This is an overview of how to use functions in the feedr
package to transform and visual your RFID data. Note that while we’ve tried to explain the code in detail, if you are new to R, you may benefit from an introduction to coding in R before tackling your data with the feedr
package
This package contains functions to perform three broad services:
There are also three ‘user-interface’ (UI) functions that can be used to simplify the use of these functions (see below).
In the normal order of operations, several functions depend on the output of other functions.
For example, to get daily activity patterns, you could get your data from the animalnexus database with dl_data()
, turn this raw data to visits data with visits()
, turn visits data to activity data with activity()
and finally, turn activity data into daily activity patterns with daily()
.
Two data frames, finches
and chickadees
are included in the feedr
package for the purpose of demonstration.
head(finches)
## animal_id date time logger_id species age sex site_name lon lat
## 1 0620000514 2016-01-28 2016-01-28 12:34:25 2200 House Finch AHY F Kamloops, BC -120.3612 50.66778
## 2 0620000514 2016-01-28 2016-01-28 12:34:28 2200 House Finch AHY F Kamloops, BC -120.3612 50.66778
## 3 041868D861 2016-01-28 2016-01-28 12:35:41 2200 House Finch AHY M Kamloops, BC -120.3612 50.66778
## 4 06200004F8 2016-01-28 2016-01-28 12:35:52 2200 House Finch AHY F Kamloops, BC -120.3612 50.66778
## 5 06200004F8 2016-01-28 2016-01-28 12:35:59 2200 House Finch AHY F Kamloops, BC -120.3612 50.66778
## 6 06200004F8 2016-01-28 2016-01-28 12:36:02 2200 House Finch AHY F Kamloops, BC -120.3612 50.66778
head(chickadees)
## animal_id date time logger_id experiment lat lon
## 1 06200004BF 2016-01-11 2016-01-11 10:48:49 exp2-GR10DATA exp2 53.89086 -122.8193
## 2 062000038F 2016-01-11 2016-01-11 10:48:50 exp2-GR10DATA exp2 53.89086 -122.8193
## 3 06200004E4 2016-01-11 2016-01-11 10:48:55 exp2-GR10DATA exp2 53.89086 -122.8193
## 4 06200004BF 2016-01-11 2016-01-11 10:50:02 exp2-GR10DATA exp2 53.89086 -122.8193
## 5 06200004E4 2016-01-11 2016-01-11 10:51:07 exp2-GR10DATA exp2 53.89086 -122.8193
## 6 06200004BE 2016-01-11 2016-01-11 10:51:51 exp2-GR10DATA exp2 53.89086 -122.8193
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