nfactor(s3$State.code.68)->s3$State.code.68\n\ndata.frame(State=99,grouped_cv=wtd.var(l1$calories,weight=l1$weight)^0.5/wtd.mean(l1$calories,weight=l1$weight))->cv3\n\nfor (i in c(1:35)) {\n subset(s3,as.numeric(s3$State.code.68)==i)->s3sub\n data.frame(State=i,grouped_cv=wtd.var(s3sub$value,s3sub$sum_weight)^0.5/wtd.mean(s3sub$value,s3sub$sum_weight))->t1\n rbind(cv3,t1)->cv3\n}\n\n# CV_from regression model\nregdata->p\nexp(predict.lm(reg))->p$predicted_cal\n\ndata.frame(State=99,predicted_cv=wtd.var(p$predicted_cal,weight=p$weight)^0.5/wtd.mean(p$predicted_cal,weight=p$weight),adjr2=summary(reg)$adj.r.squared)->cv2\n\n\n#data.frame(State=0,predicted_cv=0,adjr2=0)->e\n\nfor (i in c(1:35)) {\n subset(regdata,as.numeric(p$State.code.68)==i)->dd\n factor(dd$state_region)->dd$state_region\nfmla <- as.formula(\n ifelse(length(levels(dd$state_region))==1,\"log_cal~sector+sex+AgeChild+AgeAdult+foodprice+log(MPCE)\",\"log_cal~sector+sex+AgeChild+AgeAdult+foodprice+log(MPCE)+state_region\"))\n\n\n lm(fmla,data="" exp(predict.lm(regstate))->dd$predicted_cal\n wtd.var(dd$predicted_cal,weight=dd$weight)^0.5/wtd.mean(dd$predicted_cal,weight=dd$weight)->cvs\n data.frame(State=i,predicted_cv=cvs,adjr2=summary(regstate)$adj.r.squared)->e1\n rbind(cv2,e1)->cv2\n}\n\nsubset(cv2,select=-adjr2)->cv2\n\nmerge(cv1,cv3,by=\"State\")->t\nmerge(t,cv2,by=\"State\")->t\nmerge(t,statecode,by.x=\"State\",by.y=\"State.code.68\",all.x=TRUE)->t\nt$State.68[t$State==99]<-\"India\"\nround(t$grouped_cv,4)->t$grouped_cv\nround(t$adjusted_cv,4)->t$adjusted_cv\nround(t$predicted_cv,4)->t$predicted_cv\nnames(t)<-c(\"State.code.68\",\"CV (unit-level data)\",\"CV (grouped data)\",\"CV (based on regression model)\",\"State\")\nt->finvar\nt[order(t$State),c(5,2,3,4)]" ((:colname-names) (:rowname-names) (:result-params "replace" "value") (:result-type . value) (:comments . "") (:shebang . "") (:cache . "no") (:padline . "") (:noweb . "no") (:tangle . "no") (:exports . "results") (:results . "replace value") (:hlines . "no") (:session . "type2") (:colnames . "yes") (:hline . "yes")))

org-babel-execute-src-block(nil)

org-babel-execute-src-block-maybe()

org-babel-execute-maybe()

org-babel-execute-safely-maybe()

run-hook-with-args-until-success(org-babel-execute-safely-maybe)

org-ctrl-c-ctrl-c(nil)

call-interactively(org-ctrl-c-ctrl-c nil nil)

command-execute(org-ctrl-c-ctrl-c)

On 28-May-2016, at 10:31 pm, Charles C. Berry <address@hidden> wrote:

p.s. one more thing - below

On Sat, 28 May 2016, Charles C. Berry wrote:

On Sat, 28 May 2016, William Denton wrote:

On 28 May 2016, Vikas Rawal wrote:
Thanks John. Appreciate that you cared to respond to such a vague query. I am at a loss with this one. It does not happen all the time. I think it happens when I am processing large datasets, and CPUs and RAM of my system are struggling to keep up. But I could be wrong.
I've had the same kind of thing happen---but C-g (sometimes many) to kill the command, then rerunning, usually works without any trouble. Some strange combination of CPU and RAM and all that, the kind of thing that's not easily reproducible.

Try this: customize `debug-on-quit' to `t' (and set for current session).

Then when you have to quit via C-g, you will get a backtrace showing where the process was hanging and how it got there. This might be helpful in figuring out what is going on.

Run your code and when you finally have to C-g out copy the *Backtrace* buffer and report it back here (or on the ESS list if appropriate).

After you copy the buffer, you should type 'q' in the *Backtrace* buffer to finish up or you may have some odd messages and hangups afterwards.

Chuck

From:	Vikas Rawal
Subject:	Re: [O] Trouble evaluating R source code blocks with C-c C-c
Date:	Sun, 29 May 2016 18:43:58 +0530