version 0.0.2

cran · Mar 24, 2017 · 23825bc · 23825bc
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diff --git a/ChangeLog b/ChangeLog
@@ -0,0 +1,5 @@
+RchivalTag versions
+
+RchivalTag_0.0.2 -- March 22, 2017 (Robert Bauer)
+	- small bug in hist_tad/hist_tat functions fixed
+        - adding example on back-to-back histogram for day vs night TaD-data (see: ?hist_tad)
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: RchivalTag
 Type: Package
 Title: Analyzing Archival Tagging Data
-Version: 0.0.1
-Date: 2017-03-20
+Version: 0.0.2
+Date: 2017-03-22
 Author: Robert Bauer
 Maintainer: Robert Bauer <robert.bauer@ird.fr>
 Description: A set of functions to generate, access and analyze standard data products from archival tagging data.
@@ -11,7 +11,7 @@ Imports: plyr, akima, maptools, graphics, stats, raster, rgeos, ncdf4,
         maps, mapdata, grDevices, oceanmap, sp, methods, PBSmapping
 License: CC BY-NC-SA 4.0
 LazyLoad: yes
-Packaged: 2017-03-21 09:28:11 UTC; robert
+Packaged: 2017-03-24 18:02:44 UTC; robert
 Repository: CRAN
-Date/Publication: 2017-03-21 15:20:26 UTC
+Date/Publication: 2017-03-24 19:07:25 UTC
 NeedsCompilation: no
diff --git a/MD5 b/MD5
@@ -1,10 +1,11 @@
-c28cb13ebc4fbb1df366c8e538aea8e1 *DESCRIPTION
+f21d4892b5423153dfd9779fe78baa10 *ChangeLog
+b77853cad60e51624f87ea04db3e2bb3 *DESCRIPTION
 8b43c3895e01857f02fedd5ed61ff8a3 *NAMESPACE
 3e611abed9a9cac153c07f2681682a8e *R/bin_TempTS.r
 4496a3299a5b651e488ff184fb363094 *R/get_DaytTimeLimits.r
 5a9b700d9095f7dabdb593f7ceef78fc *R/get_thermalstrat.r
 d0238a97d31e3bc0bb39758ffea3a359 *R/hidden_functions.r
-75c153561406b2fc85838be932a5900d *R/hist_tad.r
+ac03eff18ad7382bce1a16ef3e447dfa *R/hist_tad.r
 983d14437b0aabbcaba53a1669868b7d *R/image_TempDepthProfiles.r
 2488fc005f517758e20112941f5bbcaa *R/interpolate_TempDepthProfiles.r
 3f4dd32dc5378d04c152e1d89c4f2800 *R/plot_TS.r
@@ -14,7 +15,7 @@ a64d0ede8100a59f1bd71108932d4c30 *R/plot_geopos.r
 ee79db69ed156d837cfb299d11475653 *R/read_histos.r
 57d180cfc03aa9be75938c0f018ef197 *R/tad_summary.r
 976cabc1b75f6dbea28566ad6cfa031c *R/ts2histos.r
-ae93e1e353b6fd7ce811ff8b44212f64 *inst/doc/RchivalTag.pdf
+9544216ac9dfc9c625c8ef7fa1f5a6e9 *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
@@ -24,18 +25,18 @@ ec53f903951050e6c4f96a0b7be076b8 *inst/example_files/104659-Series.csv
 17ec587e2ccd909540cb761cac3c267c *inst/example_files/15P1019-104659-1-GPE3.nc
 091f7f38953b7f084ee750e93d97b978 *man/RchivalTag.Rd
 328353bb9a7cc8f29c0078f5ea8a85bb *man/bin_TempTS.Rd
-3310f1e72fedf454cedb0f944f2282e1 *man/classify_DayTime.Rd
+8fbcb76a6fac0d2643d6aa7c0db38d33 *man/classify_DayTime.Rd
 4b01620123bacb718d7543dab21a8734 *man/combine_histos.Rd
 1463031ac752cc657542d708e9e7bfef *man/get_DayTimeLimits.Rd
 f979938df6f52db266c41491f8e65e03 *man/get_thermalstrat.Rd
-ca8dd355686d7597c88afea4fe7ed4c0 *man/hist_tad.Rd
-a42873b0e3a6bde221d987242f72f4e5 *man/hist_tat.Rd
+fa3b7ffb000adad1753f9538c59f55df *man/hist_tad.Rd
+6ad3f7777d9b2c2f65d353e258a70af7 *man/hist_tat.Rd
 502c3b2d3a0f7679e23191b4054c72ee *man/image_TempDepthProfiles.Rd
 9037e0a4076a8aa9f8dfbd8fc563efb3 *man/interpolate_TempDepthProfiles.Rd
 8fad25981fae098bc1b4760e7b8709c0 *man/merge_histos.Rd
 e4a4857d2d9b80ec016faae515d881f7 *man/plot_TS.Rd
-05d8b6e6538acd960c4015322dac7059 *man/plot_geopos.Rd
+3f96d82782905a5d9f87894034ab6e92 *man/plot_geopos.Rd
 948318f09b613af552f4d887f67a794a *man/read_PDT.Rd
 4ff61484a4a2ecfb0264d10b1933e6d1 *man/read_histos.Rd
-aaf50a330671dd46328f1451b7100baf *man/ts2histos.Rd
+cf2de73b343be4014402c023a8fb54a2 *man/ts2histos.Rd
 1e725d644d978f8637cd1e90c560bbe2 *man/unmerge_histos.Rd
diff --git a/R/hist_tad.r b/R/hist_tad.r
@@ -187,7 +187,7 @@ hist_tad <- function(df,
 
