Nothing
## ---- echo=FALSE, include=FALSE-----------------------------------------------
knitr::opts_chunk$set(collapse = TRUE)
## ---- eval=F------------------------------------------------------------------
# library("move")
# loginStored <- movebankLogin(username="user", password="password")
## ---- eval=F------------------------------------------------------------------
# searchMovebankStudies(x="oose", login=loginStored)
## ---- eval=F------------------------------------------------------------------
# getMovebankID("Ocelots on Barro Colorado Island, Panama",login=loginStored)
## ---- eval=F------------------------------------------------------------------
# getMovebankStudy(study="Ocelots on Barro Colorado Island, Panama",login=loginStored)
## ---- eval=F------------------------------------------------------------------
# getMovebankSensors(study="Ocelots on Barro Colorado Island, Panama",login=loginStored)
## ---- eval=F------------------------------------------------------------------
# getMovebankSensors(login=loginStored)
## ---- eval=F------------------------------------------------------------------
# getMovebankSensorsAttributes(study="Ocelots on Barro Colorado Island, Panama",login=loginStored)
## ---- eval=F------------------------------------------------------------------
# getMovebankAnimals(study="Ocelots on Barro Colorado Island, Panama",login=loginStored)
## ---- eval=F------------------------------------------------------------------
# getMovebankReferenceTable(study="Ocelots on Barro Colorado Island, Panama",login=loginStored)
## ---- eval=F------------------------------------------------------------------
# bci_ocelot <- getMovebankData(study="Ocelots on Barro Colorado Island, Panama", login=loginStored)
## ---- eval=F------------------------------------------------------------------
# # for one individual
# bobby <- getMovebankData(study="Ocelots on Barro Colorado Island, Panama", animalName="Bobby", login=loginStored)
## ---- eval=F------------------------------------------------------------------
# # for several individuals
# ocelot2ind <- getMovebankData(study="Ocelots on Barro Colorado Island, Panama", animalName=c("Bobby","Darlen"), login=loginStored)
## ---- eval=F------------------------------------------------------------------
# # download all data between "2003-03-22 17:44:00.000" and "2003-04-22 17:44:00.000"
# bci_ocelot_range1 <- getMovebankData(study="Ocelots on Barro Colorado Island, Panama", login=loginStored,
# timestamp_start="20030322174400000",
# timestamp_end="20030422174400000")
#
# # alternative:
# t <- strptime("20030322174400",format="%Y%m%d%H%M%S", tz='UTC')
# bci_ocelot_ranget <- getMovebankData(study="Ocelots on Barro Colorado Island, Panama", login=loginStored,
# timestamp_start=t,
# timestamp_end=t+as.difftime(31,units='days'))
## ---- eval=F------------------------------------------------------------------
# # download all data before "2003-07-24 20:00:00.000"
# bci_ocelot_range2 <- getMovebankData(study="Ocelots on Barro Colorado Island, Panama", login=loginStored,
# timestamp_end="20030724200000000")
#
## ---- eval=F------------------------------------------------------------------
# # download all data after "2003-07-01 20:00:00.000" only for "Bobby"
# bobby_range <- getMovebankData(study="Ocelots on Barro Colorado Island, Panama", login=loginStored, animalName="Bobby",
# timestamp_start="20030701200000000")
## ---- eval=F------------------------------------------------------------------
# bci_ocelot <- getMovebankData(study="Ocelots on Barro Colorado Island, Panama", login=loginStored,
# removeDuplicatedTimestamps=TRUE)
## ---- eval=F------------------------------------------------------------------
# ## fist get the animal names of the study
# animalDF <- getMovebankAnimals(study="Ocelots on Barro Colorado Island, Panama",login=loginStored)
# animalNames <- unique(animalDF$local_identifier[animalDF$number_of_events>0]) ## to make sure only to include the animals that actually have locations
#
# ## if one is sure that all individuals in the study have locations, this is a shorter way to go
# # animalNames <- unique(getMovebankAnimals(study="Ocelots on Barro Colorado Island, Panama",login=loginStored)$local_identifier)
#
# ## OPTION 1: create a loop to download each individual and afterwards create a MoveStack (if study is very large, maybe option 2 is better)
# animalList <- lapply(animalNames, function(x){
# print(paste0(x," (",match(x,animalNames), " of ", length(animalNames),")"))
# getMovebankData(study="Ocelots on Barro Colorado Island, Panama", animalName=x, login=loginStored, removeDuplicatedTimestamps=T)
# })
# ocelotsMS <- moveStack(animalList, forceTz="UTC")
#
# ## OPTION 2: if the study is very large, loading and handling the large moveStack might be very time consuming and somewhat frustrating. Therefore it might be a good idea to save each individual separately as e.g. a .RData file, and do subsequent analysis always looping through all the single individual files
# animalList <- lapply(animalNames, function(x){
# print(paste0(x," (",match(x,animalNames), " of ", length(animalNames),")"))
# ocelot <- getMovebankData(study="Ocelots on Barro Colorado Island, Panama", animalName=x, login=loginStored, removeDuplicatedTimestamps=T)
# save(file=paste0("/path/to/my/folder/OcelotsIndv/",x,".RData"), ocelot)
# })
## ---- eval=F------------------------------------------------------------------
# getMovebankLocationData(study=74496970 , sensorID="GPS",
# animalName="DER AR439", login=loginStored)
## ---- eval=F------------------------------------------------------------------
# # get acceleration data for all individuals of the study between the "2013-08-15 15:00:00.000" and "2013-08-15 15:01:00.000"
# getMovebankLocationData(study=74496970 , sensorID=653, login=loginStored,
# timestamp_start="20130815150000000",
# timestamp_end="20130815150100000")
## ---- eval=F------------------------------------------------------------------
# ## fist get the animal names of the study
# animalNames <- unique(getMovebankAnimals(study=74496970,login=loginStored)$local_identifier)
#
# ## OPTION 1: create a loop to download each individual and afterwards rbind into one large data.frame (if study is very large, maybe option 2 is better). Use the "TryCatch" function in case there are individuals with no data.
