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| File | Version | Author | Date | Message |
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| Rmd | bbb4c58 | Eric Tytell | 2022-10-23 | Final analysis scripts |
| Rmd | 65e217e | Eric Tytell | 2022-09-16 | Updated code |
| Rmd | 23b4a3f | Eric Tytell | 2022-09-06 | Starting to compare a different phylogeny |
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.3.6 ✔ purrr 0.3.4
✔ tibble 3.1.8 ✔ dplyr 1.0.9
✔ tidyr 1.2.0 ✔ stringr 1.4.1
✔ readr 2.1.2 ✔ forcats 0.5.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(ggbeeswarm)
library(phytools)Loading required package: ape
Loading required package: maps
Attaching package: 'maps'
The following object is masked from 'package:purrr':
maplibrary(patchwork)
library(here)here() starts at /Users/etytel01/Documents/Vertebrae/Codelibrary(nlme)
Attaching package: 'nlme'
The following object is masked from 'package:dplyr':
collapselibrary(ape)
library(geiger)
library(ggtree)Warning: package 'ggtree' was built under R version 4.2.1ggtree v3.4.2 For help: https://yulab-smu.top/treedata-book/
If you use the ggtree package suite in published research, please cite
the appropriate paper(s):
Guangchuang Yu, David Smith, Huachen Zhu, Yi Guan, Tommy Tsan-Yuk Lam.
ggtree: an R package for visualization and annotation of phylogenetic
trees with their covariates and other associated data. Methods in
Ecology and Evolution. 2017, 8(1):28-36. doi:10.1111/2041-210X.12628
G Yu. Data Integration, Manipulation and Visualization of Phylogenetic
Trees (1st ed.). Chapman and Hall/CRC. 2022. ISBN: 9781032233574
LG Wang, TTY Lam, S Xu, Z Dai, L Zhou, T Feng, P Guo, CW Dunn, BR
Jones, T Bradley, H Zhu, Y Guan, Y Jiang, G Yu. treeio: an R package
for phylogenetic tree input and output with richly annotated and
associated data. Molecular Biology and Evolution. 2020, 37(2):599-603.
doi: 10.1093/molbev/msz240
Attaching package: 'ggtree'
The following object is masked from 'package:nlme':
collapse
The following object is masked from 'package:ape':
rotate
The following object is masked from 'package:tidyr':
expandlibrary(emmeans)
library(car)Loading required package: carData
Attaching package: 'car'
The following object is masked from 'package:dplyr':
recode
The following object is masked from 'package:purrr':
somelibrary(Hmisc)Loading required package: lattice
Loading required package: survival
Loading required package: Formula
Attaching package: 'Hmisc'
The following object is masked from 'package:ape':
zoom
The following objects are masked from 'package:dplyr':
src, summarize
The following objects are masked from 'package:base':
format.pval, unitscitation()
To cite R in publications use:
R Core Team (2022). R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria.
URL https://www.R-project.org/.
A BibTeX entry for LaTeX users is
@Manual{,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2022},
url = {https://www.R-project.org/},
}
We have invested a lot of time and effort in creating R, please cite it
when using it for data analysis. See also 'citation("pkgname")' for
citing R packages.print(getRversion())[1] '4.2.0'citation("nlme")
To cite package 'nlme' in publications use:
Pinheiro J, Bates D, R Core Team (2022). _nlme: Linear and Nonlinear
Mixed Effects Models_. R package version 3.1-159,
<https://CRAN.R-project.org/package=nlme>.
Pinheiro JC, Bates DM (2000). _Mixed-Effects Models in S and S-PLUS_.
Springer, New York. doi:10.1007/b98882
<https://doi.org/10.1007/b98882>.
To see these entries in BibTeX format, use 'print(<citation>,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.packageVersion("nlme")[1] '3.1.159'citation("ape")
To cite ape in a publication please use:
Paradis E. & Schliep K. 2019. ape 5.0: an environment for modern
phylogenetics and evolutionary analyses in R. Bioinformatics 35:
526-528.
