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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(patchwork)
library(emmeans)
library(gt)
library(here)

here() starts at /Users/etytel01/Documents/Vertebrae/Code

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.

PGLSmodels <- readRDS(here('output/PGLSmodels.Rds'))
vertdata_all <- read_csv(here("output/vertdata_centered.csv"))

Rows: 571 Columns: 71

── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (17): Species, MatchGenus, MatchSpecies, Family, BodyShape, Habitat_Init...
dbl (54): Indiv, Pos, SL, CBL_old_raw, alpha_Pos_raw, d_raw, D_Pos_raw, alph...


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.

modeltests <- read_csv(here("output/modeltests.csv"))

Rows: 19 Columns: 19

── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): var, term
dbl (16): p.value, total_eff, benthic_demersal_p, benthic_pelagic_p, demersa...
lgl  (1): model


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.

overallmeans <-
  vertdata %>%
  group_by(Habitat) %>%
  dplyr::summarize(across(c(alphaPos_mn, alphaAnt_mn, CBL_mn, d_mn, DAnt_mn, DPos_mn,
                     alphaPos_80, alphaAnt_80, CBL_80, d_80, DAnt_80, DPos_80,
                     alphaPos_vtx, alphaAnt_vtx, CBL_vtx, d_vtx, DAnt_vtx, DPos_vtx,
                     alphaPos_quad, alphaAnt_quad, CBL_quad, d_quad, DAnt_quad, DPos_quad), 
                   list(mn = mean, se = ~ sd(.x) / sqrt(length(.x)))))

overallmeans

# A tibble: 3 × 49
  Habitat  alphaPos_mn_mn alphaPos_mn_se alphaAnt_mn_mn alphaAnt_mn_se CBL_mn_mn
  <chr>             <dbl>          <dbl>          <dbl>          <dbl>     <dbl>
1 benthic            67.5           2.39           71.5           2.54    0.0219
2 demersal           70.6           2.87           74.9           2.70    0.0229
3 pelagic            63.9           3.03           66.8           2.95    0.0249
# … with 43 more variables: CBL_mn_se <dbl>, d_mn_mn <dbl>, d_mn_se <dbl>,
#   DAnt_mn_mn <dbl>, DAnt_mn_se <dbl>, DPos_mn_mn <dbl>, DPos_mn_se <dbl>,
#   alphaPos_80_mn <dbl>, alphaPos_80_se <dbl>, alphaAnt_80_mn <dbl>,
#   alphaAnt_80_se <dbl>, CBL_80_mn <dbl>, CBL_80_se <dbl>, d_80_mn <dbl>,
#   d_80_se <dbl>, DAnt_80_mn <dbl>, DAnt_80_se <dbl>, DPos_80_mn <dbl>,
#   DPos_80_se <dbl>, alphaPos_vtx_mn <dbl>, alphaPos_vtx_se <dbl>,
#   alphaAnt_vtx_mn <dbl>, alphaAnt_vtx_se <dbl>, CBL_vtx_mn <dbl>, …

modelmeans <-
  modeltests %>%
  select(var, ends_with("mn"), ends_with("se")) %>%
  pivot_longer(ends_with("mn") | ends_with("se"), names_to = "HabitatEff", values_to = "value") %>%
  separate(HabitatEff, sep = "_", into = c("Habitat", "Eff")) %>%
  unite(var, c(var, Eff)) %>%
  pivot_wider(names_from = var, values_from = value)

