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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | edeae3c | Eric Tytell | 2021-12-30 | Rename notebooks to indicate order |
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──✓ ggplot2 3.3.5 ✓ purrr 0.3.4
✓ tibble 3.1.4 ✓ dplyr 1.0.7
✓ tidyr 1.1.3 ✓ stringr 1.4.0
✓ readr 2.0.1 ✓ forcats 0.5.1── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(phytools)Loading required package: apeLoading required package: maps
Attaching package: 'maps'The following object is masked from 'package:purrr':
maplibrary(rfishbase)
library(here)here() starts at /Users/etytel01/Documents/Vertebrae/Codefullvertmeas <- read_csv(here('data', "MasterVert_Measurements.csv")) %>%
separate(MatchSpecies, into=c("MatchGenus", "MatchSpecies"), sep="_")Rows: 585 Columns: 54── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (16): Species, MatchSpecies, Family, Body Shape, Habitat_Initial, Habita...
dbl (38): Indiv, Pos, SL, CBL_raw, alpha_Pos_raw, d_raw, D_Pos_raw, alpha_An...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.fullvertmeas# A tibble: 585 × 55
Species MatchGenus MatchSpecies Family Indiv Pos SL CBL_raw
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Alectis_ciliaris <NA> <NA> <NA> 1 40 799 24.9
2 Alectis_ciliaris <NA> <NA> <NA> 1 50 799 34.4
3 Alectis_ciliaris <NA> <NA> <NA> 1 60 799 35.8
4 Alectis_ciliaris <NA> <NA> <NA> 1 70 799 36.6
5 Alectis_ciliaris <NA> <NA> <NA> 1 80 799 35.2
6 Alectis_ciliaris <NA> <NA> <NA> 1 90 799 32.8
7 Amia_calva <NA> <NA> <NA> 1 30 1438 11.8
8 Amia_calva <NA> <NA> <NA> 1 40 1438 31.8
9 Amia_calva <NA> <NA> <NA> 1 50 1438 15.6
10 Amia_calva <NA> <NA> <NA> 1 60 1438 32.1
# … with 575 more rows, and 47 more variables: alpha_Pos_raw <dbl>,
# d_raw <dbl>, D_Pos_raw <dbl>, alpha_Ant_raw <dbl>, D_Ant_raw <dbl>,
# CBL <dbl>, alpha_Pos <dbl>, d <dbl>, D_Pos <dbl>, alpha_Ant <dbl>,
# D_Ant <dbl>, Pt1x <dbl>, Pt1y <dbl>, Pt2x <dbl>, Pt2y <dbl>, Pt3x <dbl>,
# Pt3y <dbl>, Pt4x <dbl>, Pt4y <dbl>, Pt5x <dbl>, Pt5y <dbl>, Pt6x <dbl>,
# Pt6y <dbl>, Pt7x <dbl>, Pt7y <dbl>, Body Shape <chr>,
# Habitat_Initial <chr>, Habitat_Friedman <chr>, Habitat_FishBase <chr>, …fullvertmeas %>%
filter(is.na(Water_Type))# A tibble: 0 × 55
# … with 55 variables: Species <chr>, MatchGenus <chr>, MatchSpecies <chr>,
# Family <chr>, Indiv <dbl>, Pos <dbl>, SL <dbl>, CBL_raw <dbl>,
# alpha_Pos_raw <dbl>, d_raw <dbl>, D_Pos_raw <dbl>, alpha_Ant_raw <dbl>,
# D_Ant_raw <dbl>, CBL <dbl>, alpha_Pos <dbl>, d <dbl>, D_Pos <dbl>,
# alpha_Ant <dbl>, D_Ant <dbl>, Pt1x <dbl>, Pt1y <dbl>, Pt2x <dbl>,
# Pt2y <dbl>, Pt3x <dbl>, Pt3y <dbl>, Pt4x <dbl>, Pt4y <dbl>, Pt5x <dbl>,
# Pt5y <dbl>, Pt6x <dbl>, Pt6y <dbl>, Pt7x <dbl>, Pt7y <dbl>, …This is the whole Betancur-R tree.
tree <- read.tree(here('data', '12862_2017_958_MOESM2_ESM.tre'))Get the names of species from the tree.
