Processes and calculates daily and weekly averages of 'GreenFeed' data. Handles data filtering, aggregation, and summarization to facilitate further analysis.
Arguments
- data
a data frame with daily or finalized 'GreenFeed' data
- start_date
a character string representing the start date of the study (format: "mm/dd/yyyy")
- end_date
a character string representing the end date of the study (format: "mm/dd/yyyy")
- param1
an integer representing the number of records per day to be consider for analysis
- param2
an integer representing the number of days with records per week to be consider for analysis
- min_time
an integer representing the minimum number of minutes for a records to be consider for analysis. By default min_time is 2
Value
A list of two data frames:
- daily_data
data frame with daily processed 'GreenFeed' data
- weekly_data
data frame with weekly processed 'GreenFeed' data
Examples
file <- system.file("extdata", "StudyName_GFdata.csv", package = "greenfeedr")
datafile <- readr::read_csv(file)
#> Rows: 256 Columns: 21
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (6): AnimalName, RFID, StartTime, EndTime, InterruptingTags, RunTime
#> dbl (13): FeederID, CO2GramsPerDay, CH4GramsPerDay, O2GramsPerDay, H2GramsP...
#> lgl (1): WasInterrupted
#> time (1): GoodDataDuration
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
gf_data <- process_gfdata(
data = datafile,
start_date = "2024-05-13",
end_date = "2024-05-25",
param1 = 2,
param2 = 3,
min_time = 2
)
#> Using param1 = 2 , param2 = 3 , min_time = 2
#> CH4: 387.24 +- 67.37
#> CH4 CV = 17.4%
#> CO2: 11198.47 +- 1427.23
#> CO2 CV = 12.7%
#> O2: 7578.41 +- 917.46
#> O2 CV = 12.1%
#> H2: NaN +- NA
#> H2 CV = NA%
head(gf_data)
#> $daily_data
#> # A tibble: 61 × 9
#> RFID week day n minutes CH4GramsPerDay CO2GramsPerDay
#> <chr> <dbl> <dttm> <int> <dbl> <dbl> <dbl>
#> 1 840003… 1 2024-05-13 00:00:00 7 25.9 453. 11995.
#> 2 840003… 1 2024-05-14 00:00:00 5 14.5 245. 7704.
#> 3 840003… 1 2024-05-15 00:00:00 5 17.3 428. 11108.
#> 4 840003… 1 2024-05-19 00:00:00 2 5.65 403. 13532.
#> 5 840003… 1 2024-05-14 00:00:00 3 8.45 263. 8321.
#> 6 840003… 1 2024-05-15 00:00:00 4 10.8 432. 11721.
#> 7 840003… 1 2024-05-16 00:00:00 2 4.73 427. 13417.
#> 8 840003… 1 2024-05-16 00:00:00 4 15.5 504. 12996.
#> 9 840003… 1 2024-05-17 00:00:00 3 11.0 374. 11524.
#> 10 840003… 1 2024-05-18 00:00:00 2 6.66 411. 11474.
#> # ℹ 51 more rows
#> # ℹ 2 more variables: O2GramsPerDay <dbl>, H2GramsPerDay <dbl>
#>
#> $weekly_data
#> # A tibble: 10 × 9
#> RFID week nDays nRecords TotalMin CH4GramsPerDay CO2GramsPerDay
#> <chr> <dbl> <int> <int> <dbl> <dbl> <dbl>
#> 1 840003234513955 1 4 19 63.4 394. 10907.
#> 2 840003234513977 1 3 9 24.0 372. 10859.
#> 3 840003250681234 1 4 11 42.1 466. 12546.
#> 4 840003250681655 1 5 19 51.3 340. 10969.
#> 5 840003250681661 1 3 14 59.3 354. 10934.
#> 6 840003250681664 1 5 17 62.6 506. 13682.
#> 7 840003250681689 1 6 15 59.7 407. 11378.
#> 8 840003250681711 1 4 10 29.7 430. 12428.
#> 9 840003250681766 1 5 15 53.8 312. 9210.
#> 10 840003250681788 1 6 30 101. 293. 9070.
#> # ℹ 2 more variables: O2GramsPerDay <dbl>, H2GramsPerDay <dbl>
#>