version 0.0.7

ChangeLog

@@ -1,5 +1,15 @@
 RchivalTag versions
 
+upcoming:
+	- add argument "show_time_limits" in plot_TS/DepthTS
+
+RchivalTag_0.0.7 -- January 29, 2018 (Robert Bauer)
+        - interp-function from the akima package replaced in internal calls by the approx function of the stats-package
+	- added function resample_TS
+
+RchivalTag_0.0.6 -- June 9, 2017 (Robert Bauer)
+	- small bug fixed in read_histos
+
 RchivalTag_0.0.5 -- June 8, 2017 (Robert Bauer)
 	- adding average and standard deviation estimation for depth and temperature values insisde the read_histos and ts2histos functions
 

DESCRIPTION

@@ -1,17 +1,17 @@
 Package: RchivalTag
 Type: Package
 Title: Analyzing Archival Tagging Data
-Version: 0.0.5
-Date: 2017-06-08
+Version: 0.0.7
+Date: 2018-05-25
 Author: Robert Bauer
-Maintainer: Robert Bauer <robert.bauer@ird.fr>
+Maintainer: Robert Bauer <r.bauer@profish-technology.de>
 Description: A set of functions to generate, access and analyze standard data products from archival tagging data.
 Depends: R (>= 3.0.1)
-Imports: plyr, akima, maptools, graphics, stats, raster, rgeos, ncdf4,
-        maps, mapdata, grDevices, oceanmap, sp, methods, PBSmapping
-License: CC BY-NC-SA 4.0
+Imports: plyr, maptools, graphics, stats, raster, rgeos, ncdf4, maps,
+        mapdata, grDevices, oceanmap, sp, methods, PBSmapping
+License: GPL (>= 3)
 LazyLoad: yes
-Packaged: 2017-06-08 14:45:25 UTC; robert
+Packaged: 2018-05-29 20:13:18 UTC; robert
 Repository: CRAN
-Date/Publication: 2017-06-08 15:08:22 UTC
+Date/Publication: 2018-05-29 21:17:56 UTC
 NeedsCompilation: no

