You can use fid_monitor() to monitor the feed intake of
each pig in the nedap or fire pig performance test station as well as
the total feed intake of all pigs.
# Load CSV data
data <- data.table::fread("C:/Users/Dell/Documents/projects/pptsdm_data/ppt_monitor_test_data.csv")
# Feed intake monitor
fid_monitor(data = data, station_type = "nedap", save_path = "C:/Users/Dell/Downloads/test")You can use function of station_monitor() to monitor pig
performance test data from nedap and fire pig performance test stations.
This function can monitor the changes in the number of pigs, total
visitation time of pigs, total number of visits, changes in total feed
intake, and also monitor the changes in pig weights for a specific pig
performance test station.
# Load CSV data
data <- data.table::fread("C:/Users/Dell/Documents/projects/pptsdm_data/ppt_monitor_test_data.csv")
# Station monitor
station_monitor(data = data, station_type = "nedap", save_path = "C:/Users/Dell/Downloads/test")table_monitor serves as a complement to two other
monitoring functions. It primarily aims to analyze and monitor, based on
the CSV data from the past n days of measurement stations, the daily
counts of missing records, extreme weight recordings, total feeding time
at each measurement station, total feeding amount per pen per day,
average weight per pen per day, and feed intake information in each
hour.
# 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: 0.02Combine the fid_monitor(),
station_monitor(), and table_monitor()
functions together. You can use monitor_all() to monitor
all by hands.
# Load CSV data
data <- data.table::fread("C:/Users/Dell/Documents/projects/pptsdm_data/ppt_monitor_test_data.csv")
monitor_all(data, begin_date = "2024-05-01", days = 5, save_path = "C:/Users/Dell/Downloads/test")
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 3 rows containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_col()`).
#> Removed 1 row containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 3 rows containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 3 rows containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_col()`).
#> $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
#> 7: 507 1 1 1 0 0 3
#> 8: 508 0 0 0 0 1 1
#> 9: 509 0 0 1 0 2 3
#> 10: 510 0 1 2 1 0 4
#>
#> $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
#> 7: 507 7.59 3.85 4.29 2.41 8.43
#> 8: 508 1.47 1.37 2.63 1.32 6.10
#> 9: 509 6.84 4.62 4.31 6.78 8.13
#> 10: 510 6.74 3.12 7.58 4.41 8.57
#>
#> $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
#> 7: 507 79 78 70 83 83 17.45 15.67
#> 8: 508 68 73 76 76 82 18.12 17.93
#> 9: 509 117 130 116 118 123 17.97 16.86
#> 10: 510 89 96 66 68 70 19.07 19.84
#> 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
#> 7: 15.85 15.88 16.28
#> 8: 17.49 18.09 17.18
#> 9: 17.39 17.98 16.58
#> 10: 17.89 18.17 17.72
#>
#> $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
#> 7: 507 NA 0.010 0.010 0.010 NA NA 0.03
#> 8: 508 NA NA NA NA NA 0.010 0.01
#>
#> $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
#> 7: 507 44.87 41.04 43.98 43.24 44.58 217.71
#> 8: 508 44.22 45.83 43.71 44.74 43.99 222.50
#> 9: 509 48.30 48.09 51.44 50.91 50.07 248.80
#> 10: 510 43.86 47.92 44.62 44.72 46.01 227.12
#>
#> $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
#>
#> $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
#>
#> $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
