Vert Pairs
Last updated: 2022-04-11
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Knit directory: Code/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 23908bd | Eric Tytell | 2021-12-30 | Test site build again |
| 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(patchwork)
library(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:tidyr':
expandlibrary(here)here() starts at /Users/etytel01/Documents/Vertebrae/Codeverttree <- readRDS(here('output/vert_tree.rds'))
verttree_data <- 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.verttree_data <-
as_tibble(verttree) %>%
left_join(verttree_data %>%
rename(label = FullName))Joining, by = "label"Pairs of species
Look for pairs of species that have the same parent node but differ in habitats.
pairs <-
verttree_data %>%
filter(!is.na(Habitat)) %>%
group_by(parent) %>%
arrange(Habitat) %>%
mutate(nsib = n(),
diffhab = any(Habitat != first(Habitat)),
compare = str_c(str_sub(Habitat, start=1, end=1), collapse="-")) %>%
ungroup() %>%
filter(nsib == 2) %>% # & branch.length <= 100) %>%
select(-nsib) %>%
arrange(parent, Habitat)Then look at a few specific variables and see whether they increase or decrease across the habitats.
pairslong <-
pairs %>%
select(Habitat, parent, branch.length, d_mn, alphaPos_mn, diffhab, compare) %>%
pivot_longer(c(d_mn, alphaPos_mn),
names_to="var", values_to="value") %>%
group_by(var, parent) %>%
arrange(Habitat) %>%
mutate(change = case_when(abs(lead(value) - value)/value < 0.05 ~ 'same',
lead(value) > value ~ 'increase',
lead(value) < value ~ 'decrease')) %>%
fill(change) %>%
mutate(change = factor(change),
parent = factor(parent)) %>%
ungroup() %>%
arrange(var, parent, Habitat)
pairslong# A tibble: 92 × 8
Habitat parent branch.length diffhab compare var value change
<chr> <fct> <dbl> <lgl> <chr> <chr> <dbl> <fct>
1 pelagic 84 54.2 FALSE p-p alphaPos_mn 91.5 decrease
2 pelagic 84 54.2 FALSE p-p alphaPos_mn 85.5 decrease
3 benthic 90 47.5 TRUE b-d alphaPos_mn 67.8 decrease
4 demersal 90 47.5 TRUE b-d alphaPos_mn 52.2 decrease
5 demersal 94 82.5 FALSE d-d alphaPos_mn 67.2 increase
6 demersal 94 82.5 FALSE d-d alphaPos_mn 72.8 increase
7 demersal 96 126. FALSE d-d alphaPos_mn 73.8 increase
8 demersal 96 126. FALSE d-d alphaPos_mn 88.1 increase
9 demersal 99 104. FALSE d-d alphaPos_mn 68.2 increase
10 demersal 99 104. FALSE d-d alphaPos_mn 112. increase
# … with 82 more rowspairslong %>%
filter(var == 'd_mn' & diffhab) %>%
ggplot(aes(x = Habitat, y = value, group=fct_cross(var, parent), linetype = change, color=diffhab)) +
geom_line()
Construct a transformation for point size that is the inverse of branch lengths, so that long branches would have small points.
branch_len_trans <- function()
scales::trans_new('branch_len',
function(x) 400/x,
function(x) 400/x,
domain = c(0, Inf))Plot the pairs
paircolors <- c('#1b9e77','#d95f02','#7570b3')pairslong %>%
filter(diffhab, var == "d_mn")# A tibble: 26 × 8
Habitat parent branch.length diffhab compare var value change
<chr> <fct> <dbl> <lgl> <chr> <chr> <dbl> <fct>
1 benthic 90 47.5 TRUE b-d d_mn 0.00117 same
2 demersal 90 47.5 TRUE b-d d_mn 0.0012 same
3 demersal 101 129. TRUE d-p d_mn 0.00473 decrease
4 pelagic 101 129. TRUE d-p d_mn 0.00292 decrease
5 benthic 105 41.5 TRUE b-d d_mn 0.00187 same
6 demersal 105 41.5 TRUE b-d d_mn 0.00182 same
7 benthic 127 59.2 TRUE b-p d_mn 0.00278 decrease
8 pelagic 127 59.2 TRUE b-p d_mn 0.000317 decrease
9 demersal 128 29.1 TRUE d-p d_mn 0.00218 decrease
10 pelagic 128 29.1 TRUE d-p d_mn 0.00075 decrease
# … with 16 more rowspairplot <- function(df, varname) {
df %>%
filter(diffhab) %>%
filter(str_detect(var, varname)) %>%
ggplot(aes(x = Habitat, y = value, group=fct_cross(var, parent),
