Quarto
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Running Code
When you click the Render button a document will be generated that includes both content and the output of embedded code. You can embed code like this:
Loading required package: Matrix
Attaching package: 'lmerTest'
The following object is masked from 'package:lme4':
lmer
The following object is masked from 'package:stats':
step
library (emmeans)library (car)
Loading required package: carData
-- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --
v dplyr 1.1.2 v readr 2.1.4
v forcats 1.0.0 v stringr 1.5.0
v ggplot2 3.4.3 v tibble 3.2.1
v lubridate 1.9.2 v tidyr 1.3.0
v purrr 1.0.1
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x tidyr::expand() masks Matrix::expand()
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
x tidyr::pack() masks Matrix::pack()
x dplyr::recode() masks car::recode()
x purrr::some() masks car::some()
x tidyr::unpack() masks Matrix::unpack()
i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library (RColorBrewer)library (ggplot2)library (MASS)
Attaching package: 'MASS'
The following object is masked from 'package:dplyr':
select
library (agricolae)library (vegan)
Loading required package: permute
Loading required package: lattice
Registered S3 methods overwritten by 'vegan':
method from
plot.rda klaR
predict.rda klaR
print.rda klaR
This is vegan 2.6-4
library (dplyr)library (readr)library (DT)library (ggplot2)library (quantreg)
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
You can add options to executable code like this
rm (list = ls ())setwd ("C:/Users/anune/OneDrive/Desktop/PIC_DataAnalysis_files" )<- read.csv ("PIC_65_FIRE.AN.1.csv" )<- mutate (data_PIC,day_0 = dmy (START_DAY), ENTRY_DATE = date (as.POSIXct (ENTRY_TIME, format= "%m/%d/%Y %H:%M" )),DAYS_IN_FEED = as.numeric (ENTRY_DATE - day_0))summary (data_PIC$ DAYS_IN_FEED)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-364.0 14.0 31.0 31.2 48.0 68.0 25
<- group_by (data_PIC, ID)head (data_PIC)
# A tibble: 6 x 19
# Groups: ID [3]
ID LINE SIRE DAM LITTER PEN FARM ENTRY_TIME EXIT_TIME STAY_IN
<int> <int> <int> <int> <int> <chr> <int> <chr> <chr> <int>
1 96251326 65 91032775 9.22e7 7.80e7 B0113 774 10/27/202~ 10/27/20~ 649
2 96251327 65 91032775 9.22e7 7.80e7 B0113 774 10/27/202~ 10/27/20~ 1948
3 96251327 65 91032775 9.22e7 7.80e7 B0113 774 10/27/202~ 10/27/20~ 169
4 96284921 65 90218081 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 627
5 96284921 65 90218081 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 768
6 96284921 65 90218081 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 413
# i 9 more variables: FEED_INTK <int>, ENTRY_WT <int>, EXIT_WT <int>,
# FEEDER_NO <int>, START_DAY <chr>, OFFTEST_DAY <chr>, day_0 <date>,
# ENTRY_DATE <date>, DAYS_IN_FEED <dbl>
# A tibble: 6 x 19
# Groups: ID [5]
ID LINE SIRE DAM LITTER PEN FARM ENTRY_TIME EXIT_TIME STAY_IN
<int> <int> <int> <int> <int> <chr> <int> <chr> <chr> <int>
1 98350892 65 93693188 9.41e7 7.92e7 B0602 774 6/15/2023~ 6/15/202~ 423
2 98350892 65 93693188 9.41e7 7.92e7 B0602 774 6/15/2023~ 6/15/202~ 1237
3 98350934 65 93693188 9.41e7 7.92e7 B0602 774 6/19/2023~ 6/19/202~ 183