#> 7: 507 4 1 5 3 1 2 3 5 4 3 3
#> 8: 508 5 3 1 4 3 3 3 3 4 2 3
#> 9: 509 3 5 5 1 3 2 2 4 6 4 3
#> 10: 510 3 NA 3 2 3 2 1 3 2 3 3
#> 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
#> 7: 6 5 7 3 4 5 5 4 4 NA NA 5
#> 8: 5 4 3 3 6 5 4 3 3 4 4 3
#> 9: 8 5 5 10 8 12 4 7 3 7 9 3
#> 10: 3 4 2 3 7 5 3 3 2 3 4 2
#> 23
#> <int>
#> 1: 4
#> 2: NA
#> 3: 4
#> 4: 3
#> 5: 2
#> 6: 1
#> 7: 1
#> 8: 2
#> 9: 6
#> 10: 4
#>
#> $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
#> 7: 507 41.03 13.78 51.38 44.87 20.98 28.35 39.22 56.10 45.87 49.72 40.87
#> 8: 508 48.92 21.32 6.23 29.35 47.57 70.88 40.70 41.55 50.60 45.07 34.77
#> 9: 509 31.62 78.10 18.28 13.12 26.80 14.27 27.50 66.20 48.47 56.48 53.45
#> 10: 510 20.72 NA 58.38 30.07 59.98 47.35 12.75 55.05 42.08 57.80 54.02
#> 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
#> 7: 53.45 39.77 44.08 61.90 59.02 59.58 51.63 63.30 54.67 NA NA 48.00
#> 8: 71.40 58.88 55.20 19.07 56.52 55.00 74.32 52.77 45.13 30.10 28.83 25.30
#> 9: 60.33 32.93 46.25 58.73 50.37 63.55 49.32 70.85 42.20 34.58 34.50 7.27
#> 10: 53.03 79.35 35.85 58.28 66.23 54.53 67.92 48.00 25.55 32.32 40.65 27.12
#> 23
#> <num>
#> 1: 37.85
#> 2: NA
#> 3: 15.00
#> 4: 41.63
#> 5: 15.62
#> 6: 0.83
#> 7: 9.15
#> 8: 21.32
#> 9: 15.37
#> 10: 36.28
#>
#> $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
#> 7: 507 1.73 0.60 2.06 2.28 0.82 1.35 1.88 2.61 1.94 2.10 1.48
#> 8: 508 1.81 0.80 0.19 1.09 2.23 3.16 1.59 1.97 1.99 1.92 1.21
#> 9: 509 1.55 4.37 1.29 0.80 1.32 0.45 1.22 3.39 2.64 2.89 2.88
#> 10: 510 0.82 NA 2.50 1.27 2.87 1.63 0.54 2.55 1.76 2.52 2.54
#> 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
#> 7: 2.43 2.05 1.60 2.75 2.82 2.41 2.75 3.26 3.07 NA NA 2.02
#> 8: 3.38 2.73 2.04 0.87 2.19 2.23 3.39 2.64 2.18 1.21 1.39 0.84
#> 9: 3.29 1.44 1.73 2.42 2.29 2.67 3.44 3.86 1.83 1.59 1.53 0.40
#> 10: 2.24 3.77 1.20 2.77 2.19 2.42 3.39 2.52 1.06 1.61 1.62 0.91
#> 23
#> <num>
#> 1: 1.07
#> 2: NA
#> 3: 0.90
#> 4: 1.79
#> 5: 0.64
#> 6: 0.02
#> 7: 0.56
#> 8: 0.92
#> 9: 0.81
#> 10: 1.30You can use monitor_schedule to set when to run the
fid_monitor()、station_monitor() and
table_monitor().
# Set monitor task
monitor_schedule(
taskname = "ppt_csv_monitor",
schedule = "DAILY",
starttime = "10:05",
startdate = format(Sys.Date(), "%Y/%m/%d"),
rscript_args = list(house_width = "1",
days = 5,
begin_date = "2024-05-01",
csv_path = "C:/Users/Dell/Documents/projects/pptsdm_data",
save_path = "C:/Users/Dell/Downloads/test"))
#> [1] "SUCCESS: The scheduled task \"ppt_csv_monitor\" has successfully been created."
# Delete monitor task
taskscheduleR::taskscheduler_delete("ppt_csv_monitor")
#> [1] 0process_data() is used to preprocess the read CSV
data.
# Load CSV data
data <- data.table::fread("C:/Users/Dell/Documents/projects/pptsdm_data/ppt_monitor_test_data.csv")
processed_data <- process_data(data = data)
head(processed_data)
#> responder location date seq_days seq_in_day seq_in_location
#> <char> <char> <IDat> <int> <int> <int>
#> 1: 2311763 503 2024-05-28 1 1 10
#> 2: 2311763 503 2024-05-28 1 2 35
#> 3: 2311763 503 2024-05-28 1 3 43
#> 4: 2311763 503 2024-05-28 1 4 45
#> 5: 2311763 503 2024-05-28 1 5 50
#> 6: 2311763 503 2024-05-28 1 6 52
#> feed_intake weight
#> <num> <num>
#> 1: 724 71000
#> 2: 12 70500
#> 3: 10 70000
#> 4: 0 70000
#> 5: 0 70000
#> 6: 1447 70500house_weight() is primarily used to monitor the average
weight of pigs in each barn.