# animalList <- lapply(animalNames, function(x){
# print(paste0(x," (",match(x,animalNames), " of ", length(animalNames),")"))
# tryCatch(getMovebankLocationData(study=74496970, animalName=x, sensorID="GPS", login=loginStored), error=function(e) NULL)
# })
# storksDF <- do.call("rbind", animalList)
#
# ## OPTION 2: if the study is very large, loading and handling the large data.frame might be very time consuming and somewhat frustrating. Therefore it might be a good idea to save each individual separately as e.g. a .RData file, and do subsequent analysis always looping through all the single individual files. Use the "TryCatch" function in case there are individuals with no data.
# animalList <- lapply(animalNames, function(x){
# print(paste0(x," (",match(x,animalNames), " of ", length(animalNames),")"))
# tryCatch({storkDF <- getMovebankLocationData(study=74496970, animalName=x, sensorID="GPS", login=loginStored)
# save(file=paste0("/path/to/my/folder/StorkIndv/",x,".RData"), storkDF)
# }, error=function(e) NULL)
# })
## ---- eval=F------------------------------------------------------------------
# getMovebankNonLocationData(study=74496970 , sensorID="Acceleration",
# animalName="DER AR439", login=loginStored)
## ---- eval=F------------------------------------------------------------------
# # get acceleration data for all individuals of the study between the "2013-08-15 15:00:00.000" and "2013-08-15 15:01:00.000"
# getMovebankNonLocationData(study=74496970 , sensorID=2365683, login=loginStored,
# timestamp_start="20130815150000000",
# timestamp_end="20130815150100000")
## ---- eval=F------------------------------------------------------------------
# mymove <- getMovebankData(study=74496970, login=loginStored,
# animalName="DER AR439",includeExtraSensors=TRUE)
## ---- eval=F------------------------------------------------------------------
# ## to get a data.frame containing the data for the non-location sensors use the "unUsedRecords" function
# nonlocation <- as.data.frame(unUsedRecords(mymove))
## ---- eval=F------------------------------------------------------------------
# ## fist get the animal names of the study
# animalNames <- unique(getMovebankAnimals(study=74496970,login=loginStored)$local_identifier)
#
# ## OPTION 1: create a loop to download each individual and afterwards rbind into one large data.frame (if study is very large, maybe option 2 is better). Use the "TryCatch" function in case there are individuals with no data.
# animalList <- lapply(animalNames, function(x){
# print(paste0(x," (",match(x,animalNames), " of ", length(animalNames),")"))
# tryCatch(getMovebankNonLocationData(study=74496970, animalName=x, sensorID=2365683, login=loginStored), error=function(e) NULL)
# })
# storksACC <- do.call("rbind", animalList)
#
# ## OPTION 2: if the study is very large, loading and handling the large data.frame might be very time consuming and somewhat frustrating. Therefore it might be a good idea to save each individual separately as e.g. a .RData file, and do subsequent analysis always looping through all the single individual files. Use the "TryCatch" function in case there are individuals with no data.
# animalList <- lapply(animalNames, function(x){
# print(paste0(x," (",match(x,animalNames), " of ", length(animalNames),")"))
# tryCatch({storkACC <- getMovebankNonLocationData(study=74496970, animalName=x, sensorID=2365683, login=loginStored)
# save(file=paste0("/path/to/my/folder/StorkIndv/",x,".RData"), storkACC)
# }, error=function(e) NULL)
# })
## ---- eval=F------------------------------------------------------------------
# getDataRepositoryData("doi:10.5441/001/1.2k536j54")
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