pierreqi/r_code_50k · Datasets at Hugging Face

c9f453c83004857c493b378499f28f6d0f386d12

37c42e6f4ae8de0c08bcadf9371a672852f06305

/R/read_length_plot.R

f1d4e97d607e494c06f4cb2ae6d43bf5641e8473

[ "MIT" ]

permissive

bontus/riboWaltz

3b1bea17e9c463f6f5a5bd948827b17bb2f507b2

0fe32ea8afa032bbe94ff71bde6591fb1bfcba05

refs/heads/master

2021-04-12T08:40:43.509914

2018-03-28T09:07:29

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2018-03-21T13:20:45

2018-03-21T13:20:44

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read_length_plot.R

#' Plot read length distributions. #' #' For a specified sample this function plots the read length distribution. It #' is possible to visualise the distribution for all the read lengths or to #' restrict the graphical output to a sub-range of read lengths. #' #' @param data A list of data frames from either \code{\link{bamtolist}} or #' \code{\link{bedtolist}}. #' @param sample A character string specifying the name of the sample of #' interest. #' @param cl An integer value in \emph{[1,100]} specifying the confidence level #' for restricting the plot to a sub-range of read lengths. By default it is #' set to 100, meaning that the whole distribution is displayed. #' @return A list containing a ggplot2 object, and a data frame with the #' associated data. #' @examples #' data(reads_list) #' #' ## Visualise distribution for all the read lengths #' lendist_whole <- rlength_distr(reads_list, sample = "Samp1", cl = 100) #' lendist_whole[["plot"]] #' #' ## Visualise the metaheatmaps for a sub-range of read lengths (the middle #' 95%) #' lendist_sub95 <- rlength_distr(reads_list, sample = "Samp1", cl = 95) #' lendist_sub95[["plot"]] #' @import ggplot2 #' @export rlength_distr <- function(data, sample, cl = 100) { df <- data[[sample]] dist <- table(factor(df$length, levels = c(min(df$length):max(df$length)))) dist <- data.frame(length = names(dist), count = as.vector((dist/sum(dist)) * 100)) xmin <- quantile(df$length, (1 - cl/100)/2) xmax <- quantile(df$length, 1 - (1 - cl/100)/2) p <- ggplot(dist, aes(as.numeric(as.character(length)), count)) + geom_bar(stat = "identity", fill = "gray80") + labs(title = sample, x = "Read length", y = "Count (%)") + theme_bw(base_size = 18) + theme(plot.title = element_text(hjust = 0.5)) + scale_x_continuous(limits = c(xmin-0.5, xmax+0.5), breaks = seq(xmin + ((xmin) %% 2), xmax, by=floor((xmax-xmin)/7))) output<-list() output[["plot"]]<-p output[["df"]]<-dist return(output) }

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/GarriCorrelations.R

ed5aeea9be4a2c05226699e16d8bf36695455314

[]

no_license

ca384/2020-garri-correlation-script

ef9bb809497faa9f0da6ae0a7db49708cec4d51e

48075ba428090215a2a0c34a5af542a698c98040

refs/heads/main

2023-03-09T09:08:51.442713

2021-02-08T15:47:16

335,162,354

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2021-02-08T15:47:17

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GarriCorrelations.R

## 2020-garri-correlation-script # Scripts for 2020 cassava roots and garri analysis ##script for correlation of garri traits library(here) i_am("GarriCorrelations.R") garri.38 <- read.csv(here("2020complete_umu_oto_garri_data.csv"), header=TRUE) dim(garri.38) str(garri.38) garri.38$genotypes.garri<-as.factor(garri.38$genotypes.garri) garri.38$LOCATION<-as.factor(garri.38$LOCATION) garri.38$garri<-round(log(garri.38$GARRI.YIELD.),3) garri.38$garri.plot<-round(log(garri.38$garri.yield.plot), 3) garri.38$peels<-round(garri.38$WT.OF.PEEL*100/garri.38$fresh.ROOT.WT.,3) garri.38$peeled.root<-round(garri.38$WT.OF.PEELED.ROOT.*100/garri.38$fresh.ROOT.WT.,3) garri.38$dewatered.mash<-round(garri.38$WT.OF.DEWATERED.MASH*100/garri.38$fresh.ROOT.WT.,3) ## plots hist(garri.38$dewatered.mash) hist(garri.38$peels) hist(garri.38$garri) hist(garri.38$garri.plot) hist(garri.38$peeled.root) hist(garri.38$dmcov) hist(garri.38$dmc) boxplot(garri.38$dmcov~garri.38$LOCATION) boxplot(garri.38$garri.plot~garri.38$LOCATION) boxplot(garri.38$dmc~garri.38$LOCATION, main="Dry matter content by specific gravity ",ylab = "DMC (%)",xlab="LOCATION") ##Removing outliers using the boxplot method outliers <- boxplot(garri.38$dewatered.mash,plot=FALSE)$out #by setting plot to false the boxplot is hidden garri.38[which(garri.38$dewatered.mash%in% outliers ),] #will bring out the rows and columns containing the outlier m <- garri.38[-which(garri.38$dewatered.mash%in% outliers),] #will make a subset of those that are not outlier boxplot(m$dewatered.mash~m$LOCATION,main="Proportion of dewatered mash per fresh weight",ylab = "Dewatered mash (%)",xlab="Location") library(dplyr) subset.garri.38<-select(garri.38,LOCATION,peels,peeled.root,dewatered.mash,dmc,dmcov,garri.plot,garri) subset.garri.38 <- subset.garri.38 %>% rename(d.mash=dewatered.mash) dim(subset.garri.38) umu.38<-subset(subset.garri.38, LOCATION=="umudike") umu.38$LOCATION<-NULL oto.38<-subset(subset.garri.38, LOCATION=="OTOBI") oto.38$LOCATION<-NULL library(corrplot) corr.umu<-round(cor(umu.38,use="complete.obs"),3) corplot.umu<-corrplot(corr.umu, type = "upper", order = "hclust",tl.col = "black" , tl.srt = 90, cl.pos = "n") write.csv(corplot.umu, file=here("correlation.umu_new.csv")) corre.oto<-round(cor(oto.38,use="complete.obs"),3) corplot.oto<-corrplot(corre.oto, type = "upper", order = "hclust",tl.col = "black" , tl.srt = 90, cl.pos = "n") write.csv(corplot.oto,file=here("correlation.oto_new.csv")) ##The traits of the two correlation plots are not in the same order

401c1a646e3e2a2c02f422eb677af74886e0c101

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/analysis_kenya_evi2records.R

ff6c86ec9f0a58b3d9845c2f90ef52f877c341bb

[]

no_license

erickokuto/r_scripts

0f205d8bc95fd17d4a38eff8847fec45844371be

bda8c6773a18a97b394e60616d8820bc693411f7

refs/heads/master

2021-01-10T12:37:15.492375

2016-03-03T09:36:49

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analysis_kenya_evi2records.R

source("Phenology_Functions_Erick.R") #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> #Record without gaps both using INLA and spDTyn #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> require(raster) require(foreach) require(INLA) require(hydromad) require(zoo) require(dlm) require(R2BayesX) #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> #Really Data with gaps #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Real_evi2_lists <- list.files(file.path("F:/CLIM_DATA/tranzoia_county/other Results/INLA/realData/RealDataObs"), pattern="*.tif", full.names=TRUE) Real_evi2_rasters<-foreach(files=1:length(Real_evi2_lists), .packages="raster", .combine=c, .verbose=FALSE) %do% raster(Real_evi2_lists[[files]]) Real_stk.Data.evi2<-stack(Real_evi2_rasters, bands=NULL, native=FALSE, RAT=TRUE) Real_stk.Data.evi2[Real_stk.Data.evi2 == 255] <- NA writeRaster(Real_stk.Data.evi2, "F:/CLIM_DATA/tranzoia_county/other Results/rasterImages/stkRealUnsmoothed.tif") plot(Real_stk.Data.evi2[[2]]) #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> #Simulated Records with gaps #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Sim_evi2_lists <- list.files(file.path("F:/CLIM_DATA/tranzoia_county/other Results/INLA/realData/RealDataObs"), pattern="*.tif", full.names=TRUE) Sim_evi2_rasters<-foreach(files=1:length(Sim_evi2_lists), .packages="raster", .combine=c, .verbose=FALSE) %do% raster(Sim_evi2_lists[[files]]) Sim_stk.Data.evi2<-stack(Sim_evi2_rasters, bands=NULL, native=FALSE, RAT=TRUE) Sim_stk.Data.evi2[Sim_stk.Data.evi2 == 255] <- NA writeRaster(Sim_stk.Data.evi2, "F:/CLIM_DATA/tranzoia_county/other Results/rasterImages/stkSimUnsmoothed.tif") #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> #Records gap fillied using INLA package #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> inla_evi2_lists <- list.files(file.path("F:/CLIM_DATA/tranzoia_county/other Results/INLA/realData/RealDataFitted"), pattern="*.tif", full.names=TRUE) inla_evi2_rasters<-foreach(files=1:length(inla_evi2_lists), .packages="raster", .combine=c, .verbose=FALSE) %do% raster(inla_evi2_lists[[files]]) inla_stk.Data.evi2<-stack(inla_evi2_rasters, bands=NULL, native=FALSE, RAT=TRUE) inla_stk.Data.evi2[inla_stk.Data.evi2 == 255] <- NA writeRaster(inla_stk.Data.evi2, "F:/CLIM_DATA/tranzoia_county/other Results/rasterImages/stkReal_inla_smoothed.tif") plot(inla_stk.Data.evi2[10]) #>>>>>>>>INLA with simulated data >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> inla_evi2_lists22 <- list.files(file.path("F:/CLIM_DATA/tranzoia_county/other Results/INLA/SimData/SimDataFitted"), pattern="*.tif", full.names=TRUE) inla_evi2_rasters22<-foreach(files=1:length(inla_evi2_lists22), .packages="raster", .combine=c, .verbose=FALSE) %do% raster(inla_evi2_lists22[[files]]) inla_stk.Data.evi22<-stack(inla_evi2_rasters22, bands=NULL, native=FALSE, RAT=TRUE) inla_stk.Data.evi22[inla_stk.Data.evi22 == 255] <- NA writeRaster(inla_stk.Data.evi22, "F:/CLIM_DATA/tranzoia_county/other Results/rasterImages/stkSim_inla_smoothed.tif") #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> #Analysis #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> spDTyn_evi2_lists1 <- list.files(file.path("F:/CLIM_DATA/tranzoia_county/other Results/spDTyn/RealData/RealDataFitted"), pattern="*.tif", full.names=TRUE) spDTyn_evi2_rasters1<-foreach(files=1:length(spDTyn_evi2_lists1), .packages="raster", .combine=c, .verbose=FALSE) %do% raster(spDTyn_evi2_lists1[[files]]) spDTyn_stk.Data.evi2Pred<-stack(spDTyn_evi2_rasters1, bands=NULL, native=FALSE, RAT=TRUE) spDTyn_stk.Data.evi2Pred[spDTyn_stk.Data.evi2Pred == 255] <- NA writeRaster(spDTyn_stk.Data.evi2Pred, "F:/CLIM_DATA/tranzoia_county/other Results/rasterImages/stkReal_spDTyn_smoothed.tif") #>>>>>>>>spDTyn with simulated data >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> spDTyn_evi2_lists2 <- list.files(file.path("F:/CLIM_DATA/tranzoia_county/other Results/spDTyn/SimData/SimDataFitted"), pattern="*.tif", full.names=TRUE) spDTyn_evi2_rasters2<-foreach(files=1:length(spDTyn_evi2_lists2), .packages="raster", .combine=c, .verbose=FALSE) %do% raster(spDTyn_evi2_lists2[[files]]) spDTyn_stk.Data.evi2Pred2<-stack(spDTyn_evi2_rasters2, bands=NULL, native=FALSE, RAT=TRUE) spDTyn_stk.Data.evi2Pred2[spDTyn_stk.Data.evi2Pred2 == 255] <- NA writeRaster(spDTyn_stk.Data.evi2Pred2, "F:/CLIM_DATA/tranzoia_county/other Results/rasterImages/stkSim_spDTyn_smoothed.tif") #Observation used obsUsed=obs.used.fun(stk=Real_stk.Data.evi2) writeRaster(obsUsed, "F:/CLIM_DATA/tranzoia_county/other Results/Results/obsUsed_tranzoia.tif") levelplot(obsUsed[[1]]) plot(Real_stk.Data.evi2[[4]]) #kenya records percentage observation used Real_evi2_lists <- list.files(file.path("F:/CLIM_DATA/Kenya_evi2_Records/kenya_10kmclipped"), pattern="*.tif", full.names=TRUE) Real_evi2_rasters<-foreach(files=1:length(Real_evi2_lists), .packages="raster", .combine=c, .verbose=FALSE) %do% raster(Real_evi2_lists[[files]]) Real_stk.Data.evi2<-stack(Real_evi2_rasters, bands=NULL, native=FALSE, RAT=TRUE) obsUsed=obs.used.fun(stk=Real_stk.Data.evi2) writeRaster(obsUsed, "F:/CLIM_DATA/Kenya_evi2_Records/Results/Obs.used/obsUsed_kenya.tif") require(maptools) proj <- CRS('+proj=longlat +ellps=WGS84') ##Change to your folder mapaSHP <- readShapeLines('F:/CLIM_DATA/Kenya_evi2_Records/kenyacounties/counties/counties.shp', proj4string=proj) p <- spplot(obsUsed, par.settings=BuRdTheme) p + layer(sp.lines(mapaSHP, lwd=0.8, col='black')) #The analysis or E and RMSE E_With_INLA_Fitted=r2.function2(obs.stk=Real_stk.Data.evi2, sim.stk=inla_stk.Data.evi2) writeRaster(E_With_INLA_Fitted, "F:/CLIM_DATA/tranzoia_county/other Results/Results/E_With_INLA_smoothed_RealData.tif") levelplot(E_With_INLA_Fitted) E_With_spDTyn_Fitted=r2.function2(obs.stk=Real_stk.Data.evi2, sim.stk=spDTyn_stk.Data.evi2Pred) writeRaster(E_With_spDTyn_Fitted, "F:/CLIM_DATA/tranzoia_county/other Results/Results/E_With_spDTyn_smoothed_RealData.tif") levelplot(E_With_spDTyn_Fitted) #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> E_With_INLA_Fitted_Sim=r2.function2(obs.stk=Sim_stk.Data.evi2, sim.stk=inla_stk.Data.evi22) writeRaster(E_With_INLA_Fitted_Sim, "F:/CLIM_DATA/tranzoia_county/other Results/Results/E_With_INLA_smoothed__SimData.tif") levelplot(E_With_INLA_Fitted_Sim) E_With_spDTyn_Fitted_Sim=r2.function2(obs.stk=Sim_stk.Data.evi2, sim.stk=spDTyn_stk.Data.evi2Pred2) writeRaster(E_With_spDTyn_Fitted_Sim, "F:/CLIM_DATA/tranzoia_county/other Results/Results/E_With_spDTyn_smoothed_SimData.tif") levelplot(E_With_spDTyn_Fitted_Sim) #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> #RMSE RMSE_With_INLA_Fitted=funrmse(obs.stk=Real_stk.Data.evi2, sim.stk=inla_stk.Data.evi2) writeRaster(RMSE_With_INLA_Fitted, "F:/CLIM_DATA/tranzoia_county/other Results/Results/RMSE_With_INLA_smoothed_RealData.tif") spplot(RMSE_With_INLA_Fitted) RMSE_With_spDTyn_Fitted=funrmse(obs.stk=Real_stk.Data.evi2, sim.stk=spDTyn_stk.Data.evi2Pred) writeRaster(RMSE_With_spDTyn_Fitted, "F:/CLIM_DATA/tranzoia_county/other Results/Results/RMSE_With_spDTyn_smoothed_RealData.tif") spplot(RMSE_With_spDTyn_Fitted) #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> RMSE_With_INLA_Fitted_Sim=funrmse(obs.stk=Sim_stk.Data.evi2, sim.stk=inla_stk.Data.evi22) writeRaster(RMSE_With_INLA_Fitted_Sim, "F:/CLIM_DATA/tranzoia_county/other Results/Results/RMSE_With_INLA_smoothed__SimData.tif") spplot(RMSE_With_INLA_Fitted_Sim) RMSE_With_spDTyn_Fitted_Sim=funrmse(obs.stk=Sim_stk.Data.evi2, sim.stk=spDTyn_stk.Data.evi2Pred2) writeRaster(RMSE_With_spDTyn_Fitted_Sim, "F:/CLIM_DATA/tranzoia_county/other Results/Results/RMSE_With_spDTyn_smoothed_SimData.tif") spplot(RMSE_With_spDTyn_Fitted_Sim) #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> #ploting characters with rasterVis package #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> require(maptools) proj <- CRS('+proj=longlat +ellps=WGS84') ##Change to your folder mapaSHP <- readShapeLines('F:/CLIM_DATA/tranzoia_county/trans.shp', proj4string=proj) p <- spplot(RMSE_With_INLA_Fitted, par.settings=BuRdTheme) p + layer(sp.lines(mapaSHP, lwd=0.8, col='darkgray')) #>>>>>>>>> library(gridExtra) p1 <- levelplot(RMSE_With_INLA_Fitted) p2 <- levelplot(RMSE_With_spDTyn_Fitted) grid.arrange(p1, p2, ncol=2) #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> #Latitudinal means and standard deviations #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> DF<-read.csv("F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/Lat_means/kenya_lat_evi2.csv") attach(DF) names(DF) require(ggplot2) eb_raw<- aes(ymax = raw.mean + raw.sd, ymin = raw.mean - raw.sd) eb_sgolay<- aes(ymax = sgolay.mean + sgolay.sd, ymin = sgolay.mean - sgolay.sd) eb_spde_non_dlm <- aes(ymax = nonDLMspde.mean + nonDLMspde.sd, ymin = nonDLMspde.mean - nonDLMspde.sd) ebs_kalman <- aes(ymax = DLM.kalman.mean + DLM.kalman.sd, ymin = DLM.kalman.mean - DLM.kalman.sd) eb_spde_dlm <- aes(ymax = DLMspde.mean + DLMspde.sd, ymin = DLMspde.mean - DLMspde.sd) eb_sp_spde <- aes(ymax = sp.spDTyn.mean + sp.spDTyn.sd, ymin = sp.spDTyn.mean - sp.spDTyn.sd) eb_sp_spDTyn <- aes(ymax = sp.spde.mean + sp.spde.sd, ymin = sp.spde.mean - sp.spde.sd) ############################################################################################### # DISTRIBUTION OF MEANS ############################################################################################### require(gridExtra) nonDLM <- ggplot(DF, aes(Latitude)) nonDLM <- nonDLM + geom_ribbon(eb_raw, alpha = 0.5, fill='grey') + geom_line(aes(y=raw.mean), colour="Black") nonDLM <- nonDLM + geom_ribbon(eb_sgolay, alpha = 0.5, fill='light blue') + geom_line(aes(y=sgolay.mean), colour="Dark Blue") nonDLM <- nonDLM + geom_ribbon(eb_spde_non_dlm , alpha = 0.5, fill='light green') + geom_line(aes(y=nonDLMspde.mean), colour="Dark Green") + coord_flip() + theme(panel.background = element_rect(fill='white', colour='black')) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.border = element_blank()) + xlab(NULL) + ylab(NULL) + theme(axis.text.x=element_blank()) + theme(axis.text.y=element_text(size=20,face="bold")) #annotate("text", x=41, y=-3.3, label="A",size=8,fontface="bold.italic",colour="black",parse=TRUE) + # scale_x_continuous(limits = c(33.5, 42), # breaks=c(33.5, 36, 39, 42)) + # scale_y_continuous(limits = c(-5, 6), # # breaks=c(-5, -2, 2, 6)) # scale_x_continuous(limits = c(-5, 6), # breaks=c(-5, -2, 2, 6)) + # scale_y_continuous(limits = c(33.5, 42), # breaks=c(33.5, 36, 39, 42)) pFeb<- ggplot(DF, aes(Latitude)) pFeb <- pFeb + geom_ribbon(ebraw2, alpha = 0.5, fill='grey') + geom_line(aes(y=feb.raw.mean), colour="Black") pFeb <- pFeb + geom_ribbon(ebsgolay2, alpha = 0.5, fill='light blue') + geom_line(aes(y=feb.sgolay.mean), colour="Dark Blue") pFeb <- pFeb + geom_ribbon(ebwhit2, alpha = 0.5, fill='light green') + geom_line(aes(y=feb.whit.mean), colour="Dark Green") pFeb <- pFeb + geom_ribbon(ebspde2, alpha = 0.5, fill='Orange') + geom_line(aes(y=feb.spde.mean), colour="Red") + coord_flip() + #theme(plot.margin=unit(c(1,1,1,1),"cm"))+ theme(panel.background = element_rect(fill='white', colour='black')) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.border = element_blank()) + xlab(NULL) + ylab(NULL) + theme(axis.text.x=element_blank()) + theme(axis.text.y=element_blank()) + annotate("text", x=-50, y=-0.4, label="Feb",size=8,fontface="bold.italic",colour="black",parse=TRUE) + scale_x_continuous(limits = c(-60,60), breaks=c(-60,0,60)) + scale_y_continuous(limits = c(-0.5, 1), breaks=c(-0.5,0,1)) pMar<- ggplot(DF, aes(Latitude)) pMar <- pMar + geom_ribbon(ebraw3, alpha = 0.5, fill='grey') + geom_line(aes(y=mar.raw.mean), colour="Black") pMar <- pMar + geom_ribbon(ebsgolay3, alpha = 0.5, fill='light blue') + geom_line(aes(y=mar.sgolay.mean), colour="Dark Blue") pMar <- pMar + geom_ribbon(ebwhit3, alpha = 0.5, fill='light green') + geom_line(aes(y=mar.whit.mean), colour="Dark Green") pMar <- pMar + geom_ribbon(ebspde3, alpha = 0.5, fill='Orange') + geom_line(aes(y=mar.spde.mean), colour="Red") + coord_flip() + #theme(plot.margin=unit(c(-0.5,-0.5,-0.5,1),"cm"))+ theme(panel.background = element_rect(fill='white', colour='black')) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.border = element_blank()) + xlab(NULL) + ylab(NULL) + theme(axis.text.x=element_blank()) + theme(axis.text.y=element_text(size=20,face="bold")) + annotate("text", x=-50, y=-0.4, label="Mar",size=8,fontface="bold.italic",colour="black",parse=TRUE) + scale_x_continuous(limits = c(-60,60), breaks=c(-60,0,60)) + scale_y_continuous(limits = c(-0.5, 1), breaks=c(-0.5,0,1)) #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> # observation available before smoothing #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> require(maptools) proj <- CRS('+proj=longlat +ellps=WGS84') mapaSHP <- readShapeLines('F:/CLIM_DATA/Kenya_evi2_Records/kenyacounties/counties/counties.shp', proj4string=proj) # p <- spplot(obsUsed, par.settings=BuRdTheme) # p + layer(sp.lines(mapaSHP, lwd=0.8, col='black')) # require(rasterVis) require(RColorBrewer) lists.obs = list.files(file.path("F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/obs_used"),pattern="*.tif", full.names=TRUE) obs.used.image = foreach(files=1:length(lists.obs), .combine=c, .verbose=FALSE) %do% raster(lists.obs[[files]]) stk_obs_used=stack(obs.used.image, bands=NULL, native=FALSE, RAT=TRUE) ### Show all the colour schemes available require(RColorBrewer) require(gimms) #blrd.palette <- colorRampPalette(c("blue", "red"), space = "Lab") p1=spplot(stk_obs_used, xlim = bbox(stk_obs_used)[1, ], ylim = bbox(stk_obs_used)[2, ], colorkey=list(labels = list(cex = 1.5,fontface='plain'), space='right'), col.regions = colorRampPalette(brewer.pal(9, "YlOrRd")), at=seq(70, 100, 1), strip=FALSE, scales = list(draw = TRUE), main=list("", col="black",cex=2, fontface='bold'), # panel = function(x, y, z,...) { # panel.levelplot.raster(x, y, z,...) # panel.text(x = 40.5, y = -3.2, # labels=Names[panel.number()], # col.text = "black", # cex=1.5,fontface='bold') # }, contour=FALSE, layout=c(1, 1)) p1 + layer(sp.lines(mapaSHP, lwd=0.8, col='black')) #################################################### ## Categorical data #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> lists.obs = list.files(file.path("F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/obs_used"),pattern="*.tif", full.names=TRUE) obs.used.image = foreach(files=1:length(lists.obs), .combine=c, .verbose=FALSE) %do% raster(lists.obs[[files]]) rr=obs.used.image require(raster) fcat=function(x){ifelse(x < 50, 1, ifelse(x >= 50 & x < 70, 2, ifelse(x >= 70 & x < 90, 3, ifelse(x >= 90, 4)))) } obs.used.cat=calc(rr, fcat) obs.rat <- ratify(obs.used.cat) rat <- levels(obs.rat)[[1]] rat$obs_available <- c('< 40%', '40-60%', '60-80%', '> 80%') rat$class <- c('A', 'B', 'C', 'D') levels(obs.rat) <- rat obs.rat p1.new=levelplot(obs.rat, col.regions=c('midnightblue', 'brown', 'indianred1', 'palegreen')) p1.new + layer(sp.lines(mapaSHP, lwd=0.8, col='black')) ## with 'att' you can choose another variable from the RAT # levelplot(r, att=2, col.regions=c('palegreen', 'midnightblue', 'indianred1')) # levelplot(r, att='class', col.regions=c('palegreen', 'midnightblue', 'indianred1')) #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> # pixel level means #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> lists.means = list.files(file.path("F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/means"),pattern="*.tif", full.names=TRUE) means.images = foreach(files=1:length(lists.means), .combine=c, .verbose=FALSE) %do% raster(lists.means[[files]]) stk_means=stack(means.images[[7]], means.images[[3]], means.images[[4]], means.images[[1]], means.images[[2]], means.images[[6]], means.images[[5]],bands=NULL, native=FALSE, RAT=TRUE) require(bfastSpatial) non_Dlm_spde_mean=summaryBrick(stack(means.images[[4]],means.images[[1]]), fun="mean", na.rm=TRUE) writeRaster(non_Dlm_spde_mean, "F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/means/non_Dlm_spde_mean.tif", overwrite=TRUE) require(rasterVis) blrd.palette <- colorRampPalette(c("blue", "red"), space = "Lab") Names <- c("A", "B", "C","D", "E","F", "G") p2=spplot(stk_means, xlim = bbox(stk_means)[1, ], ylim = bbox(stk_means)[2, ], colorkey=list(labels = list(cex = 1.5,fontface='plain'), space='right'), col.regions = colorRampPalette(brewer.pal(9, "YlOrRd")), at=seq(0, 0.6, 0.01), strip=FALSE, aspect="fill", scales = list(draw = FALSE), main=list(""), panel = function(x, y, z,...) { panel.levelplot.raster(x, y, z,...) panel.text(x = 41, y = -3.3, labels=Names[panel.number()], col.text = "black", cex=1.5,fontface='bold') }, contour=FALSE, layout=c(2, 4)) p2 + layer(sp.lines(mapaSHP, lwd=0.8, col='black')) #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> # pixel level standard deviations #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> lists.sd = list.files(file.path("F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/sd"),pattern="*.tif", full.names=TRUE) sd.images = foreach(files=1:length(lists.sd), .combine=c, .verbose=FALSE) %do% raster(lists.sd[[files]]) stk_sd=stack(sd.images[[7]], sd.images[[3]], sd.images[[4]], sd.images[[1]], sd.images[[2]], sd.images[[6]], sd.images[[5]],bands=NULL, native=FALSE, RAT=TRUE) require(rasterVis) Names <- c("A", "B", "C","D", "E","F", "G") p3=spplot(stk_sd, xlim = bbox(stk_sd)[1, ], ylim = bbox(stk_sd)[2, ], colorkey=list(labels = list(cex = 1.5,fontface='plain'), space='right'), col.regions = colorRampPalette(brewer.pal(9, "YlOrRd")), at=seq(0, 0.3, 0.01), strip=FALSE, aspect="fill", scales = list(draw = FALSE), main=list(""), panel = function(x, y, z,...) { panel.levelplot.raster(x, y, z,...) panel.text(x = 41, y = -3.3, labels=Names[panel.number()], col.text = "black", cex=1.5,fontface='bold') }, contour=FALSE, layout=c(2, 4)) p3 + layer(sp.lines(mapaSHP, lwd=0.8, col='black')) #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> # root mean squared error analysis #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> lists.rmse = list.files(file.path("F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/rmse"),pattern="*.tif", full.names=TRUE) rmse.images = foreach(files=1:length(lists.rmse), .combine=c, .verbose=FALSE) %do% raster(lists.rmse[[files]]) stk_rmse=stack(rmse.images[[3]], rmse.images[[4]], rmse.images[[1]], rmse.images[[2]], rmse.images[[6]], rmse.images[[5]],bands=NULL, native=FALSE, RAT=TRUE) require(bfastSpatial) rmse_non_DLM_sgolay=summaryBrick(stack(rmse.images[[4]],rmse.images[[3]]), fun="mean", na.rm=TRUE) writeRaster(rmse_non_DLM_sgolay, "F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/rmse/rmse_non_DLM_sgolay.tif", overwrite=TRUE) rmse_DLM_kalman=summaryBrick(stack(rmse.images[[1]],rmse.images[[3]]), fun="mean", na.rm=TRUE) writeRaster(rmse_DLM_kalman, "F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/rmse/rmse_DLM_kalman.tif", overwrite=TRUE) rmse_DLM_spde=summaryBrick(stack(rmse.images[[1]],rmse.images[[2]]), fun="mean", na.rm=TRUE) writeRaster(rmse_DLM_spde, "F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/rmse/rmse_DLM_spde.tif", overwrite=TRUE) require(rasterVis) Names <- c("A", "B", "C","D", "E","F") p4=spplot(stk_rmse, xlim = bbox(stk_rmse)[1, ], ylim = bbox(stk_rmse)[2, ], colorkey=list(labels = list(cex = 1.5,fontface='plain'), space='right'), col.regions = colorRampPalette(brewer.pal(9, "YlOrRd")), at=seq(0, 0.3, 0.01), strip=FALSE, aspect="fill", scales = list(draw = FALSE), main=list(""), panel = function(x, y, z,...) { panel.levelplot.raster(x, y, z,...) panel.text(x = 41, y = -3.3, labels=Names[panel.number()], col.text = "black", cex=1.5,fontface='bold') }, contour=FALSE, layout=c(2, 3)) p4 + layer(sp.lines(mapaSHP, lwd=0.8, col='black')) #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> # coefficient of efficiency E analysis #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> E_non_DLM_sgolay=(1-rmse.images[[3]])-0.1 writeRaster(E_non_DLM_sgolay, "F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/coefficient_E/E_non_DLM_sgolay.tif", overwrite=TRUE) E_non_DLM_spde=(1-rmse.images[[4]])-0.1 writeRaster(E_non_DLM_spde, "F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/coefficient_E/E_non_DLM_spde.tif", overwrite=TRUE) E_DLM_kalman=(1-rmse.images[[1]])-0.1 writeRaster(E_DLM_kalman, "F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/coefficient_E/E_DLM_kalman.tif", overwrite=TRUE) E_DLM_spde=(1-rmse.images[[2]])-0.1 writeRaster(E_DLM_spde, "F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/coefficient_E/E_DLM_spde.tif", overwrite=TRUE) E_sp_spDTyn=(1-rmse.images[[6]])-0.1 writeRaster(E_sp_spDTyn, "F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/coefficient_E/E_sp_spDTyn.tif", overwrite=TRUE) E_sp_spde=(1-rmse.images[[5]])-0.1 writeRaster(E_sp_spde, "F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/coefficient_E/E_sp_spde.tif", overwrite=TRUE) #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> lists.E = list.files(file.path("F:/CLIM_DATA/Kenya_evi2_Records/Results/outputs/coefficient_E"),pattern="*.tif", full.names=TRUE) E.images = foreach(files=1:length(lists.E), .combine=c, .verbose=FALSE) %do% raster(lists.E[[files]]) stk_E=stack(E.images[[3]], E.images[[4]], E.images[[1]], E.images[[2]], E.images[[6]], E.images[[5]],bands=NULL, native=FALSE, RAT=TRUE) require(rasterVis) Names <- c("A", "B", "C","D", "E","F") p5=spplot(stk_E, xlim = bbox(stk_E)[1, ], ylim = bbox(stk_E)[2, ], colorkey=list(labels = list(cex = 1.5,fontface='plain'), space='right'), col.regions = colorRampPalette(brewer.pal(9, "YlOrRd")), at=seq(0.7, 1, 0.01), strip=FALSE, aspect="fill", scales = list(draw = FALSE), main=list(""), panel = function(x, y, z,...) { panel.levelplot.raster(x, y, z,...) panel.text(x = 41, y = -3.3, labels=Names[panel.number()], col.text = "black", cex=1.5,fontface='bold') }, contour=FALSE, layout=c(2, 3)) p5 + layer(sp.lines(mapaSHP, lwd=0.8, col='black')) require(hydroGOF) ?nseStat ##>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> fitb <- harmonic.regression(yyy, inputtime=month) mu = fitb$coeffs[1] cos.amplitude = fitb$coeffs[2] sin.amplitude = fitb$coeffs[3] amplitude = fitb$pars[1] phase = fitb$pars[2] ?harmonic.regression #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> # Using method two #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> require(season) ?cosinor fit = cosinor(yyy ~ 1, date="month", type = 'monthly', data = data.frame(yyy, month), family = gaussian(), offsetmonth = FALSE) res2$Coefficients res2$coefficients[3] coef(fit)[2] mu = coef(res2)["Intercept"] cos.amplitude = coef(res2)["cosw"] sin.amplitude = coef(res2)["sinw "] amplitude = fitb$pars[1] phase = fitb$pars[2] data(CVD) # model to fit an annual pattern to the monthly cardiovascular disease data f = c(12) tau = c(130,10) res12 = nscosinor(data=CVD, response='adj', cycles=f, niters=500, burnin=100, tau=tau) summary(res12) plot(res12) ## cardiovascular disease data (offset based on number of days in... ## ...the month scaled to an average month length) data(CVD) res = cosinor(cvd~1, date='month', data=CVD, type='monthly', family=poisson(), offsetmonth=TRUE) summary(res) seasrescheck(res$residuals) # check the residuals # stillbirth data data(stillbirth) head(stillbirth) res = cosinor(stillborn~1, date='dob', data=stillbirth, family=binomial(link='cloglog')) summary(res) plot(res) # hourly indoor temperature data data(indoor) head(indoor) res = cosinor(bedroom~1, date='datetime', type='hourly', data=indoor) summary(res)

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/rBEADS_v2.0/source/MappabilityCorrection_v1.1.R

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Przemol/rbeads_old

22f5a9685cfb8894e0c070600230ccc99201d83a

11aef08ca1f42c9fbbbcbf55897db452edd36351

refs/heads/master

2020-05-17T09:06:14.462865

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MappabilityCorrection_v1.1.R

# TODO: Add comment # # Author: przemol ############################################################################### MappabilityCorrection <- function(GCnormTrack, mappabilityTrack=NULL, genome=Celegans) { catTime("Mappability correction", e={ # 0) Old way (multyply than divide by the same value in DIV step) #cov_GCmap <- cov * cmap.fd # 1) masking regions of mappability lower than 100 (mappability score=0.25) from precalculated mappability track #GCnormTrack[cmap.fd < 0.25] <- NA if (genome@provider_version == 'ce6') { #names(GCnormTrack) <- seqnames(Celegans)[c(1:4,7,5,6)] #GCnormTrack[mappabilityTrack[c(1:4,7,5,6)] < 100] <- NA for(i in names(GCnormTrack)) { cat('.') GCnormTrack[[i]][mappabilityTrack[[i]] < 100] <- NA } } else if (genome@provider_version == 'hg19') { for(i in names(GCnormTrack)) { cat('.') GCnormTrack[[i]][mappabilityTrack[[i]] < 100] <- NA } } else if (genome@provider_version == 'mm9') { for(i in names(GCnormTrack)) { cat('.') GCnormTrack[[i]][mappabilityTrack[[i]] < 100] <- NA } } else { warning('Unnkonown genome!') } # 2) masking regions within lowest 5% of all read values # cov_GCmap[cov.r <= quantile(as.vector(cov.r), 0.05)] <- NA }) return(GCnormTrack) } catTime <- function(..., e=NULL, file="", gc=FALSE) { cat(..., "...", sep="", file=file, append=TRUE) cat("\t<", system.time(e, gcFirst=gc)[3], "s>\n", sep="", file=file, append=TRUE) }

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/R/save.entry.item1.R

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senickel/surveycleanup

bdf0298cea14493506045444589b0c392e1fbac4

435a02c8ed42afc467d24820930facbeae660747

refs/heads/master

2020-04-07T16:21:28.226489

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save.entry.item1.R

#' save.entry.item1 #' #' check nominal if they are nominal #' @param Click here and there #' @keywords nominal #' @keywords #' @export #' @examples #' checking.nominal() #' @importFrom magrittr %>% #' save.entry.item1<-function(item1,input) { if (is.na(item1[1,1])) item1[1,1]<-"" if (item1[1,1]!=""|item1[1,2]!="") { item.all<-data.frame(oldval=item1X[,1],oldlab=item1X[,2],newval=item1[,1],newlab=item1[,2],del="") # fill up changed ones item.all[1:nrow(item1),3]<-sapply(1:nrow(item1),function(x1) { input[[paste0("val",x1)]] }) item.all[1:nrow(item1),4]<-sapply(1:nrow(item1),function(x1) { input[[paste0("lab",x1)]] }) item.all[1:nrow(item1),5]<-ifelse(sapply(1:nrow(item1),function(x1) { input[[paste0("del",x1)]] }),0,1) createtrue<-sapply(paste0("cval",c(1:5)),function(x) { if (input[[x]]!="") return(1) return(0) }) %>% sum if (createtrue>0) { item.new<-data.frame(oldval=rep("",createtrue),oldlab=rep("",createtrue), newval=sapply(1:createtrue,function(x) input[[paste0("cval",x)]]), newlab=sapply(1:createtrue,function(x) input[[paste0("clab",x)]]), del=sapply(1:createtrue,function(x) input[[paste0("cdel",x)]])) item.all<-rbind(item.all,item.new) } edit.data.item1(item.all,idd) save.csv.version(responses) } }

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/R/wallet-buys.r

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zamorarr/rcoinbase

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e3c97694c8cdefafab23e52b7f32e6558fd92957

refs/heads/master

2021-08-29T19:33:08.909098

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wallet-buys.r

#' List buys #' #' Lists buys for an account. #' #' @param account_id Account Id #' @export #' @family wallet-buys #' @references \url{https://developers.coinbase.com/api/v2#list-buys} get_buys <- function(account_id) { endpoint <- paste("accounts", account_id, "buys", sep = "/") coinbase_get(endpoint) } #' Show a buy #' #' Show an individual buy. #' #' @param account_id Account Id #' @param buy_id Buy Id #' @export #' @family wallet-buys #' @references \url{https://developers.coinbase.com/api/v2#show-a-buy} get_buy <- function(account_id, buy_id) { endpoint <- paste("accounts", account_id, "buys", buy_id, sep = "/") coinbase_get(endpoint) }

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/12.11.table2.R

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rebeccagreenblatt/brfss_code

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refs/heads/master

2020-05-27T18:39:09.943349

2019-05-27T00:51:37

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r

12.11.table2.R

library(survey) library(dplyr) asthnow <- read.csv("BRFSS_Dec_Edits/12.11.asthnow_final.csv") table(asthma$ASTHMA, useNA = "ifany") table(asthnow$ASTHNOW, useNA = "ifany") asthnow$age_cat <- cut(asthnow$AGE, breaks = c(21, 34, 44, 54, 64, 100)) never_asthma <- filter(asthnow, ASTHNOW == 0) never_asthma <- select(never_asthma, SEX, age_cat, RACE, EDUCA, INCOME, BMI, SMOKER) current_asthma <- filter(asthnow, ASTHNOW == 1) current_asthma <- select(current_asthma, SEX, age_cat, RACE, EDUCA, INCOME, BMI, SMOKER) ##current_asthma current_counts <- unlist(lapply(current_asthma, table)) current_props <- unlist(lapply(current_asthma, function(col){prop.table(table(col))})) current_combin <- paste0(formatC(current_counts, format="d",big.mark=","), ":", format(round(100*current_props, 2), nsmall=2)) names(current_combin) <- names(current_counts) d <- current_combin current_final <- rbind(t(t(d[c(2,1)])), "", t(t(d[c(12,9,11,8,10)])), "", t(t(d[c(14,15,13)])), "", t(t(d[c(17,18,16)])), "", t(t(d[c(22,23,19,20,21)])), "", t(t(d[c(26,25,24)])), "", t(t(d[c(3:7)]))) ##never_asthma never_counts <- unlist(lapply(never_asthma, table)) never_props <- unlist(lapply(never_asthma, function(col){prop.table(table(col))})) never_combin <- paste0(formatC(never_counts, format="d",big.mark=","), ":", format(round(100*never_props, 2), nsmall=2)) names(never_combin) <- names(never_counts) d <- never_combin never_final <- rbind(t(t(d[c(2,1)])), "", t(t(d[c(12,9,11,8,10)])), "", t(t(d[c(14,15,13)])), "", t(t(d[c(17,18,16)])), "", t(t(d[c(22,23,19,20,21)])), "", t(t(d[c(26,25,24)])), "", t(t(d[c(3:7)]))) all <- cbind(current_final, never_final) colnames(all) <- c("current", "never") write.csv(all, file = "BRFSS_Dec_Edits/12.11.table2.csv") ##weighted column library(survey) never_asthma <- filter(asthnow, ASTHNOW == 0) never_asthma <- select(never_asthma, SEX, age_cat, RACE, EDUCA, INCOME, BMI, SMOKER, CNTYWT, STSTR) current_asthma <- filter(asthnow, ASTHNOW == 1) current_asthma <- select(current_asthma, SEX, age_cat, RACE, EDUCA, INCOME, BMI, SMOKER, CNTYWT, STSTR) never_asthma$asth <- "never" current_asthma$asth <- "current" all <- rbind(never_asthma, current_asthma) all$asth.f <- factor(all$asth) options(survey.lonely.psu = "adjust") des <- svydesign(ids=~1, strata=~STSTR, weights=~as.numeric(CNTYWT), data=all) sex <- prop.table(svytable(~SEX+asth.f, des), 2)[c(2,1),] race <- prop.table(svytable(~RACE+asth.f, des), 2)[c(5,2,4,1,3),] educa <- prop.table(svytable(~EDUCA+asth.f, des), 2)[c(2,3,1),] inc <- prop.table(svytable(~INCOME+asth.f, des), 2)[c(2,3,1),] bmi <- prop.table(svytable(~BMI+asth.f, des), 2)[c(4,5,1,2,3),] smoker <- prop.table(svytable(~SMOKER+asth.f, des), 2)[c(3,2,1),] age <- prop.table(svytable(~age_cat+asth.f, des), 2) sex <- format(round(100*sex, 1), nsmall=1) race <- format(round(100*race, 1), nsmall=1) educa <- format(round(100*educa, 1), nsmall=1) inc <- format(round(100*inc, 1), nsmall=1) bmi <- format(round(100*bmi, 1), nsmall=1) smoker <- format(round(100*smoker, 1), nsmall=1) age <- format(round(100*age, 1), nsmall=1) r <- rep("", 2) combin <- rbind(sex, r, race, r, educa, r, inc, r, bmi, r, smoker, r, age) write.csv(combin, "BRFSS_Dec_Edits/12.11.weighted_table2_column.csv")

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2-explore_Giladi_et_al_get_marker_genes_filter_celltypes.R

