Analysis for the AABA poster
Sarah E Taylor
2024-03-13
Last updated: 2024-06-20
Checks: 6 1
Knit directory: LocksofLineage/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | e61fc16 | Sarah E Taylor | 2024-06-20 | Build site. |
| Rmd | 24ecbbf | Sarah E Taylor | 2024-06-17 | Added code to save the formatted dataframes. |
| html | 768856f | Sarah E Taylor | 2024-03-17 | Build site. |
| Rmd | 3818f0b | Sarah E Taylor | 2024-03-17 | Analysis for the poster. |
Set up
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(janitor)
Attaching package: 'janitor'
The following objects are masked from 'package:stats':
chisq.test, fisher.testlibrary(ape)
Attaching package: 'ape'
The following object is masked from 'package:dplyr':
wherelibrary(phytools)Loading required package: maps
Attaching package: 'maps'
The following object is masked from 'package:purrr':
map#Library for upset plot
library(ComplexUpset)
library(eulerr)#Map the families onto the superfamilies
superfamily_mapping <- data.frame(
family = c("Lorisidae", "Galagonidae", "Daubentoniidae","Indridae", "Lemuridae", "Cheirogaleidae", "Megaladapidae", "Tarsiidae", "Cebidae", "Callitrichidae", "Hylobatidae", "Pongidae", "Hominidae", "Cercopithecidae"),
superfamily = c("Lorisiformes", "Lorisiformes", "Lemuriformes", "Lemuriformes", "Lemuriformes", "Lemuriformes", "Lemuriformes","Tarsiiformes", "Platyrrhini", "Platyrrhini", "Hominoidea", "Hominoidea", "Hominoidea", "Cercopithecoidea"))
df_trait_values <- read_csv("data/Raw_Data/data_to_use.csv") %>%
clean_names() %>%
mutate(
natal_coat = if_else(natal_coat == "Yes", 1, 0),
sexual_dichromatism = if_else(sexual_dichromatism == "Yes", 1, 0)
) %>%
mutate(
natal_coat_type_simple = case_when(
natal_coat_type %in% c("Con to dad", "con to both", "con to mom") ~ "conspicuous",
natal_coat_type == "incon" ~ "inconspicuous",
TRUE ~ "none" # This catches all other cases
)
) %>%
mutate(
natal_coat_conspicuous = ifelse(natal_coat_type_simple == "conspicuous", 1, 0),
natal_coat_inconspicuous = ifelse(natal_coat_type_simple == "inconspicuous", 1, 0),
natal_coat_present = ifelse(natal_coat_type_simple %in% c("conspicuous", "inconspicuous"), 1, 0)
) %>%
mutate(
maturation_color_change = case_when(
natal_coat_type == "Con to dad" ~ "Males only",
natal_coat_type == "con to mom" ~ "Females only",
natal_coat_type == "con to both" ~ "Both",
TRUE ~ "None"
),
maturation_males_only = as.integer(maturation_color_change == "Males only"),
maturation_females_only = as.integer(maturation_color_change == "Females only"),
maturation_both = as.integer(maturation_color_change == "Both"),
maturation_none = as.integer(maturation_color_change == "None")
) %>%
mutate(sexual_dichromatism_complete = ifelse(sexual_dichromatism_type == "Complete", 1, 0),
sexual_dichromatism_partial = ifelse(sexual_dichromatism_type == "Partial", 1, 0),
sexual_dichromatism_present = ifelse(sexual_dichromatism_type %in% c("Complete", "Partial"), 1, 0)
) %>%
mutate(
all_color_traits = ifelse(natal_coat | sexual_dichromatism |
maturation_both | maturation_females_only | maturation_males_only, 1, 0)
)%>%
select(
family, genus, species,
natal_coat, natal_coat_type, natal_coat_type_simple, natal_coat_conspicuous, natal_coat_inconspicuous, natal_coat_present,
sexual_dichromatism, sexual_dichromatism_type, sexual_dichromatism_complete, sexual_dichromatism_partial, sexual_dichromatism_present,
size_dimorphism, maturation_color_change,
maturation_males_only, maturation_females_only,
maturation_both, maturation_none,
all_color_traits
)Rows: 238 Columns: 17
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (16): family, Genus, species, subspecies, Sexual_dimorphism, Sexual_Dimo...
