Process FCSV files from Cymatogaster
Last updated: 2022-04-11
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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(here)here() starts at /Users/etytel01/Documents/Vertebrae/CodeProcess Cymatogaster data
Dana processed the Cymatogaster aggregata scans separately, so we need to add them to the MasterVert_Measuremenst file.
They’re in one file per vertebra:
datadir <- 'data/Cymatogaster'
vertfiles <- c('Cymatogaster_aggregata_30.fcsv',
'Cymatogaster_aggregata_40.fcsv',
'Cymatogaster_aggregata_50.fcsv',
'Cymatogaster_aggregata_60.fcsv',
'Cymatogaster_aggregata_70.fcsv',
'Cymatogaster_aggregata_80.fcsv',
'Cymatogaster_aggregata_90.fcsv')
bodyfile <- 'Cymatogaster_aggregata_bodyMarks.fcsv'filenames <- map_chr(vertfiles, ~here(file.path(datadir, .x)))Load in the coordinates
coords <-
map_dfr(filenames, ~read_csv(.x, skip=2)) %>%
select(x,z,label)Rows: 7 Columns: 14── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): # columns = id, label, associatedNodeID
dbl (10): x, y, z, ow, ox, oy, oz, vis, sel, lock
lgl (1): desc
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.Rows: 7 Columns: 14── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): # columns = id, label, associatedNodeID
dbl (10): x, y, z, ow, ox, oy, oz, vis, sel, lock
lgl (1): desc
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.Rows: 7 Columns: 14── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): # columns = id, label, associatedNodeID
dbl (10): x, y, z, ow, ox, oy, oz, vis, sel, lock
lgl (1): desc
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.Rows: 7 Columns: 14── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): # columns = id, label, associatedNodeID
dbl (10): x, y, z, ow, ox, oy, oz, vis, sel, lock
lgl (1): desc
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.Rows: 7 Columns: 14── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): # columns = id, label, associatedNodeID
dbl (10): x, y, z, ow, ox, oy, oz, vis, sel, lock
lgl (1): desc
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.Rows: 7 Columns: 14── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): # columns = id, label, associatedNodeID
dbl (10): x, y, z, ow, ox, oy, oz, vis, sel, lock
lgl (1): desc
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.Rows: 7 Columns: 14── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): # columns = id, label, associatedNodeID
dbl (10): x, y, z, ow, ox, oy, oz, vis, sel, lock
lgl (1): desc
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(coords)# A tibble: 6 × 3
x z label
<dbl> <dbl> <chr>
1 -104. 430. Cymatogaster_aggregata_30-2
2 -139. 431. Cymatogaster_aggregata_30-3
3 -114. 405. Cymatogaster_aggregata_30-4
4 -131. 406. Cymatogaster_aggregata_30-5
5 -121. 414. Cymatogaster_aggregata_30-6
6 -122. 414. Cymatogaster_aggregata_30-7The x coordinate from the file is what we call y in the data set, and the z coordinate is x, so rename the columns. Plus extract the point number and position from the labels.
coordsxy <-
coords %>%
mutate(Pos = str_extract(label, "(?<=_)\\d+"),
PtNum = str_extract(label, "(?<=-)\\d+"),
PtNum = as.numeric(PtNum) - 1) %>%
rename(y = x, x = z)
coordsxy# A tibble: 49 × 5
y x label Pos PtNum
<dbl> <dbl> <chr> <chr> <dbl>
1 -104. 430. Cymatogaster_aggregata_30-2 30 1
2 -139. 431. Cymatogaster_aggregata_30-3 30 2
3 -114. 405. Cymatogaster_aggregata_30-4 30 3
4 -131. 406. Cymatogaster_aggregata_30-5 30 4
5 -121. 414. Cymatogaster_aggregata_30-6 30 5
6 -122. 414. Cymatogaster_aggregata_30-7 30 6
7 -120. 414. Cymatogaster_aggregata_30-8 30 7
8 -101. 551. Cymatogaster_aggregata_40-2 40 1
9 -121. 553. Cymatogaster_aggregata_40-3 40 2
10 -105. 524. Cymatogaster_aggregata_40-4 40 3
# … with 39 more rowsCheck the coordinates and make sure they make sense based on the previous data.
coordsxy %>%
group_by(Pos) %>%
ggplot(aes(x = x, y = y, color=Pos, label=PtNum)) +
geom_point() + geom_text(hjust=-0.1, vjust=0.5)
Make the wide data set like we have in the MasterVert_Measurements file.
coordswide <-
coordsxy %>%
pivot_wider(values_from = c(x, y), id_cols = Pos,
names_from = "PtNum", names_glue = "Pt{PtNum}{.value}")
coordswide# A tibble: 7 × 15
Pos Pt1x Pt2x Pt3x Pt4x Pt5x Pt6x Pt7x Pt1y Pt2y Pt3y Pt4y
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 30 430. 431. 405. 406. 414. 414. 414. -104. -139. -114. -131.