# Jake Yeung # Date of Creation: 2019-12-05 # File: ~/projects/scchic/scripts/rstudioserver_analysis/BM_all_merged/2-explore_Giladi_et_al.R # Explore the scRNAseq dataset : filter celltypes manually rm(list=ls()) library(dplyr) library(tidyr) library(ggplot2) library(data.table) library(Matrix) Vectorize(plog2p <- function(p){ return(ifelse(p == 0, 0, p * log2(p))) }, vectorize.args = "p") CalculateEntropy <- function(p, normalize.p = FALSE){ if (normalize.p){ p <- p / sum(p) } S <- -sum(plog2p(p)) return(S) } # Functions --------------------------------------------------------------- ReadGiladi <- function(inf, remove.first.col = TRUE){ dat <- fread(inf) %>% dplyr::rename(gene = V1) if (remove.first.col){ dat$gene <- NULL } return(dat) } # Load marker genes ------------------------------------------------------ outrds <- "/home/jyeung/hub_oudenaarden/jyeung/data/scChiC/public_data/Giladi_et_al_2018/diff_exprs_Giladi_seurat.celltypes_filt.rds" outpdf <- "/home/jyeung/hub_oudenaarden/jyeung/data/scChiC/public_data/Giladi_et_al_2018/diff_exprs_Giladi_seurat.celltypes_filt.pdf" pdf(outpdf, useDingbats = FALSE) inf.markers <- "/home/jyeung/data/from_cluster/public_data/Giladi_et_al_2018/41556_2018_121_MOESM4_ESM.markergenes.xlsx" assertthat::assert_that(file.exists(inf.markers)) library(xlsx) markers <- xlsx::read.xlsx(inf.markers, sheetIndex = 1) # Load annotations from Giladi's email ----------------------------------- inf.meta <- "~/hpc/scChiC/public_data/Giladi_et_al_2018/tier3_annotation.txt" assertthat::assert_that(file.exists(inf.meta)) dat.meta <- fread(inf.meta) # filter markers markers.keep <- c("Car1", "core", "Siglech", "Prg2", "Ccl5", "Prss34", "Cd74", "Fcrla", "Ltf") dat.meta <- subset(dat.meta, marker %in% markers.keep) cbPalette <- c("#696969", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#006400", "#FFB6C1", "#32CD32", "#0b1b7f", "#ff9f7d", "#eb9d01", "#7fbedf") ggplot(dat.meta, aes(x = x, y = y, color = marker)) + geom_point() + theme_bw() + theme(aspect.ratio=1, panel.grid.major = element_blank(), panel.grid.minor = element_blank()) # Load data --------------------------------------------------------------- indir <- "~/data/from_cluster/public_data/Giladi_et_al_2018" inf.meta <- file.path(indir, "GSE92575_metadata.txt") infs <- list.files(path = indir, pattern = "*.txt.gz", full.names = TRUE) names(infs) <- sapply(infs, function(x) strsplit(basename(x), split = "\\.")[[1]][[1]]) # get gene list genes <- ReadGiladi(infs[[1]], remove.first.col = FALSE)$gene dats <- lapply(infs, ReadGiladi, remove.first.col = TRUE) %>% bind_cols() meta <- as.data.frame(fread(inf.meta, skip = 14)) rownames(meta) <- meta$well # cells <- meta$well cells <- dat.meta$V1 # integrate tier3 annotation into thiis dats.filt <- dats[, ..cells] dats.filt <- as.matrix(dats.filt) rownames(dats.filt) <- genes # split into tiers and then calculate entropy? library(Seurat) pbmc <- CreateSeuratObject(counts = dats.filt, project = "pbmc3k", min.cells = 3, min.features = 200, meta.data = meta) pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000) pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000) # Identify the 10 most highly variable genes top10 <- head(VariableFeatures(pbmc), 10) # plot variable features with and without labels plot1 <- VariableFeaturePlot(pbmc) plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE) # CombinePlots(plots = list(plot1, plot2)) all.genes <- rownames(pbmc) pbmc <- ScaleData(pbmc, features = all.genes) pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc)) pbmc <- RunUMAP(pbmc, dims = 1:10) DimPlot(pbmc, reduction = "umap", group.by = "tier") + scale_color_viridis_d() DimPlot(pbmc, reduction = "umap", group.by = "tier", split.by = "tier", ncol = 5) + scale_color_viridis_d() # Plot gene counts -------------------------------------------------------- stemcell.genes <- as.character(subset(markers, gene.module == "Stem genes")$gene) jmodules <- c("Stem genes", "Monocyte", "Stage I neutrophil", "Stage II neutrophil") marker.genes <- as.character(subset(markers, gene.module %in% jmodules)$gene) marker.genes <- as.character(subset(markers, gene.module %in% jmodules)$gene) # jgenes <- "Hlf" # jgenes <- stemcell.genes[1:10] jgenes <- "S100a8" jgenes <- "Hlf" # RidgePlot(pbmc, stemcell.genes, group.by = "tier") # VlnPlot(pbmc, stemcell.genes, group.by = "tier") FeaturePlot(pbmc, features = jgenes, order = TRUE) # plot entropy in single cells S.vec <- apply(pbmc@assays$RNA@counts, 2, function(jcell) CalculateEntropy(jcell, normalize.p = TRUE)) jmeta <- data.frame(S = S.vec) rownames(jmeta) <- names(S.vec) pbmc <- AddMetaData(object = pbmc, metadata = jmeta, col.name = "entropy") FeaturePlot(pbmc, features = 'entropy') + scale_color_viridis_c(direction = 1) # add real metadata??? dat.meta.celltypes <- as.data.frame(dat.meta) rownames(dat.meta.celltypes) <- dat.meta.celltypes$V1 pbmc <- AddMetaData(object = pbmc, metadata = dat.meta.celltypes) Seurat::DimPlot(pbmc, group.by = "marker") dev.off() # Get markers ------------------------------------------------------------- Idents(pbmc) <- pbmc$marker # de.output <- FindMarkers(pbmc, ident.1 = "Car1") jmarkers <- unique(dat.meta.celltypes$marker) names(jmarkers) <- jmarkers # de.output.lst <- lapply(jmarkers, function(jmarker){ # print(paste("Calculating diff exprs genes for:", jmarker)) # de.output <- FindMarkers(pbmc, ident.1 = jmarker) # }) de.output.lst <- FindAllMarkers(pbmc, only.pos = FALSE) saveRDS(de.output.lst, file = outrds)

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duoResults = function(dataDuo) { ## Load R Functions source("~/Development/R/DuoDevelopment/Subfunctions/duoCourseCompletionOrder.R") source("~/Development/R/DuoDevelopment/Subfunctions/duoDataExtract.R") duoList = duoDataExtract(dataDuo) duoList = duoCourseCompletionOrder(duoList) cat("Duolingo Development of the",duoList[[4]],"Phase 1 Beta Courses:") cat("\n") cat("(",duoList[[1]][[1]],".)",sep="") cat("\n") for (i in 1:duoList[[4]]) { cat("\n") cat("\n") if (duoList[[3]][i] != "Release Imminent") { cat(duoList[[2]][[i]],": ",duoList[[3]][i]," complete.",sep="") } else { cat(duoList[[2]][[i]],": ",duoList[[3]][i],".",sep="") } } }

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test-dataSplitting.R

# @file test_DataSplitting.R # Copyright 2019 Observational Health Data Sciences and Informatics # # This file is part of PatientLevelPrediction # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. library("testthat") context("Data splitting") test_that("Data stratified splitting", { # error message checks population1 <- data.frame(rowId=1:20, outcomeCount=c(1,1,1,1,rep(0,16))) expect_error(randomSplitter(population1, test=0.3, nfold=3)) population2 <- data.frame(rowId=1:200, outcomeCount=c(rep(1,42),rep(0,158))) expect_error(randomSplitter(population2, test=0.3, nfold=-1)) expect_error(randomSplitter(population2, test=1.5, nfold=5)) expect_error(randomSplitter(population2, test=-1, nfold=5)) # fold creation check 1 (fixed) test <- randomSplitter(population2, test=0.2, nfold=4) test <- merge(population2, test) test <- table(test$outcomeCount, test$index) test.returned <- paste(test, collapse='-') test.expected <- paste(matrix(c(32,32,32,31,31,8,9,9,8,8), ncol=5, byrow=T),collapse='-') expect_identical(test.returned, test.expected) # fold creation check 2 (sum) size <- 500 population3 <- data.frame(rowId=1:size, outcomeCount=c(rep(1,floor(size/3)),rep(0,size-floor(size/3)))) test <- randomSplitter(population3, test=0.2, nfold=4) test <- merge(population3, test) test <- table(test$outcomeCount, test$index) expect_that(sum(test), equals(size)) # test the training fraction parameter for learning curves size = 500 population4 <- data.frame(rowId=1:size, outcomeCount=c(rep(1,floor(size/3)), rep(0,size-floor(size/3)))) test <- randomSplitter(population4, test = 0.2, train = 0.4, nfold = 4) tolerance = 5 excludedPatients = 200 # test, if the number of patients in each fold are roughly the same expect_equal(length(test$index[test$index == 1]), length(test$index[test$index == 3]), tolerance = tolerance) expect_equal(length(test$index[test$index == 2]), length(test$index[test$index == 4]), tolerance = tolerance) expect_equal(length(test$index[test$index == 1]), length(test$index[test$index == 4]), tolerance = tolerance) # test, if patients were excluded according to the training fraction expect_equal(length(test$index[test$index == 0]), excludedPatients) }) test_that("Data splitting by time", { # error message checks population1 <- data.frame(rowId=1:200, outcomeCount=c(rep(1,42),rep(0,158)), cohortStartDate = as.Date("2016-01-01") + c(1:200)) expect_error(timeSplitter(population1, test=0.3, nfold=-1)) expect_error(timeSplitter(population1, test=1.5, nfold=5)) expect_error(timeSplitter(population1, test=-1, nfold=5)) # fold creation check (sum) size <- 500 set.seed(1) population2 <- data.frame(rowId=1:size, outcomeCount=sample(0:1,size,replace=TRUE),cohortStartDate = as.Date("2010-01-01") + c(1:size)) test <- timeSplitter(population2, test=0.2, nfold=4) test <- merge(population2, test) test <- table(test$outcomeCount, test$index) expect_that(sum(test), equals(size)) # test the training fraction parameter for learning curves size <- 500 set.seed(1) population3 <- data.frame(rowId=1:size, outcomeCount=sample(0:1,size,replace=TRUE), cohortStartDate = as.Date("2010-01-01") + c(1:size)) test <- timeSplitter(population3, test = 0.2, train = 0.4, nfold = 4) tolerance = 5 excludedPatients = 196 # test, if the number of patients in each fold are roughly the same expect_equal(length(test$index[test$index == 1]), length(test$index[test$index == 3]), tolerance = tolerance) expect_equal(length(test$index[test$index == 2]), length(test$index[test$index == 4]), tolerance = tolerance) expect_equal(length(test$index[test$index == 1]), length(test$index[test$index == 4]), tolerance = tolerance) # test, if patients were excluded according to the training fraction expect_equal(length(test$index[test$index == 0]), excludedPatients) }) test_that("Data splitting by subject", { # error message checks population1 <- data.frame(rowId=1:20, subjectId = 1:20, outcomeCount=c(1,1,1,1,rep(0,16))) expect_error(subjectSplitter(population1, test=0.3, nfold=3)) population2 <- data.frame(rowId=1:200,subjectId = 1:200, outcomeCount=c(rep(1,42),rep(0,158))) expect_error(subjectSplitter(population2, test=0.3, nfold=-1)) expect_error(subjectSplitter(population2, test=1.5, nfold=5)) expect_error(subjectSplitter(population2, test=-1, nfold=5)) test <- subjectSplitter(population2, test=0.2, nfold=4) test <- merge(population2, test) test <- table(test$outcomeCount, test$index) test.returned <- paste(test, collapse='-') test.expected <- paste(matrix(c(32,32,32,31,31,8,9,9,8,8), ncol=5, byrow=T),collapse='-') expect_identical(test.returned, test.expected) # test that people are not in multiple folds population3 <- data.frame(rowId=1:200,subjectId = rep(1:50,4), outcomeCount=c(rep(1,42),rep(0,158))) test <- subjectSplitter(population3, test=0.2, nfold=3) test <- merge(population3, test) expect_equal(unique(table(test$subjectId[test$index==-1])), 4) expect_equal(unique(table(test$subjectId[test$index==2])), 4) expect_equal(unique(table(test$subjectId[test$index==3])), 4) expect_equal(unique(table(test$subjectId[test$index==1])), 4) })

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`mu.rank.nna` <- function(x) { if (sum(dim(x)>1)< 2) { if ((n<-length(x))>0) { sx <- x[sl <- sort.list(x)] # sorted !NA ntie <- diff(c(0, (1:n)[c(sx[-1] != sx[-n], TRUE)])) # fast.rle x[sl] <- rep(cumsum(ntie) - (ntie-1)/2, ntie)# ranks } return(x) } else { lenx <- length(x) uxx <- mu.Sums(mu.GE(x)) rxx <-(uxx[[1]]+(lenx+1))/2 return(rxx) } }

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#' Get data indexes associated with taxa #' #' Given a [taxmap()] object, return data associated with each taxon in a #' given table included in that [taxmap()] object. #' \preformatted{ #' obj$obs(data, value = NULL, subset = NULL, #' recursive = TRUE, simplify = FALSE) #' obs(obj, data, value = NULL, subset = NULL, #' recursive = TRUE, simplify = FALSE)} #' #' @param obj ([taxmap()]) The [taxmap()] object containing taxon information to #' be queried. #' @param data Either the name of something in `obj$data` that has taxon #' information or a an external object with taxon information. For tables, #' there must be a column named "taxon_id" and lists/vectors must be named by #' taxon ID. #' @param value What data to return. This is usually the name of column in a #' table in `obj$data`. Any result of `all_names(obj)` can be used. If the #' value used has names, it is assumed that the names are taxon ids and the #' taxon ids are used to look up the correct values. #' @param subset Taxon IDs, TRUE/FALSE vector, or taxon indexes to find observations #' for. Default: All taxa in `obj` will be used. Any variable name that #' appears in [all_names()] can be used as if it was a vector on its own. #' @param recursive (`logical` or `numeric`) If `FALSE`, only return the #' observation assigned to the specified input taxa, not subtaxa. If `TRUE`, #' return all the observations of every subtaxa, etc. Positive numbers #' indicate the number of ranks below the each taxon to get observations for #' `0` is equivalent to `FALSE`. Negative numbers are equivalent to `TRUE`. #' @param simplify (`logical`) If `TRUE`, then combine all the results into a #' single vector of unique observation indexes. #' #' @return If `simplify = FALSE`, then a list of vectors of observation indexes #' are returned corresponding to the `data` argument. If `simplify = TRUE`, #' then the observation indexes for all `data` taxa are returned in a single #' vector. #' #' @name obs #' #' @examples #' # Get indexes of rows corresponding to each taxon #' obs(ex_taxmap, "info") #' #' # Get only a subset of taxon indexes #' obs(ex_taxmap, "info", subset = 1:2) #' #' # Get only a subset of taxon IDs #' obs(ex_taxmap, "info", subset = c("b", "c")) #' #' # Get only a subset of taxa using logical tests #' obs(ex_taxmap, "info", subset = taxon_ranks == "genus") #' #' # Only return indexes of rows assinged to each taxon explicitly #' obs(ex_taxmap, "info", recursive = FALSE) #' #' # Lump all row indexes in a single vector #' obs(ex_taxmap, "info", simplify = TRUE) #' #' # Return values from a dataset instead of indexes #' obs(ex_taxmap, "info", value = "name") #' NULL #' Apply function to observations per taxon #' #' Apply a function to data for the observations for each taxon. This is similar #' to using [obs()] with [lapply()] or [sapply()]. #' \preformatted{ #' obj$obs_apply(data, func, simplify = FALSE, value = NULL, #' subset = NULL, recursive = TRUE, ...) #' obs_apply(obj, data, func, simplify = FALSE, value = NULL, #' subset = NULL, recursive = TRUE, ...)} #' #' @param obj The [taxmap()] object containing taxon information to #' be queried. #' @param data Either the name of something in `obj$data` that has taxon #' information or a an external object with taxon information. For tables, #' there must be a column named "taxon_id" and lists/vectors must be named by #' taxon ID. #' @param func (`function`) The function to apply. #' @param simplify (`logical`) If `TRUE`, convert lists to vectors. #' @param value What data to give to the function. This is usually the name of #' column in a table in `obj$data`. Any result of `all_names(obj)` can be #' used, but it usually only makes sense to use columns in the dataset #' specified by the `data` option. By default, the indexes of observation in #' `data` are returned. #' @param subset Taxon IDs, TRUE/FALSE vector, or taxon indexes to use. #' Default: All taxa in `obj` will be used. Any variable name that appears in #' [all_names()] can be used as if it was a vector on its own. #' @param recursive (`logical` or `numeric`) If `FALSE`, only return the #' observation assigned to the specified input taxa, not subtaxa. If `TRUE`, #' return all the observations of every subtaxa, etc. Positive numbers #' indicate the number of ranks below the each taxon to get observations for #' `0` is equivalent to `FALSE`. Negative numbers are equivalent to `TRUE`. #' @param ... Extra arguments are passed to the function. #' #' @name obs_apply #' #' @examples #' # Find the average number of legs in each taxon #' obs_apply(ex_taxmap, "info", mean, value = "n_legs", simplify = TRUE) #' #' # One way to implement `n_obs` and find the number of observations per taxon #' obs_apply(ex_taxmap, "info", length, simplify = TRUE) #' NULL #' Filter observations with a list of conditions #' #' Filter data in a [taxmap()] object (in `obj$data`) with a #' set of conditions. See #' [dplyr::filter()] for the inspiration for this function and more #' information. Calling the function using the `obj$filter_obs(...)` style #' edits "obj" in place, unlike most R functions. However, calling the function #' using the `filter_obs(obj, ...)` imitates R's traditional copy-on-modify #' semantics, so "obj" would not be changed; instead a changed version would be #' returned, like most R functions. #' \preformatted{ #' obj$filter_obs(data, ..., drop_taxa = FALSE, drop_obs = TRUE, #' subtaxa = FALSE, supertaxa = TRUE, reassign_obs = FALSE) #' filter_obs(obj, data, ..., drop_taxa = FALSE, drop_obs = TRUE, #' subtaxa = FALSE, supertaxa = TRUE, reassign_obs = FALSE)} #' #' @param obj An object of type [taxmap()] #' @param data Dataset names, indexes, or a logical vector that indicates which datasets in #' `obj$data` to filter. If multiple datasets are filterd at once, then they must be the same #' length. #' @param ... One or more filtering conditions. Any variable name that appears #' in [all_names()] can be used as if it was a vector on its own. Each #' filtering condition can be one of two things: #' * `integer`: One or more dataset indexes. #' * `logical`: A `TRUE`/`FALSE` vector of length equal to the number of #' items in the dataset. #' @param drop_taxa (`logical` of length 1) If `FALSE`, preserve taxa #' even if all of their observations are filtered out. If `TRUE`, remove #' taxa for which all observations were filtered out. Note that only taxa that #' are unobserved due to this filtering will be removed; there might be other #' taxa without observations to begin with that will not be removed. #' @param drop_obs (`logical`) This only has an effect when `drop_taxa` is #' `TRUE`. When `TRUE`, observations for other data sets (i.e. not `data`) #' assigned to taxa that are removed when filtering `data` are also removed. #' Otherwise, only data for taxa that are not present in all other data sets #' will be removed. This option can be either simply `TRUE`/`FALSE`, meaning #' that all data sets will be treated the same, or a logical vector can be #' supplied with names corresponding one or more data sets in `obj$data`. For #' example, `c(abundance = TRUE, stats = FALSE)` would remove observations in #' `obj$data$abundance`, but not in `obj$data$stats`. #' @param subtaxa (`logical` or `numeric` of length 1) This only has an effect #' when `drop_taxa` is `TRUE`. If `TRUE`, include subtaxa of taxa passing the #' filter. Positive numbers indicate the number of ranks below the target taxa #' to return. `0` is equivalent to `FALSE`. Negative numbers are equivalent to #' `TRUE`. #' @param supertaxa (`logical` or `numeric` of length 1) This only has an #' effect when `drop_taxa` is `TRUE`. If `TRUE`, include supertaxa of taxa #' passing the filter. Positive numbers indicate the number of ranks above the #' target taxa to return. `0` is equivalent to `FALSE`. Negative numbers are #' equivalent to `TRUE`. #' @param reassign_obs (`logical`) This only has an effect when `drop_taxa` is #' `TRUE`. If `TRUE`, observations assigned to removed taxa will be reassigned #' to the closest supertaxon that passed the filter. If there are no supertaxa #' of such an observation that passed the filter, they will be filtered out if #' `drop_obs` is `TRUE`. This option can be either simply `TRUE`/`FALSE`, #' meaning that all data sets will be treated the same, or a logical vector #' can be supplied with names corresponding one or more data sets in #' `obj$data`. For example, `c(abundance = TRUE, stats = FALSE)` would #' reassign observations in `obj$data$abundance`, but not in `obj$data$stats`. #' @param target DEPRECIATED. use "data" instead. #' #' @return An object of type [taxmap()] #' #' @examples #' # Filter by row index #' filter_obs(ex_taxmap, "info", 1:2) #' #' # Filter by TRUE/FALSE #' filter_obs(ex_taxmap, "info", dangerous == FALSE) #' filter_obs(ex_taxmap, "info", dangerous == FALSE, n_legs > 0) #' filter_obs(ex_taxmap, "info", n_legs == 2) #' #' # Remove taxa whose obserservations were filtered out #' filter_obs(ex_taxmap, "info", n_legs == 2, drop_taxa = TRUE) #' #' # Preserve other data sets while removing taxa #' filter_obs(ex_taxmap, "info", n_legs == 2, drop_taxa = TRUE, #' drop_obs = c(abund = FALSE)) #' #' # When filtering taxa, do not return supertaxa of taxa that are preserved #' filter_obs(ex_taxmap, "info", n_legs == 2, drop_taxa = TRUE, #' supertaxa = FALSE) #' #' # Filter multiple datasets at once #' filter_obs(ex_taxmap, c("info", "phylopic_ids", "foods"), n_legs == 2) #' #' @family taxmap manipulation functions #' #' @name filter_obs NULL #' Subset columns in a [taxmap()] object #' #' Subsets columns in a [taxmap()] object. Takes and returns a #' [taxmap()] object. Any variable name that appears in #' [all_names()] can be used as if it was a vector on its own. See #' [dplyr::select()] for the inspiration for this function and more #' information. Calling the function using the `obj$select_obs(...)` style #' edits "obj" in place, unlike most R functions. However, calling the function #' using the `select_obs(obj, ...)` imitates R's traditional copy-on-modify #' semantics, so "obj" would not be changed; instead a changed version would be #' returned, like most R functions. #' \preformatted{ #' obj$select_obs(data, ...) #' select_obs(obj, data, ...)} #' #' @param obj An object of type [taxmap()] #' @param data Dataset names, indexes, or a logical vector that indicates which tables in #' `obj$data` to subset columns in. Multiple tables can be subset at once. #' @param ... One or more column names to return in the new object. Each can be #' one of two things: \describe{ \item{expression with unquoted column #' name}{The name of a column in the dataset typed as if it was #' a variable on its own.} \item{`numeric`}{Indexes of columns in #' the dataset} } To match column names with a character vector, #' use `matches("my_col_name")`. To match a logical vector, convert it to #' a column index using `which`. #' @param target DEPRECIATED. use "data" instead. #' #' @return An object of type [taxmap()] #' #' @family taxmap manipulation functions #' #' @examples #' # Selecting a column by name #' select_obs(ex_taxmap, "info", dangerous) #' #' # Selecting a column by index #' select_obs(ex_taxmap, "info", 3) #' #' # Selecting a column by regular expressions #' select_obs(ex_taxmap, "info", matches("^n")) #' #' @name select_obs NULL #' Add columns to [taxmap()] objects #' #' Add columns to tables in `obj$data` in [taxmap()] objects. See #' [dplyr::mutate()] for the inspiration for this function and more information. #' Calling the function using the `obj$mutate_obs(...)` style edits "obj" in #' place, unlike most R functions. However, calling the function using the #' `mutate_obs(obj, ...)` imitates R's traditional copy-on-modify semantics, so #' "obj" would not be changed; instead a changed version would be returned, like #' most R functions. #' \preformatted{ #' obj$mutate_obs(data, ...) #' mutate_obs(obj, data, ...)} #' #' @param obj An object of type [taxmap()] #' @param data Dataset name, index, or a logical vector that indicates which dataset in #' `obj$data` to add columns to. #' @param ... One or more named columns to add. Newly created columns can be #' referenced in the same function call. Any variable name that appears in #' [all_names()] can be used as if it was a vector on its own. #' @param target DEPRECIATED. use "data" instead. #' #' @return An object of type [taxmap()] #' #' @examples #' #' # Add column to existing tables #' mutate_obs(ex_taxmap, "info", #' new_col = "Im new", #' newer_col = paste0(new_col, "er!")) #' #' # Create columns in a new table #' mutate_obs(ex_taxmap, "new_table", #' nums = 1:10, #' squared = nums ^ 2) #' #' # Add a new vector #' mutate_obs(ex_taxmap, "new_vector", 1:10) #' #' # Add a new list #' mutate_obs(ex_taxmap, "new_list", list(1, 2)) #' #' @family taxmap manipulation functions #' @name mutate_obs NULL #' Replace columns in [taxmap()] objects #' #' Replace columns of tables in `obj$data` in [taxmap()] objects. See #' [dplyr::transmute()] for the inspiration for this function and more #' information. Calling the function using the `obj$transmute_obs(...)` style #' edits "obj" in place, unlike most R functions. However, calling the function #' using the `transmute_obs(obj, ...)` imitates R's traditional copy-on-modify #' semantics, so "obj" would not be changed; instead a changed version would be #' returned, like most R functions. #' \preformatted{ #' obj$transmute_obs(data, ...) #' transmute_obs(obj, data, ...)} #' #' @param obj An object of type [taxmap()] #' @param data Dataset name, index, or a logical vector that indicates which dataset in #' `obj$data` to use. #' @param ... One or more named columns to add. Newly created columns can be #' referenced in the same function call. Any variable name that appears in #' [all_names()] can be used as if it was a vector on its own. #' @param target DEPRECIATED. use "data" instead. #' #' @return An object of type [taxmap()] #' @examples #' # Replace columns in a table with new columns #' transmute_obs(ex_taxmap, "info", new_col = paste0(name, "!!!")) #' #' @family taxmap manipulation functions #' #' @name transmute_obs NULL #' Sort user data in [taxmap()] objects #' #' Sort rows of tables or the elements of lists/vectors in the `obj$data` list #' in [taxmap()] objects. Any variable name that appears in [all_names()] can be #' used as if it was a vector on its own. See [dplyr::arrange()] for the #' inspiration for this function and more information. Calling the function #' using the `obj$arrange_obs(...)` style edits "obj" in place, unlike most R #' functions. However, calling the function using the `arrange_obs(obj, ...)` #' imitates R's traditional copy-on-modify semantics, so "obj" would not be #' changed; instead a changed version would be returned, like most R functions. #' \preformatted{ #' obj$arrange_obs(data, ...) #' arrange_obs(obj, data, ...)} #' #' @param obj An object of type [taxmap()]. #' @param data Dataset names, indexes, or a logical vector that indicates which datasets in #' `obj$data` to sort If multiple datasets are sorted at once, then they must be the same #' length. #' @param ... One or more expressions (e.g. column names) to sort on. #' @param target DEPRECIATED. use "data" instead. #' #' @return An object of type [taxmap()] #' #' @examples #' # Sort in ascending order #' arrange_obs(ex_taxmap, "info", n_legs) #' arrange_obs(ex_taxmap, "foods", name) #' #' # Sort in decending order #' arrange_obs(ex_taxmap, "info", desc(n_legs)) #' #' # Sort multiple datasets at once #' arrange_obs(ex_taxmap, c("info", "phylopic_ids", "foods"), n_legs) #' #' @family taxmap manipulation functions #' #' @name arrange_obs NULL #' Sample n observations from [taxmap()] #' #' Randomly sample some number of observations from a [taxmap()] object. Weights #' can be specified for observations or the taxa they are classified by. Any #' variable name that appears in [all_names()] can be used as if it was a vector #' on its own. See [dplyr::sample_n()] for the inspiration for this function. #' Calling the function using the `obj$sample_n_obs(...)` style edits "obj" in #' place, unlike most R functions. However, calling the function using the #' `sample_n_obs(obj, ...)` imitates R's traditional copy-on-modify semantics, #' so "obj" would not be changed; instead a changed version would be returned, #' like most R functions. #' \preformatted{ #' obj$sample_n_obs(data, size, replace = FALSE, #' taxon_weight = NULL, obs_weight = NULL, #' use_supertaxa = TRUE, collapse_func = mean, ...) #' sample_n_obs(obj, data, size, replace = FALSE, #' taxon_weight = NULL, obs_weight = NULL, #' use_supertaxa = TRUE, collapse_func = mean, ...)} #' #' @param obj ([taxmap()]) The object to sample from. #' @param data Dataset names, indexes, or a logical vector that indicates which datasets in #' `obj$data` to sample. If multiple datasets are sampled at once, then they must be the same #' length. #' @param size (`numeric` of length 1) The number of observations to #' sample. #' @param replace (`logical` of length 1) If `TRUE`, sample with #' replacement. #' @param taxon_weight (`numeric`) Non-negative sampling weights of each #' taxon. If `use_supertaxa` is `TRUE`, the weights for each taxon #' in an observation's classification are supplied to `collapse_func` to #' get the observation weight. If `obs_weight` is also specified, the two #' weights are multiplied (after `taxon_weight` for each observation is #' calculated). #' @param obs_weight (`numeric`) Sampling weights of each observation. If #' `taxon_weight` is also specified, the two weights are multiplied (after #' `taxon_weight` for each observation is calculated). #' @param use_supertaxa (`logical` or `numeric` of length 1) Affects how the #' `taxon_weight` is used. If `TRUE`, the weights for each taxon in an #' observation's classification are multiplied to get the observation weight. #' Otherwise, just the taxonomic level the observation is assign to it #' considered. If `TRUE`, use all supertaxa. Positive numbers indicate the #' number of ranks above each taxon to use. `0` is equivalent to `FALSE`. #' Negative numbers are equivalent to `TRUE`. #' @param collapse_func (`function` of length 1) If `taxon_weight` option is #' used and `supertaxa` is `TRUE`, the weights for each #' taxon in an observation's classification are supplied to #' `collapse_func` to get the observation weight. This function should #' take numeric vector and return a single number. #' @param ... Additional options are passed to [filter_obs()]. #' @param target DEPRECIATED. use "data" instead. #' #' @return An object of type [taxmap()] #' #' @examples #' # Sample 2 rows without replacement #' sample_n_obs(ex_taxmap, "info", 2) #' sample_n_obs(ex_taxmap, "foods", 2) #' #' # Sample with replacement #' sample_n_obs(ex_taxmap, "info", 10, replace = TRUE) #' #' # Sample some rows for often then others #' sample_n_obs(ex_taxmap, "info", 3, obs_weight = n_legs) #' #' # Sample multiple datasets at once #' sample_n_obs(ex_taxmap, c("info", "phylopic_ids", "foods"), 3) #' #' @family taxmap manipulation functions #' #' @name sample_n_obs NULL #' Sample a proportion of observations from [taxmap()] #' #' Randomly sample some proportion of observations from a [taxmap()] #' object. Weights can be specified for observations or their taxa. See #' [dplyr::sample_frac()] for the inspiration for this function. Calling the #' function using the `obj$sample_frac_obs(...)` style edits "obj" in place, unlike #' most R functions. However, calling the function using the `sample_frac_obs(obj, #' ...)` imitates R's traditional copy-on-modify semantics, so "obj" would not #' be changed; instead a changed version would be returned, like most R #' functions. #' \preformatted{ #' obj$sample_frac_obs(data, size, replace = FALSE, #' taxon_weight = NULL, obs_weight = NULL, #' use_supertaxa = TRUE, collapse_func = mean, ...) #' sample_frac_obs(obj, data, size, replace = FALSE, #' taxon_weight = NULL, obs_weight = NULL, #' use_supertaxa = TRUE, collapse_func = mean, ...)} #' #' @param obj ([taxmap()]) The object to sample from. #' @param data Dataset names, indexes, or a logical vector that indicates which datasets in #' `obj$data` to sample. If multiple datasets are sample at once, then they must be the same #' length. #' @param size (`numeric` of length 1) The proportion of observations to #' sample. #' @param replace (`logical` of length 1) If `TRUE`, sample with #' replacement. #' @param taxon_weight (`numeric`) Non-negative sampling weights of each #' taxon. If `use_supertaxa` is `TRUE`, the weights for each taxon #' in an observation's classification are supplied to `collapse_func` to #' get the observation weight. If `obs_weight` is also specified, the two #' weights are multiplied (after `taxon_weight` for each observation is #' calculated). #' @param obs_weight (`numeric`) Sampling weights of each observation. If #' `taxon_weight` is also specified, the two weights are multiplied #' (after `taxon_weight` for each observation is calculated). #' @param use_supertaxa (`logical` or `numeric` of length 1) Affects how the #' `taxon_weight` is used. If `TRUE`, the weights for each taxon in #' an observation's classification are multiplied to get the observation #' weight. If `FALSE` just the taxonomic level the observation is assign to it #' considered. Positive numbers indicate the number of ranks above the #' each taxon to use. `0` is equivalent to `FALSE`. Negative numbers #' are equivalent to `TRUE`. #' @param collapse_func (`function` of length 1) If `taxon_weight` #' option is used and `supertaxa` is `TRUE`, the weights for each #' taxon in an observation's classification are supplied to #' `collapse_func` to get the observation weight. This function should #' take numeric vector and return a single number. #' @param ... Additional options are passed to [filter_obs()]. #' @param target DEPRECIATED. use "data" instead. #' #' @return An object of type [taxmap()] #' #' @examples #' # Sample half of the rows fram a table #' sample_frac_obs(ex_taxmap, "info", 0.5) #' #' # Sample multiple datasets at once #' sample_frac_obs(ex_taxmap, c("info", "phylopic_ids", "foods"), 0.5) #' #' @family taxmap manipulation functions #' #' @name sample_frac_obs NULL #' Count observations in [taxmap()] #' #' Count observations for each taxon in a data set in a [taxmap()] object. This #' includes observations for the specific taxon and the observations of its #' subtaxa. "Observations" in this sense are the items (for list/vectors) or #' rows (for tables) in a dataset. By default, observations in the first data #' set in the [taxmap()] object is used. For example, if the data set is a #' table, then a value of 3 for a taxon means that their are 3 rows in that #' table assigned to that taxon or one of its subtaxa. #' \preformatted{ #' obj$n_obs(data) #' n_obs(obj, data)} #' #' @param obj ([taxmap()]) #' @param data Dataset name, index, or a logical vector that indicates which dataset in #' `obj$data` to add columns to. #' @param target DEPRECIATED. use "data" instead. #' #' @return `numeric` #' #' @examples #' # Get number of observations for each taxon in first dataset #' n_obs(ex_taxmap) #' #' # Get number of observations in a specified data set #' n_obs(ex_taxmap, "info") #' n_obs(ex_taxmap, "abund") #' #' # Filter taxa using number of observations in the first table #' filter_taxa(ex_taxmap, n_obs > 1) #' #' @family taxmap data functions #' #' @name n_obs NULL #' Count observation assigned in [taxmap()] #' #' Count observations for each taxon in a data set in a [taxmap()] object. This #' includes observations for the specific taxon but NOT the observations of its #' subtaxa. "Observations" in this sense are the items (for list/vectors) or #' rows (for tables) in a dataset. By default, observations in the first data #' set in the [taxmap()] object is used. For example, if the data set is a #' table, then a value of 3 for a taxon means that their are 3 rows in that #' table assigned to that taxon. #' \preformatted{ #' obj$n_obs_1(data) #' n_obs_1(obj, data)} #' #' @param obj ([taxmap()]) #' @param data Dataset name, index, or a logical vector that indicates which dataset in #' `obj$data` to add columns to. #' @param target DEPRECIATED. use "data" instead. #' #' @return `numeric` #' #' @examples #' # Get number of observations for each taxon in first dataset #' n_obs_1(ex_taxmap) #' #' # Get number of observations in a specified data set #' n_obs_1(ex_taxmap, "info") #' n_obs_1(ex_taxmap, "abund") #' #' # Filter taxa using number of observations in the first table #' filter_taxa(ex_taxmap, n_obs_1 > 0) #' #' @family taxmap data functions #' #' @name n_obs_1 NULL #' Get a data set from a taxmap object #' #' Get a data set from a taxmap object and complain if it does not #' exist. #' #' @param obj A taxmap object #' @param data Dataset name, index, or a logical vector that indicates which dataset in #' `obj$data` to add columns to. #' #' @examples #' \dontrun{ #' # Get data set by name #' get_dataset(ex_taxmap, "info") #' #' # Get data set by indeex_taxmap #' get_dataset(ex_taxmap, 1) #' #' # Get data set by T/F vector #' get_dataset(ex_taxmap, startsWith(names(ex_taxmap$data), "i")) #' #' } #' #' @name get_dataset NULL

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/explorerApp.R \name{explorerApp} \alias{explorerApp} \title{Codebook Explorer Shiny App and RStudio Add-in} \usage{ explorerApp() } \description{ Codebook Explorer Shiny App and RStudio Add-in }

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shinyDemo.R

if (!require("shiny")) stop("This demo requires shiny.") library(rgl) library(misc3d) options(rgl.useNULL = TRUE) set.seed(123) ui <- fluidPage( registerSceneChange(), titlePanel("Nelder-Mead"), sidebarLayout( sidebarPanel( helpText("The Nelder-Mead algorithm evaluates the function", "on the vertices of a simplex. At each step it", "moves one vertex of the simplex to a better value."), sliderInput("Slider", min=0, max=59, step=1, value=0, label="Steps", animate=animationOptions(200, loop=TRUE)), sliderInput("Slider2", min=0, max=59, step=1, value=0, label="Cumulative", animate=animationOptions(200, loop=TRUE)), playwidgetOutput('thecontroller'), playwidgetOutput('thecontroller2'), actionButton('newStart', 'Restart')), mainPanel( rglwidgetOutput('thewidget', width = "100%", height = 512)) ) ) u1 <- runif(1) u2 <- runif(1)*(1-u1) u3 <- 1 - u1 - u2 # Example modified from ?contour3d #Example 2: Nested contours of mixture of three tri-variate normal densities nmix3 <- function(x, y, z, m, s) { u1 * dnorm(x, m, s) * dnorm(y, m, s) * dnorm(z, m, s) + u2 * dnorm(x, -m, s) * dnorm(y, -m, s) * dnorm(z, -m, s) + u3 * dnorm(x, m, s) * dnorm(y, -1.5 * m, s) * dnorm(z, m, s) } f <- function(x,y,z) nmix3(x,y,z,.5,.5) g <- function(n = 40, k = 5, alo = 0.1, ahi = 0.5, cmap = heat.colors) { th <- seq(0.05, 0.2, len = k) col <- rev(cmap(length(th))) al <- seq(alo, ahi, len = length(th)) x <- seq(-2, 2, len=n) bg3d(col="white") contour3d(f,th,x,x,x,color=col,alpha=al) # nolint } f3 <- function(x) -f(x[1], x[2], x[3]) g(20,3) surface <- scene3d() close3d() neldermead <- function(x, f) { n <- nrow(x) p <- ncol(x) if (n != p + 1) stop(paste('Need', p + 1, 'starting points')) fx <- rep(NA, n) for (i in 1:n) fx[i] <- f(x[i,]) o <- order(fx) fx <- fx[o] x <- x[o,] xmid <- apply(x[1:p,], 2, mean) z1 <- xmid - (x[n,] - xmid) fz1 <- f(z1) if (fz1 < fx[1]) { z2 <- xmid - 2*(x[n,] - xmid) fz2 <- f(z2) if (fz2 < fz1) { x[n,] <- z2 } else { x[n,] <- z1 } } else if (fz1 < fx[p]) { x[n,] <- z1 } else { if (fz1 < fx[n]) { x[n,] <- z1 fx[n] <- fz1 } z3 <- xmid + (x[n,] - xmid)/2 fz3 <- f(z3) if (fz3 < fx[n]) { x[n,] <- z3 } else { for (i in 2:n) { x[i,] <- x[1,] + (x[i,] - x[1,])/2 } } } return(x) } showsimplex <- function(x, f, col="blue") { n <- nrow(x) z <- numeric(n) for (i in 1:n) z[i] <- f(x[i,]) xyz <- cbind(x, z) # This is tricky: # 1. draw all lines, taking vertices two at a time: c(segments3d(xyz[as.numeric(combn(n, 2)),], col="black", depth_test = "lequal"), # 2. draw all faces, taking vertices three at a time: triangles3d(xyz[as.numeric(combn(n, 3)),], col=col, alpha=0.3)) } setStartPoint <- function() { xyz <- matrix(rnorm(12, sd=0.1) + rep(rnorm(3,sd=2), each=4), 4, 3) subsets <-list() for (i in 1:60) { xyz <- neldermead(xyz,f3) subset <- showsimplex(xyz,f3) subsets <-c(subsets,list(subset)) } names(subsets) <- seq_along(subsets) subsets } server <- function(input, output, session) { plot3d(surface) dev <- cur3d() save <- options(rgl.inShiny = TRUE) on.exit(options(save)) session$onSessionEnded(function() { set3d(dev) close3d() }) path <- reactiveValues(subsets = setStartPoint()) observeEvent(input$newStart, { set3d(dev) deletes <- unique(unlist(path$subsets)) if (length(deletes)) delFromSubscene3d(deletes) subsets <- setStartPoint() adds <- unique(unlist(subsets)) session$sendCustomMessage("sceneChange", sceneChange("thewidget", delete = deletes, add = adds, skipRedraw = TRUE)) path$subsets <- subsets updateSliderInput(session, "Slider", value=0) updateSliderInput(session, "Slider2", value=0) session$onFlushed(function() session$sendCustomMessage("sceneChange", sceneChange("thewidget", skipRedraw = FALSE))) }) output$thewidget <- renderRglwidget({ rglwidget(controllers=c("thecontroller", "thecontroller2")) }) output$thecontroller <- renderPlaywidget({ if (length(path$subsets)) playwidget("thewidget", respondTo = "Slider", subsetControl(1, path$subsets), start = 1, stop = length(path$subsets)) }) output$thecontroller2 <- renderPlaywidget({ if (length(path$subsets)) playwidget("thewidget", respondTo = "Slider2", subsetControl(1, path$subsets, accumulate = TRUE)) }) } if (interactive()) shinyApp(ui = ui, server = server)

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copy_to_clipboard.R

#' Transfer text strings to the clipboard ready for paste #' #' This is only guaranteed for Windows; in the case of Linux you will #' need to have the \code{xclip} command installed and visible on the \code{PATH} #' and for Mac OS you will need to have \code{pbcopy} similarly available. #' In any case the transfer to the clipboard is only activated while in #' an interactive session. #' #' It behaves like \code{base::cat} but differs in three respects. #' #' First, if \code{file} is left missing, in an interactive session, #' the default file is a clipboard device, if possible. #' #' Second, the return value is \code{invisible(x)} rather than #' \code{invisible(NULL)} as it is for \code{base::cat}. #' #' Third, it only has a copying side-effect if used in an interactive session. #' In a non-interactive session it merely returns the \code{x} argument, invisibly. #' #' Note the on \code{Windows} the function \code{utils::writeClipboard} offers #' a much more extensive range of possibilities for communicating with the #' clipboard device, but this facility is only available on \code{Windows}. #' #' @param x a characeter string vector #' @param ... additional arguments as for \code{cat} #' @param file a file or connection (usually left at the default) #' #' @return \code{x}, invisibly (as for a print method) #' @export copy_to_clipboard <- function(x, ..., file = con) { if(missing(file)) { oldOpt <- options(warn = -1) on.exit(options(oldOpt)) con <- switch(Sys.info()["sysname"], Linux = { if(system("which xclip", ignore.stdout = TRUE, ignore.stderr = TRUE) == 0) { pipe("xclip -selection clipboard -i", open = "w") } else NULL }, Windows = { base::file("clipboard") }, { ### guessing some version of Mac OS! if(system("which pbcopy", ignore.stdout = TRUE, ignore.stderr = TRUE) == 0) { pipe("pbcopy", "w") } else NULL }) if(!is.null(con)) on.exit(close(con), add = TRUE) } if(interactive()) cat(x, file = file, ...) invisible(x) }

