Quarto
Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org .
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>
[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 ...
.. ..$ : int [1:183] 427 428 429 719 720 2169 2467 2468 2469 2660 ...
.. ..$ : int [1:249] 731 732 733 734 2181 2182 2482 2483 2484 2485 ...
.. ..$ : int [1:205] 745 746 1035 1036 1037 2187 2188 2492 2493 2494 ...
.. ..$ : int [1:196] 753 754 1136 1137 1138 1528 1529 1530 2196 2197 ...
.. ..$ : 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 ...
.. ..$ : int [1:220] 1061 1062 1063 1587 1588 1813 1814 2742 2743 2744 ...
.. ..$ : 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 ...
.. ..$ : int [1:173] 560 561 562 1625 1626 2150 2151 2470 2471 2472 ...
.. ..$ : int [1:251] 638 639 640 641 1638 1639 1640 1641 1642 1643 ...
.. ..$ : 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 ...
.. ..$ : int [1:192] 735 736 737 738 2032 2033 2034 2124 2125 2126 ...
.. ..$ : int [1:209] 766 767 768 769 866 867 1038 1039 1040 1041 ...
.. ..$ : 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 ...
.. ..$ : int [1:156] 711 891 892 893 1131 1132 1548 1549 2713 2714 ...
.. ..$ : int [1:186] 136 137 138 910 911 912 913 1143 1144 1145 ...
.. ..$ : int [1:192] 225 226 347 348 1237 1238 1239 1644 1645 2201 ...
.. ..$ : 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 ...
.. ..$ : int [1:250] 192 193 370 371 1349 1350 1351 1531 1532 1533 ...
.. ..$ : int [1:153] 194 195 196 1352 1353 1553 1554 2012 2013 2916 ...
.. ..$ : int [1:220] 211 212 213 214 1354 1355 1576 1577 1578 2022 ...
.. ..$ : int [1:192] 227 1356 1357 1358 1596 1597 1598 1599 2027 2028 ...
.. ..$ : int [1:269] 434 435 1359 1360 1361 1600 1601 1602 1603 1604 ...
.. ..$ : int [1:237] 440 441 442 1362 1363 1364 1365 1614 1615 2045 ...
.. ..$ : int [1:318] 455 456 457 1384 1385 1630 1631 1632 1633 1634 ...
.. ..$ : 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"
# A tibble: 6 x 20
# Groups: ID [3]
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 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 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>
starting httpd help server ... done
<- 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
<- data_PIC %>% group_by (Social_Group) %>% arrange (EXIT_TIME) %>% mutate (line = row_number ())
ggplot(data_PIC.arrange, aes(x = EXIT_TIME, fill = Social_Group)) +
geom_bar(position = “dodge”) +
labs(title = “Interaction Between Social Groups Over Time”,
x = “EXIT_TIME”,
y = “Count”) +
theme_minimal()
The echo: false option disables the printing of code (only output is displayed).
#| warning: true #| echo: true <- table (data_PIC$ Social_Group)print (social_group_table)
B0111_16-Mar-23_15-May-23 B0111_27-Oct-22_2-Jan-23 B0111_5-Jan-23_13-Mar-23
3006 3534 2923
B0113_16-Mar-23_15-May-23 B0113_27-Oct-22_2-Jan-23 B0113_5-Jan-23_13-Mar-23
3815 3234 3353
