Title: | Fetch and use data from the Motus Wildlife Tracking System |
---|---|
Description: | Retrieve, filter, and visualize telemetry data from the Motus Wildlife Tracking System <https://motus.org>. |
Authors: | Birds Canada [aut, cre], John Brzustowski [aut], Denis Lepage [aut], Steffi LaZerte [ctb], Joey Bernard [ctb], Lucas Berrigan [ctb], Tara Crewe [ctb], Zoe Crysler [ctb], Jeremy Hussell [ctb], Catherine Jardine [ctb], Amie MacDonald [ctb], Stuart Mackenzie [ctb], Paul Morrill [ctb], Josh Sayers [ctb], Philip Taylor [ctb] |
Maintainer: | Birds Canada <[email protected]> |
License: | GPL-3 |
Version: | 6.1.1 |
Built: | 2024-11-01 05:10:46 UTC |
Source: | https://github.com/MotusWTS/motus |
Download or resume a download of the activity
table in an existing Motus
database. Batch activity refers to the number of hits detected during a given
batch. Batches with large numbers of hits may indicate interference and thus
unreliable hits.
activity(src, resume = FALSE)
activity(src, resume = FALSE)
src |
SQLite connection. Result of |
resume |
Logical. Resume a download? Otherwise the table is removed and the download is started from the beginning. |
This function is automatically run by the tagme()
function with
resume = TRUE
.
If an activity
table doesn't exist, it will be created prior to
downloading. If there is an existing activity
table, this will update the
records.
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Access 'activity' table library(dplyr) a <- tbl(sql_motus, "activity") # If interrupted and you want to resume ## Not run: my_tags <- activity(sql_motus, resume = TRUE)
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Access 'activity' table library(dplyr) a <- tbl(sql_motus, "activity") # If interrupted and you want to resume ## Not run: my_tags <- activity(sql_motus, resume = TRUE)
Download or resume a download of the activityAll
table in an existing Motus
database. Batch activity refers to the number of hits detected during a given
batch. Batches with large numbers of hits may indicate interference and thus
unreliable hits.
activityAll(src, resume = FALSE)
activityAll(src, resume = FALSE)
src |
SQLite connection. Result of |
resume |
Logical. Resume a download? Otherwise the table is removed and the download is started from the beginning. |
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Get all activity ## Not run: sql_motus <- activityAll(sql_motus) # Access 'activityAll' table library(dplyr) a <- tbl(sql_motus, "activityAll") # If interrupted and you want to resume ## Not run: sql_motus <- activityAll(sql_motus, resume = TRUE)
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Get all activity ## Not run: sql_motus <- activityAll(sql_motus) # Access 'activityAll' table library(dplyr) a <- tbl(sql_motus, "activityAll") # If interrupted and you want to resume ## Not run: sql_motus <- activityAll(sql_motus, resume = TRUE)
Verifies the version of the package against the admInfo
table of a .motus
file. Those should match if the updateMotusDb()
function has been properly
applied by the tagme()
function.
checkVersion(src)
checkVersion(src)
src |
SQLite connection. Result of |
A detections is "ambiguous" if the motus tag finder could not tell which of several tags was detected, because they all produce the same signal and were active at the same time. The motus tag finder uses tag deployment and lifetime metadata to decide what tags to seek when, and notices when it can't distinguish between two or more of them. Detections of such tags during these periods of overlap are assigned a negative motus tag ID that represents from 2 to 6 possible real motus tags. The ambiguities might be real (i.e. two or more tags transmitting the same signal and active at the same time), or due to errors in tag registration or deployment metadata.
clarify(src, id, from, to, all.mine = FALSE)
clarify(src, id, from, to, all.mine = FALSE)
src |
SQLite connection. Result of |
id |
if not missing, a vector of negative motus ambiguous tag IDs for
which you wish to claim detections. If missing, all tags are claimed over
any period specified by |
from |
Character. If not missing, the start time for your claim to
ambiguous detections of tag(s) |
to |
Character. If not missing, the end time for your claim to ambiguous
detections of tag(s) |
all.mine |
Logical. If TRUE, claim all ambiguous detections. In this
case, |
This function serves two purposes:
called with only a database, it reports the numbers of ambiguous detections and what they could represent.
called with id
, it lets you claim some of the ambiguities as your own
tag, so that in subsequent processing, they will appear to be yours.
This function does not (yet?) report your claim to motus.org
WARNING: you cannot undo a claim within a copy of the database. If
unsure, copy the .motus file first, then run clarify
on only
one copy.
If both from
and to
are missing, then all detections of ambiguous tag(s)
id
are claimed.
Parameters id
, from
, and to
are recycled to the length of the longest
item.
When you claim an ambiguous tag T
for a period, any runs of T
which
overlap that period at all are claimed entirely, even if they extend beyond
the period; i.e. runs are not split.
With no parameters, returns a summary data frame of ambiguous tag detections
## Not run: s <- tagme(57) # get the tag database for project 57 clarify(s) # report on the ambiguous tag detections in s clarify(all.mine = TRUE) # claim all ambiguous tag detections as mine clarify(id = -57) # claim all detections of ambiguous tag -57 as mine clarify(id = c(-72, -88, -91), from = "2017-01-02", to = "2017-05-06") # claim all detections of ambiguous tags -72, -88, and -91 from # January 2 through May 6, 2017, as mine ## End(Not run)
## Not run: s <- tagme(57) # get the tag database for project 57 clarify(s) # report on the ambiguous tag detections in s clarify(all.mine = TRUE) # claim all ambiguous tag detections as mine clarify(id = -57) # claim all detections of ambiguous tag -57 as mine clarify(id = c(-72, -88, -91), from = "2017-01-02", to = "2017-05-06") # claim all detections of ambiguous tags -72, -88, and -91 from # January 2 through May 6, 2017, as mine ## End(Not run)
Create a new filter records that can be applied to runs
createRunsFilter(src, filterName, motusProjID = NA, descr = NA, update = FALSE)
createRunsFilter(src, filterName, motusProjID = NA, descr = NA, update = FALSE)
src |
SQLite connection. Result of |
filterName |
Character. Unique name given to the filter |
motusProjID |
Character. Optional project ID attached to the filter in order to share with other users of the same project. |
descr |
Character. Optional filter description detailing what the filter is meant to do |
update |
Logical. Whether the filter record gets updated when a filter with the same name already exists. |
an integer filterID
Deletes a filter by name or project ID.