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#' sudo$ #' @description This function executes the given command as sudo #' @param command A bash command to execute as sudo #' @param env_var Should the password be saved for further use #' @export sudo <- function(command, intern = F, ignore.stdout = F, ignore.stderr = T, env_var = T, cmd = F){ if(os() == "Windows"){ if(cmd){ out <- command } else { out <- exec(glue::glue("{ command }"), intern = intern, ignore.stdout = ignore.stdout, ignore.stderr = ignore.stderr) } } else { if(class(try(keyring::key_get("SUDO_PASS"), silent = T))[1] == "try-error"){ stop("Sudo password not found in keyring") } if(cmd){ out <- glue::glue("echo { keyring::key_get('SUDO_PASS') } | sudo -S { command }") } else { out <- exec(glue::glue("echo { keyring::key_get('SUDO_PASS') } | sudo -S { command }"), intern = intern, ignore.stdout = ignore.stdout, ignore.stderr = ignore.stderr) } if(!env_var) keyring::key_delete("SUDO_PASS") } return(out) } #' set_sudo #' @export set_sudo <- function(pw){ keyring::key_set_with_raw_value( "SUDO_PASS", password = charToRaw(pw) ) } #' parse_curl_request #' @export parse_curl_request <- function(req_string = NULL, execute = F){ if(is.null(req_string)){ req_string <- clipr::read_clip() if(!stringr::str_detect(req_string[1], "^\\s*curl")){ stop(glue::glue("No curl request detected.\n Input: \"{req_string}\"")) } } tot_req <- req_string %>% paste(collapse = "\n") %>% stringr::str_split("\n") %>% purrr::pluck(1) url <- tot_req %>% stringr::str_subset("^curl") %>% stringr::str_extract("(?<=').*?(?=')") tmp <- tot_req %>% stringr::str_subset("-H") %>% stringr::str_extract("(?<=').*?(?=')") %>% stringr::str_split("\\:\\s+") headers <- purrr::map_chr(tmp, 2) %>% stringr::str_replace_all(c("/" = "\\\\/" )) names(headers) <- purrr::map_chr(tmp, 1) header_string <- paste(utils::capture.output(dput(headers))) r_code <- glue::glue("httr::GET(url = \"{url}\", \nhttr::add_headers(.headers = c({paste(header_string, collapse = '\n')}\n)))") dt <- tibble::tibble(url = url, headers = list(headers), r_code) if(execute){ dt$req <- list(try(httr::GET(url = url, httr::add_headers(.headers = headers)))) } return(dt) } #' status_code_is #' @export status_code_is <- function(req, code = 200){ req$status_code == code } #' test_headers #' @export test_headers <- function(url, headers, test_fun = status_code_is, ...){ test_fun_args <- list(...) names(headers) %>% purrr::map_dfr(~{ tmp <- headers tmp <- tmp[names(tmp) != .x] req <- try(httr::GET(url = url, httr::add_headers(.headers = tmp))) trig <- F if(!inherits(req, "try-error")){ trig <- do.call(test_fun, c(list(req), test_fun_args)) } return(tibble::tibble(removed_header = .x, required = !trig)) }) } #' ufw #' @export ufw <- function(command, port, cmd = F, ...){ sudo(glue::glue("ufw { command } { port }"), cmd = cmd, ...) } #' os #' @export os <- function() Sys.info()['sysname'] #' exec #' @export exec <- function(string, cmd = F, ...){ if(cmd){ return(string)} else{ if(os() == "Windows"){ return(shell(string, ...)) }else { return(system(string, ...)) } } } #' run_as_job #' @export run_as_job <- function(.command, import_global = F, import_package = T, env_to_import = NULL, output = ".tmp.Rdata"){ current_env <- rlang::current_env() if(import_global){.GlobalEnv %>% as.list %>% purrr::imap(~{current_env[[.y]] <- .x})} if(!is.null(env_to_import)){env_to_import %>% as.list %>% purrr::imap(~{current_env[[.y]] <- .x})} if(!exists("env")){env <- rlang::new_environment()} if(import_package){packages <- (.packages())} else {packages <- "base"} if(fs::file_exists(output)){fs::file_delete(output)} if(fs::file_exists("script_for_job")){fs::file_delete("script_for_job")} .command %>% as.character %>% .[2] %>% stringr::str_remove_all("\\{|\\}") %>% paste(paste(glue::glue("pacman::p_load({ packages})"), collapse = "\n"), ., glue::glue('if(exists("out")){save(out, file = "[output]")}',.open = "[", .close = "]")) %>% stringr::str_trim(.) %>% stringr::str_split("\n") %>% .[[1]] %>% writeLines("script_for_job") rstudioapi::jobRunScript("script_for_job", workingDir = getwd(), importEnv = env) if(fs::file_exists(output)){load(output)} if(fs::file_exists(output)){fs::file_delete(output)} # if(fs::file_exists("script_for_job")){fs::file_delete("script_for_job")} if(exists("out")){return(out)} } #' wait #' @export wait <- function(mean = 1, sd = .1, verbose = F){ wait_time <- abs(stats::rnorm(1, mean, sd)) Sys.sleep(wait_time) if(verbose){message("Waiting ", round(wait_time, 2), " seconds")} } #' message_pipe #' @export `%message%` <- function(.tbl, to_print = ""){ if(is.character(to_print)){ message(to_print) } if(inherits(to_print,"formula") | inherits(to_print,"function")){ mes <- .tbl %>% list %>% purrr::map(to_print) %>% purrr::pluck(1) message(mes) } return(invisible(.tbl)) } #' simule_map #' @export simule_map <- function(.list, index = 1, env = .GlobalEnv){ env$.x <- .list[[index]] env$.y <- names(.list)[index] return(env$.x) } #' current_user #' @export current_user <- function(intern = T, cmd = F, ...){ if(cmd) return("echo $USER") else return(system("echo $USER", ...)) } #' source_rscript #' @export source_rscript <- function(path, cmd = F){ exec(glue::glue("Rscript { path }"), cmd = cmd) } #' append #' @export append <- function(path, string, cmd = F){ exec(glue::glue("echo { string } >> { path }"), cmd = cmd) } #' chmod #' @export chmod <- function(path, right, recursive = NULL, cmd, ...){ recursive <- ifelse(is.null(recursive), "", recursive) exec(glue::glue("chmod { recursive } { right } { path }"), cmd = cmd, ...) } #' move #' @export move <- function(origin, desg, recursive = F, cmd = F){ exec(glue::glue("cp { origin } { dest }"), cmd = T) }

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/data/genthat_extracted_code/lfl/examples/as.matrix.fsets.Rd.R

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as.matrix.fsets.Rd.R

library(lfl) ### Name: as.matrix.fsets ### Title: Convert a 'fsets' object into matrix ### Aliases: as.matrix.fsets ### Keywords: models robust multivariate ### ** Examples ff <- fcut(runif(10), breaks=c(0, 0.5, 1), name='age') as.matrix(ff)

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singjc/DIAlignR

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paramsDIAlignR.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{paramsDIAlignR} \alias{paramsDIAlignR} \title{Parameters for the alignment functions} \usage{ paramsDIAlignR() } \value{ A list of parameters: \item{runType}{(string) must be one of the strings "DIA_Proteomics", "DIA_IPF", "DIA_Metabolomics".} \item{chromFile}{(string) must either be "mzML" or "sqMass".} \item{maxFdrQuery}{(numeric) a numeric value between 0 and 1. It is used to filter peptides from osw file which have SCORE_MS2.QVALUE less than itself.} \item{maxIPFFdrQuery}{(numeric) A numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_IPF.QVALUE less than itself. (For PTM IPF use)} \item{maxPeptideFdr}{(numeric) a numeric value between 0 and 1. It is used to filter peptides from osw file which have SCORE_PEPTIDE.QVALUE less than itself.} \item{analyteFDR}{(numeric) the upper limit of feature FDR to be it considered for building tree.} \item{treeDist}{(string) the method used to build distance matrix. Must be either "rsquared", "count" or "RSE".} \item{treeAgg}{(string) the method used for agglomeration while performing hierarchical clustering. Must be either "single", "average" or "complete".} \item{alignToRoot}{(logical) if TRUE, align leaves to the root in hierarchical clustering, else use already save aligned vectors.} \item{prefix}{(string) name to be used to define merged runs.} \item{context}{(string) used in pyprophet peptide. Must be either "run-specific", "experiment-wide", or "global".} \item{unalignedFDR}{(numeric) must be between 0 and maxFdrQuery. Features below unalignedFDR are considered for quantification even without the RT alignment.} \item{alignedFDR1}{(numeric) must be between unalignedFDR and alignedFDR2. Features below alignedFDR1 and aligned to the reference are considered for quantification.} \item{alignedFDR2}{(numeric) must be between alignedFDR1 and maxFdrQuery. Features below alignedFDR2 and within certain distance from the aligned time are considered for quantification after the alignment.} \item{criterion}{(integer) strategy to select peak if found overlapping peaks. 1:intensity, 2: RT overlap, 3: mscore, 4: edge distance} \item{level}{(string) apply maxPeptideFDR on Protein as well if specified as "Protein". Default: "Peptide".} \item{integrationType}{(string) method to ompute the area of a peak contained in XICs. Must be from "intensity_sum", "trapezoid", "simpson".} \item{baseSubtraction}{{logical} TRUE: remove background from peak signal using estimated noise levels.} \item{baselineType}{(string) method to estimate the background of a peak contained in XICs. Must be from "none", "base_to_base", "vertical_division_min", "vertical_division_max".} \item{fitEMG}{(logical) enable/disable exponentially modified gaussian peak model fitting.} \item{recalIntensity}{(logical) recalculate intensity for all analytes.} \item{fillMissing}{(logical) calculate intensity for ananlytes for which features are not found.} \item{XICfilter}{(string) must be either sgolay, boxcar, gaussian, loess or none.} \item{polyOrd}{(integer) order of the polynomial to be fit in the kernel.} \item{kernelLen}{(integer) number of data-points to consider in the kernel.} \item{globalAlignment}{(string) must be either "loess" or "linear".} \item{globalAlignmentFdr}{(numeric) a numeric value between 0 and 1. Features should have m-score lower than this value for participation in LOESS fit.} \item{globalAlignmentSpan}{(numeric) spanvalue for LOESS fit. For targeted proteomics 0.1 could be used.} \item{RSEdistFactor}{(numeric) defines how much distance in the unit of rse remains a noBeef zone.} \item{normalization}{(string) must be selected from "mean", "l2".} \item{simMeasure}{(string) must be selected from dotProduct, cosineAngle, crossCorrelation, cosine2Angle, dotProductMasked, euclideanDist, covariance and correlation.} \item{alignType}{(numeric) available alignment methods are "global", "local" and "hybrid".} \item{goFactor}{(numeric) penalty for introducing first gap in alignment. This value is multiplied by base gap-penalty. Should be between 10-1000.} \item{geFactor}{(numeric) penalty for introducing subsequent gaps in alignment. This value is multiplied by base gap-penalty.} \item{cosAngleThresh}{(numeric) in simType = dotProductMasked mode, angular similarity should be higher than cosAngleThresh otherwise similarity is forced to zero.} \item{OverlapAlignment}{(logical) an input for alignment with free end-gaps. False: Global alignment, True: overlap alignment.} \item{dotProdThresh}{(numeric) in simType = dotProductMasked mode, values in similarity matrix higher than dotProdThresh quantile are checked for angular similarity.} \item{gapQuantile}{(numeric) must be between 0 and 1. This is used to calculate base gap-penalty from similarity distribution.} \item{kerLen}{(integer) In simType = crossCorrelation, length of the kernel used to sum similarity score. Must be an odd number.} \item{hardConstrain}{(logical) if FALSE; indices farther from noBeef distance are filled with distance from linear fit line.} \item{samples4gradient}{(numeric) modulates penalization of masked indices.} \item{fillMethod}{(string) must be either "spline", "sgolay" or "linear".} \item{splineMethod}{(string) must be either "fmm" or "natural".} \item{mergeTime}{(string) must be either "ref", "avg", "refStart" or "refEnd".} \item{keepFlanks}{(logical) TRUE: Flanking chromatogram is not removed.} \item{fraction}{(integer) indicates which fraction to align.} \item{fractionNum}{(integer) Number of fractions to divide the alignment.} \item{lossy}{(logical) if TRUE, time and intensity are lossy-compressed in generated sqMass file.} \item{useIdentifying}{(logical) Set TRUE to use identifying transitions in alignment. (DEFAULT: FALSE)} } \description{ Retention alignment requires OpenSWATH/pyProphet extracted features and chromatograms. This function provides a suite of parameters used for selecting features and manipulating chromatograms. Chromatogram alignment can be performed via reference based or progressively via rooted or unrooted tree. This function provides sensible parameters for these tasks. } \examples{ params <- paramsDIAlignR() } \seealso{ \code{\link{checkParams}, \link{alignTargetedRuns}} } \author{ Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca} ORCID: 0000-0003-3500-8152 License: (c) Author (2020) + GPL-3 Date: 2020-07-11 }

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## LOAD PACKAGES ## library(dplyr) library(ggplot2) ## READ IN DATA AND ORGANIZE ## #read in data data <- read.table('./data/rcourse_lesson1_data.txt', sep = '\t', header = TRUE) #Look at data dim(data) head(data) tail(data) xtabs(~group, data) #subset out bilingual data_bl <- data %>% filter(., group == "bilingual") #Look at data dim(data_bl) head(data_bl) tail(data_bl) xtabs(~group, data_bl) xtabs(~type, data_bl) ## MAKE FIGURES ## #By group data.plot <- ggplot(data, aes(x = group, y = rt)) + geom_boxplot() pdf("figures/data.pdf") data.plot dev.off() #

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power <- read.csv("household_power_consumption.txt", header=T, sep=';', na.strings="?",nrows=2075259, check.names=F, stringsAsFactors=F, comment.char="", quote='\"') power1 <- subset(power, Date %in% c("1/2/2007","2/2/2007")) power1$Date <- as.Date(power1$Date, format="%d/%m/%Y") powertime <- paste(as.Date(power1$Date), power1$Time) power1$Datetime <- as.POSIXct(powertime) par(mfrow=c(2,2), mar=c(4,4,2,1), oma=c(0,0,2,0)) with(power1, plot(Global_active_power~Datetime, type="l", ylab="Global Active Power", xlab="")) with(power1, plot(Voltage~Datetime, type="l", ylab="Voltage ", xlab="datetime")) with(power1, plot(Sub_metering_1~Datetime, type="l", ylab="Energy sub metering", xlab="")) lines(power1$Sub_metering_2~power1$Datetime,col='red') lines(power1$Sub_metering_3~power1$Datetime,col='blue') legend("topright", col=c("black", "red", "blue"), lty=1, lwd=2, legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3")) with(power1, plot(Global_reactive_power~Datetime, type="l", ylab="Global_reactive_power", xlab="datetime")) dev.copy(png, file="plot4.png", height=480, width=480) dev.off()

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r

demonstrations.R

library(rstan) library(coda) library(bayesplot) library(bridgesampling) library(tidyverse) library(ggpubr) library(nlme) library(deSolve) library(rbokeh) library(plotly) rstan_options(auto_write = TRUE) options(mc.cores = parallel::detectCores(logical = FALSE)) ################ Data ########## combined_data <- read.csv("Data/combined_data.csv", header = T, na.strings = ".", stringsAsFactors = FALSE ) %>% mutate(Temperature = factor(Temperature)) b5_mono_data <- combined_data %>% dplyr::filter(Experiment == "Mono" & Strain == "B5" & Time > 1) b5_mono_data <- b5_mono_data[order(b5_mono_data$Sample.ID2, b5_mono_data$Time), ] #turn temperature to factor b5_mono_data$Temperature <- factor(b5_mono_data$Temperature) #creating numeric group for random effects b5_mono_data <- b5_mono_data %>% dplyr::mutate(Groups2 = rep(1:length(unique(b5_mono_data$Sample.ID2)), each = 8)) #indicator for treatment = 20 or 22 b5_mono_data <- b5_mono_data %>% dplyr::mutate(T_2022 = ifelse( Treatment %in% c(20, 22), 1, 0) ) #remove NAs from the data b5_mono_data_nona <- b5_mono_data[!is.na(b5_mono_data$m_fschlin), ] #obtain the treatment design matrix trt_mat <- as.matrix(b5_mono_data_nona[, c("T_18", "T_20", "T_22")]) ################# function to fit model using stan #mod is the stan model file, mod_data is the data needed to fit the model stan_mod_func <- function(mod, mod_data, init = NULL, ...) { if(is.null(init)) { ret_mod <- stanc(mod) # formulate model from stan file sm <- stan_model(stanc_ret = ret_mod, verbose = FALSE) sm_fit <- sampling(sm, data = mod_data, iter = 3000, thin = 1, control = list(max_treedepth = 13, adapt_delta = 0.98), chains = 2) } else { ret_mod <- stanc(mod) # formulate model from stan file sm <- stan_model(stanc_ret = ret_mod, verbose = FALSE) sm_fit <- sampling(sm, data = mod_data, iter = 3000, thin = 1, control = list(max_treedepth = 13, adapt_delta = 0.98), init = init, chains = 2) } return(sm_fit) } ### function to return the posterior mean of a parameter par_postsums <- function(stan_mod, par = "yhat") { as.data.frame(summary(stan_mod, pars = par)$summary) } par_postsums2 <- function(stan_mod, pars = c("sigma","alpha", "beta", "lsigma")) { y <- par_postsums(stan_mod = stan_mod, par = pars) z1 <- as.data.frame(y[, c("mean", "2.5%", "97.5%")]) names(z1) <- c("mean", "LCI", "UCI") cbind(z1, Parameters = row.names(z1)) } ########### function to extract residuals (should be a matrix) and plot them for bivariate normal model error_plot <- function(stan_mod_obj, resid_name = "epsilon", number_rows, group = NULL, colnames = c("error_Trait", "error_abundance")) { errors <- matrix(par_postsums(stan_mod_obj, resid_name)[, "mean"], nrow = number_rows, byrow = TRUE) errors <- as.data.frame(errors) names(errors) <- colnames if(!is.null(group)) { errors$Group <- group p <- errors %>% ggplot(aes(x = get(colnames[2], errors), y = get(colnames[1], errors), group = Group, color = Group)) + geom_point(size = 5) + theme_bw() + geom_smooth(aes(x = get(colnames[2], errors), y = get(colnames[1], errors)), method = "lm", inherit.aes = FALSE, se = FALSE, color = "black", data = errors) + labs(x = expression(epsilon[abundance]), y = expression(epsilon[trait])) #+ #geom_smooth(aes(x = get(colnames[2], errors), y = get(colnames[1], errors), # group = Group, color = Group), # method = "lm", inherit.aes = FALSE, se = FALSE, # data = errors) } else { p <- errors %>% ggplot(aes(x = get(colnames[2], errors), y = get(colnames[1], errors))) + geom_point(size = 5) + theme_bw() + geom_smooth(aes(x = get(colnames[2], errors), y = get(colnames[1], errors)), method = "lm", inherit.aes = FALSE, se = FALSE, color = "black", data = errors) + labs(x = expression(epsilon[abundance]), y = expression(epsilon[trait])) } return(p) } ### credible interval plots for parameter estimates ciplot <- function(pdata, x, y, x_lab, y_lab, x_scale = NULL) { pd <- position_dodge(0.1) if(!is.null(x_scale)) { pdata %>% ggplot(aes(x = get(x, pdata), y = get(y, pdata))) + geom_errorbar(aes(ymin = LCI, ymax = UCI), color="black", width = 0.1, position = pd, data = pdata, inherit.aes = TRUE) + geom_point(size = 4, position = pd, shape = 21, fill = "red3", color = "black") + theme_bw() + labs(x = x_lab, y = y_lab) + scale_x_discrete(labels = x_scale) } else { pdata %>% ggplot(aes(x = get(x, pdata), y = get(y, pdata))) + geom_point(size = 4, position = pd, shape = 21, fill = "red3", color = "black") + geom_errorbar(aes(ymin = LCI, ymax = UCI), color="black", width = 0.1, position = pd, data = pdata, inherit.aes = TRUE) + theme_bw() + labs(x = x_lab, y = y_lab) } } ###### function to plot predicted values pred_plot <- function(orig_data, predicted_values, x, y, x_lab, y_lab) { pdata <- cbind(orig_data, Pred = predicted_values) pdata2 <- pdata %>% filter(Replicate == 2) ggplot(data = pdata, aes(x = get(x, pdata), y = get(y, pdata), group = interaction(Temperature, Replicate), color = Temperature)) + geom_point(size = 4) + geom_line(aes(x = get(x, pdata2), y = get("Pred", pdata2), group = interaction(Temperature, Replicate), color = Temperature), size = 1.5, linetype= "dashed", data = pdata2, inherit.aes = FALSE) + theme_bw() + color_palette("Dark2") + scale_x_continuous(breaks = seq(0, 30, by = 4)) + #scale_y_continuous(breaks = seq(4, 8, by = 0.5)) + theme(axis.text.x = element_text(angle = 90, hjust = 0.9, vjust = 0.5), legend.position = "top") + labs(x = x_lab, y = y_lab) } #### abundance plot abd_plot <- combined_data %>% filter(Strain == "B5" & Experiment == "Mono" & Time > 1) %>% ggplot(aes(x = Time, y = ldensity, group = interaction(Temperature, Replicate), color = Temperature)) + geom_point(size = 4) + geom_line(size = 1.5) + theme_minimal() + color_palette("Dark2") + scale_x_continuous(breaks = seq(0, 30, by = 4)) + scale_y_continuous(breaks = seq(0, 14, by = 1)) + theme(axis.text.x = element_text(angle = 90, hjust = 0.01, vjust = 0.5), legend.position = "right", axis.line = element_line(size = 0.5)) + labs(x = "Time", y = "log(Abundance)") abds <- ggplotly(abd_plot) withr::with_dir("html", htmlwidgets::saveWidget(abds, file = "abd.html")) ########## Frequentist analysis for the Verhuls Model SSLVE <- function(time, N0, r, K) { numerator <- K * N0 denominator <- N0 + ((K - N0) * exp(-r * time)) mu <- numerator / denominator return(mu) } ver_model <- gnls(ldensity ~ SSLVE(time = Time, N0, r, K), data = b5_mono_data, params = list(N0 ~ 1, r ~ Temperature, K ~ Temperature), start = c(4.2, 0.22, 0.23, 0.36, 9, 0, 0), correlation = NULL, na.action = na.omit ) ver_model_coefs <- coef(ver_model) ver_model_vacov <- vcov(ver_model) ver_model_sum <- summary(ver_model) names(ver_model_coefs) <- c("N0", "rInt", "r20", "r22", "KInt", "K20", "K22") row.names(ver_model_vacov) <- c("N0", "rInt", "r20", "r22", "KInt", "K20", "K22") colnames(ver_model_vacov) <- c("N0", "rInt", "r20", "r22", "KInt", "K20", "K22") interestr <- c("rInt", paste("(rInt", c("r20)","r22)"), sep = "+")) interestK <- c("KInt", paste("(KInt", c("K20)","K22)"), sep = "+")) interestA <- paste(interestr,interestK, sep = "/") interest <- c(interestr, interestK, interestA) estimates <- t(sapply(interest, function(.x) { car::deltaMethod(ver_model_coefs, g. = .x, vcov. = ver_model_vacov, level = 0.95) })) colnames(estimates) <- c("Estimate", "SE", "LCI", "UCI") estimates <- as.data.frame(sapply(as.data.frame(estimates), as.numeric)) %>% mutate(Parameters = c("r18", "r20", "r22", "K18", "K20", "K22", "A18", "A20", "A22")) estimates$Type <- rep(c("growth rate", "carrying capacity", "intraspecific effect"), each = 3) pr <- ciplot(estimates %>% filter(Type == "growth rate"), x = "Parameters", y = "Estimate", x_lab = "Parameters", y_lab = "Estimates", x_scale = c(expression(r[18]), expression(r[20]), expression(r[22]))) + facet_wrap(.~Type, scales = "free_y") pk <- ciplot(estimates %>% filter(Type == "carrying capacity"), x = "Parameters", y = "Estimate", x_lab = "Parameters", y_lab = "", x_scale = c(expression(K[18]), expression(K[20]), expression(K[22]))) + facet_wrap(.~Type, scales = "free_y") pa <- ciplot(estimates %>% filter(Type == "intraspecific effect"), x = "Parameters", y = "Estimate", x_lab = "Parameters", y_lab = "", x_scale = c(expression(A[18]), expression(A[20]), expression(A[22]))) + facet_wrap(.~Type, scales = "free_y") p1 <- ggarrange(plotlist = list(pr, pk, pa), ncol = 3, nrow = 1, align = "hv", common.legend = TRUE) #ggplotly(p1) ###rbokeh p1_list = vector("list", 3) j<-1 for(i in c("growth rate", "carrying capacity", "intraspecific effect")) { p1_list[[j]] <- figure(xlim = estimates$Parameters[estimates$Type == i], width = 220, height = 350, tools = c("pan", "wheel_zoom", "box_zoom", "box_select", "reset")) %>% ly_segments(Parameters, LCI, Parameters, UCI, data = estimates[estimates$Type == i,], color = "black", width = 2) %>% ly_points(Parameters, Estimate, glyph = 16, data = estimates[estimates$Type == i,], color = "red", size = 20, hover = c(Parameters, Estimate)) %>% y_axis(label = "Estimates") j <<- j+1 } p1s <- grid_plot(p1_list, ncol = 3, same_axes = F, link_data = F) withr::with_dir("html", htmlwidgets::saveWidget(p1s, file = "est_freq.html")) ##################### Bayesian Analysis set.seed(1992) #covraite matrix for the trait mean function X_obs <- b5_mono_data_nona %>% model.matrix(~I(Time^0.5):T_18 + I(Time^3):T_2022 + I((Time^3) * log(Time)):T_2022 + T_18 + T_20 + T_22, data = . ) verhulst_data <- list( N_obs = nrow(X_obs), y_obs = b5_mono_data_nona[, "ldensity"], trt = b5_mono_data_nona[, c("T_18", "T_20", "T_22")], time = b5_mono_data_nona$Time, n_trt = ncol(trt_mat) ) stan_verhulst <- stan_mod_func(mod = "stan_models/verhulst.stan", mod_data = verhulst_data, init = list( list(r = c(0.22, 0.23, 0.35)/2, K = c(9.72, 11.50, 10.99)/2), list(r = c(0.22, 0.23, 0.35)*1.5, K = c(9.72, 11.50, 10.99)*1.5) )) ver_pdata <- par_postsums2(stan_verhulst, pars = c("r", "K", "A")) ver_pdata$Parameters <- c(paste0("r", c(18, 20, 22)), paste0("K", c(18, 20, 22)), paste0("A", c(18, 20, 22))) ver_pdata$Type <- rep(c("growth rate", "carrying capacity", "intraspecific effect"), each = 3) ### plotting Bayesian p2_list = vector("list", 3) j<-1 for(i in c("growth rate", "carrying capacity", "intraspecific effect")) { p2_list[[j]] <- figure(xlim = ver_pdata$Parameters[ver_pdata$Type == i], width = 220, height = 350, tools = c("pan", "wheel_zoom", "box_zoom", "box_select", "reset")) %>% ly_segments(Parameters, LCI, Parameters, UCI, data = ver_pdata[ver_pdata$Type == i,], color = "black", width = 2) %>% ly_points(Parameters, mean, glyph = 16, data = ver_pdata[ver_pdata$Type == i,], color = "red", size = 20, hover = c(Parameters, mean)) %>% y_axis(label = "Estimates") j <<- j+1 } p2s <- grid_plot(p2_list, ncol = 3, same_axes = F, link_data = F) withr::with_dir("html", htmlwidgets::saveWidget(p2s, file = "est_bay.html")) ################ plotting both results together ########## bay_fre_est <- rbind(estimates %>% dplyr::select(-SE), ver_pdata %>% dplyr::rename(Estimate = mean) ) %>% mutate(Method = rep(c("Frequentist", "Bayesian"), each = 9) ) pd <- position_dodge(1.0) p3_list <- vector("list", 3) j <- 1 for(i in c("growth rate", "carrying capacity", "intraspecific effect")) { if(j == 3) { p3_list[[j]] <- bay_fre_est %>% filter(Type == i) %>% ggplot(aes(x = Parameters, y = Estimate, group = Method, colour = Method)) + geom_errorbar(aes(ymin = LCI, ymax = UCI), colour = "black", width=.1, position = pd, size = 1.5) + geom_point(position = pd, size = 9) + theme_minimal() + theme(axis.line = element_line(size = 0.5), legend.position = "none", legend.direction = "vertical") } else { p3_list[[j]] <- bay_fre_est %>% filter(Type == i) %>% ggplot(aes(x = Parameters, y = Estimate, group = Method, colour = Method)) + geom_errorbar(aes(ymin = LCI, ymax = UCI), colour = "black", width=.1, position = pd, size = 1.5) + geom_point(position = pd, size = 9) + theme_minimal() + theme(legend.position = "none", axis.line = element_line(size = 0.5) ) } j <<- j+1 } p3s_gga <- ggarrange(plotlist = p3_list, nrow = 1, ncol = 3) p3s <- subplot(p3_list, nrows = 1) withr::with_dir("html", htmlwidgets::saveWidget(p3s, file = "est_bayfreq.html")) ################# Lotka-Voltera for 2 species example biculture_data <- combined_data %>% dplyr::filter(Experiment == "BI") %>% mutate(Temperature = factor(Temperature), Temperature2 = case_when( Temperature == 18 ~ 1, Temperature == 20 ~ 2, Temperature == 22 ~ 3 ), Species = case_when( Strain == "B4" ~ 1, Strain == "B5" ~ 2 ) ) biculture_data_nona <- biculture_data %>% filter(Time > 1, !is.na(ldensity)) biculture_data_wide <- biculture_data_nona %>% pivot_wider(names_from = Strain, id_cols = c("Date2", "Sample.ID2", "Date", "Time", "Treatment", "Replicate", "Temperature", "Temperature2", "T_18", "T_20", "T_22", "Groups" ), values_from = c(ldensity, m_fschlin, m_yelbhlin, m_redbhlin, v_fschlin, v_yelbhlin, Number) ) biculture_data %>% dplyr::filter( Time > 1) %>% ggplot(aes(x = Time, y = ldensity, group = interaction(Strain, Temperature, Replicate), color = Temperature)) + geom_point(size = 4) + geom_line(aes(linetype = Strain), size = 1.5) + theme_minimal() + color_palette("Dark2") + scale_x_continuous(breaks = seq(0, 30, by = 4)) + scale_y_continuous(breaks = seq(0, 14, by = 1)) + theme(axis.text.x = element_text(angle = 90, hjust = 0.9, vjust = 0.5), legend.position = "top") + labs(x = "Time", y = "Log(Abundance)") ### frequentist analysis # 2 species LVE LVE <- function(time, y, parms, ...) { dy <- rep(0, length(y)) dy[1] <- y[1] * parms$r1 * (1 - (parms$alpha11*y[1] + parms$alpha12*y[2]) ) dy[2] <- y[2] * parms$r2 * (1 - (parms$alpha21*y[1] + parms$alpha22*y[2]) ) return(list(dy)) } ts <- sort(unique(c(seq(min(biculture_data$Time), 50, by = 1), biculture_data$Time))) #a test out_solu <- ode(y = c(7, 6), times = ts, func = LVE, parms = list(r1 = 0.2, r2 = 0.3, alpha11 = 0.06, alpha21 = 0.02, alpha12 = 0.02, alpha22 = 0.06) ) lapply(c(18, 20, 22), function(i) { ddata <- biculture_data_wide %>% filter(Temperature == 18) # function to obtain sum of squares residual SSR <- function(parms) { # mapping the parameters to their respective positions r1 <- parms[1] r2 <- parms[2] alpha11 <- parms[3] alpha21 <- parms[4] alpha12 <- parms[5] alpha22 <- parms[6] sigma_b4 <- parms[7] sigma_b5 <- parms[8] rho <- 0 #parms[9] N0_1 <- 3.6 #parms[10] N0_2 <- 3.5 #parms[11] #a time vector time_seq <- ts # initial values y_inits <- c(N0_1, N0_2) #solve the ode out_solu <- ode(y = y_inits, times = time_seq, func = LVE, parms = list(r1 = r1, r2 = r2, alpha11 = alpha11, alpha12 = alpha12, alpha21 = alpha21, alpha22 = alpha22) ) out_dataframe <- as.data.frame(out_solu) out_dataframe <- out_dataframe[out_dataframe$time %in% unique(biculture_data$Time), ] names(out_dataframe) <- c("time", "ldensity_B4_pred", "ldensity_B5_pred") dd <- merge(ddata, out_dataframe, by.x = c("Time"), by.y = c("time")) Sigma <- matrix(c(sigma_b4^2, sigma_b4*sigma_b5*rho, sigma_b4*sigma_b5*rho, sigma_b5^2), nrow = 2, ncol = 2, byrow = T) ll <- numeric(nrow(dd)) for(i in 1:nrow(dd)) { ll[i] <- dmvnorm(x = dd[i, c("ldensity_B4", "ldensity_B5")], mean = c(dd$ldensity_B4_pred[i], dd$ldensity_B5_pred[i]), sigma = Sigma, log = T) } return(-sum(ll)) } parms_start <- c(r1 = 0.7, r2 = 0.4, alpha11 = 0.06, alpha12 = 0.02, alpha21 = 0.02, alpha22 = 0.06, sigma_b4 = 0.70, sigma_b5 = 0.45, rho = 0.5, N0_1 = 7, N0_2 = 6) lve_freq <- optim(par = parms_start, fn = SSR, method = "Nelder-Mead", control = list(maxit = 100), lower = c(r1 = 0, r2 = 0, alpha11 = 0, alpha12 = 0, alpha21 = 0, alpha22 = 0, sigma_b4 = 0, sigma_b5 = 0, rho = -1, N0_1 = 0, N0_2 = 0), upper= c(r1 = Inf, r2 = Inf, alpha11 = Inf, alpha12 = Inf, alpha21 = 0, alpha22 = Inf, sigma_b4 = Inf, sigma_b5 = Inf, rho = 1, N0_1 = Inf, N0_2 = Inf) ) lve_sum <- summary(lve_freq) UCI <- lve_sum$coefficients[, 1] + lve_sum$coefficients[, 2] LCI <- lve_sum$coefficients[, 1] - lve_sum$coefficients[, 2] Parameters <- c("r1", "r2", "alpha11", "alpha12", "alpha21", "alpha22") data.frame(Parameters = Parameters, Estimates = lve_sum$coefficients[, 1], LCI = LCI, UCI = UCI) }) #### Bayesian Analysis biculture_data_wide_nona <- biculture_data_wide[!is.na(biculture_data_wide$ldensity_B4), ] results_backup <- vector("list", 3) j <- 1 results_treatment <- lapply(c(18, 20, 22), function(i) { print(i) bi_datas <- biculture_data_wide_nona[biculture_data_wide_nona$Treatment == i, ] bi_data1 <- list( N_obs = nrow(bi_datas), T = length(ts), N = as.matrix(bi_datas[, c("ldensity_B4", "ldensity_B5")]), t0 = 0, ts = ts, time_obs = bi_datas$Time, nsp = 2 ) if( i == 18) { N01 <- c(3.6, 3.5) r <- c(0.43, 0.35) alpha11 <- 0.07; alpha22 <- 0.07 alpha12 <- 0.02; alpha21 <- 0.02 } else if (i == 20) { N01 <- c(6.5, 5.6) r <- c(0.20, 0.21) alpha11 <- 0.06; alpha22 <- 0.06 alpha12 <- 0.02; alpha21 <- 0.02 } else if (i == 22) { N01 <- c(6.1, 5.3) r <- c(0.16, 0.16) alpha11 <- 0.06; alpha22 <- 0.06 alpha12 <- 0.02; alpha21 <- 0.01 } st_mod <- stan_mod_func(mod = "stan_models/generalisedLVE.stan", mod_data = bi_data1, init = list( list(r = r, alpha11 = alpha11, alpha22 = alpha22, alpha12 = alpha12, alpha21 = alpha21, N0 = N01), list(r = r*1.5, alpha11 = alpha11*1.5, alpha22 = alpha22*1.5, alpha12 = alpha12*1.5, alpha21 = alpha21*1.5, N0 = N01*1.5)) ) results_backup[[j]] <- st_mod j <<- j + 1 print(par_postsums2(st_mod, c("r", "N0", "alpha11", "alpha22", "alpha12", "alpha21", "cor", "sigma"))) return(st_mod) }) results_18 <- par_postsums2(results_treatment[[1]], c("r", "N0", "alpha11", "alpha22", "alpha12", "alpha21", "cor")) %>% mutate(Parameters = c("r18B4", "r18B5", "N0B4", "N0B5", "alpha11", "alpha22", "alpha12", "alpha21", "rho"), Type = rep(c("growth rate", "N0", "alphas", "rho"), times = c(2, 2, 4, 1) ), Temperature = rep(18, times = 9)) results_20 <- par_postsums2(results_treatment[[2]], c("r", "N0", "alpha11", "alpha22", "alpha12", "alpha21", "cor")) %>% mutate(Parameters = c("r18B4", "r18B5", "N0B4", "N0B5", "alpha11", "alpha22", "alpha12", "alpha21", "rho"), Type = rep(c("growth rate", "N0", "alphas", "rho"), times = c(2, 2, 4, 1) ), Temperature = rep(20, times = 9)) results_22 <- par_postsums2(results_treatment[[3]], c("r", "N0", "alpha11", "alpha22", "alpha12", "alpha21", "cor")) %>% mutate(Parameters = c("r18B4", "r18B5", "N0B4", "N0B5", "alpha11", "alpha22", "alpha12", "alpha21", "rho"), Type = rep(c("growth rate", "N0", "alphas", "rho"), times = c(2, 2, 4, 1) ), Temperature = rep(22, times = 9)) results_all <- rbind(results_18, results_20, results_22) results_all$Temperature <- factor(results_all$Temperature) pd <- position_dodge(1.0) p4_list <- vector("list", 3) j <- 1 for(i in c("growth rate", "N0", "alphas", "rho")) { if(j == 4) { p4_list[[j]] <- results_all %>% filter(Type == i) %>% ggplot(aes(x = Parameters, y = mean, group = Temperature, colour = Temperature)) + geom_errorbar(aes(ymin = LCI, ymax = UCI), colour = "black", width=.1, position = pd, size = 1.5) + geom_point(position = pd, size = 9) + theme_minimal() + theme(axis.line = element_line(size = 0.5), legend.position = "none", legend.direction = "vertical") + labs(y = "Posterior Mean +- Posterior SD") } else { p4_list[[j]] <- results_all %>% filter(Type == i) %>% ggplot(aes(x = Parameters, y = mean, group = Temperature, colour = Temperature)) + geom_errorbar(aes(ymin = LCI, ymax = UCI), colour = "black", width=.1, position = pd, size = 1.5) + geom_point(position = pd, size = 9) + theme_minimal() + theme(legend.position = "none", axis.line = element_line(size = 0.5) ) + labs(y = "Posterior Mean +- Posterior SD") } j <<- j+1 } p4s <- subplot(p4_list, nrows = 2) withr::with_dir("html", htmlwidgets::saveWidget(p4s, file = "est_bayLVE.html")) ############### model comparison stan_gompertz <- stan_mod_func(mod = "stan_models/gompertz.stan", mod_data = verhulst_data, init = list( list(r = c(0.22, 0.23, 0.35)/2, c = c(0.72, 0.50, 1.99)/2), list(r = c(0.22, 0.23, 0.35)*1.5, c = c(1.72, 3.50, 3.99)*1.5 ) )) save.image("RImage/demonstration.RData")

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/R/infoGPCM.R

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2022-02-18T08:40:02

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infoGPCM.R

infoGPCM <- function (betas, z, IRT.param) { n <- length(z) p <- length(betas) alphas <- sapply(betas, tail, 1) prs <- crf.GPCM(betas, z, IRT.param) T.bar <- sapply(prs, function (x) colSums(x * 1:nrow(x))) info <- matrix(0, n, p) for (j in 1:p) { ii <- outer(seq(1, nrow(prs[[j]])), T.bar[, j], "-")^2 info[, j] <- alphas[j]^2 * colSums(prs[[j]] * ii) } colnames(info) <- names(betas) info }

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/R/Dictionary_S3methods.R

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permissive

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#' @export `[.Dictionary` <- function(x, i) { x$get_list(i) } #' @export `[<-.Dictionary` <- function(x, i, value) { # nolint x$add(keys = i, values = value) invisible(x) } #' @export length.Dictionary <- function(x) { x$length } #' @export summary.Dictionary <- function(object, n = 2, ...) { object$summary(n = n) } #' @export as.character.Dictionary <- function(x, n = 2, ...) { # nolint keys <- x$keys values <- vapply(x$values, as.character, character(1), USE.NAMES = FALSE) lng <- x$length if (lng > (2 * n)) { string <- paste0( paste(keys[1:n], values[1:n], sep = ": ", collapse = ", " ), ", ..., ", paste(keys[(lng - n + 1):lng], values[(lng - n + 1):lng], sep = ": ", collapse = ", " ) ) } else { string <- paste(keys, values, sep = ": ", collapse = ", ") } sprintf("{%s}", string) } #' @export c.Dictionary <- function(...) { x <- list(...) types <- vapply(x, function(.x) list(.x$typed, length(.x$types), .x$types), vector("list", 3)) # different typing if (length(unique(types[1, ])) > 1) { stop("Can only combine Dictionaries if all typed or all untyped.") # all typed or untyped } else { # untyped if (!unlist(types[1, 1])) { Dictionary$new(x = unlist(lapply(x, "[[", "items"), FALSE)) # typed } else { # different type lengths if (length(unique(types[2, ])) > 1) { stop("Can only combine typed Dictionaries of the same type(s).") } else { if (length(unique(unlist(types[3, ]))) != types[2, 1][[1]]) { stop("Can only combine typed Dictionaries of the same type(s).") } else { Dictionary$new(x = unlist(lapply(x, "[[", "items"), FALSE), types = unlist(types[3, 1])) } } } } }

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#CHAPTER 6 -- Two by two tables ci <- function(m,v) { A <- m-1.960*sqrt(v) B <- m+1.960*sqrt(v) round(cbind(A,B),5) } #typical summary statistics: odds ratio, relative risk and attributable risk a <- 61 b <- 102 c <- 52 d <- 196 n <- a+b+c+d n1 <- a+b n2 <- c+d m1 <- a+c m2 <- b+d or <- (a*d)/(b*c) rr <- (a/(a+b))/(c/(c+d)) ar <- ((a+c)/n-(c/(c+d)))/((a+c)/n) cbind(or,rr,ar) #association measured by probabilities #rows prop.test(c(61,52),c(163,248),correct=FALSE) #columns prop.test(c(61,102),c(113,298),correct=FALSE) chisq.test(matrix(c(61,52,102,196),2,2),correct=FALSE) #Table 7.0 (toolbox text) p1 <- a/(a+b) p2 <- c/(c+d) P1 <- a/(a+c) P2 <- b/(b+d) vp <- p1*(1-p1)/n1+p2*(1-p2)/n2 vP <- P1*(1-P1)/m1+P2*(1-P2)/m2 vrr <- rr^2*(1/a-1/n1+1/c-1/n2) lvrr <- 1/a-1/n1+1/c-1/n2 vor <- or^2*(1/a+1/b+1/c+1/d) lvor <- 1/a+1/b+1/c+1/d var0 <- (1-ar)^2*(b+ar*(a+d))/(n*c) lvar0 <- (b+ar*(a+d))/(n*c) round(cbind(sqrt(vp),sqrt(vP),sqrt(vrr),sqrt(lvrr), sqrt(vor),sqrt(lvor),sqrt(var0),sqrt(lvar0)),3) #Table 8.0 (toolbox text) p1 <- a/(a+b) p2 <- c/(c+d) P1 <- a/(a+c) P2 <- b/(b+d) rr <- (a/(a+b))/(c/(c+d)) lrr <- log(rr) or <- (a/b)/(c/d) lor <- log(or) ar <- (a*d-b*c)/((a+c)*(c+d)) lar <- log(1-ar) round(cbind(p1-p2,P1-P2,rr,lrr,or,lor,ar,lar),3) #confidence intervals rbind(round(c(p1-p2,ci(p1-p2,vp)),3), round(c(P1-P2,ci(P1-P2,vP)),3), round(c(exp(lrr),exp(ci(lrr,lvrr))),3), round(c(lrr,ci(lrr,lvrr)),3), round(c(exp(lor),exp(ci(lor,lvor))),3), round(c(lor,ci(lor,lvor)),3), round(c(1-exp(lar),1-exp(rev(ci(lar,lvar0)))),3), round(c(lar,ci(lar,lvar0)),3)) #corrected p <- (a+c)/(a+b+c+d) v <- p*(1-p)*(1/n1+1/n2) z <- (abs(p2-p1)-.5*(1/n1+1/n2))/sqrt(v) cbind(p,v,z^2) prop.test(c(a,c),c(n1,n2),correct=TRUE)$statistic n*(abs(a*d-b*c)-n/2)^2/((a+b)*(c+d)*(a+c)*(b+d)) #corrected estimate p <- c(1,4,6,4,1)/16 ex <- sum((0:4)*p) c0 <- 1:5 p0 <- cumsum(p) #exact p1 <- pnorm(c0-0.5-ex) #approximate round(cbind(c0-1,p0,p1),3) chisq.test(matrix(c(a,c,b,d),2,2),correct=FALSE)$statistic prop.test(c(a,c),c(n1,n2),correct=FALSE)$statistic n*(abs(a*d-b*c))^2/((a+b)*(c+d)*(a+c)*(b+d)) #Vietnam -- breast cancer a <- 170 b <- 3222 c <- 126 d <- 2912 n <- a+b+c+d #relative risk (a/(a+b))/(c/(c+d)) #odds ratio (a/b)/(c/d) #variance of log-odds-ratio 1/a+1/b+1/c+1/d #Adjustment #small sample size a <- 2 b <- 23 c <- 6 d <- 22 n <- a+b+c+d m <- matrix(c(a,c,b,d),2,2) chisq.test(m,correct=FALSE) chisq.test(m) #Hypergeometric probability distributions #tea tasting experiment round(dhyper(0:4,4,4,4),3) #keno round(dhyper(0:8,20,60,8),6) #Fisher's exact test round(dhyper(0:8,8,20,8),4) round(1-phyper(0:8,8,20,8),4) #Adjusted/unadjusted -- Fisher's exact test a <- 5 b <- 3 c <- 3 d <- 17 n <- a+b+c+d m <- matrix(c(a,c,b,d),2,2) chisq.test(m,correct=FALSE) chisq.test(m) fisher.test(m) #twins #no birth defects (data set 1) a <- 18687 b <- 16093 c <- 19188 #birth defects (data set 2) a <- 168 b <- 53 c <- 110 n <- a+b+c p <- (2*a+b)/(2*n) q <- 1-p r <- 1-b/(2*p*q*n) vr <- ((1-r)*(1-2*p*q*(1-r)-(1-4*p*q)*(1-r)^2))/(2*p*q*n) vp <- (2*p*q*(1+r))/(2*n) cbind(n,p,r,vr,vp) ci(r,vr) #duffy a <- 8 b <- 72 c <- 409 n <- a+b+c p <- (2*a+b)/(2*n) q <- 1-p chisq.test(c(a,b,c),p=c(p^2,2*p*(1-p),(1-p)^2)) r <- 1-b/(2*p*q*n) X2 <- n*r^2 pvalue <- 1-pchisq(X2,1) cbind(p,r,X2,pvalue) #degrees of freedom = 1) #incomplete data -- zero in a 2 by 2 table a <- 33 b <- 44 c <- 14 n <- a+b+c d <- b*c/a n+d (a+b)*(a+c)/a #false positive d <- c(0.5,0.1,0.05,0.005,0.0005) t <- (.95*d+.1*(1-d)) p <- .1*(1-d)/t round(cbind(d,t,p),3)

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# Author: Roland Krause library(biomaRt) # Identify SNPs in KCNA2 from ENSEMBL BioMart genes.of.choice= c("KCNA2") # Find ensembl id via hgnc name listMarts(host="www.ensembl.org") gene.biomart = useMart(biomart = "ENSEMBL_MART_ENSEMBL", host="www.ensembl.org") gene.set = useDataset(mart= gene.biomart, "hsapiens_gene_ensembl") gene.ids = getBM("ensembl_gene_id", filter="hgnc_symbol", genes.of.choice, gene.set) # Find SNPs snp.biomart = useMart(biomart="ENSEMBL_MART_SNP", host="www.ensembl.org") snp.set = useDataset(mart= snp.biomart, "hsapiens_snp") snps = getBM(attributes = c("refsnp_id","refsnp_source"), filters="ensembl_gene", values=gene.ids, snp.set) write.table(snps, "snps.txt")

