feedeffir

The goal of feedeffir is to process feed efficiency files and calculate total dry matter intake, metabolic body weight, delta body weight, and milk energy.

Installation

You can install the development version of feedeffir from GitHub with:

# install.packages("pak")
pak::pak("GMBog/feedeffir")

feedeffir handles two main types of data:

1. Raw Data:
   • Description: This includes unprocessed datasets directly from the farm. Examples are raw milk weights from DC and AFI system, feed intake files from RIC2 Discover system, and milk composition files.
   • Usage: Functions that operate on raw data are designed for pre-processing and compilation.
2. Compiled Feed Efficiency Data:
   • Description: This includes compiled datasets from raw data and include metrics such as milk weights, milk composition, and feed intakes.
   • Usage: Functions targeting compiled files focus on analyzing feed efficiency traits. They require data that has already been processed from raw inputs.

How to Use

To work with raw data, use functions to preprocess and create the initial data sets:

• process_VRfiles()
• process_DC_milkw()
• process_AFI_milkw()

To compile data, use the compiler() function with different types of files (body weights, milk weights, milk composition, and VR).

To analyze compiled feed efficiency files, use functions which expect compiled data:

• process_bw()
• process_intakes()
• calculate_milke()

For detailed examples of how to use these functions, refer to the Examples section.

Examples

This is a basic example which shows you how to solve a common problem:

library(feedeffir)

# Process VR files with intakes
data <- process_VRfiles(
  exp = "Study1",
  VRfile = system.file("extdata", "VR240724.DAT", package = "feedeffir"),
  bins = seq(1, 32),
  save_dir = tempdir()
)
#> The VR file was processed and the result saved at /var/folders/8n/lmf4l1hs7jz2m86j5g16k21c0000gn/T//RtmpTQwVbw

head(data)
#> # A tibble: 6 × 6
#>   TrialID Date       Visible_ID Feed  FedKg Obs  
#>   <chr>   <date>          <dbl> <chr> <dbl> <lgl>
#> 1 Study1  2024-07-24       1009 TMR01  54.1 NA   
#> 2 Study1  2024-07-24       1023 TMR01  50.2 NA   
#> 3 Study1  2024-07-24       1035 TMR01  46.2 NA   
#> 4 Study1  2024-07-24       1281 TMR01  61.7 NA   
#> 5 Study1  2024-07-24       1291 TMR01  64.5 NA   
#> 6 Study1  2024-07-24       1334 TMR01  59.7 NA

# The next step is to compile the processed VR files
# compile_VRfiles(dir = "~/Downloads/files/",
#                compfile = "UW_Study1_CompiledIntakes.xlsx",
#                data = data)



# Process milk weights from Dairy Comp
data <- process_DC_milkw(exp = "Study1", 
                         file_path = system.file("extdata", "MilkWeight_DCfile.xls", package = "feedeffir"), 
                         save_dir = tempdir())
#> Number of cows in file: 64
#> Time range:
#> 2024-09-14
#> 2024-09-13
#> 2024-09-12
#> 2024-09-11
#> 2024-09-10
#> 2024-09-09
#> 2024-09-08
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    0.00   45.00   53.00   52.88   62.00   89.00

head(data)
#>   Trial_ID       Date MilkNum Visible_ID MilkLbs
#> 1   Study1 2024-09-14      PM       1001      66
#> 2   Study1 2024-09-14      PM       1068      53
#> 3   Study1 2024-09-14      PM       1069      51
#> 4   Study1 2024-09-14      PM       1074      57
#> 5   Study1 2024-09-14      PM       1092      49
#> 6   Study1 2024-09-14      PM       1097      44