Description Usage Arguments Value Examples
View source: R/data_cleaning.R
Cleans a raw animal GPS dataset, implementing a standardized procedure to remove impossible values
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df |
data frame in standardized format (e.g., from a raw spreadsheet) |
dtype |
data type, iGotU or Columbus P-1 |
prep |
reformat columns if all required columns are not present, defaults to True |
filters |
filter bad data points, defaults to true |
aniid |
identification code for the animal |
gpsid |
identification code for the GPS device |
maxrate |
maximum rate of travel (meters/minute) between consecutive points |
maxcourse |
maximum distance (meters) between consecutive points |
maxdist |
maximum geographic distance (meters) between consecutive points |
maxtime |
maximum time (minutes) between consecutive points |
tz_in |
input time zone, defaults to UTC |
tz_out |
output time zone, defaults to UTC |
data frame of clean animal GPS data
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | # Clean a data frame from csv
## Read igotU data
bannock_df <- read.csv(system.file("extdata", "demo_nov19/Bannock_2017_101_1149.csv",
package = "animaltracker"), skipNul=TRUE)
## Clean and filter
clean_location_data(bannock_df, dtype = "igotu", filters = TRUE, aniid = 1149,
gpsid = 101, maxrate = 84, maxdist = 840, maxtime = 100)
## Clean without filtering
clean_location_data(bannock_df, dtype = "igotu", filters = FALSE, aniid = 1149,
gpsid = 101, maxrate = 84, maxdist = 840, maxtime = 100)
# Clean a data frame from txt
## Read Columbus P-1 data
columbus_df <- read_columbus(system.file("extdata", "demo_columbus.TXT",
package = "animaltracker"))
## Clean and filter
clean_location_data(columbus_df, dtype = "columbus", filters = TRUE, aniid = 1149,
gpsid = 101, maxrate = 84, maxdist = 840, maxtime = 100)
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