deleteRunsFilter(src, filterName, motusProjID = NA, clearOnly = FALSE)
deleteRunsFilter(src, filterName, motusProjID = NA, clearOnly = FALSE)
src |
SQLite connection. Result of |
filterName |
Character. Unique name given to the filter |
motusProjID |
Character. Optional project ID attached to the filter in order to share with other users of the same project. |
clearOnly |
Logical. When true, only remove the probability records associated with the filter, but retain the filter itself |
the integer filterID
of the filter deleted
Deprecated batches are removed from the online database but not from
local data files. This function fetches a list of deprecated batches
(stored in the 'deprecated' table), and, optionally, removes these batches
from all tables that reference batchID
s
deprecateBatches(src, fetchOnly = FALSE, ask = TRUE)
deprecateBatches(src, fetchOnly = FALSE, ask = TRUE)
src |
SQLite connection. Result of |
fetchOnly |
Logical. Only fetch batches that are deprecated. Don't remove deprecated batches from other tables. |
ask |
Logical. Ask for confirmation when removing deprecated batches |
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Access 'deprecated' table using tbl() from dplyr library(dplyr) tbl(sql_motus, "deprecated") # See that there are deprecated batches in the data filter(tbl(sql_motus, "alltags"), batchID == 6000) # Fetch deprecated batches deprecateBatches(sql_motus, fetchOnly = TRUE) # Remove deprecated batches (will ask for confirmation unless ask = FALSE) deprecateBatches(sql_motus, ask = FALSE) # See that there are NO more deprecated batches in the data filter(tbl(sql_motus, "alltags"), batchID == 6000) ## End(Not run)
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Access 'deprecated' table using tbl() from dplyr library(dplyr) tbl(sql_motus, "deprecated") # See that there are deprecated batches in the data filter(tbl(sql_motus, "alltags"), batchID == 6000) # Fetch deprecated batches deprecateBatches(sql_motus, fetchOnly = TRUE) # Remove deprecated batches (will ask for confirmation unless ask = FALSE) deprecateBatches(sql_motus, ask = FALSE) # See that there are NO more deprecated batches in the data filter(tbl(sql_motus, "alltags"), batchID == 6000) ## End(Not run)
alltags
by activity
The activity
table is used to identify batches with too much noise.
Depending on the value of return
these are filtered out, returned, or
identified in the alltags
view with the column probability
. No changes
to the database are made.
filterByActivity( src, return = "good", view = "alltags", minLen = 3, maxLen = 5, maxRuns = 100, ratio = 0.85 )
filterByActivity( src, return = "good", view = "alltags", minLen = 3, maxLen = 5, maxRuns = 100, ratio = 0.85 )
src |
SQLite connection. Result of |
return |
Character. One of "good" (return only 'good' runs), "bad"
(return only 'bad' runs), "all" (return all runs, but with a new
|
view |
Character. Which view to use, one of "alltags" (faster) or "alltagsGPS" (with GPS data). |
minLen |
Numeric. The minimum run length to allow (equal to or below this, all runs are 'bad') |
maxLen |
Numeric. The maximum run length to allow (equal to or above this, all runs are 'good') |
maxRuns |
Numeric. The cutoff of number of runs in a batch (see Details) |
ratio |
Numeric. The ratio cutoff of runs length 2 to number of runs in a batch (see Details) |
Runs are identified by the following:
All runs with a length >= maxLen
are GOOD
All runs with a length <= minLen
are BAD
Runs with a length between minLen
and maxLen
are BAD IF both of the
following is true:
belong to a batch where the number of runs is >= maxRuns
the ratio of runs with a length of 2 to the number of runs total
is >= ratio
tbl_SQLiteConnection
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() tbl_good <- filterByActivity(sql_motus) tbl_bad <- filterByActivity(sql_motus, return = "bad") tbl_all <- filterByActivity(sql_motus, return = "all")
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() tbl_good <- filterByActivity(sql_motus) tbl_bad <- filterByActivity(sql_motus, return = "bad") tbl_all <- filterByActivity(sql_motus, return = "all")
Return the projects and receivers which are accessible by the given credentials
getAccess()
getAccess()
## Not run: getAccess() ## End(Not run)
## Not run: getAccess() ## End(Not run)
To improve speed, the alltags
view doesn't include GPS-related variables
such as gpsLat
, gpsLon
, or gpsAlt
. There is a alltagsGPS
view that
does include GPS-related variables, but this will take time to load. This
function accepts a source and returns the GPS data associated with the
hitID
s in the alltags
view. Optionally, users can supply a
subset of the alltags
view to return only GPS data associated with the
specific hitID
s present in the subset.
getGPS(src, data = NULL, by = "daily", cutoff = NULL, keepAll = FALSE)
getGPS(src, data = NULL, by = "daily", cutoff = NULL, keepAll = FALSE)
src |
SQLite connection. Result of |
data |
SQLite connection or data.frame. Optional subset of the |
by |
Numeric/Character. Either the time in minutes over which to join GPS locations to hits, or "daily" or "closest". To join GPS locations by daily time blocks or by the closest temporal match (see Details). |
cutoff |
Numeric. The maximum allowable time in minutes between hit and
GPS timestamps when matching hits to GPS with |
keepAll |
Logical. Return all hits regardless of whether they have a GPS match? Defaults to FALSE. |
There are three different methods for matching GPS data to hitID
s
all related to timestamps (ts
).
by = X
Where X
is a duration in minutes. ts
is converted to a
specific time block of duration X
. Median GPS lat/longs for the time block
are returned, matching associated hitID
time blocks.
by = "daily"
(the default). Similar to by = X
except the duration is
24hr.
by = "closest"
Individual GPS lat/lons are returned, matching the
closest hitID
timestamp. Use cutoff
to specify the maximum
allowable time between timestamps (defaults to none).
Data frame linking hitID to gpsLat, gpsLon and gpsAlt.
When by = 'daily'
or by = 'X'
, output includes:
hitID
- the ID associated with the hit
gpsLat
\ gpsLon
\ gpsAlt
- the median location calculated from the
available GPS points
gpsTs_min
\ gps_Ts_max
- the range of GPS timestamps associated with
the GPS points binned
When by = 'closest'
or by = 'X'
, output includes:
hitID
- the ID associated with the hit
gpsID
- the ID of the closest GPS point aligned with the hitID
gpsLat
\ gpsLon
\ gpsAlt
- the location of the GPS point
gpsTs
- the timestamp of the GPS point
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Match hits to GPS within 24hrs (daily) of each other my_gps <- getGPS(sql_motus) my_gps # Note that the sample data doesn't have GPS hits so this will be an # empty data frame for project 176. # Match hits to GPS within 15min of each other my_gps <- getGPS(sql_motus, by = 15) my_gps # Match hits to GPS according to the closest timestamp my_gps <- getGPS(sql_motus, by = "closest") my_gps # Match hits to GPS according to the closest timestamp, but limit to within # 20min of each other my_gps <- getGPS(sql_motus, by = "closest", cutoff = 20) my_gps # To return all hits, regardless of whether they match a GPS record my_gps <- getGPS(sql_motus, keepAll = TRUE) my_gps # Alternatively, use the alltagsGPS view: dplyr::tbl(sql_motus, "alltagsGPS")
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Match hits to GPS within 24hrs (daily) of each other my_gps <- getGPS(sql_motus) my_gps # Note that the sample data doesn't have GPS hits so this will be an # empty data frame for project 176. # Match hits to GPS within 15min of each other my_gps <- getGPS(sql_motus, by = 15) my_gps # Match hits to GPS according to the closest timestamp my_gps <- getGPS(sql_motus, by = "closest") my_gps # Match hits to GPS according to the closest timestamp, but limit to within # 20min of each other my_gps <- getGPS(sql_motus, by = "closest", cutoff = 20) my_gps # To return all hits, regardless of whether they match a GPS record my_gps <- getGPS(sql_motus, keepAll = TRUE) my_gps # Alternatively, use the alltagsGPS view: dplyr::tbl(sql_motus, "alltagsGPS")
Receiver database files have names like "SG-1234BBBK06EA.motus" or "Lotek-12345.motus", and project database files have names like "project-52.motus".
