The table_monitor function processes input data and generates an Excel workbook
with various monitoring statistics and conditional formatting. It is designed for
analyzing and visualizing data related to house monitoring, feed intake, and other
relevant metrics over a specified period.
table_monitor(data, house_width = "1", days, ref_date = Sys.Date(), save_path)data.frame, required. The input data frame containing the data to be processed.
character, optional. A character string representing the house width. Default is "1".
integer, optional. An integer representing the number of days for the analysis.
Date, optional.
A Date object representing the reference date. Default is the current date
(Sys.Date()).
character, required. A character string representing the path where the Excel file will be saved. Must be a non-empty string ending with ".xlsx".
A list containing various monitoring statistics:
responder_na: Data frame with responder NA statistics.
extreme_weight: Data frame with extreme weight outlier statistics.
feed_time_n: Data frame with feed time statistics.
low_feedintake: Data frame with low feed intake statistics.
all_feedintake: Data frame with all feed intake statistics.
mean_feedintake: Data frame with mean feed intake statistics.
house_weight: Data frame with house weight statistics.
visit_n_hour: Data frame with the number of visits per hour.
feed_time_hour: Data frame with feed time per hour.
feed_intake_hour: Data frame with feed intake per hour.
The save_path parameter must be a valid path ending with ".xlsx". Ensure that
the data parameter is a data frame and contains the necessary columns for
processing.
# Load CSV data
data <- data.table::fread("C:/Users/Dell/Documents/projects/pptsdm_data/ppt_monitor_test_data.csv")
# Monitor station and data
res <- table_monitor(data = data, days = 5, save_path = "C:/Users/Dell/Downloads/test")
# Monitor the number of times 'na' appears in the last n days
head(res$responder_na)
#> location 06-03 06-04 06-05 06-06 06-07 total_nas
#> <char> <int> <int> <int> <int> <int> <num>
#> 1: 501 0 0 0 0 0 0
#> 2: 502 2 1 1 3 2 9
#> 3: 503 0 2 2 0 0 4
#> 4: 504 2 0 2 0 0 4
#> 5: 505 0 0 0 0 0 0
#> 6: 506 0 0 0 0 0 0
# Monitor the percentage of extreme weight records in the last n days
head(res$extreme_weight)
#> Key: <location>
#> location 06-03 06-04 06-05 06-06 06-07
#> <int> <num> <num> <num> <num> <num>
#> 1: 501 7.07 4.31 4.12 2.78 5.79
#> 2: 502 5.38 6.86 3.12 6.67 7.32
#> 3: 503 4.82 5.32 6.17 5.19 5.21
#> 4: 504 5.95 3.70 2.41 5.41 3.53
#> 5: 505 1.28 3.37 5.05 4.67 11.30
#> 6: 506 7.69 7.46 4.35 1.28 7.50
# Monitor the visiting time and frequency of pigs in the last n days
head(res$feed_time_n)
#> Key: <location>
#> location n_06-03 n_06-04 n_06-05 n_06-06 n_06-07 time_06-03 time_06-04
#> <int> <int> <int> <int> <int> <int> <num> <num>
#> 1: 501 99 116 97 108 121 17.62 18.14
#> 2: 502 93 102 96 75 82 17.24 16.38
#> 3: 503 83 94 81 77 96 18.15 16.92
#> 4: 504 84 81 83 74 85 18.61 17.66
#> 5: 505 78 89 99 107 115 16.82 17.51
#> 6: 506 65 67 69 78 80 18.65 17.29
#> time_06-05 time_06-06 time_06-07
#> <num> <num> <num>
#> 1: 18.64 18.62 18.54
#> 2: 16.92 16.48 16.30
#> 3: 15.49 15.84 15.86
#> 4: 16.87 17.10 16.49
#> 5: 16.73 17.26 16.33
#> 6: 17.48 19.43 18.44
# Monitor the low feedintake over the last n days
head(res$low_feedintake)
#> location responder 06-03 06-04 06-05 06-06 06-07 sum_feedintake
#> <int> <int> <num> <num> <num> <num> <num> <num>
#> 1: 507 2315012 NA NA NA 0.029 0.003 0.03
#> 2: 502 NA 0.081 0.046 0.056 0.087 0.027 0.30
#> 3: 510 NA NA 0.048 0.208 0.014 NA 0.27
#> 4: 503 NA NA 0.212 0.022 NA NA 0.23
#> 5: 509 NA NA NA 0.010 NA 0.152 0.16
#> 6: 504 NA 0.026 NA 0.023 NA NA 0.05
# Monitor the total feed intake over the last n days
head(res$all_feedintake)
#> Key: <location>
#> location 06-03 06-04 06-05 06-06 06-07 all_feedintake
#> <char> <num> <num> <num> <num> <num> <num>
#> 1: 501 41.30 43.52 46.97 45.56 46.34 223.69
#> 2: 502 46.95 46.13 49.73 46.41 50.10 239.32
#> 3: 503 51.22 49.40 47.49 48.06 48.47 244.65
#> 4: 504 48.68 49.71 48.49 48.85 47.98 243.70
#> 5: 505 43.64 46.99 47.73 47.40 44.47 230.23
#> 6: 506 47.60 46.10 45.98 50.43 47.72 237.83
# Monitor the average feed intake over the last n days
head(res$mean_feedintake)
#> Key: <date>
#> date 5_feed all_feedintake 5_n 5_mean_feed
#> <char> <num> <num> <int> <num>
#> 1: 06-03 460.51 460.51 147 3.13
#> 2: 06-04 464.41 464.41 146 3.18
#> 3: 06-05 469.79 469.79 145 3.24
#> 4: 06-06 470.22 470.22 146 3.22
#> 5: 06-07 469.56 469.56 146 3.22
# Monitor the average weight per pen over the last n days
head(res$house_weight)