# Load CSV data
data <- data.table::fread("C:/Users/Dell/Documents/projects/pptsdm_data/ppt_monitor_test_data.csv")
head(house_weight(data = data, days = 5))
#> 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 80.61responder_na() primarily monitors the 访问组
for each station.
# Load CSV data
data <- data.table::fread("C:/Users/Dell/Documents/projects/pptsdm_data/ppt_monitor_test_data.csv")
print(responder_na(data = data, days = 5))
#> 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
#> 7: 507 1 1 1 0 0 3
#> 8: 508 0 0 0 0 1 1
#> 9: 509 0 0 1 0 2 3
#> 10: 510 0 1 2 1 0 4extreme_time_n() primarily monitors abnormal weight
records, access frequency, and access times.
# Load CSV data
data <- data.table::fread("C:/Users/Dell/Documents/projects/pptsdm_data/ppt_monitor_test_data.csv")
print(extreme_time_n(data = data, days = 5))
#> $weight_outlier
#> 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
#> 7: 507 7.59 3.85 4.29 2.41 8.43
#> 8: 508 1.47 1.37 2.63 1.32 6.10
#> 9: 509 6.84 4.62 4.31 6.78 8.13
#> 10: 510 6.74 3.12 7.58 4.41 8.57
#>
#> $feedintake
#> 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
#> 7: 507 79 78 70 83 83 17.45 15.67
#> 8: 508 68 73 76 76 82 18.12 17.93
#> 9: 509 117 130 116 118 123 17.97 16.86
#> 10: 510 89 96 66 68 70 19.07 19.84
#> 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
#> 7: 15.85 15.88 16.28
#> 8: 17.49 18.09 17.18
#> 9: 17.39 17.98 16.58
#> 10: 17.89 18.17 17.72low_feedintake() primarily monitors individuals with low
feed intake.
# Load CSV data
data <- data.table::fread("C:/Users/Dell/Documents/projects/pptsdm_data/ppt_monitor_test_data.csv")
print(low_feedintake(data = data, days = 5))
#> 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
#> 7: 507 NA 0.010 0.010 0.010 NA NA 0.03
#> 8: 508 NA NA NA NA NA 0.010 0.01all_feedintake() is used to monitor the total daily feed
intake for each station.
# Load CSV data
data <- data.table::fread("C:/Users/Dell/Documents/projects/pptsdm_data/ppt_monitor_test_data.csv")
print(all_feedintake(data = data, days = 5))
#> 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
#> 7: 507 44.87 41.04 43.98 43.24 44.58 217.71
#> 8: 508 44.22 45.83 43.71 44.74 43.99 222.50
#> 9: 509 48.30 48.09 51.44 50.91 50.07 248.80
#> 10: 510 43.86 47.92 44.62 44.72 46.01 227.12number_location() is used to calculate the number of
pigs at each station.
# Load CSV data
data <- data.table::fread("C:/Users/Dell/Documents/projects/pptsdm_data/ppt_monitor_test_data.csv")
print(number_location(data = data, house_width = "1", days = 5))
#> Key: <date>
#> date 5
#> <char> <int>
#> 1: 06-03 147
#> 2: 06-04 146
#> 3: 06-05 145
#> 4: 06-06 146
#> 5: 06-07 146house_feedintake() is used to calculate the total feed
intake at each measurement station.
# Load CSV data
data <- data.table::fread("C:/Users/Dell/Documents/projects/pptsdm_data/ppt_monitor_test_data.csv")
print(house_feedintake(data = data, house_width = "1", days = 5))
#> Key: <date>
#> date 5 all_feedintake
#> <char> <num> <num>
#> 1: 06-03 460.51 460.51
#> 2: 06-04 464.41 464.41
#> 3: 06-05 469.79 469.79
#> 4: 06-06 470.22 470.22
#> 5: 06-07 469.56 469.56hour_stat() is used to monitor the number of visits,
visit times, and feed intake of pigs at each measurement station for
every hour within a 24-hour period.