dbl (1): Size_Dimorphism
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.#read in mammal tree
mammaltree <- read.tree("data/Raw_Data/MamPhy_BDvr_Completed_v2_tree0000.tre")
summary(mammaltree)
Phylogenetic tree: mammaltree
Number of tips: 5987
Number of nodes: 5986
Branch lengths:
mean: 2.680715
variance: 24.17565
distribution summary:
Min. 1st Qu. Median 3rd Qu. Max.
0.0000000 0.5284341 1.3073255 2.9454665 106.6007500
No root edge.
First ten tip labels: X_Shuotherium
X_Pseudotribos
X_Asfaltomylos
X_Obdurodon
Zaglossus_bartoni
Zaglossus_bruijnii
Zaglossus_attenboroughi
Tachyglossus_aculeatus
Ornithorhynchus_anatinus
X_Teinolophos
No node labels.# Assuming mammaltree has already been loaded with read.tree() as in the provided code
Binary_traits_combined <- df_trait_values %>%
unite("species", genus, species, sep = "_") %>%
mutate(species = str_to_title(species)) %>%
mutate(family = str_to_title(family)) %>%
filter(species %in% mammaltree$tip.label)
# Format tree to match data
pruned.tree <- drop.tip(mammaltree, setdiff(mammaltree$tip.label, Binary_traits_combined$species))
data_pruned_ordered <- Binary_traits_combined %>%
arrange(match(species, pruned.tree$tip.label)) %>%
left_join(superfamily_mapping, by = "family") %>%
column_to_rownames("species")# Optionally save the data frame to a csv
write.csv(data_pruned_ordered, file = "~/Desktop/GitHub/LocksofLineage/data/data_pruned_ordered.csv", row.names = TRUE)
# Optionally save the tree into a .nex file
write.nexus(pruned.tree, file = "pruned_tree.nex")Run the phytools fitPagel model
natal_coats <- setNames(data_pruned_ordered$natal_coat,rownames(data_pruned_ordered))
sexual_dichromatism <- setNames(data_pruned_ordered$sexual_dichromatism,rownames(data_pruned_ordered))
size_dimorphism <- setNames(data_pruned_ordered$size_dimorphism,rownames(data_pruned_ordered))
all_color_traits <- setNames(data_pruned_ordered$all_color_traits,rownames(data_pruned_ordered))# Correlations between natal coats and sexual dichromatism
natal_coats_and_sexual_dichrom_pagel <- fitPagel(pruned.tree, natal_coats, sexual_dichromatism)
anova(natal_coats_and_sexual_dichrom_pagel) log(L) d.f. AIC weight
independent -284.6806 4 577.3613 2.328933e-08
natal_coats_and_sexual_dichrom_pagel -263.1054 8 542.2107 1.000000e+00# Correlations between size dimorphism and the color traits (natal coats and sexual dichromatism)
# size_and_color_pagel <- fitPagel(pruned.tree, size_dimorphism, all_color_traits)# Plot the natal coat and sexual dichromatism model
plot(natal_coats_and_sexual_dichrom_pagel, lwd.by.rate=TRUE)
| Version | Author | Date |
|---|---|---|
| 768856f | Sarah E Taylor | 2024-03-17 |
#plot(size_and_color_pagel, lwd.by.rate=TRUE)Other plots for the poster
#scale_fill_manual(values=c("Lorisidae" = "darkseagreen", "Galagonidae"= "mediumseagreen", "Daubentoniidae" = "chocolate4", "Indridae" = "chocolate2", "Lemuridae" = "salmon", "Cheirogaleidae" = "coral3", "Megaladapidae" = "sienna3", "Tarsiidae" = "gold", "Cebidae" = "cyan", "Callitrichidae" = "turquoise", "Cercopithecidae" = "burlywood2", "Hylobatidae" = "maroon", "Pongidae" = "violetred2", "Hominidae" = "deeppink1"),)
# Color palatte for streps
tailwind_colors <- c(
"Blue" = "#000cee",
"Zaffre" = "#2107a7",
"ElectricIndigo" = "#6200ff",
"Indigo" = "#5c0096",
