This is a quick tutorial to get you started with loading in your own data. feedr includes several wrapper functions that can be used to load and format your data.

These functions work with either raw RFID logger data (downloaded directly from the loggers) or database data downloaded from http://animalnexus.ca directly or with the dl_data() function.

A note about file structure

It’s important to remember that when specifying file locations, you must either specify a complete file location (e.g. “/home/steffi/Desktop/data.csv”) or an appropriate relative file location. A relative file location would be something like: “./Data/data.csv” which points to a file called data.csv which is in a folder called “Data”. The “./” indicates that “Data” is in the current working directory. If we used “../” that would indicate that “Data” was one directory up.

Also, remember that file locations are relative to where R’s working directory is, and this is not necessarily the same place as the R script with which you are working.

If you are using RStudio, it is highly recommended that you specify an RStudio project in the directory which holds your scripts. This way, anytime you open the file, the working directly is automatically set to your script directly.

This tutorial assumes that your data is stored in a folder called “Data” which is in turn stored in your R scripts folder.

load_raw()

This loads and formats a raw data file downloaded directly from RFID loggers setup in the same manner as the Thompson Rivers University loggers.

In the raw form, this data looks like:

GR10DATA
06200004BF 01/11/16 10:48:49
062000038F 01/11/16 10:48:50
06200004E4 01/11/16 10:48:55
06200004BF 01/11/16 10:50:02
06200004E4 01/11/16 10:51:07

When we use load_raw() to import it, we get this:

r1 <- load_raw("./Data/Raw/exp2/GR10DATA_2016_01_16.TXT")
## Loading file ./Data/Raw/exp2/GR10DATA_2016_01_16.TXT...
head(r1)
##    animal_id       date                time logger_id
## 1 06200004BF 2016-01-11 2016-01-11 10:48:49  GR10DATA
## 2 062000038F 2016-01-11 2016-01-11 10:48:50  GR10DATA
## 3 06200004E4 2016-01-11 2016-01-11 10:48:55  GR10DATA
## 4 06200004BF 2016-01-11 2016-01-11 10:50:02  GR10DATA
## 5 06200004E4 2016-01-11 2016-01-11 10:51:07  GR10DATA
## 6 06200004BE 2016-01-11 2016-01-11 10:51:51  GR10DATA

Note that the logger_id has been extracted from the first line of the file. This is done by matching an expected pattern against the the contents of the first line (these patterns are called ‘Regular Expressions’).

The default pattern matches GR or GPR followed by 1 or 2 digits. If you need to specify a different pattern you can do so. For example, if your loggers were labeled “Logger_10” or “Logger_01”:

Logger_10
06200004BF 01/11/16 10:48:49
062000038F 01/11/16 10:48:50
06200004E4 01/11/16 10:48:55
06200004BF 01/11/16 10:50:02
06200004E4 01/11/16 10:51:07
r1 <- load_raw("./Data/Raw/exp2/Logger_10_2016_01_16.TXT", logger_pattern = "Logger_[0-9]{2}")
## Loading file ./Data/Raw/exp2/Logger_10_2016_01_16.TXT...
head(r1)
##    animal_id       date                time logger_id
## 1 06200004BF 2016-01-11 2016-01-11 10:48:49 Logger_10
## 2 062000038F 2016-01-11 2016-01-11 10:48:50 Logger_10
## 3 06200004E4 2016-01-11 2016-01-11 10:48:55 Logger_10
## 4 06200004BF 2016-01-11 2016-01-11 10:50:02 Logger_10
## 5 06200004E4 2016-01-11 2016-01-11 10:51:07 Logger_10
## 6 06200004BE 2016-01-11 2016-01-11 10:51:51 Logger_10

Details

Alternatively, you can determine where load_raw() gets extra details from by specifying the details argument (defaults to details = 1).

For example:

1. details = 0

logger_id is in the file name, defined by the pattern logger_pattern (If logger_id is also the first line, need to skip that first line).