B0211_12-Jan-23_20-Mar-23 B0211_23-Mar-23_22-May-23 B0211_3-Nov-22_9-Jan-23
2650 3303 3522
B0213_12-Jan-23_20-Mar-23 B0213_23-Mar-23_22-May-23 B0213_3-Nov-22_9-Jan-23
3295 2704 3038
B0311_10-Nov-22_16-Jan-23 B0311_19-Jan-23_27-Mar-23 B0311_29-Mar-23_29-May-23
2819 2940 3066
B0313_10-Nov-22_16-Jan-23 B0313_19-Jan-23_27-Mar-23 B0313_29-Mar-23_29-May-23
3220 2866 2707
B0411_17-Nov-22_23-Jan-23 B0411_26-Jan-23_3-Apr-23 B0411_5-Apr-23_5-Jun-23
4047 3867 2714
B0413_17-Nov-22_23-Jan-23 B0413_26-Jan-23_3-Apr-23 B0413_5-Apr-23_5-Jun-23
3399 3748 3247
B0502_13-Apr-23_12-Jun-23 B0506_2-Feb-23_10-Apr-23 B0506_23-Nov-22_30-Jan-23
3182 2709 2266
B0602_20-Apr-23_19-Jun-23 B0606_1-Dec-22_6-Feb-23 B0606_9-Feb-23_17-Apr-23
2526 3745 3830
B0706_16-Feb-23_24-Apr-23 B0706_8-Dec-22_13-Feb-23 B0906_2-Mar-23_8-May-23
3329 3794 3256
B0906_22-Dec-22_27-Feb-23 B1006_29-Dec-22_6-Mar-23 B1006_9-Mar-23_9-May-23
2713 2859 3037
<- as.data.frame (social_group_table)print (social_group_df)
Var1 Freq
1 B0111_16-Mar-23_15-May-23 3006
2 B0111_27-Oct-22_2-Jan-23 3534
3 B0111_5-Jan-23_13-Mar-23 2923
4 B0113_16-Mar-23_15-May-23 3815
5 B0113_27-Oct-22_2-Jan-23 3234
6 B0113_5-Jan-23_13-Mar-23 3353
7 B0211_12-Jan-23_20-Mar-23 2650
8 B0211_23-Mar-23_22-May-23 3303
9 B0211_3-Nov-22_9-Jan-23 3522
10 B0213_12-Jan-23_20-Mar-23 3295
11 B0213_23-Mar-23_22-May-23 2704
12 B0213_3-Nov-22_9-Jan-23 3038
13 B0311_10-Nov-22_16-Jan-23 2819
14 B0311_19-Jan-23_27-Mar-23 2940
15 B0311_29-Mar-23_29-May-23 3066
16 B0313_10-Nov-22_16-Jan-23 3220
17 B0313_19-Jan-23_27-Mar-23 2866
18 B0313_29-Mar-23_29-May-23 2707
19 B0411_17-Nov-22_23-Jan-23 4047
20 B0411_26-Jan-23_3-Apr-23 3867
21 B0411_5-Apr-23_5-Jun-23 2714
22 B0413_17-Nov-22_23-Jan-23 3399
23 B0413_26-Jan-23_3-Apr-23 3748
24 B0413_5-Apr-23_5-Jun-23 3247
25 B0502_13-Apr-23_12-Jun-23 3182
26 B0506_2-Feb-23_10-Apr-23 2709
27 B0506_23-Nov-22_30-Jan-23 2266
28 B0602_20-Apr-23_19-Jun-23 2526
29 B0606_1-Dec-22_6-Feb-23 3745
30 B0606_9-Feb-23_17-Apr-23 3830
31 B0706_16-Feb-23_24-Apr-23 3329
32 B0706_8-Dec-22_13-Feb-23 3794
33 B0906_2-Mar-23_8-May-23 3256
34 B0906_22-Dec-22_27-Feb-23 2713
35 B1006_29-Dec-22_6-Mar-23 2859
36 B1006_9-Mar-23_9-May-23 3037
<- table (data_PIC$ PEN)dim (PEN1)
B0111 B0113 B0211 B0213 B0311 B0313 B0411 B0413 B0502 B0506 B0602 B0606 B0706
9463 10402 9475 9037 8825 8793 10628 10394 3182 4975 2526 7575 7123
B0906 B1006
5969 5896
<- table (data_PIC$ ID, data_PIC$ Social_Group)dim (ID_SocialGroup)
B0111_16-Mar-23_15-May-23 B0111_27-Oct-22_2-Jan-23
96251326 0 0
96251327 0 0
96251328 0 0
96251346 0 0
96263569 0 0
B0111_5-Jan-23_13-Mar-23 B0113_16-Mar-23_15-May-23
96251326 0 0
96251327 0 0
96251328 0 0
96251346 0 0
96263569 0 0
B0113_27-Oct-22_2-Jan-23
96251326 193
96251327 136
96251328 221
96251346 194
96263569 200
rowSums (ID_SocialGroup > 0 ) %>% table ()colSums (ID_SocialGroup > 0 ) %>% table ()
.
11 13 14 15 16
1 1 6 8 20