MD5

@@ -1,21 +1,22 @@
-945aa68c1b9d7a4d8ad943bc1eeb1e29 *ChangeLog
-0007eb65be4967d918c1e99192d06838 *DESCRIPTION
-8b43c3895e01857f02fedd5ed61ff8a3 *NAMESPACE
+48378a23996c5c36a7af63a4fe49b14a *ChangeLog
+ca252f705f96c14c96685bddfc3aa78e *DESCRIPTION
+b54e32662a12d2b81787a4f682f1c4d1 *NAMESPACE
 3e611abed9a9cac153c07f2681682a8e *R/bin_TempTS.r
 4496a3299a5b651e488ff184fb363094 *R/get_DaytTimeLimits.r
 5a9b700d9095f7dabdb593f7ceef78fc *R/get_thermalstrat.r
 9281c3eeba89e7956ccd608dbe37b449 *R/hidden_functions.r
-ac03eff18ad7382bce1a16ef3e447dfa *R/hist_tad.r
+5ac87505d4ce1fddd94ed63f22358e1a *R/hist_tad.r
 983d14437b0aabbcaba53a1669868b7d *R/image_TempDepthProfiles.r
-2488fc005f517758e20112941f5bbcaa *R/interpolate_TempDepthProfiles.r
-e28c2a2a8bd12c5abacc6c51f9eee033 *R/plot_TS.r
+f140d14958f35b369d985549faf09104 *R/interpolate_TempDepthProfiles.r
+4f23960900b703c431dce7bd4d234f41 *R/plot_TS.r
 2f9e8ba0d608fcfe6c2659144482860d *R/plot_errorbars.r
-a64d0ede8100a59f1bd71108932d4c30 *R/plot_geopos.r
-3cb18a2b337441ed7d8450742ac72ac8 *R/read_PDT.r
-e362f87ea5523a988fe8ced9fb0ed9bf *R/read_histos.r
-57d180cfc03aa9be75938c0f018ef197 *R/tad_summary.r
-425a0884decdb20bd58e6bc15eb63e1d *R/ts2histos.r
-4da50e452a8f5ed5e4bb6b99f6360aea *inst/doc/RchivalTag.pdf
+177d612d06254e3546611ba4ed9d40e7 *R/plot_geopos.r
+0eb2b0cf5f62cbcfa87ef5a481c9099f *R/read_PDT.r
+3fd728d011bc739fbf9f91b762a50e83 *R/read_histos.r
+bd7e8a9472e36c875630df2426efe181 *R/resample_TS.r
+d69209aa2ca0fc2b3ded519d17abaf67 *R/tad_summary.r
+04e6bb671d405ce429932e613c7ff71b *R/ts2histos.r
+c00e19b0796efdc9fb569743a307fee3 *inst/doc/RchivalTag.pdf
 5ed8535044dcec09af091dca0617ac41 *inst/example_files/104659-Histos.csv
 14919538772cfbcc8dde94b889ede6f9 *inst/example_files/104659-PDTs.csv
 ec53f903951050e6c4f96a0b7be076b8 *inst/example_files/104659-Series.csv
@@ -28,15 +29,16 @@ ec53f903951050e6c4f96a0b7be076b8 *inst/example_files/104659-Series.csv
 8fbcb76a6fac0d2643d6aa7c0db38d33 *man/classify_DayTime.Rd
 4b01620123bacb718d7543dab21a8734 *man/combine_histos.Rd
 1463031ac752cc657542d708e9e7bfef *man/get_DayTimeLimits.Rd
-f979938df6f52db266c41491f8e65e03 *man/get_thermalstrat.Rd
+46fc6d6c92d9f5c63c1ff3fac713e87f *man/get_thermalstrat.Rd
 fa3b7ffb000adad1753f9538c59f55df *man/hist_tad.Rd
 6ad3f7777d9b2c2f65d353e258a70af7 *man/hist_tat.Rd
-502c3b2d3a0f7679e23191b4054c72ee *man/image_TempDepthProfiles.Rd
-9037e0a4076a8aa9f8dfbd8fc563efb3 *man/interpolate_TempDepthProfiles.Rd
+3d974bc1c0bbf712a0efb6b3410b3662 *man/image_TempDepthProfiles.Rd
+b33c418b50b07821675316cdb7ba83e8 *man/interpolate_TempDepthProfiles.Rd
 8fad25981fae098bc1b4760e7b8709c0 *man/merge_histos.Rd
-e4a4857d2d9b80ec016faae515d881f7 *man/plot_TS.Rd
-3f96d82782905a5d9f87894034ab6e92 *man/plot_geopos.Rd
-948318f09b613af552f4d887f67a794a *man/read_PDT.Rd
+c262e2ee99b13daa238fa54a64d78321 *man/plot_TS.Rd
+0bb4921d4fe1c12d2fa9181b9329f5d5 *man/plot_geopos.Rd
+e19c52488e7158af704fa84a58e3052c *man/read_PDT.Rd
 1d41623f8b790451fc7617e197718285 *man/read_histos.Rd
+dc2dbf6dfbc12462a5eb86f3a874f059 *man/resample_TS.Rd
 2d5e6915b7c43315df150ba1721e75ee *man/ts2histos.Rd
 1e725d644d978f8637cd1e90c560bbe2 *man/unmerge_histos.Rd

NAMESPACE

@@ -11,10 +11,10 @@ import("maps")
 import("mapdata")
 import("oceanmap")
 import("sp")
+importFrom("stats", "approx")
 importFrom("methods", "is")
 import("PBSmapping")
 importFrom("plyr",ddply)
-importFrom("akima",interp)
 importFrom("stats", "median", "sd", "spline")
 # Export all names
 exportPattern("^[^\\.]")

R/hist_tad.r

@@ -55,6 +55,7 @@ hist_tad <- function(df,
       hist_list <- hist_list_new
     }
     IDs <- names(hist_list[[Type]])
+    if(length(IDs) > 1) cat('data from several IDs found (that will be plotted seperately):',paste(IDs,sep=", "),"\n")
     for(ID in IDs){
       df <- hist_list[[Type]][[ID]]$df
       bin_breaks <- hist_list[[Type]][[ID]]$bin_breaks
@@ -94,7 +95,7 @@ hist_tad <- function(df,
       #     if("dtime" %in% names(df) & length(grep('Night', names(df))) == 0) df$Night <- df$dtime
 
       if(length(split_by) > 0){
-        if(missing(split_levels) & split_by == "day.time") split_levels <- c("Night", "Day")
+        if(missing(split_levels) & split_by == "daytime") split_levels <- c("Night", "Day")
         if(missing(split_levels)) split_levels <- unique(df[[split_by]])
         if(length(split_by) > 1) stop("argument split_by providing the vector name to split TaD is > 1. please revise")
         if(length(grep(split_by, names(df))) == 0) stop("vector name to split TaD data does not exist. please revise!")
@@ -120,9 +121,9 @@ hist_tad <- function(df,
       }
 
       if(missing(main)) main <- ""
-
       tdb <- paste0(bin_prefix, 1:length(bin_breaks))
       if(length(tad.df) < length(bin_prefix)) tad.df[[tdb[which(!(tdb %in% names(tad.df)))[1]]]] <- NA # add additional column if required
+
       tad.sm <- .tad_summary(tad.df, vars=split_by, bin_prefix=bin_prefix)
       #     head(tad.sm)
 