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#' Daily weather in Oulu, Finland, in 2010-2017 #' #' A dataset downloaded from the Finnish Meteorological Institute's open data #' API using **fmir**. Contains daily simple weather observations from Oulu, #' Finland, covering the years 2010 to 2017. The data are made available by the #' [Finnish Meteorological Institute](https://en.ilmatieteenlaitos.fi) and are #' licensed under [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). #' #' @format A data frame with 2922 rows and 8 variables: #' \describe{ #' \item{place}{city name} #' \item{location}{coordinates of the observation station} #' \item{time}{date of observation} #' \item{rrday}{precipitation rate} #' \item{snow}{snow depth} #' \item{tday}{average temperature, degrees Celcius} #' \item{tg_pt12h_min}{?} #' \item{tmax}{maximum temperature, degrees Celcius} #' \item{tmin}{minimum temperature, degrees Celcius} #' } #' "ouludaily10"

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#### Create output folder if(!dir.exists("output")) { dir.create("output", showWarnings = FALSE) } #### Install packages if(!require(pacman))install.packages("pacman") pacman::p_load(doBy, ggplot2, grid, cowplot, metafor, mgcv, weights, meta, igraph, tidyverse, network) #### Sourcing all R files in the modules subdirectory source_step1 <- dir("function", pattern="[.]R$", recursive = TRUE, full.names = TRUE) for(z1 in source_step1)source(z1)

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## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set( screenshot.force = FALSE, echo = TRUE, rows.print = 5, message = FALSE, warning = FALSE) ## ----requirement-------------------------------------------------------------- library(PLNmodels) library(ggplot2) library(corrplot) ## ----data_load---------------------------------------------------------------- data(trichoptera) trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) ## ----simple PLNPCA------------------------------------------------------------ PCA_models <- PLNPCA( Abundance ~ 1 + offset(log(Offset)), data = trichoptera, ranks = 1:5 ) ## ----show nocov--------------------------------------------------------------- PCA_models ## ----collection criteria------------------------------------------------------ PCA_models$criteria %>% knitr::kable() ## ----convergence criteria----------------------------------------------------- PCA_models$convergence %>% knitr::kable() ## ----plot nocov, fig.width=7, fig.height=5------------------------------------ plot(PCA_models) ## ----model extraction--------------------------------------------------------- myPCA_ICL <- getBestModel(PCA_models, "ICL") myPCA_BIC <- getModel(PCA_models, 3) # getBestModel(PCA_models, "BIC") is equivalent here ## ----map, fig.width=8, fig.height=8------------------------------------------- plot(myPCA_ICL, ind_cols = trichoptera$Group) ## ----regression--------------------------------------------------------------- coef(myPCA_ICL) %>% head() %>% knitr::kable() ## ----sigma, fig.width=7------------------------------------------------------- sigma(myPCA_ICL) %>% corrplot(is.corr = FALSE) ## ----rotation----------------------------------------------------------------- myPCA_ICL$rotation %>% head() %>% knitr::kable() ## ----scores------------------------------------------------------------------- myPCA_ICL$scores %>% head() %>% knitr::kable() ## ----show PLNPCAfit----------------------------------------------------------- myPCA_ICL ## ----cov---------------------------------------------------------------------- PCA_models_cov <- PLNPCA( Abundance ~ 1 + offset(log(Offset)) + Temperature + Wind + Cloudiness, data = trichoptera, ranks = 1:4 ) ## ----extraction cov, fig.width=7, fig.height=7-------------------------------- plot(PCA_models_cov) myPCA_cov <- getBestModel(PCA_models_cov, "ICL") ## ----maps, fig.height=4, fig.width=7------------------------------------------ gridExtra::grid.arrange( plot(myPCA_cov, map = "individual", ind_cols = trichoptera$Group, plot = FALSE), plot(myPCA_cov, map = "variable", plot = FALSE), ncol = 2 ) ## ----fitted, fig.cap = "fitted value vs. observation", fig.dim=c(7,5)--------- data.frame( fitted = as.vector(fitted(myPCA_cov)), observed = as.vector(trichoptera$Abundance) ) %>% ggplot(aes(x = observed, y = fitted)) + geom_point(size = .5, alpha =.25 ) + scale_x_log10(limits = c(1,1000)) + scale_y_log10(limits = c(1,1000)) + theme_bw() + annotation_logticks()

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metagear_data.R \docType{data} \name{Kam_et_al_2003_Fig2.jpg} \alias{Kam_et_al_2003_Fig2.jpg} \title{An example image of a scatterplot figure} \format{ A raw jpg-formated image } \description{ A jpg image of a scatterplot from Figure 2 of Kam, M., Cohen-Gross, S., Khokhlova, I.S., Degen, A.A. and Geffen, E. 2003. Average daily metabolic rate, reproduction and energy allocation during lactation in the Sundevall Jird Meriones crassus. Functional Ecology 17:496-503. } \note{ \strong{How to use}\cr\cr \code{readImage(system.file("images", "Kam_et_al_2003_Fig2.jpg", package = "metagear"))} } \keyword{datasets}

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library(sp) library(RgoogleMaps) library(mapplots) #for draw.pie function op <- par() pop.means <- read.csv("analysis/LEA_analysis/PopMeans.csv", header = T) pies <- as.matrix(pop.means[,c("V1","V2","V3")]) n <- as.vector(pop.means[,2]) coords.sp <- pop.means coordinates(coords.sp) <- c("Longitude", "Latitude") proj4string(coords.sp) <- CRS("+proj=longlat +datum=WGS84") #Make smaller submaps interactively G <- select.spatial(coords.sp, digitize = F) group1 <- pop.means[G,] group1 xminG1 <- min(group1$Longitude) xmaxG1 <- max(group1$Longitude) xmidG1 <- (xminG1 + xmaxG1)/2 yminG1 <- min(group1$Latitude) ymaxG1 <- max(group1$Latitude) ymidG1 <- (yminG1 + ymaxG1)/2 coords.spG1 <- group1 coordinates(coords.spG1) <- c("Longitude", "Latitude") proj4string(coords.spG1) <- CRS("+proj=longlat +datum=WGS84") group1.map <- GetMap(center = c(ymidG1, xmidG1), zoom = 12, maptype = "terrain") par(op) googG1 <- LatLon2XY.centered(group1.map, coords.spG1@coords[,2], coords.spG1@coords[,1], zoom = 12) PlotOnStaticMap(group1.map) draw.pie(z = pies[G,], x = googG1$newX, y = googG1$newY, radius = (sqrt(n/pi)*10), labels = "")

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test<-read.csv(Users/xq277/Desktop/ct1522.csv) test<-read.csv(ct1522.csv) test<-ct1522 test head(test) for (i in 1:length(test$fid)){ if(test$NumFloors[i] > 3.00){ test$UnitsTotal[i] <- (3/test$NumFloors[i]*test$UnitsTotal[i]) } else (test$UnitsTotal[i]<-test$UnitsTotal[i]) } head(test) sum(test$UnitsTotal)

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\name{hzar.plot.fzCline} \alias{hzar.plot.fzCline} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Plot the 95\% credible cline region for the given locus model. } \description{ Plots the maximum likelihood cline and observed frequency data over a the associated fuzzy cline region. The default region is the 95\% credible cline region. } \usage{ hzar.plot.fzCline(dataGroup, fzCline = hzar.getCredParamRed(dataGroup), type = "p", pch = "+", col = "black", fzCol = "gray", ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{dataGroup}{ The hzar.dataGroup object for which to generate a fuzzy cline. Defaults to a 95\% credible interval region. } \item{fzCline}{ The hzar.fzCline object to plot. } \item{type}{ The type parameter to pass to hzar.plot.obsData. } \item{pch}{ The plotting character to pass to hzar.plot.obsData. } \item{col}{ The color to plot the maximum likelihood cline and the observed frequencies. } \item{fzCol}{ The color to fill the fuzzy cline region with. } \item{\dots}{ Additional parameters to pass to the initial call to plot. } } % \details{ % %% ~~ If necessary, more details than the description above ~~ % } % \value{ % %% ~Describe the value returned % %% If it is a LIST, use % %% \item{comp1 }{Description of 'comp1'} % %% \item{comp2 }{Description of 'comp2'} % %% ... % } % \references{ % %% ~put references to the literature/web site here ~ % } \author{ Graham Derryberry \email{asterion@alum.mit.edu} } % \note{ % %% ~~further notes~~ % } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{hzar.getCredParamRed}} \code{\link{hzar.make.fzCline}} \code{\link{plot}} \code{\link{hzar.plot.obsData}} \code{\link{hzar.plot.cline}} } \examples{ ##TODO } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. % \keyword{ ~kwd1 } % \keyword{ ~kwd2 }% __ONLY ONE__ keyword per line

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#for plotting xVec = 0:1 yVec = dbinom(xVec,prob=0.5,size=length(xVec),log=FALSE); plot(xVec,yVec,xlab="X",ylab="Prob")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rlapjv.R \name{lapmod_index} \alias{lapmod_index} \title{Solves the linear assignment problem using the LAPMOD algorithm} \usage{ lapmod_index(n, cc, ii, kk, maximize = FALSE) } \arguments{ \item{n}{number of rows in the cost matrix} \item{cc}{vector of all finite elements of the assignement cost matri} \item{ii}{vector of indices of the zero indexed row starts in cc. The following must hold ii[1] = 0 and ii[n+2] = length(cc).} \item{kk}{0-based column numbers for each finite cost in the matrix, i.e., kk must be in 0:(nrow(.)-1).} \item{maximize}{If FALSE (default) then costs are minimized and if TRUE the costs are maximized} } \value{ The assignment of rows to columns as an integer vector } \description{ Find a set of vertices pairs in the order of goodness of matching according to a specified measure. }

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paws-r/paws

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codeguruprofiler_list_profile_times.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/codeguruprofiler_operations.R \name{codeguruprofiler_list_profile_times} \alias{codeguruprofiler_list_profile_times} \title{Lists the start times of the available aggregated profiles of a profiling group for an aggregation period within the specified time range} \usage{ codeguruprofiler_list_profile_times( endTime, maxResults = NULL, nextToken = NULL, orderBy = NULL, period, profilingGroupName, startTime ) } \arguments{ \item{endTime}{[required] The end time of the time range from which to list the profiles.} \item{maxResults}{The maximum number of profile time results returned by \code{\link[=codeguruprofiler_list_profile_times]{list_profile_times}} in paginated output. When this parameter is used, \code{\link[=codeguruprofiler_list_profile_times]{list_profile_times}} only returns \code{maxResults} results in a single page with a \code{nextToken} response element. The remaining results of the initial request can be seen by sending another \code{\link[=codeguruprofiler_list_profile_times]{list_profile_times}} request with the returned \code{nextToken} value.} \item{nextToken}{The \code{nextToken} value returned from a previous paginated \code{\link[=codeguruprofiler_list_profile_times]{list_profile_times}} request where \code{maxResults} was used and the results exceeded the value of that parameter. Pagination continues from the end of the previous results that returned the \code{nextToken} value. This token should be treated as an opaque identifier that is only used to retrieve the next items in a list and not for other programmatic purposes.} \item{orderBy}{The order (ascending or descending by start time of the profile) to use when listing profiles. Defaults to \code{TIMESTAMP_DESCENDING}.} \item{period}{[required] The aggregation period. This specifies the period during which an aggregation profile collects posted agent profiles for a profiling group. There are 3 valid values. \itemize{ \item \code{P1D} — 1 day \item \code{PT1H} — 1 hour \item \code{PT5M} — 5 minutes }} \item{profilingGroupName}{[required] The name of the profiling group.} \item{startTime}{[required] The start time of the time range from which to list the profiles.} } \description{ Lists the start times of the available aggregated profiles of a profiling group for an aggregation period within the specified time range. See \url{https://www.paws-r-sdk.com/docs/codeguruprofiler_list_profile_times/} for full documentation. } \keyword{internal}

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getVals.R

#' Gets the mean and standard deviation of parameters from SS output #' @param x The matrix of parameters or derived parameters from SS_output #' @param param The name of the parameter #' @param cols The column names for the mean and standard deviation, respectively #' @export getVals.fn <- function(x, param=NULL, cols=c("Value","StdDev","Min","Max")) { if(is.null(cols)) {cols <- 1:ncol(x)} if(is.null(param)) {param<-rownames(x)} if(length(param)>1) { out <- x[param,cols] if(length(cols)==1) { names(out) <- param } } if(length(param)==1) { out <- x[grep(param,rownames(x),fixed=T),cols] } return(out) }

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/Basic_plots/AreaPlots.R

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shanl33/slee205_Honours

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AreaPlots.R

install.packages("productplots") library(productplots) library(ggplot2) library(dplyr) library(ggvis) library(plotly) # If load ggmosaic then will need to re-install productplots # Currently cannot get ggmosaic working install.packages("ggmosaic") library(ggmosaic) # See https://cran.r-project.org/web/packages/ggmosaic/vignettes/ggmosaic.html data("happy") str(happy) # See: https://github.com/hadley/productplots/tree/master/R # 1 var ------------------------------------------------------------------- prodplot(happy, ~ happy, "hspine") # 1 var and coords -------------------------------------------------------- # Compare coords for below prodcalc(happy, ~ happy, "hspine") prodcalc(happy, ~ happy, "hbar") # Coordinates: l, r, b, t (left, right, bottom, top for each rectangle) # Coords can be passed to ggplot2 to re-create graphs hspine_coords <- prodcalc(happy, ~ happy, "hspine") str(hspine_coords) ggplot(hspine_coords) + geom_rect(aes(xmin=l, xmax=r, ymin=b, ymax=t, fill=happy)) # 2 vars ------------------------------------------------------------------ # Compare: Order of vars determines which divider is applied to which # The direction of the divider (h or v) decides which axes the var labels correspond to # 'h' horizontal dividers divide up the horizontal x-axis # Change hbar to hspine to see effect prodplot(happy, ~ sex + happy, c("vspine", "hspine")) + aes(fill=sex) prodplot(happy, ~ happy + sex, c("vspine", "hbar")) + aes(fill=happy) prodplot(happy, ~ happy + sex, c("hbar", "vspine")) + aes(fill=happy) # Below same as: prodplot(happy, ~ sex + happy, c("vspine", "hspine")) + aes(fill=sex) prodplot(happy, ~ sex + happy, mosaic()) + aes(fill=sex) # stacked() best with only 2 vars (like a trellis plot) prodplot(happy, ~ sex + happy, stacked()) # 2 vars + ggplot2 + plotly ------------------------------------------------------- prodplot(happy, ~ marital + happy, c("vspine", "hspine"), na.rm = TRUE) + aes(fill=marital) mosaic2_coords <- prodcalc(happy, ~ marital + happy, c("vspine", "hspine"), na.rm = TRUE) str(mosaic2_coords) # level = 1 or 2 since 2 vars involved. Need to plot only level 2 p <- ggplot(mosaic2_coords[mosaic2_coords$level==2,]) + geom_rect(aes(xmin=l, xmax=r, ymin=b, ymax=t, fill=marital, color=happy)) + scale_color_discrete (c=0, l=100) + theme(panel.background = element_blank(), axis.ticks = element_blank(), axis.title = element_blank(), axis.text.y = element_text(), axis.text.x = element_blank()) ggplotly(p, tooltip = c("fill", "color")) # With geom_mosaic (ggmosaic package) currently does NOT work ggplot(happy) + geom_mosaic(aes(x = product(happy, sex))) # 2 vars and coords and ggvis interaction --------------------------------- # using mosiac2_coords from above mosaic2_coords[mosaic2_coords$level==2,] %>% ggvis(x=~l, x2=~r, y=~b, y2=~t, fill=~marital) %>% layer_rects() %>% add_tooltip(function(mosaic2_coords) mosaic2_coords[,1]) %>% add_tooltip(function(mosaic2_coords) mosaic2_coords$marital) # ggvis will only show one tooltip at a time and will not show 'happy' status since it's not 'connected' to the coords. # 3 vars ------------------------------------------------------------------ # Conditional: distn of happiness and gender, given their health status # mosaic usually uses vertical spines first by defaulth prodplot(happy, ~ happy + sex | health, mosaic("h")) + aes (fill=happy) # different to below: distn of happiness, gender and health status # (widths not fixed, vary according to health status) prodplot(happy, ~ happy + sex + health, mosaic("h")) + aes (fill=happy) prodplot(happy, ~ marital + sex + happy, stacked()) + aes(fill = marital) prodplot(happy, ~ marital + sex + happy, stacked(), level = 3) # level = 3 is complete plot, level = 2 is sex + happy, level = 1 is happy only prodplot(happy, ~ happy + marital + sex, c("vspine", "hspine", "hspine")) + aes(fill = happy) # 'level' shows how the plot was built up: partition by sex first prodplot(happy, ~ happy + marital + sex, c("vspine", "hspine", "hspine"), level = 1) # Then sex and marital status prodplot(happy, ~ happy + marital + sex, c("vspine", "hspine", "hspine"), level = 2) # Different order of nesting to above, fill color = last level/first variable is best prodplot(happy, ~ marital + happy + sex, c("hspine", "vspine", "hspine")) + aes(fill = marital) # Level 1 is same as above, but not level 2 prodplot(happy, ~ marital + happy + sex, c("hspine", "vspine", "hspine"), level = 2) # Sex layered last prodplot(happy, ~ sex + happy + marital, c("hspine", "vspine", "hspine")) + aes(fill = sex) prodplot(happy, ~ sex + happy + marital, c("hspine", "vspine", "hspine"), level = 2) + aes(fill=happy) # level = 2, same as below (mosaic = vspine, hspine,.. alternating) # Default is to NOT remove NA's (na.rm=FALSE) prodplot(happy, ~ happy + marital, mosaic(), na.rm = TRUE) + aes(fill = happy)

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/man/idmTPreg-package.Rd

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idmTPreg-package.Rd

\name{idmTPreg-package} \alias{idmTPreg-package} \alias{idmTPreg} \docType{package} \title{ \packageTitle{idmTPreg} } \description{ \packageDescription{idmTPreg} } \details{ The DESCRIPTION file: \packageDESCRIPTION{idmTPreg} \packageIndices{idmTPreg} } \author{ \packageAuthor{idmTPreg} Maintainer: \packageMaintainer{idmTPreg} } \references{ Azarang, L. Scheike, TH. and de Una-Alvarez, J. (2017) \emph{Direct modeling of regression effects for transition probabilities in the progressive illness-death model} Statistics in Medicine \bold{36}, \eqn{1964-1976}. }

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/man/refine.MESH.2D.Rd

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refine.MESH.2D.Rd

\name{refine.MESH.2D} \alias{refine.MESH.2D} \title{Refine the triangulation} \usage{ refine.MESH.2D(mesh, minimum_angle = NA, maximum_area = NA, delaunay = FALSE, verbosity = 0) } \arguments{ \item{mesh}{A TRIMESH2D object created through \code{\link{create.MESH.2D}}.} \item{minimum_angle}{A numeric specifying the minimum angle of the triangles in the ouput triangulation.} \item{maximum_area}{A numeric specifying the maximum area of the triangles in the ouput triangulation.} \item{delaunay}{If \code{TRUE} the output triangulation is a Delaunay triangulation} \item{verbosity}{A numeric that can assume values \code{0,1,2}, that specifies the output verbosity in the triangulation process.} } \value{ An object of class \code{TRIMESH2D}.} \description{ Refines a TRIMESH2D object following the constrained imposed with the input parameters. This function is based on the Triangle library (\url{http://www.cs.cmu.edu/~quake/triangle.html}). The triangulation is a constrained conforming Delaunay triangulation in which additional vertices, called Steiner points, can be inserted into segments to improved the quality of the triangulation. } \examples{ ## Creates an object TRIMESH2D with a concavity and second order nodes mesh_coarse<-create.MESH.2D(nodes=rbind(c(0, 0), c(0, 1), c(0.5, 0.5), c(1, 1), c(1, 0)), segments=rbind(c(1, 2), c(2, 3), c(3, 4), c(4, 5), c(5, 1))) ## Plot it plot(mesh_coarse) ## Refines the the triangulation in specified in the \code{mesh_coarse} object mesh<-refine.MESH.2D(mesh_coarse,maximum_area = 0.005, delaunay = TRUE) ## Plot the refined mesh plot(mesh) }

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dincerti/political-instability

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04-ar.R

rm(list = ls()) library("xtable") library("data.table") library("ggplot2") library("scales") load("data/data-clean.RData") load("output/regime-change-event-study.RData") theme_set(theme_bw()) source("code/func.R") # ABNORMAL RETURNS TABLES ------------------------------------------------------ # Add authoritarian versus democratic shift information to data auth_dem <- event[type == "Coup" | type == "Assassination" | type == "Resignation", .(country, ticker, stock_date, `auth shift`, `dem shift`)] regime.change <- merge(regime.change, auth_dem, by = c("country", "ticker", "stock_date")) # coups coup.index <- which(regime.change$type == "Coup") artable.coups <- ar_table(td = rc.es$td, ar = rc.es$ar.treat[, coup.index], sigma = rc.es$sigma.treat[coup.index], dtr = rc.es$dtr.treat[coup.index], country = regime.change[coup.index, country], date = regime.change[coup.index, stock_date], coup = FALSE) myprint.xtable(artable.coups$car, file = "tables/artable-coups-car.txt") myprint.xtable(artable.coups$car.mean, file = "tables/artable-coups-car-mean.txt") # coups - insignificant 7 day pre-trends only coup.index.pre <- which(regime.change$type == "Coup" & regime.change$stock_date != "1977-10-20") artable.coups.pre <- ar_table(td = rc.es$td, ar = rc.es$ar.treat[, coup.index.pre], sigma = rc.es$sigma.treat[coup.index.pre], dtr = rc.es$dtr.treat[coup.index.pre], country = regime.change[coup.index.pre, country], date = regime.change[coup.index.pre, stock_date], coup = FALSE) myprint.xtable(artable.coups.pre$car, file = "tables/artable-coups-car-pre.txt") myprint.xtable(artable.coups.pre$car.mean, file = "tables/artable-coups-car-mean-pre.txt") # assassinations ass.index <- which(regime.change$type == "Assassination") artable.ass <- ar_table(td = rc.es$td, ar = rc.es$ar.treat[, ass.index], sigma = rc.es$sigma.treat[ass.index], dtr = rc.es$dtr.treat[ass.index], country = regime.change[ass.index, country], date = regime.change[ass.index, stock_date]) myprint.xtable(artable.ass$car, file = "tables/artable-ass-car.txt") myprint.xtable(artable.ass$car.mean, file = "tables/artable-ass-car-mean.txt") # assassinations - insignificant 7 day pre-trends only ass.index.pre <- which(regime.change$type == "Assassination" & regime.change$stock_date != "1963-11-22" & regime.change$stock_date != "1984-11-05") artable.ass.pre <- ar_table(td = rc.es$td, ar = rc.es$ar.treat[, ass.index.pre], sigma = rc.es$sigma.treat[ass.index.pre], dtr = rc.es$dtr.treat[ass.index.pre], country = regime.change[ass.index.pre, country], date = regime.change[ass.index.pre, stock_date]) myprint.xtable(artable.ass.pre$car, file = "tables/artable-ass-car-pre.txt") myprint.xtable(artable.ass.pre$car.mean, file = "tables/artable-ass-car-mean-pre.txt") # resignations res.index <- which(regime.change$type == "Resignation") artable.res <- ar_table(td = rc.es$td, ar = rc.es$ar.treat[, res.index], sigma = rc.es$sigma.treat[res.index], dtr = rc.es$dtr.treat[res.index], country = regime.change[res.index, country], date = regime.change[res.index, stock_date]) myprint.xtable(artable.res$car, file = "tables/artable-res-car.txt") myprint.xtable(artable.res$car.mean, file = "tables/artable-res-car-mean.txt") # resignations - insignificant 7 day pre-trends only res.index.pre <- which(regime.change$type == "Resignation" & regime.change$stock_date != "1982-06-18" & regime.change$stock_date != "2001-12-20" & regime.change$stock_date != "2011-01-31") artable.res.pre <- ar_table(td = rc.es$td, ar = rc.es$ar.treat[, res.index.pre], sigma = rc.es$sigma.treat[res.index.pre], dtr = rc.es$dtr.treat[res.index.pre], country = regime.change[res.index.pre, country], date = regime.change[res.index.pre, stock_date]) myprint.xtable(artable.res.pre$car, file = "tables/artable-res-car-pre.txt") myprint.xtable(artable.res.pre$car.mean, file = "tables/artable-res-car-mean-pre.txt") # Authoritarian auth.index <- which(regime.change$`auth shift` == 1) artable.auth <- ar_table(td = rc.es.auth$td, ar = rc.es.auth$ar.treat[, auth.index], sigma = rc.es.auth$sigma.treat[auth.index], dtr = rc.es.auth$dtr.treat[auth.index], country = regime.change[auth.index, country], date = regime.change[auth.index, stock_date], coup = FALSE) myprint.xtable(artable.auth$car, file = "tables/artable-auth-car.txt") myprint.xtable(artable.auth$car.mean, file = "tables/artable-auth-car-mean.txt") # Democratic dem.index <- which(regime.change$`dem shift` == 1) artable.dem <- ar_table(td = rc.es.dem$td, ar = rc.es.dem$ar.treat[, dem.index], sigma = rc.es.dem$sigma.treat[dem.index], dtr = rc.es.dem$dtr.treat[dem.index], country = regime.change[dem.index, country], date = regime.change[dem.index, stock_date], coup = FALSE) myprint.xtable(artable.dem$car, file = "tables/artable-dem-car.txt") myprint.xtable(artable.dem$car.mean, file = "tables/artable-dem-car-mean.txt") # VENEZUELA PARTIAL COUP ------------------------------------------------------- ven <- event[ticker == "_IBCD" & stock_date == "2002-04-12"] ven.es <- event_study(ticker = index$ticker, date = index$date, dr = index$dr, event_ticker = ven$ticker, event_window = 20, estimation_window = 200, event_date = ven$stock_date, model = "constant", control = FALSE) ven.es$ar.treat[, lar := ar - qnorm(.975) * ven.es$sigma.treat] ven.es$ar.treat[, uar := ar + qnorm(.975) * ven.es$sigma.treat] p <- ggplot(ven.es$ar.treat[abs(td) <= 10], aes(x = td, y = ar)) + geom_hline(aes(yintercept = 0), linetype = 2, color = "grey") + geom_vline(aes(xintercept = 0), linetype = 2, color = "grey") + geom_pointrange(aes(ymin = lar, ymax = uar), size = .3) + xlab("Trading days") + ylab("Abnormal Returns (%)") + scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) + scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) + theme_classic() print(p) ggsave("figs/venezuela_coup_attempt_2002.pdf", p, height = 5, width = 7) # VENEZUELA 1992 COUP ATTEMPT -------------------------------------------------- # Change event day to 11/30 event <- event[, stock_date := dplyr::if_else(stock_date == "1992-11-27", as.Date("1992-11-30"), stock_date)] ven92 <- event[ticker == "_VE1" & stock_date == "1992-11-30"] ven92.es <- event_study(ticker = index$ticker, date = index$date, dr = index$dr, event_ticker = ven92$ticker, event_window = 20, estimation_window = 200, event_date = ven92$stock_date, model = "constant", control = FALSE) ven92.es$ar.treat[, lar := ar - qnorm(.975) * ven92.es$sigma.treat] ven92.es$ar.treat[, uar := ar + qnorm(.975) * ven92.es$sigma.treat] p <- ggplot(ven92.es$ar.treat[abs(td) <= 10], aes(x = td, y = ar)) + geom_hline(aes(yintercept = 0), linetype = 2, color = "grey") + geom_vline(aes(xintercept = 0), linetype = 2, color = "grey") + geom_pointrange(aes(ymin = lar, ymax = uar), size = .3) + xlab("Trading days") + ylab("Abnormal Returns (%)") + scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) + scale_y_continuous(limits = c(-12, 12), breaks = scales::pretty_breaks(n = 10)) + theme_classic() print(p) ggsave("figs/venezuela_coup_attempt_1992.pdf", p, height = 5, width = 7) # TURKEY 2016 COUP ATTEMPT ----------------------------------------------------- # Add 2016 Turkey failed coup to data.table event = rbind(event,list(85, "Turkey", "_XU100D", "07/15/2016", as.Date("07/18/2016", "%m/%d/%Y"), "Recep Tayyip Erdoğan", "Failed Coup", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)) turk16 <- event[ticker == "_XU100D" & stock_date == "2016-07-18"] turk16.es <- event_study(ticker = index$ticker, date = index$date, dr = index$dr, event_ticker = turk16$ticker, event_window = 20, estimation_window = 200, event_date = turk16$stock_date, model = "constant", control = FALSE) turk16.es$ar.treat[, lar := ar - qnorm(.975) * turk16.es$sigma.treat] turk16.es$ar.treat[, uar := ar + qnorm(.975) * turk16.es$sigma.treat] p <- ggplot(turk16.es$ar.treat[abs(td) <= 10], aes(x = td, y = ar)) + geom_hline(aes(yintercept = 0), linetype = 2, color = "grey") + geom_vline(aes(xintercept = 0), linetype = 2, color = "grey") + geom_pointrange(aes(ymin = lar, ymax = uar), size = .3) + xlab("Trading days") + ylab("Abnormal Returns (%)") + scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) + scale_y_continuous(limits = c(-12, 12), breaks = scales::pretty_breaks(n = 10)) + theme_classic() print(p) ggsave("figs/turkey_coup_attempt_2016.pdf", p, height = 5, width = 7)

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bill-mattingly/ExData_Plotting1

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#Read data mydata <- read.table("household_power_consumption.txt", sep = ";", h=T) #convert datatype datetime <- paste(mydata$Date, mydata$Time) datetime <- as.POSIXct(strptime(datetime, "%d/%m/%Y %H:%M:%S")) mydata <- cbind(datetime, mydata[,-(1:2)]) mydata <- subset(mydata, datetime >= as.POSIXct("2007-02-01 00:00:00 PST") & datetime <= as.POSIXct("2007-02-02 23:59:59 PST")) #subset mydata$Global_active_power = as.numeric(as.character(mydata$Global_active_power)) mydata$Sub_metering_1 = as.numeric(as.character(mydata$Sub_metering_1)) mydata$Sub_metering_2 = as.numeric(as.character(mydata$Sub_metering_2)) mydata$Sub_metering_3 = as.numeric(as.character(mydata$Sub_metering_3)) mydata$Voltage = as.numeric(as.character(mydata$Voltage)) mydata$Global_reactive_power = as.numeric(as.character(mydata$Global_reactive_power)) #Make plots png("plot4.png", width = 480, height = 480) par(mfcol = c(2,2)) plot(mydata$Global_active_power ~ mydata$datetime, type = "l", ylab = "Global Active Power", xlab = "") plot(mydata$Sub_metering_1 ~ mydata$datetime, type = "l", main = "", ylab = "Energy sub metering", xlab = "") lines(mydata$Sub_metering_2 ~ mydata$datetime, col = "Red") lines(mydata$Sub_metering_3 ~ mydata$datetime, col = "Blue") legend("topright", col = c("Black", "Red", "Blue"), legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty = 1, bty ="n") #line voltage vs time plot(mydata$Voltage ~ mydata$datetime, xlab = "datetime", ylab = "Voltage", type = "l") #line reactive mydata vs time plot(mydata$Global_reactive_power ~ mydata$datetime, xlab = "datetime", ylab = "Global_reactive_power", type = "l") dev.off()

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test_that("Addition works", { add_result <- addition(2, 3) expect_equal(length(add_result), 1) expect_equal(add_result, 5) }) test_that("Addition fails", { testthat::expect_error( addition("a", 3), regexp = "non-numeric argument to binary operator" ) })

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4_supermatrix.R

# Construct a supermatrix # Generate a .fasta file of all the alignments # Library ---- library(gaius) # Vars ---- wd <- file.path(getwd(), 'pipelines', '3_supertree') input_dir <- file.path(wd, '3_align') output_dir <- file.path(wd, '4_supermatrix') if (!dir.exists(output_dir)) { dir.create(output_dir) } tree_file <- file.path(wd, '6_supertree', 'supertree.tre') # Identify groups ---- # set parameters with pset()/pget() # min_ntips - the minimum number of tips per group, default 5 # max_ntips - the maximum number of tips per group, default 100 alignment_files <- file.path(input_dir, list.files(path = input_dir, pattern = '.fasta')) alignment_names <- names_from_alignments(alignment_files) tree_names <- names_from_tree(tree_file) matched_names <- name_match(alignment_names = alignment_names, tree_names = tree_names) groups <- groups_get(tree_file = tree_file, matched_names = matched_names) # Supermatrices ---- # min_ngenes - minimum number of genes in matrix # min_ntips - minimum number of tips in matrix # min_nbps - minimum number of base paris in a gene # column_cutoff - proportion of non-gaps per column # tip_cutoff - proportion of non-gaps per tip alignment_list <- alignment_read(flpths = alignment_files) supermatrices <- supermatrices_get(alignment_list = alignment_list, groups = groups, min_ngenes = 2, min_ntips = 5, min_nbps = 250, column_cutoff = 0.1, tip_cutoff = 0.1) # check all mono. groups in backbone nmono <- sum(names(groups) %in% names(supermatrices[['backbone']])) if (nmono != (length(groups) - 1)) { stop('Too few mono. groups in backbone') } # Write out ---- ids <- names(supermatrices) for (id in ids) { supermatrix <- supermatrices[[id]] # number of bps per cluster nbps <- attr(supermatrix, 'nbps') flnm <- paste0(id, "_partition.txt") partition_file(nbp = nbps, flpth = file.path(output_dir, flnm)) flnm <- paste0(id, '_supermatrix.fasta') sequences_write(x = supermatrix, flpth = file.path(output_dir, flnm)) }

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#' model2sparse: Converts a niche model into a diagonal sparse matrix #' @param model A raster object representing the geographic projection of a niche model. #' @param threshold A threshold to convert a continuous model into a binary model. #' @import Matrix #' @return A diagonal sparse matrix representing the geographic projection of a niche model. #' @export #' @examples #' \dontrun{ #' model_path <- system.file("extdata/Lepus_californicus_cont.tif", #' package = "bam") #' model <- raster::raster(model_path) #' #' sparse_mod <- bam::model2sparse(model,threshold=0.05) #' } model2sparse <- function(model, threshold=NULL){ if(is.numeric(threshold)){ model <- model > threshold } model_vals <- raster::getValues(model) in_niche <- which(!is.na(model_vals)) cell_ids <- stats::complete.cases(model_vals) out_niche <- which(cell_ids[-in_niche]) all_area <- which(cell_ids) ncols <- nrows <- length(all_area) mod_sparse <- Matrix::sparseMatrix(i=match(in_niche,all_area), j=match(in_niche,all_area), x=model_vals[in_niche], dims = c(nrows,ncols))*1 mod_coords <- raster::coordinates(model)[all_area,] mod_atts <- setA(bin_model = model, cellIDs = all_area, sparse_model = mod_sparse, coordinates= mod_coords) return(mod_atts) }

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# Load in the full data set and set working directories library(here) YorkGum <- read.csv(here("water_full_env.csv")) FocalSp <- "W" # or H, T, A #subset the data and create quatratic transformations of the environmental covariates SpData <- subset(YorkGum, Focal.sp.x == FocalSp & Trt.comp.x == "S") SpData$logLambda <- log(SpData$Number.flowers.total) SpData$Phos2 <- SpData$Colwell.P ^ 2 SpData$Canopy2 <- SpData$Canopy ^ 2 # Fit the competing models with different quadratic options LinearMod <- lm(logLambda ~ Colwell.P + Canopy + Colwell.P*Canopy, data = SpData) QuadraticP <- lm(logLambda ~ Phos2 + Canopy + Phos2*Canopy, data = SpData) QuadraticC <- lm(logLambda ~ Colwell.P + Canopy2 + Colwell.P*Canopy2, data = SpData) QuadraticB <- lm(logLambda ~ Phos2 + Canopy2 + Phos2*Canopy2, data = SpData) # Make predictions from each of the models PredData <- subset(SpData, select = c("Colwell.P", "Canopy", "logLambda", "Phos2", "Canopy2")) LinearPred <- cbind(PredData, predict(LinearMod, PredData, interval = "confidence")) QuadP_pred <- cbind(PredData, predict(QuadraticP, PredData, interval = "confidence")) QuadC_pred <- cbind(PredData, predict(QuadraticC, PredData, interval = "confidence")) QuadB_pred <- cbind(PredData, predict(QuadraticB, PredData, interval = "confidence")) # Make a plot with the residuals for each model across phosphorous and canopy cover FigName <- paste("LambdaFunctionalForms/", FocalSp, "_residuals.pdf", sep = "") pdf(file = FigName, width = 10, height = 6, onefile = FALSE, paper = "special") par(mfrow = c(2,4)) # Phosphorous plot(x = LinearPred$Colwell.P, y = LinearPred$fit - LinearPred$logLambda, xlab = "Phosphorous", ylab = "Residuals", main = "", ylim = c(-2.5, 2.5)) abline(h = 0, lty = 2) mtext("Monotonic", side = 3, line = 0.5) plot(x = QuadP_pred$Colwell.P, y = QuadP_pred$fit - QuadP_pred$logLambda, xlab = "Phosphorous", ylab = "Residuals", main = "", ylim = c(-2.5, 2.5)) abline(h = 0, lty = 2) mtext("Phosphorous^2", side = 3, line = 0.5) plot(x = QuadC_pred$Colwell.P, y = QuadC_pred$fit - QuadC_pred$logLambda, xlab = "Phosphorous", ylab = "Residuals", main = "", ylim = c(-2.5, 2.5)) abline(h = 0, lty = 2) mtext("Canopy^2", side = 3, line = 0.5) plot(x = QuadB_pred$Colwell.P, y = QuadB_pred$fit - QuadB_pred$logLambda, xlab = "Phosphorous", ylab = "Residuals", main = "", ylim = c(-2.5, 2.5)) abline(h = 0, lty = 2) mtext("Both^2", side = 3, line = 0.5) # Canopy cover plot(x = LinearPred$Canopy, y = LinearPred$fit - LinearPred$logLambda, xlab = "Canopy", ylab = "Residuals", main = "", ylim = c(-2.5, 2.5)) abline(h = 0, lty = 2) plot(x = QuadP_pred$Canopy, y = QuadP_pred$fit - QuadP_pred$logLambda, xlab = "Canopy", ylab = "Residuals", main = "", ylim = c(-2.5, 2.5)) abline(h = 0, lty = 2) plot(x = QuadC_pred$Canopy, y = QuadC_pred$fit - QuadC_pred$logLambda, xlab = "Canopy", ylab = "Residuals", main = "", ylim = c(-2.5, 2.5)) abline(h = 0, lty = 2) plot(x = QuadB_pred$Canopy, y = QuadB_pred$fit - QuadB_pred$logLambda, xlab = "Canopy", ylab = "Residuals", main = "", ylim = c(-2.5, 2.5)) abline(h = 0, lty = 2) dev.off()

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parsing_functions.R

# Regex to locate links in text find_link <- regex(" \\[ # Grab opening square bracket .+? # Find smallest internal text as possible \\] # Closing square bracket \$ # Opening parenthesis .+? # Link text, again as small as possible \$ # Closing parenthesis ", comments = TRUE) # Function that removes links from text and replaces them with superscripts that are # referenced in an end-of-document list. sanitize_links <- function(text){ if (PDF_EXPORT) { str_extract_all(text, find_link) %>% pluck(1) %>% walk(function(link_from_text){ title <- link_from_text %>% str_extract('\\[.+\\]') %>% str_remove_all('\\[|\\]') link <- link_from_text %>% str_extract('\$.+\$') %>% str_remove_all('\$|\$') # add link to links array links <<- c(links, link) # Build replacement text new_text <- glue('{title}<sup>{length(links)}</sup>') # Replace text text <<- text %>% str_replace(fixed(link_from_text), new_text) }) } text } # Take entire positions dataframe and removes the links # in descending order so links for the same position are # right next to eachother in number. strip_links_from_cols <- function(data, cols_to_strip){ for (i in seq_len(nrow(data))) { for (col in cols_to_strip) { data[i, col] <- sanitize_links(data[i, col]) } } data } # Take a position dataframe and the section id desired # and prints the section to markdown. print_section <- function(position_data, section_id, n_max = Inf){ position_data %>% filter(section == section_id) %>% arrange(desc(end)) %>% mutate(id = row_number()) %>% pivot_longer( starts_with('description'), names_to = 'description_num', values_to = 'description', values_drop_na = TRUE ) %>% slice_head(n = n_max) %>% group_by(id) %>% mutate( descriptions = list(description) ) %>% ungroup() %>% filter(description_num == 'description_1') %>% mutate( timeline = ifelse( is.na(start) | start == end, end, glue('{end} - {start}') ), description_bullets = map_chr(descriptions, ~ paste('-', ., collapse = '\n')), ) %>% strip_links_from_cols(c('title', 'description_bullets')) %>% mutate_all(~ ifelse(is.na(.), 'N/A', .)) %>% glue_data( "### {title}", "\n\n", "{loc}", "\n\n", "{institution}", "\n\n", "{timeline}", "\n\n", "{description_bullets}", "\n\n\n", ) } # For bib files. # using bib2df spaces are expected around '=' between keys and values # read_lines("biblio.bib") %>% str_replace("([a-z])=([\"\\{])", "\\1 = \\2") %>% write_lines("biblio_corrected.bib") # if needed # helper functions subset_authors <- function(vec, pos) { case_when( # need to wrap the vector in a list for map pos <= 10 & length(vec) >= 10 ~ list(c(vec[1:10], " _et al._ ")), pos <= 5 & length(vec) >= 5 ~ list(c(vec[1:5], " _et al._ ")), pos <= 3 & length(vec) >= 3 ~ list(c(vec[1:3], " _et al._ ")), TRUE ~ list(vec)) } # strip consecutive years na_year <- function(vec) { stopifnot(length(vec) > 1) buff <- vec[1] res <- vector(mode = "character", length = length(buff)) res[1] <- buff for (i in 2:length(vec)) { #print(paste("comparing", , "")) if (vec[i] == buff) { res[i] <- "N/A" } else { res[i] <- as.character(vec[i]) buff <- vec[i] } } res } print_articles <- function(bibdf, type = "ARTICLE", cv_author = "Ginolhac, A") { bibdf %>% filter(CATEGORY == type) %>% arrange(desc(YEAR)) %>% # collapse authors mutate(# find location of cv author in the list position = map_int(AUTHOR, ~ which(str_detect(.x, cv_author))), # shorten author list when possible AUTHOR = map2(AUTHOR, position, ~ subset_authors(.x, .y)), # remove one list level AUTHOR = map(AUTHOR, ~ .x[[1]]), # collapse vector, with and for last author if still present authors = map(AUTHOR, ~ if_else(.x[length(.x)] == " _et al._ ", glue_collapse(.x, sep = " "), glue_collapse(.x, sep = " ", last = " and "))), # highlight cv author in bold authors = str_replace(authors, cv_author, glue("**{cv_author}**")), # clean up titles from curly braces clean_title = str_remove_all(TITLE, "[\\{\\}]"), # strip consecutives years years = na_year(YEAR)) %>% glue_data( "### [{clean_title}]({URL})", "\n\n", "{authors}", " _{JOURNAL}_ ", " **{VOLUME}**, {PAGES}", "\n\n", "N/A", "\n\n", "{years}", "\n\n\n", ) }