4 98332707 65 93423760 9.42e7 7.92e7 B0602 774 6/19/2023~ 6/19/202~ 6
5 98350859 65 93693188 9.41e7 7.92e7 B0602 774 6/19/2023~ 6/19/202~ 14
6 98340247 65 93513866 9.42e7 7.92e7 B0602 774 6/19/2023~ 6/19/202~ 29
# i 9 more variables: FEED_INTK <int>, ENTRY_WT <int>, EXIT_WT <int>,
# FEEDER_NO <int>, START_DAY <chr>, OFFTEST_DAY <chr>, day_0 <date>,
# ENTRY_DATE <date>, DAYS_IN_FEED <dbl>
# A tibble: 114,263 x 19
# Groups: ID [548]
ID LINE SIRE DAM LITTER PEN FARM ENTRY_TIME EXIT_TIME STAY_IN
<int> <int> <int> <int> <int> <chr> <int> <chr> <chr> <int>
1 96251326 65 9.10e7 9.22e7 7.80e7 B0113 774 10/27/202~ 10/27/20~ 649
2 96251327 65 9.10e7 9.22e7 7.80e7 B0113 774 10/27/202~ 10/27/20~ 1948
3 96251327 65 9.10e7 9.22e7 7.80e7 B0113 774 10/27/202~ 10/27/20~ 169
4 96284921 65 9.02e7 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 627
5 96284921 65 9.02e7 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 768
6 96284921 65 9.02e7 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 413
7 96284921 65 9.02e7 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 18
8 96284921 65 9.02e7 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 171
9 96284921 65 9.02e7 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 29
10 96284921 65 9.02e7 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 118
# i 114,253 more rows
# i 9 more variables: FEED_INTK <int>, ENTRY_WT <int>, EXIT_WT <int>,
# FEEDER_NO <int>, START_DAY <chr>, OFFTEST_DAY <chr>, day_0 <date>,
# ENTRY_DATE <date>, DAYS_IN_FEED <dbl>
The echo: false option disables the printing of code (only output is displayed).
#| warning: true #| echo: true class (data_PIC)
[1] "grouped_df" "tbl_df" "tbl" "data.frame"
[1] "ID" "LINE" "SIRE" "DAM" "LITTER"
[6] "PEN" "FARM" "ENTRY_TIME" "EXIT_TIME" "STAY_IN"
[11] "FEED_INTK" "ENTRY_WT" "EXIT_WT" "FEEDER_NO" "START_DAY"
[16] "OFFTEST_DAY" "day_0" "ENTRY_DATE" "DAYS_IN_FEED"
gropd_df [114,263 x 19] (S3: grouped_df/tbl_df/tbl/data.frame)
$ ID : int [1:114263] 96251326 96251327 96251327 96284921 96284921 96284921 96284921 96284921 96284921 96284921 ...
$ LINE : int [1:114263] 65 65 65 65 65 65 65 65 65 65 ...
$ SIRE : int [1:114263] 91032775 91032775 91032775 90218081 90218081 90218081 90218081 90218081 90218081 90218081 ...
$ DAM : int [1:114263] 92185339 92185339 92185339 90208620 90208620 90208620 90208620 90208620 90208620 90208620 ...
$ LITTER : int [1:114263] 78043216 78043216 78043216 78064883 78064883 78064883 78064883 78064883 78064883 78064883 ...
$ PEN : chr [1:114263] "B0113" "B0113" "B0113" "B0113" ...
$ FARM : int [1:114263] 774 774 774 774 774 774 774 774 774 774 ...
$ ENTRY_TIME : chr [1:114263] "10/27/2022 12:51" "10/27/2022 12:00" "10/27/2022 13:03" "10/27/2022 7:27" ...
$ EXIT_TIME : chr [1:114263] "10/27/2022 13:02" "10/27/2022 12:32" "10/27/2022 13:06" "10/27/2022 7:37" ...
$ STAY_IN : int [1:114263] 649 1948 169 627 768 413 18 171 29 118 ...
$ FEED_INTK : int [1:114263] 265 514 57 85 196 88 3 33 4 14 ...
$ ENTRY_WT : int [1:114263] 1056 1086 791 473 613 691 591 588 555 551 ...
$ EXIT_WT : int [1:114263] 791 572 734 388 417 603 588 555 551 537 ...