modelmeans

# A tibble: 3 × 39
  Habitat  alphaPos_mn_mn alphaPos_mn_se CBL_quad_mn CBL_quad_se d_mn_mn d_mn_se
  <chr>             <dbl>          <dbl>       <dbl>       <dbl>   <dbl>   <dbl>
1 benthic            97.4           13.5    -0.0228       0.0386 0.00313 9.70e-4
2 demersal           88.7           13.3    -0.0288       0.0381 0.00303 9.58e-4
3 pelagic            84.0           13.7     0.00649      0.0392 0.00234 9.84e-4
# … with 32 more variables: d_vtx_mn <dbl>, d_vtx_se <dbl>, CBL_vtx_mn <dbl>,
#   CBL_vtx_se <dbl>, alphaAnt_mn_mn <dbl>, alphaAnt_mn_se <dbl>,
#   DAnt_quad_mn <dbl>, DAnt_quad_se <dbl>, alphaAnt_quad_mn <dbl>,
#   alphaAnt_quad_se <dbl>, DPos_quad_mn <dbl>, DPos_quad_se <dbl>,
#   fineness_mn <dbl>, fineness_se <dbl>, alphaPos_quad_mn <dbl>,
#   alphaPos_quad_se <dbl>, DPos_mn_mn <dbl>, DPos_mn_se <dbl>,
#   DAnt_mn_mn <dbl>, DAnt_mn_se <dbl>, alphaPos_vtx_mn <dbl>, …

reversemeasurements <- function(df) {
  df %>%
    mutate(Pt1x = DPos / 2 / tan(alphaPos/2 * pi/180),
           Pt2x = DPos / 2 / tan(alphaPos/2 * pi/180),
           Pt3x = -DAnt / 2 / tan(alphaAnt/2 * pi/180),
           Pt4x = -DAnt / 2 / tan(alphaAnt/2 * pi/180),
           Pt5x = 0,
           Pt6x = 0,
           Pt7x = 0,
           Pt8x = d/2 * tan(alphaPos/2 * pi/180),
           Pt9x = d/2 * tan(alphaPos/2 * pi/180),
           Pt10x = -d/2 * tan(alphaAnt/2 * pi/180),
           Pt11x = -d/2 * tan(alphaAnt/2 * pi/180),
           Pt1y = DPos / 2,
           Pt2y = -DPos / 2,
           Pt3y = DAnt / 2,
           Pt4y = -DAnt / 2,
           Pt5y = 0,
           Pt6y = d / 2,
           Pt7y = -d / 2,
           Pt8y = d / 2,
           Pt9y = -d / 2,
           Pt10y = d / 2,
           Pt11y = -d / 2)
}

vertshape <-
  modelmeans %>%
  rename_with(~ str_replace(.x, "_mn_mn", "")) %>%
  reversemeasurements()

vertshape <-
  vertshape %>%
  mutate(Shape1x = Pt1x,
         Shape2x = Pt2x,
         Shape3x = Pt9x,
         Shape4x = Pt11x,
         Shape5x = Pt4x,
         Shape6x = Pt3x,
         Shape7x = Pt10x,
         Shape8x = Pt8x,
         Shape9x = Pt1x,
         Shape1y = Pt1y,
         Shape2y = Pt2y,
         Shape3y = Pt9y,
         Shape4y = Pt11y,
         Shape5y = Pt4y,
         Shape6y = Pt3y,
         Shape7y = Pt10y,
         Shape8y = Pt8y,
         Shape9y = Pt1y)

vertshape <-
  vertshape %>%
  select(!starts_with("Pt")) %>%
  pivot_longer(starts_with("Shape"), names_to = "Pt", values_to = "value") %>%
  extract(Pt, into = c("Num", "XY"), regex = "Shape(\\d+)(x|y)") %>%
  pivot_wider(names_from = XY, values_from = value)

vertshape_panel <-
  vertshape %>%
  ggplot(aes(x = x, y = y, color = Habitat, fill = Habitat)) +
  geom_path() +
  geom_polygon(alpha = 0.2) +
  coord_fixed() +
  scale_color_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2"),
                     guide = "none") +
  scale_fill_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2"),
                     guide = "none") +
  annotate("line", x = c(0.01, 0.01), y = c(-0.003, -0.007), color = "black") +
  annotate("text", x = 0.01, y = -0.005, label = "0.004 BL", angle=90, size=4, vjust = 1.3) +
  theme_minimal() +
  theme(line = element_blank(),
        text = element_blank(),
        title = element_blank())

vertshape_panel

modeltests %>%
  filter(str_detect(var, "alphaPos_mn") | str_detect(var, "d_mn")) %>%
  arrange(var)