A BibTeX entry for LaTeX users is
@Article{,
title = {ape 5.0: an environment for modern phylogenetics and evolutionary analyses in {R}},
author = {E. Paradis and K. Schliep},
journal = {Bioinformatics},
year = {2019},
volume = {35},
pages = {526-528},
}
As ape is evolving quickly, you may want to cite also its version
number (found with 'library(help = ape)' or 'packageVersion("ape")').packageVersion("ape")[1] '5.6.2'citation("geiger")
To cite medusa, auteur, or geiger in a publication use:
medusa
Alfaro Michael E, Francesco Santini, Chad Brock, Hugo Alamillo, Alex
Dornburg, Daniel L Rabosky, Giorgio Carnevale, and Luke J Harmon.
2009. Nine exceptional radiations plus high turnover explain species
diversity in jawed vertebrates. PNAS 106:13410-13414.
auteur
Eastman Jonathan M, Michael E Alfaro, Paul Joyce, Andrew L Hipp, and
Luke J Harmon. 2011. A novel comparative method for identifying
shifts in the rate of character evolution on trees. Evolution
65:3578-3589.
MECCA
Slater Graham J, Luke J Harmon, Daniel Wegmann, Paul Joyce, Liam J
Revell, and Michael E Alfaro. 2012. Fitting models of continuous
trait evolution to incompletely sampled comparative data using
approximate Bayesian computation. Evolution 66:752-762.
geiger-orig
Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and
Wendell Challenger. 2008. GEIGER: investigating evolutionary
radiations. Bioinformatics 24:129-131.
geiger
Pennell Matthew W, Jonathan M Eastman, Graham J Slater, Joseph W
Brown, Josef C Uyeda, Richard G FitzJohn, Michael E Alfaro, and Luke
J Harmon. 2014. geiger v2.0: an expanded suite of methods for fitting
macroevolutionary models to phylogenetic trees. Bioinformatics
30:2216-2218.
To see these entries in BibTeX format, use 'print(<citation>,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.packageVersion("geiger")[1] '2.0.10'Load data
vertdata_sum <- read_csv(here("output/vertdata_summary.csv")) |>
mutate(MatchSpecies = str_c(MatchGenus, MatchSpecies, sep = '_')) |>
select(-MatchGenus)Rows: 82 Columns: 46
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): Species, Habitat, Water_Type, MatchSpecies, MatchGenus, alltaxon, ...
dbl (38): Indiv, fineness, CBL_med, CBL_max, CBL_mn, d_med, d_max, d_mn, alp...
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.Phylogeny
This is the tree from the Fish Tree of Life: https://fishtreeoflife.org/
tree <- read.tree(here('data/actinopt_12k_treePL.tre.xz'))Get the names of species from the tree.
allspecies <- tibble(tree$tip.label)
colnames(allspecies) <- c('Species')
head(allspecies)# A tibble: 6 × 1
Species
<chr>
1 Gambusia_marshi
2 Gambusia_panuco
3 Gambusia_regani
4 Gambusia_aurata
5 Gambusia_hurtadoi
6 Gambusia_gaigei Set up the tip number (just the row)
allspecies$Tip <- seq_len(nrow(allspecies))The double species name for Fundulus confuses things, so we’ll just correct it manually.