#> Key: <date>
#> date house_5
#> <char> <num>
#> 1: 06-03 75.08
#> 2: 06-04 76.42
#> 3: 06-05 77.64
#> 4: 06-06 79.34
#> 5: 06-07 80.61
# Monitor visit time in each hour over the last 1 day.
head(res$visit_n_hour)
#> Key: <location>
#> location 0 1 2 3 4 5 6 7 8 9 10
#> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1: 501 3 5 4 4 4 3 4 9 5 5 8
#> 2: 502 4 1 3 4 1 2 4 4 4 4 4
#> 3: 503 2 7 4 3 1 3 3 3 4 2 7
#> 4: 504 4 4 1 5 1 2 3 3 3 5 4
#> 5: 505 5 9 4 3 2 4 4 2 4 5 3
#> 6: 506 3 4 2 3 1 1 3 2 4 2 4
#> 11 12 13 14 15 16 17 18 19 20 21 22
#> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1: 4 4 6 4 9 10 6 6 4 5 3 2
#> 2: 3 3 4 5 5 5 4 5 5 4 1 5
#> 3: 5 4 4 4 4 5 7 5 4 4 1 6
#> 4: 5 3 5 3 9 4 5 3 4 2 1 3
#> 5: 8 7 6 6 6 7 8 6 4 5 4 1
#> 6: 6 2 4 6 3 5 4 3 3 4 5 5
#> 23
#> <int>
#> 1: 4
#> 2: NA
#> 3: 4
#> 4: 3
#> 5: 2
#> 6: 1
# Monitor feed intake time in each hour over the last 1 day.
head(res$feed_time_hour)
#> Key: <location>
#> location 0 1 2 3 4 5 6 7 8 9 10
#> <int> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 501 9.57 33.95 48.88 59.77 34.53 36.38 50.50 41.57 52.70 55.47 48.17
#> 2: 502 36.35 12.25 37.57 53.27 11.37 18.67 36.13 62.23 42.80 49.67 48.58
#> 3: 503 18.97 39.23 40.38 21.13 6.62 29.62 50.40 41.62 71.25 23.22 50.48
#> 4: 504 33.45 56.98 7.00 68.78 12.65 25.65 34.40 28.45 36.30 60.68 56.87
#> 5: 505 30.82 34.20 17.30 25.02 18.45 46.52 52.07 21.17 45.72 74.60 44.17
#> 6: 506 66.65 50.72 16.48 31.30 8.08 18.82 58.05 47.10 57.83 46.55 54.50
#> 11 12 13 14 15 16 17 18 19 20 21 22
#> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 60.68 48.07 48.53 51.03 52.95 43.55 64.73 60.97 55.23 45.68 49.97 21.82
#> 2: 45.80 25.98 51.55 58.68 61.32 56.58 64.05 47.82 65.83 41.63 6.27 43.73
#> 3: 40.33 21.33 50.57 64.28 55.67 48.55 54.83 62.55 41.50 53.48 5.27 45.27
#> 4: 60.58 41.45 68.10 47.85 44.65 60.88 56.72 51.77 46.52 19.98 4.45 23.38
#> 5: 40.10 48.08 32.12 52.98 57.20 44.18 57.08 67.37 56.15 63.82 33.68 1.47
#> 6: 73.53 50.00 50.40 76.15 42.93 58.73 62.28 56.82 58.42 50.52 42.28 27.53
#> 23
#> <num>
#> 1: 37.85
#> 2: NA
#> 3: 15.00
#> 4: 41.63
#> 5: 15.62
#> 6: 0.83
# Monitor feed intake in each hour over the last 1 day.
head(res$feed_intake_hour)
#> Key: <location>
#> location 0 1 2 3 4 5 6 7 8 9 10
#> <int> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 501 0.40 1.10 2.05 2.18 1.90 1.41 2.23 2.26 1.85 2.29 2.13
#> 2: 502 1.73 0.81 1.80 2.45 0.53 1.00 1.76 3.04 1.86 2.58 2.39
#> 3: 503 0.84 1.90 2.20 1.07 0.31 1.37 2.44 2.53 3.84 1.01 2.41
#> 4: 504 1.32 2.61 0.35 3.07 0.72 1.52 1.75 1.34 1.66 2.63 2.85
#> 5: 505 1.29 1.16 0.56 1.21 0.89 2.02 2.39 0.97 2.24 2.87 2.32
#> 6: 506 2.60 2.15 0.72 1.43 0.47 0.66 2.44 2.02 2.72 2.09 2.13
#> 11 12 13 14 15 16 17 18 19 20 21 22
#> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 2.28 1.94 2.48 1.78 2.20 1.51 2.42 2.71 2.80 2.26 2.27 0.80
#> 2: 2.19 1.50 2.63 2.89 2.98 2.89 3.69 2.80 3.33 2.38 0.36 2.50
#> 3: 1.92 1.13 2.52 3.35 3.10 2.25 3.01 3.18 1.96 2.48 0.21 2.52
#> 4: 2.91 2.38 3.44 2.35 2.11 3.10 2.93 2.80 2.17 0.88 0.28 1.02
#> 5: 1.47 2.22 1.67 2.65 2.06 2.15 1.64 3.80 3.38 3.34 1.44 0.08
#> 6: 3.11 1.73 2.24 3.74 1.47 1.79 3.10 2.63 3.00 2.27 1.91 1.30
#> 23
#> <num>
#> 1: 1.07
#> 2: NA
#> 3: 0.90
#> 4: 1.79
#> 5: 0.64
#> 6: 0.02