# A tibble: 2 × 19
  var           p.value total_eff benthic_demersa… benthic_pelagic… demersal_pelagi…
  <chr>           <dbl>     <dbl>            <dbl>            <dbl>            <dbl>
1 alphaPos_mn 0.0000437     1.05          0.000899          0.00191           0.413 
2 d_mn        0.0135        0.860         0.805             0.0132            0.0310
# … with 13 more variables: statistic <dbl>, term <chr>, df <dbl>, model <lgl>,
#   benthic_demersal_eff <dbl>, benthic_pelagic_eff <dbl>,
#   demersal_pelagic_eff <dbl>, benthic_mn <dbl>, demersal_mn <dbl>,
#   pelagic_mn <dbl>, benthic_se <dbl>, demersal_se <dbl>, pelagic_se <dbl>

dyl = 0.0005
yl = max(vertdata$d_mn) + dyl

d_mn_panel <- ggplot(vertdata, aes(x = Habitat, y = d_mn, color = Habitat, shape = Habitat)) +
  geom_quasirandom(width=0.3, alpha = 0.5) +
  geom_line(data = modelmeans, aes(x = Habitat, y = d_mn_mn, group = 1), color = "black") +
  geom_pointrange(data = modelmeans, 
                  aes(x = Habitat, y = d_mn_mn, 
                      ymin = d_mn_mn - d_mn_se, ymax = d_mn_mn + d_mn_se), size = 0.8) +
  annotate("line", x = c(2,3), y = c(yl, yl), color = "black") +
  annotate("text", x = 2.5, y = yl, label = "* 0.37", size = 3, vjust = -0.2) +
  annotate("line", x = c(1,3), y = c(yl + dyl, yl + dyl), color = "black") +
  annotate("text", x = 2, y = yl+dyl, label = "* 0.43", size = 3, vjust = -0.2) +
  labs(y = "Mean foramen\ndiameter (BL)") +
  scale_x_discrete(labels = c("b", "d", "p")) +
  scale_shape_manual(values = c(15, 19, 4)) +
  scale_color_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2")) +
  theme_bw() + theme(aspect.ratio = 0.7)

d_mn_panel

dyl = 5
yl = max(vertdata$alphaPos_mn) + dyl

alphaPos_mn_panel <- ggplot(vertdata, aes(x = Habitat, y = alphaPos_mn, color = Habitat, shape = Habitat)) +
  geom_quasirandom(width=0.3, alpha = 0.5) +
  geom_line(data = modelmeans, aes(x = Habitat, y = alphaPos_mn_mn, group = 1), color = "black") +
  geom_pointrange(data = modelmeans, 
                  aes(x = Habitat, y = alphaPos_mn_mn, 
                      ymin = alphaPos_mn_mn - alphaPos_mn_se, ymax = alphaPos_mn_mn + alphaPos_mn_se), size = 0.8) +
  annotate("line", x = c(1,2), y = c(yl, yl), color = "black") +
  annotate("text", x = 1.5, y = yl, label = "*** 0.34", size = 3, vjust = -0.2) +
  annotate("line", x = c(1,3), y = c(yl + dyl, yl + dyl), color = "black") +
  annotate("text", x = 2, y = yl+dyl, label = "** 0.52", size = 3, vjust = -0.2) +
  labs(y = "Mean posterior\ncone angle (deg)") +
  scale_x_discrete(labels = c("b", "d", "p")) +
  scale_shape_manual(values = c(15, 19, 4)) +
  scale_color_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2")) +
  theme_bw() + theme(aspect.ratio = 0.7)

