greenfeedr provides a set of functions that help you work with GreenFeed data:
get_gfdata() downloads GreenFeed data via API.report_gfdata() downloads and generates markdown
reports of daily and final GreenFeed data.process_gfdata() processes and averages daily or final
GreenFeed data.pellin() processes pellet intakes from GreenFeed
units.viseat() processes GreenFeed visits.Most of these use the same daily and final data from GreenFeed system.
More complete information about how to use greenfeedr can be found in:
You can install the development version of greenfeedr from GitHub with:
# install.packages("pak")
pak::pak("GMBog/greenfeedr")Here we present an example of how to use
process_gfdata():
library(greenfeedr)Note that we received the finalized data (or Summarized Data) for our study using GreenFeed from C-Lock Inc. So, now we need to process all the daily records obtained.
The data looks like (first 5 cols):
| RFID | FID | Start Time | End Time | Good Data Duration | Hour Of Day | CO2 Massflow (g/d) | CH4 Massflow (g/d) | O2 Massflow (g/d) |
|---|---|---|---|---|---|---|---|---|
| 840003250681664 | 1 | 2024-05-13 09:33:24 | 2024-05-13 09:36:31 | 1899-12-31 00:02:31 | 9.556666 | 10541.00 | 466.9185 | 6821.710 |
| 840003250681664 | 1 | 2024-05-13 10:25:44 | 2024-05-13 10:32:40 | 1899-12-31 00:06:09 | 10.428889 | 14079.59 | 579.3398 | 8829.182 |
| 840003250681799 | 1 | 2024-05-13 12:29:02 | 2024-05-13 12:45:19 | 1899-12-31 00:10:21 | 12.483889 | 9273.30 | 302.3902 | 6193.614 |
| 840003250681664 | 1 | 2024-05-13 13:06:20 | 2024-05-13 13:12:14 | 1899-12-31 00:04:00 | 13.105555 | 14831.44 | 501.0839 | 10705.166 |
| 840003250681664 | 1 | 2024-05-13 14:34:58 | 2024-05-13 14:41:52 | 1899-12-31 00:04:55 | 14.582778 | 20187.44 | 759.9457 | 11080.463 |
| 840003234513955 | 1 | 2024-05-13 14:59:14 | 2024-05-13 15:11:50 | 1899-12-31 00:03:42 | 14.987223 | 13994.72 | 472.2763 | 8997.816 |
The first step is to investigate the total number of records, records per day, and days with records per week we have in our GreenFeed data.
To do this we will use the process_gfdata() function and
test threshold values that will define the records we will retain for
further analysis. Note that the function includes :
param1 is the number of records per
day.
param2 is the number of days with
records per week.
min_time is the minimum duration of a
record.
We can make an iterative process evaluating all possible combinations of parameters. Then, we define the parameters as follows:
# Define the parameter space for param1 (i), param2 (j), and min_time (k):
i <- seq(1, 3)
j <- seq(3, 7)
k <- seq(2, 5)
# Generate all combinations of i, j, and k
param_combinations <- expand.grid(param1 = i, param2 = j, min_time = k)Interestingly, we have 60 combinations of our 3 parameters (param1, param2, and min_time).
The next step, is to evaluate the function
process_gfdata() with the defined set of parameters. Note
that the function can handle as argument a file path to the data files
or the data as data frame.
# Helper function to call process_gfdata and extract relevant information
process_and_summarize <- function(param1, param2, min_time) {
data <- process_gfdata(
data = finaldata,
start_date = "2024-05-13",
end_date = "2024-05-25",
param1 = param1,
param2 = param2,
min_time = min_time
)
# Extract daily_data and weekly_data
daily_data <- data$daily_data
weekly_data <- data$weekly_data
# Calculate the required metrics
records_d <- nrow(daily_data)
cows_d <- length(unique(daily_data$RFID))
mean_dCH4 <- mean(daily_data$CH4GramsPerDay, na.rm = TRUE)