vertdata_sum <-
vertdata_sum |>
mutate(MatchSpecies = if_else(Species == 'Fundulus_heteroclitus',
'Fundulus_heteroclitus_heteroclitus',
MatchSpecies))vertdata <- left_join(vertdata_sum, allspecies,
by=c("MatchSpecies"="Species")) %>%
select(-(ends_with(".x") | ends_with(".y")))
vertdata# A tibble: 82 × 46
Species Indiv Habitat Water…¹ Match…² allta…³ Order Family finen…⁴ CBL_med
<chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
1 Abramis_b… 1 pelagic freshw… Alburn… Actino… Cypr… Cypri… 8.95 0.0166
2 Alectis_c… 1 pelagic marine Alecti… Actino… Cara… Caran… 8.75 0.0346
3 Alosa_pse… 1 pelagic anadro… Alosa_… Actino… Clup… Clupe… 7.39 0.0165
4 Amia_calva 1 demers… freshw… Amia_c… Actino… Amii… Amiid… 6.72 0.00983
5 Ammodytes… 1 demers… marine Ammody… Actino… Uran… Ammod… 16.9 0.0132
6 Anodontos… 1 pelagic freshw… Doroso… Actino… Clup… Clupe… 4.66 0.0228
7 Anoplarch… 1 benthic marine Anopla… Actino… Perc… Stich… 8.62 0.0195
8 Anoplarch… 1 demers… marine Anopla… Actino… Perc… Stich… 9.51 0.0156
9 Anoplogas… 1 pelagic marine Anoplo… Actino… Bery… Anopl… 5.04 0.0287
10 Aphareus_… 1 pelagic marine Aphare… Actino… Ince… Lutja… 5.01 0.0312
# … with 72 more rows, 36 more variables: CBL_max <dbl>, CBL_mn <dbl>,
# d_med <dbl>, d_max <dbl>, d_mn <dbl>, alphaAnt_med <dbl>,
# alphaAnt_max <dbl>, alphaAnt_mn <dbl>, alphaPos_med <dbl>,
# alphaPos_max <dbl>, alphaPos_mn <dbl>, DAnt_med <dbl>, DAnt_max <dbl>,
# DAnt_mn <dbl>, DPos_med <dbl>, DPos_max <dbl>, DPos_mn <dbl>,
# dBW_med <dbl>, dBW_max <dbl>, dBW_mn <dbl>, DAntBW_med <dbl>,
# DAntBW_max <dbl>, DAntBW_mn <dbl>, DPosBW_med <dbl>, DPosBW_max <dbl>, …Drop species without a match
vertdata %>%
filter(is.na(Tip)) %>%
distinct(Species, .keep_all=TRUE) %>%
select(Species, MatchSpecies, Tip, Habitat)# A tibble: 0 × 4
# … with 4 variables: Species <chr>, MatchSpecies <chr>, Tip <int>,
# Habitat <chr>vertdata <-
vertdata %>%
filter(!is.na(Tip))ourspecies <-
vertdata %>%
distinct(Species, .keep_all=TRUE)verttree <- keep.tip(tree, tip=as.vector(ourspecies$Tip))Replace the names of species in the tree with our matched ones.
verttree$tip.label <-
ourspecies |>
arrange(Tip) |>
pull(Species)plotTree(verttree)
vertdata_sp <-
vertdata %>%
distinct(Species, .keep_all = TRUE) %>%
mutate(rowname = Species) %>%
column_to_rownames(var = "rowname")left_join(as_tibble(verttree),
vertdata_sp %>%
rownames_to_column("label") %>%
select(label, Habitat)) %>%
tidytree::as.treedata() %>%
ggtree(layout = 'circular') + # geom_tiplab() +
geom_tippoint(aes(color = Habitat))Joining, by = "label"
Check if tree and data match
length(verttree$tip.label)[1] 82nrow(vertdata_sp)[1] 82name.check(verttree, vertdata_sp)[1] "OK"Merge the measurements and the tree
verttree_data <-
as_tibble(verttree) %>%
left_join(vertdata_sp %>%
rownames_to_column("label") %>%
select(label, Habitat, alltaxon, Order, Family, fineness,
ends_with("80"), ends_with("max"), ends_with("med"), ends_with("slope"), ends_with("quad")))Joining, by = "label"Save out the tree data. There is something subtly different about saving the data out as a csv file and saving it as an RDS file. The base class of the tree is tbl_tree, but when we load it back in from a csv, despite having exactly the same data, it won’t work with the tidytree functions. So we save it in an RDS file, which preserves the class.