@@ -199,11 +200,11 @@ hist_tad <- function(df,
           ylabels2 <- cbind(c(intToUtf8(8805), ylabels[2:length(ylabels)]), c(ylabels[1], ylabels[1:length(ylabels)-1]-1))
           ylabels2a <- apply(ylabels2, 1, function(x) paste(x[1], x[2], sep=""))
           ylabels2a[2:length(ylabels)] <- apply(ylabels2, 1, function(x) paste(x[1], x[2], sep="-"))[2:length(ylabels)]
-          
-          axis(2, pos=xlim[1]+yaxis.pos, at=(0.5:(ncol(raw)-0.5)), lwd="", labels=ylabels2a, las=1)
+
+          axis(2, pos=xlim[1]+yaxis.pos, at=(0.5:(length(ylabels2a)-0.5)), lwd="", labels=ylabels2a, las=1)
 
         }else{
-          axis(2, pos=xlim[1]+yaxis.pos, at=(0:ncol(raw)), c("", ylabels), las=1)
+          axis(2, pos=xlim[1]+yaxis.pos, at=(0:length(ylabels)), c("", ylabels), las=1)
         }      
 
         if(xlab2.side == 1){

R/interpolate_TempDepthProfiles.r

@@ -44,14 +44,18 @@ interpolate_TempDepthProfiles <- function(ts, Temp_field="Temperature", ID_key="
         k$Depth <- k[[var]]
         k2 <- plyr::ddply(k[,which(names(k) %in% c('date','Depth','Temperature'))],c("date","Depth"),function(x)c(Temperature=mean(x$Temperature)))
 
-        xx <- c(k2$date,k2$date+1)
-        yy <- c(k2$Depth,k2$Depth)
-        zz <- c(k2$Temperature,k2$Temperature)
-        temp <- akima::interp(x=xx,y=yy,z=zz,linear=T,duplicate='mean',yo=depths) # interpolate data per day
-        Temperature_matrix[,sids] <- temp$z[1,]
+        ### old code depending on akima-package: (RchivalTag-package versions < 0.07) :
+        # xx <- c(k2$date,k2$date+1)
+        # yy <- c(k2$Depth,k2$Depth)
+        # zz <- c(k2$Temperature,k2$Temperature)
+        # temp <- akima::interp(x=xx,y=yy,z=zz,linear=T,duplicate='mean',yo=depths) # interpolate data per day
+        # Temperature_matrix[,sids] <- temp$z[1,]
 
-        #         dd <- paste0('Date_',format.Date(d,'%Y%m%d'))
+        ### new code: (RchivalTag-package versions >= 0.07) :
+        temp <- approx(x = k2$Depth, xout = depths, y = k2$Temperature)
+        Temperature_matrix[,sids] <- temp$y
 
+        #         dd <- paste0('Date_',format.Date(d,'%Y%m%d'))
         #         df <- data.frame(Depth=depths,Temperature=temp$z[1,],Date=as.Date(d))
         #         out_list[[paste0(Data_Source,'.',id)]][[dd]] <- df[which(!is.na(df$Temperature)),]
       }

R/plot_TS.r

@@ -45,7 +45,7 @@ empty.plot_TS <- function(xlim, ylim, xticks_interval, ylab="", xlab="Time (UTC)
 
 plot_DepthTS <- plot_TS <- function(df, y="Depth", xlim, ylim, xticks_interval,
                                     ylab=y, xlab="Time (UTC)", main, main.line=1, plot_info=TRUE, 
-                                    ID, ID_label="Serial", 
+                                    ID, ID_label="Serial", #show.temp=F,
                                     plot_DayTimePeriods=TRUE, twilight.set="ast", 
                                     type="l", las=1, xaxs="i", yaxs="i", cex=1, plot_box=TRUE, bty="l", Return=FALSE, ...){
   if(missing(ID)) ID <- unique(df[[ID_label]])
@@ -57,7 +57,7 @@ plot_DepthTS <- plot_TS <- function(df, y="Depth", xlim, ylim, xticks_interval,
   if(!("date.long" %in% names(df))) stop('no "date.long" vector provided! please revise.')
 