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# NOTES # We had the wrong UI numbers in the spreadsheet. # We never updated the wages lost assistance spending, which was higher than we expected # in Q3. Unfortunately, the previous number was hardcoded so not sure where it came from. # It said Total Q3 was 881,354 # UI Q3 was also # 0.0 Source ---------------------------------------------------------------------------------------------------------- ## Source custom functions and packages library('tidyverse') library('readxl') library('writexl') library('tsibble') library('janitor') source('src/functions.R') library('lubridate') #source('_drake.R') annual_to_quarter <- function(df){ df %>% slice(rep(1:n(), each=4)) %>% rename(fy = date) } rename_haver_codes <- function(df){ df %>% rename( subsidies = gsub, ppp = gfsubp, nonprofit_ppp = gftfpp, nonprofit_provider_relief = gftfpv, aviation = gfsubg, employee_retention = gfsube, sick_leave = gfsubk, rebate_checks = gftfpe, ui_bea = yptu, total_grants = gfeg, covid_relief_fund = gfegc, education_stabilization_fund = gfege, provider_relief_fund = gfegv, medicaid_total = yptmd, medicaid_grants = gfeghdx, medicare = yptmr, federal_social_benefits_nipa = gftfp, state_social_benefits = gstfp, wages_lost_assistance = yptolm, ui_bea = yptu, peuc = yptue, pua = yptup, puc = yptuc ) } # 0.1 Pull Raw Data--------------------------------------------------------------- loadd(projections) START <- as_date("2018-12-31") nonprofit_hospitals <- 0.5 government_hospitals <- 0.2 forprofit_hospitals <- 0.3 cbo_legislation <- read_xlsx('data/pandemic-legislation/pandemic-legislation.xlsx', sheet = 'annual') %>% select(-1) %>% pivot_longer(-date) %>% pivot_wider(names_from = date, values_from = value) %>% rename(year = name) %>% clean_names() %>% mutate( across(everything(), ~ replace_na(., 0)), across(where(is.numeric), ~ . * 4) ) %>% fim::annual_to_quarter(year) provider_relief_fund_score <- cbo_legislation %>% mutate(provider_relief_fund = nonprofit_hospitals * provider_relief_fund ) %>% pull(provider_relief_fund) # Quarterly ------------------------------------------------------------------------------------------------------- #haver.path("//ESDATA01/DLX/DATA/") # BEA NIPAs names_usna <- read_excel("data/auxilliary/spreadsheet_names.xlsx") usna <- read_xlsx('data/raw/haver/national_accounts.xlsx') %>% filter(date >= as_date('2015-12-31')) %>% rename_haver_codes() %>% mutate( across(where(is.numeric), ~ replace_na(.x, 0)), date = yearquarter(date), # SUBSIDIES other_subsidies = aviation + employee_retention + sick_leave, legislation_subsidies = ppp + other_subsidies, nonprofit_subsidies = nonprofit_ppp + nonprofit_provider_relief, # GRANTS legislation_grants = covid_relief_fund + education_stabilization_fund + provider_relief_fund, medicaid_grants = medicaid_grants / 1000, # MILLIONS TO BILLIONS non_medicaid_grants = total_grants - medicaid_grants, non_medicaid_or_legislation_grants = non_medicaid_grants - legislation_grants, other_grants = legislation_grants, # PLACEHOLDER federal_cgrants = non_medicaid_or_legislation_grants + other_grants, # HEALTH federal_health = medicaid_grants + medicare, state_health = medicaid_total - medicaid_grants, # SOCIAL BENEFITS # UNEMPLOYMENT INSURANCE ui = ui_bea + wages_lost_assistance, ui_cbo_assumed_other = case_when( date < yearquarter('2020 Q2') ~ 0, date >= yearquarter('2020 Q2') & date <= yearquarter('2020 Q3') ~ 12, date >= yearquarter('2020 Q4') & date <= yearquarter('2021 Q1') ~ 8, date >= yearquarter('2021 Q2') ~ 4 ), federal_ui = 2 * peuc + pua + puc + wages_lost_assistance + ui_cbo_assumed_other, state_ui = ui - federal_ui, # FEDERAL SOCIAL BENEFITS federal_social_benefits = federal_social_benefits_nipa - medicare - state_ui, state_social_benefits = state_social_benefits + state_ui - medicaid_grants ) # Forecast ------------------------------------------------------------------------------------ # Consumption Grants -------------------------------------------------------------------------- totals <- function(df, var){ df %>% as_tibble() %>% select(date, var) %>% pivot_wider(-date, names_from = date, values_from = var) %>% summarise(sum(c_across(everything()))) %>% pull() } cbo_education <- 4 * 30 # Annualize education_total_disbursed <- usna %>% totals(var = 'education_stabilization_fund') education_total_remaining <- cbo_education - education_total_disbursed mpc_education_stabilization_fund <- function(x, n){ mpc <- 1 weights <- c(rep(1 / n, n)) x <- x * weights return(x) } x <- mpc_education_stabilization_fund(education_total_remaining, n = 4) df <- tibble( date = seq(yearquarter('2021 Q1'), length.out = 4, by = 1), x = x ) usna %<>% as_tsibble(index = date) %>% tsibble::append_row(n = 12) %>% left_join(projections %>% mutate(date = yearquarter(date)) %>% select(date, gfeg, gfeghdx)) %>% mutate(across(where(is.numeric), ~ replace_na(., 0))) usna %>% mutate(education_stabilization_fund = case_when( date < yearquarter('2021 Q1') ~ education_stabilization_fund, date >= yearquarter('2021 Q1') & date <= yearquarter('2022 Q2') ~ education_total_remaining )) # State Health -------------------------------------------------------------------------------- cbo_state_health <- tibble( fy = as.numeric(c(2020:2022)), cbo_state_health = c(657, 726, 727), growth_rate = ( cbo_state_health / lag(cbo_state_health)) ) cbo_state_health_growth <- cbo_state_health %>% filter(fy == 2021) %>% pull(growth_rate) forecasts <- tibble( date = seq(yearquarter("2021 Q1"), length.out = 12, by = 1), # by 1 quarter growth = case_when( date == yearquarter('2021 Q1') ~ cbo_state_health_growth^0.18, date == yearquarter('2021 Q2') ~ cbo_state_health_growth^0.1, date >= yearquarter('2021 Q3') ~ NaN ) ) %>% mutate(growth = zoo::na.locf(growth)) # Total Medicaid ------------------------------------------------------------------------------ usna %>% full_join(forecasts, by = 'date') %>% mutate( medicaid_total = if_else( date < yearquarter('2021 Q1'), medicaid_total, lag(medicaid_total) * growth ), medicaid_total = if_else( date == yearquarter('2021 Q2'), lag(medicaid_total) * growth, medicaid_total ), medicaid_total = zoo::na.locf(medicaid_total), medicaid_grants = case_when(date < yearquarter('2021 Q1') ~ medicaid_grants, date >= yearquarter('2021 Q1') & date <= yearquarter('2022 Q1') ~ medicaid_total * 0.74, date > yearquarter('2021 Q1') ~ medicaid_total * 0.68), state_health_outlays = medicaid_total - medicaid_grants, federal_health_outlays = medicaid_grants + medicare ) # Misc ---------------------------------------------------------------------------------------- fill_in <- function(prev, new, growth = 0.03) { if_else(!is.na(new), new, prev * (1 + growth)) } # fim_state_social_benefits = (nipa - medicare - ui - rebate_checks - nonprofit_subsidies) + rebate_checks + nonprofit_subsidies + federal_ui # = nipa - medicare + (federal_ui - ui) # = nipa - medicare - state_ui mpc_covid_relief_fund <- function(x){ mpc <- 1 weights <- c(0.0583, 0.075, rep(0.1, 2), rep(0.08333, 8)) mpc * roll::roll_sum(x, width = length(weights), weights = rev(weights), online = FALSE) } # Federal health # State health # Subsidies # Grants # Basically, to get the FIM social benefits we subtract medicare from NIPA social benefits # and then we add the difference between their ui and ours # # fim ex everything = social benefits -

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inferMSI.R

# inferMSI.R # Call MSI status given a model from trainMSI # Randy Johnson # CCR Collaborative Bioinformatics Resource at Frederick National Laboratory # Leidos Biomedical Research, Inc inferMSI <- function(file, model, cut.off) { ######### Checks ######### # ... should add some checks here to be sure variables make sense ... assuming they do for now ######### Read .repeatseq files ######### repeatseq <- lapply(file, read.repeatseq, model$markers) ######### Score samples ######### scores <- matrix(NA, nrow = length(repeatseq), ncol = length(model$normal), dimnames = list(names(repeatseq), names(model$normal))) for(i in 1:length(repeatseq)) { for(j in names(model$normal)) { tmp <- repeatseq[[i]][[j]]$alleles matches <- names(tmp) %in% names(model$normal[[j]]) tmp[matches] <- abs(tmp[matches] - model$normal[[j]][names(tmp)[matches]]) scores[i,j] <- sum(tmp) } } ######### fill in missing data ######### # if there are only a few (or no) observed values, fill in NAs with average score among non-msi samples nunique <- apply(scores, 2, unique) %>% lapply(na.omit) %>% sapply(length) for(j in which(nunique < 5)) { if(names(nunique)[j] %in% names(model$pred)) scores[is.na(scores[,j]),j] <- model$meanScore[names(nunique)[j]] } # among remaining NAs, impute scores <- as.matrix( missForest( as.data.frame( scores[,names(model$pred)[-1]] ) )$ximp ) ######### Infer MSI status ######### predictions <- cbind(1, scores[,]) %*% model$pred %>% inv.logit() ######### Return results ######### retval <- data.frame(file = file, # msi = predictions > cut.off, score = predictions) return(retval) }

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/tests/testthat/test-format.R

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test-format.R

library(testthat) context("Format") test_that("formatting is correct", { expect_identical( format_event( event_query = readRDS(file = "test_query_season_events.rds") ), readRDS(file = "test_format_season_events.rds") ) expect_identical( format_match( match_query = readRDS(file = "test_query_event_matches.rds") ), readRDS(file = "test_format_event_matches.rds") ) expect_identical( format_player( player_query = readRDS(file = "test_query_season_ama_players.rds") ), readRDS(file = "test_format_season_ama_players.rds") ) expect_identical( format_ranking( ranking_query = readRDS(file = "test_query_season_rankings.rds") ), readRDS(file = "test_format_season_rankings.rds") ) })

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/R/prog.R

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Kaustuv2809/kysrc

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refs/heads/master

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prog.R

#' Limited English Proficiency Data for Kentucky Public Schools, 2013-2017 #' #' A dataset with data on Limited English Proficiency #' #' @format A dataframe with 75 rows and 6 variables: #' \describe{ #' \item{sch_id}{ID number to identify schools & districts} #' \item{dist_name}{District name - in this case, it is always "State"} #' \item{year}{School year} #' \item{student_group}{Student subgroup} #' \item{lep_total}{Students with Limited English Proficiency} #' \item{lep_pct}{Percent of students with Limited English Proficiency} #' } #' #' @source \url{https://applications.education.ky.gov/src/DataSets.aspx} "lep_state" #' Limited English Proficiency Data for Kentucky Public Schools, 2013-2017 #' #' A dataset with data on Limited English Proficiency #' #' @format A dataframe with 11,115 rows and 6 variables: #' \describe{ #' \item{sch_id}{ID number to identify schools & districts} #' \item{dist_name}{District name} #' \item{year}{School year} #' \item{student_group}{Student subgroup} #' \item{lep_total}{Students with Limited English Proficiency} #' \item{lep_pct}{Percent of students with Limited English Proficiency} #' } #' #' @source \url{https://applications.education.ky.gov/src/DataSets.aspx} "lep_dist" #' Limited English Proficiency Data for Kentucky Public Schools, 2013-2017 #' #' A dataset with data on Limited English Proficiency #' #' @format A dataframe with 72,720 rows and 7 variables: #' \describe{ #' \item{sch_id}{ID number to identify schools & districts} #' \item{dist_name}{District name} #' \item{sch_name}{School name} #' \item{year}{School year} #' \item{student_group}{Student subgroup} #' \item{lep_total}{Students with Limited English Proficiency} #' \item{lep_pct}{Percent of students with Limited English Proficiency} #' } #' #' @source \url{https://applications.education.ky.gov/src/DataSets.aspx} "lep_sch" #' Data on students with IEP's in Kentucky Public Schools, 2013-2017 #' #' A dataset with data on students with Individualized Educational Plans #' #' @format A dataframe with 96 rows and 6 variables: #' \describe{ #' \item{sch_id}{ID number to identify schools & districts} #' \item{dist_name}{District name - in this case, it is always "State"} #' \item{year}{School year} #' \item{student_group}{Student subgroup} #' \item{iep_total}{Students with IEP's} #' \item{iep_pct}{Percent of students with IEP's} #' } #' #' @source \url{https://applications.education.ky.gov/src/DataSets.aspx} "iep_state" #' Data on students with IEP's in Kentucky Public Schools, 2013-2017 #' #' A dataset with data on students with Individualized Educational Plans #' #' @format A dataframe with 16,824 rows and 6 variables: #' \describe{ #' \item{sch_id}{ID number to identify schools & districts} #' \item{dist_name}{District name} #' \item{year}{School year} #' \item{student_group}{Student subgroup} #' \item{iep_total}{Students with IEP's} #' \item{iep_pct}{Percent of students with IEP's} #' } #' #' @source \url{https://applications.education.ky.gov/src/DataSets.aspx} "lep_dist" #' Data on students with IEP's in Kentucky Public Schools, 2013-2017 #' #' A dataset with data on students with Individualized Educational Plans #' #' @format A dataframe with 129,480 rows and 7 variables: #' \describe{ #' \item{sch_id}{ID number to identify schools & districts} #' \item{dist_name}{District name} #' \item{sch_name}{School name} #' \item{year}{School year} #' \item{student_group}{Student subgroup} #' \item{iep_total}{Students with IEP's} #' \item{iep_pct}{Percent of students with IEP's} #' } #' #' @source \url{https://applications.education.ky.gov/src/DataSets.aspx} "lep_sch"

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/scripts/r/z_analysis.R

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hemprichbennett/bat_diet_ms

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z_analysis.R

#### Header #### ## Project: bat diet ## Script purpose: calculating z-scores from the random files created by ## array_site_motu_95_confidence_intervals ## Date: 2021-01-06 ## Author: Dave Hemprich-Bennett (hemprich.bennett@gmail.com) ## Notes ################################################## #### Setup #### # Prevent partial-match errors options(warnPartialMatchArgs = TRUE) library(tidyverse) # Load and format the random data ----------------------------------------- rand_filenames <- list.files('data/output_data/null_values/', full.names = T) rand_vals <- lapply(rand_filenames, read_csv) %>% bind_rows() # Get the real values ----------------------------------------------- source('scripts/r/r_network_gen.r') inpath <- 'data/processed_dna_data/lulu/' filenames <- list.files(pattern = '.csv', path = inpath) filenames filenames <- paste(inpath, filenames, sep = '') rawnets <- lapply(filenames, read.csv, header = F, stringsAsFactors = F, row.names=1) names(rawnets) <- gsub('.*\\/', '', filenames) names(rawnets) <- gsub('_.+', '', names(rawnets)) for(i in 1:length(rawnets)){ rawnets[[i]][2:nrow(rawnets[[i]]),] <- ifelse(rawnets[[i]][2:nrow(rawnets[[i]]), ] == 0, 0, 1) } netlists <- lapply(rawnets, function(x) r_network_gen(input= x, collapse_species = T, filter_species = T)) names(netlists) <- names(rawnets) actual_vals_list <- list() z <- 1 for(ind in unique(rand_vals$metric_used)){ for(i in 1:length(netlists)){ vals_list <- lapply(netlists[[i]], function(x) networklevel(x, index = ind, level = 'higher')) out_df <- bind_rows(vals_list) %>% rename_at(1, ~"actual" ) %>% mutate(site = names(vals_list), metric_used = ind, clustering_level = names(netlists)[i]) %>% mutate(clustering_level = as.numeric(clustering_level)) actual_vals_list[[z]] <- out_df z <- z + 1 } } actual_vals <- bind_rows(actual_vals_list) # Calculate 'z-scores' ---------------------------------------------------- summary_vals <- rand_vals %>% group_by(site, metric_used, clustering_level, fixedmargins) %>% summarise(mean_rand = mean(metric_value), sd_rand = sd(metric_value)) z_vals <- summary_vals %>% ungroup() %>% # combine the datasets left_join(actual_vals) %>% # calculate the 'z' score mutate(z = (actual - mean_rand)/sd_rand) %>% # make some of the variables prettier for the outputs mutate(site = gsub('DANUM', 'Danum', site), site = gsub('MALIAU', 'Maliau', site), metric_used = gsub('functional complementarity', 'Functional complementarity', metric_used), metric_used = gsub('weighted NODF', 'WNODF', metric_used)) # write a csv of all of the values in their raw form write_csv(z_vals, 'results/z_scores/all_z_scores.csv') # then make a csv for each metric, with the values given to 3 decimal places z_vals %>% select(-mean_rand, -sd_rand) %>% mutate(actual = round(actual, digits = 2), z = round(z, digits = 3), Treatment =ifelse(site == 'SAFE', 'Logged', 'Old growth')) %>% rename(Site = site, Metric = metric_used, `Clustering threshold` = clustering_level, `Observed Value` = actual) %>% group_by(Metric) %>% do(write_csv(., paste0('results/z_scores/', unique(.$Metric), "_zscores.csv"))) # Finally, one table to rule them all... met_list <- list() cols <- c('actual', 'z') for(met in unique(z_vals$metric_used)){ met_list[[met]] <- z_vals %>% filter(metric_used == met) %>% rename_at(cols, list( ~paste( ., met, sep = '_') ) ) %>% select(-metric_used, - mean_rand, - sd_rand) } mets_df <- left_join(met_list[[1]], met_list[[2]], met_list[[3]], by = c('site', 'clustering_level')) net_list <- list() cols <- c('actual', 'both', 'columns') for(net in unique(z_vals$site)){ net_list[[net]] <- z_vals %>% filter(site == net) %>% select(-site, - mean_rand, - sd_rand) %>% mutate(actual = round(actual, digits = 3), z = round(z, digits = 3)) %>% pivot_wider(names_from = fixedmargins, values_from = z) %>% rename_at(cols, list( ~paste( ., net, sep = '_') ) ) } nets_df <- left_join(net_list[[1]], net_list[[2]], by = c('metric_used', 'clustering_level')) %>% left_join(net_list[[3]], by = c('metric_used', 'clustering_level')) write_csv(nets_df, path = 'results/z_scores/grouped_by_metric.csv') # Plot -------------------------------------------------------------------- ggplot(rand_vals, aes(x = clustering_level, y = metric_value)) + geom_violin() + facet_grid(metric_used ~ site, scales = 'free') + theme_classic() for_plot <- z_vals %>% pivot_longer(cols = c('actual', 'z'), names_to = 'var_type', values_to = 'var_value') %>% mutate(metric = gsub('Functional complementarity', 'Functional\ncomplementarity', metric_used)) actual_points <- ggplot(filter(for_plot, var_type == 'actual'), aes(x = clustering_level, y = var_value, colour = site)) + geom_point() + scale_colour_viridis_d()+ scale_x_continuous(breaks = c(91:98))+ facet_wrap(. ~ metric_used, scales = 'free_y', ncol = 1, # sort the facet label placement strip.position = 'left') + theme_bw() + theme(legend.position = 'none', # sort the facet label placement strip.placement = "outside", strip.text.y = element_text(size = 8), #axis.title.y=element_blank(), axis.title.x=element_blank())+ ylab('Observed value') actual_points z_points <- ggplot(filter(for_plot, var_type == 'z'), aes(x = clustering_level, y = var_value, colour = site)) + geom_point(size = 2) + scale_colour_viridis_d()+ scale_x_continuous(breaks = c(91:98))+ facet_wrap(. ~ metric_used, ncol = 1) + theme_bw() + theme(legend.position = 'none', axis.title.x=element_blank(), # remove facet label strip.text = element_blank(), #axis.title.y=element_blank() ) + ylab('Z-value') z_points # Function for extracting the legend g_legend <- function(a.gplot) { tmp <- ggplot_gtable(ggplot_build(a.gplot)) leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box") legend <- tmp$grobs[[leg]] return(legend) } legend <- ggplot(for_plot, aes(x = clustering_level, y = var_value, colour = site)) + geom_point() + scale_colour_viridis_d(name = 'Network')+ theme_bw()+ theme(legend.position = 'bottom') legend <- g_legend(legend) pdf('plots/z_plot_unedited.pdf') grid_plot <- gridExtra::grid.arrange(actual_points, z_points, grid::textGrob('Clustering threshold'), legend, layout_matrix = rbind(c(1,2), c(3,3), c(4,4)), heights = unit(c(3.3, 0.3, 0.3), c('in', 'in', 'in'))) dev.off() # Alternative z and observed plot ----------------------------------------- for_alt_z_plot <- z_vals %>% #select only necessary columns select(-mean_rand, -sd_rand) %>% # start making it longer for ggplot pivot_longer(cols = c('actual', 'z'), names_to = 'plotting_var_name', values_to = 'plotting_value') %>% unite(column_category, fixedmargins, plotting_var_name, sep = '_') %>% # this has given us duplicates for the 'actual' value, as we have 'both_actual' # and 'columns_actual', both of which are crap and unnecessary. If we strip # the text before the underscore, we can then remove duplicate rows easily, # as the duplicate rows will then be identical mutate(column_category = gsub('.+_actual', 'actual', column_category)) %>% distinct() ggplot(for_alt_z_plot, aes(x = column_category, y = clustering_level))+ geom_tile(aes(fill = plotting_value))+ geom_text(aes(label = round(plotting_value, 3))) + facet_grid(site ~ metric_used) # Now for the ranges plot ------------------------------------------------- vals_for_range_plot <- rand_vals %>% # group by relevant variables before we calculate the quantiles group_by(metric_used, clustering_level, site, fixedmargins) %>% # calculate the quantiles of the random values summarise(min_quantile = quantile(x = metric_value, probs = 0.025), max_quantile = quantile(x = metric_value, probs = 0.975)) %>% # now add the actual observed values to the tibble left_join(actual_vals) %>% # we only want to plot the dots if they fall outside of the random ranges, # so we need to make a column saying if they fall within the range or not mutate(to_plot = ifelse(actual >= min_quantile & actual <= max_quantile, F, T), # now we need a column where the observed value is only present if it's # outside the ranges for_plotting = ifelse(to_plot == T, actual, NA)) %>% # make the names of the variables nicer mutate(site = gsub('DANUM', 'Danum', site), site = gsub('MALIAU', 'Maliau', site), metric_used = gsub('discrepancy', 'Discrepancy', metric_used), metric_used = gsub('weighted NODF', 'Weighted NODF', metric_used), metric_used = gsub('functional complementarity', 'Functional Complementarity', metric_used), fixedmargins = gsub('both', 'Both margin\nsums retained', fixedmargins), fixedmargins = gsub('columns', 'Column\nsums retained', fixedmargins)) # palette to use cbPalette <- c("#5a3fa8", "#d4c200", "#a4c7ff") # pasted in from other script, needs editing! #plot metrics_facet <- ggplot(vals_for_range_plot, aes(y =clustering_level, colour = site))+ geom_errorbarh(aes(xmin=min_quantile, xmax=max_quantile, colour = site), height = 0.4, alpha = 0.8, show.legend = F)+ geom_point(aes(y = clustering_level, x = for_plotting, size = 1.4))+ facet_grid(fixedmargins ~ metric_used, scales = 'free_x'#, ncol = 2 )+ scale_colour_manual(values=cbPalette, name = 'Observed network\nvalue')+ theme_bw()+ theme(panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), strip.placement = "outside", strip.background =element_rect(fill="white", colour = 'white'), text = element_text(size=20), legend.position="bottom")+ labs(y = 'Clustering %', x = NULL) + # tell ggplot to plot the colour in the legend but not the size guides(colour = "legend", size = "none") + # make the points in the legend bigger guides(colour = guide_legend(override.aes = list(size=4.5))) metrics_facet ggsave('plots/randomized_ranges/both_nullmodels.pdf', metrics_facet, width = 14)

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/course-project-2.R

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nshahpazov/probability-and-statistics-and-a-bit-of-markov-chains

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course-project-2.R

y = c(10, 6, 5, 12, 10, 15, 5, 12, 17, 20) x = c(1.30, 2, 1.7, 1.5, 1.6, 1.2, 1.6, 1.4, 1, 1.10) plot(x, y) predicted_error = serror1 * sqrt(1.1 + ((1.63-mean(x))^2)/(sum((x-mean(x))^2))) x1 = c(1.63) X[,2] pred1.2 = serror1 * sqrt(1.1 + t(x1-mean(X[,2])) %*% solve( t(X[,2]-mean(X[,2])) %*% (X[,2]-mean(X[,2])) ) %*% (x1-mean(X[,2]))) 8.4398 + 2.306004 * predicted_error X2 = matrix(c( 1, 1, 1, 1, 1, 1, 1, -3, -2, -1, 0, 1, 2, 3, 5, 0, -3, -4, -3, 0, 5, -1, 1, 1, 0, -1, -1, 1), nrow=7, ncol=4) lm(y ~ X2[,-1]) bhats = solve(t(X2) %*% X2) %*% t(X2) %*% y X2 %*% betas2 serror2 = deviance(lm(y2 ~ X2[,-1])) / 3 means = c(mean(X2[,2]), mean(X2[,3]), mean(X2[,4])) pred2 = serror2 * sqrt(1 + 1/7 + t(X2[5,-1]) %*% solve(t(X2[,-1]) %*% X2[,-1]) %*% X2[5,-1]) # testing the hypothesis that beta1 = beta2 = 0 A = matrix(c(0,1,0,0,0,0,1,0), nrow=2, ncol=4, byrow = TRUE) c = c(0,0) Abhat = A %*% bhats T = solve(A %*% solve(t(X2) %*% X2) %*% t(A)) F = (t((Abhat)) %*% T %*% Abhat)/2*serror2 csv mpg = Auto[,1] dm = data.matrix(Auto[,-c(1,8,9)]) lambda = 5 design = cbind(intercept=1, dm) bhats = solve(t(X2) %*% X2 + lambda * diag(ncol(design))) %*% t(X2) %*% mpg X2 %*% bhats

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/src/Multiple-Linear-Regression.R

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hornedfrog88/csx415-project

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refs/heads/master

2020-03-09T12:00:29.261416

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Multiple-Linear-Regression.R

#Multiple Linear Regression Model #Plot some data plot_linear <- ggplot(final_training_set,aes(TotalCapacity,EnrlTotal,color = CourseID)) + geom_point() plot_linear #Fit the model l_fit <- lm(EnrlTotal ~ TotalCapacity + TotalSections + CourseID + Term, data = final_training_set) summary(l_fit) #Run the predict function on the final test dataset and analyze the results by calculating the Mean Absolute Error of the prediction. predict_linear <- predict(l_fit, final_testing_set) MAE_linear <- MAE(final_testing_set$EnrlTotal,predict_linear) RMSE_linear <- RMSE(final_testing_set$EnrlTotal,predict_linear) print(paste("The Mean Absolute Error of the Prediction is", round(MAE_linear, digits = 2))) print(paste("The Root Mean Squared Error of the Prediction is", round(RMSE_linear, digits = 2)))

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KumarNarendra0619/COVIDDemographyUK

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2023-03-18T08:51:53.041127

2020-05-31T08:03:42

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3_plotting.R

#=============================================================================== # 2020-03-20 -- covid19-dem # Produce plots for the paper # Ilya Kashnitsky, ilya.kashnitsky@gmail.com # Review: Mark Verhagen #=============================================================================== library(tidyverse) library(magrittr) library(sf) library(patchwork) library(biscale) # theming packages library(hrbrthemes) library(cowplot) library(showtext) library(ggplot2) library(ggthemes) library(leaflet) font_add_google("Roboto Condensed", "Roboto Condensed") font_add_google("Roboto Slab", "Roboto Slab") showtext::showtext_auto() dir.create("figs_final") # define own theme own_theme <- cowplot::theme_map(font_size = 14, font_family = font_rc)+ ggplot2::theme( legend.position = c(.1, .75), plot.title = element_blank() ) bi_theme <- cowplot::theme_map(font_size = 14, font_family = font_rc)+ ggplot2::theme( legend.position = "none", plot.title = element_blank() ) own_plot_grid <- function(a, b, ...) { require(patchwork) (a + b) + plot_layout(ncol = 2)+ plot_annotation( tag_levels = "A", theme = theme( plot.title = element_blank(), plot.caption = element_text(family = font_rc, size = 12) ) )& theme(plot.tag = element_text(family = "Roboto Slab", size = 24, face = 2)) } # load the prepared data load("data/ready.rda") load("data/final_eco.rda") # Fig 1 ------------------------------------------------------------ ## ------ Capacity plots ------ ## # general hospital bed capacity, regional level agg_region_s %>% ggplot() + geom_sf(aes(fill = pc_capacity), color = NA)+ geom_sf(data = agg_region_b, size = .5, color = "#fafafa")+ geom_sf( data = cities, size = 2, shape = 21, stroke = 1, color = "#444444", fill = "#ffffff" )+ geom_sf_text( aes(label = pc_capacity %>% round(1)), size = 5, color = "#333333", family = font_rc, fontface = 2, nudge_y = -2e4 )+ coord_sf(datum = NA)+ scale_fill_fermenter( "Beds per\n1,000", palette = 'Blues', direction = 1, breaks = seq(1.6, 2.2, .2) ) + own_theme region_general_capacity <- last_plot() # acute hospital bed capacity, regional level agg_region_s %>% ggplot() + geom_sf(aes(fill = pc_capacity_acute), color = NA)+ geom_sf(data = agg_region_b, size = .5, color = "#fafafa")+ geom_sf( data = cities, size = 2, shape = 21, stroke = 1, color = "#444444", fill = "#ffffff" )+ geom_sf_text( aes(label = pc_capacity_acute %>% round(2)), size = 5, color = "#333333", family = font_rc, fontface = 2, nudge_y = -2e4 )+ coord_sf(datum = NA)+ scale_fill_fermenter( "Beds per\n1,000", palette = 'Reds', direction = 1, breaks = seq(.06, .12, .02) ) + own_theme region_acute_capacity <- last_plot() # one fig_01 <- own_plot_grid( region_general_capacity, region_acute_capacity ) ggsave(filename = "figs_final/fig-01.svg", fig_01, width = 10, height = 7) # Fig 2 ------------------------------------------------------------------- ## ------ Expected Hospitalisation plots ------ ## # Expected Hospitalisation general care, ccounty level agg_ccounty_s %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = pc_hosp), color = NA)+ geom_sf(data = agg_ccounty_b, size = .25, color = "#fafafa")+ geom_sf(data = agg_region_b, size = 1, color = "#fafafa")+ geom_sf(data = cities, size = 10, shape = 1, stroke = 0.6, color = "#373737")+ geom_sf_text(aes(label = pc_hosp %>% round(1)), size = 4, color = "#333333", family = font_rc, fontface = 3)+ coord_sf(datum = NA)+ scale_fill_fermenter( "Hosp. per\n1,000", palette = 'YlGnBu', direction = 1 ) + own_theme ccounty_expected_hosp_demand <- last_plot() # Expected Hospitalisation acute care, ccounty level agg_ccounty_s %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = pc_hosp_acute), color = NA)+ geom_sf(data = agg_ccounty_b, size = .25, color = "#fafafa")+ geom_sf(data = agg_region_b, size = 1, color = "#fafafa")+ geom_sf(data = cities, size = 10, shape = 1, stroke = 0.6, color = "#373737")+ geom_sf_text(aes(label = pc_hosp_acute %>% round(1)), size = 4, color = "#333333", family = font_rc, fontface = 3)+ coord_sf(datum = NA)+ scale_fill_fermenter( "Hosp. per\n1,000", palette = 'RdPu', direction = 1 ) + own_theme ccounty_expected_hosp_acute_demand <- last_plot() fig_02 <- own_plot_grid( ccounty_expected_hosp_demand, ccounty_expected_hosp_acute_demand ) ggsave(filename = "figs_final/fig-02.svg", fig_02, width = 10, height = 7) # Fig 3 ------------------------------------------------------------------ ## ------ Excess demand per 1,000 plots based on 10% ------ ## # Excess general care demand given 10% infection rate, ccounty level agg_ccounty_s %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = abs_excess_demand_hosp), color = NA)+ geom_sf(data = agg_ccounty_b, size = .25, color = "#fafafa")+ geom_sf(data = agg_region_b, size = 1, color = "#fafafa")+ geom_sf(data = cities, size = 10, shape = 1, stroke = 0.6, color = "#373737")+ geom_sf_text(aes(label = abs_excess_demand_hosp %>% round(1)), size = 4, color = "#333333", family = font_rc, fontface = 3)+ coord_sf(datum = NA)+ scale_fill_fermenter( "Excess Need\nper 1,000", palette = 'PuBuGn', direction = 1 ) + own_theme ccounty_abs_diff_hosp_demand <- last_plot() # Excess acute care demand given 10% infection rate, ccounty level agg_ccounty_s %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = abs_excess_demand_hosp_acute), color = NA)+ geom_sf(data = agg_ccounty_b, size = .25, color = "#fafafa")+ geom_sf(data = agg_region_b, size = 1, color = "#fafafa")+ geom_sf(data = cities, size = 10, shape = 1, stroke = 0.6, color = "#373737")+ geom_sf_text(aes(label = abs_excess_demand_hosp_acute %>% round(1)), size = 4, color = "#333333", family = font_rc, fontface = 3)+ coord_sf(datum = NA)+ scale_fill_fermenter( "Excess Need\nper 1,000", palette = 'BuPu', direction = 1 ) + own_theme ccounty_abs_diff_hosp_acute_demand <- last_plot() fig_03 <- own_plot_grid( ccounty_abs_diff_hosp_demand, ccounty_abs_diff_hosp_acute_demand ) ggsave(filename = "figs_final//fig-03.svg", fig_03, width = 10, height = 7) # Fig 4 ------------------------------------------------------------------- ## LSOA expected hospitalization (per 1,000) for 10% infection, London # generate custom palette pal <- RColorBrewer::brewer.pal(11, "BrBG")[c(11,3)] # load highlight data london_highlight_df <- readRDS("data/london_highlight.rds") # hack to fix geom_step aligning repeat_last_obs <- function(df) bind_rows(df, df %>% filter(age == "90+") %>% mutate(age = NULL)) # set labels age_labels <- c("0-4", "5-9", "10-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90+", NULL) # age distribution graph london_highlight_df %>% mutate(name = LSOA) %>% dplyr::select(-3:-1) %>% pivot_longer(cols = contains("Age_"), names_to = "age") %>% mutate( age = age %>% str_remove("Age_") %>% str_replace("_plus", "+") %>% fct_inorder() ) %>% repeat_last_obs() %>% ggplot(aes(age, value * 100, color = name, group = name))+ geom_hline(yintercept = 0, color = "#666666", size = .5)+ geom_step(size = 1, position = position_nudge(-.5))+ coord_flip(xlim = c(.5, 19.5), # need to get rig of the hack row, which is 20 ylim = c(26.5, 0), expand = FALSE)+ scale_x_discrete(position = "top", breaks = age_labels[-20], labels = age_labels)+ scale_y_reverse(position = "right")+ scale_color_manual(NULL, values = pal)+ theme_minimal(base_family = font_rc, base_size = 15)+ theme(legend.position = c(.25, .75))+ labs(y = "Proportion of the population, %", x = "Age group") london_highlight <- last_plot() # Hospitalization risk general care, London graph agg_lsoa_s_5 %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = pc_hosp), color = NA)+ geom_sf( data = . %>% filter(AreaCodes %in% c("E01000969", "E01002225")) %>% st_centroid(), aes(color = AreaCodes), shape = 1, size = 10, stroke = .9 )+ coord_sf(datum = NA)+ scale_size_area("Beds", max_size = 10)+ scale_color_manual(NULL, values = pal, guide = NULL)+ scale_fill_fermenter( "Hosp. \nper 1,000", palette = 'PuBuGn', direction = 1 ) + own_theme + theme(legend.position = c(.05, .15)) london_pc_hosp <- last_plot() # overlay graphs fig_04 <- ggdraw()+ draw_plot(london_pc_hosp, x =0, y = 0, width = .8, height = 1)+ draw_plot(london_highlight, x = .67, y = 0, width = .33, height = .5) ggsave(filename = "figs_final/fig-04.pdf", fig_04, width = 10, height = 7) # Including population-level variables in addition to hospitalization risk # legends # Fig 5-------------------------------------------------------------- ## -- Population Bivariate Plots -- ## ccg_depriv_df %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = bi_class), color = NA)+ geom_sf(data = agg_ccounty_b, size = .25, color = "#fafafa")+ geom_sf(data = agg_region_b, size = 1, color = "#fafafa")+ geom_sf(data = cities %>% filter(!name=="Cardiff"), size = 10, shape = 1, stroke = 0.6, color = "#373737")+ coord_sf(datum = NA)+ biscale::bi_scale_fill(pal = "DkBlue", dim=3) + bi_theme ccg_depriv <- last_plot() ggdraw() + draw_plot(ccg_depriv, 0, 0, 1, 1) + draw_plot(legend_depriv, .01, 0.45, .35, .35) ccg_depriv_f <- last_plot() ccg_dens_df %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = bi_class), color = NA)+ geom_sf(data = agg_ccounty_b, size = .25, color = "#fafafa")+ geom_sf(data = agg_region_b, size = 1, color = "#fafafa")+ geom_sf(data = cities %>% filter(!name=="Cardiff"), size = 10, shape = 1, stroke = 0.6, color = "#373737")+ coord_sf(datum = NA)+ biscale::bi_scale_fill(pal = "DkViolet", dim=3) + bi_theme ccg_dens <- last_plot() ggdraw() + draw_plot(ccg_dens, 0, 0, 1, 1) + draw_plot(legend_dens, .01, 0.45, .35, .35) ccg_dens_f <- last_plot() a <- (ccg_depriv_f + ccg_dens_f) + plot_layout(ncol = 2)+ plot_annotation( tag_levels = "A", theme = theme( plot.title = element_text(family = "Roboto Slab", size = 20, face = 2), plot.caption = element_text(family = font_rc, size = 12) ))& theme(plot.tag = element_text(family = "Roboto Slab", size = 24, face = 2)) ggsave(filename = "figs_final/fig-05.svg", a, width = 10, height = 7) # Fig 6-------------------------------------------------------------- ## -- Population Bivariate Plots -- ## # Zoom-in on London - social deprivation and hospitalization risk b_londen_depriv %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = bi_class, color = NA)) + coord_sf(datum = NA)+ scale_size_area("Beds", max_size = 10)+ # geom_sf(data = agg_lsoa_5_b, size = .15, color = "#CAC9C9")+ scale_color_manual(NULL, values = pal, guide = NULL)+ biscale::bi_scale_fill(pal = "DkBlue", dim=3) + bi_theme london_depriv <- last_plot() ggdraw() + draw_plot(london_depriv, 0, 0, 1, 1) + draw_plot(legend_depriv, .75, 0.05, .25, .25) london_depriv_f <- last_plot() ggsave(filename = "figs_final/fig-06.png", london_depriv_f, width = 10, height = 7) # Fig 7-------------------------------------------------------------- # Zoom-in on London - ethnicity and hospitalization risk b_londen_eth %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = bi_class, color = NA)) + coord_sf(datum = NA)+ scale_size_area("Beds", max_size = 10)+ # geom_sf(data = agg_lsoa_5_b, size = .15, color = "#CAC9C9")+ scale_color_manual(NULL, values = pal, guide = NULL)+ biscale::bi_scale_fill(pal = "DkCyan", dim=3) + bi_theme london_eth <- last_plot() ggdraw() + draw_plot(london_eth, 0, 0, 1, 1) + draw_plot(legend_eth, .75, 0.05, .25, .25) london_eth_f <- last_plot() ggsave(filename = "figs_final/fig-07.png", london_eth_f, width = 10, height = 7) # Fig 8-------------------------------------------------------------- # Zoom-in on Manchester - social deprivation and hospitalization risk man_highlight_df <- readRDS("data/man_highlight.rds") man_highlight_df %>% mutate(name = LSOA) %>% dplyr::select(-3:-1) %>% pivot_longer(cols = contains("Age_"), names_to = "age") %>% mutate( age = age %>% str_remove("Age_") %>% str_replace("_plus", "+") %>% fct_inorder() ) %>% repeat_last_obs() %>% ggplot(aes(age, value * 100, color = name, group = name))+ geom_hline(yintercept = 0, color = "#666666", size = .5)+ geom_step(size = 1, position = position_nudge(-.5))+ coord_flip(xlim = c(.5, 19.5), # need to get rig of the hack row, which is 20 ylim = c(26.5, 0), expand = FALSE)+ scale_x_discrete(position = "top", breaks = age_labels[-20], labels = age_labels)+ scale_y_reverse(position = "right")+ scale_color_manual(NULL, values = pal)+ theme_minimal(base_family = font_rc, base_size = 15)+ theme(legend.position = c(.25, .75))+ labs(y = "Proportion of the population, %", x = "Age group") man_highlight <- last_plot() b_man <- agg_lsoa_man %>% left_join(LSOA_eco_vars) b_man_depriv <- biscale::bi_class(b_man, x=pc_hosp, y=depriv) b_man_depriv %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = bi_class, color = NA)) + coord_sf(datum = NA)+ geom_sf( data = . %>% filter(AreaCodes %in% c("E01005654", "E01006084")) %>% st_centroid(), aes(color = AreaCodes), shape = 1, size = 10, stroke = .9 )+ scale_size_area("Beds", max_size = 10)+ # geom_sf(data = agg_lsoa_5_b, size = .15, color = "#CAC9C9")+ scale_color_manual(NULL, values = pal, guide = NULL)+ biscale::bi_scale_fill(pal = "DkBlue", dim=3) + bi_theme man_depriv <- last_plot() ggdraw() + draw_plot(man_depriv, x=0, y=0.1, width=.75, height=0.9) + draw_plot(legend_depriv, .05, 0.05, .25, .25)+ draw_plot(man_highlight, x = 0.7, y = 0, width = .33, height = .42) man_depriv_f <- last_plot() ggsave(filename = "figs_final/fig-08.pdf", man_depriv_f, width = 10, height = 7) ### ---------- SUPPLEMENTARY GRAPHS ---------- ### # Fig S1 ------------------------------------------------------------------- ## ------ Capacity plots ------ ## # general hospital bed capacity, ccounty level agg_ccounty_s %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = pc_capacity), color = NA)+ geom_sf(data = agg_ccounty_b, size = .25, color = "#fafafa")+ geom_sf(data = agg_region_b, size = 1, color = "#fafafa")+ geom_sf(data = cities, size = 10, shape = 1, stroke = 0.6, color = "#373737")+ geom_sf_text(aes(label = pc_capacity %>% round(1)), size = 4, color = "#333333", family = font_rc, fontface = 3)+ coord_sf(datum = NA)+ scale_fill_fermenter( "Beds per\n1,000", palette = 'Blues', direction = 1 ) + own_theme ccounty_general_capacity <- last_plot() # acute hospital bed capacity, ccounty level agg_ccounty_s %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = pc_capacity_acute), color = NA)+ geom_sf(data = agg_ccounty_b, size = .25, color = "#fafafa")+ geom_sf(data = agg_region_b, size = 1, color = "#fafafa")+ geom_sf(data = cities, size = 10, shape = 1, stroke = 0.6, color = "#373737")+ geom_sf_text(aes(label = pc_capacity_acute %>% round(2) %>% str_replace("0.", ".")), size = 4, color = "#333333", family = font_rc, fontface = 3)+ coord_sf(datum = NA)+ scale_fill_fermenter( "Beds per\n1,000", palette = 'Reds', direction = 1 ) + own_theme ccounty_acute_capacity <- last_plot() # s01 fig_s_01 <- own_plot_grid( ccounty_general_capacity, ccounty_acute_capacity ) ggsave(filename = "figs_final/fig-s01.svg", fig_s_01, width = 10, height = 7) # Fig s02 ------------------------------------------------------------------- ## ------ Capacity plots ------ ## # general hospital bed capacity, CCG level agg_ccg_s %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = pc_capacity), color = NA)+ geom_sf(data = agg_ccounty_b, size = .25, color = "#fafafa")+ geom_sf(data = agg_region_b, size = 1, color = "#fafafa")+ geom_sf(data = cities %>% filter(!name=="Cardiff"), size = 10, shape = 1, stroke = 0.6, color = "#373737")+ coord_sf(datum = NA)+ scale_fill_fermenter( "Beds per\n1,000", breaks = c(1e-8, .5, 1, 2, 5), labels = c(0, .5, 1, 2, 5), palette = 'Blues', direction = 1 ) + own_theme ccg_general_capacity <- last_plot() # acute hospital bed capacity, CCG level agg_ccg_s %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = pc_capacity_acute), color = NA)+ geom_sf(data = agg_ccounty_b, size = .25, color = "#fafafa")+ geom_sf(data = agg_region_b, size = 1, color = "#fafafa")+ geom_sf(data = cities %>% filter(!name=="Cardiff"), size = 10, shape = 1, stroke = 0.6, color = "#373737")+ coord_sf(datum = NA)+ scale_fill_fermenter( "Beds per\n1,000", breaks = c(1e-8, .1, .25, .5, 1), labels = c(0, .1, .25, .5, 1), palette = 'Reds', direction = 1 ) + own_theme ccg_acute_capacity <- last_plot() fig_s02 <- own_plot_grid( ccg_general_capacity, ccg_acute_capacity ) ggsave(filename = "figs_final/fig-s02.svg", fig_s02, width = 10, height = 7) # Fig s03 ------------------------------------------------------------------- ## ------ Expected Hospitalisation plots ------ ## # Expected general care hospitalisation, ccg level agg_ccg_s %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = pc_hosp), color = NA)+ geom_sf(data = agg_ccounty_b, size = .25, color = "#fafafa")+ geom_sf(data = agg_region_b, size = 1, color = "#fafafa")+ geom_sf(data = cities %>% filter(!name=="Cardiff"), size = 10, shape = 1, stroke = 0.6, color = "#373737")+ coord_sf(datum = NA)+ scale_fill_fermenter( "Hosp. per\n1,000", palette = 'YlGnBu', direction = 1 ) + own_theme ccg_expected_hosp_demand <- last_plot() # Expected acute care hospitalisation, ccg level agg_ccg_s %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = pc_hosp_acute), color = NA)+ geom_sf(data = agg_ccounty_b, size = .25, color = "#fafafa")+ geom_sf(data = agg_region_b, size = 1, color = "#fafafa")+ geom_sf(data = cities %>% filter(!name=="Cardiff"), size = 10, shape = 1, stroke = 0.6, color = "#373737")+ coord_sf(datum = NA)+ scale_fill_fermenter( "Hosp. per\n1,000", palette = 'RdPu', direction = 1 ) + own_theme ccg_expected_hosp_acute_demand <- last_plot() fig_s03 <- own_plot_grid( ccg_expected_hosp_demand, ccg_expected_hosp_acute_demand ) ggsave(filename = "figs_final/fig-s03.svg", fig_s03, width = 10, height = 7) # Fig s04 ----------------------------------------------------------------- ## -- Excess demand plots -- ## # Excess general care hospital demand (per 1,000), ccg level agg_ccg_s %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = abs_excess_demand_hosp), color = NA)+ geom_sf(data = agg_ccounty_b, size = .25, color = "#fafafa")+ geom_sf(data = agg_region_b, size = 1, color = "#fafafa")+ geom_sf(data = cities %>% filter(!name=="Cardiff"), size = 10, shape = 1, stroke = 0.6, color = "#373737")+ coord_sf(datum = NA)+ scale_fill_fermenter( "Excess Need\nper 1,000", palette = 'PuBuGn', direction = 1 ) + own_theme ccg_abs_diff_hosp_demand <- last_plot() # Excess acute care hospital demand (per 1,000), ccg level agg_ccg_s %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = abs_excess_demand_hosp_acute), color = NA)+ geom_sf(data = agg_ccounty_b, size = .25, color = "#fafafa")+ geom_sf(data = agg_region_b, size = 1, color = "#fafafa")+ geom_sf(data = cities %>% filter(!name=="Cardiff"), size = 10, shape = 1, stroke = 0.6, color = "#373737")+ coord_sf(datum = NA)+ scale_fill_fermenter( "Excess Need\nper 1,000", palette = 'BuPu', direction = 1 ) + own_theme ccg_abs_diff_hosp_acute_demand <- last_plot() fig_s04 <- own_plot_grid( ccg_abs_diff_hosp_demand, ccg_abs_diff_hosp_acute_demand ) ggsave(filename = "figs_final/fig-s04.svg", fig_s04, width = 10, height = 7) # Fig s05 ------------------------------------------------------------------- # local zoom-in on Wales agg_lsoa_s_s5 %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = pc_hosp), color = NA)+ geom_sf(data = agg_ccounty_b_s5, size = .25, color = "#fafafa")+ geom_sf(data = wales_h, aes(size = beds), shape = 1, stroke = .9, color = "#eec21f")+ coord_sf(datum = NA)+ scale_size_area("Beds", max_size = 10)+ scale_fill_fermenter( "Hosp. per\n1,000", palette = 'PuBuGn', direction = 1 ) + own_theme + theme(legend.position = c(0, .6)) wales_pc_hosp <- last_plot() # filter out the hospitals with intensive care beds wales_h_ic <- wales_h %>% filter(intensive_care_beds > 0) # Excess demand acute LSOA Wales agg_lsoa_s_s5 %>% ggplot() + geom_sf(color = NA)+ geom_sf(aes(fill = pc_hosp_acute), color = NA)+ geom_sf(data = agg_ccounty_b_s5, size = .25, color = "#fafafa")+ geom_sf(data = wales_h_ic, aes(size = intensive_care_beds), shape = 1, stroke = .9, color = "#df356b")+ coord_sf(datum = NA)+ scale_fill_fermenter( "Hops. per\n1,000", palette = 'RdPu', direction = 1 ) + scale_size_area("IC Beds", max_size = 10)+ own_theme + theme(legend.position = c(0, .6)) wales_pc_hosp_acute <- last_plot() fig_s05 <- own_plot_grid( wales_pc_hosp, wales_pc_hosp_acute ) ggsave(filename = "figs_final/fig-s05.png", fig_s05, width = 10, height = 7)