$ FEEDER_NO : int [1:114263] 5 5 5 5 5 5 5 5 5 5 ...
$ START_DAY : chr [1:114263] "27-Oct-22" "27-Oct-22" "27-Oct-22" "27-Oct-22" ...
$ OFFTEST_DAY : chr [1:114263] "2-Jan-23" "2-Jan-23" "2-Jan-23" "2-Jan-23" ...
$ day_0 : Date[1:114263], format: "2022-10-27" "2022-10-27" ...
$ ENTRY_DATE : Date[1:114263], format: "2022-10-27" "2022-10-27" ...
$ DAYS_IN_FEED: num [1:114263] 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "groups")= tibble [548 x 2] (S3: tbl_df/tbl/data.frame)
..$ ID : int [1:548] 96251326 96251327 96251328 96251346 96263569 96263570 96263592 96263593 96263594 96263848 ...
..$ .rows: list<int> [1:548]
.. ..$ : int [1:193] 1 80 81 624 625 626 965 966 967 1714 ...
.. ..$ : int [1:136] 2 3 86 87 632 633 968 1721 1819 1820 ...
.. ..$ : int [1:221] 92 93 94 251 252 983 984 985 986 1729 ...
.. ..$ : int [1:194] 102 103 987 988 989 1735 1745 1746 1747 1824 ...
.. ..$ : int [1:200] 107 108 109 110 111 263 264 265 1000 1001 ...
.. ..$ : int [1:208] 112 113 114 115 618 619 620 621 1078 1079 ...
.. ..$ : int [1:315] 129 256 257 258 259 260 261 381 382 1327 ...
.. ..$ : int [1:253] 266 267 268 387 388 389 390 547 548 549 ...
.. ..$ : int [1:177] 394 395 396 563 564 595 596 597 622 623 ...
.. ..$ : int [1:244] 262 408 409 410 411 604 605 606 607 615 ...
.. ..$ : int [1:250] 416 417 418 608 609 610 627 628 629 630 ...
.. ..$ : int [1:203] 4 5 6 7 8 9 10 11 12 13 ...
.. ..$ : int [1:216] 15 16 17 18 19 687 688 689 690 691 ...
.. ..$ : int [1:212] 20 21 22 23 24 25 26 27 28 29 ...
.. ..$ : int [1:159] 30 31 32 33 68 712 713 952 953 1073 ...
.. ..$ : int [1:181] 73 74 75 76 724 725 726 727 957 958 ...
.. ..$ : int [1:218] 82 83 739 740 741 969 970 971 972 973 ...
.. ..$ : int [1:224] 34 88 89 90 757 758 759 974 975 976 ...
.. ..$ : int [1:221] 95 96 97 98 770 771 772 773 990 991 ...
.. ..$ : int [1:184] 35 36 37 38 104 105 1002 1003 1004 1053 ...
.. ..$ : int [1:390] 39 40 121 122 123 124 125 550 551 552 ...
.. ..$ : int [1:192] 41 42 43 383 384 567 568 1335 1336 1337 ...
.. ..$ : int [1:225] 44 391 598 599 600 649 650 651 1762 1893 ...
.. ..$ : int [1:153] 397 398 601 602 603 667 668 1076 1077 1895 ...
.. ..$ : int [1:296] 45 46 47 48 49 50 419 420 611 612 ...
.. ..$ : int [1:224] 51 704 705 706 707 708 954 955 956 1694 ...
.. ..$ : int [1:231] 52 53 59 60 61 62 63 714 715 716 ...
.. ..$ : int [1:179] 64 65 66 67 728 729 730 1325 1326 1709 ...
.. ..$ : int [1:225] 69 70 71 72 742 743 744 938 939 940 ...
.. ..$ : int [1:259] 77 78 79 749 750 751 752 959 960 961 ...
.. ..$ : int [1:208] 84 85 760 761 962 963 964 1731 1732 1733 ...
