This still lacks the newest depth scale, however, so it’s better to -use a custom file.
-coresumm <- read_csv("dat/SectionSummary.csv")# Rows: 142 Columns: 10
-# ── Column specification ──────────────────────────────────────────────────────
-# Delimiter: ","
-# chr (3): Hole, Type, Sect
-# dbl (7): Exp, Site, Core, Top depth CSF-B (m), Bottom depth CSF-B (m), top_d...
-#
-# 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.Now that we have the metadata, we can attach it to the images.
imgs <- imgs |>
tidylog::left_join(coresumm |>
- mutate(Sect = str_replace(Sect, "CC", "8") |>
+ mutate(Sc = ifelse(str_detect(Sc, "CC"), "8", Sc) |>
parse_integer()),
by = c('site' = 'Site',
- 'hole' = 'Hole',
- 'core' = 'Core',
- 'type' = 'Type',
- 'section' = 'Sect'))# left_join: added 5 columns (Exp, Top depth CSF-B (m), Bottom depth CSF-B (m), top_depth, bot_depth)# > rows only in x 0# > rows only in y (111)# > matched rows 31# > =====# > rows total 31# left_join: added 5 columns (Leg, LL(m), CL(m), Top(mbsf), Comment)# > rows only in x 0# > rows only in y (1,042)# > matched rows 31# > =======# > rows total 31and make proper plots of our sections
corepics <- imgs |>
## slice(1) |> # try it out for a single image
ggplot(aes(xmin = 0, xmax = 1,
- ymin = `Bottom depth CSF-B (m)`,
- ymax = `Top depth CSF-B (m)`)) +
- # here we turn the filename into the filepath, relative to this project
- geom_textured_rect(aes(image = paste0("dat/croppedsections/", file))) +
+ ymin = `Top(mbsf)` + `LL(m)`,
+ ymax = `Top(mbsf)`)) +
scale_y_reverse() +
+ # here we turn the filename into the filepath, relative to this project
+ geom_textured_rect(aes(image = paste0("dat/croppedsections/", file)),
+ # below settings are essential so that section images get
+ # stretched!
+ nrow = 1, ncol = 1,
+ img_width = unit(1, "null"),
+ img_height = unit(1, "null"),
+ interpolate = FALSE) +
facet_grid(cols = vars(hole)) +
+ labs(y = "Depth (mbsf)")
+corepics +
+ # show the core and section number
geom_text(aes(x = 1.2, label = paste0(core, "-", section),
- y = `Top depth CSF-B (m)` +
- 0.5 * (`Bottom depth CSF-B (m)` - `Top depth CSF-B (m)`)),
- size = 2, hjust = 1)
-corepics
## agem <- read_excel("dat/Agemodel.xlsx")
agem <- tribble( ~ depth_mbsf, ~ Age_GTS12,
+ 260, 15,
290, 20,
315, 22,
323, 22.7,
@@ -730,36 +731,36 @@ agemodel
geom_line() +
geom_point() +
# annotate cores we have pics for
- geom_rug(aes(y = top_depth),
+ geom_rug(aes(y = `Top(mbsf)`),
colour = "grey",
inherit.aes = FALSE,
data = imgs) +
scale_y_reverse()
Apply the agemodel. I’m assuming that the top_age and bot_age are in mbsf, because the rmcd column in the agemodel is empty!
imgs <- imgs |>
mutate(top_age = approx(x = agem$depth_mbsf, y = agem$Age_GTS12,
- xout = top_depth)$y,
+ xout = `Top(mbsf)`)$y,
bot_age = approx(x = agem$depth_mbsf, y = agem$Age_GTS12,
- xout = bot_depth)$y)Add the age information to the colourlog
newflat <- flat |>
# tidylog is nice because it prints some messages about how the join went.
tidylog::left_join(coresumm |>
- mutate(Sect = str_replace(Sect, "CC", "8") |>
+ mutate(Sc = ifelse(str_detect(Sc, "CC"), "8", Sc) |>
parse_integer()),
by = c('site' = 'Site',
- 'hole' = 'Hole',
- 'core' = 'Core',
- 'type' = 'Type',
- 'section' = 'Sect'))# left_join: added 5 columns (Exp, Top depth CSF-B (m), Bottom depth CSF-B (m), top_depth, bot_depth)# > rows only in x 0# > rows only in y (134)# > matched rows 977# > =====# > rows total 977# left_join: added 5 columns (Leg, LL(m), CL(m), Top(mbsf), Comment)# > rows only in x 0# > rows only in y (1,065)# > matched rows 977# > =======# > rows total 977glimpse(newflat)# Rows: 977
# Columns: 37
@@ -795,11 +796,11 @@ agemodel
# $ mean_blue_values2 <dbl> 42.14176, 44.79482, 41.49701, …
# $ standard_deviation_of_blue_values2 <dbl> 13.73924, 16.90331, 14.81062, …
# $ median_of_blue_values2 <dbl> 41, 41, 38, 38, 34, 35, 35, 35…
-# $ Exp <dbl> 159, 159, 159, 159, 159, 159, …
-# $ `Top depth CSF-B (m)` <dbl> 274, 274, 274, 274, 274, 274, …
-# $ `Bottom depth CSF-B (m)` <dbl> 275.489, 275.489, 275.489, 275…
-# $ top_depth <dbl> 300.02, 300.02, 300.02, 300.02…
-# $ bot_depth <dbl> 301.509, 301.509, 301.509, 301…The above metadata gives us the depth of each top of the section, but @@ -807,21 +808,21 @@
depthcalc <- newflat %>%
# add the section depth (in mbsf) and the top depth (in cm).
- mutate(depth = `Top depth CSF-B (m)` + distance_from_core_top2 * 0.01)
+ mutate(depth = `Top(mbsf)` + distance_from_core_top2 * 0.01)
# inspect the new depth
depthcalc %>%
- select(`Top depth CSF-B (m)`, top_depth, distance_from_core_top2, depth) |>
+ select(`Top(mbsf)`, distance_from_core_top2, depth) |>
tail()# # A tibble: 6 × 4
-# `Top depth CSF-B (m)` top_depth distance_from_core_top2 depth
-# <dbl> <dbl> <dbl> <dbl>
-# 1 283. 309. 24.5 284.
-# 2 283. 309. 25.5 284.
-# 3 283. 309. 26.5 284.
-# 4 283. 309. 27.5 284.
-# 5 283. 309. 28.5 284.
-# 6 283. 309. 29.5 284.# # A tibble: 6 × 3
+# `Top(mbsf)` distance_from_core_top2 depth
+# <dbl> <dbl> <dbl>
+# 1 283. 24.5 284.
+# 2 283. 25.5 284.
+# 3 283. 26.5 284.
+# 4 283. 27.5 284.
+# 5 283. 28.5 284.
+# 6 283. 29.5 284.We now have our entire colourlog and the associated core depth!
Save plot and data
# make sure that corepics and colourplot have the same depth scale
(corepics +
@@ -886,7 +887,7 @@ calculate colour
axis.text.y = element_blank(),
axis.ticks.y = element_blank())) +
plot_layout(ncol = 2, widths = c(.1, .9))
ggsave('imgs/colouranalysis.pdf', width = 10, height = 25, units = c('cm'))
write_csv(colourlog, file = "out/colourdata.csv")