getMotusDBSrc( recv = NULL, proj = NULL, create = FALSE, dbDir = motus_vars$dbDir )
getMotusDBSrc( recv = NULL, proj = NULL, create = FALSE, dbDir = motus_vars$dbDir )
recv |
receiver serial number |
proj |
integer motus project number
exactly one of |
create |
Is this a new database? Default: FALSE. Same semantics as for
|
dbDir |
path to folder with existing receiver databases Default:
|
a src_sqlite for the receiver; if the receiver is new, this database will be empty, but have the correct schema.
Specifically the runID
and motusTagID
, ambigID
and tsBegin
to tsEnd
(timestamp) range of runs, filtered by optional parameters. The
match.partial
parameter (default = TRUE) determines how timestamp filtering
works. When match.partial
is FALSE, runID
's are only included when both
tsBegin
and tsEnd
falls between ts.min
and ts.max
(only includes runs
when they entirely contained in the specified range). When match.partial is
TRUE, runID
's are returned whenever the run partially matches the specified
period.
getRuns( src, ts.min = NA, ts.max = NA, match.partial = TRUE, motusTagID = c(), ambigID = c() )
getRuns( src, ts.min = NA, ts.max = NA, match.partial = TRUE, motusTagID = c(), ambigID = c() )
src |
SQLite connection. Result of |
ts.min |
minimum timestamp used to filter the dataframe, Default: NA |
ts.max |
maximum timestamp used to filter the dataframe, Default: NA |
match.partial |
whether runs that partially overlap the specified ts range are included, Default: TRUE |
motusTagID |
vector of Motus tag ID's used to filter the resulting dataframe, Default: c() |
ambigID |
vector of ambig ID's used to filter the resulting dataframe, Default: c() |
a dataframe containing the runID, the motusTagID and the ambigID (if applicable) of runs
Returns a dataframe of the runsFilters
records matching a filter name (and
optionally a project ID) stored in the local database.
getRunsFilters(src, filterName, motusProjID = NA)
getRunsFilters(src, filterName, motusProjID = NA)
src |
SQLite connection. Result of |
filterName |
Character. Unique name given to the filter |
motusProjID |
Character. Optional project ID attached to the filter in order to share with other users of the same project. |
a database connection to src
Download or resume a download of the gpsAll
table in an existing Motus
database. Batch activity refers to the number of hits detected during a given
batch. Batches with large numbers of hits may indicate interference and thus
unreliable hits.
gpsAll(src, resume = TRUE)
gpsAll(src, resume = TRUE)
src |
SQLite connection. Result of |
resume |
Logical. Resume a download? Otherwise the table is removed and the download is started from the beginning. |
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Get all GPS points ## Not run: sql_motus <- gpsAll(sql_motus) # Access 'gpsAll' table library(dplyr) g <- tbl(sql_motus, "gpsAll") # gpsAll resumes a previous download by default # If you want to delete this original data and do a fresh download, # use resume = FALSE ## Not run: sql_motus <- gpsAll(sql_motus, resume = FALSE)
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Get all GPS points ## Not run: sql_motus <- gpsAll(sql_motus) # Access 'gpsAll' table library(dplyr) g <- tbl(sql_motus, "gpsAll") # gpsAll resumes a previous download by default # If you want to delete this original data and do a fresh download, # use resume = FALSE ## Not run: sql_motus <- gpsAll(sql_motus, resume = FALSE)
Returns a dataframe of the filters stored in the local database.
listRunsFilters(src)
listRunsFilters(src)
src |
SQLite connection. Result of |
a dataframe
Updates the entire metadata for receivers and tags from Motus server.
Contrary to tagme()
, this function retrieves the entire set of metadata for
tags and receivers, and not only those pertinent to the detections in your
local file.
metadata(src, projectIDs = NULL, replace = TRUE, delete = FALSE)
metadata(src, projectIDs = NULL, replace = TRUE, delete = FALSE)
src |
SQLite connection. Result of |
projectIDs |
optional integer vector of Motus projects IDs for which metadata should be obtained; default: NULL, meaning obtain metadata for all tags and receivers that your permissions allow. |
replace |
logical scalar; if TRUE (default), existing data replace the existing metadata with the newly acquired ones. |
delete |
logical scalar; Default = FALSE. if TRUE, the entire metadata tables are cleared (for all projects) before re-importing the metadata. |
tagme()
provides an option to update only the metadata
relevant to a specific project or receiver file.
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Add extended metadata to your file ## Not run: metadata(sql_motus) # Access different metadata tables library(dplyr) tbl(sql_motus, "species") tbl(sql_motus, "projs") tbl(sql_motus, "tagDeps") # Etc.
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Add extended metadata to your file ## Not run: metadata(sql_motus) # Access different metadata tables library(dplyr) tbl(sql_motus, "species") tbl(sql_motus, "projs") tbl(sql_motus, "tagDeps") # Etc.
motus
is an R package for retrieving telemetry data from the Motus
Wildlife Tracking System https://motus.org.
For a detailed walk-though and instructions check out the walk-throughs and articles!
Commonly used functions:
Download telemetry data
Create data filters
Summarize data
Plot data
Sunrises and sets
Motus Wildlife Tracking System https://motus.org
Any requests to the motus data server after calling this function will require re-entering a username and password.
motusLogout()
motusLogout()
This function just resets these items to NULL:
motus_vars$authToken
motus_vars$userLogin
motus_vars$userPassword
Due to their active bindings, subsequent calls to any functions that need them will prompt for a login.
TRUE.
Download or resume a download of the 'nodeData' table in an existing Motus
database. nodeData
contains information regarding the 'health' of portable
node units.
nodeData(src, resume = FALSE)
nodeData(src, resume = FALSE)
src |
SQLite connection. Result of |
resume |
Logical. Resume a download? Otherwise the table is removed and the download is started from the beginning. |
This function is automatically run by the tagme()
function with
resume = TRUE
.
If an nodeData
table doesn't exist, it will be created prior to
downloading. If there is an existing nodeData
table, this will update the
records.
Note that only records for CTT tags will have the possibility of
nodeData
.
Node metadata is found in the nodeDeps
table, updated along with other
metadata.