alphaPos_mn_panel

dyl = 0
yl = max(vertdata$CBL_mn) + dyl

CBL_mn_panel <- ggplot(vertdata, aes(x = Habitat, y = CBL_mn, color = Habitat, shape = Habitat)) +
  geom_quasirandom(width=0.3, alpha = 0.5) +
  geom_line(data = modelmeans, aes(x = Habitat, y = CBL_mn_mn, group = 1), color = "black") +
  geom_pointrange(data = modelmeans, 
                  aes(x = Habitat, y = CBL_mn_mn, 
                      ymin = CBL_mn_mn - CBL_mn_se, ymax = CBL_mn_mn + CBL_mn_se), size = 0.8) +
  #annotate("line", x = c(1,2), y = c(yl, yl), color = "black") +
  #annotate("text", x = 1.5, y = yl, label = "*** 0.34", size = 3, vjust = -0.2) +
  #annotate("line", x = c(1,3), y = c(yl + dyl, yl + dyl), color = "black") +
  #annotate("text", x = 2, y = yl+dyl, label = "** 0.52", size = 3, vjust = -0.2) +
  labs(y = "Centrum body\nlength (BL)") +
  scale_x_discrete(labels = c("b", "d", "p")) +
  scale_shape_manual(values = c(15, 19, 4)) +
  scale_color_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2")) +
  theme_bw() + theme(aspect.ratio = 0.7)

CBL_mn_panel

alphaPos_mn_panel / d_mn_panel / CBL_mn_panel / vertshape_panel + 
  plot_annotation(tag_levels = 'A') +
  plot_layout(guides = 'collect') &
  theme(legend.position = "bottom",
        panel.border = element_blank(), axis.line = element_line())

ggsave(here('output/mean_d_alphaPos_CBL.pdf'), width = 3, units = "in")

Saving 3 x 5 in image

Distribution along the body

Posterior cone angle

alphaPosvPosPanel <-
  vertdata_all %>%
  filter(Pos >= 0.4) %>%
  ggplot(aes(x = Pos, y = alphaPos, color=Habitat, fill=Habitat, group=Habitat)) +
    stat_summary(fun.data = "mean_se", geom="ribbon", alpha=0.5) +
    stat_summary(fun = "mean", geom="line") +
  scale_shape_manual(values = c(15, 19, 4)) +
  scale_color_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2")) +
  scale_fill_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2")) +
  labs(x = "Position (BL)", y = "Posterior cone\nangle (deg)") +
  theme_bw() + theme(aspect.ratio = 0.7)

alphaPosvPosPanel

Centrum body length

modeltests %>%
  filter(str_detect(var, "CBL")) %>%
  arrange(var)

# A tibble: 3 × 19
  var      p.value total_eff benthic_demersal… benthic_pelagic… demersal_pelagi…
  <chr>      <dbl>     <dbl>             <dbl>            <dbl>            <dbl>
1 CBL_mn   0.316       0.281             0.327           0.610           0.997  
2 CBL_quad 0.00385     0.963             0.635           0.0231          0.00378
3 CBL_vtx  0.0404      0.730             0.670           0.133           0.0359 
# … with 13 more variables: statistic <dbl>, term <chr>, df <dbl>, model <lgl>,
#   benthic_demersal_eff <dbl>, benthic_pelagic_eff <dbl>,
#   demersal_pelagic_eff <dbl>, benthic_mn <dbl>, demersal_mn <dbl>,
#   pelagic_mn <dbl>, benthic_se <dbl>, demersal_se <dbl>, pelagic_se <dbl>

CBLvPosPanel <-
  vertdata_all %>%
  filter(Pos >= 0.4) %>%
  ggplot(aes(x = Pos, y = CBL, color=Habitat, fill=Habitat, group=Habitat)) +
    stat_summary(fun.data = "mean_se", geom="ribbon", alpha=0.5) +
    stat_summary(fun = "mean", geom="line") +
  scale_shape_manual(values = c(15, 19, 4)) +
  scale_color_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2")) +
  scale_fill_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2")) +
  labs(x = "Position (BL)", y = "Centrum body\nlength (BL)") +
  theme_bw() + theme(aspect.ratio = 0.7)

