Simmental Animal Locations
Troy Rowan
2020-07-31
Last updated: 2020-08-31
Checks: 6 1
Knit directory: local_adaptation_sequence/
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|---|---|---|---|---|
| Rmd | 27eb0df | Troy Rowan | 2020-08-31 | Simmental data dump locations |
Reading in phenotypes, pedigree, etc.
Pulling lab IDs for genotype dump
This is from the animal table as of August 21st, 2020 Harly turned this into an RDS file using one of her scripts and weird suite of packages (almost broke her computer) This behaved a bit curiously as I kept dropping ~10 K animals from the Simmental data sheet that I read in. Turns out that they had repeated records for mature weight, so the same cow could be listed multiple times. Upon filtering those out and matching as many animals as possible to a Reg or Ref_ID based on their ASA Registration number, I get 100,559 distinct lab IDs. They’re written out and sent to Bob for a data dump on CIFS prior to imputation on Lewis.
There are 99,932 individuals that are adequately accoutned for in the database
# A tibble: 98,432 x 2
# Groups: asa_nbr [98,432]
asa_nbr n
<chr> <int>
1 2378503 7
2 2585172 7
3 2618240 7
4 2671799 7
5 2724093 7
6 2724097 7
7 2724108 7
8 2724110 7
9 2724117 7
10 2724129 7
# ... with 98,422 more rowsfunction (..., sep = " ", collapse = NULL)
.Internal(paste(list(...), sep, collapse))
<bytecode: 0x0000000014cf61e0>
<environment: namespace:base>[1] 99932Completeness of data
98,335 individuals (unique ASA reg numbers) match up to entries in the database
colSums(!is.na(sim_animals))
asa_nbr 110710
breeder_zip 109352
owner_zip 105406
bw 96064
bw_adj 110710
bw_cg 109549
ww 89595
ww_adj 110710
ww_cg 86410
yw 64608
yw_adj 110293
mw 46580
international_id 110703# A tibble: 1 x 1
n
<int>
1 98335It actually looks
Simmental Location Counts
K=9 All Variables (Seed #1)
# A tibble: 9 x 2
# Groups: region [9]
region n
<chr> <int>
1 Arid Prairie 1069
2 Corn Belt 13887
3 Desert 16
4 Fescue Belt 24485
5 Foothills 1787
6 Forested Mountains 8912
7 High Plains 29195
8 Southeast 6733
9 Upper Midwest 20270K=9 All Variables (Seed #2)
# A tibble: 8 x 2
# Groups: region [8]
region n
<chr> <int>
1 Arid Prairie 1501
2 Desert 16
3 Fescue Belt 30276
4 Foothills 1391
5 Forested Mountains 7606
6 High Plains 31632
7 Southeast 10518
8 Upper Midwest 25723K=9 Three Variables ()
Seed doesn’t appear to cause any issues here
# A tibble: 9 x 2
# Groups: region [9]
region n
<chr> <int>
1 Arid Prairie 2460
2 Desert 1435
3 Fescue Belt 26561
4 Foothills 18684
5 Forested Mountains 1436
6 High Plains 34624
7 Rainforest 15
8 Southeast 11828
9 Upper Midwest 15700K = 10 Counts
Something is up with these region assignments… Way too many individuals say that they’re from “VERY HOT PLACE”
Simmental Location Maps
K = 9 Map
K=10 Map
R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.0 stringr_1.4.0 dplyr_0.8.5 readr_1.3.1
[5] tidyr_1.0.3 tibble_3.0.1 tidyverse_1.3.0 here_0.1
[9] ggcorrplot_0.1.3 corrr_0.4.2 factoextra_1.0.7 ggplot2_3.3.0
[13] purrr_0.3.4 cowplot_1.0.0 ggthemes_4.2.0 maps_3.3.0
[17] RStoolbox_0.2.6 fpc_2.2-7 raster_3.3-7 rgdal_1.5-12
[21] sp_1.4-2 knitr_1.28 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] colorspace_1.4-1 ellipsis_0.3.0 class_7.3-15
[4] modeltools_0.2-23 mclust_5.4.6 rprojroot_1.3-2
[7] fs_1.4.1 rstudioapi_0.11 farver_2.0.3
[10] ggrepel_0.8.2 flexmix_2.3-15 prodlim_2019.11.13
[13] fansi_0.4.1 lubridate_1.7.8 xml2_1.3.2
[16] codetools_0.2-16 splines_3.6.1 doParallel_1.0.15
[19] robustbase_0.93-6 jsonlite_1.6.1 pROC_1.16.2
[22] caret_6.0-86 broom_0.5.6 cluster_2.1.0
[25] kernlab_0.9-29 dbplyr_1.4.3 rgeos_0.5-3
[28] compiler_3.6.1 httr_1.4.1 backports_1.1.6
[31] assertthat_0.2.1 Matrix_1.2-17 cli_2.0.2
[34] later_1.0.0 htmltools_0.4.0 tools_3.6.1
[37] gtable_0.3.0 glue_1.4.0 reshape2_1.4.4
[40] Rcpp_1.0.4.6 cellranger_1.1.0 vctrs_0.2.4
[43] nlme_3.1-140 iterators_1.0.12 timeDate_3043.102
[46] gower_0.2.2 xfun_0.13 rvest_0.3.5
[49] lifecycle_0.2.0 XML_3.99-0.3 DEoptimR_1.0-8
[52] MASS_7.3-51.4 scales_1.1.0 ipred_0.9-9
[55] hms_0.5.3 promises_1.1.0 parallel_3.6.1
[58] yaml_2.2.1 geosphere_1.5-10 rpart_4.1-15
[61] stringi_1.4.6 highr_0.8 foreach_1.5.0
[64] lava_1.6.7 rlang_0.4.6 pkgconfig_2.0.3
[67] prabclus_2.3-2 evaluate_0.14 lattice_0.20-38
[70] recipes_0.1.13 labeling_0.3 tidyselect_1.0.0
[73] plyr_1.8.6 magrittr_1.5 R6_2.4.1
[76] generics_0.0.2 DBI_1.1.0 pillar_1.4.4
[79] haven_2.2.0 whisker_0.4 withr_2.2.0
[82] survival_2.44-1.1 nnet_7.3-12 modelr_0.1.7
[85] crayon_1.3.4 utf8_1.1.4 rmarkdown_2.1
[88] grid_3.6.1 readxl_1.3.1 data.table_1.12.8
[91] git2r_0.27.1 ModelMetrics_1.2.2.2 reprex_0.3.0
[94] digest_0.6.25 diptest_0.75-7 httpuv_1.5.2
[97] stats4_3.6.1 munsell_0.5.0