For example, if the file looks like:

Logger_21
06200004BF 01/11/16 10:48:49
062000038F 01/11/16 10:48:50
06200004E4 01/11/16 10:48:55
06200004BF 01/11/16 10:50:02
06200004E4 01/11/16 10:51:07

Then this pattern won’t work because it matches the first line, not the file name:

load_raw("./Data/Raw/exp2/Logger_Data.TXT", logger_pattern = "Logger_[0-9]{2}", details = 0, skip = 1)
## Loading file ./Data/Raw/exp2/Logger_Data.TXT...
## Error: logger_id not detected in file name

But this pattern will work:

load_raw("./Data/Raw/exp2/Logger_Data.TXT", logger_pattern = "Logger", details = 0, skip = 1)
## Loading file ./Data/Raw/exp2/Logger_Data.TXT...
##    animal_id       date                time logger_id
## 1 06200004BF 2016-01-11 2016-01-11 10:48:49    Logger
## 2 062000038F 2016-01-11 2016-01-11 10:48:50    Logger
## 3 06200004E4 2016-01-11 2016-01-11 10:48:55    Logger
## 4 06200004BF 2016-01-11 2016-01-11 10:50:02    Logger
## 5 06200004E4 2016-01-11 2016-01-11 10:51:07    Logger
## 6 06200004BE 2016-01-11 2016-01-11 10:51:51    Logger

2. details = 1

logger_id is in the first line of the file, also defined by the pattern logger_pattern

load_raw("./Data/Raw/exp2/Logger_Data.TXT", logger_pattern = "Logger_[0-9]{2}", details = 1)
## Loading file ./Data/Raw/exp2/Logger_Data.TXT...
##    animal_id       date                time logger_id
## 1 06200004BF 2016-01-11 2016-01-11 10:48:49 Logger_21
## 2 062000038F 2016-01-11 2016-01-11 10:48:50 Logger_21
## 3 06200004E4 2016-01-11 2016-01-11 10:48:55 Logger_21
## 4 06200004BF 2016-01-11 2016-01-11 10:50:02 Logger_21
## 5 06200004E4 2016-01-11 2016-01-11 10:51:07 Logger_21
## 6 06200004BE 2016-01-11 2016-01-11 10:51:51 Logger_21

3. details = 2

logger_id is in the first line of the file, and lat/lon information is on the second line, in the format of “latitude, longitude” both in decimal format (spacing doesn’t matter, but the comma does):

Such a file would look like this:

Logger_21
53.89086,-122.81933
06200004BF 01/11/16 10:48:49
062000038F 01/11/16 10:48:50
06200004E4 01/11/16 10:48:55
06200004BF 01/11/16 10:50:02

And would be loaded by load_raw() like this:

load_raw("./Data/Raw/exp2/Logger_Data.TXT", logger_pattern = "Logger_[0-9]{2}", details = 2)
## Loading file ./Data/Raw/exp2/Logger_Data.TXT...
##    animal_id       date                time logger_id      lat       lon
## 1 06200004BF 2016-01-11 2016-01-11 10:48:49 Logger_21 53.89086 -122.8193
## 2 062000038F 2016-01-11 2016-01-11 10:48:50 Logger_21 53.89086 -122.8193
## 3 06200004E4 2016-01-11 2016-01-11 10:48:55 Logger_21 53.89086 -122.8193
## 4 06200004BF 2016-01-11 2016-01-11 10:50:02 Logger_21 53.89086 -122.8193
## 5 06200004E4 2016-01-11 2016-01-11 10:51:07 Logger_21 53.89086 -122.8193
## 6 06200004BE 2016-01-11 2016-01-11 10:51:51 Logger_21 53.89086 -122.8193

For more information on how to write Regular Expression patterns, see documentation for “Regular Expressions” (e.g. http://www.regular-expressions.info/tutorial.html)

load_raw_all()

The function load_raw_all() is a wrapper function which will automatically load and combine data contained in several different files in a single folder, or in a nested series of folders. Other files can be present, but all data files must be identifiable by a pattern (pattern argument) in the file name.