2 40 551. 553. 524. 526. 538. 538. 537. -101. -121. -105. -124.
3 50 684. 685. 657. 660. 671. 671. 671. -86.0 -104. -89.0 -108.
4 60 813. 815. 789. 789. 801. 801. 801. -75.2 -94.7 -77.0 -96.1
5 70 940. 939. 915. 913. 926. 926. 926. -66.8 -86.7 -68.7 -88.7
6 80 1065. 1064. 1040. 1039. 1052. 1052. 1052. -64.9 -83.4 -64.3 -84.0
7 90 1194. 1192. 1169. 1167. 1181. 1181. 1181. -74.0 -92.7 -71.3 -90.3
# … with 3 more variables: Pt5y <dbl>, Pt6y <dbl>, Pt7y <dbl>Functions for 2d and 3d distance.
dist2 <- function(x1,y1, x2,y2) {
sqrt((x2 - x1)^2 + (y2 - y1)^2)
}
dist3 <- function(x1,y1,z1, x2,y2,z2) {
sqrt((x2 - x1)^2 + (y2 - y1)^2 + (z2 - z1)^2)
}This calculates the different variables based on the points. (Pulled from Excel functions)
vertmeasurements <- function(df) {
df %>%
mutate(CBL_raw = dist2(Pt1x, Pt1y, Pt4x, Pt4y),
CBL2_raw = (abs(Pt1x - Pt3x) + abs(Pt2x - Pt4x))/2,
alpha_Pos_raw = acos((dist2(Pt5x,Pt5y, Pt2x,Pt2y)^2 + dist2(Pt5x,Pt5y, Pt1x,Pt1y)^2 -
dist2(Pt1x,Pt1y, Pt2x,Pt2y)^2) /
(2 * dist2(Pt5x,Pt5y, Pt2x,Pt2y) * dist2(Pt5x,Pt5y, Pt1x,Pt1y))) * 180/pi,
alpha_Ant_raw = acos((dist2(Pt5x,Pt5y, Pt4x,Pt4y)^2 + dist2(Pt5x,Pt5y, Pt3x,Pt3y)^2 -
dist2(Pt3x,Pt3y, Pt4x,Pt4y)^2) /
(2 * dist2(Pt5x,Pt5y, Pt4x,Pt4y) * dist2(Pt5x,Pt5y, Pt3x,Pt3y))) * 180/pi,
d_raw = dist2(Pt6x,Pt6y, Pt7x,Pt7y),
D_Pos_raw = dist2(Pt1x,Pt1y, Pt2x,Pt2y),
D_Ant_raw = dist2(Pt3x,Pt3y, Pt4x,Pt4y))
}
reversemeasurements <- function(df) {
df %>%
mutate(Pt1x = D_Pos_raw / 2 / tan(alpha_Pos_raw/2 * pi/180),
Pt2x = D_Pos_raw / 2 / tan(alpha_Pos_raw/2 * pi/180),
Pt3x = -D_Ant_raw / 2 / tan(alpha_Ant_raw/2 * pi/180),
Pt4x = -D_Ant_raw / 2 / tan(alpha_Ant_raw/2 * pi/180),
Pt5x = 0,
Pt6x = 0,
Pt7x = 0,
Pt1y = D_Pos_raw / 2,
Pt2y = -D_Pos_raw / 2,
Pt3y = D_Ant_raw / 2,
Pt4y = -D_Ant_raw / 2,
Pt5y = 0,
Pt6y = d_raw / 2,
Pt7y = -d_raw / 2)
}coordswide <-
coordswide %>%
vertmeasurements()
coordswide# A tibble: 7 × 22
Pos Pt1x Pt2x Pt3x Pt4x Pt5x Pt6x Pt7x Pt1y Pt2y Pt3y Pt4y
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 30 430. 431. 405. 406. 414. 414. 414. -104. -139. -114. -131.
2 40 551. 553. 524. 526. 538. 538. 537. -101. -121. -105. -124.
3 50 684. 685. 657. 660. 671. 671. 671. -86.0 -104. -89.0 -108.