CBLvPosPanel

dyl = 1
yl = max(vertdata$CBL_vtx) + dyl

CBLvtxPanel <- ggplot(vertdata, aes(x = Habitat, y = CBL_vtx, color = Habitat, shape = Habitat)) +
  geom_quasirandom(width=0.3, alpha = 0.5) +
  geom_line(data = modelmeans, aes(x = Habitat, y = CBL_vtx_mn, group = 1), color = "black") +
  geom_pointrange(data = modelmeans, 
                  aes(x = Habitat, y = CBL_vtx_mn, 
                      ymin = CBL_vtx_mn - CBL_vtx_se, ymax = CBL_vtx_mn + CBL_vtx_se), size = 0.8) +
  #annotate("line", x = c(1,2), y = c(yl, yl), color = "black") +
  #annotate("text", x = 1.5, y = yl, label = "*** 0.34", size = 3, vjust = -0.2) +
  annotate("line", x = c(2,3), y = c(yl + dyl, yl + dyl), color = "black") +
  annotate("text", x = 2.5, y = yl+dyl, label = "* 0.36", size = 3, vjust = -0.2) +
  labs(y = "Fit location of\nlongest vertebra (BL)") +
  scale_x_discrete(labels = c("b", "d", "p")) +
  scale_shape_manual(values = c(15, 19, 4)) +
  scale_color_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2")) +
  theme_bw() # + theme(aspect.ratio = 0.7)

CBLvtxPanel

dyl = 0.01
yl = max(vertdata$CBL_quad) + dyl

CBLquadPanel <- ggplot(vertdata, aes(x = Habitat, y = CBL_quad, color = Habitat, shape = Habitat)) +
  geom_quasirandom(width=0.3, alpha = 0.5) +
  geom_line(data = modelmeans, aes(x = Habitat, y = CBL_quad_mn, group = 1), color = "black") +
  geom_pointrange(data = modelmeans, 
                  aes(x = Habitat, y = CBL_quad_mn, 
                      ymin = CBL_quad_mn - CBL_quad_se, ymax = CBL_quad_mn + CBL_quad_se), size = 0.8) +
  annotate("line", x = c(2,3), y = c(yl, yl), color = "black") +
  annotate("text", x = 2.5, y = yl, label = "** -0.48", size = 3, vjust = -0.2) +
  annotate("line", x = c(1,3), y = c(yl + dyl, yl + dyl), color = "black") +
  annotate("text", x = 2, y = yl+dyl, label = "* -0.40", size = 3, vjust = -0.2) +
  labs(y = "Centrum body length\nquad. coeff. (BL^2)") +
  scale_x_discrete(labels = c("b", "d", "p")) +
  scale_shape_manual(values = c(15, 19, 4)) +
  scale_color_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2")) +
  theme_bw() # + theme(aspect.ratio = 0.7)

CBLquadPanel

Foramen diameter

dvPosPanel <-
  vertdata_all %>%
  filter(Pos >= 0.4) %>%
  ggplot(aes(x = Pos, y = d, color=Habitat, fill=Habitat, group=Habitat)) +
    stat_summary(fun.data = "mean_se", geom="ribbon", alpha=0.5) +
    stat_summary(fun = "mean", geom="line") +
  scale_shape_manual(values = c(15, 19, 4)) +
  scale_color_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2")) +
  scale_fill_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2")) +
  labs(x = "Position (BL)", y = "Foramen\ndiameter (BL)") +
  theme_bw() + theme(aspect.ratio = 0.7)

dvPosPanel

modeltests %>%
  filter(str_detect(var, "d_")) %>%
  arrange(var)

# A tibble: 3 × 19
  var      p.value total_eff benthic_demersal… benthic_pelagic… demersal_pelagi…
  <chr>      <dbl>     <dbl>             <dbl>            <dbl>            <dbl>
1 d_mn   0.0135        0.860         0.805               0.0132           0.0310
2 d_quad 0.436         0.220         0.446               1.00             0.745 
3 d_vtx  0.0000251     0.799         0.0000937           0.0239           1.00  
# … with 13 more variables: statistic <dbl>, term <chr>, df <dbl>, model <lgl>,
#   benthic_demersal_eff <dbl>, benthic_pelagic_eff <dbl>,
#   demersal_pelagic_eff <dbl>, benthic_mn <dbl>, demersal_mn <dbl>,
#   pelagic_mn <dbl>, benthic_se <dbl>, demersal_se <dbl>, pelagic_se <dbl>