"DarkViolet" = "#9c00b8",
"HotMagenta" = "#ea2cda",
"Fandango" = "#b8008a",
"Cyan (RGB)" = "#00fffb",
"Spring Green" = "#00f56a",
"Forest Green" = "#009138",
"Gold" = "#ffd500",
"Pumpkin" = "#ff6a00",
"Turkey Red" = "#ac0000"
)
# Define the traits
set_attributes <- c(
'natal_coat_present',
'natal_coat_conspicuous',
'size_dimorphism',
'sexual_dichromatism_complete',
'sexual_dichromatism_partial',
'maturation_males_only',
'maturation_females_only',
'maturation_both')
# Create the plot
upset(
data_pruned_ordered,
set_attributes,
base_annotations = list(
'Intersection size' = intersection_size(
counts = TRUE,
mapping = aes(fill = family) # Ensure 'family' is the correct column
) + scale_fill_manual(values = c(
"Lorisidae" = "#ea2cda",
"Galagonidae" = "#b8008a",
"Daubentoniidae" = "#9c00b8",
"Indridae" = "#5c0096",
"Lemuridae" = "#000CEE",
"Cheirogaleidae" = "#6200ff",
"Megaladapidae" = "#2107a7",
"Tarsiidae" = "#00fffb",
"Cebidae" = "#00f56a",
"Callitrichidae" = "#009138",
"Cercopithecidae" = "#ffd500",
"Hylobatidae" = "#ff6a00",
"Pongidae" = "#ac0000",
"Hominidae" = "#ac0000"
))
),
width_ratio = 0.1
)Warning in upset_data(data, intersect, mode = mode, encode_sets = encode_sets,
: Converting non-logical columns to binary: natal_coat_present,
natal_coat_conspicuous, size_dimorphism, sexual_dichromatism_complete,
sexual_dichromatism_partial, maturation_males_only, maturation_females_only,
maturation_bothWarning in upset_data(data, intersect, mode = mode, encode_sets = encode_sets,
: Detected missing values in the columns indicating sets, coercing to FALSEWarning in plot_theme(plot): The `legend.text.align` theme element is not
defined in the element hierarchy.Warning in plot_theme(plot): The `legend.title.align` theme element is not
defined in the element hierarchy.Warning in plot_theme(plot): The `legend.text.align` theme element is not
defined in the element hierarchy.Warning in plot_theme(plot): The `legend.title.align` theme element is not
defined in the element hierarchy.Warning in plot_theme(plot): The `legend.text.align` theme element is not
defined in the element hierarchy.Warning in plot_theme(plot): The `legend.title.align` theme element is not
defined in the element hierarchy.
upset(
data_pruned_ordered, set_attributes, width_ratio=0.1,
annotations =list(
'Family Percentages'=list(
aes=aes(x=intersection, fill=family),
geom=list(
geom_bar(stat='count', position='fill', na.rm=TRUE),
geom_text(
aes(
label=!!aes_percentage(relative_to='group'),
group=family,
color=ifelse(family == 'Cercopithecidae', 'show', 'hide')
),
stat='count',
position=position_fill(vjust = .5)
),
scale_y_continuous(labels=scales::percent_format())
)
)
)
)Warning in upset_data(data, intersect, mode = mode, encode_sets = encode_sets,
: Converting non-logical columns to binary: natal_coat_present,
natal_coat_conspicuous, size_dimorphism, sexual_dichromatism_complete,
sexual_dichromatism_partial, maturation_males_only, maturation_females_only,
maturation_bothWarning in upset_data(data, intersect, mode = mode, encode_sets = encode_sets,
: Detected missing values in the columns indicating sets, coercing to FALSEWarning: The dot-dot notation (`..prop..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(prop)` instead.
ℹ The deprecated feature was likely used in the ComplexUpset package.
