Type:	Package
Title:	Process and Report 'GreenFeed' Data
Version:	1.2.1
Maintainer:	Guillermo Martinez-Boggio <guillermo.martinezboggio@wisc.edu>
Description:	Provides tools for downloading, processing, and reporting daily and finalized 'GreenFeed' data.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	dplyr, ggplot2, httr, lubridate, purrr, readr, readxl, rmarkdown, stats, stringr, tidyr
Depends:	R (≥ 2.10)
Suggests:	knitr, testthat (≥ 3.0.0)
Config/testthat/edition:	3
BugReports:	https://github.com/GMBog/greenfeedr/issues
URL:	https://github.com/GMBog/greenfeedr
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-04-15 17:57:07 UTC; GuillermoMartinez
Author:	Guillermo Martinez-Boggio [cre, aut, cph], Meredith Harrison [ctb], Patrick Lutz [ctb]
Repository:	CRAN
Date/Publication:	2025-04-17 17:40:02 UTC

Compare Preliminary and Finalized 'GreenFeed' Data

Description

Compare preliminary and finalized 'GreenFeed' data.

Arguments

Value

Data frame including records removed from preliminary and final reports

Examples

# Datasets with preliminary and finalized GreenFeed data
prelimrep <- system.file("extdata", "StudyName_GFdata.csv", package = "greenfeedr")
finalrep <- system.file("extdata", "StudyName_FinalReport.xlsx", package = "greenfeedr")

data <- compare_gfdata(
  prelimrep,
  finalrep,
  start_date = "2024-05-13",
  end_date = "2024-05-20"
)

Convert 'GreenFeed' Unit Number

Description

Processes the parameter unit to format it correctly as a comma-separated string, regardless of whether it is provided as a numeric, character, or list/vector.

Usage

convert_unit(unit, t)

Arguments

Value

A character string of the unit(s) in the correct comma-separated format.

Examples

# Example 1: Providing unit as a character vector
unit <- c("592", "593")
convert_unit(unit, 1)

# Example 2: Providing unit as a single numeric
unit <- 592
convert_unit(unit, 1)

# Example 3: Providing unit as a comma-separated character string
unit <- "592, 593"
convert_unit(unit, 1)

# Example 4: Providing unit as a list
unit <- list(592, 593)
convert_unit(unit, 1)

Check date format and transform in a usable one

Description

Check date format. If the format is wrong ('NA') an error message is printed, else it will formatted the date in the correct way (YYYY-MM-DD)

Usage

ensure_date_format(date_input)

Arguments

Value

A character string representing the date in 'YYYY-MM-DD' format

Examples

# Example of correct date formats
ensure_date_format("2024-08-30") # "2024-08-30"
ensure_date_format("30/08/2024") # "2024-08-30"

# Example of incorrect date formats
tryCatch(
  {
    ensure_date_format("Aug-30")
  },
  error = function(e) {
    message(e$message)
  }
)

Evaluate all combination of parameters

Description

Evaluate parameters that best fit your 'GreenFeed' data

Usage

eval_gfparam(data, start_date, end_date, cutoff)

Arguments

Value

A data frame with the mean, SD, and CV for gas production using all possible combination of parameters

Examples

file <- readr::read_csv(system.file("extdata", "StudyName_GFdata.csv", package = "greenfeedr"))
eval <- eval_gfparam(data = file,
                     start_date = "2024-05-13",
                     end_date = "2024-05-20"
                    )

Detect outliers in data using mean and standard deviation

Description

Detect outliers using the mean and standard deviation.

Usage

filter_within_range(v, cutoff)

Arguments

Value

A logical vector of the same length as 'v', where each element is 'TRUE' if the corresponding value in 'v' falls within the specified range, and 'FALSE' otherwise.

Examples

# Sample data
data <- c(10, 12, 14, 15, 20, 25, 30, 100)

# Detect values within 3 standard deviations from the mean
filter_within_range(data, cutoff = 3)

# Result:
# [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
# Explanation: All values fall within 2.5 standard deviations from the mean.

# Detect values within 1 standard deviation from the mean
filter_within_range(data, cutoff = 1)

# Result:
# [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
# Explanation: All values except 100 fall within 1 standard deviation from the mean.

Download Preliminary and Raw 'GreenFeed' Data via 'API'

Description

Downloads preliminary and raw 'GreenFeed' data from the 'C-Lock Inc.' server via an 'API'. Retrieves data based on specified parameters (login, date range, and units), and provides a CSV file with the 'GreenFeed' preliminary data.

Usage

get_gfdata(
  user,
  pass,
  d = "visits",
  type = 2,
  exp = NA,
  unit,
  start_date,
  end_date = Sys.Date(),
  save_dir = tempdir()
)

Arguments

Value

A CSV file with the specified data (visits or raw) saved in the provided directory.

Examples

# Please replace "your_username" and "your_password" with your actual 'GreenFeed' credentials.
# By default, the function downloads the preliminary 'GreenFeed' data,
# if raw data is needed use options: "feed", "rfid", or "cmds"

get_gfdata(
  user = "your_username",
  pass = "your_password",
  d = "visits",
  type = 2,
  exp = "StudyName",
  unit = c(304, 305),
  start_date = "2024-01-01",
  end_date = Sys.Date(),
  save_dir = tempdir()
)

Check for 'API' Credentials

Description

Check if necessary 'API' credentials are available in the environment.