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Access `nodeData` table library(dplyr) a <- tbl(sql_motus, "nodeData") # If you just want to download `nodeData` ## Not run: my_tags <- nodeData(sql_motus)
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Access `nodeData` table library(dplyr) a <- tbl(sql_motus, "nodeData") # If you just want to download `nodeData` ## Not run: my_tags <- nodeData(sql_motus)
Plot latitude/longitude vs time (UTC rounded to the hour) for each tag using motus detection data. Coordinate is by default taken from a receivers deployment latitude in metadata.
plotAllTagsCoord( data, coordinate = "recvDeployLat", ts = "ts", tagsPerPanel = 5 )
plotAllTagsCoord( data, coordinate = "recvDeployLat", ts = "ts", tagsPerPanel = 5 )
data |
a selected table from motus data, eg. "alltags", or a data.frame
of detection data including at a minimum variables for |
coordinate |
column name from which to obtain location values, by
default it is set to |
ts |
Character. Name of column with timestamp values, defaults to |
tagsPerPanel |
number of tags in each panel of the plot, by default this is 5 |
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltags") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Plot tbl file tbl_alltags with default GPS latitude data and 5 tags per panel plotAllTagsCoord(tbl_alltags) # Plot an sql file tbl_alltags with 10 tags per panel plotAllTagsCoord(tbl_alltags, tagsPerPanel = 10) # Plot dataframe df_alltags using receiver deployment latitudes with default # 5 tags per panel plotAllTagsCoord(df_alltags, coordinate = "recvDeployLat") # Plot dataframe df_alltags using LONGITUDES and 10 tags per panel # But only works if non-NA "gpsLon"! ## Not run: plotAllTagsCoord(df_alltags, coordinate = "gpsLon", tagsPerPanel = 10) # Plot dataframe df_alltags using lat for select motus tagIDs plotAllTagsCoord(filter(df_alltags, motusTagID %in% c(19129, 16011, 17357)), tagsPerPanel = 1)
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltags") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Plot tbl file tbl_alltags with default GPS latitude data and 5 tags per panel plotAllTagsCoord(tbl_alltags) # Plot an sql file tbl_alltags with 10 tags per panel plotAllTagsCoord(tbl_alltags, tagsPerPanel = 10) # Plot dataframe df_alltags using receiver deployment latitudes with default # 5 tags per panel plotAllTagsCoord(df_alltags, coordinate = "recvDeployLat") # Plot dataframe df_alltags using LONGITUDES and 10 tags per panel # But only works if non-NA "gpsLon"! ## Not run: plotAllTagsCoord(df_alltags, coordinate = "gpsLon", tagsPerPanel = 10) # Plot dataframe df_alltags using lat for select motus tagIDs plotAllTagsCoord(filter(df_alltags, motusTagID %in% c(19129, 16011, 17357)), tagsPerPanel = 1)
Plot deployment (ordered by latitude) vs time (UTC) for each tag
plotAllTagsSite(data, coordinate = "recvDeployLat", tagsPerPanel = 5)
plotAllTagsSite(data, coordinate = "recvDeployLat", tagsPerPanel = 5)
data |
a selected table from .motus data, eg. "alltags", or a data.frame
of detection data including at a minimum variables for |
coordinate |
column of receiver latitude/longitude values to use,
defaults to |
tagsPerPanel |
number of tags in each panel of the plot, default is 5 |
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltags") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Plot detections of dataframe df_alltags by site ordered by latitude, with # default 5 tags per panel plotAllTagsSite(df_alltags) # Plot detections of dataframe df_alltags by site ordered by latitude, with # 10 tags per panel plotAllTagsSite(df_alltags, tagsPerPanel = 10) # Plot detections of tbl file tbl_alltags by site ordered by receiver # deployment latitude plotAllTagsSite(tbl_alltags, coordinate = "recvDeployLon") # Plot tbl file tbl_alltags using 3 tags per panel for species Red Knot plotAllTagsSite(filter(tbl_alltags, speciesEN == "Red Knot"), tagsPerPanel = 3)
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltags") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Plot detections of dataframe df_alltags by site ordered by latitude, with # default 5 tags per panel plotAllTagsSite(df_alltags) # Plot detections of dataframe df_alltags by site ordered by latitude, with # 10 tags per panel plotAllTagsSite(df_alltags, tagsPerPanel = 10) # Plot detections of tbl file tbl_alltags by site ordered by receiver # deployment latitude plotAllTagsSite(tbl_alltags, coordinate = "recvDeployLon") # Plot tbl file tbl_alltags using 3 tags per panel for species Red Knot plotAllTagsSite(filter(tbl_alltags, speciesEN == "Red Knot"), tagsPerPanel = 3)
Plots total number of detections across all tags, and total number of tags
detected per day for a specified site. Depends on siteSumDaily()
.
plotDailySiteSum(data, recvDeployName)
plotDailySiteSum(data, recvDeployName)
data |
a selected table from .motus data, eg. "alltagsGPS", or a
data.frame of detection data including at a minimum variables for
|
recvDeployName |
name of site to plot |
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltagsGPS") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Plot of all tag detections at site Longridge using dataframe df_alltags plotDailySiteSum(df_alltags, recvDeployName = "Longridge") # Plot of all tag detections at site Niapiskau using tbl file tbl_alltags plotDailySiteSum(df_alltags, recvDeployName = "Niapiskau")
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltagsGPS") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Plot of all tag detections at site Longridge using dataframe df_alltags plotDailySiteSum(df_alltags, recvDeployName = "Longridge") # Plot of all tag detections at site Niapiskau using tbl file tbl_alltags plotDailySiteSum(df_alltags, recvDeployName = "Niapiskau")
Google map of routes of Motus tag detections coloured by ID. User defines a date range to show points for receivers that were operational at some point during the date range.
plotRouteMap( src, maptype = "osm", zoom = NULL, start_date = NULL, end_date = NULL, lim_lat = NULL, lim_lon = NULL, data, lat, lon, recvStart, recvEnd )
plotRouteMap( src, maptype = "osm", zoom = NULL, start_date = NULL, end_date = NULL, lim_lat = NULL, lim_lon = NULL, data, lat, lon, recvStart, recvEnd )
src |
SQLite connection. Result of |
maptype |
Character. Map tiles to use. Must be one of |
zoom |
Integer. Override the calculated zoom level to increase or decrease the resolution of the map tiles. |
start_date |
Character. Optional start date for routes. |
end_date |
Character. Optional end date for routes. |
lim_lat |
Numeric vector. Optional latitudinal plot limits. |
lim_lon |
Numeric vector. Optional longitudinal plot limits. |
data |
Defunct, use |
lat |
Defunct |
lon |
Defunct |
recvStart |
Defunct |
recvEnd |
Defunct |
By default this function uses OSM maps (Open Street Map). OSM and many other map tiles are released under specific licences, which generally require that you give attribution at a minimum. See OSM for more details on their tiles, but remember to check what other groups require if you use their tiles.
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Plot route map of all detection data, with "osm" maptype, and receivers # active between 2016-01-01 and 2017-01-01 plotRouteMap(sql_motus, start_date = "2016-01-01", end_date = "2016-12-31")
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Plot route map of all detection data, with "osm" maptype, and receivers # active between 2016-01-01 and 2017-01-01 plotRouteMap(sql_motus, start_date = "2016-01-01", end_date = "2016-12-31")
Plot tag ID vs time for all tags detected by site, coloured by antenna
bearing. Input is expected to be a data frame, database table, or database.