.. ..$ : int [1:238] 54 55 56 91 996 997 998 999 1045 1046 ...
.. ..$ : int [1:189] 99 100 101 948 949 950 951 1055 1056 1756 ...
.. ..$ : int [1:204] 106 1005 1006 1060 1763 1764 2186 2510 2511 2512 ...
.. ..$ : int [1:217] 116 117 118 553 554 555 1317 1318 1319 2191 ...
.. ..$ : int [1:125] 119 120 565 566 932 2526 2527 2708 3644 4674 ...
.. ..$ : int [1:178] 126 127 128 569 634 635 1133 1134 1135 2715 ...
.. ..$ : int [1:184] 385 386 636 637 1152 1153 1154 2726 2727 2728 ...
.. ..$ : int [1:105] 1086 1087 2204 2205 2206 2207 4504 4505 4506 4864 ...
.. ..$ : int [1:200] 392 393 669 670 1801 1802 2740 2741 3670 3671 ...
.. ..$ : int [1:219] 399 400 401 402 403 404 405 406 407 678 ...
.. ..$ : int [1:239] 412 413 414 415 696 697 698 699 1911 1912 ...
.. ..$ : int [1:229] 421 422 423 424 425 426 709 710 717 718 ...
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.. ..$ : int [1:270] 762 763 764 765 1155 1156 1157 1158 1543 1544 ...
.. ..$ : int [1:163] 1049 1050 1051 1564 1565 1796 1797 2718 2719 2720 ...
.. ..$ : int [1:183] 1057 1058 1059 1566 1567 1568 1569 1570 1571 1803 ...
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.. ..$ : int [1:224] 542 543 544 545 546 1605 1606 1607 1608 1609 ...
.. ..$ : int [1:144] 556 557 558 559 1621 1622 1623 1624 1921 2139 ...
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.. ..$ : int [1:169] 642 643 1969 1970 1971 2170 2171 2239 2240 2241 ...
.. ..$ : int [1:233] 747 748 1811 1812 2005 2006 2007 2328 2329 2599 ...
.. ..$ : int [1:277] 368 685 686 1052 1919 1920 2014 2015 2016 2017 ...
.. ..$ : int [1:191] 721 722 723 1975 1976 1977 2183 2184 2242 2243 ...
.. ..$ : int [1:241] 755 756 1139 1140 1141 1142 1978 1979 1980 2189 ...
.. ..$ : int [1:293] 652 653 654 655 656 1159 1160 1161 1987 1988 ...
.. ..$ : int [1:203] 657 658 659 1798 1799 1800 1994 1995 1996 2302 ...
.. ..$ : int [1:254] 700 701 1922 2019 2020 2021 2031 2361 2362 2363 ...
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.. ..$ : int [1:224] 671 672 673 868 869 870 871 1042 1043 1044 ...
.. ..$ : int [1:217] 154 269 270 271 272 273 274 914 915 1148 ...
.. ..$ : int [1:345] 167 168 169 170 171 300 301 302 1193 1194 ...
.. ..$ : int [1:207] 181 182 183 329 330 331 1212 1213 1214 1215 ...
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.. ..$ : int [1:176] 468 469 470 1007 1008 1009 1010 1011 1309 1310 ...
.. ..$ : int [1:208] 354 355 356 357 358 359 360 361 362 432 ...
.. ..$ : int [1:199] 369 438 439 1253 1254 1255 1256 1257 2245 2246 ...
.. ..$ : int [1:265] 449 450 1258 1259 1260 1261 1262 1263 2079 2080 ...
.. ..$ : int [1:175] 201 202 203 336 337 338 339 1217 1218 1219 ...
.. ..$ : int [1:204] 451 452 453 454 1306 1307 1308 2092 2093 2094 ...
.. ..$ : int [1:189] 471 472 473 474 1956 1957 2082 2310 2601 2602 ...
.. ..$ : int [1:208] 489 490 1958 1959 1960 2083 2084 2330 2331 2332 ...
.. ..$ : int [1:152] 498 499 1961 1962 1963 2095 2096 2097 2333 2629 ...