Please report the issue at
<https://github.com/krassowski/complex-upset/issues>.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.Warning in plot_theme(plot): The `legend.text.align` theme element is not
defined in the element hierarchy.Warning in plot_theme(plot): The `legend.title.align` theme element is not
defined in the element hierarchy.Warning in plot_theme(plot): The `legend.text.align` theme element is not
defined in the element hierarchy.Warning in plot_theme(plot): The `legend.title.align` theme element is not
defined in the element hierarchy.Warning in plot_theme(plot): The `legend.text.align` theme element is not
defined in the element hierarchy.Warning in plot_theme(plot): The `legend.title.align` theme element is not
defined in the element hierarchy.Warning in plot_theme(plot): The `legend.text.align` theme element is not
defined in the element hierarchy.Warning in plot_theme(plot): The `legend.title.align` theme element is not
defined in the element hierarchy.
VennDiag <- euler(c("A" = 62 + 67, "B" = 13 + 67, "A&B" = 67))
plot(VennDiag, quantities = TRUE, font = 1, cex = 1, alpha = 0.5, fill=c("#F38C79","#D8EDDB"), labels = c("Natal Coats", "Sexual Dichromatism"))
| Version | Author | Date |
|---|---|---|
| 768856f | Sarah E Taylor | 2024-03-17 |
sessionInfo()R version 4.3.3 (2024-02-29)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Denver
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] eulerr_7.0.2 ComplexUpset_1.3.3 phytools_2.1-1 maps_3.4.2
[5] ape_5.8 janitor_2.2.0 lubridate_1.9.3 forcats_1.0.0
[9] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
[13] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
[17] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] tidyselect_1.2.1 farver_2.1.2 optimParallel_1.0-2
[4] fastmap_1.2.0 combinat_0.0-8 promises_1.3.0
[7] digest_0.6.35 timechange_0.3.0 lifecycle_1.0.4
[10] processx_3.8.4 polylabelr_0.2.0 magrittr_2.0.3
[13] compiler_4.3.3 rlang_1.1.3 sass_0.4.9
[16] tools_4.3.3 igraph_2.0.3 utf8_1.2.4
[19] yaml_2.3.8 knitr_1.45 phangorn_2.11.1
[22] clusterGeneration_1.3.8 labeling_0.4.3 bit_4.0.5
[25] mnormt_2.1.1 scatterplot3d_0.3-44 expm_0.999-9
[28] withr_3.0.0 numDeriv_2016.8-1.1 polyclip_1.10-6
[31] grid_4.3.3 fansi_1.0.6 git2r_0.33.0
[34] colorspace_2.1-0 scales_1.3.0 iterators_1.0.14
[37] MASS_7.3-60.0.1 cli_3.6.2 crayon_1.5.2
[40] rmarkdown_2.27 generics_0.1.3 rstudioapi_0.16.0
[43] httr_1.4.7 tzdb_0.4.0 cachem_1.1.0
[46] parallel_4.3.3 vctrs_0.6.5 Matrix_1.6-5
[49] jsonlite_1.8.8 callr_3.7.6 patchwork_1.2.0
[52] hms_1.1.3 bit64_4.0.5 foreach_1.5.2
[55] jquerylib_0.1.4 glue_1.7.0 codetools_0.2-19
[58] ps_1.7.6 stringi_1.8.4 gtable_0.3.5
[61] later_1.3.2 quadprog_1.5-8 munsell_0.5.1
[64] pillar_1.9.0 htmltools_0.5.8.1 R6_2.5.1
[67] doParallel_1.0.17 rprojroot_2.0.4 vroom_1.6.5
[70] evaluate_0.23 lattice_0.22-5 highr_0.10
[73] snakecase_0.11.1 httpuv_1.6.15 bslib_0.7.0
[76] Rcpp_1.0.12 fastmatch_1.1-4 coda_0.19-4.1
[79] nlme_3.1-164 whisker_0.4.1 xfun_0.44
[82] fs_1.6.4 getPass_0.2-4 pkgconfig_2.0.3