The data must contain "ts", "antBearing", "fullID", "recvDeployName",
"recvDeployLat", "recvDeployLon", and optionally "gpsLat" and "gpsLon". If
GPS lat/lon are included, they will be used rather than recvDeployLat/Lon.
These data are generally contained in the alltags
or the alltagsGPS
views. If a motus database is submitted, the alltagsGPS
view will be used.
plotSite(df_src, sitename = NULL, ncol = NULL, nrow = NULL, data)
plotSite(df_src, sitename = NULL, ncol = NULL, nrow = NULL, data)
df_src |
Data frame, SQLite connection, or SQLite table. An SQLite
connection would be the result of |
sitename |
Character vector. Subset of sites to plot. If |
ncol |
Numeric. Passed on to |
nrow |
Numeric. Passed on to |
data |
Defunct, use |
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltagsGPS") # Plot all sites within file for tbl file tbl_alltags plotSite(tbl_alltags) # Plot only detections at a specific site; Piskwamish plotSite(tbl_alltags, sitename = "Piskwamish") # For more custom filtering, convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- collect(tbl_alltags) # Plot only detections for specified tags for data.frame df_alltags plotSite(filter(df_alltags, motusTagID %in% c(16047, 16037, 16039)))
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltagsGPS") # Plot all sites within file for tbl file tbl_alltags plotSite(tbl_alltags) # Plot only detections at a specific site; Piskwamish plotSite(tbl_alltags, sitename = "Piskwamish") # For more custom filtering, convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- collect(tbl_alltags) # Plot only detections for specified tags for data.frame df_alltags plotSite(filter(df_alltags, motusTagID %in% c(16047, 16037, 16039)))
Plot signal strength vs time for all tags detected at a specified site, coloured by antenna
plotSiteSig(data, recvDeployName)
plotSiteSig(data, recvDeployName)
data |
a selected table from .motus data, eg. "alltags", or a data.frame
of detection data including at a minimum variables for |
recvDeployName |
name of |
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltags") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Plot all tags for site Piskwamish plotSiteSig(tbl_alltags, recvDeployName = "Piskwamish") # Plot select tags for site Piskwamish plotSiteSig(filter(df_alltags, motusTagID %in% c(16037, 16039, 16035)), recvDeployName = "Netitishi")
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltags") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Plot all tags for site Piskwamish plotSiteSig(tbl_alltags, recvDeployName = "Piskwamish") # Plot select tags for site Piskwamish plotSiteSig(filter(df_alltags, motusTagID %in% c(16037, 16039, 16035)), recvDeployName = "Netitishi")
Plot signal strength vs time for specified tag, faceted by site (ordered by latitude) and coloured by antenna
plotTagSig(data, motusTagID)
plotTagSig(data, motusTagID)
data |
a selected table from .motus data, eg. "alltagsGPS", or a data.frame of detection data including at a minimum variables for motusTagID, sig, ts, antBearing, recvDeployLat, fullID, recvDeployName, gpsLat, gpsLon |
motusTagID |
a numeric motusTagID to display in plot |
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltagsGPS") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Plot signal strength of a specified tag using dataframe df_alltags plotTagSig(df_alltags, motusTagID = 16047) # Plot signal strength of a specified tag using tbl file tbl_alltags plotTagSig(tbl_alltags, motusTagID = 16035)
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltagsGPS") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Plot signal strength of a specified tag using dataframe df_alltags plotTagSig(df_alltags, motusTagID = 16047) # Plot signal strength of a specified tag using tbl file tbl_alltags plotTagSig(tbl_alltags, motusTagID = 16035)
Converts a data frame with a list of lat/lons to a spatial data frame with
MULTILINES defining paths by tag id. Useful for plotting with
ggplot2::geom_sf()
. Will silently remove single points.
points2Path(df, by = "fullID", lat = "recvDeployLat", lon = "recvDeployLon")
points2Path(df, by = "fullID", lat = "recvDeployLat", lon = "recvDeployLon")
df |
Data frame. Could be the result of |
by |
Character. Column defining the tag id over which to group points into paths. Defaults to "fullID". |
lat |
Character. Name of column with latitude values, defaults to
|
lon |
Character. Name of column with longitude values, defaults to
|
Spatial data frame with MULTILINE paths
Creates a dataframe consisting of detections of tags that are detected at two or more receiver at the same time.
simSiteDet(data)
simSiteDet(data)
data |
a selected table from .motus data, eg. "alltags", or a data.frame
of detection data including at a minimum variables for |
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltags") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # To get a data.frame of just simultaneous detections from a tbl file # tbl_alltags simSites <- simSiteDet(tbl_alltags) # To get a data.frame of just simultaneous detections from a dataframe # df_alltags simSites <- simSiteDet(df_alltags)
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltags") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # To get a data.frame of just simultaneous detections from a tbl file # tbl_alltags simSites <- simSiteDet(tbl_alltags) # To get a data.frame of just simultaneous detections from a dataframe # df_alltags simSites <- simSiteDet(df_alltags)
Creates a summary of the first and last detection at a site, the length of time between first and last detection, the number of tags, and the total number of detections at a site. Plots total number of detections across all tags, and total number of tags detected at each site.
siteSum(data, units = "hours")
siteSum(data, units = "hours")
data |
a selected table from .motus data, eg. "alltagsGPS", or a data.frame of detection data including at a minimum variables for motusTagID, sig, recvDeployLat, recvDeployLon, recvDeployName, ts, gpsLat, and gpsLon |
units |
units to display time difference, defaults to "hours", options include "secs", "mins", "hours", "days", "weeks" |
a data.frame with these columns:
site: site
first_ts: time of first detection at specified site
last_ts: time of last detection at specified site
tot_ts: total amount of time between first and last detection at specified site, output in specified unit (defaults to "hours")
num.tags: total number of unique tags detected at specified site
num.det: total number of tag detections at specified site
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltagsGPS") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Create site summaries for all sites within detection data with time in # default hours using data.frame df_alltags site_summary <- siteSum(tbl_alltags) # Create site summaries for only select sites with time in minutes sub <- filter(df_alltags, recvDeployName %in% c("Niapiskau", "Netitishi", "Old Cur", "Washkaugou")) site_summary <- siteSum(sub, units = "mins") # Create site summaries for only a select species, Red Knot site_summary <- siteSum(filter(df_alltags, speciesEN == "Red Knot"))
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltagsGPS") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Create site summaries for all sites within detection data with time in # default hours using data.frame df_alltags site_summary <- siteSum(tbl_alltags) # Create site summaries for only select sites with time in minutes sub <- filter(df_alltags, recvDeployName %in% c("Niapiskau", "Netitishi", "Old Cur", "Washkaugou")) site_summary <- siteSum(sub, units = "mins") # Create site summaries for only a select species, Red Knot site_summary <- siteSum(filter(df_alltags, speciesEN == "Red Knot"))
Creates a summary of the first and last daily detection at a site, the length
of time between first and last detection, the number of tags, and the total
number of detections at a site for each day. Same as siteSum()
, but daily
by site.