sd_dCH4 <- sd(daily_data$CH4GramsPerDay, na.rm = TRUE)
CV_dCH4 <- sd(daily_data$CH4GramsPerDay, na.rm = TRUE) / mean(daily_data$CH4GramsPerDay, na.rm = TRUE)
mean_dCO2 <- mean(daily_data$CO2GramsPerDay, na.rm = TRUE)
sd_dCO2 <- sd(daily_data$CO2GramsPerDay, na.rm = TRUE)
CV_dCO2 <- sd(daily_data$CO2GramsPerDay, na.rm = TRUE) / mean(daily_data$CO2GramsPerDay, na.rm = TRUE)
records_w <- nrow(weekly_data)
cows_w <- length(unique(weekly_data$RFID))
mean_wCH4 <- mean(weekly_data$CH4GramsPerDay, na.rm = TRUE)
sd_wCH4 <- sd(weekly_data$CH4GramsPerDay, na.rm = TRUE)
CV_wCH4 <- sd(weekly_data$CH4GramsPerDay, na.rm = TRUE) / mean(weekly_data$CH4GramsPerDay, na.rm = TRUE)
mean_wCO2 <- mean(weekly_data$CO2GramsPerDay, na.rm = TRUE)
sd_wCO2 <- sd(weekly_data$CO2GramsPerDay, na.rm = TRUE)
CV_wCO2 <- sd(weekly_data$CO2GramsPerDay, na.rm = TRUE) / mean(weekly_data$CO2GramsPerDay, na.rm = TRUE)
# Return a summary row
return(data.frame(
param1 = param1,
param2 = param2,
min_time = min_time,
records_d = records_d,
cows_d = cows_d,
mean_dCH4 = round(mean_dCH4, 1),
sd_dCH4 = round(sd_dCH4, 1),
CV_dCH4 = round(CV_dCH4, 2),
mean_dCO2 = round(mean_dCO2, 1),
sd_dCO2 = round(sd_dCO2, 1),
CV_dCO2 = round(CV_dCO2, 2),
records_w = records_w,
cows_w = cows_w,
mean_wCH4 = round(mean_wCH4, 1),
sd_wCH4 = round(sd_wCH4, 1),
CV_wCH4 = round(CV_wCH4, 2),
mean_wCO2 = round(mean_wCO2, 1),
sd_wCO2 = round(sd_wCO2, 1),
CV_wCO2 = round(CV_wCO2, 2)
))
}
# Apply helper function to all combinations and combine results into a data frame
data <- param_combinations %>%
purrr::pmap_dfr(process_and_summarize)Finally, the results from our function will be placed in a data frame with the following structure:
| param1 | param2 | min_time | records_d | cows_d | mean_dCH4 | sd_dCH4 | CV_dCH4 | mean_dCO2 | sd_dCO2 | CV_dCO2 | records_w | cows_w | mean_wCH4 | sd_wCH4 | CV_wCH4 | mean_wCO2 | sd_wCO2 | CV_wCO2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 3 | 2 | 184 | 25 | 380.2 | 110.5 | 0.29 | 11429.1 | 2531.7 | 0.22 | 33 | 19 | 382.4 | 54.8 | 0.14 | 11488.1 | 1428.0 | 0.12 |
| 2 | 3 | 2 | 116 | 20 | 380.8 | 84.9 | 0.22 | 11450.6 | 2076.6 | 0.18 | 22 | 15 | 392.7 | 58.7 | 0.15 | 11630.5 | 1421.7 | 0.12 |
| 3 | 3 | 2 | 75 | 19 | 373.1 | 86.7 | 0.23 | 11394.2 | 2185.9 | 0.19 | 12 | 10 | 377.1 | 62.1 | 0.16 | 11458.8 | 1429.4 | 0.12 |
| 1 | 4 | 2 | 184 | 25 | 380.2 | 110.5 | 0.29 | 11429.1 | 2531.7 | 0.22 | 25 | 15 | 389.9 | 50.4 | 0.13 | 11685.1 | 1266.2 | 0.11 |
| 2 | 4 | 2 | 116 | 20 | 380.8 | 84.9 | 0.22 | 11450.6 | 2076.6 | 0.18 | 17 | 14 | 380.5 | 51.6 | 0.14 | 11367.5 | 1264.2 | 0.11 |
| 3 | 4 | 2 | 75 | 19 | 373.1 | 86.7 | 0.23 | 11394.2 | 2185.9 | 0.19 | 6 | 5 | 359.4 | 41.9 | 0.12 | 11310.2 | 1595.7 | 0.14 |
| 1 | 5 | 2 | 184 | 25 | 380.2 | 110.5 | 0.29 | 11429.1 | 2531.7 | 0.22 | 21 | 15 | 380.2 | 48.1 | 0.13 | 11444.3 | 1182.3 | 0.10 |
| 2 | 5 | 2 | 116 | 20 | 380.8 | 84.9 | 0.22 | 11450.6 | 2076.6 | 0.18 | 8 | 7 | 361.5 | 38.8 | 0.11 | 11247.0 | 1250.1 | 0.11 |
| 3 | 5 | 2 | 75 | 19 | 373.1 | 86.7 | 0.23 | 11394.2 | 2185.9 | 0.19 | 4 | 3 | 360.3 | 50.0 | 0.14 | 11555.9 | 2000.6 | 0.17 |
| 1 | 6 | 2 | 184 | 25 | 380.2 | 110.5 | 0.29 | 11429.1 | 2531.7 | 0.22 | 14 | 11 | 378.1 | 50.6 | 0.13 | 11208.1 | 1316.1 | 0.12 |
That gives the user an idea of what are the pros and cons of being more or less conservative when processing GreenFeed data for analysis. In general, the more conservative the parameters are, the fewer records are retained in the data.
If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub.