Make Figure 1
Last updated: 2022-04-11
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| 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(ggbeeswarm)
library(phytools)Loading required package: apeLoading 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(ggtree)ggtree v3.0.2 For help: https://yulab-smu.top/treedata-book/
If you use ggtree in published research, please cite the most appropriate paper(s):
1. Guangchuang Yu. Using ggtree to visualize data on tree-like structures. Current Protocols in Bioinformatics, 2020, 69:e96. doi:10.1002/cpbi.96
2. Guangchuang Yu, Tommy Tsan-Yuk Lam, Huachen Zhu, Yi Guan. Two methods for mapping and visualizing associated data on phylogeny using ggtree. Molecular Biology and Evolution 2018, 35(12):3041-3043. doi:10.1093/molbev/msy194
3. 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
Attaching package: 'ggtree'The following object is masked from 'package:ape':
rotateThe following object is masked from 'package:tidyr':
expandlibrary(plotly)
Attaching package: 'plotly'The following object is masked from 'package:ggplot2':
last_plotThe following object is masked from 'package:stats':
filterThe following object is masked from 'package:graphics':
layoutFigure 1
For this figure, we need to identify three species from the three habitat classes that have clearly different vertebrae.
vertdata <- read_csv(here('output/vertdata_summary_lm_species.csv'))Rows: 77 Columns: 99── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (7): Species, Habitat, Water_Type, MatchSpecies, MatchGenus, FullName, ...
dbl (92): fineness, CBL_med, CBL_max, CBL_mn, d_med, d_max, d_mn, alphaAnt_m...
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.pairs <- read_csv(here('output/vertdata_pairs.csv'))Rows: 46 Columns: 104── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): label, Species, Habitat, Water_Type, MatchSpecies, MatchGenus, Fam...
dbl (95): parent, node, branch.length, fineness, CBL_med, CBL_max, CBL_mn, d...
lgl (1): diffhab
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.plot_ly(data = vertdata, type = "scatter", mode = "markers") %>%
add_trace(x = ~Habitat, y = ~d_med, type = "box",
text = ~Species, hoverinfo = "text",
boxpoints = "all", jitter = 0.2)Warning: Can't display both discrete & non-discrete data on same axisWarning: 'box' objects don't have these attributes: 'mode'
Valid attributes include:
'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'uid', 'ids', 'customdata', 'meta', 'selectedpoints', 'hoverinfo', 'hoverlabel', 'stream', 'transforms', 'uirevision', 'y', 'x', 'x0', 'y0', 'dx', 'dy', 'xperiod', 'yperiod', 'xperiod0', 'yperiod0', 'xperiodalignment', 'yperiodalignment', 'name', 'q1', 'median', 'q3', 'lowerfence', 'upperfence', 'notched', 'notchwidth', 'notchspan', 'boxpoints', 'jitter', 'pointpos', 'boxmean', 'mean', 'sd', 'orientation', 'quartilemethod', 'width', 'marker', 'line', 'fillcolor', 'whiskerwidth', 'offsetgroup', 'alignmentgroup', 'selected', 'unselected', 'text', 'hovertext', 'hovertemplate', 'hoveron', 'xcalendar', 'ycalendar', 'xaxis', 'yaxis', 'idssrc', 'customdatasrc', 'metasrc', 'hoverinfosrc', 'ysrc', 'xsrc', 'q1src', 'mediansrc', 'q3src', 'lowerfencesrc', 'upperfencesrc', 'notchspansrc', 'meansrc', 'sdsrc', 'textsrc', 'hovertextsrc', 'hovertemplatesrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'A tibble: 14 × 100
Species Habitat Water_Type MatchSpecies MatchGenus fineness CBL_med CBL_max
Choose example species close to the median for their group:
- benthic: Barbichthys laevis (Sucker barb) or Myoxocephalus polyacanthocephalus (Sculpin)
- demersal: Poecilia reticulata (Guppy)
- pelagic: Sphyraena sphyraena (Barracuda)
We’ll use the sculpin as an example benthic species, because we have good histology data for it.