siteSumDaily(data, units = "hours")
siteSumDaily(data, units = "hours")
data |
a selected table from .motus data, eg. "alltagsGPS", or a
data.frame of detection data including at a minimum variables for
|
units |
units to display time difference, defaults to "hours", options include "secs", "mins", "hours", "days", "weeks" |
a data.frame with these columns:
recvDeployName: site name of deployment
date: date that is being summarized
first_ts: time of first detection on specified "date" at "recvDeployName"
last_ts: time of last detection on specified "date" at "recvDeployName"
tot_ts: total amount of time between first and last detection at "recvDeployName" on "date, output in specified unit (defaults to "hours")
num.tags: total number of unique tags detected at "recvDeployName", on "date"
num.det: total number of detections at "recvDeployName", on "date"
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltagsGPS") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Create site summaries for all sites within detection data with time in # minutes using tbl file tbl_alltags daily_site_summary <- siteSumDaily(tbl_alltags, units = "mins") # Create site summaries for only select sites with time in minutes using tbl # file tbl_alltags sub <- filter(tbl_alltags, recvDeployName %in% c("Niapiskau", "Netitishi", "Old Cut", "Washkaugou")) daily_site_summary <- siteSumDaily(sub, units = "mins") # Create site summaries for only a select species, Red Knot, with default # time in hours using data frame df_alltags daily_site_summary <- siteSumDaily(filter(df_alltags, speciesEN == "Red Knot"))
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltagsGPS") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Create site summaries for all sites within detection data with time in # minutes using tbl file tbl_alltags daily_site_summary <- siteSumDaily(tbl_alltags, units = "mins") # Create site summaries for only select sites with time in minutes using tbl # file tbl_alltags sub <- filter(tbl_alltags, recvDeployName %in% c("Niapiskau", "Netitishi", "Old Cut", "Washkaugou")) daily_site_summary <- siteSumDaily(sub, units = "mins") # Create site summaries for only a select species, Red Knot, with default # time in hours using data frame df_alltags daily_site_summary <- siteSumDaily(filter(df_alltags, speciesEN == "Red Knot"))
Creates a dataframe of transitions between sites; detections are ordered by detection time, then "transitions" are identified as the period between the final detection at site x (possible "departure"), and the first detection (possible "arrival") at site y (ordered chronologically). Each row contains the last detection time and lat/lon of site x, first detection time and lat/lon of site y, distance between the site pair, time between detections, rate of movement between detections, and bearing between site pairs.
siteTrans(data, latCoord = "recvDeployLat", lonCoord = "recvDeployLon")
siteTrans(data, latCoord = "recvDeployLat", lonCoord = "recvDeployLon")
data |
a selected table from .motus data, eg. "alltagsGPS", or a
data.frame of detection data including at a minimum variables for |
latCoord |
a variable with numeric latitude values, defaults to
|
lonCoord |
a variable with numeric longitude values, defaults to
|
a data.frame with these columns:
fullID: fullID of Motus registered tag
ts.x: time of last detection of tag at site.x ("departure" time)
lat.x: latitude of site.x
lon.x: longitude of site.x
site.x: first site in transition pair (the "departure" site)
ts.y: time of first detection of tag at site.y ("arrival" time)
lat.y: latitude of site.y
lon.y: longitude of site.y
site.y: second site in transition pair (the "departure" site)
tot_ts: length of time between ts.x and ts.y (in seconds)
dist: total straight line distance between site.x and site.y (in metres),
see sensorgnome::latLonDist()
for details
rate: overall rate of movement (tot_ts/dist), in metres/second
bearing: bearing between first and last detection sites, see bearing function in geosphere package for more details
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltagsGPS") ## convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # View all site transitions for all detection data from tbl file tbl_alltags transitions <- siteTrans(tbl_alltags) # View site transitions for only tag 16037 from data.frame df_alltags using # gpsLat/gpsLon transitions <- siteTrans(filter(df_alltags, motusTagID == 16037), latCoord = "gpsLat", lonCoord = "gpsLon")
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltagsGPS") ## convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # View all site transitions for all detection data from tbl file tbl_alltags transitions <- siteTrans(tbl_alltags) # View site transitions for only tag 16037 from data.frame df_alltags using # gpsLat/gpsLon transitions <- siteTrans(filter(df_alltags, motusTagID == 16037), latCoord = "gpsLat", lonCoord = "gpsLon")
Sets, resets or returns the "motus.timeout" global option used by all API
access functions (including tagme()
). If timeout
is a number and reset
is FALSE
, the API timeout is set to timeout
number of seconds. If reset
is TRUE
, the API timeout is reset to the default of 120 seconds. If no
timeout
is defined and reset = FALSE
, the current value of the timeout
is returned.
srvTimeout(timeout, reset = FALSE)
srvTimeout(timeout, reset = FALSE)
timeout |
Numeric. Number of seconds to wait for a response from the server. Increase if you're working with a project that requires extra time to process and serve the data. |
reset |
Logical. Whether to reset the timeout to the default (120s;
default |
By default the timeout is 120s, which generally should
give the server sufficient time to prepare the data without having the user
wait for too long if the API is unavailable. However, some projects take
unusually long to compile the data, so a longer timeout may be warranted in
those situations. This is equivalent to options(motus.timeout = timeout)
Nothing. Or, if timeout
is missing and reset = FALSE
, the current
timeout value.
srvTimeout() # get the timeout value srvTimeout(5) # set the timeout value srvTimeout() # get the timeout value ## Not run: # No problem with default timeouts t <- tagme(176, new = TRUE) # But setting the timeout too short results in a server timeout srvTimeout(0.001) t <- tagme(176, new = TRUE) ## End(Not run)
srvTimeout() # get the timeout value srvTimeout(5) # set the timeout value srvTimeout() # get the timeout value ## Not run: # No problem with default timeouts t <- tagme(176, new = TRUE) # But setting the timeout too short results in a server timeout srvTimeout(0.001) t <- tagme(176, new = TRUE) ## End(Not run)
Create and add sunrise and sunset columns to tag data. Can take a motus database table, but will always return a collected data frame. Requires data containing at least latitude, longitude, and time.
sunRiseSet( df_src, lat = "recvDeployLat", lon = "recvDeployLon", ts = "ts", data )
sunRiseSet( df_src, lat = "recvDeployLat", lon = "recvDeployLon", ts = "ts", data )
df_src |
Data frame, SQLite connection, or SQLite table. An SQLite
connection would be the result of |
lat |
Character. Name of column with latitude values, defaults to
|
lon |
Character. Name of column with longitude values, defaults to
|
ts |
Character. Name of column with timestamp values, defaults to |
data |
Defunct, use |
Note that this will always return the sunrise and sunset of the local date. For example, 2023-01-01 04:00:00 in Central North American time is 2023-01-01 in UTC, but 2023-01-01 20:00:00 is actually the following date in UTC. Because Motus timestamps are UTC, times are first converted to their local time zone time using the lat/lon coordinates before extracting the date. Thus:
A UTC timestamp of 1672624800 for Winnipeg, Canada is 2023-01-02 02:00:00 UTC and 2023-01-01 20:00:00 local time
Therefore sunRiseSet()
calculates the sunrise/sunset times for 2023-01-01
(not for 2023-01-02)
These sunrise/sunset times are returned in UTC: 2023-01-01 14:27:50 UTC and 2023-01-01 22:38:30 UTC
Note that the UTC timestamp 2023-01-02 02:00:00 is later than the sunset time of 2023-01-01 22:38:30 UTC. This makes sense, as we know that the timestamp is ~8pm local time, well after sunset in the winter for that date.