.. ..$ : int [1:197] 275 276 277 278 279 280 281 282 283 284 ...
.. ..$ : int [1:168] 293 511 1344 1345 1972 1973 2366 2367 2368 2396 ...
.. ..$ : int [1:239] 303 304 305 306 307 308 529 530 531 1974 ...
.. ..$ : int [1:186] 130 131 132 317 318 319 320 321 322 323 ...
.. ..$ : int [1:174] 143 144 332 1020 1346 1984 1985 1986 2697 2851 ...
.. ..$ : int [1:166] 145 340 341 1021 1022 1023 1024 1347 1997 1998 ...
.. ..$ : int [1:157] 163 164 349 1520 2000 2001 2002 2091 2877 3399 ...
.. ..$ : int [1:121] 180 350 363 1348 1521 1522 2003 2004 2878 2879 ...
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.. ..$ : int [1:153] 194 195 196 1352 1353 1553 1554 2012 2013 2916 ...
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.. ..$ : int [1:241] 286 287 288 289 458 459 460 461 849 850 ...
.. ..$ : int [1:380] 294 295 296 309 310 475 476 477 478 854 ...
.. ..$ : int [1:149] 311 312 479 480 863 864 865 1415 2236 2237 ...
.. .. [list output truncated]
.. ..@ ptype: int(0)
..- attr(*, ".drop")= logi TRUE
$ PEN <- as.factor (data_PIC$ PEN)$ Social_Group <- paste (data_PIC$ PEN, data_PIC$ START_DAY, data_PIC$ OFFTEST_DAY, sep = "_" )head (data_PIC$ Social_Group)
[1] "B0113_27-Oct-22_2-Jan-23" "B0113_27-Oct-22_2-Jan-23"
[3] "B0113_27-Oct-22_2-Jan-23" "B0113_27-Oct-22_2-Jan-23"
[5] "B0113_27-Oct-22_2-Jan-23" "B0113_27-Oct-22_2-Jan-23"
<- group_by (data_PIC, Social_Group)
# A tibble: 114,263 x 20
# Groups: Social_Group [36]
ID LINE SIRE DAM LITTER PEN FARM ENTRY_TIME EXIT_TIME STAY_IN
<int> <int> <int> <int> <int> <fct> <int> <chr> <chr> <int>
1 96251326 65 9.10e7 9.22e7 7.80e7 B0113 774 10/27/202~ 10/27/20~ 649
2 96251327 65 9.10e7 9.22e7 7.80e7 B0113 774 10/27/202~ 10/27/20~ 1948
3 96251327 65 9.10e7 9.22e7 7.80e7 B0113 774 10/27/202~ 10/27/20~ 169
4 96284921 65 9.02e7 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 627
5 96284921 65 9.02e7 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 768
6 96284921 65 9.02e7 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 413
7 96284921 65 9.02e7 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 18
8 96284921 65 9.02e7 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 171
9 96284921 65 9.02e7 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 29
10 96284921 65 9.02e7 9.02e7 7.81e7 B0113 774 10/27/202~ 10/27/20~ 118
# i 114,253 more rows
# i 10 more variables: FEED_INTK <int>, ENTRY_WT <int>, EXIT_WT <int>,
# FEEDER_NO <int>, START_DAY <chr>, OFFTEST_DAY <chr>, day_0 <date>,
# ENTRY_DATE <date>, DAYS_IN_FEED <dbl>, Social_Group <chr>
<- arrange (data_PIC, Social_Group, ENTRY_TIME, by_group = TRUE )%>% mutate (line= row_number ())
# A tibble: 114,263 x 21
# Groups: Social_Group [36]
ID LINE SIRE DAM LITTER PEN FARM ENTRY_TIME EXIT_TIME STAY_IN
<int> <int> <int> <int> <int> <fct> <int> <chr> <chr> <int>
1 97900500 65 9.20e7 9.15e7 7.86e7 B0111 774 3/15/2023~ 3/15/202~ 62
2 97887847 65 8.83e7 9.34e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 13
3 97916804 65 9.20e7 9.37e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 144