Usage

has_credentials()

Value

A logical value: 'TRUE' if both 'API_USER' and 'API_PASS' environment variables are set (i.e., not 'NA'); 'FALSE' otherwise

Examples

# Example 1: When environment variables are set
Sys.setenv(API_USER = "my_username", API_PASS = "my_password")
has_credentials()
# Expected output: TRUE

# Example 2: When one or both environment variables are not set
Sys.unsetenv("API_USER")
Sys.unsetenv("API_PASS")
has_credentials()
# Expected output: FALSE

# Clean up by removing environment variables
Sys.unsetenv("API_USER")
Sys.unsetenv("API_PASS")

Process 'GreenFeed' Pellet Intakes

Description

Processes 'feedtimes' file from 'GreenFeed' system. Food drops are used to calculate pellet intakes per animal. Aggregates data to provide insights into the feeding behavior and pellet consumption of the animals during a study.

Arguments

Value

An Excel file with pellet intakes for all animals and days within the specified period is saved to save_dir. The file is named "Pellet_Intakes_YYYY-MM-DD_YYYY-MM-DD.csv".

Examples

# You should provide the 'feedtimes' file provided by C-Lock.
# it could be a list of files if you have data from multiple units to combine
path <- system.file("extdata", "feedtimes.csv", package = "greenfeedr")

# You must include the grams of pellets per cup based on the result obtained from the 10-drops test

# If the user include an rfid file, the structure should be in col1 AnimalName or Visual ID, and
# col2 the RFID or TAG_ID. The file could be save in different formats (.xlsx, .csv, or .txt).
RFIDs <- system.file("extdata", "RFID_file.csv", package = "greenfeedr")

pellin(
  unit = 1,
  gcup = 34,
  start_date = "2024-05-13",
  end_date = "2024-05-25",
  save_dir = tempdir(),
  rfid_file = RFIDs,
  file_path = path
)

Process Preliminary and Finalized 'GreenFeed' Data

Description

Processes and calculates daily and weekly averages of 'GreenFeed' data. Handles data filtering, aggregation, and summarization to facilitate further analysis.

Arguments

Value

A list of three data frames:

Examples

file <- system.file("extdata", "StudyName_GFdata.csv", package = "greenfeedr")
datafile <- readr::read_csv(file)

gf_data <- process_gfdata(
  data = datafile,
  start_date = "2024-05-13",
  end_date = "2024-05-25",
  param1 = 2,
  param2 = 3,
  min_time = 2
)
head(gf_data)

Process RFID Data

Description

Processes RFID data from animals in the study.

Usage

process_rfid_data(rfid_file)

Arguments

Value

A data frame with standardized column names ('FarmName' and 'RFID'). If the input is invalid or if no valid data is provided, the function returns 'NULL'.

Examples

# Example with a data frame
df <- data.frame(
  V1 = c("Farm1", "Farm2", "Farm3"),
  V2 = c("12345", "67890", "54321")
)
processed_df <- process_rfid_data(df)
message(processed_df)

# Example with invalid input
invalid_data <- process_rfid_data(NULL)
# Expected output: message "RFID is NA. It is recommended to include it." and NULL
message(invalid_data)

Download and Report 'GreenFeed' Data

Description

Generates a PDF report of preliminary and finalized 'GreenFeed' data. The report includes: number of animals using 'GreenFeed' and plots with distribution of records and gas production. If the preliminary option is used, the data is retrieved from the 'C-Lock Inc.' server through an 'API', otherwise the data processed by 'C-Lock Inc.' must be provided to generate the report.

Arguments

Value

A CSV file with preliminary 'GreenFeed' data and a PDF report with a description of the preliminary or finalized data

Examples

# Please replace "your_username" and "your_password" with your actual 'GreenFeed' credentials.
# The data range must be fewer than 180 days
# Example without rfid_file (by default NA)

report_gfdata(
  user = "your_username",
  pass = "your_password",
  input_type = "prelim",
  exp = "StudyName",
  unit = 1,
  start_date = "2023-01-01",
  end_date = Sys.Date(),
  save_dir = tempdir(),
  plot_opt = "All"
)

Process 'GreenFeed' Visits

Description

Processes 'GreenFeed' visits and food drops for a requested period. Generates a list of animals not visiting the 'GreenFeed' to manage them, and a description of animals visiting the 'GreenFeed'.

Arguments

Value

A list of two data frames:

Examples

# You should provide the feedtimes files.
# it could be a list of files if you have data from multiple units to combine
path <- system.file("extdata", "feedtimes.csv", package = "greenfeedr")

# If the user include an rfid file, the structure should be in col1 AnimalName or VisualID, and
# col2 the RFID or TAG_ID. The file could be save in different formats (.xlsx, .csv, or .txt).
RFIDs <- system.file("extdata", "RFID_file.csv", package = "greenfeedr")

data <- viseat(
  file_path = path,
  unit = 1,
  start_date = "2024-05-13",
  end_date = "2024-05-25",
  rfid_file = RFIDs
)