Original data (as a flat data frame), with the following additional columns:
sunrise
- Time of sunrise in UTC for that row's date and location
sunset
- Time of sunset in UTC for that row's date and location
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # For SQLite Data base----------------------------------------------- sun <- sunRiseSet(sql_motus) # For specific SQLite table/view ------------------------------------ library(dplyr) tbl_alltagsGPS <- tbl(sql_motus, "alltagsGPS") sun <- sunRiseSet(tbl_alltagsGPS) # For a flattened data frame ---------------------------------------- df_alltagsGPS <- collect(tbl_alltagsGPS) sun <- sunRiseSet(df_alltagsGPS) # Using alternate lat/lons ------------------------------------------ # Get sunrise and sunset information from tbl_alltags using gps lat/lon # Note this will only work if there are non-NA values in gpsLat/gpsLon ## Not run: sun <- sunRiseSet(tbl_alltagsGPS, lat = "gpsLat", lon = "gpsLon")
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # For SQLite Data base----------------------------------------------- sun <- sunRiseSet(sql_motus) # For specific SQLite table/view ------------------------------------ library(dplyr) tbl_alltagsGPS <- tbl(sql_motus, "alltagsGPS") sun <- sunRiseSet(tbl_alltagsGPS) # For a flattened data frame ---------------------------------------- df_alltagsGPS <- collect(tbl_alltagsGPS) sun <- sunRiseSet(df_alltagsGPS) # Using alternate lat/lons ------------------------------------------ # Get sunrise and sunset information from tbl_alltags using gps lat/lon # Note this will only work if there are non-NA values in gpsLat/gpsLon ## Not run: sun <- sunRiseSet(tbl_alltagsGPS, lat = "gpsLat", lon = "gpsLon")
This is the main motus function for accessing and updating your data. This
function downloads motus data to a local SQLite data base in the name of
project-XXX.motus
or RECIVER_NAME.motus
. If you are having trouble with
a particular data base timing out on downloads, see srvTimeout()
for
options.
tagme( projRecv, update = TRUE, new = FALSE, dir = getwd(), countOnly = FALSE, forceMeta = FALSE, rename = FALSE, skipActivity = FALSE, skipNodes = FALSE, skipDeprecated = FALSE )
tagme( projRecv, update = TRUE, new = FALSE, dir = getwd(), countOnly = FALSE, forceMeta = FALSE, rename = FALSE, skipActivity = FALSE, skipNodes = FALSE, skipDeprecated = FALSE )
projRecv |
Numeric. Project code from motus.org, or character receiver serial number. |
update |
Logical. Download and merge new data (Default |
new |
Logical. Create a new database (Default |
dir |
Character. Path to the folder where you are storing databases
IF |
countOnly |
Logical. If |
forceMeta |
Logical. If |
rename |
Logical. If current SQLite database is of an older data
version, automatically rename that database for backup purposes and
download the newest version. If |
skipActivity |
Logical. Skip checking for and downloading |
skipNodes |
Logical. Skip checking for and downloading |
skipDeprecated |
Logical. Skip fetching list of deprecated batches
stored in |
a SQLite Connection for the (possibly updated) database, or a data
frame of counts if countOnly = TRUE
.
tellme()
, which is a synonym for
tagme(..., countOnly = TRUE)
## Not run: # Create and update a local tag database for motus project 14 in the # current directory t <- tagme(14, new = TRUE) # Update and open the local tag database for motus project 14; # it must already exist and be in the current directory t <- tagme(14) # Update and open the local tag database for a receiver; # it must already exist and be in the current directory t <- tagme("SG-1234BBBK4567") # Open the local tag database for a receiver, without # updating it t <- tagme("SG-1234BBBK4567", update = FALSE) # Open the local tag database for a receiver, but # tell 'tagme' that it is in a specific directory t <- tagme("SG-1234BBBK4567", dir = "Projects/gulls") # Update all existing project and receiver databases in the current working # directory tagme() ## End(Not run)
## Not run: # Create and update a local tag database for motus project 14 in the # current directory t <- tagme(14, new = TRUE) # Update and open the local tag database for motus project 14; # it must already exist and be in the current directory t <- tagme(14) # Update and open the local tag database for a receiver; # it must already exist and be in the current directory t <- tagme("SG-1234BBBK4567") # Open the local tag database for a receiver, without # updating it t <- tagme("SG-1234BBBK4567", update = FALSE) # Open the local tag database for a receiver, but # tell 'tagme' that it is in a specific directory t <- tagme("SG-1234BBBK4567", dir = "Projects/gulls") # Update all existing project and receiver databases in the current working # directory tagme() ## End(Not run)
For running examples and testing out motus functionality, it can be useful to
work with sample data set. You can download the most up-to-date copy of this
data yourself (to project-176.motus
) with the username and password both
"motus.sample".
tagmeSample(db = "project-176.motus")
tagmeSample(db = "project-176.motus")
db |
Character. Name of sample data base to load. The sample data is "project-176.motus". |
sql_motus <- tagme(176, new = TRUE)
Or you can use this helper function to grab an in-memory copy bundled in this package.
In memory version of the sample database.