In this example our data files are stored in a folder called raw and there are several sets of data, each corresponding to an individual experiment which are then stored in their own folder called exp1, exp2, etc. Logger data files are identifiable by the characters ‘DATA’ present in the name (as in the above example), which is the default pattern:

r <- load_raw_all(r_dir = "./Data/Raw")
## Loading file ./Data/Raw/exp2/GR10DATA_2016_01_16.TXT...
## Loading file ./Data/Raw/exp2/GR10DATA_2016_01_18.TXT...
## Empty file skipped: ./Data/Raw/exp2/GR10DATA_2016_01_18.TXT
## Loading file ./Data/Raw/exp2/GR10DATA_2016_01_25.TXT...
## Loading file ./Data/Raw/exp2/GR11DATA_2016_01_16.TXT...
## Loading file ./Data/Raw/exp2/GR11DATA_2016_01_18.TXT...
## Loading file ./Data/Raw/exp2/GR11DATA_2016_01_20.TXT...
## Loading file ./Data/Raw/exp2/GR11DATA_2016_01_25.TXT...
## Loading file ./Data/Raw/exp2/GR12DATA_2016_01_16.TXT...
## Loading file ./Data/Raw/exp2/GR12DATA_2016_01_18.TXT...
## Loading file ./Data/Raw/exp2/GR12DATA_2016_01_25.TXT...
## Loading file ./Data/Raw/exp2/GR13DATA_2016_01_16.TXT...
## Loading file ./Data/Raw/exp2/GR13DATA_2016_01_18.TXT...
## Loading file ./Data/Raw/exp2/GR13DATA_2016_01_20.TXT...
## Loading file ./Data/Raw/exp2/GR13DATA_2016_01_25.TXT...
## Loading file ./Data/Raw/exp3/GR10DATA_2016_02_01.TXT...
## Loading file ./Data/Raw/exp3/GR10DATA_2016_02_06.TXT...
## Loading file ./Data/Raw/exp3/GR10DATA_2016_02_13.TXT...
## Loading file ./Data/Raw/exp3/GR10DATA_2016_02_16.TXT...
## Loading file ./Data/Raw/exp3/GR11DATA_2016_02_06.TXT...
## Loading file ./Data/Raw/exp3/GR11DATA_2016_02_10.TXT...
## Loading file ./Data/Raw/exp3/GR11DATA_2016_02_13.TXT...
## Loading file ./Data/Raw/exp3/GR11DATA_2016_02_16.TXT...
## Loading file ./Data/Raw/exp3/GR12DATA_2016_02_01.TXT...
## Loading file ./Data/Raw/exp3/GR12DATA_2016_02_06.TXT...
## Loading file ./Data/Raw/exp3/GR12DATA_2016_02_10.TXT...
## Loading file ./Data/Raw/exp3/GR12DATA_2016_02_13.TXT...
## Loading file ./Data/Raw/exp3/GR12DATA_2016_02_16.TXT...
## Loading file ./Data/Raw/exp3/GR13DATA_2016_02_06.TXT...
## Loading file ./Data/Raw/exp3/GR13DATA_2016_02_13.TXT...
## Loading file ./Data/Raw/exp3/GR13DATA_2016_02_16.TXT...
head(r)
##    animal_id       date                time logger_id
## 1 06200004BF 2016-01-11 2016-01-11 10:48:49  GR10DATA
## 2 062000038F 2016-01-11 2016-01-11 10:48:50  GR10DATA
## 3 06200004E4 2016-01-11 2016-01-11 10:48:55  GR10DATA
## 4 06200004BF 2016-01-11 2016-01-11 10:50:02  GR10DATA
## 5 06200004E4 2016-01-11 2016-01-11 10:51:07  GR10DATA
## 6 06200004BE 2016-01-11 2016-01-11 10:51:51  GR10DATA
summary(r)
##       animal_id          date                 time                        logger_id   
##  062000038D: 4374   Min.   :2016-01-06   Min.   :2016-01-06 13:18:44   GR10DATA:5563  
##  062000038F: 2750   1st Qu.:2016-01-14   1st Qu.:2016-01-14 13:16:54   GR11DATA:8697  
##  0700EE2B10: 2614   Median :2016-01-21   Median :2016-01-21 12:12:26   GR12DATA:5649  
##  062000014F: 2138   Mean   :2016-01-25   Mean   :2016-01-25 17:32:23   GR13DATA:8819  
##  0700ED9E0E: 1809   3rd Qu.:2016-02-05   3rd Qu.:2016-02-05 09:11:10                  
##  0700EE0E42: 1675   Max.   :2016-02-16   Max.   :2016-02-16 14:30:29                  
##  (Other)   :13368