dyl = 1
yl = max(vertdata$d_vtx) + dyl

dvtxPanel <- ggplot(vertdata, aes(x = Habitat, y = d_vtx, color = Habitat, shape = Habitat)) +
  geom_quasirandom(width=0.3, alpha = 0.5) +
  geom_line(data = modelmeans, aes(x = Habitat, y = d_vtx_mn, group = 1), color = "black") +
  geom_pointrange(data = modelmeans, 
                  aes(x = Habitat, y = d_vtx_mn, 
                      ymin = d_vtx_mn - d_vtx_se, ymax = d_vtx_mn + d_vtx_se), size = 0.8) +
  annotate("line", x = c(1,2), y = c(yl, yl), color = "black") +
  annotate("text", x = 1.5, y = yl, label = "*** -0.40", size = 3, vjust = -0.2) +
  annotate("line", x = c(1,3), y = c(yl + dyl, yl + dyl), color = "black") +
  annotate("text", x = 2, y = yl+dyl, label = "* -0.40", size = 3, vjust = -0.2) +
  labs(y = "Fit location of\nlargest foramen (BL)") +
  scale_x_discrete(labels = c("b", "d", "p")) +
  scale_shape_manual(values = c(15, 19, 4)) +
  scale_color_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2")) +
  theme_bw() # + theme(aspect.ratio = 0.7)

dvtxPanel

dyl = 0.01
yl = max(vertdata$d_quad) + dyl

dQuadPanel <- ggplot(vertdata, aes(x = Habitat, y = d_quad, color = Habitat, shape = Habitat)) +
  geom_quasirandom(width=0.3, alpha = 0.5) +
  geom_line(data = modelmeans, aes(x = Habitat, y = d_quad_mn, group = 1), color = "black") +
  geom_pointrange(data = modelmeans, 
                  aes(x = Habitat, y = d_quad_mn, 
                      ymin = d_quad_mn - d_quad_se, ymax = d_quad_mn + d_quad_se), size = 0.8) +
  #annotate("line", x = c(2,3), y = c(yl, yl), color = "black") +
  #annotate("text", x = 2.5, y = yl, label = "** -0.48", size = 3, vjust = -0.2) +
  #annotate("line", x = c(1,3), y = c(yl + dyl, yl + dyl), color = "black") +
  #annotate("text", x = 2, y = yl+dyl, label = "* -0.40", size = 3, vjust = -0.2) +
  labs(y = "Foramen diameter\nquad. coeff. (BL^2)") +
  scale_x_discrete(labels = c("b", "d", "p")) +
  scale_shape_manual(values = c(15, 19, 4)) +
  scale_color_manual(values = c(benthic="chocolate4", demersal = "gold", pelagic = "deepskyblue2")) +
  theme_bw() # + theme(aspect.ratio = 0.7)

dQuadPanel

Combined figure

alphaPosvPosPanel / dvPosPanel / CBLvPosPanel + plot_annotation(tag_levels = 'A') +
  plot_layout(guides = 'collect') &
  theme(legend.position = "bottom",
        panel.border = element_blank(), axis.line = element_line())

ggsave(here('output/BodyDistribution.pdf'), width = 3, units = "in")