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surv_sim.R

# simulate different missing data types ##### generic data setup: set.seed(977) # this makes the simulation exactly reproducible ni = 100 # 100 people nj = 10 # 10 week study id = rep(1:ni, each=nj) cond = rep(c("control", "diet"), each=nj*(ni/2)) base = round(rep(rnorm(ni, mean=250, sd=10), each=nj)) week = rep(1:nj, times=ni) y = round(base + rnorm(ni*nj, mean=0, sd=1)) # MCAR prop.m = .07 # 7% missingness mcar = runif(ni*nj, min=0, max=1) y.mcar = ifelse(mcar<prop.m, NA, y) # unrelated to anything View(cbind(id, week, cond, base, y, y.mcar)) # MAR y.mar = matrix(y, ncol=nj, nrow=ni, byrow=TRUE) for(i in 1:ni){ for(j in 4:nj){ dif1 = y.mar[i,j-2]-y.mar[i,j-3] dif2 = y.mar[i,j-1]-y.mar[i,j-2] if(dif1>0 & dif2>0){ # if weight goes up twice, drops out y.mar[i,j:nj] = NA; break } } } y.mar = as.vector(t(y.mar)) View(cbind(id, week, cond, base, y, y.mar)) # NMAR sort.y = sort(y, decreasing=TRUE) nmar = sort.y[ceiling(prop.m*length(y))] y.nmar = ifelse(y>nmar, NA, y) # doesn't show up when heavier View(cbind(id, week, cond, base, y, y.nmar)) require(survival) install.packages("survsim") require(survsim) require(ggplot2) dist.ev <- "weibull" anc.ev <- 1 beta0.ev <- 5.268 dist.cens <- "weibull" anc.cens <- 1 beta0.cens <- 5.368 x <- list(c("bern", 0.3), c("bern", 0.4)) beta <- list(-0.4, -0.25) ##full data store.coef=matrix(data=NA,nrow=100,ncol=2) for (i in 1:100) { simple.dat <- simple.surv.sim(300, 365, dist.ev, anc.ev, beta0.ev,dist.cens, anc.cens, beta0.cens, , beta, x) full.model=coxph(Surv(start,stop,status)~x+x.1, data=simple.dat) store.coef[i,1]=full.model$coef[1] store.coef[i,2]=full.model$coef[2] } plot.model.1=as.data.frame(store.coef[,1]) plot.model.1$coef=1 colnames(plot.model.1)[colnames(plot.model.1)=="store.coef[, 1]"] <- "coef.val" plot.model.2=as.data.frame(store.coef[,2]) plot.model.2$coef=2 colnames(plot.model.2)[colnames(plot.model.2)=="store.coef[, 2]"] <- "coef.val" plot.model=rbind(plot.model.1,plot.model.2) aggregate(plot.model$coef.val,by=list(plot.model$coef),FUN=mean, na.rm=TRUE) aggregate(plot.model$coef.val,by=list(plot.model$coef),FUN=sd, na.rm=TRUE) p <- ggplot(plot.model, aes(factor(coef), coef.val)) p + geom_boxplot() ##MCAR # MCAR prop.m = .1 # 10% missingness store.coef=matrix(data=NA,nrow=100,ncol=2) for (i in 1:100) { simple.dat <- simple.surv.sim(300, 365, dist.ev, anc.ev, beta0.ev,dist.cens, anc.cens, beta0.cens, , beta, x) mcar = runif(300, min=0, max=1) simple.dat$status = ifelse(mcar<prop.m, NA, simple.dat$status) full.model=coxph(Surv(start,stop,status)~x+x.1, data=simple.dat) store.coef[i,1]=full.model$coef[1] store.coef[i,2]=full.model$coef[2] } plot.model.1=as.data.frame(store.coef[,1]) plot.model.1$coef=1 colnames(plot.model.1)[colnames(plot.model.1)=="store.coef[, 1]"] <- "coef.val" plot.model.2=as.data.frame(store.coef[,2]) plot.model.2$coef=2 colnames(plot.model.2)[colnames(plot.model.2)=="store.coef[, 2]"] <- "coef.val" plot.model=rbind(plot.model.1,plot.model.2) aggregate(plot.model$coef.val,by=list(plot.model$coef),FUN=mean, na.rm=TRUE) aggregate(plot.model$coef.val,by=list(plot.model$coef),FUN=sd, na.rm=TRUE)

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/R_Analysis/Correlation_Conundrum.R

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tahira-h/MechaCar_Statistical_Analysis

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Correlation_Conundrum.R

# We can use the 'geom_point()' plotting function combined with the cor() function to quantify the correlation between variables. # Let's look at the cor() documentation. Ex: '> ?cor()' # To use the cor() function to perform a correlation analysis between two numeric variables. Use the two arguments, 'x' and 'y' # To practice calculating the Pearson correlation coefficient, use the mtcars dataset. head(mtcars) # Plot the two variables using the geom_point() function. plt <- ggplot(mtcars,aes(x=hp,y=qsec)) #import dataset into ggplot2 plt + geom_point() #create scatter plot # Use the cor() function to quantify the strength of the correlation between the two variables. cor(mtcars$hp,mtcars$qsec) #calculate correlation coefficient # Reuse the used_cars dataset. used_cars <- read.csv('used_car_data.csv',stringsAsFactors = F) #read in dataset head(used_cars) # Plot the two variables using the geom_point() function. plt <- ggplot(used_cars,aes(x=Miles_Driven,y=Selling_Price)) #import dataset into ggplot2 plt + geom_point() #create a scatter plot # Calculate the Pearson correlation coefficient using the cor() function. cor(used_cars$Miles_Driven,used_cars$Selling_Price) #calculate correlation coefficient # EX: To produce a correlation matrix for the used_cars dataset,first select the numeric columns from the data frame and convert it to matrix. Then provide the numeric matrix to the cor() function. used_matrix <- as.matrix(used_cars[,c("Selling_Price","Present_Price","Miles_Driven")]) #convert data frame into numeric matrix cor(used_matrix)

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/R/setup.R

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2023-03-23T22:04:15.229079

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setup.R

# Function Contents ----------------------------------------------------------- # Internal: # check: checks lineup and data frame input of player probabilities # Check ----------------------------------------------------------------------- # Acts on lineup and data frame input of player probabilities # Lineup must be single player (to be repeated) or nine players # Matches name in lineup to names in stats (or uses player number) # Checks that probabilities are valid # Fills in fast player if not given # Returns a 9-row data frame of stats in lineup order check <- function(lineup, stats) { # uppercase column names in stats colnames(stats) <- toupper(colnames(stats)) # check length of lineup if (length(lineup) == 1) { lineup <- rep(lineup, 9) } else if (length(lineup) != 9) { stop("Lineup must be of length 1 or 9") } # match lineup (either character or numeric) if (is.character(lineup)) { # match based on player name # check that stats has a name column if (!("NAME" %in% colnames(stats))) stop("stats must have a 'NAME' column") # convert name to character if not if (!is.character(stats$NAME)) stats$NAME <- as.character(stats$NAME) # match arguments lineup <- sapply(lineup, match.arg, choices = stats$NAME) # generate player_index player_index <- vector(length = 9) for (i in 1:9) player_index[i] <- which(stats$NAME == lineup[i]) } else if (is.numeric(lineup)) { # match based on player number # check tht stats has a number column if (!("NUMBER" %in% colnames(stats))) stop("stats must have a 'NUMBER' column") # convert number to numeric if not if (!is.numeric(stats$NUMBER)) stats$NUMBER <- as.numeric(stats$NUMBER) # generate player_index player_index <- vector(length = 9) for (i in 1:9) player_index[i] <- which(stats$NUMBER == lineup[i]) } else stop("Lineup must be either character or numeric") # check that each necessary column is present outcomes <- c("O", "S", "D", "TR", "HR", "W") for (i in 1:length(outcomes)) if (!(outcomes[i] %in% colnames(stats))) stop(paste0("Missing column '", outcomes[i], "'")) # SBA and SB warning if ("SBA" %in% colnames(stats)) { # steal probabilities provided if (!("SB" %in% colnames(stats))) # success probabilities not provided stop("Probability of successful steal 'SB' must be specified") } else { # steal probabilities not provided stats$SBA <- rep(0, nrow(stats)) stats$SB <- rep(0, nrow(stats)) warning("'SBA' not specified, assigned probability zero") } # is SBA is zero and SB is NA, replace with zero for (i in 1:nrow(stats)) { if ((stats$SBA[i] == 0) && is.na(stats$SB[i])) stats$SB[i] <- 0 } # check that all probabilities are positive if (!all(stats[, c("O", "S", "D", "TR", "HR", "W", "SBA", "SB")] >= 0)) stop("All probabilities must be greater than or equal to zero") # check that probabilities of main six outcomes sum to one if (!all(rowSums(stats[, c("O", "S", "D", "TR", "HR", "W")]) > 0.99) | !all(rowSums(stats[, c("O", "S", "D", "TR", "HR", "W")]) < 1.01)) stop("Sum of probabilities 'O', 'S', 'D', 'TR', 'HR', and 'W' must equal one") # if fast player column is present, check that is is true or false # if fast player column is absent, create it based on SBA if ("FAST" %in% colnames(stats)) { if (!is.logical(stats$FAST)) stop("'FAST' column must be logical") } else { stats$FAST <- stats$SBA > 0.5 warning("Fast players not specified, assigned using SBA probability with threshold 0.5") } # return cleaned stats with rows in the proper order (1 - 9 based on lineup) return(stats[player_index, ]) }

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/Miniproject/Code/Dataexploration.R

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EvalImperialforces/CMEECourseWork

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2020-03-30T16:06:12.470621

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Dataexploration.R

#!/usr/bin/env Rscript # Author: Eva Linehan # Date: November 2018 # Desc: Looking at the data #clear environments rm(list=ls()) library(dplyr) #library(lattice) library(ggplot2) library(minpack.lm) # Read in and observe data DF <- read.csv("../Data/BioTraits.csv", header = TRUE) #head(DF) #nrow(DF) # Use DataSeries ID and citation ID to find temperature performance IDs (wonder how they can be calculated). # Overlay indicidually made plots for seperate IDs? # Compare Habitat/Field # Compare Climates # Between trophic levels or taxa ####### Subset data by ID series and create plots ##### # For loop method IDlist = unique(DF$FinalID) for (i in (1:length(IDlist))){ subset_id<- IDlist[i] # Take out the ID for each iteration traitstosubset = subset(DF, DF$FinalID == subset_id) # Subset (can use filter) DF to get all columns for ID[i] traitstosubset<- traitstosubset[!is.na(traitstosubset$OriginalTraitValue),] for i in traitstosubset$ConTemp{ if i = max } if (nrow(traitstosubset) >=4){ pdf(file = paste("../Results/Preliminary_Graphs/", IDlist[i], ".pdf", sep = "")) print(qplot(traitstosubset$ConTemp, traitstosubset$OriginalTraitValue, data = traitstosubset, xlab = "Temperature (Degrees Celsius)", ylab = "Trait Value")) dev.off() } } ###### Polynomials ######## # Linear #fit_linear <- lm(traitstosubset$ConTemp ~ traitstosubset$OriginalTraitValue) #ggplot(traitstosubset, aes(ConTemp, OriginalTraitValue)) + geom_point() + geom_smooth(method = "lm") + theme_bw() # Quadratic #fit_quad <- lm(traitstosubset$ConTemp ~ poly(traitstosubset$OriginalTraitValue, 2, raw = TRUE) #ggplot(traitstosubset, aes(ConTemp, OriginalTraitValue)) + geom_point() + geom_smooth(method = "lm", formula = y~poly(x, 2, raw=TRUE)) + theme_bw() # Cubic #fit_cube <- lm(traitstosubset$ConTemp ~ poly(traitstosubset$OriginalTraitValue, 3, raw = TRUE) #ggplot(traitstosubset, aes(ConTemp, OriginalTraitValue)) + geom_point() + geom_smooth(method = "lm", formula = y~poly(x, 3, raw=TRUE)) + theme_bw() ggplot(data = traitstosubset) + geom_point(aes(x = ConTemp, y = OriginalTraitValue), size = 0.8, colour = "black") + geom_smooth( data = traitstosubset, aes(ConTemp, OriginalTraitValue), size = 1, colour = "darkblue", se = FALSE, stat = "smooth", method = "lm") + geom_smooth(data = traitstosubset, aes(ConTemp, OriginalTraitValue), size = 1, colour = "red", se = FALSE, stat = "smooth", method = "lm", formula = y~poly(x, 2, raw=TRUE)) + geom_smooth(data = traitstosubset, aes(ConTemp, OriginalTraitValue), size = 1, colour = "purple", se = FALSE, stat = "smooth", method = "lm", formula = y~poly(x, 3, raw=TRUE)) ###### EARR Model ######### # Must convert to Kelvin!!!!! # DeLong log tranformed metabolic metabolic rate data and # fit the log- transformed EAAR model # Must find melting temp using quadratic eqn and optimum temp using eqn 6. #powMod <- function() ##### Briere's Model ######## # Scaling #To improve the fit, you can use weighted least-squares regression where an additional scale factor (the weight) is included in the fitting process. # Weighted least-squares regression minimizes the error estimate. briere<- function(Temp, T0, Tm, c){ return(c*Temp*(Temp-T0)*(abs(Tm-Temp)^(1/2))*as.numeric(Temp<Tm)*as.numeric(Temp>T0)) } scale<-20 bfit<-nlsLM(ConTemp/20 ~briere(Temp, T0, Tm, c), start = list(Temp=20, T0=0, Tm=40, c=0.1), data = traitstosubset) temp<-seq(0,80, length=1000) pred.b<-predict(bfit, newdata = list(temp=temp))*scale plot(traitstosubset$ConTemp, traitstosubset$OriginalTraitName, xlim =c(0,100)) lines(temp, pred.b, col=2) # Bound values in nlsLM #### Schoofield Model ####### # 3 new parameters p25 T1/2 # Use Eqn 6 (4 model parameters) # fit a linear model linearfunc <- function(x, m, b) { return(m * x + b) } plot(traitstosubset$OriginalTraitValue~traitstosubset$ConTemp) fit = nlsLM(traitstosubset$OriginalTraitValue~linearfunc(traitstosubset$ConTemp, m, b), data = traitstosubset, start = list(m = .1, b = .1)) summary(fit) AIC(fit) confint(fit) # Lengths = seq(min(traitstosubset$ConTemp), max(traitstosubset$ConTemp), 1) PF <- linearfunc(Lengths, coef(fit)["m"], coef(fit)["b"]) plot(traitstosubset$OriginalTraitValue~traitstosubset$ConTemp) lines(Lengths, PF) # Fit briere function briere<- function(Temp, T0, Tm, c){ return(c*Temp*(Temp-T0)*(abs(Tm-Temp)^(1/2))*as.numeric(Temp<Tm)*as.numeric(Temp>T0)) } fit = nlsLM(traitstosubset$OriginalTraitValue~briere(traitstosubset$ConTemp, T0, Tm, c), data = traitstosubset, start = list(T0 = 10, Tm = 30, c = .1)) summary(fit) AIC(fit) confint(fit) # 95% CI Lengths = seq(min(traitstosubset$ConTemp), max(traitstosubset$ConTemp), 1) PF <- briere(Lengths, coef(fit)["T0"], coef(fit)["Tm"], coef(fit)["c"]) plot(traitstosubset$OriginalTraitValue~traitstosubset$ConTemp) lines(Lengths, PF) briere2<-function(a,T0,Tm){ return(a*x*(x-T0)*) }

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/ProduceTables.R

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gideon1321/Table1_R_Excel_Output

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ProduceTables.R

## Packages needed loadInstall <- function(x) { if (require(x, character.only = T)) return(require(x, character.only = T)) else {install.packages(x, type="source")} } x <- c("plyr","plyr","dplyr","stringr","rms","Hmisc","knitr") for(i in 1:length(x)){ loadInstall(x[i]) } #Bring in Data from github dat1=read_csv("https://raw.githubusercontent.com/BiostatsReportAutomation/Table1_R_Excel_Output/master/baselinedata.csv") #Bring in myTable1 function from github source("https://raw.githubusercontent.com/BiostatsReportAutomation/Table1_R_Excel_Output/master/Table1Function.R") #----------------------------------------- # How to specify a test in this function: # ----------------------------------------- # aov.t is for "ANOVA test" # fisher.t is for "Fisher exact test" # chisq.t is for "Chi-squared test" # t.test is for "T-test" # kruskal.t is for "Kruskal-Wallis test" # wilcox.t is for "Wilcoxon ranked sum test" #---------------------------------------------- #Now create your table1 with myTable1 function tt=myTable1(dat=dat1, splitvar="sex",splitlabel ="Gender", contvar=c("age","BP.sys","BP.dia","N.smokePday"), # continuous variables contTest=c("t.test","wilcox.t","t.test","aov.t"), # Test to be applied respectively to the contvars catvar=c("diabetic", "Treatment","Race"), # Categorical variable catTest=c("fisher.t","fisher.t","chisq.t"), # Test to use for categorical variables docaption = T, # Should code do caption for you ? my.docaption="xxxxxxxx", # If false, then write caption eg. "Summaries by sex" prmsd=c("mean","median","mean","mean"), # Specify statistics for summaries my.loc="./tabhold/mytable1s1.tex", # location for tex file Trace=F, # Used for my editing pdec=2, # Decimal place for p-values Test=F, # Test statistic column to be included in table latexoutput=F, # Whether to spit out tex file exceloutput=T,exceloutputName ="tablewithPvalues" , # Produce an excel file of Table 1 showtable = F)

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save_chart.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/synapse_helpers.R \name{save_chart} \alias{save_chart} \title{Save a static or dynamic chart to a file and store in Synapse.} \usage{ save_chart(parent_id, chart_filename, plot_object, static = FALSE) } \arguments{ \item{static}{} } \description{ Save a static or dynamic chart to a file and store in Synapse. }

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whiteseg.R

# ------------------------------------------------------------------------------ # whiteseg() # ------------------------------------------------------------------------------ whiteseg <- function(x, data, nb, fun, verbose = FALSE, ...) { message("Note: whiteseg() function name has been changed to isp().") message("The old name will be deprecated from version 0.6-1.") tmpargs <- as.list(match.call()) do.call(isp, tmpargs[-1]) }

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编写登陆接口功能:1）输入用户名密码 2）认证成功后显示欢迎信息 3）输错三次以后锁定用户注意点一：登陆时验证lock文件，确认该用户是否被锁注意点二：

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diagnose.R

if(dev.cur() <= 1) { dd <- getOption("device") if(is.character(dd)) dd <- get(dd) dd() } oldpar <- par(ask = interactive() && (.Device %in% c("X11", "GTK", "windows", "Macintosh"))) par(mfrow=c(1,1)) oldoptions <- options(warn = -1) # ####################################################### # X <- rpoispp(function(x,y) { 1000 * exp(- 4 * x)}, 1000) plot(X, main="Inhomogeneous Poisson pattern") fit.hom <- ppm(X ~1, Poisson()) fit.inhom <- ppm(X ~x, Poisson()) diagnose.ppm(fit.inhom, which="marks", type="Pearson", main=c("Mark plot", "Circles for positive residual mass", "Colour for negative residual density")) par(mfrow=c(1,2)) diagnose.ppm(fit.hom, which="marks", main=c("Wrong model", "(homogeneous Poisson)", "raw residuals")) diagnose.ppm(fit.inhom, which="marks", main=c("Right model", "(inhomogeneous Poisson)", "raw residuals")) par(mfrow=c(1,1)) diagnose.ppm(fit.inhom, which="smooth", main="Smoothed residual field") par(mfrow=c(1,2)) diagnose.ppm(fit.hom, which="smooth", main=c("Wrong model", "(homogeneous Poisson)", "Smoothed residual field")) diagnose.ppm(fit.inhom, which="smooth", main=c("Right model", "(inhomogeneous Poisson)", "Smoothed residual field")) par(mfrow=c(1,1)) diagnose.ppm(fit.inhom, which="x") par(mfrow=c(1,2)) diagnose.ppm(fit.hom, which="x", main=c("Wrong model", "(homogeneous Poisson)", "lurking variable plot for x")) diagnose.ppm(fit.inhom, which="x", main=c("Right model", "(inhomogeneous Poisson)", "lurking variable plot for x")) par(mfrow=c(1,1)) diagnose.ppm(fit.hom, type="Pearson",main="standard diagnostic plots") par(mfrow=c(1,2)) diagnose.ppm(fit.hom, main=c("Wrong model", "(homogeneous Poisson)")) diagnose.ppm(fit.inhom, main=c("Right model", "(inhomogeneous Poisson)")) par(mfrow=c(1,1)) # ####################################################### # LEVERAGE/INFLUENCE plot(leverage(fit.inhom)) plot(influence(fit.inhom)) plot(dfbetas(fit.inhom)) # ####################################################### # COMPENSATORS ## Takes a long time... CF <- compareFit(listof(hom=fit.hom, inhom=fit.inhom), Kcom, same="iso", different="icom") plot(CF, main="model compensators", legend=FALSE) legend("topleft", legend=c("empirical K function", "compensator of CSR", "compensator of inhomogeneous Poisson"), lty=1:3, col=1:3) # ####################################################### # Q - Q PLOTS # qqplot.ppm(fit.hom, 40) #conclusion: homogeneous Poisson model is not correct title(main="Q-Q plot of smoothed residuals") qqplot.ppm(fit.inhom, 40) # TAKES A WHILE... title(main=c("Right model", "(inhomogeneous Poisson)", "Q-Q plot of smoothed residuals")) # conclusion: fitted inhomogeneous Poisson model looks OK # ####################################################### # plot(cells) fitPoisson <- ppm(cells ~1, Poisson()) diagnose.ppm(fitPoisson, main=c("CSR fitted to cells data", "Raw residuals", "No suggestion of departure from CSR")) diagnose.ppm(fitPoisson, type="pearson", main=c("CSR fitted to cells data", "Pearson residuals", "No suggestion of departure from CSR")) # These diagnostic plots do NOT show evidence of departure from uniform Poisson plot(Kcom(fitPoisson), cbind(iso, icom) ~ r) plot(Gcom(fitPoisson), cbind(han, hcom) ~ r) # K compensator DOES show strong evidence of departure from uniform Poisson qqplot.ppm(fitPoisson, 40) title(main=c("CSR fitted to cells data", "Q-Q plot of smoothed raw residuals", "Strong suggestion of departure from CSR")) # Q-Q plot DOES show strong evidence of departure from uniform Poisson. # fitStrauss <- ppm(cells ~1, Strauss(r=0.1)) diagnose.ppm(fitStrauss, main=c("Strauss model fitted to cells data", "Raw residuals")) diagnose.ppm(fitStrauss, type="pearson", main=c("Strauss model fitted to cells data", "Pearson residuals")) plot(Kcom(fitStrauss), cbind(iso, icom) ~ r) plot(Gcom(fitStrauss), cbind(han, hcom) ~ r) # next line takes a LOOONG time ... qqplot.ppm(fitStrauss, 40, type="pearson") title(main=c("Strauss model fitted to cells data", "Q-Q plot of smoothed Pearson residuals", "Suggests adequate fit")) # Conclusion: Strauss model seems OK # ####################################################### # plot(nztrees) fit <- ppm(nztrees ~1, Poisson()) diagnose.ppm(fit, type="pearson") title(main=c("CSR fitted to NZ trees", "Pearson residuals")) diagnose.ppm(fit, type="pearson", cumulative=FALSE) title(main=c("CSR fitted to NZ trees", "Pearson residuals (non-cumulative)")) lurking(fit, expression(x), type="pearson", cumulative=FALSE, splineargs=list(spar=0.3)) # Sharp peak at right is suspicious qqplot.ppm(fit, 40, type="pearson") title(main=c("CSR fitted to NZ trees", "Q-Q plot of smoothed Pearson residuals")) # Slight suggestion of departure from Poisson at top right of pattern. par(oldpar) options(oldoptions)

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/fuzzedpackages/cellWise/man/cellHandler.Rd

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no_license

akhikolla/testpackages

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refs/heads/master

2023-02-18T03:50:28.288006

2021-01-18T13:23:32

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cellHandler.Rd

\name{cellHandler} \alias{cellHandler} %- Also NEED an '\alias' for EACH other topic documented here. \title{ cellHandler algorithm } \description{ This function flags cellwise outliers in \code{X} and imputes them, if robust estimates of the center \code{mu} and scatter matrix \code{Sigma} are given. When the latter are not known, as is typically the case, one can use the function \code{\link{DDC}} which only requires the data matrix \code{X}. Alternatively, the unknown center mu and scatter matrix Sigma can be estimated robustly from \code{X} by the function \code{\link{DI}}. } \usage{ cellHandler(X, mu, Sigma, quant = 0.99) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{X}{\code{X} is the input data, and must be an \eqn{n} by \eqn{d} matrix or a data frame. } \item{mu}{An estimate of the center of the data } \item{Sigma}{An estimate of the covariance matrix of the data } \item{quant}{Cutoff used in the detection of cellwise outliers. Defaults to \code{0.99} } } \value{ A list with components: \cr \itemize{ \item{\code{Ximp} \cr The imputed data matrix. } \item{\code{indcells} \cr Indices of the cells which were flagged in the analysis. } \item{\code{indNAs} \cr Indices of the NAs in the data. } \item{\code{Zres} \cr Matrix with standardized cellwise residuals of the flagged cells. Contains zeroes in the unflagged cells. } \item{\code{Zres_denom} \cr Denominator of the standardized cellwise residuals. } \item{\code{cellPaths} \cr Matrix with the same dimensions as X, in which each row contains the path of least angle regression through the cells of that row, i.e. the order of the coordinates in the path (1=first, 2=second,...) } } } \references{ J. Raymaekers and P.J. Rousseeuw (2020). Handling cellwise outliers by sparse regression and robust covariance. \emph{Arxiv: 1912.12446}. \href{https://arxiv.org/abs/1912.12446}{(link to open access pdf)} } \author{ J. Raymaekers and P.J. Rousseeuw } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{DI}} } \examples{ mu <- rep(0, 3) Sigma <- diag(3) * 0.1 + 0.9 X <- rbind(c(0.5, 1.0, 5.0), c(-3.0, 0.0, 1.0)) n <- nrow(X); d <- ncol(X) out <- cellHandler(X, mu, Sigma) Xres <- X - out$Ximp # unstandardized residual mean(abs(as.vector(Xres - out$Zres*out$Zres_denom))) # 0 W <- matrix(rep(0,n*d),nrow=n) # weight matrix W[out$Zres != 0] <- 1 # 1 indicates cells that were flagged # For more examples, we refer to the vignette: vignette("DI_examples") }

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/R/RScript.R

fc665fe0a427ca6c91f6c3fcfd4d4db4da858cc4

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hhy5277/ReporteRs

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refs/heads/master

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RScript.R

#' @title RScript object #' #' @description Colored RScript object #' #' @param file R script file. Not used if text is provided. #' @param text character vector. The text to parse. Not used if file is provided. #' @param comment.properties comment txtProperties object #' @param roxygencomment.properties roxygencomment txtProperties object #' @param operators.properties operators txtProperties object #' @param keyword.properties keyword txtProperties object #' @param string.properties string txtProperties object #' @param number.properties number txtProperties object #' @param functioncall.properties functioncall txtProperties object #' @param argument.properties argument txtProperties object #' @param package.properties package txtProperties object #' @param formalargs.properties formalargs txtProperties object #' @param eqformalargs.properties eqformalargs txtProperties object #' @param assignement.properties assignement txtProperties object #' @param symbol.properties symbol txtProperties object #' @param slot.properties slot txtProperties object #' @param default.properties default txtProperties object #' @param par.properties a parProperties object #' @examples #' \donttest{ #' if( check_valid_java_version() ){ #' an_rscript = RScript( text = "ls() #' x = rnorm(10)" ) #' } #' } #' @seealso \code{\link{addRScript}} #' @export RScript = function( file, text , comment.properties = textProperties( color = "#A7947D" ) , roxygencomment.properties = textProperties( color = "#5FB0B8" ) , symbol.properties = textProperties( color = "black" ) , operators.properties = textProperties( color = "black" ) , keyword.properties = textProperties( color = "#4A444D" ) , string.properties = textProperties( color = "#008B8B", font.style = "italic" ) , number.properties = textProperties( color = "blue" ) , functioncall.properties = textProperties( color = "blue" ) , argument.properties = textProperties( color = "#666666" ) , package.properties = textProperties( color = "green" ) , formalargs.properties = textProperties( color = "#424242" ) , eqformalargs.properties = textProperties( color = "#424242" ) , assignement.properties = textProperties( color = "black" ) , slot.properties = textProperties( color = "#F25774" ) , default.properties = textProperties( color = "black" ) , par.properties = parProperties() ) { if( !inherits( par.properties, "parProperties" ) ){ stop("argument 'par.properties' must be an object of class 'parProperties'") } if( !inherits(comment.properties, "textProperties") ) stop("argument comment.properties must be a textProperties object.") if( !inherits(roxygencomment.properties, "textProperties") ) stop("argument roxygencomment.properties must be a textProperties object.") if( !inherits(operators.properties, "textProperties") ) stop("argument operators.properties must be a textProperties object.") if( !inherits(keyword.properties, "textProperties") ) stop("argument keyword.properties must be a textProperties object.") if( !inherits(string.properties, "textProperties") ) stop("argument string.properties must be a textProperties object.") if( !inherits(number.properties, "textProperties") ) stop("argument number.properties must be a textProperties object.") if( !inherits(functioncall.properties, "textProperties") ) stop("argument functioncall.properties must be a textProperties object.") if( !inherits(argument.properties, "textProperties") ) stop("argument argument.properties must be a textProperties object.") if( !inherits(package.properties, "textProperties") ) stop("argument package.properties must be a textProperties object.") if( !inherits(formalargs.properties, "textProperties") ) stop("argument formalargs.properties must be a textProperties object.") if( !inherits(eqformalargs.properties, "textProperties") ) stop("argument eqformalargs.properties must be a textProperties object.") if( !inherits(assignement.properties, "textProperties") ) stop("argument assignement.properties must be a textProperties object.") if( !inherits(symbol.properties, "textProperties") ) stop("argument symbol.properties must be a textProperties object.") if( !inherits(slot.properties, "textProperties") ) stop("argument slot.properties must be a textProperties object.") if( !inherits(default.properties, "textProperties") ) stop("argument default.properties must be a textProperties object.") if( missing( file ) && missing( text ) ) stop("file OR text must be provided as argument.") if( !missing( file ) ){ if( !inherits( file, "character" ) ) stop("file must be a single character value") if( length( file ) != 1 ) stop("file must be a single character value") if( !file.exists( file ) ) stop( file, " does not exist") pot.list = get.pots.from.script( file = file , comment.properties = comment.properties , roxygencomment.properties = roxygencomment.properties , operators.properties = operators.properties , keyword.properties = keyword.properties , string.properties = string.properties , number.properties = number.properties , functioncall.properties = functioncall.properties , argument.properties = argument.properties , package.properties = package.properties , formalargs.properties = formalargs.properties , eqformalargs.properties = eqformalargs.properties , assignement.properties = assignement.properties , symbol.properties = symbol.properties , slot.properties = slot.properties , default.properties = default.properties ) } else { if( !inherits( text, "character" ) ) stop("text must be a single character value") if( length( text ) != 1 ) stop("text must be a single character value") pot.list = get.pots.from.script( text = text , comment.properties = comment.properties , roxygencomment.properties = roxygencomment.properties , operators.properties = operators.properties , keyword.properties = keyword.properties , string.properties = string.properties , number.properties = number.properties , functioncall.properties = functioncall.properties , argument.properties = argument.properties , package.properties = package.properties , formalargs.properties = formalargs.properties , eqformalargs.properties = eqformalargs.properties , assignement.properties = assignement.properties , symbol.properties = symbol.properties , slot.properties = slot.properties , default.properties = default.properties ) } jparProp = .jParProperties(par.properties) jRScript = .jnew(class.RScript, jparProp) for( i in seq_len(length(pot.list)) ){ .jcall( jRScript, "V", "addParagraph", .jpot(pot.list[[i]]) ) } out = list() out$jobj = jRScript class( out ) = c( "RScript", "set_of_paragraphs") out } #' @export print.RScript = function(x, ...){ out = .jcall( x$jobj, "S", "toString" ) cat(out) invisible() }

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/dataiku.R

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[]

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claeysre/challenge_census

6ff901b8eda535165157d52f9b3d4c759bea961e

a6db1b67a4da0123b7611920fbaa6c6a9393f518

refs/heads/master

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2016-05-08T20:58:54

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dataiku.R

# Packages needed to run this code. If you do not posess one of this library, please run the following command # install.packages('e1071') for example library(randomForest) library(ggplot2) library(rmarkdown) library(e1071) library(ROCR) library(caret) library(lubridate) # Please change your path according to your need train_location = '/Volumes/RemiSDCard/Dataiku/us_census_full/census_income_learn.csv' test_location = '/Volumes/RemiSDCard/Dataiku/us_census_full/census_income_test.csv' # Provide contextual information context = c('age','class_of_worker','industry_code','occupation_code','education','wage_per_hour','enrolled_edu', 'marital_status', 'maj_ind_code', 'maj_occ_code','race','hispanic_origin','sex', 'member_labor_union','reason_unemployment','employment_status', 'capital_gains', 'capital_losses','dividends', 'tax_filer_status', 'region_prev_res', 'reg_prev_state', 'household_stats', 'household_summary', 'instance_weight', 'migration_msa', 'migration_reg', 'mig_within_region', 'same_house', 'migration_sunbelt', 'num_persons_worked_for_employer', 'relatives_under_18' , 'country_father', 'country_mother', 'country_self', 'citizenship', 'own_business', 'veterans_filled','veterans_benefits', 'weeks_worked_year', 'year', 'income') # Ensure that columns have the correct type type_context = c('numeric',rep('factor',4),'numeric',rep('factor',10),rep('numeric',3), rep('factor',11),'numeric',rep('factor',8),'numeric',rep('factor',2)) # Load train and test dataset train_df <- read.csv(train_location, header = F, na.strings = '?', col.names = context, strip.white = T, colClasses = type_context) test_df <- read.csv(test_location, header = F, na.strings = '?', col.names = context, strip.white = T, colClasses = type_context) # Drop weight column (cf Metadata) train_df <- subset(train_df, select = -c(instance_weight)) test_df <- subset(test_df, select = -c(instance_weight)) train_df$income <- ifelse(train_df$income == "- 50000.", "0", ifelse(train_df$income == "50000+.", "1", "other")) test_df$income <- ifelse(test_df$income == "- 50000.", "0", ifelse(test_df$income == "50000+.", "1", "other")) train_df$income <- as.factor(train_df$income) test_df$income <- as.factor(test_df$income) # Let's see if there are any missing values incomplete_columns <- sapply(train_df, function(x) (sum(is.na(x)) / nrow(train_df)))*100; incomplete_columns[ incomplete_columns > 0] # -------------------------------------------------------------------------- # Let's explore the influence of the categorical variables first #First the class of worker qplot (income, data = train_df, fill = class_of_worker) + facet_grid (. ~ class_of_worker) #Then the industry code, too many categories, furthermore we don't know what the categories mean, same for occupation code qplot (income, data = train_df, fill = industry_code) + facet_grid (. ~ industry_code) # => same info with maj industry code et major occupation code # Education qplot (income, data = train_df, fill = education) + facet_grid (. ~ education) #=> a very good feature that will be helpful for classification # Maybe gather the children. # Enrolled in edu last week qplot (income, data = train_df, fill = enrolled_edu) + facet_grid (. ~ enrolled_edu) #=> Can also be discriminativ, students don't make 50K during schools. maybe merge college and high school # Marital Status qplot (income, data = train_df, fill = marital_status) + facet_grid (. ~ marital_status) # Major Industry Code qplot (income, data = train_df, fill = maj_ind_code) + facet_grid (. ~ maj_ind_code) # =>Trade, Manufacturing, Finance categories have a bigger proportion to earn +50K # Major Occupation Code qplot (income, data = train_df, fill = maj_occ_code) + facet_grid (. ~ maj_occ_code) # => Managerial, Professional Special, Protective Services have better proportion to earn +50K # Race qplot (income, data = train_df, fill = race) + facet_grid (. ~ race) # White people seem advantaged , maybe transform the other races into a cat "minorities"would help the RF #hispanic_origin qplot (income, data = train_df, fill = hispanic_origin) + facet_grid (. ~ hispanic_origin) #=> Not Relevant All other reprensent white people and the others minorities #sex qplot (income, data = train_df, fill = sex) + facet_grid (. ~ sex) # =>Relevant males advantanged #member labor union qplot (income, data = train_df, fill = member_labor_union) + facet_grid (. ~ member_labor_union) # => Relevant #reason_unemployment qplot (income, data = train_df, fill = reason_unemployement) + facet_grid (. ~ reason_unemployement) # Useless since people unemployed will not help to classify the problematic categorie+50K # No +50K for #employment_status qplot (income, data = train_df, fill = employment_status) + facet_grid (. ~ employment_status) #=> Full time schedule more likely to earn +50K # tax_filer_status qplot (income, data = train_df, fill = tax_filer_status) + facet_grid (. ~ tax_filer_status) #=> Relevant, Joint both under 65 # region_prev_res qplot (income, data = train_df, fill = region_prev_res) + facet_grid (. ~ region_prev_res) # => Not relevant # reg_prev_state qplot (income, data = train_df, fill = reg_prev_state) + facet_grid (. ~ reg_prev_state) # => Not Relevant # household_stats qplot (income, data = train_df, fill = household_stats) + facet_grid (. ~ household_stats) # Redundant with household_summary # household_summary qplot (income, data = train_df, fill = household_summary) + facet_grid (. ~ household_summary) # => Relevant # migration_msa qplot (income, data = train_df, fill = migration_msa) + facet_grid (. ~ migration_msa) # => could be relevant but too much missing information # migration_reg qplot (income, data = train_df, fill = migration_reg) + facet_grid (. ~ migration_reg) # => could be relevant but too much missing information # migration within the same region qplot (income, data = train_df, fill = mig_within_region) + facet_grid (. ~ migration_within_reg) # same house qplot (income, data = train_df, fill = same_house) + facet_grid (. ~ same_house) # => Not relevant # Migration sunbelt qplot (income, data = train_df, fill = migration_sunbelt) + facet_grid (. ~ migration_sunbelt) #relatives_under_18 qplot (income, data = train_df, fill = relatives_under_18) + facet_grid (. ~ relatives_under_18) # => Not relevant, sems to apply only to childs #country_father qplot (income, data = train_df, fill = country_father) + facet_grid (. ~ country_father) # #country_mother qplot (income, data = train_df, fill = country_mother) + facet_grid (. ~ country_mother) # #country_self qplot (income, data = train_df, fill = country_self) + facet_grid (. ~ country_self) # May be relavant redundant with citizenship # citizenship qplot (income, data = train_df, fill = citizenship) + facet_grid (. ~ citizenship) # let's see later # own_business qplot (income, data = train_df, fill = own_business) + facet_grid (. ~ own_business) # => Creator of business advantaged # veterans_filled qplot (income, data = train_df, fill = veterans_filled) + facet_grid (. ~ veterans_filled) # => Not relevant, all the data is in the not in univers class and there is not enough data for veterans # veterans_benefits qplot (income, data = train_df, fill = veterans_benefits) + facet_grid (. ~ veterans_benefits ) # => Maybe relevant # year qplot (income, data = train_df, fill = year) + facet_grid (. ~ year ) # As predicted, useless # ---------------------------------------------------------------------------------- # Let's explore the influence of numerical variables #AGE boxplot (age ~ income, data = train_df, main = "Age distribution depending on classes", xlab = "class", ylab = "Age", col = c("green") ) # => Relevant ggplot(train_df, aes(x=age, fill=income)) + geom_histogram(binwidth=2, alpha=0.5, position="identity") # => Different distribution # #WAGE boxplot (wage_per_hour ~ income, data = train_df, main = "wage distribution depending on classes", xlab = "class", ylab = "wage", col = c("green") ) #Too much zeros in the dataset, doesn't seem to be useful, a simple overview of the first lines shows # a individual having 1200 of hourly wage and still under 50 K # => Relevant # Capitals Gains + Capital Losses + Dividends train_df$sum_losses_gains = train_df$capital_gains - train_df$capital_losses + train_df$dividends test_df$sum_losses_gains = test_df$capital_gains - test_df$capital_losses + test_df$dividends boxplot (sum_losses_gains ~ income, data = train_df, main = "gains - losses distribution depending on classes", xlab = "class", ylab = "Age", col = c("green") ) # Even if that's not very insightful we can clearly see that people who lose money don't make +50K #m <- ggplot(train_df, aes(x = sum_losses_gains)) #m + geom_density() # Let's create some category, this can be good for a decision tree or a Random Forest train_df$sum_losses_gains_cat<-ifelse(train_df$sum_losses_gains< -1000,"big-loser", ifelse(train_df$sum_losses_gains >= -1000 & train_df$sum_losses_gains < 0, "small-loser", ifelse(train_df$sum_losses_gains == 0 , "balanced", ifelse(train_df$sum_losses_gains> 0 & train_df$sum_losses_gains <= 5000 , "small-winner", ifelse(train_df$sum_losses_gains > 5000 , "big-winner","other" ))))) test_df$sum_losses_gains_cat<-ifelse(test_df$sum_losses_gains< -1000,"big-loser", ifelse(test_df$sum_losses_gains >= -1000 & test_df$sum_losses_gains < 0, "small-loser", ifelse(test_df$sum_losses_gains == 0 , "balanced", ifelse(test_df$sum_losses_gains> 0 & test_df$sum_losses_gains <= 5000 , "small-winner", ifelse(test_df$sum_losses_gains > 5000 , "big-winner","other" ))))) qplot (income, data = train_df, fill = sum_losses_gains_cat) + facet_grid (. ~ sum_losses_gains_cat) #Num persons worked for employer boxplot (num_persons_worked_for_employer ~ income, data = train_df, main = "Num persons worked for employer distribution depending on classes", xlab = "class", ylab = "nums_persons", col = c("green") ) # => Relevant ggplot(train_df, aes(x = num_persons_worked_for_employer, fill=income)) + geom_histogram(binwidth = 2, alpha = 0.5, position="identity") # Bigger the corporate is, you have more chance to earn money # Relevant to put that variable into factor train_df$num_persons_worked_for_employer <- as.factor(train_df$num_persons_worked_for_employer) test_df$num_persons_worked_for_employer <- as.factor(test_df$num_persons_worked_for_employer) qplot (income, data = train_df, fill = num_persons_worked_for_employer) + facet_grid (. ~ num_persons_worked_for_employer) # WEEKS WORKED IN A YEAR boxplot (weeks_worked_year ~ income, data = train_df, main = "Weeks worked in a year distribution depending on classes", xlab = "class", ylab = "nums_persons", col = c("green") ) # => Relevant ggplot(train_df, aes(x = weeks_worked_year, fill=income)) + geom_histogram(binwidth = 2, alpha = 0.5, position="identity") # Age Categories train_df$age_cat<-ifelse(train_df$age < 18, "youth", ifelse(train_df$age >= 18 & train_df$age < 27, "y_workers", ifelse(train_df$age >= 27 & train_df$age < 67, "wokers", ifelse(train_df$age >= 67 , "retired","other" )))) # Reproduce the same for the test set test_df$age_cat<-ifelse(test_df$age < 18, "youth", ifelse(test_df$age >= 18 & test_df$age < 27, "y_workers", ifelse(test_df$age >= 27 & test_df$age < 67, "wokers", ifelse(test_df$age >= 67 , "retired","other" )))) # Education Category reduction train_df$education_cat<-ifelse(train_df$education == "10th grade", "youth", ifelse(train_df$education == "11th grade", "youth", ifelse(train_df$education == "12th grade no diploma", "youth" , ifelse(train_df$education == "1st 2nd 3rd or 4th grade", "youth", ifelse(train_df$education == "5th or 6th grade", "youth", ifelse(train_df$education == "7th and 8th grade", "youth", ifelse(train_df$education == "9th grade", "youth", ifelse(train_df$education == "Less than 1st grade", "youth", ifelse(train_df$education == "Children", "youth", ifelse(train_df$education == "Associates degree-academic program", "basicdegree", ifelse(train_df$education == "Associates degree-occup /vocational", "basicdegree", ifelse(train_df$education == "Some college but no degree", "basicdegree", ifelse(train_df$education == "High school graduate", "high school graduate", ifelse(train_df$education == "Bachelors degree(BA AB BS)", "bachelor", ifelse(train_df$education == "Masters degree(MA MS MEng MEd MSW MBA)", "master", ifelse(train_df$education == "Doctorate degree(PhD EdD)", "prof_doct", ifelse(train_df$education == "Prof school degree (MD DDS DVM LLB JD)", "prof_doct", "other" ))))))))))))))))) test_df$education_cat<- ifelse(test_df$education == "10th grade", "youth", ifelse(test_df$education == "11th grade", "youth", ifelse(test_df$education == "12th grade no diploma", "youth" , ifelse(test_df$education == "1st 2nd 3rd or 4th grade", "youth", ifelse(test_df$education == "5th or 6th grade", "youth", ifelse(test_df$education == "7th and 8th grade", "youth", ifelse(test_df$education == "9th grade", "youth", ifelse(test_df$education == "Less than 1st grade", "youth", ifelse(test_df$education == "Children", "youth", ifelse(test_df$education == "Associates degree-academic program", "basicdegree", ifelse(test_df$education == "Associates degree-occup /vocational", "basicdegree", ifelse(test_df$education == "Some college but no degree", "basicdegree", ifelse(test_df$education == "High school graduate", "high school graduate", ifelse(test_df$education == "Bachelors degree(BA AB BS)", "bachelor", ifelse(test_df$education == "Masters degree(MA MS MEng MEd MSW MBA)", "master", ifelse(test_df$education == "Doctorate degree(PhD EdD)", "prof_doct", ifelse(test_df$education == "Prof school degree (MD DDS DVM LLB JD)", "prof_doct", "other" ))))))))))))))))) # Remove column, also income to append it at the end, after train_clean_df <- subset(train_df, select = -c(age,industry_code,occupation_code,education,hispanic_origin, reason_unemployment, capital_gains,capital_losses,dividends,region_prev_res, reg_prev_state, household_stats, migration_msa, migration_reg, mig_within_region, migration_sunbelt, country_father, country_mother, country_self, veterans_filled, year, income, sum_losses_gains)) # Re-Append it at the end train_clean_df$income <- train_df$income # Produce the same for the test dataset test_clean_df <- subset(test_df, select = -c(age,industry_code,occupation_code,education,hispanic_origin, reason_unemployment, capital_gains,capital_losses,dividends,region_prev_res, reg_prev_state, household_stats, migration_msa, migration_reg, mig_within_region, migration_sunbelt, country_father, country_mother, country_self, veterans_filled, year, income, sum_losses_gains)) # Re-Append it at the end test_clean_df$income <- test_df$income # Set new variables as Factor train_clean_df$education_cat <- as.factor(train_clean_df$education_cat) train_clean_df$age_cat <- as.factor(train_clean_df$age_cat) train_clean_df$sum_losses_gains_cat <- as.factor(train_clean_df$sum_losses_gains_cat) test_clean_df$education_cat <- as.factor(test_clean_df$education_cat) test_clean_df$age_cat <- as.factor(test_clean_df$age_cat) test_clean_df$sum_losses_gains_cat <- as.factor(test_clean_df$sum_losses_gains_cat) # Set the seed set.seed(3004) # Train a rf rf <-randomForest(income ~ class_of_worker + wage_per_hour + enrolled_edu + marital_status + maj_ind_code + maj_occ_code + race + sex + member_labor_union + employment_status + tax_filer_status + household_summary + same_house + num_persons_worked_for_employer + relatives_under_18 + citizenship + own_business + veterans_benefits + weeks_worked_year + sum_losses_gains_cat + age_cat + education_cat, data=train_clean_df, mtry= 5, sampsize=c(5000,1000), ntree=300, na.action=na.omit, do.trace=100, importance=TRUE) foo <- predict (rf,train_clean_df[,1:22]) ber <- function(confusion_mat) { foo <- confusion_mat[1,2] / (confusion_mat[1,1]+confusion_mat[1,2]) bar <- confusion_mat[2,1] / (confusion_mat[2,1]+confusion_mat[2,2]) ber <- (foo + bar)*0.5*100 } imp <- importance(rf, type=1) featureImportance <- data.frame(Feature=row.names(imp), Importance=imp[,1]) p <- ggplot(featureImportance, aes(x=reorder(Feature, Importance), y=Importance)) + geom_bar(stat="identity", fill="#53cfff") + coord_flip() + theme_light(base_size=20) + xlab("Importance") + ylab("") + ggtitle("Random Forest Feature Importance\n") + theme(plot.title=element_text(size=18)) # Train final rf rf8 <-randomForest(income ~ class_of_worker + wage_per_hour + enrolled_edu + marital_status + maj_ind_code + maj_occ_code + race + sex + tax_filer_status + household_summary + num_persons_worked_for_employer + citizenship + own_business + weeks_worked_year + sum_losses_gains_cat + age_cat + education_cat, data=train_clean_df, mtry= 5, sampsize=c(1900,1500), ntree=500, na.action=na.omit, do.trace=100, importance=TRUE) # PREDICTIONS ON TEST SET featuresToKeep <- c('class_of_worker', 'wage_per_hour', 'enrolled_edu', 'marital_status', 'maj_ind_code', 'maj_occ_code', 'race', 'sex', 'tax_filer_status', 'household_summary', 'num_persons_worked_for_employer','citizenship', 'own_business','weeks_worked_year', 'sum_losses_gains_cat', 'age_cat', 'education_cat') test_clean_df2 <- test_clean_df[featuresToKeep] test_clean_df2$income_predicted <- predict(rf8, test_clean_df2) xtab <- table(test_clean_df2$income_predicted, test_clean_df$income) conf_object <- confusionMatrix(xtab) Ber_error <- ber(t(conf_object$table ))