write_csv(vertdata_sp, here('output/vertdata_summary_species.csv'))
saveRDS(verttree, here('output/vert_tree.rds'))
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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 utils datasets methods base
other attached packages:
[1] Hmisc_4.7-1 Formula_1.2-4 survival_3.3-1 lattice_0.20-45
[5] car_3.1-0 carData_3.0-5 emmeans_1.8.0 ggtree_3.4.2
[9] geiger_2.0.10 nlme_3.1-159 here_1.0.1 patchwork_1.1.2
[13] phytools_1.2-0 maps_3.4.0 ape_5.6-2 ggbeeswarm_0.6.0
[17] forcats_0.5.2 stringr_1.4.1 dplyr_1.0.9 purrr_0.3.4
[21] readr_2.1.2 tidyr_1.2.0 tibble_3.1.8 ggplot2_3.3.6
[25] tidyverse_1.3.2
loaded via a namespace (and not attached):
[1] readxl_1.4.1 backports_1.4.1 fastmatch_1.1-3
[4] workflowr_1.7.0 igraph_1.3.4 lazyeval_0.2.2
[7] splines_4.2.0 digest_0.6.29 yulab.utils_0.0.5
[10] htmltools_0.5.3 fansi_1.0.3 checkmate_2.1.0
[13] magrittr_2.0.3 optimParallel_1.0-2 googlesheets4_1.0.1
[16] cluster_2.1.3 tzdb_0.3.0 modelr_0.1.9
[19] vroom_1.5.7 jpeg_0.1-9 colorspace_2.0-3
[22] rvest_1.0.3 haven_2.5.1 xfun_0.32
[25] crayon_1.5.1 jsonlite_1.8.0 phangorn_2.9.0
[28] glue_1.6.2 gtable_0.3.1 gargle_1.2.0
[31] abind_1.4-5 scales_1.2.1 mvtnorm_1.1-3
[34] DBI_1.1.3 Rcpp_1.0.9 plotrix_3.8-2
[37] htmlTable_2.4.1 xtable_1.8-4 gridGraphics_0.5-1
[40] tidytree_0.4.0 bit_4.0.4 foreign_0.8-82
[43] subplex_1.8 deSolve_1.33 htmlwidgets_1.5.4
[46] httr_1.4.4 RColorBrewer_1.1-3 ellipsis_0.3.2
[49] farver_2.1.1 pkgconfig_2.0.3 nnet_7.3-17
[52] sass_0.4.2 dbplyr_2.2.1 deldir_1.0-6
[55] utf8_1.2.2 labeling_0.4.2 ggplotify_0.1.0
[58] tidyselect_1.1.2 rlang_1.0.4 later_1.3.0
[61] munsell_0.5.0 cellranger_1.1.0 tools_4.2.0
[64] cachem_1.0.6 cli_3.3.0 generics_0.1.3
[67] broom_1.0.1 evaluate_0.16 fastmap_1.1.0
[70] yaml_2.3.5 bit64_4.0.5 knitr_1.40
[73] fs_1.5.2 whisker_0.4 aplot_0.1.6
[76] xml2_1.3.3 compiler_4.2.0 rstudioapi_0.14
[79] beeswarm_0.4.0 png_0.1-7 reprex_2.0.2
[82] treeio_1.20.2 clusterGeneration_1.3.7 bslib_0.4.0
[85] stringi_1.7.8 highr_0.9 Matrix_1.4-1
[88] vctrs_0.4.1 pillar_1.8.1 lifecycle_1.0.1
[91] combinat_0.0-8 jquerylib_0.1.4 estimability_1.4.1
[94] data.table_1.14.2 httpuv_1.6.5 R6_2.5.1
[97] latticeExtra_0.6-30 promises_1.2.0.1 gridExtra_2.3
[100] vipor_0.4.5 codetools_0.2-18 MASS_7.3-56
[103] assertthat_0.2.1 rprojroot_2.0.3 withr_2.5.0
[106] mnormt_2.1.0 expm_0.999-6 parallel_4.2.0
[109] hms_1.1.2 rpart_4.1.16 quadprog_1.5-8
[112] grid_4.2.0 ggfun_0.0.7 coda_0.19-4
[115] rmarkdown_2.16 googledrive_2.0.0 git2r_0.30.1
[118] numDeriv_2016.8-1.1 scatterplot3d_0.3-41 lubridate_1.8.0
[121] base64enc_0.1-3 interp_1.1-3