Saving 3 x 5 in image

PGLSmodels

# A tibble: 19 × 19
   var             p.value total_eff benthic_demersa… benthic_pelagic… demersal_pelagi…
   <chr>             <dbl>     <dbl>            <dbl>            <dbl>            <dbl>
 1 alphaPos_mn   0.0000437     1.05         0.000899           0.00191          0.413  
 2 CBL_quad      0.00385       0.963        0.635              0.0231           0.00378
 3 d_mn          0.0135        0.860        0.805              0.0132           0.0310 
 4 d_vtx         0.0000251     0.799        0.0000937          0.0239           1.00   
 5 CBL_vtx       0.0404        0.730        0.670              0.133            0.0359 
 6 alphaAnt_mn   0.0948        0.626        0.884              0.168            0.0836 
 7 DAnt_quad     0.208         0.524        0.855              0.186            0.306  
 8 alphaAnt_quad 0.167         0.521        0.556              0.499            0.175  
 9 DPos_quad     0.271         0.472        0.947              0.253            0.326  
10 fineness      0.279         0.456        0.788              0.492            0.260  
11 alphaPos_quad 0.216         0.448        0.452              0.718            0.274  
12 DPos_mn       0.340         0.410        0.735              0.623            0.336  
13 DAnt_mn       0.413         0.384        0.918              0.544            0.384  
14 alphaPos_vtx  0.495         0.327        0.713              0.514            0.798  
15 CBL_mn        0.316         0.281        0.327              0.610            0.997  
16 DPos_vtx      0.516         0.237        0.549              0.703            0.985  
17 d_quad        0.436         0.220        0.446              1.00             0.745  
18 alphaAnt_vtx  0.691         0.178        0.738              0.988            0.812  
19 DAnt_vtx      0.923         0.115        0.987              0.954            0.917  
# … with 13 more variables: statistic <dbl>, term <chr>, df <dbl>,
#   model <list>, benthic_demersal_eff <dbl>, benthic_pelagic_eff <dbl>,
#   demersal_pelagic_eff <dbl>, benthic_mn <dbl>, demersal_mn <dbl>,
#   pelagic_mn <dbl>, benthic_se <dbl>, demersal_se <dbl>, pelagic_se <dbl>

tab <-
  PGLSmodels %>%
  select(var, statistic, p.value, ends_with("eff"), -total_eff) %>%
  separate(var, into = c("var", "posstat")) %>%
  mutate(posstat = if_else(var == "fineness", "mn", posstat)) %>%
  # group_by(var) %>%
  # group_modify(~ add_row(.x,.before=0)) %>%
  # mutate(posstat = if_else(is.na(posstat), "x", posstat)) %>%
  mutate(posstat = factor(posstat, levels = c("x", "mn", "vtx", "quad")),
         posstat = fct_recode(posstat, x = "x", mean = "mn", vertex = "vtx", "quad. coef." = "quad")) %>%
  ungroup() %>%
  mutate(var = case_when(
    var == "alphaAnt"   ~   "Anterior cone angle",
    var == "alphaPos"   ~   "Posterior cone angle",
    var == "CBL"   ~   "Centrum body length",
    var == "DAnt"   ~   "Anterior cone diameter",
    var == "DPos"   ~   "Posterior cone diameter",
    var == "d"   ~   "Foramen diameter",
    var == "fineness"   ~   "Fineness"
  )) %>%
  arrange(var, posstat) %>%
  gt(
    groupname_col = "var",
    rowname_col = "posstat"
  ) %>%
  fmt_number(
    columns = "statistic",
    suffixing = FALSE,
    n_sigfig = 2
  ) %>%
  fmt_number(
    columns = "p.value",
    decimals = 3
  ) %>%
  fmt_number(
    columns = c("benthic_demersal_eff", "benthic_pelagic_eff", "demersal_pelagic_eff"),
    decimals = 2,
    force_sign = TRUE
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_body(rows = p.value < 0.05)
  ) %>%
  cols_label(
    var = md("Measurement"),
    posstat = "",
    statistic = md("F2,74"),
    p.value = md("p"),
    benthic_demersal_eff = md("b - d"),
    benthic_pelagic_eff = md("b - p"),
    demersal_pelagic_eff = md("d - p")
  ) %>%
  tab_style(
    locations = cells_column_labels(columns = c("var", "posstat", "statistic", "p.value")),
    style = cell_text(v_align = "middle",
                      align = "center")
  ) %>%
  tab_stubhead("Measurement") %>%
  tab_style(
    locations = cells_stubhead(),
    style = cell_text(v_align = "middle")
  ) %>%
  tab_spanner(
    label = "Effect sizes",
    columns = c("benthic_demersal_eff", "benthic_pelagic_eff", "demersal_pelagic_eff")
  ) %>%
  fmt_missing(columns = 1:7,
              missing_text = "")

Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [10].

