# reads Time Series Data from Archival Tags reads Time Series Data (e.g. Depth and Temperature) from Archival Tags (Supported Models: MiniPAT, sPAT, recovered mk10, mk9 from [Wildlife Computers](https://wildlifecomputers.com/) as well as LOTEK PSAT Models [LOTEK](https://www.lotek.com/products/psat-series/). Models from other Manufactorers might be supported as well. ```r read_TS(ts_file, header=TRUE, sep=",", skip = 0, date_format, lang_format = "en", tz = "UTC") ``` ## Arguments - `ts_file`: character string indicating the name of a standard [Wildlife Computers](https://wildlifecomputers.com/) file to read or the data.frame of a manually loaded histogram data file. The file is assumed to include the columns `Day`, `Time` (or a preformatted date-time vector termed `datetime` in "UTC" format.) as well as at one of the subsequent columns `DeployID`, `Ptt` and `Serial` to distinguish data from indiviudal tags. - `header`: a logical value indicating whether the file contains the names of the variables as its first line. If missing, the value is determined from the file format: header is set to TRUE if and only if the first row contains one fewer field than the number of columns. - `sep`: the field separator character. Values on each line of the file are separated by this character. If sep = "" the separator is 'white space', that is one or more spaces, tabs, newlines or carriage returns. - `skip`: integer: the number of lines of the data file to skip before beginning to read data. - `date_format, lang_format, tz`: character strings indicating the date format, language format and the corresponding time zone, defined by the vectors Date and Time (by default: date_format="%d-%b-%Y %H:%M:%S", lang_format="en", tz='UTC') If formatting fails, please check as well the input language format, defined by `lang_format` (and use abbrviations such as "en" for English,"es" for Spanish, "fr" for French, etc.) as well. ## Details This function reads a time series data file from archival tags. Data sets are "completed" to facilitate an assessment of the data coverage (i.e. by ts2histos or hist_tad ). ## Returns A data frame (data.frame ) containing a representation of the data in the file. ## See Also ts2histos , hist_tad , plot_TS ## Author(s) Robert K. Bauer ## Examples ```r ### load sample depth and temperature time series data from miniPAT: ts_file <- system.file("example_files/104659-Series.csv",package="RchivalTag") ts_df <- read_TS(ts_file) head(ts_df) ## other date_format: ts_file2 <- system.file("example_files/104659-Series_date_format2.csv",package="RchivalTag") # ts_miniPAT2 <- read_TS(ts_file2) # run to see error message ts_miniPAT2 <- read_TS(ts_file2,date_format = "%d-%m-%Y %H:%M:%S") head(ts_miniPAT2) ## other date_format and lang_format: ts_file_ES <- system.file("example_files/104659-Series_date_format_ES.csv",package="RchivalTag") # ts_miniPAT_ES <- read_TS(ts_file_ES) # run to see error message ts_miniPAT_ES <- read_TS(ts_file_ES,skip=1,sep=";",header = TRUE, date_format = "%d/%b/%y %H:%M:%S",lang_format = "es") head(ts_miniPAT_ES) ## load same data in LOTEK format ts_file <- system.file("example_files/104659_PSAT_Dive_Log.csv",package="RchivalTag") ts_df <- read_TS(ts_file,date_format="%m/%d/%Y %H:%M:%S") head(ts_df) ## attention no identifier (Ptt, Serial, DeployID) included! ts_df$DeployID <- ts_df$Ptt <- "104659" ## example 1) convert only DepthTS data to daily TaD frequencies: tad_breaks <- c(0, 2, 5, 10, 20, 50, 100, 200, 300, 400, 600, 2000) tat_breaks <- c(10,12,15,17,18,19,20,21,22,23,24,27) histos <- ts2histos(ts_df, tad_breaks = tad_breaks, tat_breaks = tat_breaks) histos$TAD$merged$df$nperc ## check completeness of TAD data sets histos$TAT$merged$df$nperc ## check completeness of TAT data sets # histos <- ts2histos(ts_df, tad_breaks = tad_breaks, tat_breaks = tat_breaks,min_perc = 90) ### example 2) add daytime (Day vs Night) information and plot results # add daytime periods during plot-function call and return extended data set # ts_df$Lon <- 5; ts_df$Lat <- 43 # plot_DepthTS(ts_df, plot_DayTimePeriods = TRUE, xlim = unique(ts_df$date)[2:3]) # ts_df2 <- plot_DepthTS(ts_df, plot_DayTimePeriods = TRUE, Return = TRUE) # names(ts_df) # names(ts_df2) ### add daytime periods before function call # ts_df_extended <- get_DayTimeLimits(ts_df) # plot_DepthTS(ts_df_extended, plot_DayTimePeriods = TRUE) # plot_DepthTS(ts_df_extended, plot_DayTimePeriods = TRUE, twilight.set = "naut") ```

read_TS function