allspecies <- tibble(tree$tip.label)
colnames(allspecies) <- c('FullName')
head(allspecies)# A tibble: 6 × 1
FullName
<chr>
1 Rajidae_Leucoraja_erinacea_G01356
2 Callorhinchidae_Callorhinchus_milii_G01235
3 Latimeriidae_Latimeria_chalumnae_G01347
4 Neoceratodontidae_Neoceratodus_forsteri_G01534
5 Protopteridae_Protopterus_aethiopicus_annectens_G01451
6 Lepidosirenidae_Lepidosiren_paradoxa_G01352 And split the names into family, genus, and species.
allspecies <-
allspecies %>% separate(FullName, sep='_', into=c('Family', 'Genus', 'Species'),
extra='drop', remove=FALSE)Set up the tip number (just the row)
allspecies$Tip <- seq_len(nrow(allspecies))Now let’s look at just the distinct species in our data set.
ourspecies <-
fullvertmeas %>%
distinct(Species, .keep_all=TRUE) %>%
separate(Species, sep='_', into=c("Genus", "Species")) %>%
select(Genus, Species, Family, MatchGenus, MatchSpecies)
ourspecies# A tibble: 85 × 5
Genus Species Family MatchGenus MatchSpecies
<chr> <chr> <chr> <chr> <chr>
1 Alectis ciliaris <NA> <NA> <NA>
2 Amia calva <NA> <NA> <NA>
3 Anoplogaster cornuta <NA> <NA> <NA>
4 Aphareus furca <NA> <NA> <NA>
5 Catostomus catostomus <NA> <NA> <NA>
6 Cephalopholis argus <NA> <NA> <NA>
7 Chaetostoma lineopunctatum <NA> <NA> <NA>
8 Centropomus undecimalis <NA> <NA> <NA>
9 Osteoglossum bicirrhosum <NA> <NA> <NA>
10 Chanos chanos <NA> <NA> <NA>
# … with 75 more rowsJoin up our species with the data table of all of the species.
ourspecies <-
ourspecies %>%
left_join(allspecies, by = c("Genus", "Species")) %>%
mutate(Family = coalesce(Family.x, Family.y)) %>%
select(-Family.x, -Family.y)We are working to set up new columns called MatchGenus and MatchSpecies and that are the matching genus and species in the Betancur tree.
For species that have matches in the big tree (so that Tip is not NA), the “MatchSpecies” and “MatchGenus” are just the same as Genus and Species.
ourspecies <-
ourspecies %>%
mutate(MatchSpecies = if_else(!is.na(Tip), Species, MatchSpecies),
MatchGenus = if_else(!is.na(Tip), Genus, MatchGenus))For species that aren’t in the Betancur tree, we have to find the most appropriate other species. First, find species in our data set that are not in the Betancur tree.
missingspecies <- anti_join(ourspecies, allspecies, by=c("Genus", "Species"))
missingspecies# A tibble: 29 × 7
Genus Species MatchGenus MatchSpecies FullName Tip Family
<chr> <chr> <chr> <chr> <chr> <int> <chr>
1 Catostomus catostomus <NA> <NA> <NA> NA <NA>
2 Chaetostoma lineopunctatum <NA> <NA> <NA> NA <NA>
3 Roeboides affinis <NA> <NA> <NA> NA <NA>
4 Myripristis jacobus <NA> <NA> <NA> NA <NA>
5 Mirorictus taningi Searsia koefoedi <NA> NA Platytr…
6 Ophidion scrippsae <NA> <NA> <NA> NA <NA>
7 Oncorhynchus gorbuscha <NA> <NA> <NA> NA <NA>
8 Coryphaenoides filifer <NA> <NA> <NA> NA <NA>
9 Anodontostoma chacunda Dorosoma cepedianum <NA> NA Clupeid…
10 Apteronotus cuchillejo <NA> <NA> <NA> NA <NA>
# … with 19 more rowsLook for species in the Betancur tree in the same genera as the missing species:
left_join(missingspecies, allspecies, by=c("Genus")) %>%
group_by(Genus, Species.x) %>%
summarize(n = sum(!is.na(Species.y)))`summarise()` has grouped output by 'Genus'. You can override using the `.groups` argument.# A tibble: 29 × 3
# Groups: Genus [27]
Genus Species.x n
<chr> <chr> <int>
1 Abramis brama 0
2 Ammodytes personatus 2
3 Anodontostoma chacunda 0
4 Anoplarchus insignis 0
5 Anoplarchus purpurescens 0
6 Apodichthys flavidus 0
7 Apteronotus cuchillejo 1
8 Catostomus catostomus 6
9 Chaetostoma lineopunctatum 1
10 Coryphaenoides filifer 4
# … with 19 more rowsFor species and genera with 1 or more matches in the Betancur tree, we can assume that the phylogenetic relationship is the same as another species in the genus, so long as our data set doesn’t have more than one species in that genus.