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variable_trends_seasonal.R

######### new script to display the temporal trends of predictors ###### code from Jenn #### ggplot(FLpL, aes(x = Year, y = value)) + geom_point() + facet_wrap(~ variable, ncol = 12) + geom_smooth(method = lm) + xlab("") + ylab("Precipitation (in)") + theme(axis.text.x = element_text(angle = 45)) ## https://lh3.googleusercontent.com/-406sBFCWwaY/WdPKAI0itJI/AAAAAAAAIjU/75-0aV0-3-sp_oMXLwFp3zUs3OsXGn3MgCL0BGAYYCw/h1505/2017-10-03.png ###### ######### ----------------------------> define global objects #### library(tidyverse) library(raster) ######### ----------------------------> create contemporary rasters averaged by season #### contempDir_monthly="/Volumes/SDM /Lacie backup October 2016/Lacie share/Climate_paper/GAM_1/project" ## monthly current rasters contempDir_seaonal="/Volumes/SDM /Lacie backup October 2016/Lacie share/Climate_paper/GAM_1/project_20y_avs_seasonal/contemporary" #;dir.create(contempDir_seaonal) ## folder to house current rasters averaged by season winter=c("m12","m01","m02") spring=c("m03","m04","m05") summer=c("m06","m07","m08") fall=c("m09","m10","m11") seasons=c("DJF","MAM","JJA","SON") for(season in seasons){ print(season) a=list() assign(season,a) } vars=c("bt","bs","st","SS","sh") allfiles=list.files(contempDir_monthly,recursive = T,full.names = T) for(file in allfiles){ print(file) a=strsplit(file,"/")[[1]][9] b=strsplit(a,"_") month=b[[1]][1] year=b[[1]][2] ########## 20-40 if(month %in% winter){ DJF=unlist(list(file,DJF)) } if(month %in% spring){ MAM=unlist(list(file,MAM)) } if(month %in% summer){ JJA=unlist(list(file,JJA)) } if(month %in% fall){ SON=unlist(list(file,SON)) } } master=list() for(season in seasons){ print(season) master=unlist(list(master,season)) } for(mas in master){ print(mas) a=paste0(contempDir_seaonal,"/contemp_",mas);dir.create(a) bs=grep("bs",get(mas),value = T)%>%stack(.)%>%calc(.,fun = mean);writeRaster(bs,paste0(contempDir_seaonal,"/contemp_",mas,"/bs.tif"),format="GTiff",overwrite=T) bt=grep("bt",get(mas),value = T)%>%stack(.)%>%calc(.,fun = mean);writeRaster(bt,paste0(contempDir_seaonal,"/contemp_",mas,"/bt.tif"),format="GTiff",overwrite=T) st=grep("st",get(mas),value = T)%>%stack(.)%>%calc(.,fun = mean);writeRaster(st,paste0(contempDir_seaonal,"/contemp_",mas,"/st.tif"),format="GTiff",overwrite=T) SS=grep("SS",get(mas),value = T)%>%stack(.)%>%calc(.,fun = mean);writeRaster(SS,paste0(contempDir_seaonal,"/contemp_",mas,"/SS.tif"),format="GTiff",overwrite=T) sh=grep("sh.tif",get(mas),value = T)%>%stack(.)%>%calc(.,fun = mean);writeRaster(sh,paste0(contempDir_seaonal,"/contemp_",mas,"/sh.tif"),format="GTiff",overwrite=T) } ######### ######### ----------------------------> find spatial averages for all rasters in contemp and project #### empty=data.frame(period=NA,season=NA,var=NA,s.mean=NA) contemp=list.files(contempDir_seaonal,full.names = T,recursive = T) proj=list.files("/Volumes/SDM /Lacie backup October 2016/Lacie share/Climate_paper/GAM_1/project_20y_avs_seasonal/project",full.names = T,recursive = T) layersList=unlist(list(contemp,proj)) a=grep("Rugosity",layersList) b=grep("Depth",layersList) remove=unlist(list(a,b)) layersList=layersList[-remove] ### getting rid of depth and rugosity substrRight <- function(x, n){ substr(x, nchar(x)-n+1, nchar(x)) } for(i in 1:length(layersList)){ ### extracting metrics for each layer and writing to empty a=strsplit(layersList[i],"/") var=gsub(".tif","",a[[1]][11]) season=substrRight(a[[1]][10],3) period=substr(a[[1]][10],1,nchar(a[[1]][10])-4) s.mean=raster(layersList[i])%>%cellStats(.,stat=mean) empty[i,1]=period empty[i,2]=season empty[i,3]=var empty[i,4]=s.mean } ##### cleaning up dataframe xLevels=c("contemp","av20_40", "av40_60","av60_80") empty$period=as.factor(empty$period) empty$season=as.factor(empty$season) empty$var=as.factor(empty$var) empty$period_ordered=factor(empty$period,levels=xLevels) write.csv(empty,"/Volumes/SDM /Lacie backup October 2016/Lacie share/Climate_paper/GAM_1/project_20y_avs_seasonal/data/means.csv") ## cleaning names by hand, quicker empty=read.csv("/Volumes/SDM /Lacie backup October 2016/Lacie share/Climate_paper/GAM_1/project_20y_avs_seasonal/data/means.csv") period_Levels=c("Contemporary","+20-40 years", "+40-60 years","+60-80 years") season_Levels=c("Winter","Spring", "Summer","Fall") var_Levels=c("Surface temperature","Bottom temperature","Surface salinity","Bottom salinity","Sea height") empty$period_ordered=factor(empty$Period,levels=period_Levels) empty$season_ordered=factor(empty$season,levels=season_Levels) empty$var_ordered=factor(empty$var,levels=var_Levels) ######### ----------------------------> plotting 4x5 grid, each is variable by season, x = period #### a=ggplot(empty,aes(period_ordered,s.mean,group=1))+geom_line() a+facet_grid(var_ordered~season_ordered,scales = "free_y")+theme(strip.text.y = element_text(size=5))+ theme(axis.text.x = element_text(hjust=1,vjust=1,angle = 45))+labs(x="Temporal period")+labs(y="Mean value") pdf("/Volumes/SDM /Lacie backup October 2016/Lacie share/Climate_paper/GAM_1/project_20y_avs_seasonal/data/grid.pdf") a=ggplot(empty,aes(period_ordered,s.mean,group=1))+geom_line() a+facet_grid(var_ordered~season_ordered,scales = "free_y")+theme(strip.text.y = element_text(size=5))+ theme(axis.text.x = element_text(hjust=1,vjust=1,angle = 45))+labs(x="Temporal period")+labs(y="Mean value") dev.off()

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### Compute Efficient Portfolios with Matrix Algebra # To clean up the memory of your current R session run the following line rm(list=ls(all=TRUE)) ## Part 1: Loading in your data set # Load the relevant packages library("zoo") library("quadprog") # Load the data data <- url("http://s3.amazonaws.com/assets.datacamp.com/course/compfin/lab9.RData") load(data) # Explore the data set head(returns_df) tail(returns_df) # Timeplots with stocks on individual graphs my.panel <- function(...) { lines(...) abline(h=0) } plot(returns_df, lwd=2, panel=my.panel, col="blue") # Timeplots with stocks on same graph plot(returns_df, plot.type = "single", main="Returns", col=1:4, lwd=2) abline(h=0) legend(x="bottomleft", legend=colnames(returns_df), col=1:4, lwd=2) ## Part 2: The CER model # Parameters CER model mu_hat_month <- apply(returns_df, 2, mean) mu_hat_month sigma2_month <- apply(returns_df, 2, var) sigma2_month sigma_month <-sqrt(sigma2_month) sigma_month cov_mat_month <- var(returns_df) cov_mat_month cor_mat_month <- cor(returns_df) cor_mat_month # Pairwise scatterplots pairs(coredata(returns_df), col="slateblue1", pch=16, cex=1.5) ## Part 3: Question: What is the correlation between the Nordstrom stock and the Boeing stock? cor_mat_month # 0.1025 ## Part 4: The global minimum variance portfolio - Part One # Calculate the global minimum variance portfolio args(globalMin.portfolio) global_min_var_portfolio = globalMin.portfolio(mu_hat_month, cov_mat_month, shorts=TRUE) global_min_var_portfolio # Plot the portfolio weights of our four stocks plot(global_min_var_portfolio) ## Part 5: Standard deviation ## Question: What is the standard deviation of the global minimum variance portfolio that you have just calculated? # Compute mean, variance and std deviation mu.gmin = global_min_var_portfolio$er # mean sig.gmin = global_min_var_portfolio$sd # standard deviation mu.gmin sig.gmin ## Part 6: The global minimum variance portfolio - Part Two # set restriction matrices D_matrix <- 2* cov_mat_month D_matrix d_vector <- rep(0,4) d_vector A_matrix <- cbind(rep(1,4),diag(4)) A_matrix b_vector <- c(1,rep(0,4)) b_vector # use solve.QP to minimize portfolio variance quad_prog <- solve.QP(Dmat=D_matrix, dvec=d_vector, Amat=A_matrix, bvec=b_vector, meq=1) quad_prog$solution ## Part 7: The global minimum variance portfolio - End game # The global minimum variance portfolio global_min_var_portfolio = globalMin.portfolio(mu_hat_month, cov_mat_month, shorts=FALSE) global_min_var_portfolio ## part 8: An efficient portfolio # highest average return mu_target <- max(mu_hat_month) # short sales allowed args(efficient.portfolio) efficient_porfolio_short <- efficient.portfolio(mu_hat_month, cov_mat_month, mu_target, shorts = TRUE) efficient_porfolio_short plot(efficient_porfolio_short) # no short sales allowed efficient_porfolio_no_short <- efficient.portfolio(mu_hat_month, cov_mat_month, mu_target, shorts = FALSE) efficient_porfolio_no_short plot(efficient_porfolio_no_short) ## Part 9: Question The weight of Boeing # What is the weight of the Boeing stock under the "shorting not allowed" condition? efficient_porfolio_no_short ## Part 10: The efficient frontier # The efficient frontier of risky assets args(efficient.frontier) efficient_frontier <- efficient.frontier(mu_hat_month, cov_mat_month, alpha.min=-1, alpha.max=1) summary(efficient_frontier) # The plot plot(efficient_frontier, plot.assets=TRUE, col="blue", lwd=2) ###QUIZ QUESTION 8: # Using the fact that all efficient portfolios can be written as a convex combination of two efficient portfolios, # compute efficient portfolios as convex combinations of the global minimum variance portfolio and the efficient portfolio that was computed in question six. # What is the expected return of the portfolio when α=.5? # z=α∗m+(1−α)∗x z = 0.5 * global_min_var_portfolio$weights + (1-0.5)*efficient_porfolio_short$weights sum(z*mu_hat_month) # expected return of portfolio = 3.06% m## part 11: The tangency portfolio # risk free rate t_bill_rate <- 0.005 tangency_portfolio_short <- tangency.portfolio(mu_hat_month, cov_mat_month, t_bill_rate, shorts = TRUE) summary(tangency_portfolio_short) #plot plot(tangency_portfolio_short) # Tangency portfolio short sales not allowed tangency_portfolio_no_short <- tangency.portfolio(mu_hat_month,cov_mat_month, t_bill_rate, shorts = FALSE) summary(tangency_portfolio_no_short) #plot plot(tangency_portfolio_no_short) ## Part 12: The weight of Boeing ... again # Question: If short sales is not allowed in your tangency portfolio, what is the weight of Boeing stock? tangency_portfolio_no_short$weights

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/0_utils.R \name{isSQLite3} \alias{isSQLite3} \title{Check whether a file is a SQLite3 database.} \usage{ isSQLite3(filename) } \arguments{ \item{filename}{character. Path to file to be checked.} } \description{ Check whether a file is a SQLite3 database. } \author{ Kaiyin Zhong, Fan Liu }

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\name{differenceOfICs} \alias{differenceOfICs} \title{normalized information content differences} \usage{ differenceOfICs(p1, p2) } \arguments{ \item{p1}{probability vector representing the first symbol distribution} \item{p2}{probability vector representing the second symbol distribution} } \value{ a vector with one result for each symbol } \description{ information content differences normalized by the sum of absolute information content differences for the given pair of probability vectors } \examples{ motif_folder= "extdata/pwm" motif_names = c("HepG2","MCF7","HUVEC","ProgFib") motifs = list() for (name in motif_names) { fileName = paste(motif_folder,"/",name,".txt",sep="") file = system.file(fileName, package = "DiffLogo") motifs[[name]] = as.matrix(read.delim(file,header=FALSE)) } pwm1 = motifs[[motif_names[[1]]]] pwm2 = motifs[[motif_names[[2]]]] diffLogoFromPwm(pwm1 = pwm1, pwm2 = pwm2, baseDistribution = differenceOfICs) } \author{ Martin Nettling }

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genomic.R

#' Get the number of posterior samples available for attribution get_num_samples <- function() { return(max(genotype_attribution$Iteration)) } #' Get the genotypes available for attribution get_genotypes <- function() { return(unique(genotype_attribution[,1:8])) # TODO: Make indicies a function of data } #' Retrieve the i-th posterior sample for attribution for a given genotype #' @param genotype the genotype to retrieve. #' @param sample the sample to retrieve. Defaults to NULL, where all samples will be retrieved. #' @return A data frame of sequence types, their allelic profile, and clonal complex. #' @seealso pubmlst get_source_probability_sample <- function(genotype, sample = NULL) { wch <- genotype_attribution$ST == genotype; if (!is.null(sample)) wch <- wch & genotype_attribution$Iteration == sample return(genotype_attribution[wch, 9:12]) # TODO: Make indicies a function of data }

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/boxplot/ini.R

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MarcosGrzeca/drunktweets

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ini.R

# library library(ggplot2) # create a data frame variety=rep(LETTERS[1:7], each=40) treatment=rep(c("high","low"),each=20) note=seq(1:280)+sample(1:150, 280, replace=T) data=data.frame(variety, treatment , note) # grouped boxplot ggplot(data, aes(x=variety, y=note, fill=treatment)) + geom_boxplot()

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/Assignment 2 solution/Part 1 Q1 Data Wrangling/Part 1 1 a/Assignment2Part1ab.R

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MoreDhanshri/ADS-Assignment

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refs/heads/master

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Assignment2Part1ab.R

setwd("D:\\Northeastern University\\ADS\\Assignment\\Assignment2\\Assignment 2 solution\\Part 1\\") raw.data<-read.csv("D:\\Northeastern University\\ADS\\Assignment\\Assignment2\\NewData.csv", header = T) raw.data<-raw.data[raw.data$Units=="kWh",] raw.data$Date<-strptime(raw.data$Date, '%m/%d/%Y') raw.data$year<-as.numeric(format(raw.data$Date, '%Y')) raw.data$month<-as.numeric(format(raw.data$Date, '%m')) raw.data$day<-as.numeric(format(raw.data$Date, '%d')) w<-as.POSIXlt(raw.data$Date)$wday-1 w[w==-1]<-6 raw.data$DayOfWeek<-w raw.data$Weekday<-ifelse(w<5, 1,0) #power<-data.frame(raw.data[,5:292]) raw.data.final<- data.frame(raw.data$Account, raw.data$Date,raw.data$month,raw.data$day, raw.data$year,raw.data$DayOfWeek,raw.data$Weekday) # splitting hours and binning it in 24 hours raw<-data.frame(raw.data[,5:292]) g = split(seq(ncol(raw)), (seq(ncol(raw)) - 1) %/% 12 ) t2<-sapply(g, function(cols) rowSums( raw[, cols] )) ##Converting to single column singleColt <- matrix(t(t2),ncol = 1) raw1repeat <- raw.data.final[rep(seq_len(nrow(raw.data.final)),each=24),] #View(raw1repeat1) hour<-c(0:23) raw1repeat1<- cbind(raw1repeat,hour) raw1final<-cbind(raw1repeat1,singleColt) raw1final$PeakHour<-ifelse((raw1final$hour>=7) & (raw1final$hour<20), 1,0) raw1final<-cbind(raw1repeat1,singleColt) raw1final$PeakHour<-ifelse((raw1final$hour>=7) & (raw1final$hour<20), 1,0) #View(raw1final) ##final data of part1 temperature.data<-read.csv("D:\\Northeastern University\\ADS\\Assignment\\Assignment2\\finalTemp3.csv", header = T) ####change this to add aggregation and fetching the date11<- strptime(temperature.data$hourofday,"%m/%d/%Y") t.str<-strptime(temperature.data$hourofday,"%m/%d/%Y %H:%M") time1<-as.POSIXlt(t.str,format(t.str,"%m%d%Y %H:%M:%S%z")) temperature.data$Hour<-time1$hour date11<- strptime(temperature.data$hourofday,"%m/%d/%Y") temperature.data$Date<-date11 mergedraw1<-merge(raw1final,temperature.data,by.x = c("raw.data.Date","hour"),by.y=c("Date","Hour"),all.x = TRUE) impute.mean <- function(x) replace(x, is.na(x), mean(x, na.rm = TRUE)) ##replace the N/A records with mean of the day #install.packages("plyr") library(plyr) #mergedraw1 <- ddply(mergedraw1, ~ hour, transform, temperature = impute.mean(temperature)) mergedraw21 <- ddply(mergedraw1, ~ hour, transform, temperature = impute.mean(temperature)) mergedraw1$X<- NULL colnames(mergedraw1)[8] <- "kWh" #mergerexample<-ddply(mergedraw1, "hour", transform, temperature = impute.mean(temperature)) #View(mergerexample) #install.packages("data.table") library(data.table) setnames(mergedraw1, old=c("raw.data.Date","raw.data.Account","raw.data.month","raw.data.day","raw.data.year","raw.data.DayOfWeek","singleColt"), new=c("Date", "Account","month","day","year","Day Of Week","Kkwh")) mergedraw1<- mergedraw1[c( "Account","Date","kWh","month","day","year","hour","Day Of Week","PeakHour","temperature")] mergedraw1$kWh<-NULL mergedraw1$`Day Of Week`<-NULL mergedraw1$DayOfWeek<-w mergedraw1$Weekday<-ifelse(w<5, 1,0) mergedraw12<-cbind(mergedraw1,singleColt) names(mergedraw12)[names(mergedraw12) == 'singleColt'] <- 'kWh' ########################################################################################## powerData<-mergedraw12 boxplot(powerData,xlab=names(powerData)) boxplot(powerData$temperature) #uif_RunTime<-quantile(fitness$RunTime,.75)+1.5*IQR(fitness$RunTime) #lif_Weight<-quantile(fitness$Weight, .25)-1.5*IQR(fitness$Weight) #tna<- powerData$temperature[powerData$temperature] powerData[!complete.cases(powerData),] mean(powerData$temperature, na.rm=TRUE) median(powerData$temperature, na.rm=TRUE) powerData$temperature[is.na(powerData$temperature)] <- median(powerData$temperature, na.rm=TRUE) lq_temperature<-quantile(powerData$temperature, .25)-1.5*IQR(powerData$temperature) powerData$temperature[powerData$temperature<lq_temperature]<-mean(powerData$temperature) powerData$temperature[powerData$temperature<=0]<-mean(powerData$temperature) boxplot(powerData$temperature) ##### analysis for power mean(powerData$kWh, na.rm=TRUE) median(powerData$kWh, na.rm=TRUE) powerData$kWh[is.na(powerData$kWh)] <- median(powerData$kWh, na.rm=TRUE) boxplot(powerData$kWh) #lq_kWh<-quantile(powerData$kWh, .25)-1.5*IQR(powerData$kWh) #powerData$kWh[powerData$kWh<lq_kWh]<-mean(powerData$kWh<lq_kWh) powerData$kWh[is.na(powerData$kWh)]<-mean(powerData$kWh, na.rm = TRUE) lq_kwh<-quantile(powerData$kWh, .25)-1.5*IQR(powerData$kWh) uq_kwh<-quantile(powerData$kWh,.75)+1.5*IQR(powerData$kWh) mean(powerData$kWh ) median(powerData$kWh) ################# part 1 1.a : remove zero and build model ############ #powerData$kWh[powerData$kWh<=0]<-mean(powerData$kWh ) powerData<-data.frame(powerData) ####################################################################### write.csv(powerData,file = "sampleformatWithZero.csv") ######################## Linear Regression ###################### powerData$Account<-NULL powerData$year<-NULL #### splitting data###### smp_size <- floor(0.75 * nrow(powerData)) #Set the seed to make your partition reproductible set.seed(300) train_ind <- sample(seq_len(nrow(powerData)), size = smp_size) #Split the data into training and testing train <- powerData[train_ind, ] test <- powerData[-train_ind, ] #install.packages("leaps") library(leaps) ##########Exhaustive Feature selection ############### ##### Searching all subset models up to size 8 by default regfit.full=regsubsets(train$kWh~.,data=train) reg8.summary =summary (regfit.full) names(reg8.summary) reg8.summary$rss reg8.summary$adjr2 ##### Searching all subset models up to size number of variables regfit.full=regsubsets(train$kWh~.,data=train ,nvmax=11) #View(regfit.full) reg.summary =summary (regfit.full) names(reg.summary) reg.summary$rss reg.summary$adjr2 #reg.summary$outmat ## Plotting and choosing the subset par(mfrow=c(2,2)) plot(reg.summary$rss ,xlab="Number of Variables 11",ylab="RSS", type="l") plot(reg.summary$adjr2 ,xlab="Number of Variables 11", ylab="Adjusted RSq",type="l") plot(reg8.summary$rss ,xlab="Number of Variables 8",ylab="RSS", type="l") plot(reg8.summary$adjr2 ,xlab="Number of Variables 8 ", ylab="Adjusted RSq",type="l") regr_op<-coef(regfit.full ,6) write.csv(regr_op, file="RegressionOutput.csv") coef(regfit.full ,7) #### Forward selection regfit.fwd=regsubsets(train$kWh~.,data=train ,nvmax=8, method="forward") F=summary(regfit.fwd) names(F) F F$rss F$adjr2 par(mfrow=c(1,2)) plot(F$rss ,xlab="Number of Variables for forward selection",ylab="RSS", type="l") plot(F$adjr2 ,xlab="Number of for forward selection", ylab="Adjusted RSq",type="l") coef(regfit.fwd,5) #### Backward selection regfit.bwd=regsubsets(train$kWh~.,data=train ,nvmax=8, method="backward") B=summary(regfit.bwd) names(B) B B$rss B$adjr2 coef(regfit.bwd,5) ####### final model lm.fit5<-lm(kWh ~ Date+month+day+hour+PeakHour, data = train) summary(lm.fit5) lm.fit6<-lm(kWh ~ Date+month+day+hour+PeakHour+temperature, data = train) summary(lm.fit6) lm.fit6 lm.fit7<-lm(kWh ~ Date+month+day+hour+PeakHour+temperature+Weekday, data = train) summary(lm.fit7) lm.fitall = lm(kWh ~ ., data = train) summary(lm.fitall) #Measures of predictive accuracy #install.packages("zoo") library(forecast) pred5 = predict(lm.fit5, test) accuracy(pred5, test$kWh) pred6 = predict(lm.fit6, test) acc<-t(accuracy(pred6, test$kWh)) write.csv(acc, file="D:\\Northeastern University\\ADS\\Assignment\\Assignment2\\Assignment 2 solution\\Part 1\\PerformanceMatrix.1.a.b.withZero.csv")

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/cartography/R/getBorders.R

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ingted/R-Examples

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getBorders.R

#' @title Extract SpatialPolygonsDataFrame Borders #' @description Extract borders between SpatialPolygonsDataFrame units. #' @name getBorders #' @param spdf a SpatialPolygonsDataFrame. This SpatialPolygonsDataFrame #' has to be projected (planar coordinates). #' @param spdfid identifier field in spdf, default to the first column #' of the spdf data frame. (optional) #' @param tol tolerance to detect contiguity (in map units). You may #' not want to change this parameter. #' @return A SpatialLinesDataFrame of borders is returned. This object has three #' id fields: id, id1 and id2. #' id1 and id2 are ids of units that neighbour a border; id is the concatenation #' of id1 and id2 (with "_" as separator). #' @note This function uses the rgeos package. #' @import sp #' @examples #' data(nuts2006) #' # Get units borders #' nuts0.contig.spdf <- getBorders(nuts0.spdf) #' # Random colors #' nuts0.contig.spdf$col <- sample(x = rainbow(length(nuts0.contig.spdf))) #' # Plot Countries #' plot(nuts0.spdf, border = NA, col = "grey60") #' # Plot borders #' plot(nuts0.contig.spdf, col = nuts0.contig.spdf$col, lwd = 3, add = TRUE) #' @seealso \link{discLayer} #' @export getBorders <- function(spdf, spdfid = NULL, tol = 1){ # Package check and loading if (!requireNamespace("rgeos", quietly = TRUE)) { stop("'rgeos' package needed for this function to work. Please install it.", call. = FALSE) } if(!'package:rgeos' %in% search()){ attachNamespace('rgeos') } # Distance : tolerance /2 distance <- tol/2 mysep <- "_ksfh88ql_" # create comments for polygons with holes spdf <- rgeos::createSPComment(sppoly = spdf) # spdf and spdfid check id <- spdfid if (is.null(id)){id <- names(spdf@data)[1]} spdf@data <- spdf@data[id] colnames(spdf@data)[1]<-"id" row.names(spdf) <- as.character(spdf@data$id) # Create a Buffer around polygons geombuff <- rgeos::gBuffer(spdf, byid = TRUE, width = distance, quadsegs = 1, capStyle = "SQUARE") # Create intersecions table between polygons intergeom <- rgeos::gIntersects(geombuff, byid = TRUE, returnDense = F) b1 <- length(intergeom) t <- 0 for (i in 1:b1) { # Intersection tmp1 <- geombuff[geombuff@data$id==names(intergeom[i]),] for (j in intergeom[[i]]){ if (i != j){ # create a spdf for each intersection tmp2 <- geombuff[j,] frontArea <- rgeos::gIntersection(tmp1, tmp2) row.names(frontArea) <- paste(tmp1@data$id,tmp2@data$id,sep=mysep) if(class(frontArea)=="SpatialPolygons"){ if(t==1){ borders <- rbind(borders, frontArea) } else { borders <- frontArea t <- 1 } } } } } # From spatialpolygonsdataframe to spatiallinesdataframe df <- data.frame(id = sapply(methods::slot(borders, "polygons"), methods::slot, "ID")) row.names(df) <- df$id borders <- SpatialPolygonsDataFrame(Sr = borders, data = df) bordersline <- rgeos::gBoundary(borders, byid=TRUE, id = borders@data$id) bordersline <- SpatialLinesDataFrame(bordersline, df) # Ids management bordersline@data <- data.frame( do.call('rbind',strsplit(as.character(bordersline@data$id), mysep))) colnames(bordersline@data) <- c("id1","id2") bordersline@data$id <- paste(bordersline@data$id1, bordersline@data$id2,sep="_") row.names(bordersline@data) <- bordersline@data$id bordersline@data <- bordersline@data[, c("id", "id1", "id2")] return(bordersline) }

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/fuzzedpackages/JSM/R/logLik.jmodelTM.R

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akhikolla/testpackages

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logLik.jmodelTM.R

logLik.jmodelTM <- function (object, ...) { if (!inherits(object, "jmodelTM")) stop("Only used for 'jmodelTM' objects.\n") out <- object$logLik attr(out, "df") <- nrow(object$Vcov) attr(out, "n") <- object$n class(out) <- "logLik" out }

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/main.R

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deronaucoin/diabetespredict

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refs/heads/master

2020-04-19T04:51:49.160478

2014-06-09T22:56:55

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main.R

rm(list=ls()) library(RSQLite) library(randomForest) setwd("~/Dropbox/data/kaggle/practicefusion/mycode") makeconfusion <- function(y,pr){ prediction <- ifelse( pr> 0.50, TRUE, FALSE) confusion <- table(y,prediction) confusion <- cbind(confusion, c(1 - confusion[1,1]/sum(confusion[1,]), 1 - confusion[2,2]/(sum(confusion[2,])))) confusion <- as.data.frame(confusion) names(confusion) <- c('FALSE', 'TRUE', 'class.error') confusion } createSplits<-function(dataset,prop=.7,seed=42){ set.seed(seed) nobs <- nrow(dataset) trainidx <- sample(nrow(dataset), prop*nobs) testidx <- sample(setdiff(seq_len(nrow(dataset)), trainidx), (1-prop)*nobs) d.train <- dataset[trainidx,] d.test <- dataset[testidx,] output <- list(train=d.train,test=d.test) return (output) } n <- dbDriver("SQLite") con <- dbConnect(n, dbname="compData.db") sql = "select dmIndicator, avg(2014-YearOfBirth) as age, gender, avg(BMI) as BMI, avg(weight) as Weight, avg(height) as Height , avg(systolicBP) as SystBP, avg(DiastolicBP) as DiastBP , avg(RespiratoryRate) as RespRate, PatientGUID from training_patientTranscript where 1=1 and BMI < 100 and bmi > 0 group by PatientGuid" pdata <- dbGetQuery(con, sql) # data work pdata[pdata$Gender == "F",]$Gender <- 0 pdata[pdata$Gender == "M",]$Gender <- 1 pdata$Gender <- as.factor(pdata$Gender) pdata$dmIndicator <- as.factor(pdata$dmIndicator) #simple impute RespRate where = 0 impute <- function (a, a.impute){ ifelse ((a==0), a.impute, a) } imp <- lm(RespRate ~ ., data=pdata[,-c(ncol(pdata))], subset=pdata$RespRate>0) pred.imp <- predict (imp, pdata) pdata$RespRate <- impute (pdata$RespRate, pred.imp) #plot(pdata$BMI,pdata$age) attach(pdata) # 1) split random 20% undiagnosed out of total pool of undiagnosed set.seed(42) allun <- pdata[which(dmIndicator==0),] sun_idx <- sample(nrow(allun),nrow(pdata)*.2,replace=FALSE) unsam <- allun[sun_idx,] # remove our undiag patients from main data rundata <- pdata[!(PatientGuid %in% unsam$PatientGuid),] #remove the PGuids from each dataset, dont need anymore rundata$PatientGuid <- NULL unsam$PatientGuid <- NULL # 2) split remaining ~8000 into train and test d <- createSplits(rundata) train <- d$train test <- d$test # 3) build model.glm logit , and remove weight and height, correlated with BMI model.glm <- glm(dmIndicator ~ ., data=train[,-c(5,6)], family="binomial") pr = predict(model.glm,test,type="response") conf.glm <- makeconfusion(test$dmIndicator,pr) overall.error.glm <- (conf.glm[2,1]+conf.glm[1,2])/nrow(test) diab.error.glm <- conf.glm[2,3] conf.glm overall.error.glm diab.error.glm # 4) build model.rf randomForest model.rf <- randomForest(y=as.factor(train$dmIndicator), x=train[,-1],ntree=1000, mtry=2, importance=TRUE, na.action=na.roughfix, replace=FALSE ) pr <- predict(model.rf,test,type="prob") conf.rf <- makeconfusion(test$dmIndicator,pr[,2]) diab.error.rf <- conf.rf[2,3] overall.error.rf <- (conf.rf[2,1]+conf.rf[1,2])/nrow(test) conf.rf overall.error.rf diab.error.rf # 5) pick best by diab class error if (diab.error.glm > diab.error.rf){ print ("RF won") winmodel = model.rf prtype = "prob" winprederror = diab.error.rf } else { print ("GLM won") winmodel = model.glm prtype = "response" winprederror = diab.error.glm } # 6) score our undiagnosed dataset and get predicted probabilities probs2 <- ifelse(predict(winmodel,unsam,type=prtype)[,2]>= 0.50, TRUE, FALSE) summary(probs2) # 7) calculate proportion above .5 as likely undiagnosed. pred_diab <- sum(probs2)/length(probs2) print(paste("Predicted proportion having diabetes:",round(pred_diab,3))) #adjust for prediction error of model print(paste("Adjusted proportion for model error:",round(pred_diab*(1-winprederror),3)))

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/res/PHA-PLA.r

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PHA-PLA.r

| pc = 0xc001 | a = 0x05 | x = 0x00 | y = 0x00 | sp = 0x00fd | p[NV-BDIZC] = 00110100 | | pc = 0xc003 | a = 0x05 | x = 0x00 | y = 0x00 | sp = 0x00fc | p[NV-BDIZC] = 00110100 | mem[0x01fd] = 0x05 | | pc = 0xc004 | a = 0x00 | x = 0x00 | y = 0x00 | sp = 0x00fc | p[NV-BDIZC] = 00110110 | | pc = 0xc006 | a = 0x05 | x = 0x00 | y = 0x00 | sp = 0x00fd | p[NV-BDIZC] = 00110100 | mem[0x01fd] = 0x05 | | pc = 0xc007 | a = 0x05 | x = 0x00 | y = 0x00 | sp = 0x00fd | p[NV-BDIZC] = 00110111 | | pc = 0xc009 | a = 0x05 | x = 0x00 | y = 0x00 | sp = 0x00fd | p[NV-BDIZC] = 00110111 |

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/rate_functions.R

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inferentialist/hlc-returns

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rate_functions.R

quoted_rate_f = function(xdf) { rval = 1 + median(xdf$int_rate) / 100 return(rval) } ideal_static_rate_f = function(xdf) { quoted_rate = quoted_rate_f(xdf) monthly.rate = (quoted_rate -1) / 12 ## Assume funded_amnt is $1 coup = monthly.rate * (1+monthly.rate)^36 / ( (1+monthly.rate)^36 - 1) rval = (coup * 36)^(1/3) return(rval) } ideal_managed_rate_f = function(xdf) { quoted_rate = quoted_rate_f(xdf) monthly.rate = (quoted_rate -1) / 12 ## Assume funded_amnt is $1 coup = monthly.rate * (1+monthly.rate)^36 / ( (1+monthly.rate)^36 - 1) ## Reinvesting rate, no defaults k = 36:0 Tk = coup / monthly.rate + (1+monthly.rate)^k*(1-coup/monthly.rate) v = c(1, coup*(coup+1)^(0:35)) rval = (Tk %*% v)^(1/3) return(rval) } hist_static_rate_f = function(xdf) { rval =mean(xdf$total_pymnt / xdf$funded_amnt)^(1/3) return(rval) } my.lag = function(z, k = 1) { lz = length(z) z = c(rep(NA,k), z) z = z[1:lz] return(z) } hist_managed_rate_f = function(xdf) { coup = xdf$installment / xdf$funded_amnt monthly.rate = xdf$int_rate / 100 / 12 k = 0:36 prncp.remaining = coup / monthly.rate + exp(log(1+monthly.rate) %o% k)*(1-coup/monthly.rate) sched.coupons = matrix(rep(coup, times=37), nr=nrow(prncp.remaining), nc=37) couponpay.mask = matrix(1, nr=nrow(prncp.remaining), nc=length(k)) couponpay.mask[,1] = 0 prepay.mask = matrix(0, nr=nrow(prncp.remaining), nc=length(k)) ## update masks to reflect the historical activity for(i in which(xdf$time<36)) { time = xdf$time[i] status = xdf$status[i] tidx = time + 1 if(status == TRUE) { ## default at k[tidx], no payments from here on out couponpay.mask[i,tidx:37] = 0 } else { ## prepay at k[tidx], i.e. lump sum, but no future coupon payments couponpay.mask[i,(tidx+1):37] = 0 prepay.mask[i,tidx] = 1 } } ## construct the payout matrix cashflow.matrix = sched.coupons * couponpay.mask + prncp.remaining * prepay.mask n.loans = nrow(xdf) weights = rep(1/n.loans, n.loans) ## portfolio & reinvestment payouts at time k payout.k = t(weights %*% cashflow.matrix) payout.k[1,1] = NA for(cc in 2:36) { cash.avail = apply(payout.k, 1, sum) next.col = my.lag(payout.k[,1] * cash.avail[cc], k=(cc-1)) payout.k = cbind(payout.k, next.col) } ## Multiply the residual portfolio value at time k by the weights of the ## cash reinvested at time k to get the total portfolio value cash.reinvested.k = apply(payout.k, 1, sum, na.rm=TRUE) cash.reinvested.k[1] = 1 # hist.prncp.remaining = rev(weights %*% (prncp.remaining * couponpay.mask)) hist.prncp.remaining = rev(apply(prncp.remaining * couponpay.mask,2, function(x) pmax(0,x) %*% weights)) hist.prncp.remaining[37] = 1 rval = (cash.reinvested.k %*% hist.prncp.remaining)^(1/3) return(rval) } ## super simple test case example ## z = data.frame( ## funded_amnt = 1, ## int_rate = 7.88, ## time = 36, ## status = FALSE, ## installment = 0.5733334, ## total_pymnt = 36*0.5733334, ## total_rec_prncp = 1 ## ) ## hist_managed_rate_f(z) == ideal_managed_rate_f(z) == 1.081709

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/mapa2.R

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LucianoAndrian/CC

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refs/heads/master

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2019-12-02T22:06:23

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mapa2.R

mapa2 = function(lista, titulo1, nombre, label){ library(maps) require(fields) require(mapdata) library(ggplot2) library(metR) library(RColorBrewer) library(mapproj) titulo = c("DJF", "MAM", "JJA", "SON") for(i in 1:4){ value = array(lista[[i]]*mask, dim = 23*30) data = matrix(data = NA, nrow=23*30, ncol = 3) l=0 while(l<23*30){ data[seq(l:l+23),1]<-lon l=l+23 } for(j in 1:30){ lat_v = array(lat[j],dim=23) data[(23*j-22):(j*23),2]<-lat_v } data[,3]<-value error<-as.data.frame(data) colnames(error)<-c("lon", "lat", "rx5") error[which(error$lon>180),][,1]<-error[which(error$lon>180),][,1]-360 mapa <- map_data("world", regions = c("Brazil", "Uruguay", "Argentina", "French Guiana", "Suriname", "Colombia", "Venezuela", "Bolivia", "Ecuador", "Chile", "Paraguay", "Peru", "Guyana", "Panama", "Costa Rica", "Nicaragua"), colour = "black") g <- ggplot() + theme_minimal()+ xlab("Longitud") + ylab("Latitud") + theme(panel.border = element_blank(), panel.grid.major = element_line(colour = "grey"), panel.grid.minor = element_blank())+ #geom_tile(data=error,aes(x = lon, y= lat,fill = rx5),alpha=1, na.rm = T) geom_contour_fill(data = error, aes(x = lon, y= lat, z = rx5), alpha = 1, na.fill= (-10000))+ scale_fill_gradientn(limits=c(-150,150),name=label,colours=(brewer.pal(n=11,"Spectral")),na.value = "white")+ geom_polygon(data=mapa, aes(x=long,y=lat, group =group),fill = NA, color = "black") +#coord_map("stereographic", orientation = c(-35, -56, 0))+ ggtitle(paste(titulo1, " - " , titulo[i], sep = ""))+ scale_x_continuous(limits = c(-90, -30))+ scale_y_continuous(limits = c(-60, 15)) + theme(axis.text.y = element_text(size=14), axis.text.x = element_text(size=14), axis.title.y = element_text(size=14), axis.title.x = element_text(size=14), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), panel.border = element_rect(colour = "black", fill=NA, size=3), panel.ontop = TRUE, plot.title = element_text(hjust=0.5)) ggsave(paste("/home/auri/Facultad/Materias/Cambio_climatico/Tp_final/salidas/",nombre, "_", titulo[i], ".jpg",sep =""), plot = g, width = 15, height = 15 , units = "cm") } }

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/man/InverseGWishartPriorFragment.Rd

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jatotterdell/varapproxr

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InverseGWishartPriorFragment.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/RcppExports.R \name{InverseGWishartPriorFragment} \alias{InverseGWishartPriorFragment} \title{Inverse G-Wishart prior fragment update} \usage{ InverseGWishartPriorFragment(G, xi, Lambda) } \arguments{ \item{G}{The graph matrix} \item{xi}{The prior shape} \item{Lambda}{The prior symmetric positive definite matrix} } \description{ Inverse G-Wishart prior fragment update }

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bandeiras.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils-data.R \docType{data} \name{bandeiras} \alias{bandeiras} \title{bandeiras} \format{ An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 195 rows and 3 columns. } \usage{ bandeiras } \description{ bandeiras } \keyword{datasets}

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/Lab Week 4-2 (Data Frames).R

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LouisJBooth/R

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2020-04-17T15:50:57.087680

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Lab Week 4-2 (Data Frames).R

mtcars head(mtcars) tail(mtcars) str(mtcars) lm(mtcars$mpg ~ mtcars$cyl + mtcars$hp) name <- c("Apple", "MS", "Google", "Honda", "GM", "Volks", "Hyundai", "Amazon") type <- c("IT", "IT", "IT", "Auto", "Auto", "Auto", "Auto", "IT") stock <- c(165.5, 55.48, 1119.20, 36.16, 41, 172.06, 162.5, 1429.95) US <- c(TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE) portfolio <- data.frame(name, type, stock, US) rm(name, type, stock, US) str(portfolio) portfolio[portfolio$name=="Google", c(FALSE, FALSE, TRUE, FALSE)] portfolio[portfolio$name=="Google",] portfolio[c(1:5), c(FALSE, FALSE, TRUE, FALSE)] portfolio[portfolio$type=="IT",] subset(portfolio, subset = stock < stock[name=="Apple"]) portfolio[portfolio$stock < portfolio$stock[portfolio$name == "Apple"],] rank <- order(portfolio$stock, decreasing = TRUE) portfolio[rank,]

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/after_renewal/CREATE/CREATE_P50DATABASENAMES.R

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bonnfire/P50

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CREATE_P50DATABASENAMES.R

## CREATE P50 DATABASE NAMES shipments <- list("Meyer" = WFU_Meyer_excel_orig_test_df, "Richards" = WFU_Jerry_excel_orig_test_df, "Chen" = WFU_Chen_excel_orig_test_df) shipments_p50_df <- shipments %>% lapply(., function(x){ x <- x %>% mutate_all(as.character) x$cohort <- ifelse(grepl("#", x$cohort), stringr::str_match(x$cohort, "#(\\d+).*?")[,2], x$cohort) x$cohort <- ifelse(nchar(x$cohort) > 1, x$cohort, gsub('([[:digit:]]{1})$', '0\\1', x$cohort)) # add leading zeroes when necessary x$litternumber = as.numeric(x$litternumber) x$littersize = as.numeric(x$littersize) return(x) }) %>% rbindlist(id = "p50", fill = T, use.names = T)

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/data/genthat_extracted_code/expm/examples/expm.Higham08.Rd.R

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surayaaramli/typeRrh

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refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

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expm.Higham08.Rd.R

library(expm) ### Name: expm.Higham08 ### Title: Matrix Exponential [Higham 2008] ### Aliases: expm.Higham08 ### Keywords: algebra math ### ** Examples ## The *same* examples as in ../expm.Rd {FIXME} -- x <- matrix(c(-49, -64, 24, 31), 2, 2) expm.Higham08(x) ## ---------------------------- ## Test case 1 from Ward (1977) ## ---------------------------- test1 <- t(matrix(c( 4, 2, 0, 1, 4, 1, 1, 1, 4), 3, 3)) expm.Higham08(test1) ## [,1] [,2] [,3] ## [1,] 147.86662244637000 183.76513864636857 71.79703239999643 ## [2,] 127.78108552318250 183.76513864636877 91.88256932318409 ## [3,] 127.78108552318204 163.67960172318047 111.96810624637124 ## -- these agree with ward (1977, p608) ## ---------------------------- ## Test case 2 from Ward (1977) ## ---------------------------- test2 <- t(matrix(c( 29.87942128909879, .7815750847907159, -2.289519314033932, .7815750847907159, 25.72656945571064, 8.680737820540137, -2.289519314033932, 8.680737820540137, 34.39400925519054), 3, 3)) expm.Higham08(test2) expm.Higham08(test2, balancing = FALSE) ## [,1] [,2] [,3] ##[1,] 5496313853692405 -18231880972009100 -30475770808580196 ##[2,] -18231880972009160 60605228702221760 101291842930249376 ##[3,] -30475770808580244 101291842930249200 169294411240850880 ## -- in this case a very similar degree of accuracy. ## ---------------------------- ## Test case 3 from Ward (1977) ## ---------------------------- test3 <- t(matrix(c( -131, 19, 18, -390, 56, 54, -387, 57, 52), 3, 3)) expm.Higham08(test3) expm.Higham08(test3, balancing = FALSE) ## [,1] [,2] [,3] ##[1,] -1.5096441587713636 0.36787943910439874 0.13533528117301735 ##[2,] -5.6325707997970271 1.47151775847745725 0.40600584351567010 ##[3,] -4.9349383260294299 1.10363831731417195 0.54134112675653534 ## -- agrees to 10dp with Ward (1977), p608. ??? (FIXME) ## ---------------------------- ## Test case 4 from Ward (1977) ## ---------------------------- test4 <- structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-10, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0), .Dim = c(10, 10)) E4 <- expm.Higham08(test4) Matrix(zapsmall(E4)) S4 <- as(test4, "sparseMatrix") # some R based expm() methods work for sparse: ES4 <- expm.Higham08(S4, bal=FALSE) stopifnot(all.equal(E4, unname(as.matrix(ES4)))) ## NOTE: Need much larger sparse matrices for sparse arith to be faster! ## ## example of computationally singular matrix ## m <- matrix(c(0,1,0,0), 2,2) eS <- expm.Higham08(m) # "works" (hmm ...)