tab

Measurement	F2,74	p	Effect sizes
Measurement	F2,74	p	b - d	b - p	d - p
Anterior cone angle
mean	4.7	0.095	−0.04	+0.27	+0.31
vertex	0.74	0.691	−0.07	+0.02	+0.09
quad. coef.	3.6	0.167	−0.09	+0.17	+0.26
Anterior cone diameter
mean	1.8	0.413	−0.04	+0.16	+0.19
vertex	0.16	0.923	+0.01	−0.04	−0.06
quad. coef.	3.1	0.208	−0.05	−0.26	−0.21
Centrum body length
mean	2.3	0.316	−0.13	−0.14	−0.01
vertex	6.4	0.040	−0.08	+0.29	+0.36
quad. coef.	11	0.004	+0.08	−0.40	−0.48
Fineness
mean	2.6	0.279	+0.06	−0.17	−0.23
Foramen diameter
mean	8.6	0.014	+0.06	+0.43	+0.37
vertex	21	0.000	−0.40	−0.40	+0.00
quad. coef.	1.7	0.436	+0.11	+0.00	−0.11
Posterior cone angle
mean	20	0.000	+0.34	+0.53	+0.18
vertex	1.4	0.495	+0.07	+0.16	+0.09
quad. coef.	3.1	0.216	−0.11	+0.12	+0.22
Posterior cone diameter
mean	2.2	0.340	−0.07	+0.14	+0.21
vertex	1.3	0.516	+0.09	+0.12	+0.02
quad. coef.	2.6	0.271	−0.03	−0.24	−0.21

gtsave(tab, here("output/stats_table.rtf"))

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       gt_0.3.1         emmeans_1.6.3    patchwork_1.1.1 
 [5] ggbeeswarm_0.6.0 forcats_0.5.1    stringr_1.4.0    dplyr_1.0.7     
 [9] purrr_0.3.4      readr_2.0.1      tidyr_1.1.3      tibble_3.1.4    
[13] ggplot2_3.3.5    tidyverse_1.3.1 

loaded via a namespace (and not attached):
 [1] fs_1.5.0         lubridate_1.7.10 bit64_4.0.5      httr_1.4.2      
 [5] rprojroot_2.0.2  tools_4.1.2      backports_1.2.1  utf8_1.2.2      
 [9] R6_2.5.1         vipor_0.4.5      DBI_1.1.1        colorspace_2.0-2
[13] withr_2.4.2      tidyselect_1.1.1 bit_4.0.4        compiler_4.1.2  
[17] git2r_0.29.0     cli_3.0.1        rvest_1.0.1      xml2_1.3.2      
[21] sass_0.4.0       labeling_0.4.2   scales_1.1.1     checkmate_2.0.0 
[25] mvtnorm_1.1-2    commonmark_1.7   digest_0.6.27    rmarkdown_2.10  
[29] pkgconfig_2.0.3  htmltools_0.5.2  dbplyr_2.1.1     fastmap_1.1.0   
[33] highr_0.9        rlang_0.4.11     readxl_1.3.1     rstudioapi_0.13 
[37] generics_0.1.0   farver_2.1.0     jsonlite_1.7.2   vroom_1.5.4     
[41] magrittr_2.0.1   Rcpp_1.0.7       munsell_0.5.0    fansi_0.5.0     
[45] lifecycle_1.0.0  stringi_1.7.4    whisker_0.4      yaml_2.2.1      
[49] grid_4.1.2       parallel_4.1.2   promises_1.2.0.1 crayon_1.4.1    
[53] lattice_0.20-45  haven_2.4.3      hms_1.1.0        knitr_1.34      
[57] pillar_1.6.2     estimability_1.3 reprex_2.0.1     glue_1.4.2      
[61] evaluate_0.14    renv_0.14.0      modelr_0.1.8     vctrs_0.3.8     
[65] tzdb_0.1.2       httpuv_1.6.4     cellranger_1.1.0 gtable_0.3.0    
[69] assertthat_0.2.1 xfun_0.25        xtable_1.8-4     broom_0.7.9     
[73] coda_0.19-4      later_1.3.0      beeswarm_0.4.0   workflowr_1.7.0 
[77] ellipsis_0.3.2

R Notebook

Distribution along the body

Posterior cone angle

Centrum body length

Foramen diameter

Combined figure