Check for genera for which we have multiple species:
missingspecies <-
missingspecies %>%
group_by(Genus) %>%
mutate(n_in_genus = sum(!is.na(Species)))
missingspecies %>%
filter(n_in_genus > 1)# A tibble: 4 × 8
# Groups: Genus [2]
Genus Species MatchGenus MatchSpecies FullName Tip Family n_in_genus
<chr> <chr> <chr> <chr> <chr> <int> <chr> <int>
1 Anoplarchus insignis <NA> <NA> <NA> NA Stich… 2
2 Anoplarchus purpurescens <NA> <NA> <NA> NA Stich… 2
3 Xiphister atropurpureus <NA> <NA> <NA> NA Stich… 2
4 Xiphister mucosus <NA> <NA> <NA> NA Stich… 2For species where the genus only shows up once in our data set, we can choose any other species in the Betancur tree that is in the same genus
matchspecies <-
missingspecies %>%
filter(n_in_genus == 1 & is.na(MatchSpecies)) %>%
left_join(allspecies, by=c("Genus")) %>%
group_by(Genus, Species.x) %>%
mutate(Family = coalesce(Family.x, Family.y)) %>%
summarize(MatchSpecies = first(Species.y),
MatchGenus = if_else(!is.na(MatchSpecies), first(Genus), NA_character_),
Tip = first(Tip.y),
Family = first(Family)) %>%
rename(Species = Species.x)`summarise()` has grouped output by 'Genus'. You can override using the `.groups` argument.matchspecies# A tibble: 14 × 6
# Groups: Genus [14]
Genus Species MatchSpecies MatchGenus Tip Family
<chr> <chr> <chr> <chr> <int> <chr>
1 Ammodytes personatus dubius Ammodytes 1696 Ammodytidae
2 Apteronotus cuchillejo albifrons Apteronotus 248 Apteronoti…
3 Catostomus catostomus plebius Catostomus 84 Catostomid…
4 Chaetostoma lineopunctatum breve Chaetostoma 274 Loricariid…
5 Coryphaenoides filifer armatus Coryphaenoides 611 Macrouridae
6 Hirundichthys rondeletii marginatus Hirundichthys 718 Exocoetidae
7 Lumpenus sagitta fabricii Lumpenus 1241 Stichaeidae
8 Myripristis jacobus murdjan Myripristis 670 Holocentri…
9 Oncorhynchus gorbuscha nerka Oncorhynchus 466 Salmonidae
10 Ophidion scrippsae holbrookii Ophidion 1986 Ophidiidae
11 Poecilia reticulata latipinna Poecilia 732 Poeciliidae
12 Remora remora osteochir Remora 1047 Echeneidae
13 Roeboides affinis descalvadensis Roeboides 375 Characidae
14 Trachurus trachurus lathami Trachurus 1019 Carangidae Here is the full list of our species and the best matching species from the Betancur tree.