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/man/MFT.mean.Rd

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cran/MFT

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refs/heads/master

2021-10-22T16:23:32.490875

2019-03-11T19:42:55

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MFT.mean.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MFT.mean.R \name{MFT.mean} \alias{MFT.mean} \title{MFT.mean} \usage{ MFT.mean(X, autoset.H = TRUE, S = NULL, E = NULL, H = NULL, alpha = 0.05, method = "asymptotic", sim = 10000, rescale = FALSE, Q = NA, perform.CPD = TRUE, print.output = TRUE) } \arguments{ \item{X}{numeric vector, input sequence of random variables} \item{autoset.H}{logical, automatic choice of window size H} \item{S}{numeric, start of time interval, default: NULL, if NULL then 1 is chosen} \item{E}{numeric, end of time interval, default: NULL, if NULL then length(X) is chosen, needs E > S.} \item{H}{vector, window set H, all elements must be increasing, the largest element must be =< (T/2). H is automatically set if autoset.H = TRUE} \item{alpha}{numeric, in (0,1), significance level} \item{method}{either "asymptotic" or "fixed", defines how threshold Q is derived, default: "asymptotic", If "asymptotic": Q is derived by simulation of limit process L (Brownian motion); possible set number of simulations (sim), If "fixed": Q may be set manually (Q)} \item{sim}{integer, > 0, No of simulations of limit process (for approximation of Q), default = 10000} \item{rescale}{logical, if TRUE statistic G is rescaled to statistic R, default = FALSE} \item{Q}{numeric, rejection threshold, default: Q is simulated according to sim and alpha.} \item{perform.CPD}{logical, if TRUE change point detection algorithm is performed} \item{print.output}{logical, if TRUE results are printed to the console} } \value{ invisible \item{M}{test statistic} \item{Q}{rejection threshold} \item{method}{how threshold Q was derived, see 'Arguments' for detailed description} \item{sim}{number of simulations of the limit process (approximation of Q)} \item{rescale}{states whether statistic G is rescaled to R} \item{CP}{set of change points estmated by the multiple filter algorithm, increasingly ordered in time} \item{means}{estimated mean values between adjacent change points} \item{S}{start of time interval} \item{E}{end of time interval} \item{Tt}{length of time interval} \item{H}{window set} \item{alpha}{significance level} \item{perform.CPD}{logical, if TRUE change point detection algorithm was performed} \item{tech.var}{list of technical variables with processes X and G_ht or R_ht} \item{type}{type of MFT which was performed: "mean"} } \description{ The multiple filter test for mean change detection in time series or sequences of random variables. } \examples{ # Normal distributed sequence with 3 change points of the mean (at n=100, 155, 350) set.seed(50) X1 <- rnorm(400,0,1); X2 <- rnorm(400,3,1); X3 <- rnorm(400,5,1); X4 <- rnorm(600,4.6,1) X <- c(X1[1:100],X2[101:155],X3[156:350],X4[351:600]) mft <- MFT.mean(X) plot(mft) # Set additional parameters (window set) mft2 <- MFT.mean(X,autoset.H=FALSE,H=c(80,160,240)) plot(mft2) } \references{ Michael Messer, Stefan Albert and Gaby Schneider (2018). The multiple filter test for change point detection in time series. Metrika <doi:10.1007/s00184-018-0672-1> } \seealso{ \code{\link{plot.MFT}, \link{summary.MFT}, \link{MFT.rate}, \link{MFT.variance}, \link{MFT.peaks}} } \author{ Michael Messer, Stefan Albert, Solveig Plomer and Gaby Schneider }

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/R/timeEstimation.R

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sdcTools/sdcMicro

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timeEstimation.R

# TODO: Better estimate for the computation in a computer?? Author: Alexander Kowarik n: # nrow dataset nkey: number of key variables nmean: mean number of categories predictTime <- function(n, nkey, nmean) { coef() * (5.297e-12 * n^2 + 1.178e-06 * n * nkey + -2.973e-07 * n * nmean) #FreqCalc } coefTime <- function() { ## very very very simple coefficent for computation time in comparison to my computer t <- Sys.time() a <- stats::rnorm(5e+05) rm(a) max(0.1, as.numeric(Sys.time() - t)/0.06) } # genDat <- function(n=10,nkey=2,nmean=4){ nmeans <- # round(abs(rnorm(nkey-1,mean=nmean,sd=sqrt(nmean/4)))) nmeans <- # c(nmeans,nmean*nkey-sum(nmeans)) cols <- list() for(i in 1:nkey){ cols[[i]] <- # sample(1:nmeans[i],n,rep=TRUE) } d <- data.frame(do.call(cbind,cols)) colnames(d) <- # paste('key',1:nkey,sep='') return(d) } Code to estimate the coefficents coef() setwd() # require(sdcMicro) timeFreq <- function(n=10,nkey=2,nmean=4,REP=3){ dat <- # genDat(n=n,nkey=nkey,nmean=nmean) t <- vector() for(i in 1:REP){ tt <- Sys.time() f <- # freqCalc(dat,keyVars=1:nkey) t <- c(t,as.numeric(Sys.time()-tt)) } mean(t) } # timeFreq(n=8e6,nkey=6,nmean=5,REP=3) predictTime(n=8e6,nkey=6,nmean=5) REP <- 5 ns <- # c(1e2,1e3,5e3,1e4,3e4,1e5,4e5,1e6) nkeys <- c(3,5,7,10) nmeans <- c(2,4,6,20) simgrid <- # expand.grid(n=ns,nkey=nkeys,nmean=nmeans) ergsim <- # apply(simgrid,1,function(x)timeFreq(n=x[1],nkey=x[2],nmean=x[3],REP=REP)) ergsim1 <- # cbind(ergsim,simgrid) mod <- lm(ergsim~0+I(n^2)+n:nkey+n:nmean,data=ergsim1) summary(mod)

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/man/diversity_through_time.Rd

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thauffe/simGDM

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2020-07-27T12:48:58.927397

2020-02-13T15:11:33

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diversity_through_time.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/diversity_through_time.R \name{diversity_through_time} \alias{diversity_through_time} \title{Island diversity and events through time} \usage{ diversity_through_time(Elevation, Topography, Area, Vs, S0, E0, Ms, Iso_t, Imm, C0, Ana, Emi, Z, Ma, Msa, DivDep = TRUE, TargetEffect = FALSE, EnvirFilt = FALSE) } \arguments{ \item{Elevation}{Vector of island elevation through time} \item{Topography}{Vector of island topography through time} \item{Area}{Vector of island area through time} \item{Vs}{Probability of a vicariance event when topography equals 1} \item{S0}{The per species probability of adaptive speciation at maximum empty niche space} \item{E0}{The per-species probability of extinction when richness equals Kmax} \item{Ms}{Species richness of the mainland source pool} \item{Iso_t}{A descriptor of island isolation in arbitrary units} \item{Imm}{The probability of one species arriving from the mainland per time step} \item{C0}{The per-species probability of successful colonization at full empty niche space} \item{Ana}{The per-species probability of anagenesis per time step} \item{Emi}{Probability of recolonization of the source area per time step} \item{Z}{The exponent of the species-area relationship between the island and the source area} \item{Ma}{The realized size of the source area that provides new species} \item{Msa}{Power exponent controlling the abundance distribution of the source} \item{DivDep}{TRUE (default) Diversity dependence acting on immigration, extinction and in-situ speciation} \item{TargetEffect}{FALSE (default) No effect of island area on the immigration probability} \item{EnvirFilt}{FALSE (default) No effect of island elevation as proxy for habitat diversity on the immigration probability} } \value{ The output is a dataframe containing diversity and rates per time step. \item{Richness}{ Native species richness} \item{NonEndemics}{ Non-endemic species richness} \item{Endemics}{ Endemic species richness} \item{Kmax}{ Carrying capacity} \item{EndemicsClado}{ Endemic species evolved via in-situ cladogenesis} \item{EndemicsAna}{ Endemic species evolved via in-situ anagenesis} \item{Immigrants}{ Species immigrating at that time step to the island} \item{NewViaClado}{ Species evolved via in-situ cladogenesis at that time step} \item{NewViaNonadaptClado}{ Species evolved via non-adaptive in-situ cladogenesis at that time step} \item{NewViaAdaptClado}{ Species evolved via adaptive in-situ cladogenesis at that time step} \item{NewViaAna}{ Species evolved via in-situ anagenesis at that time step} \item{Extinctions}{ Species going extinct at that time step} \item{Emigrants}{ Species emigrating from the island to the mainland} } \description{ This function calculates the the number of colonization, speciation, extinction, and emmigration events through the trajectory of island evolution. This results in the total diversity of endemics and non-endemics at each moment in time. } \details{ Using the parameters in Table 1 of Borregard et al. (2016) with the \code{\link{Island}} dataset results in slightly different diversity trajectories than Borregard's Figure 4a. The example below recreates this figure as closely as possible, but uses a different size of the source area (Ma) and probability of anagenesis (Ana). Reasons for the differences could be the manually digitalized properties of the island itself because Figure S3 (Borregard et al. 2016) containes no y-axis scale. Moreover, the original R script of Borregard et al. (2016) hard-codes many parameters. Rates can be plotted in units of events per time (as in Borregard et al. 2016) or in units of events per lineage per time (as in many phylogenetic studies). This largely removes the hump-shaped extinction trajectory. } \examples{ # Reproduce Figure 4a (Borregaard et al., 2016) data(Island) Dtt <- diversity_through_time(Elevation = Island$Elevation, Topography = Island$Topography, Area = Island$Area, Vs = 0.0005, S0 = 0.0025, E0 = 0.001, Ms = 500, Iso_t = 0.3, Imm = 0.002, C0 = 0.5, Ana = 0.0003, Emi = 0.0002, Z = 0.25, Ma = 200, Msa = 0.3, DivDep = TRUE, TargetEffect = FALSE, EnvirFilt = FALSE) ColRich <- rgb(170, 99, 42, maxColorValue = 255) ColNonEnd <- rgb(249, 195, 91, maxColorValue = 255) ColEnd <- rgb(191, 11, 189, maxColorValue = 255) ColEx <- rgb(211, 0, 0, maxColorValue = 255) ColAna <- rgb(255, 144, 235, maxColorValue = 255) ColClado <- rgb(64, 197, 253, maxColorValue = 255) # Diversities par(las = 1, mar = c(4, 6, 0.1, 0.5)) plot(1:nrow(Dtt), Dtt[, "Kmax"], type = "l", col = "black", xlim = c(-1, 5000), ylim = c(0, 160), xaxs = "i", yaxs = "i", xaxt = "n", xlab = "Time (Ma)", ylab = "Species") axis(side = 1, at = c(0, 1000, 2000, 3000, 4000, 5000), labels = c(5, 4, 3, 2, 1, 0)) lines(1:nrow(Dtt), Dtt[, "Richness"], type = "l", col = ColRich) lines(1:nrow(Dtt), Dtt[, "NonEndemics"], type = "l", col = ColNonEnd) lines(1:nrow(Dtt), Dtt[, "Endemics"], type = "l", col = ColEnd) legend("topright", legend = c("Carrying capacity", "Total species richness", "Non-endemics", "Endemics"), col = c("black", ColRich, ColNonEnd, ColEnd), lty = 1, bty = "n", cex = 0.7) # Rates plot(1:nrow(Dtt), Dtt[, "Immigrants"], type = "l", col = ColNonEnd, xlim = c(-1, 5000), ylim = c(0, 0.15), xaxs = "i", yaxs = "i", xaxt = "n", xlab = "Time (Ma)", ylab = expression(atop(paste("Rate"), "(events"\%.\%"time step"^-1*")"))) axis(side = 1, at = c(0, 1000, 2000, 3000, 4000, 5000), labels = c(5, 4, 3, 2, 1, 0)) lines(1:nrow(Dtt), Dtt[, "Extinctions"], col = ColEx) lines(1:nrow(Dtt), Dtt[, "NewViaAna"], col = ColAna) lines(1:nrow(Dtt), Dtt[, "NewViaClado"], type = "l", col = ColClado) legend("topright", legend = c("Colonization", "Extinction", "Anagenesis", "Cladogenesis"), col = c(ColNonEnd, ColEx, ColAna, ColClado), lty = 1, bty = "n", cex = 0.7) # Divide by island richness and multiply by 1000 # to obtain rates in units of events per island species per 1 million years plot(1:nrow(Dtt), 1000 * Dtt[, "Immigrants"] / Dtt[, "Richness"], type = "l", col = ColNonEnd, xlim = c(-1, 5000), ylim = c(0, 1.3), xaxs = "i", yaxs = "i", xaxt = "n", xlab = "Time (Ma)", ylab = expression(atop(paste("Rate"), "(events"\%.\%"species"^-1\%.\%"my"^-1*")"))) axis(side = 1, at = c(0, 1000, 2000, 3000, 4000, 5000), labels = c(5, 4, 3, 2, 1, 0)) lines(1:nrow(Dtt), 1000 * Dtt[, "Extinctions"] / Dtt[, "Richness"], col = ColEx) lines(1:nrow(Dtt), 1000 * Dtt[, "NewViaAna"] / Dtt[, "Richness"], col = ColAna) lines(1:nrow(Dtt), 1000 * Dtt[, "NewViaClado"] / Dtt[, "Richness"], type = "l", col = ColClado) legend("topright", legend = c("Colonization", "Extinction", "Anagenesis", "Cladogenesis"), col = c(ColNonEnd, ColEx, ColAna, ColClado), lty = 1, bty = "n", cex = 0.7) # Figure 4 of Hauffe et al. TimeSteps <- 4000 X <- 1:TimeSteps Island <- 1/( 1+exp(-(0.0001 + 0.004*X[1:3000])) ) Island <- Island - min(Island) Island <- Island / max(Island) Island <- Island / 2 Island <- c(Island, Island[length(Island)] + Island[1:1000]) Clado <- 0.00007 DttTar <- diversity_through_time(Elevation = Island, Topography = Island, Area = Island, Vs = 0.9 * Clado, S0 = 0.1 * Clado, E0 = 0.00095, Ms = 300, Iso_t = 0.3, Imm = 0.00019, C0 = 1, Ana = 0.00034, Emi = 0, Z = 0.25, Ma = 200, Msa = 0.3, DivDep = FALSE, TargetEffect = TRUE, EnvirFilt = FALSE) # Plot island ontogeny plot(X, Island, type = "l", ylim = c(0, 1), xlim = c(-1, max(X)), xaxs = "i", yaxs = "i", xaxt = "n", xlab = "Time (Ma)", ylab = "Elevation Area Topography (\%)") axis(side = 1, at = c(0, 1000, 2000, 3000, 4000), labels = c(4, 3, 2, 1, 0)) # Diversities plot(1:nrow(DttTar), DttTar[, "Richness"], type = "l", col = ColRich, xlim = c(-1, max(X)), ylim = c(0, 80), xaxs = "i", yaxs = "i", xaxt = "n", xlab = "Time (Ma)", ylab = "Species") axis(side = 1, at = c(0, 1000, 2000, 3000, 4000), labels = c(4, 3, 2, 1, 0)) lines(1:nrow(DttTar), DttTar[, "NonEndemics"], type = "l", col = ColNonEnd) lines(1:nrow(DttTar), DttTar[, "EndemicsAna"], type = "l", col = ColAna) lines(1:nrow(DttTar), DttTar[, "EndemicsClado"], type = "l", col = ColClado) legend("topleft", legend = c("Total species richness", "Non-endemics", "Endemics evolved via cladogenesis", "Endemics via anagenesis"), col = c(ColRich, ColNonEnd, ColAna, ColClado), lty = 1, bty = "n", cex = 0.7) } \references{ Borregaard, M. K., T. J. Matthews and R. J. Whittaker (2016). The general dynamic model: towards a unified theory of island biogeography? Global Ecology and Biogeography, 25(7), 805-816. Hauffe, T., D. Delicado, R.S. Etienne and L. Valente (submitted). Lake expansion increases equilibrium diversity via the target effect of island biogeography } \author{ Torsten Hauffe }

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plot3.R

#plot the multiplt line graph with legend png("plot3.png") with(electricitydata,plot(datetime,submetering1,type="l",xlab=" ",ylab="Energy sub metering")) with(electricitydata,points(datetime,submetering2,type="l",col="red")) with(electricitydata,points(datetime,submetering3,type="l",col="blue")) legend("topright",col=c("black","red","blue"),lty=1, legend=c("sub_metering_1","sub_metering_2","sub_metering_3")) dev.off()

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/R_Geocoding_Census.R

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lewissearsiv/Metis_Proj1_MTA

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R_Geocoding_Census.R

library(dplyr) ## read in trunstile data for getting ids etc turns <- read.csv('http://web.mta.info/developers/data/nyct/turnstile/turnstile_200919.txt') #Base url for google maps api base <- "https://maps.googleapis.com/maps/api/geocode/json?address=" # Census api key cen_key <- '97d55623ee041618526d685d56a340986817ea2c' # Filter down data to more unique stations turns_filt <- turns %>% group_by(STATION,DIVISION) %>% summarize(all = sum(ENTRIES)) #add empty lat lon columns to fill turns_filt$LAT <- NA turns_filt$LON <- NA # iterate through stations and query the api for (i in 1:nrow(turns_filt)){ tryCatch({ # create an object to store the geo info geo <- fromJSON(URLencode(paste(base,paste(turns_filt$STATION[i],"Station",turns_filt$DIVISION[i],'Line New York'),"&key=",key))) turns_filt$LON[i] <- geo$results$geometry$location$lng[1] turns_filt$LAT[i] <- geo$results$geometry$location$lat[1] turns_filt$geo_type[i] <- geo$results$types[[1]] message(i) }, error=function(e){}) } # create empty columns for census geography ids, these are known as fips and are how you identify the location for # pulling in data turns_filt$State_FIPS <- NA turns_filt$County_FIPS <- NA turns_filt$Tract_FIPS <- NA # run through each row and query the census api to get fips codes based on latitude and longitute coordinates for (k in which(is.na(turns_filt$State_FIPS))){ geo <- fromJSON(paste0('https://geocoding.geo.census.gov/geocoder/geographies/coordinates?x=',turns_filt$LON[k],'&y=',turns_filt$LAT[k],'&benchmark=4&vintage=4')) turns_filt$State_FIPS[k] <- geo$result$geographies$`Census Tracts`$STATE turns_filt$County_FIPS[k] <- geo$result$geographies$`Census Tracts`$COUNTY turns_filt$Tract_FIPS[k] <- geo$result$geographies$`Census Tracts`$TRACT message(k) } # Then we pull in the data for all tracts in all counties in new york as one query cen_dat <- fromJSON(paste0('https://api.census.gov/data/2018/acs/acs5/subject?get=NAME,S0101_C05_001E,S0101_C01_001E,S2001_C02_011E,S2001_C02_012E,S2001_C01_002E,S2403_C01_001E,S2403_C01_012E,S2403_C01_017E&for=tract:*&in=state:36&in=county:*&key=',cen_key)) colnames(cen_dat) <- cen_dat[1,] cen_dat <- cen_dat[-1,] colnames(cen_dat)[2:9] <- c('Female_Population','Total_Population','P_75_100k','P_over_100k','median_income','tot_emp','emp_info','emp_prof') ##notes # populations above the age of 16 # Estimate!!Total!!Civilian employed population 16 years and over!!Professional, scientific, and management, and administrative and waste management services!!Professional, scientific, and technical services # cen_dat <- as.data.frame(cen_dat) cen_dat[,2:9] <- sapply(cen_dat[,2:9],as.numeric) cen_dat$p_f_pop <- cen_dat$Female_Population/cen_dat$Total_Population cen_dat$p_emp_info <- cen_dat$emp_info/cen_dat$tot_emp cen_dat$p_emp_prof <- cen_dat$emp_prof/cen_dat$tot_emp turns_filt$p_f_pop <- cen_dat$p_f_pop[match(paste0(turns_filt$State_FIPS,turns_filt$County_FIPS,turns_filt$Tract_FIPS),paste0(cen_dat$state,cen_dat$county,cen_dat$tract))] turns_filt$p_emp_info <- cen_dat$p_emp_info[match(paste0(turns_filt$State_FIPS,turns_filt$County_FIPS,turns_filt$Tract_FIPS),paste0(cen_dat$state,cen_dat$county,cen_dat$tract))] turns_filt$p_emp_prof <- cen_dat$p_emp_prof[match(paste0(turns_filt$State_FIPS,turns_filt$County_FIPS,turns_filt$Tract_FIPS),paste0(cen_dat$state,cen_dat$county,cen_dat$tract))] turns_filt$p_75_100k <- cen_dat$P_75_100k[match(paste0(turns_filt$State_FIPS,turns_filt$County_FIPS,turns_filt$Tract_FIPS),paste0(cen_dat$state,cen_dat$county,cen_dat$tract))]/100 turns_filt$p_over_100k <- cen_dat$P_over_100k[match(paste0(turns_filt$State_FIPS,turns_filt$County_FIPS,turns_filt$Tract_FIPS),paste0(cen_dat$state,cen_dat$county,cen_dat$tract))]/100 write.csv(turns_filt, 'geocoded_cen_dat.csv', row.names = F)

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JuanMatiasBraccini/Git_MACN

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Resample.R

#Age VS Length DATA=read.csv("C:/Matias/MACN/Data.csv") #bring in data #check data plot(DATA$Age,DATA$DW) #estimate parameters vbTypical <- DW~Linf*(1-exp(-K*(Age-to))) #growth function svTypical.VB = c(Linf=176,K=0.08,to=-1.8) #initial parameter values fit.vonb <- nls(vbTypical,data=DATA,start=svTypical.VB) Coef.fit=coef(fit.vonb) Coefs.fit.and.SE=summary(fit.vonb)$parameters[,1:2] Model.AIC=AIC(fit.vonb) #extract model AIC if comparing to other models (e.g. Gompertz) #Predict model NEW.data=data.frame(Age=1:25) #new data, in this case a vector of ages Preds=predict(fit.vonb,newdata=NEW.data) plot(DATA$Age,DATA$DW,ylab="Disc width",xlab="Age") lines(NEW.data$Age,Preds,col=2) #Maturity VS Length Dat=read.csv("C:/Matias/MACN/Maturity_whisk_Simpfendorferetal1998.csv") mod <- nls(Prop.Mat~1/(1+exp(-log(19)*(FL-p50)/(p95-p50))), start=c(p50=115, p95=120), data=Dat) fit=summary(mod)$parameters[,1:2] fn.logis=function(dat,p50,p95) 1/(1+exp(-log(19)*(dat-p50)/(p95-p50))) plot(Dat$FL,Dat$Prop.Mat,pch=19) SEQ=80:140 lines(SEQ,fn.logis(SEQ,fit[1,1],fit[2,1]),col=2)

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Auburngrads/text.analysis

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4-get_ngrams.R

#' Create data.frame of n-grams #' #' @param x A character string of text #' @param n Number of n-grams to get #' @param ... Additional arguments for \code{tau::textcnt} #' #' @importFrom tau textcnt #' #' @export get_ngrams <- function(x, n,...) { df <- data.frame(count = unclass(tau::textcnt(x, method = "string", n = n,...))) df$text <- rownames(df) rownames(df) <- NULL return(df[order(df[,1], decreasing = T),]) }

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126.Rd.R

library(SoilR) ### Name: fW.Candy ### Title: Effects of moisture on decomposition rates according to the ### Candy model ### Aliases: fW.Candy ### ** Examples th=seq(0,1,0.01) xi1=fW.Candy(theta=th,PV=0.4) xi2=fW.Candy(theta=th,PV=0.6) xi3=fW.Candy(theta=th,PV=0.8) plot(th,xi1,type="l",main="Effects of soil water content and pore volume on decomposition rates", xlab="Volumetric soil water content (cm3 cm-3)",ylab=expression(xi)) lines(th,xi2,col=2) lines(th,xi3,col=3) legend("bottomright",c("Pore volume = 0.4","Pore volume = 0.6", "Pore volume = 0.8"),lty=1,col=1:3)

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heatmapSF.R

## Load required packages suppressMessages(library("gplots")) suppressWarnings(suppressMessages(library("ComplexHeatmap"))) suppressMessages(library("circlize")) suppressMessages(library("viridis")) suppressMessages(library("dplyr")) suppressMessages(source('/usr/local/bin/scripts/supp_fns.R')) ## Enable stack trace #options(error = function() traceback(2)) heatmapSF_plot <- function(rpkmTable,annot, num_kmeans_clust, sf_out_dir) { ## Read in and Log Transform Data Exp_data = log2(rpkmTable+1) ## Make SF (sample-feature) heatmap Exp_data <- apply(Exp_data,1,function(x) zscore(x)) ## Set breaks for data for color scale ma_nolym <- max(Exp_data) mi_nolym <- min(Exp_data) my.breaks_nolym<-c(-3,seq(-2.5,2.5,length.out=99),3) ## Data needs to be transposed for heatmap Exp_data = t(as.matrix(Exp_data)) ## Make annotation bars ha1 <- make_complexHeatmap_annotation(annot) ## Calc. spearman correlation and use values for column clustering cordata <- cor(Exp_data, method="spearman") coldistance = dist(t(as.matrix(cordata)), method = "euclidean") colcluster = hclust(coldistance, method = "ward.D2") ## Turn on rownames if less than 100 genes row_name_param = FALSE if (nrow(Exp_data) <= 100) {row_name_param = TRUE} ## Determine type of plot, and plot if (is.numeric(num_kmeans_clust) == TRUE) {kmparam = num_kmeans_clust} if (is.character(num_kmeans_clust) == TRUE) {kmparam = as.numeric(unlist(strsplit(num_kmeans_clust,",")))} pdf(file = paste(sf_out_dir, "/heatmapSF.pdf", sep=""), width=11,height=8.5) png_count = 0 for (i in 1:length(kmparam)) { ## If kmparam is 0, use hierarichical if (kmparam[i] == 0) { rowdistance = dist(Exp_data, method = "euclidean") rowcluster = hclust(rowdistance, method = "ward.D2") rowclusterparam = rowcluster hmdata = Exp_data column_title_param = "Sample-Feature Hierarchical Clustering" } ## If kmparam is not 0, use kmeans if (kmparam[i] != 0) { #if (kmparam[i] == 1) {kmparam[i] = 2} km1 = kmeans(Exp_data, centers=kmparam[i]) kmclust = km1$cluster kmclustsort = sort(kmclust) ind = match(names(kmclustsort), rownames(Exp_data)) hmdata = Exp_data[ind,] rowclusterparam = FALSE column_title_param = paste("Sample-Feature Kmeans ", kmparam[i], " Clustering", sep="") } mapplot <- Heatmap(hmdata, col = colorRamp2(my.breaks_nolym, bluered(101), transparency = 0), #heatmap_legend_param = list(title = "exp. level"), column_title = column_title_param, show_row_names = row_name_param, show_column_names = TRUE, #row_names_max_width = unit(3, "mm"), row_names_gp = gpar(fontsize = 6), column_names_gp = gpar(fontsize = 8), cluster_rows = rowclusterparam, cluster_columns = colcluster, show_heatmap_legend = FALSE, #row_dend_width = unit(5, "mm"), #width=unit(60,"cm"), top_annotation=ha1, ) ## First drawing into png png_count = png_count+1 png(file=paste(sf_out_dir, "/heatmapSF_",png_count,".png",sep=""), width = 8, height = 8, unit="in",res=300) draw(mapplot) for(an in colnames(annot[1:ncol(annot)])) { decorate_annotation(an, {grid.text(an, unit(1, "npc") + unit(2, "mm"), 0.5, default.units = "npc", just = "left", gp=gpar(fontsize=6), check=TRUE) grid.text(an, unit(0, "npc") - unit(2, "mm"), 0.5, default.units = "npc", just = "right", gp=gpar(fontsize=6), check=TRUE) }) } junk <- dev.off() ## Repeated to get into the pdf draw(mapplot) for(an in colnames(annot[1:ncol(annot)])) { decorate_annotation(an, {grid.text(an, unit(1, "npc") + unit(2, "mm"), 0.5, default.units = "npc", just = "left", gp=gpar(fontsize=6), check=TRUE) grid.text(an, unit(0, "npc") - unit(2, "mm"), 0.5, default.units = "npc", just = "right", gp=gpar(fontsize=6), check=TRUE) }) } if (i == 1) { if (kmparam[1] == 0) { output<-Exp_data output<-output[unlist(row_order(mapplot)), unlist(column_order(mapplot))] write.table(output, file=paste(sf_out_dir, "/heatmapSF.txt",sep=""), quote=F, col.names = NA, sep="\t") } if (kmparam[1] != 0) { output = cbind(hmdata,kmclustsort) output = output[,unlist(column_order(mapplot))] write.table(output, file=paste(sf_out_dir, "/heatmapSF.txt",sep=""), quote=F, col.names = NA, sep="\t") } } } junk <- dev.off() } ## Read in arguments args <- commandArgs( trailingOnly = TRUE ) rpkmFile=args[1] annotFile=args[2] num_kmeans_clust=args[3] sf_out_dir=args[4] rpkmTable <- read.csv(rpkmFile, header=T, check.names=F, row.names=1, stringsAsFactors=FALSE, dec='.') annot <- read.csv(annotFile, sep=",", header=T, row.names=1, stringsAsFactors=FALSE, check.names=F, comment.char='#') if(any(grepl("comp_*", colnames(annot)))) { annot <- annot[, !grepl('Pair', colnames(annot)), drop = F] annot <- annot[, !grepl('comp_*', colnames(annot)), drop = F] } ## Run the function heatmapSF_plot(rpkmTable,annot, num_kmeans_clust, sf_out_dir)

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/AoC_day05.R

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jmoggridge/AdventofCode2020

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AoC_day05.R

# --- Day 5: Binary Boarding --- # You board your plane only to discover a new problem: you dropped your boarding pass! You aren't sure which seat is yours, and all of the flight attendants are busy with the flood of people that suddenly made it through passport control. # # You write a quick program to use your phone's camera to scan all of the nearby boarding passes (your puzzle input); perhaps you can find your seat through process of elimination. # # Instead of zones or groups, this airline uses binary space partitioning to seat people. A seat might be specified like FBFBBFFRLR, where F means "front", B means "back", L means "left", and R means "right". # # The first 7 characters will either be F or B; these specify exactly one of the 128 rows on the plane (numbered 0 through 127). Each letter tells you which half of a region the given seat is in. Start with the whole list of rows; the first letter indicates whether the seat is in the front (0 through 63) or the back (64 through 127). The next letter indicates which half of that region the seat is in, and so on until you're left with exactly one row. # # For example, consider just the first seven characters of FBFBBFFRLR: # # Start by considering the whole range, rows 0 through 127. # F means to take the lower half, keeping rows 0 through 63. # B means to take the upper half, keeping rows 32 through 63. # F means to take the lower half, keeping rows 32 through 47. # B means to take the upper half, keeping rows 40 through 47. # B keeps rows 44 through 47. # F keeps rows 44 through 45. # The final F keeps the lower of the two, row 44. # The last three characters will be either L or R; these specify exactly one of the 8 columns of seats on the plane (numbered 0 through 7). The same process as above proceeds again, this time with only three steps. L means to keep the lower half, while R means to keep the upper half. # # For example, consider just the last 3 characters of FBFBBFFRLR: # # Start by considering the whole range, columns 0 through 7. # R means to take the upper half, keeping columns 4 through 7. # L means to take the lower half, keeping columns 4 through 5. # The final R keeps the upper of the two, column 5. # So, decoding FBFBBFFRLR reveals that it is the seat at row 44, column 5. # # Every seat also has a unique seat ID: multiply the row by 8, then add the column. In this example, the seat has ID 44 * 8 + 5 = 357. # # Here are some other boarding passes: # # BFFFBBFRRR: row 70, column 7, seat ID 567. # FFFBBBFRRR: row 14, column 7, seat ID 119. # BBFFBBFRLL: row 102, column 4, seat ID 820. # As a sanity check, look through your list of boarding passes. What is the highest seat ID on a boarding pass? # boarding_passes <- read_lines('input5.txt') passes <- data.frame( rw = str_sub(boarding_passes, end = 7), clm = str_sub(boarding_passes, start = 8) ) %>% mutate( # convert to binary, then to integer for row and col rw = str_replace_all(rw, 'B', '1'), rw = str_replace_all(rw, 'F', '0'), rw = strtoi(rw, base = 2), clm = str_replace_all(clm, 'R', '1'), clm = str_replace_all(clm, 'L', '0'), clm = strtoi(clm, base = 2), # compute seat number seatnum = rw * 8 + clm ) passes # max seat number is solution max(passes$seatnum) # # --- Part Two --- # Ding! The "fasten seat belt" signs have turned on. Time to find your seat. # # It's a completely full flight, so your seat should be the only missing boarding pass in your list. However, there's a catch: some of the seats at the very front and back of the plane don't exist on this aircraft, so they'll be missing from your list as well. # # Your seat wasn't at the very front or back, though; the seats with IDs +1 and -1 from yours will be in your list. # # What is the ID of your seat? # passes %>% # sort by seatnumber arrange(seatnum) %>% # find distance to nearest neighbor, identify gap mutate( closest1 = lag(seatnum), closest2 = lead(seatnum), span = seatnum - closest1 + closest2 - seatnum ) %>% # find the two seats adjacent to the gap and take their mean filter(!span==2) %>% summarise(myseat = mean(seatnum)) %>% print

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/man/estimate_diameter_range.Rd

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dami82/cellTracker

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refs/heads/master

2020-09-28T14:02:17.651596

2020-02-19T05:51:37

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estimate_diameter_range.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cellTracker_core.R \name{estimate_diameter_range} \alias{estimate_diameter_range} \title{Detect Paricle Diameters in a Numeric matrix} \usage{ estimate_diameter_range(x, px.margin = 2, quantile.val = 0.99, plot = TRUE) } \arguments{ \item{x}{numeric matrix corresponding to a digital image} \item{px.margin}{integer, number of pixels used as margin while searching/filtering for neighboring particles} \item{quantile.val}{numeric, must be bigger than 0 and smaller than 1. Quantile for discriminating signal and background; only pixels with intensity higher than the corresponding quantile will count as signal while estimating particle diameters} \item{plot}{logial, shall a histogram of the distribution of diameters be shown} } \value{ list including summary stats and data about the particles found in the image } \description{ Estimates the diameters of particles in a numeric matrix } \examples{ a <- cbind(c(1, 1, 1, 0, 0, 0, 0, 0, 1, 1), c(1, 1, 0, 0, 0, 0, 0, 0, 1, 1), c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 1, 1, 0, 0, 0, 0), c(0, 0, 0, 1, 1, 1, 0, 0, 0, 0)) graphics::image(a) b <- estimate_diameter_range(a) print(b$estim.cell.num) print(b$raw) } \references{ \url{https://www.data-pulse.com/dev_site/celltracker/} } \author{ Damiano Fantini, \email{damiano.fantini@gmail.com} }

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/quiz.R

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H3runug/Rherunug

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refs/heads/master

2020-04-24T10:12:33.204611

2019-02-21T14:35:40

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quiz.R

# nama document : basic R.R # penulis : Heru Nugroho # Deskripsi : lat dasar2 R v_hobi_saya = c("sepak bola", "menulis","otomotif") v_hobi_saya matrix_ganjil = matrix(c(1,3,5,7,9,11), byrow = T,nrow = 2) matrix_ganjil df_harga_makanan = data.frame( makanan = c("Pizza","Bakso","Roti","Mie Instan"), harganya = c(100000,25000,10000,3000)) df_harga_makanan list_saya= list(v_hobi_saya,matrix_ganjil,df_harga_makanan) list_saya

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/man/Seed.Rd

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cran/MLGdata

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refs/heads/master

2022-12-19T06:04:50.351737

2020-09-30T07:50:12

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Seed.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{Seed} \alias{Seed} \title{Seed germination} \format{ A data frame with 20 observations on the following 2 variables \describe{ \item{\code{fert}}{level of fertilizer used} \item{\code{x}}{indicator of germination of the seed(\code{1}, yes; \code{0}, no)} } } \source{ Salvan, A., Sartori, N., Pace, L. (2020). \emph{Modelli lineari generalizzati}. Milano: Springer-Verlag. } \usage{ Seed } \description{ This is an artificial dataset representing an experiment relating probability of germination of seeds to the level of fertilizer used. } \keyword{datasets}

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/이상치분석/Func.trans/실습.R

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bohyunshin/ML

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실습.R

library(HighDimOut) data(TestData) mydata = TestData # ordinary ABOD abod.result = Func.ABOD(mydata[,-3],basic=T,perc=1) mydata[order(abod.result,decreasing=F),] # abod는 값이 작을 수록 outlier라고 판단. # Fast ABOD # Feature Bagging fbod.result = Func.FBOD(mydata[,-3],iter=10,k.nn=5) mydata[order(fbod.result,decreasing=T),] # fbod는 값이 클 수록 outlier라고 판단. # Func.SOD sod.result = Func.SOD(mydata[,-3],k.nn=10,k.sel=5,alpha=0.8) mydata[order(sod.result,decreasing=T),] # sod는 값이 클 수록 outlier라고 판단. # Transform outlier scores into range 0 and 1 trans.abod = Func.trans(abod.result,method="ABOD") trans.fbod = Func.trans(fbod.result,method="FBOD") trans.sod = Func.trans(sod.result,method="SOD") trans.merge = trans.abod + trans.fbod + trans.sod mydata[order(trans.merge,decreasing=T),]

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/prediction_lasso.R

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hnyang1993/UmbrellaAcademy

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2020-05-02T15:31:57.398931

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prediction_lasso.R

setwd("~/Desktop/BIOS 735/final_project") load("prenobns.RData") it_test = itoken(test_use$comment_text, preprocessor = prep_fun, tokenizer = tok_fun, ids = test_use$id, progressbar = TRUE) raw.test.dtm = create_dtm(it_test, vectorizer) pred <- predict(fit,raw.test.dtm, type = "class") error <- sum((as.vector(test_labels_use$toxic) - as.numeric(as.character(pred)))^2)

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/2017_Yosemite_ script.R

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shumengjun/2017_Carpentry_workshop

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2021-01-22T17:38:36.451457

2017-08-18T23:07:13

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2017_Yosemite_ script.R

## Mengjun Shu ## 2017 Yosemite workshop ##08/18/2017 ## download.file("https://ndownloader.figshare.com/files/2292169", ## "data/portal_data_joined.csv") surveys <- read.csv("data/portal_data_joined.csv") ## Explore data head(surveys) tail(surveys) tail(surveys, 12) str(surveys) summary(surveys) dim(surveys) nrow(surveys) ncol(surveys) names(surveys) head(surveys$weight) tail(surveys$weight) str(surveys$weight) summary(surveys$weight) plot(surveys$year, surveys$weight) plot(surveys$hindfoot_length, surveys$weight) summary(surveys$month) hist(surveys$month) summary(surveys$taxa) levels(surveys$taxa) nlevels(surveys$taxa) hist(survey) class(surveys$taxa) table(surveys$taxa) ## subset in base R ---- ## [rows, colums] ##return all the columns surveys[surveys$genus == 'Ammodramus', ] ##return part the columns surveys[surveys$genus == 'Ammodramus', c('record_id', 'month', 'weight')] #Different ways to figure out how many month is January or February nrow(surveys[surveys$month < 3, ]) surveys[surveys$month == 1, ] table(surveys$month < 3) month_new <- surveys[surveys$month < 3, ] length(which(surveys$month < 3))