   if(!missing(xlim)){
-    if(class(xlim)[1] == 'Date' | nchar(as.character(xlim[1])) == 10){
+    if(class(xlim)[1] == 'Date'){# | nchar(as.character(xlim[1])) == 10){
       xlim <- as.POSIXct(paste(xlim, '00:00:00'), tz="UTC")
       if(length(xlim) == 1) xlim <- c(xlim, xlim)
       xlim[2] <- xlim[2]+24*60*60
@@ -106,7 +106,7 @@ plot_DepthTS <- plot_TS <- function(df, y="Depth", xlim, ylim, xticks_interval,
     df$date <- as.Date(df$date.long)
   }
 
-  ### plot TS:
+  ### plot emptyTS plot:
   par(las=las, yaxs=yaxs, xaxs=xaxs,...)  
   plot(df$date.long, df[[y]], axes=FALSE, lwd=0, cex=0, xlab="", ylab="", xlim=xlim, ylim=ylim, ...)
 
@@ -162,8 +162,10 @@ plot_DepthTS <- plot_TS <- function(df, y="Depth", xlim, ylim, xticks_interval,
 
   xti <- which(xticks >= xlim[1] & xticks <=xlim[2])
   axis(1, at=xticks[xti], labels=xtick.labels[xti], xpd=TRUE, pos=par()$usr[3], cex.axis=.9*cex, lwd=0, lwd.ticks = 1)
-  if(.date.long2min(xlim[1]) != 0)  axis(1, at=xlim[1], labels=format(xlim[1], "%H:%M"), xpd=TRUE, pos=par()$usr[3], cex.axis=.9*cex, lwd=0, lwd.ticks = 1)
-  if(.date.long2min(xlim[2]) != 0)  axis(1, at=xlim[2], labels=format(xlim[2], "%H:%M"), xpd=TRUE, pos=par()$usr[3], cex.axis=.9*cex, lwd=0, lwd.ticks = 1)
+
+  ### show_time_limits
+  # if(.date.long2min(xlim[1]) != 0)  axis(1, at=xlim[1], labels=format(xlim[1], "%H:%M"), xpd=TRUE, pos=par()$usr[3], cex.axis=.9*cex, lwd=0, lwd.ticks = 1)
+  # if(.date.long2min(xlim[2]) != 0)  axis(1, at=xlim[2], labels=format(xlim[2], "%H:%M"), xpd=TRUE, pos=par()$usr[3], cex.axis=.9*cex, lwd=0, lwd.ticks = 1)
 