(Note that empty files are skipped, but identified)

If your logger files don’t have an identifiable label, but are the only csv files in the folders, you could use:

r <- load_raw_all(r_dir = "./Data/Raw", pattern = ".csv")

Extra details

In this example we have several different experiments, which we’ll probably want to identify in our data. This is where the extra_ arguments come in.

list.files("./Data/Raw")
## [1] "exp2" "exp3"

In our example, each experiment is stored in its own folder (‘exp2’ and ‘exp3’). Therefore we can tell our function to identify patterns (extra_pattern) in the file names and store the values in an extra column (extra_name):

r <- load_raw_all(r_dir = "./Data/Raw", extra_pattern = "exp[2-3]{1}", extra_name = "experiment")
## Loading file ./Data/Raw/exp2/GR10DATA_2016_01_16.TXT...
## Loading file ./Data/Raw/exp2/GR10DATA_2016_01_18.TXT...
## Empty file skipped: ./Data/Raw/exp2/GR10DATA_2016_01_18.TXT
## Loading file ./Data/Raw/exp2/GR10DATA_2016_01_25.TXT...
## Loading file ./Data/Raw/exp2/GR11DATA_2016_01_16.TXT...
## Loading file ./Data/Raw/exp2/GR11DATA_2016_01_18.TXT...
## Loading file ./Data/Raw/exp2/GR11DATA_2016_01_20.TXT...
## Loading file ./Data/Raw/exp2/GR11DATA_2016_01_25.TXT...
## Loading file ./Data/Raw/exp2/GR12DATA_2016_01_16.TXT...
## Loading file ./Data/Raw/exp2/GR12DATA_2016_01_18.TXT...
## Loading file ./Data/Raw/exp2/GR12DATA_2016_01_25.TXT...
## Loading file ./Data/Raw/exp2/GR13DATA_2016_01_16.TXT...
## Loading file ./Data/Raw/exp2/GR13DATA_2016_01_18.TXT...
## Loading file ./Data/Raw/exp2/GR13DATA_2016_01_20.TXT...
## Loading file ./Data/Raw/exp2/GR13DATA_2016_01_25.TXT...
## Loading file ./Data/Raw/exp3/GR10DATA_2016_02_01.TXT...
## Loading file ./Data/Raw/exp3/GR10DATA_2016_02_06.TXT...
## Loading file ./Data/Raw/exp3/GR10DATA_2016_02_13.TXT...
## Loading file ./Data/Raw/exp3/GR10DATA_2016_02_16.TXT...
## Loading file ./Data/Raw/exp3/GR11DATA_2016_02_06.TXT...
## Loading file ./Data/Raw/exp3/GR11DATA_2016_02_10.TXT...
## Loading file ./Data/Raw/exp3/GR11DATA_2016_02_13.TXT...
## Loading file ./Data/Raw/exp3/GR11DATA_2016_02_16.TXT...
## Loading file ./Data/Raw/exp3/GR12DATA_2016_02_01.TXT...
## Loading file ./Data/Raw/exp3/GR12DATA_2016_02_06.TXT...
## Loading file ./Data/Raw/exp3/GR12DATA_2016_02_10.TXT...
## Loading file ./Data/Raw/exp3/GR12DATA_2016_02_13.TXT...
## Loading file ./Data/Raw/exp3/GR12DATA_2016_02_16.TXT...
## Loading file ./Data/Raw/exp3/GR13DATA_2016_02_06.TXT...
## Loading file ./Data/Raw/exp3/GR13DATA_2016_02_13.TXT...
## Loading file ./Data/Raw/exp3/GR13DATA_2016_02_16.TXT...
head(r)
##    animal_id       date                time logger_id experiment
## 1 06200004BF 2016-01-11 2016-01-11 10:48:49  GR10DATA       exp2
## 2 062000038F 2016-01-11 2016-01-11 10:48:50  GR10DATA       exp2
## 3 06200004E4 2016-01-11 2016-01-11 10:48:55  GR10DATA       exp2
## 4 06200004BF 2016-01-11 2016-01-11 10:50:02  GR10DATA       exp2
## 5 06200004E4 2016-01-11 2016-01-11 10:51:07  GR10DATA       exp2
## 6 06200004BE 2016-01-11 2016-01-11 10:51:51  GR10DATA       exp2

“exp[1-2]{1}” matches the exact characters “exp” followed by either a 1 or a 2 of which there is exactly 1. The values matching this pattern are then stored in a new column called ‘experiment’.