color=change, # shape = branch.length,
linetype=change)) +
geom_line(size = 1) + #, position = position_dodge(width = 0.2)) +
geom_point() + #alpha = 0.7) + # , position = position_dodge(width = 0.2)) +
scale_size(trans = "branch_len", range = c(1, 12)) +
scale_shape_binned() +
scale_color_manual(values = paircolors) +
labs(y = varname) +
theme_bw()
}
#p1 <- pairplot(pairs, "d_max")
p2 <- pairplot(pairslong, "alphaPos") +
labs(y = 'Mean posterior\ncone angle (deg)') +
theme(aspect.ratio = 0.7)
p1 <- pairplot(pairslong, "d_mn") +
labs(y = 'Mean foramen\ndiameter (BL)') +
theme(aspect.ratio = 0.7)
p1 / p2 + plot_layout(guides = 'collect') & theme(legend.position = "bottom")
pairslong %>%
filter(var == "d_mn") %>%
filter(diffhab) %>%
group_by(change, parent) %>%
summarize(change = first(change)) %>%
summarize(n = n(), pct = n()/13 * 100)`summarise()` has grouped output by 'change'. You can override using the `.groups` argument.# A tibble: 3 × 3
change n pct
<fct> <int> <dbl>
1 decrease 8 61.5
2 increase 2 15.4
3 same 3 23.1pairslong %>%
filter(var == "d_mn") %>%
filter(!diffhab) %>%
group_by(change, parent) %>%
summarize(change = first(change)) %>%
summarize(n = n(), pct = n()/13 * 100)`summarise()` has grouped output by 'change'. You can override using the `.groups` argument.# A tibble: 2 × 3
change n pct
<fct> <int> <dbl>
1 decrease 6 46.2
2 increase 4 30.8pairslong %>%
filter(var == "alphaPos_mn") %>%
filter(diffhab) %>%
group_by(change, parent) %>%
summarize(change = first(change)) %>%
summarize(n = n(), pct = n()/13 * 100)`summarise()` has grouped output by 'change'. You can override using the `.groups` argument.# A tibble: 3 × 3
change n pct
<fct> <int> <dbl>
1 decrease 6 46.2
2 increase 6 46.2
3 same 1 7.69pairslong %>%
filter(var == "alphaPos_mn") %>%
filter(!diffhab) %>%
group_by(change, parent) %>%
summarize(change = first(change)) %>%
summarize(n = n(), pct = n()/13 * 100)`summarise()` has grouped output by 'change'. You can override using the `.groups` argument.# A tibble: 3 × 3
change n pct
<fct> <int> <dbl>
1 decrease 4 30.8
2 increase 4 30.8
3 same 2 15.4ggsave(here('output/pair_plot.pdf'), width=3, units="in")Saving 3 x 5 in imagewrite_csv(pairs, here('output/vertdata_pairs.csv'))
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 ggtree_3.0.2 patchwork_1.1.1 forcats_0.5.1
[5] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.0.1
[9] tidyr_1.1.3 tibble_3.1.4 ggplot2_3.3.5 tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] nlme_3.1-153 fs_1.5.0 lubridate_1.7.10
[4] bit64_4.0.5 httr_1.4.2 rprojroot_2.0.2
[7] tools_4.1.2 backports_1.2.1 utf8_1.2.2
[10] R6_2.5.1 DBI_1.1.1 lazyeval_0.2.2
[13] colorspace_2.0-2 withr_2.4.2 tidyselect_1.1.1
[16] bit_4.0.4 compiler_4.1.2 git2r_0.29.0
[19] cli_3.0.1 rvest_1.0.1 xml2_1.3.2
[22] labeling_0.4.2 scales_1.1.1 digest_0.6.27
[25] yulab.utils_0.0.2 rmarkdown_2.10 pkgconfig_2.0.3
[28] htmltools_0.5.2 highr_0.9 dbplyr_2.1.1
[31] fastmap_1.1.0 rlang_0.4.11 readxl_1.3.1
[34] rstudioapi_0.13 farver_2.1.0 gridGraphics_0.5-1
[37] generics_0.1.0 jsonlite_1.7.2 vroom_1.5.4
[40] magrittr_2.0.1 ggplotify_0.1.0 Rcpp_1.0.7
[43] munsell_0.5.0 fansi_0.5.0 ape_5.5
[46] lifecycle_1.0.0 stringi_1.7.4 whisker_0.4
[49] yaml_2.2.1 grid_4.1.2 parallel_4.1.2
[52] promises_1.2.0.1 crayon_1.4.1 lattice_0.20-45
[55] haven_2.4.3 hms_1.1.0 knitr_1.34
[58] pillar_1.6.2 reprex_2.0.1 glue_1.4.2
[61] evaluate_0.14 renv_0.14.0 BiocManager_1.30.16
[64] modelr_0.1.8 vctrs_0.3.8 treeio_1.16.1
[67] tzdb_0.1.2 httpuv_1.6.4 cellranger_1.1.0
[70] gtable_0.3.0 assertthat_0.2.1 xfun_0.25
[73] broom_0.7.9 tidytree_0.3.5 later_1.3.0
[76] aplot_0.1.0 rvcheck_0.1.8 workflowr_1.7.0
[79] ellipsis_0.3.2