matchspecies <-
ourspecies %>%
left_join(matchspecies, by = c("Genus", "Species")) %>%
mutate(MatchGenus = coalesce(MatchGenus.x, MatchGenus.y),
MatchSpecies = coalesce(MatchSpecies.x, MatchSpecies.y),
Family = coalesce(Family.x, Family.y),
Tip = coalesce(Tip.x, Tip.y)) %>%
select(-(ends_with('.x') | ends_with('.y'))) %>%
select(Genus, Species, MatchGenus, MatchSpecies) %>%
unite("MatchSpecies", c(MatchGenus, MatchSpecies)) %>%
unite("Species", c(Genus, Species)) %>%
mutate(MatchSpecies = if_else(MatchSpecies == "NA_NA", NA_character_, MatchSpecies))
matchspecies# A tibble: 85 × 2
Species MatchSpecies
<chr> <chr>
1 Alectis_ciliaris Alectis_ciliaris
2 Amia_calva Amia_calva
3 Anoplogaster_cornuta Anoplogaster_cornuta
4 Aphareus_furca Aphareus_furca
5 Catostomus_catostomus Catostomus_plebius
6 Cephalopholis_argus Cephalopholis_argus
7 Chaetostoma_lineopunctatum Chaetostoma_breve
8 Centropomus_undecimalis Centropomus_undecimalis
9 Osteoglossum_bicirrhosum Osteoglossum_bicirrhosum
10 Chanos_chanos Chanos_chanos
# … with 75 more rowsThese are the species we couldn’t match:
matchspecies %>%
filter(is.na(MatchSpecies))# A tibble: 4 × 2
Species MatchSpecies
<chr> <chr>
1 Anoplarchus_insignis <NA>
2 Anoplarchus_purpurescens <NA>
3 Xiphister_atropurpureus <NA>
4 Xiphister_mucosus <NA> Set up the final data table with a filled in MatchSpecies column.
fullvertmeas_matched <-
fullvertmeas %>%
unite("MatchSpecies", c(MatchGenus, MatchSpecies)) %>%
mutate(MatchSpecies = if_else(MatchSpecies == "NA_NA", NA_character_, MatchSpecies)) %>%
left_join(matchspecies, by = c("Species")) %>%
mutate(MatchSpecies = coalesce(MatchSpecies.x, MatchSpecies.y)) %>%
select(-(ends_with(".x") | ends_with(".y")))And write it out!
write_csv(fullvertmeas_matched, here('output', "MasterVert_Measurements_Matched.csv"))Load in the matched data set.
fullvertmeas <- read_csv(here('output', "MasterVert_Measurements_Matched.csv")) %>%
separate(MatchSpecies, into=c("MatchGenus", "MatchSpecies"), sep="_") %>%
relocate(MatchGenus, MatchSpecies, .after=Species)Rows: 585 Columns: 54── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (16): Species, Family, Body Shape, Habitat_Initial, Habitat_Friedman, Ha...
dbl (38): Indiv, Pos, SL, CBL_raw, alpha_Pos_raw, d_raw, D_Pos_raw, alpha_An...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.fullvertmeas# A tibble: 585 × 55
Species MatchGenus MatchSpecies Family Indiv Pos SL CBL_raw
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Alectis_ciliaris Alectis ciliaris <NA> 1 40 799 24.9