         xticks <- pretty(xlim)
         xlabels <- abs(xticks)
-        ylabels <- rev(bin_prefix)
+        ylabels <- rev(bin_breaks)
         #       if(!labeling){
         #         xlabels <- rep('', length(xlabels))
         #         ylabels <- rep('', length(ylabels))
@@ -203,7 +203,6 @@ hist_tad <- function(df,
           axis(2, pos=xlim[1]+yaxis.pos, at=(0.5:(ncol(raw)-0.5)), lwd="", labels=ylabels2a, las=1)
 
         }else{
-
           axis(2, pos=xlim[1]+yaxis.pos, at=(0:ncol(raw)), c("", ylabels), las=1)
         }      
 

diff --git a/inst/doc/RchivalTag.pdf b/inst/doc/RchivalTag.pdf
diff --git a/man/classify_DayTime.Rd b/man/classify_DayTime.Rd
@@ -55,6 +55,21 @@ get_DayTimeLimits(pos)
 #### example 1b) classify current ime of the day in Mainz, Germany:
 classify_DayTime(get_DayTimeLimits(pos))
 
+## convert 1c) back-to-back histogram showing day vs night TAD frequencies:
+### load sample depth and temperature time series data from miniPAT:
+ts_file <- system.file("example_files/104659-Series.csv",package="RchivalTag")
+ts_df <- read.table(ts_file, header = TRUE, sep = ",")
+tad_breaks <- c(0, 2, 5, 10, 20, 50, 100, 200, 300, 400, 600, 2000)
+
+ts_df$Lat <- 4; ts_df$Lon=42.5 ## required geolocations to estimate daytime
+ts_df$date.long <- strptime(paste(ts_df$Day,ts_df$Time),"\%d-\%B-\%Y \%H:\%M:\%S")
+head(ts_df)
+ts_df2 <- classify_DayTime(get_DayTimeLimits(ts_df)) # estimate daytime
+head(ts_df2)
+
+ts2histos(ts_df2, tad_breaks = tad_breaks,split_by = "daytime")
+hist_tad(ts_df2, bin_breaks = tad_breaks,split_by = "daytime", do_mid.ticks = FALSE)
+
 
 }
 
diff --git a/man/hist_tad.Rd b/man/hist_tad.Rd
@@ -68,7 +68,7 @@ a vector defining the limits (x1,x2) of the x-axis, by default c(0,100). However
 }
 
 \item{split_by}{
-Name of the logical vector by which TaD data should be splitted (e.g. day.time).
+Name of the logical vector by which TaD data should be splitted (e.g. daytime; see \link{classify_DayTime}).
 }
 
 \item{split_levels, xlab2}{
@@ -161,6 +161,17 @@ hist_tat(tat$TAT$merged, do_mid.ticks = FALSE)
 ## convert 1c) DepthTS & TemperatureTS data to daily TAD & TAT frequencies:
 ts2histos(ts_df, tad_breaks = tad_breaks, tat_breaks = tat_breaks)
 
+## convert 1d) back-to-back histogram showing day vs night TAD frequencies:
+ts_df$Lat <- 4; ts_df$Lon=42.5 ## required geolocations to estimate daytime
+ts_df$date.long <- strptime(paste(ts_df$Day,ts_df$Time),"\%d-\%B-\%Y \%H:\%M:\%S")
+head(ts_df)
+ts_df2 <- classify_DayTime(get_DayTimeLimits(ts_df)) # estimate daytime
+head(ts_df2)
+
+ts2histos(ts_df2, tad_breaks = tad_breaks,split_by = "daytime")
+hist_tad(ts_df2, bin_breaks = tad_breaks,split_by = "daytime", do_mid.ticks = FALSE)
+
+
 ## example 2) rebin daily TAD frequencies:
 tad <- ts2histos(ts_df, tad_breaks = tad_breaks)
 tad2 <- rebin_histos(hist_list = tad, tad_breaks = tad_breaks[c(1:3,6:12)])
@@ -169,6 +180,7 @@ hist_tad(tad, do_mid.ticks = FALSE) ## example for multiple individuals
 hist_tad(tad$TAD$merged, do_mid.ticks = FALSE)
 hist_tad(tad$TAD$merged, bin_breaks = tad_breaks[c(1:3,6:12)]) ## from inside hist_tad
 