# Explore the sample data tags <- tagmeSample() dplyr::tbl(tags, "activity") dplyr::tbl(tags, "alltags")
# Explore the sample data tags <- tagmeSample() dplyr::tbl(tags, "activity") dplyr::tbl(tags, "alltags")
Creates a summary for each tag of it's first and last detection time (ts
),
first and last detection site, length of time between first and last
detection, straight line distance between first and last detection site, rate
of movement, and bearing. Lat/lons are taken from gpsLat
/gpsLon
, or if
missing, from recvDeployLat
/recvDeployLon
. Bearing is calculated using
the geosphere::bearing()
function.
tagSum(df_src, data)
tagSum(df_src, data)
df_src |
Data frame, SQLite connection, or SQLite table. An SQLite
connection would be the result of |
data |
Defunct, use |
A flat data frame with the following for each tag:
fullID
- fullID
of Motus registered tag
first_ts
- Time (ts
) of first detection
last_ts
- Time (ts
) of last detection
first_site
- First detection site (recvDeployName
)
last_site
- Last detection site (recvDeployName
)
recvLat.x
- Latitude of first detection site (gpsLat
or recvDeployLat
)
recvLon.x
- Longitude of first detection site (gpsLon
or recvDeployLon
)
recvLat.y
- Latitude of last detection site (gpsLat
or recvDeployLat
)
recvLon.y
- Longitude of last detection site (gpsLon
or recvDeployLon
)
tot_ts
- Time between first and last detection (in seconds)
dist
- Straight line distance between first and last detection site (in metres)
rate
- Overall rate of movement (tot_ts
/dist
), in metres/second
bearing
- Bearing between first and last detection sites
num_det
- Number of detections summarized
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Summarize tags tag_summary <- tagSum(sql_motus) # For specific SQLite table/view (needs gpsLat/gpsLon) -------------- library(dplyr) tbl_alltagsGPS <- tbl(sql_motus, "alltagsGPS") tag_summary <- tagSum(tbl_alltagsGPS) # For a flattened data frame ---------------------------------------- df_alltagsGPS <- collect(tbl_alltagsGPS) tag_summary <- tagSum(df_alltagsGPS) # Can be filtered, e.g., for only a few tags tag_summary <- tagSum(filter(tbl_alltagsGPS, motusTagID %in% c(16047, 16037, 16039)))
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Summarize tags tag_summary <- tagSum(sql_motus) # For specific SQLite table/view (needs gpsLat/gpsLon) -------------- library(dplyr) tbl_alltagsGPS <- tbl(sql_motus, "alltagsGPS") tag_summary <- tagSum(tbl_alltagsGPS) # For a flattened data frame ---------------------------------------- df_alltagsGPS <- collect(tbl_alltagsGPS) tag_summary <- tagSum(df_alltagsGPS) # Can be filtered, e.g., for only a few tags tag_summary <- tagSum(filter(tbl_alltagsGPS, motusTagID %in% c(16047, 16037, 16039)))
Creates a summary for each tag of it's first and last detection time at each site, length of time between first and last detection of each site, and total number of detections at each site.
tagSumSite(data, units = "hours")
tagSumSite(data, units = "hours")
data |
a selected table from .motus data, eg. "alltagsGPS", or a data.frame of detection data including at a minimum variables for motusTagID, fullID, recvDeployName, ts, recvDeployLat, recvDeployLon, gpsLat, gpsLon |
units |
units to display time difference, defaults to "hours", options include "secs", "mins", "hours", "days", "weeks" |
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltagsGPS") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Create tag summaries for all tags within detection data with time in # minutes with tbl file tbl_alltags tag_site_summary <- tagSumSite(tbl_alltags, units = "mins") # Create tag summaries for only select tags with time in default hours with # data.frame df_alltags tag_site_summary <- tagSumSite(filter(df_alltags, motusTagID %in% c(16047, 16037, 16039))) # Create tag summaries for only a select species with data.frame df_alltags tag_site_summary <- tagSumSite(filter(df_alltags, speciesEN == "Red Knot"))
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # convert sql file "sql_motus" to a tbl called "tbl_alltags" library(dplyr) tbl_alltags <- tbl(sql_motus, "alltagsGPS") # convert the tbl "tbl_alltags" to a data.frame called "df_alltags" df_alltags <- tbl_alltags %>% collect() %>% as.data.frame() # Create tag summaries for all tags within detection data with time in # minutes with tbl file tbl_alltags tag_site_summary <- tagSumSite(tbl_alltags, units = "mins") # Create tag summaries for only select tags with time in default hours with # data.frame df_alltags tag_site_summary <- tagSumSite(filter(df_alltags, motusTagID %in% c(16047, 16037, 16039))) # Create tag summaries for only a select species with data.frame df_alltags tag_site_summary <- tagSumSite(filter(df_alltags, speciesEN == "Red Knot"))
"new" means data not already in your local database.
tellme(projRecv, new = FALSE, dir = getwd())
tellme(projRecv, new = FALSE, dir = getwd())
projRecv |
Numeric. Project code from motus.org, or character receiver serial number. |
new |
Logical. Create a new database (Default |
dir |
Character. Path to the folder where you are storing databases
IF |
a named list with these items:
numBatches: number of batches having data for your database
numRuns: number of runs of tags detections with new data
numHits: number of new detections
numGPS: number of new GPS fixes covering the new detections
numBytes: estimated size of download, in bytes. This is an estimate of the uncompressed size, but data are gz-compressed for transfer, so the number of bytes you have to download is typically going to be smaller than this number by a factor of 2 or more.
if you specify new = TRUE
and the database does not already exist, it
will be created (but empty).
Create and add columns for time to and time since sunrise/sunset to tag data. Can take a motus database table, but will always return a collected data frame. Requires data containing at least latitude, longitude, and time.
timeToSunriset( df_src, lat = "recvDeployLat", lon = "recvDeployLon", ts = "ts", units = "hours", data )
timeToSunriset( df_src, lat = "recvDeployLat", lon = "recvDeployLon", ts = "ts", units = "hours", data )
df_src |
Data frame, SQLite connection, or SQLite table. An SQLite
connection would be the result of |
lat |
Character. Name of column with latitude values, defaults to
|
lon |
Character. Name of column with longitude values, defaults to
|
ts |
Character. Name of column with time as numeric or POSIXct, defaults
to |
units |
Character. Units to display time difference, defaults to "hours", options include "secs", "mins", "hours", "days", "weeks". |
data |
Defunct, use |
Uses sunRiseSet()
to perform sunrise/sunset calculates, see ?sunRiseSet
for details regarding how local dates are assessed from UTC timestamps.
Original data (as a flat data frame), with the following additional columns:
sunrise
- Time of sunrise in UTC for that row's date and location
sunset
- Time of sunset in UTC for that row's date and location
ts_to_set
- Time to next sunset, in units
ts_since_set
- Time to previous sunset, in units
ts_to_rise
- Time to next sunrise after, in units
ts_since_rise
- Time to previous sunrise, in units
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Get sunrise and sunset information for alltags view with units in minutes sunrise <- timeToSunriset(sql_motus, units = "mins")
# Download sample project 176 to .motus database (username/password are "motus.sample") ## Not run: sql_motus <- tagme(176, new = TRUE) # Or use example data base in memory sql_motus <- tagmeSample() # Get sunrise and sunset information for alltags view with units in minutes sunrise <- timeToSunriset(sql_motus, units = "mins")
Write to the local database the probabilities associated with runs for a filter
writeRunsFilter( src, filterName, motusProjID = NA, df, overwrite = TRUE, delete = FALSE )
writeRunsFilter( src, filterName, motusProjID = NA, df, overwrite = TRUE, delete = FALSE )
src |
SQLite connection. Result of |
filterName |
Character. Unique name given to the filter |
motusProjID |
Character. Optional project ID attached to the filter in order to share with other users of the same project. |
df |
Data frame. Containing |
overwrite |
Logical. When |
delete |
Logical. When TRUE, removes all existing filter records
associated with the |
database connection refering to the filter created