4 97900594 65 8.82e7 9.22e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 8
5 97887845 65 8.83e7 9.34e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 713
6 97900594 65 8.82e7 9.22e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 2124
7 97900500 65 9.20e7 9.15e7 7.86e7 B0111 774 3/15/2023~ 3/15/202~ 5
8 97916804 65 9.20e7 9.37e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 132
9 97900489 65 8.83e7 9.18e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 1033
10 97900600 65 8.82e7 9.22e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 533
# i 114,253 more rows
# i 11 more variables: FEED_INTK <int>, ENTRY_WT <int>, EXIT_WT <int>,
# FEEDER_NO <int>, START_DAY <chr>, OFFTEST_DAY <chr>, day_0 <date>,
# ENTRY_DATE <date>, DAYS_IN_FEED <dbl>, Social_Group <chr>, line <int>
%>% :: select (ID, ENTRY_TIME, Social_Group)
# A tibble: 114,263 x 3
# Groups: Social_Group [36]
ID ENTRY_TIME Social_Group
<int> <chr> <chr>
1 97900500 3/15/2023 10:01 B0111_16-Mar-23_15-May-23
2 97887847 3/15/2023 10:05 B0111_16-Mar-23_15-May-23
3 97916804 3/15/2023 10:10 B0111_16-Mar-23_15-May-23
4 97900594 3/15/2023 10:14 B0111_16-Mar-23_15-May-23
5 97887845 3/15/2023 10:16 B0111_16-Mar-23_15-May-23
6 97900594 3/15/2023 10:29 B0111_16-Mar-23_15-May-23
7 97900500 3/15/2023 11:06 B0111_16-Mar-23_15-May-23
8 97916804 3/15/2023 11:10 B0111_16-Mar-23_15-May-23
9 97900489 3/15/2023 11:15 B0111_16-Mar-23_15-May-23
10 97900600 3/15/2023 11:33 B0111_16-Mar-23_15-May-23
# i 114,253 more rows
#| warning: true #| echo: true <- data_PIC %>% arrange (Social_Group, ENTRY_TIME) %>% group_by (Social_Group) %>% mutate (Follower_ID = lag (ID),Follower_Time = ifelse (is.numeric (lag (ENTRY_TIME)), log (lag (ENTRY_TIME)), NA ),Follower_Social_Group = lag (Social_Group))
# A tibble: 114,263 x 23
# Groups: Social_Group [36]
ID LINE SIRE DAM LITTER PEN FARM ENTRY_TIME EXIT_TIME STAY_IN
<int> <int> <int> <int> <int> <fct> <int> <chr> <chr> <int>
1 97900500 65 9.20e7 9.15e7 7.86e7 B0111 774 3/15/2023~ 3/15/202~ 62
2 97887847 65 8.83e7 9.34e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 13
3 97916804 65 9.20e7 9.37e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 144
4 97900594 65 8.82e7 9.22e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 8
5 97887845 65 8.83e7 9.34e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 713
6 97900594 65 8.82e7 9.22e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 2124
7 97900500 65 9.20e7 9.15e7 7.86e7 B0111 774 3/15/2023~ 3/15/202~ 5
8 97916804 65 9.20e7 9.37e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 132
9 97900489 65 8.83e7 9.18e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 1033
10 97900600 65 8.82e7 9.22e7 7.90e7 B0111 774 3/15/2023~ 3/15/202~ 533
# i 114,253 more rows
# i 13 more variables: FEED_INTK <int>, ENTRY_WT <int>, EXIT_WT <int>,
# FEEDER_NO <int>, START_DAY <chr>, OFFTEST_DAY <chr>, day_0 <date>,
# ENTRY_DATE <date>, DAYS_IN_FEED <dbl>, Social_Group <chr>,
# Follower_ID <int>, Follower_Time <lgl>, Follower_Social_Group <chr>