examplespecies <- list("Myoxocephalus_polyacanthocephalus",
"Poecilia_reticulata",
"Sphyraena_sphyraena")verttree <- readRDS(here('output/vert_tree.rds'))vertdata %>%
filter(Species %in% examplespecies)# A tibble: 3 × 99
Species Habitat Water_Type MatchSpecies MatchGenus fineness CBL_med CBL_max
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Myoxoceph… benthic marine polyacantho… Myoxoceph… 2.72 0.0191 0.0231
2 Poecilia_… demers… freshwater latipinna Poecilia 5.94 0.0262 0.0301
3 Sphyraena… pelagic marine sphyraena Sphyraena 11.5 0.0321 0.0362
# … with 91 more variables: 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>, DPosBW_mn <dbl>, d_normCBL_med <dbl>, …vertdata <-
vertdata %>%
mutate(WaterTypeShort = str_sub(Water_Type, start = 1, end = 1))highlightpairs <-
pairs %>%
distinct(parent) %>%
pull(parent)left_join(as_tibble(verttree), vertdata, by = c("label" = "FullName")) %>%
mutate(label = str_replace(Species, "_", " ")) %>%
tidytree::as.treedata() %>%
ggtree() + # layout = "circular", open.angle = 120) +
scale_y_reverse() +
geom_tiplab(aes(color = Habitat), size=1.5, offset = 5) +
geom_tippoint(aes(shape = Water_Type)) +
geom_text2(aes(label=Species, subset=Species %in% examplespecies),
hjust = 0, vjust = 0) +
geom_hilight(mapping=aes(subset = node %in% highlightpairs)) +
scale_shape_manual(values = c(3, 23, 24)) +
scale_color_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2")) +
theme(legend.position = "bottom")Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
#geom_label2(aes(label='P', subset = ispair))ggsave(here('output/plot_example_data_figure.pdf'), width=3.5, height=6, units="in")vertdata %>%
group_by(Habitat) %>%
summarize(n = n(), frac = n() / nrow(vertdata))# A tibble: 3 × 3
Habitat n frac
<chr> <int> <dbl>
1 benthic 18 0.234
2 demersal 38 0.494
3 pelagic 21 0.273vertdata %>%
group_by(Water_Type) %>%
summarize(n = n(), frac = n() / nrow(vertdata))# A tibble: 3 × 3
Water_Type n frac
<chr> <int> <dbl>
1 anadromous 1 0.0130
2 freshwater 26 0.338
3 marine 50 0.649
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] plotly_4.9.4.1 ggtree_3.0.2 here_1.0.1 patchwork_1.1.1
[5] phytools_0.7-80 maps_3.3.0 ape_5.5 ggbeeswarm_0.6.0
[9] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[13] readr_2.0.1 tidyr_1.1.3 tibble_3.1.4 ggplot2_3.3.5
[17] tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] colorspace_2.0-2 ellipsis_0.3.2 rprojroot_2.0.2
[4] fs_1.5.0 aplot_0.1.0 rstudioapi_0.13
[7] farver_2.1.0 bit64_4.0.5 fansi_0.5.0
[10] lubridate_1.7.10 xml2_1.3.2 codetools_0.2-18
[13] mnormt_2.0.2 knitr_1.34 jsonlite_1.7.2
[16] workflowr_1.7.0 broom_0.7.9 dbplyr_2.1.1
[19] BiocManager_1.30.16 compiler_4.1.2 httr_1.4.2
[22] rvcheck_0.1.8 backports_1.2.1 assertthat_0.2.1
[25] Matrix_1.3-4 fastmap_1.1.0 lazyeval_0.2.2
[28] cli_3.0.1 later_1.3.0 htmltools_0.5.2
[31] tools_4.1.2 igraph_1.2.6 coda_0.19-4
[34] gtable_0.3.0 glue_1.4.2 clusterGeneration_1.3.7
[37] fastmatch_1.1-3 Rcpp_1.0.7 cellranger_1.1.0
[40] vctrs_0.3.8 nlme_3.1-153 crosstalk_1.1.1
[43] xfun_0.25 rvest_1.0.1 lifecycle_1.0.0
[46] renv_0.14.0 phangorn_2.7.1 MASS_7.3-54
[49] scales_1.1.1 vroom_1.5.4 hms_1.1.0
[52] promises_1.2.0.1 parallel_4.1.2 expm_0.999-6
[55] yaml_2.2.1 yulab.utils_0.0.2 stringi_1.7.4
[58] highr_0.9 plotrix_3.8-2 tidytree_0.3.5
[61] rlang_0.4.11 pkgconfig_2.0.3 evaluate_0.14
[64] lattice_0.20-45 labeling_0.4.2 treeio_1.16.1
[67] htmlwidgets_1.5.4 bit_4.0.4 tidyselect_1.1.1
[70] magrittr_2.0.1 R6_2.5.1 generics_0.1.0
[73] combinat_0.0-8 DBI_1.1.1 pillar_1.6.2
[76] haven_2.4.3 whisker_0.4 withr_2.4.2
[79] scatterplot3d_0.3-41 modelr_0.1.8 crayon_1.4.1
[82] utf8_1.2.2 tmvnsim_1.0-2 tzdb_0.1.2
[85] rmarkdown_2.10 grid_4.1.2 readxl_1.3.1
[88] data.table_1.14.0 git2r_0.29.0 reprex_2.0.1
[91] digest_0.6.27 httpuv_1.6.4 numDeriv_2016.8-1.1
[94] gridGraphics_0.5-1 munsell_0.5.0 beeswarm_0.4.0
[97] viridisLite_0.4.0 ggplotify_0.1.0 vipor_0.4.5
[100] quadprog_1.5-8