Because here the loggers were RFID-enabled feeders reused for different experiments, some logger have the same id, but a different lat/lon. However, logger_ids need to be unique or we will have problems later on, so let’s create unique logger_id names:

r$logger_id <- paste(r$experiment, r$logger_id, sep = "-")
head(r)
##    animal_id       date                time     logger_id experiment
## 1 06200004BF 2016-01-11 2016-01-11 10:48:49 exp2-GR10DATA       exp2
## 2 062000038F 2016-01-11 2016-01-11 10:48:50 exp2-GR10DATA       exp2
## 3 06200004E4 2016-01-11 2016-01-11 10:48:55 exp2-GR10DATA       exp2
## 4 06200004BF 2016-01-11 2016-01-11 10:50:02 exp2-GR10DATA       exp2
## 5 06200004E4 2016-01-11 2016-01-11 10:51:07 exp2-GR10DATA       exp2
## 6 06200004BE 2016-01-11 2016-01-11 10:51:51 exp2-GR10DATA       exp2

Logger details

Because raw logger data doesn’t include logger specific details, we should probably include some extra data for use later (visualizations, etc.):

We can do this the same way as we did above (see ‘Details’ under load_raw()), or by merging our raw data with a logger index file. This method works best when you have multiple details you’d like to add (more than just lat/lon information), or when your raw files don’t contain lat/lon information already.

## Open logger index
l_index <- read.csv("./Data/chickadees_logger_index.csv")
head(l_index)
##   experiment logger_name      lat       lon
## 1       exp2    GR10DATA 53.89086 -122.8193
## 2       exp2    GR11DATA 53.88999 -122.8210
## 3       exp2    GR12DATA 53.88997 -122.8193
## 4       exp2    GR13DATA 53.89088 -122.8208
## 5       exp3    GR10DATA 53.88763 -122.8217
## 6       exp3    GR11DATA 53.88821 -122.8205
l_index$logger_id <- paste(l_index$experiment, l_index$logger_name, sep = "-")

## Merge logger index into RFID data, matching 'experiment' and 'logger_id'
r <- merge(r, l_index, by = c("experiment", "logger_id"))
head(r)
##   experiment     logger_id  animal_id       date                time logger_name      lat       lon
## 1       exp2 exp2-GR10DATA 06200004BF 2016-01-11 2016-01-11 10:48:49    GR10DATA 53.89086 -122.8193
## 2       exp2 exp2-GR10DATA 062000038F 2016-01-11 2016-01-11 10:48:50    GR10DATA 53.89086 -122.8193
## 3       exp2 exp2-GR10DATA 06200004E4 2016-01-11 2016-01-11 10:48:55    GR10DATA 53.89086 -122.8193
## 4       exp2 exp2-GR10DATA 06200004BF 2016-01-11 2016-01-11 10:50:02    GR10DATA 53.89086 -122.8193
## 5       exp2 exp2-GR10DATA 06200004E4 2016-01-11 2016-01-11 10:51:07    GR10DATA 53.89086 -122.8193
## 6       exp2 exp2-GR10DATA 06200004BE 2016-01-11 2016-01-11 10:51:51    GR10DATA 53.89086 -122.8193

This data is now ready for housekeeping or go straight to transformations!

dl_data()

This is likely the easiest way to get data (provided the data you’re interested in is hosted in the animal__nexus__ database). This function requests data from animal__nexus__ and formats it for use with the feedr transformation functions.

If you don’t specify anything, all data will be downloaded for the default site (Kamloops) and the default extra columns (loc and species) with the default timezone (America/Vancouver). See ?dl_data for more details.