   date.ticks <- xticks[which(xtick.labels == "12" & xticks >= xlim[1])]
   axis(1, at=date.ticks, labels=format(date.ticks, "%Y-%m-%d"), lwd=0, line=1, cex.axis=1*cex)
@@ -173,7 +175,57 @@ plot_DepthTS <- plot_TS <- function(df, y="Depth", xlim, ylim, xticks_interval,
   #     si <- which(.date.long2hour.dc(xticks[xti]) == 12)
   #     for(sii in si) text(xticks[xti][sii], y=max(ylim)-10, labels = "\U25d7", srt="90", cex=6, col=colors()[143], xpd=F)
   #   }
-  par(new=TRUE)
+
+
+  # if(show.temp){ ### interpolate Temperature data and add it to plot:
+    # Depth_res <- 1
+    # maxDepth <- max(ylim)
+    # depths <- seq(0,maxDepth,by=Depth_res) # sequence of depth values defining the vertical grid resolution
+    # df$date.longh <- .date2date.long(df$date,midday = F)+date.long2hour(df$date.long)*3600
+    # df$date.longh_id <- as.numeric(1+difftime(df$date.longh,min(df$date.longh),units = "hours"))
+    # date.longh_all <- seq(min(df$date.longh),max(df$date.longh),by=3600)
+    # ndates <- length(date.longh_all)
+    # Temperature_matrix <- matrix(ncol=ndates, nrow=length(depths), NA) # date-depth matrix
+    # smooth_hours <- 0
+    # smh <- smooth_hours
+    # 
+    # for(d in unique(df$date.longh_id)){
+    #   # d <- 1
+    #   df_sub <- df[which(df$date.longh_id %in% seq(d-smh,d+smh,by=1)),]
+    #   if(length(unique(df_sub$Temp)) > 1){
+    #     df_sub$Temp <- df_sub$Temp
+    #     # k2 <- plyr::ddply(df_sub[,which(names(df_sub) %in% c('date.longh_id','Depth','Temp'))],c("date.longh_id","Depth"),function(x)c(Temp=mean(x$Temp)))
+    #     # xx <- c(k2$date.longh_id,k2$date.longh_id+1)
+    #     
+    #     k2 <- plyr::ddply(df_sub[,which(names(df_sub) %in% c('date','Depth','Temp'))],c("date","Depth"),function(x)c(Temp=mean(x$Temp)))
+    #     xx <- c(k2$date,k2$date+1)
+    #     
+    #     yy <- c(k2$Depth,k2$Depth)
+    #     zz <- c(k2$Temp,k2$Temp)
+    #     temp <- akima::interp(x=xx,y=yy,z=zz,linear=T,duplicate='mean',yo=depths) # interpolate data per day
+    #     Temperature_matrix[,d] <- temp$z[1,]
+    #   }
+    # }
+    # xi <- date.longh_all
+    # yi <- depths
+    # zi <- Temperature_matrix
+    # 
+    # print(xi)
+  #   
+  #   
+  #   inst.pkg(raster)
+  #   f <- raster(zi[nrow(zi):1, ])
+  #   extent(f) <- extent(c(range(as.numeric(xi)), range(yi)))
+  #   
+  #   data(cmap)
+  #   par(new=TRUE)
+  #   image(f, xlim=as.numeric(xlim), ylim=ylim, col=cmap$light.jet, axes=F, ylab="", xlab="", add=T)#, zlim=zlim)
+  # }
+
+
+
+  ### plot vertical track:
+  par(new=T)
   plot(df$date.long, df[[y]], axes=FALSE, xlab="", ylab=ylab, ylim=ylim, xlim=xlim, type=type,xpd=TRUE,...)
   axis(2, lwd = 0, cex.axis=.9*cex, lwd.ticks=1)
   if(plot_box) box(bty=bty)

R/plot_geopos.r

@@ -1,5 +1,5 @@
 
-plot_geopos <- function(file, pos, xlim, ylim, prob_lim=.75, pal="jet", alpha=70, type="p", pch=19, add=FALSE, ...){
+plot_geopos <- function(file="", pos, xlim, ylim, prob_lim=.75, pal="jet", alpha=70, type="p", pch=19, add=FALSE, ...){
 
   cmap <- NULL
 #   oceanmap::plotmap('lion') ### check for Error in get(dbname) : object 'worldHiresMapEnv' not found
@@ -27,11 +27,11 @@ Return <- F
         header_found <- any(grepl("Most.Likely",header0))
       }
       pos <- read.csv(file, header=T,sep=',', skip=skip)
-      head(pos)
-      names(pos) <- gsub('Most.Likely.', '', names(pos))
-      names(pos) <- gsub('gitude', '', names(pos))
-      names(pos) <- gsub('itude', '', names(pos))  
     }
+    head(pos)
+    names(pos) <- gsub('Most.Likely.', '', names(pos))
+    names(pos) <- gsub('gitude', '', names(pos))
+    names(pos) <- gsub('itude', '', names(pos))  
 #     if(!missing(v_area)){
 #       r <- regions(v_area)
 #       xlim <- r$xlim

R/read_PDT.r

@@ -1,7 +1,8 @@
-read_PDT <- function(pdt_file, sep=","){
+read_PDT <- function(pdt_file, folder, sep=","){
 
   options(warn=0)
   pdt_all <- c()
+  if(!missing(folder)) pdt_file <- paste0(paste0(folder,"/"),pdt_file)
 
   for(f in pdt_file){
     cat('running pdt_file',f,"\n")

R/read_histos.r

@@ -55,8 +55,9 @@ read_histos <- function(hist_file){
         info <- add0[,which(names(add0) %in% c(identifiers,'date','date.long'))]
         nbins <- length(bb)
         madd0 <- add0[,which(names(add0) %in% paste0("Bin",1:nbins))]
-        for(ii in 1:ncol(madd0)) madd0[,ii] <- .fact2num(madd0[,ii])
+        for(icol in 1:ncol(madd0)) madd0[,icol] <- .fact2num(madd0[,icol])
 