+
 ## example 3) read, merge and plot TAD frequency data from several files:
 ## part I - read histogram data from two files:
 hist_dat_1 <- read_histos(system.file("example_files/104659-Histos.csv",package="RchivalTag"))

diff --git a/man/hist_tat.Rd b/man/hist_tat.Rd
@@ -66,7 +66,7 @@ a vector defining the limits (x1,x2) of the x-axis, by default c(0,100). However
 }
 
 \item{split_by}{
-Name of the logical vector by which TaD data should be splitted (e.g. day.time).
+Name of the logical vector by which TaD data should be splitted (e.g. daytime; see \link{classify_DayTime}).
 }
 
 \item{split_levels, xlab2}{
@@ -157,6 +157,17 @@ hist_tat(tat$TAT$merged, do_mid.ticks = FALSE)
 ## convert 1c) DepthTS & TemperatureTS data to daily TAD & TAT frequencies:
 ts2histos(ts_df, tad_breaks = tad_breaks, tat_breaks = tat_breaks)
 
+## convert 1d) back-to-back histogram showing day vs night TAD frequencies:
+ts_df$Lat <- 4; ts_df$Lon=42.5 ## required geolocations to estimate daytime
+ts_df$date.long <- strptime(paste(ts_df$Day,ts_df$Time),"\%d-\%B-\%Y \%H:\%M:\%S")
+head(ts_df)
+ts_df2 <- classify_DayTime(get_DayTimeLimits(ts_df)) # estimate daytime
+head(ts_df2)
+
+ts2histos(ts_df2, tad_breaks = tad_breaks,split_by = "daytime")
+hist_tad(ts_df2, bin_breaks = tad_breaks,split_by = "daytime", do_mid.ticks = FALSE)
+
+
 ## example 2) rebin daily TAD frequencies:
 tad <- ts2histos(ts_df, tad_breaks = tad_breaks)
 tad2 <- rebin_histos(hist_list = tad, tad_breaks = tad_breaks[c(1:3,6:12)])
@@ -165,6 +176,7 @@ hist_tad(tad, do_mid.ticks = FALSE) ## example for multiple individuals
 hist_tad(tad$TAD$merged, do_mid.ticks = FALSE)
 hist_tad(tad$TAD$merged, bin_breaks = tad_breaks[c(1:3,6:12)]) ## from inside hist_tad
 
+
 ## example 3) read, merge and plot TAD frequency data from several files:
 ## part I - read histogram data from two files:
 hist_dat_1 <- read_histos(system.file("example_files/104659-Histos.csv",package="RchivalTag"))

diff --git a/man/plot_geopos.Rd b/man/plot_geopos.Rd
@@ -85,7 +85,7 @@ plot_geopos(csv_file, add=TRUE) ## show tracks as scatter plot
 ## example 2) probability surfaces of horizontal tracks from nc-file:
 ## this can take some time as it inlcudes time consuming data processing
 nc_file <- system.file("example_files/15P1019-104659-1-GPE3.nc",package="RchivalTag") 
-plot_geopos(nc_file)
+# plot_geopos(nc_file)
 
 }
 
diff --git a/man/ts2histos.Rd b/man/ts2histos.Rd
@@ -31,7 +31,7 @@ a numeric vector, defining the depth and/or temperature breakpoints of the histo
 }
 
 \item{split_by}{
-Name of the logical vector by which TaD/TaT data shall be splitted (e.g. day.time).
+Name of the logical vector by which TaD/TaT data shall be splitted (e.g. daytime; see \link{classify_DayTime}).
 }
 
 \item{aggregate_by}{
@@ -72,6 +72,17 @@ hist_tat(tat$TAT$merged, do_mid.ticks = FALSE)
 ## convert 1c) DepthTS & TemperatureTS data to daily TAD & TAT frequencies:
 ts2histos(ts_df, tad_breaks = tad_breaks, tat_breaks = tat_breaks)
 
+## convert 1d) back-to-back histogram showing day vs night TAD frequencies:
+ts_df$Lat <- 4; ts_df$Lon=42.5 ## required geolocations to estimate daytime
+ts_df$date.long <- strptime(paste(ts_df$Day,ts_df$Time),"\%d-\%B-\%Y \%H:\%M:\%S")
+head(ts_df)
+ts_df2 <- classify_DayTime(get_DayTimeLimits(ts_df)) # estimate daytime
+head(ts_df2)
+
+ts2histos(ts_df2, tad_breaks = tad_breaks,split_by = "daytime")
+hist_tad(ts_df2, bin_breaks = tad_breaks,split_by = "daytime", do_mid.ticks = FALSE)
+
+
 ## example 2) rebin daily TAD frequencies:
 tad <- ts2histos(ts_df, tad_breaks = tad_breaks)
 tad2 <- rebin_histos(hist_list = tad, tad_breaks = tad_breaks[c(1:3,6:12)])