2 Alectis_ciliaris Alectis ciliaris <NA> 1 50 799 34.4
3 Alectis_ciliaris Alectis ciliaris <NA> 1 60 799 35.8
4 Alectis_ciliaris Alectis ciliaris <NA> 1 70 799 36.6
5 Alectis_ciliaris Alectis ciliaris <NA> 1 80 799 35.2
6 Alectis_ciliaris Alectis ciliaris <NA> 1 90 799 32.8
7 Amia_calva Amia calva <NA> 1 30 1438 11.8
8 Amia_calva Amia calva <NA> 1 40 1438 31.8
9 Amia_calva Amia calva <NA> 1 50 1438 15.6
10 Amia_calva Amia calva <NA> 1 60 1438 32.1
# … with 575 more rows, and 47 more variables: alpha_Pos_raw <dbl>,
# d_raw <dbl>, D_Pos_raw <dbl>, alpha_Ant_raw <dbl>, D_Ant_raw <dbl>,
# CBL <dbl>, alpha_Pos <dbl>, d <dbl>, D_Pos <dbl>, alpha_Ant <dbl>,
# D_Ant <dbl>, Pt1x <dbl>, Pt1y <dbl>, Pt2x <dbl>, Pt2y <dbl>, Pt3x <dbl>,
# Pt3y <dbl>, Pt4x <dbl>, Pt4y <dbl>, Pt5x <dbl>, Pt5y <dbl>, Pt6x <dbl>,
# Pt6y <dbl>, Pt7x <dbl>, Pt7y <dbl>, Body Shape <chr>,
# Habitat_Initial <chr>, Habitat_Friedman <chr>, Habitat_FishBase <chr>, …Find the matches in the full phylogeny.
fullvertmeas.species <- left_join(fullvertmeas, allspecies, by=c("MatchGenus"="Genus", "MatchSpecies"="Species"))And check to see how many species we have. It should be the same number as those in the matchspecies data set, minus those we couldn’t match.
checkspecies <-
fullvertmeas.species %>%
filter(!is.na(Tip)) %>%
distinct(Species, .keep_all = TRUE)nrow(checkspecies) == nrow(filter(matchspecies, !is.na(MatchSpecies)))[1] TRUE
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] here_1.0.1 rfishbase_3.1.9 phytools_0.7-80 maps_3.3.0
[5] ape_5.5 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[9] purrr_0.3.4 readr_2.0.1 tidyr_1.1.3 tibble_3.1.4
[13] ggplot2_3.3.5 tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] nlme_3.1-153 fs_1.5.0 bit64_4.0.5
[4] lubridate_1.7.10 progress_1.2.2 httr_1.4.2
[7] rprojroot_2.0.2 gh_1.3.0 numDeriv_2016.8-1.1
[10] tools_4.1.2 backports_1.2.1 utf8_1.2.2
[13] R6_2.5.1 DBI_1.1.1 colorspace_2.0-2
[16] withr_2.4.2 prettyunits_1.1.1 tidyselect_1.1.1
[19] mnormt_2.0.2 phangorn_2.7.1 bit_4.0.4
[22] curl_4.3.2 compiler_4.1.2 git2r_0.29.0
[25] cli_3.0.1 rvest_1.0.1 expm_0.999-6
[28] xml2_1.3.2 scales_1.1.1 quadprog_1.5-8
[31] digest_0.6.27 rmarkdown_2.10 pkgconfig_2.0.3
[34] htmltools_0.5.2 plotrix_3.8-2 dbplyr_2.1.1
[37] fastmap_1.1.0 rlang_0.4.11 readxl_1.3.1
[40] rstudioapi_0.13 generics_0.1.0 combinat_0.0-8
[43] jsonlite_1.7.2 vroom_1.5.4 magrittr_2.0.1
[46] Matrix_1.3-4 Rcpp_1.0.7 munsell_0.5.0
[49] fansi_0.5.0 lifecycle_1.0.0 scatterplot3d_0.3-41
[52] stringi_1.7.4 whisker_0.4 yaml_2.2.1
[55] clusterGeneration_1.3.7 MASS_7.3-54 grid_4.1.2
[58] parallel_4.1.2 promises_1.2.0.1 crayon_1.4.1
[61] lattice_0.20-45 haven_2.4.3 hms_1.1.0
[64] tmvnsim_1.0-2 knitr_1.34 pillar_1.6.2
[67] igraph_1.2.6 codetools_0.2-18 fastmatch_1.1-3
[70] reprex_2.0.1 glue_1.4.2 evaluate_0.14
[73] arkdb_0.0.12 renv_0.14.0 modelr_0.1.8
[76] vctrs_0.3.8 tzdb_0.1.2 httpuv_1.6.4
[79] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[82] cachem_1.0.6 xfun_0.25 broom_0.7.9
[85] coda_0.19-4 later_1.3.0 memoise_2.0.0
[88] workflowr_1.7.0 ellipsis_0.3.2