+        madd0[which(is.na(madd0),arr.ind = T)] <- 0
         add_final <- .get_histos_stats(madd0,bb)
         hist_list[[Type]][[id]]$df <- data.frame(info,add_final,stringsAsFactors = F)
       }
@@ -143,7 +144,7 @@ rebin_histos <- merge_histos <- function(hist_list, tad_breaks=NULL, tat_breaks=
   hist_list_new <- list()
   Type <- 'TAD'
   for(Type in c('TAD','TAT')){
-    vlim <- .switch_if(Type == "TAD",c(0,5000),c(0,45))
+    vlim <- .switch_if(Type == "TAD",c(0,200),c(0,45))
     IDs <- names(hist_list[[Type]])
     if(length(IDs) != 0){
       cat('\n\nmerging',Type,'data:')

R/resample_TS.r

@@ -0,0 +1,27 @@
+
+resample_TS <- function(df, tstep){
+  tsims <- list()
+  tstep0 <- tstep
+  min_tstep <- unique(as.numeric(diff(df$date.long[1:10]))) #"corresponds to raw data sets!"
+  if(tstep %% min_tstep != 0) stop('selected time step (',tstep,'s)is not a multiple of the original sampling resolution (',min_tstep,'s). Please revise!')
+  if(tstep == 0) tstep0 <- min_tstep
+
+  df$date.long <- as.POSIXct(df$date.long,tz = 'UTC')
+  df$date <- as.Date(df$date.long)
+  df$year <- as.numeric(format(df$date, "%Y"))
+  df$month <- as.numeric(format(df$date, "%m"))
+  df$day <- as.numeric(format(df$date, "%d"))
+  df$tstep <- tstep0
+
+  tstarts <- which(df$date.long < df$date.long[1]+tstep0)
+  tag <- paste(df[1,which(names(df) %in% c('Serial','tag',"Ptt","DeployID"))],collapse=" - ")
+  # head(df)
+  tstart <- tstarts[1]
+  for(tstart in tstarts){
+    cat('resampling time series data from tag',tag,'with time step', tstep, 's - repetition',tstart,"of", tail(tstarts,1),'\n')
+    ii <- which(as.character(df$date.long) %in% as.character(seq(df$date.long[tstart],df$date.long[nrow(df)],by=tstep0)))
+    ts <- df[ii,]
+    tsims[[tstart]] <- ts
+  }
+  return(tsims)
+}

R/tad_summary.r

@@ -1,5 +1,5 @@
 .tad_summary <- function(tad.df, vars=c(), bin_prefix='tad.'){
-  
+
   if(nrow(tad.df) == 0) stop("empty data frame provided! please revise!")
   treat.vars <- names(tad.df)[grep(bin_prefix,names(tad.df))]
   op <- plyr::ddply(tad.df,c(vars),function(x){

R/ts2histos.r

@@ -48,7 +48,7 @@ ts2histos <- function(ts_df, tad_breaks=NULL, tat_breaks=NULL, split_by=NULL, ag
       out <- rbind(out,  sm.df)
       #         }
       #       }
-      output[[Type]][["merged"]] <- list(df=out,bin_breaks=bin_breaks)  
+      output[[Type]][["merged"]] <- list(df=out,bin_breaks=bin_breaks,split_by=split_by)  
     }
   }
   return(output)

man/get_thermalstrat.Rd

@@ -107,8 +107,8 @@ Monterey, G., and S. Levitus (1997) Seasonal variability of mixed layer depth fo
 \examples{
 #### example 1) run on PDT file:
 ## step I) read sample PDT data file:
-setwd(system.file("example_files",package="RchivalTag"))
-PDT <- read_PDT("104659-PDTs.csv")
+path <- system.file("example_files",package="RchivalTag")
+PDT <- read_PDT("104659-PDTs.csv",folder=path)
 head(PDT)
 # 
 # ## step II) interpolate average temperature fields (MeanPDT) from PDT file:

man/image_TempDepthProfiles.Rd

@@ -78,8 +78,8 @@ Robert K. Bauer
 \examples{
 #### example 1) run on PDT file:
 ## step I) read sample PDT data file:
-setwd(system.file("example_files",package="RchivalTag"))
-PDT <- read_PDT("104659-PDTs.csv")
+path <- system.file("example_files",package="RchivalTag")
+PDT <- read_PDT("104659-PDTs.csv",folder=path)
 head(PDT)
 # 
 # ## step II) interpolate average temperature fields (MeanPDT) from PDT file: