Summarize vertebral measurements
Last updated: 2022-04-11
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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(emmeans)
library(ggbeeswarm)
library(patchwork)
library(here)here() starts at /Users/etytel01/Documents/Vertebrae/CodeLoad data
Vertebral measurements
vertdata <- read_csv(here('output', "MasterVert_Measurements_Matched.csv")) %>%
separate(MatchSpecies, into=c("MatchGenus", "MatchSpecies"), sep="_") %>%
relocate(MatchGenus, MatchSpecies, .after=Species) %>%
rename(alphaPos = alpha_Pos,
alphaAnt = alpha_Ant,
DPos = D_Pos,
DAnt = D_Ant,
BodyShape = `Body Shape`,
dBW = d_BW,
DAntBW = D_Ant_BW,
DPosBW = D_Pos_BW,
fineness = `SL/Max_BW`)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.head(vertdata)# A tibble: 6 × 55
Species MatchGenus MatchSpecies Family Indiv Pos SL CBL_raw alpha_Pos_raw
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Alecti… Alectis ciliaris <NA> 1 40 799 24.9 1.51
2 Alecti… Alectis ciliaris <NA> 1 50 799 34.4 1.18
3 Alecti… Alectis ciliaris <NA> 1 60 799 35.8 1.38
4 Alecti… Alectis ciliaris <NA> 1 70 799 36.6 1.34
5 Alecti… Alectis ciliaris <NA> 1 80 799 35.2 1.32
6 Alecti… Alectis ciliaris <NA> 1 90 799 32.8 1.31
# … with 46 more variables: d_raw <dbl>, D_Pos_raw <dbl>, alpha_Ant_raw <dbl>,
# D_Ant_raw <dbl>, CBL <dbl>, alphaPos <dbl>, d <dbl>, DPos <dbl>,
# alphaAnt <dbl>, DAnt <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>, BodyShape <chr>,
# Habitat_Initial <chr>, Habitat_Friedman <chr>, Habitat_FishBase <chr>,
# Habitat <chr>, Water_Type <chr>, Max_BW_mm <dbl>, BW_slide <dbl>, …Compute the correct CBL
vertdata <-
vertdata %>%
rename(CBL_old_raw = CBL_raw,
CBL_old = CBL) %>%
mutate(CBL_raw = (abs(Pt1x - Pt3x) + abs(Pt2x - Pt4x))/2,
CBL = CBL_raw / SL)ggplot(vertdata, aes(x = CBL_old, y = CBL)) +
geom_point()
The values are similar but not identical.
Basic plots
vertdata <-
vertdata %>%
mutate(d_normD = d / ((DAnt + DPos)/2),
d_normCBL = d / CBL,
Iratio = 1 - d^4/((DAnt + DPos)/2)^4) %>%
mutate(Pos = Pos/100,
Pos = factor(Pos))Calculate the mean of each variable at each body position.
vertdata_bypos <-
vertdata %>%
group_by(Pos) %>%
dplyr::summarize(across(c(d, CBL, alphaAnt, alphaPos, DAnt, DPos, dBW, DPosBW, DAntBW,
d_normCBL, d_normD, Iratio),
list(mn = ~ mean(.x, na.rm = TRUE),
med = ~ median(.x, na.rm = TRUE),
iqr = ~ IQR(.x, na.rm = TRUE),
sd = ~ sd(.x, na.rm = TRUE))))vertdata_bypos %>%
pivot_longer(!Pos, names_to=c("var", ".value"),
names_pattern = "(.*)_(.*)") %>%
arrange(var, Pos) %>%
ggplot(aes(x = Pos, y = med, group = 1)) +
geom_ribbon(aes(ymin = med-iqr, ymax = med+iqr), alpha = 0.5) +
geom_line() +
geom_line(aes(y = mn), color="red") +
facet_wrap(~ var, scales = "free")
vertdata <-
vertdata %>%
group_by(Pos) %>%
mutate(across(c(d, CBL, alphaAnt, alphaPos, DAnt, DPos, dBW, DAntBW, DPosBW, d_normD, d_normCBL, Iratio),
list(ctr = ~.x - median(.x, na.rm = TRUE)))) %>%
ungroup()vertdata %>%
ggplot(aes(x = Pos, y = d_ctr, color=Habitat)) +
geom_boxplot(aes(group = Pos)) +
geom_beeswarm() +
geom_line(data = filter(vertdata, d > 0.006 & Habitat == "pelagic"),
aes(group=Species, linetype=Species)) +
geom_label(data = filter(vertdata, d > 0.006 & Pos == 0.9 & Habitat == "pelagic"),
aes(label = Species)) +
facet_grid(. ~ Habitat) +
labs(x = "Vertebra position (L)",
y = "Foramen diameter (L)")
vertdata %>%
ggplot(aes(x = Pos, y = dBW_ctr, color=Habitat)) +
geom_boxplot(aes(group = Pos)) +
geom_beeswarm() +
geom_line(data = filter(vertdata, dBW_ctr > 0.05 & Habitat == "pelagic"),
aes(group=Species, linetype=Species)) +
geom_label(data = filter(vertdata, dBW_ctr > 0.05 & Pos == 0.9 & Habitat == "pelagic"),
aes(label = Species)) +
facet_grid(. ~ Habitat) +
labs(x = "Vertebra position (L)",
y = "Foramen diameter (L)")
Let’s exclude Elops and Mirorictus from the rest of the analysis for right now.
vertdata2 <-
vertdata %>%
filter((Species != "Elops_saurus") &
Species != "Mirorictus_taningi")vertdata2 %>%
filter(is.na(d_normCBL))# A tibble: 0 × 71
# … with 71 variables: Species <chr>, MatchGenus <chr>, MatchSpecies <chr>,
# Family <chr>, Indiv <dbl>, Pos <fct>, SL <dbl>, CBL_old_raw <dbl>,
# alpha_Pos_raw <dbl>, d_raw <dbl>, D_Pos_raw <dbl>, alpha_Ant_raw <dbl>,
# D_Ant_raw <dbl>, CBL_old <dbl>, alphaPos <dbl>, d <dbl>, DPos <dbl>,
# alphaAnt <dbl>, DAnt <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>, BodyShape <chr>, …write_csv(vertdata2, here('output', "vertdata_centered.csv"))plot_position_habitat_distribution <- function(df, var)
{
var <- enquo(var)
p1 <-
df %>%
filter(!is.na(!!var)) %>%
ggplot(aes(x = Pos, y = !!var, color=Habitat, fill=Habitat, group=Habitat)) +
stat_summary(fun.data = "mean_se", geom="ribbon", alpha=0.5) +
stat_summary(fun = "mean", geom="line")
p2 <-
df %>%
filter(!is.na(!!var)) %>%
ggplot(aes(x = Habitat, y = !!var, color=Habitat)) +
geom_violin() +
geom_boxplot(width=0.3, alpha=0.5) +
stat_summary(aes(group = 1), fun = "median", geom = "line")
p1 + p2 + plot_layout(widths = c(3,1), guides = 'collect')
}plot_position_habitat_distribution(vertdata2, d)
Here we’re plotting the foramen diameter (normalized by body length) relative to position on the left, and the overall distributions relative to habitat on the right. The bottom row has the overall mean pattern relative to body length subtracted.
plot_position_habitat_distribution(vertdata2, dBW)
plot_position_habitat_distribution(vertdata2, CBL)
plot_position_habitat_distribution(vertdata2, alphaAnt)
plot_position_habitat_distribution(vertdata2, alphaPos)
plot_position_habitat_distribution(vertdata2, DAnt)
Compare summary statistics
First generate the summary statistics, summarizing across the body positions.
This gives us the mean, median, and max values for each of the measurements.
vertdata_summary <-
vertdata2 %>%
filter(Pos != 0.2 & Pos != 0.3) %>%
group_by(Species, Indiv, Habitat, Water_Type, MatchSpecies, MatchGenus, fineness) %>%
dplyr::summarize(across(c(CBL, d, alphaAnt, alphaPos, DAnt, DPos,
dBW, DAntBW, DPosBW, d_normCBL, d_normD, Iratio),
list(med = ~ median(.x, na.rm = TRUE),
max = ~ max(.x, na.rm = TRUE),
mn = ~ mean(.x, na.rm = TRUE)))) %>%
ungroup()`summarise()` has grouped output by 'Species', 'Indiv', 'Habitat', 'Water_Type', 'MatchSpecies', 'MatchGenus'. You can override using the `.groups` argument.head(vertdata_summary)# A tibble: 6 × 43
Species Indiv Habitat Water_Type MatchSpecies MatchGenus fineness CBL_med
<chr> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl>
1 Abramis_bra… 1 pelagic freshwater alburnus Alburnus 8.95 0.0166
2 Alectis_cil… 1 demers… marine ciliaris Alectis 8.75 0.0346
3 Alosa_pseud… 1 pelagic anadromous pseudoharen… Alosa 7.39 0.0165
4 Amia_calva 1 demers… freshwater calva Amia 6.72 0.00983
5 Ammodytes_p… 1 benthic marine dubius Ammodytes 16.9 0.0132
6 Anodontosto… 1 pelagic freshwater cepedianum Dorosoma 4.66 0.0228
# … with 35 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>, DPosBW_mn <dbl>, …vertdata_summary %>%
select(ends_with("max") | Habitat | Species) %>%
pivot_longer(contains("max"),
names_to = "var", values_to = "value") %>%
ggplot(aes(x = Habitat, y = value, color = Habitat)) +
geom_violin() +
geom_boxplot(width=0.3, alpha=0.5) +
stat_summary(aes(group = 1), fun = "median", geom = "line") +
facet_wrap(~ var, scales = "free")
write_csv(vertdata_summary, here("output", "vertdata_summary.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 patchwork_1.1.1 ggbeeswarm_0.6.0 emmeans_1.6.3
[5] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[9] readr_2.0.1 tidyr_1.1.3 tibble_3.1.4 ggplot2_3.3.5
[13] tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] httr_1.4.2 bit64_4.0.5 vroom_1.5.4 jsonlite_1.7.2
[5] modelr_0.1.8 assertthat_0.2.1 highr_0.9 renv_0.14.0
[9] vipor_0.4.5 cellranger_1.1.0 yaml_2.2.1 pillar_1.6.2
[13] backports_1.2.1 lattice_0.20-45 glue_1.4.2 digest_0.6.27
[17] promises_1.2.0.1 rvest_1.0.1 colorspace_2.0-2 htmltools_0.5.2
[21] httpuv_1.6.4 pkgconfig_2.0.3 broom_0.7.9 haven_2.4.3
[25] xtable_1.8-4 mvtnorm_1.1-2 scales_1.1.1 whisker_0.4
[29] later_1.3.0 tzdb_0.1.2 git2r_0.29.0 farver_2.1.0
[33] generics_0.1.0 ellipsis_0.3.2 withr_2.4.2 cli_3.0.1
[37] magrittr_2.0.1 crayon_1.4.1 readxl_1.3.1 estimability_1.3
[41] evaluate_0.14 fs_1.5.0 fansi_0.5.0 xml2_1.3.2
[45] beeswarm_0.4.0 tools_4.1.2 hms_1.1.0 lifecycle_1.0.0
[49] munsell_0.5.0 reprex_2.0.1 compiler_4.1.2 rlang_0.4.11
[53] grid_4.1.2 rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.10
[57] gtable_0.3.0 DBI_1.1.1 R6_2.5.1 lubridate_1.7.10
[61] knitr_1.34 bit_4.0.4 fastmap_1.1.0 utf8_1.2.2
[65] workflowr_1.7.0 rprojroot_2.0.2 stringi_1.7.4 parallel_4.1.2
[69] Rcpp_1.0.7 vctrs_0.3.8 dbplyr_2.1.1 tidyselect_1.1.1
[73] xfun_0.25 coda_0.19-4