# Load CSV data
data <- data.table::fread("C:/Users/Dell/Documents/projects/pptsdm_data/ppt_monitor_test_data.csv")
print(hour_stat(data = data))
#> $visit_n
#> 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
#> 7: 507 4 1 5 3 1 2 3 5 4 3 3
#> 8: 508 5 3 1 4 3 3 3 3 4 2 3
#> 9: 509 3 5 5 1 3 2 2 4 6 4 3
#> 10: 510 3 NA 3 2 3 2 1 3 2 3 3
#> 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
#> 7: 6 5 7 3 4 5 5 4 4 NA NA 5
#> 8: 5 4 3 3 6 5 4 3 3 4 4 3
#> 9: 8 5 5 10 8 12 4 7 3 7 9 3
#> 10: 3 4 2 3 7 5 3 3 2 3 4 2
#> 23
#> <int>
#> 1: 4
#> 2: NA
#> 3: 4
#> 4: 3
#> 5: 2
#> 6: 1
#> 7: 1
#> 8: 2
#> 9: 6
#> 10: 4
#>
#> $feed_time
#> 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
#> 7: 507 41.03 13.78 51.38 44.87 20.98 28.35 39.22 56.10 45.87 49.72 40.87
#> 8: 508 48.92 21.32 6.23 29.35 47.57 70.88 40.70 41.55 50.60 45.07 34.77
#> 9: 509 31.62 78.10 18.28 13.12 26.80 14.27 27.50 66.20 48.47 56.48 53.45
#> 10: 510 20.72 NA 58.38 30.07 59.98 47.35 12.75 55.05 42.08 57.80 54.02
#> 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
#> 7: 53.45 39.77 44.08 61.90 59.02 59.58 51.63 63.30 54.67 NA NA 48.00
#> 8: 71.40 58.88 55.20 19.07 56.52 55.00 74.32 52.77 45.13 30.10 28.83 25.30
#> 9: 60.33 32.93 46.25 58.73 50.37 63.55 49.32 70.85 42.20 34.58 34.50 7.27
#> 10: 53.03 79.35 35.85 58.28 66.23 54.53 67.92 48.00 25.55 32.32 40.65 27.12
#> 23
#> <num>
#> 1: 37.85
#> 2: NA
#> 3: 15.00
#> 4: 41.63
#> 5: 15.62
#> 6: 0.83
#> 7: 9.15
#> 8: 21.32
#> 9: 15.37
#> 10: 36.28
#>
#> $feed_intake
#> 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
#> 7: 507 1.73 0.60 2.06 2.28 0.82 1.35 1.88 2.61 1.94 2.10 1.48
#> 8: 508 1.81 0.80 0.19 1.09 2.23 3.16 1.59 1.97 1.99 1.92 1.21
#> 9: 509 1.55 4.37 1.29 0.80 1.32 0.45 1.22 3.39 2.64 2.89 2.88
#> 10: 510 0.82 NA 2.50 1.27 2.87 1.63 0.54 2.55 1.76 2.52 2.54
#> 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
#> 7: 2.43 2.05 1.60 2.75 2.82 2.41 2.75 3.26 3.07 NA NA 2.02
#> 8: 3.38 2.73 2.04 0.87 2.19 2.23 3.39 2.64 2.18 1.21 1.39 0.84
#> 9: 3.29 1.44 1.73 2.42 2.29 2.67 3.44 3.86 1.83 1.59 1.53 0.40
#> 10: 2.24 3.77 1.20 2.77 2.19 2.42 3.39 2.52 1.06 1.61 1.62 0.91
#> 23
#> <num>
#> 1: 1.07
#> 2: NA
#> 3: 0.90
#> 4: 1.79
#> 5: 0.64
#> 6: 0.02
#> 7: 0.56
#> 8: 0.92
#> 9: 0.81
#> 10: 1.30That’s it ! This the end of the documented story of our package. All components are there.