##    animal_id       date                time logger_id            species age sex    site_name       lon      lat
## 1 062000031A 2015-09-02 2015-09-02 07:25:58      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 2 062000031A 2015-09-02 2015-09-02 07:26:01      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 3 062000031A 2015-09-03 2015-09-03 09:12:20      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 4 062000031A 2015-09-03 2015-09-03 09:12:23      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 5 062000031A 2015-09-05 2015-09-05 15:32:20      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 6 062000031A 2015-09-05 2015-09-05 15:32:23      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
r <- dl_data()
head(r)

You can specify start and end times:

r <- dl_data(start = "2015-10-01", end = "2015-10-02")
head(r)
##    animal_id       date                time logger_id     species age sex    site_name       lon      lat
## 1 06200003AA 2015-10-01 2015-10-01 07:24:44      2700 House Finch  HY   F Kamloops, BC -120.3632 50.66909
## 2 06200003AA 2015-10-01 2015-10-01 07:24:46      2700 House Finch  HY   F Kamloops, BC -120.3632 50.66909
## 3 06200003AA 2015-10-01 2015-10-01 07:24:49      2700 House Finch  HY   F Kamloops, BC -120.3632 50.66909
## 4 06200003AA 2015-10-01 2015-10-01 07:24:51      2700 House Finch  HY   F Kamloops, BC -120.3632 50.66909
## 5 06200003AA 2015-10-01 2015-10-01 07:24:53      2700 House Finch  HY   F Kamloops, BC -120.3632 50.66909
## 6 06200003AA 2015-10-01 2015-10-01 07:24:55      2700 House Finch  HY   F Kamloops, BC -120.3632 50.66909
r <- dl_data(start = "2015-10-01 09:00:00", end = "2015-10-02")
head(r)
##    animal_id       date                time logger_id     species age sex    site_name       lon      lat
## 1 041868D396 2015-10-01 2015-10-01 16:02:22      2100 House Finch AHY   F Kamloops, BC -120.3624 50.66896
## 2 041868D396 2015-10-01 2015-10-01 16:15:14      2100 House Finch AHY   F Kamloops, BC -120.3624 50.66896
## 3 041868D396 2015-10-01 2015-10-01 16:15:55      2100 House Finch AHY   F Kamloops, BC -120.3624 50.66896
## 4 041868D396 2015-10-01 2015-10-01 16:15:56      2100 House Finch AHY   F Kamloops, BC -120.3624 50.66896
## 5 041868D396 2015-10-01 2015-10-01 16:15:58      2100 House Finch AHY   F Kamloops, BC -120.3624 50.66896
## 6 041868D396 2015-10-01 2015-10-01 16:16:00      2100 House Finch AHY   F Kamloops, BC -120.3624 50.66896

You can also specify only a start or an end, all data up to or after that point will be grabbed:

r <- dl_data(end = "2015-09-06")
head(r)
##    animal_id       date                time logger_id            species age sex    site_name       lon      lat
## 1 062000031A 2015-09-02 2015-09-02 07:25:58      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 2 062000031A 2015-09-02 2015-09-02 07:26:01      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 3 062000031A 2015-09-03 2015-09-03 09:12:20      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 4 062000031A 2015-09-03 2015-09-03 09:12:23      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 5 062000031A 2015-09-05 2015-09-05 15:32:20      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 6 062000031A 2015-09-05 2015-09-05 15:32:23      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896

Extra animal or logger related details (?dl_data for more):

r <- dl_data(end = "2015-09-06", logger_details = c("loc","site_name"), animal_details = c("species", "age", "sex"))
## Warning: Argument logger_details is deprecated (all logger details will be returned)
## Warning: Argument animal_details is deprecated (all animal details will be returned)
head(r)
##    animal_id       date                time logger_id            species age sex    site_name       lon      lat
## 1 062000031A 2015-09-02 2015-09-02 07:25:58      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 2 062000031A 2015-09-02 2015-09-02 07:26:01      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 3 062000031A 2015-09-03 2015-09-03 09:12:20      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 4 062000031A 2015-09-03 2015-09-03 09:12:23      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 5 062000031A 2015-09-05 2015-09-05 15:32:20      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896
## 6 062000031A 2015-09-05 2015-09-05 15:32:23      2100 Mountain Chickadee  SY   M Kamloops, BC -120.3624 50.66896

ui_import()

ui_import() is a helper function that launches a stand-alone shiny app to give you a user-interface for importing data.

my_imported_data <- ui_import()

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