4 60 813. 815. 789. 789. 801. 801. 801. -75.2 -94.7 -77.0 -96.1
5 70 940. 939. 915. 913. 926. 926. 926. -66.8 -86.7 -68.7 -88.7
6 80 1065. 1064. 1040. 1039. 1052. 1052. 1052. -64.9 -83.4 -64.3 -84.0
7 90 1194. 1192. 1169. 1167. 1181. 1181. 1181. -74.0 -92.7 -71.3 -90.3
# … with 10 more variables: Pt5y <dbl>, Pt6y <dbl>, Pt7y <dbl>, CBL_raw <dbl>,
# CBL2_raw <dbl>, alpha_Pos_raw <dbl>, alpha_Ant_raw <dbl>, d_raw <dbl>,
# D_Pos_raw <dbl>, D_Ant_raw <dbl>The body file contains marks for the standard length.
bodydata <- read_csv(here(file.path(datadir, bodyfile)), skip=2) %>%
select(x,y,z, label)Rows: 4 Columns: 14── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): # columns = id, label, associatedNodeID
dbl (10): x, y, z, ow, ox, oy, oz, vis, sel, lock
lgl (1): desc
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.bodydata# A tibble: 4 × 4
x y z label
<dbl> <dbl> <dbl> <chr>
1 -140. -336. 48 Cymatogaster_aggregata_bodyMarks-snout
2 -122. -251. 421 Cymatogaster_aggregata_bodyMarks-neck
3 -85.1 -231. 819 Cymatogaster_aggregata_bodyMarks-anal
4 -105. -231. 1296 Cymatogaster_aggregata_bodyMarks-caudalThese I digitized directly in the CT scan in Slicer.
maxWidth.mm <- 175.3
maxWidthSlide <- 422
maxHeight.mm <- 492
maxHeightSlide <- 563Here we pull out the standard length
SL <-
bodydata %>%
mutate(Pt = str_extract(label, "(?<=Marks-)\\w+")) %>%
select(-label) %>%
pivot_wider(values_from = c(x, y, z),
names_from = "Pt", names_glue = "{Pt}{.value}") %>%
mutate(SL = dist3(snoutx,snouty,snoutz, caudalx,caudaly,caudalz)) %>%
pull(SL)And normalize all the variables.
coordswide <-
coordswide %>%
mutate(SL = SL,
Max_BW_mm = maxWidth.mm,
BW_slide = maxWidthSlide,
Max_BH_mm = maxHeight.mm,
BH_slide = maxHeightSlide,
d = d_raw / SL,
D_Ant = D_Ant_raw / SL,
D_Pos = D_Pos_raw / SL,
CBL = CBL_raw / SL,
alpha_Pos = alpha_Pos_raw,
alpha_Ant = alpha_Ant_raw,
d_BW = d_raw / Max_BW_mm,
D_Pos_BW = D_Pos_raw / Max_BW_mm,
D_Ant_BW = D_Ant_raw / Max_BW_mm,
`SL/Max_BW` = SL / Max_BW_mm,
Species = "Cymatogaster_aggregata",
Indiv = 1,
Habitat = "pelagic",
Pos = as.numeric(Pos))Merge with other data file
vertmeasdata <- read_csv(here('data/MasterVert_Measurements_old.csv'))Rows: 578 Columns: 54── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (16): Species, MatchSpecies, Family, Body Shape, Habitat_Initial, Habita...
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(vertmeasdata)# A tibble: 6 × 54
Species MatchSpecies Family Indiv Pos SL CBL_raw alpha_Pos_raw d_raw
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Alectis_cil… <NA> <NA> 1 40 799 24.9 1.51 3.57
2 Alectis_cil… <NA> <NA> 1 50 799 34.4 1.18 3.13
3 Alectis_cil… <NA> <NA> 1 60 799 35.8 1.38 2
4 Alectis_cil… <NA> <NA> 1 70 799 36.6 1.34 0.75
5 Alectis_cil… <NA> <NA> 1 80 799 35.2 1.32 0.54
6 Alectis_cil… <NA> <NA> 1 90 799 32.8 1.31 0.49
# … with 45 more variables: D_Pos_raw <dbl>, alpha_Ant_raw <dbl>,
# D_Ant_raw <dbl>, CBL <dbl>, alpha_Pos <dbl>, d <dbl>, D_Pos <dbl>,
# alpha_Ant <dbl>, D_Ant <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>,
# Body Shape <chr>, Habitat_Initial <chr>, Habitat_Friedman <chr>,
# Habitat_FishBase <chr>, Habitat <chr>, Water_Type <chr>, Max_BW_mm <dbl>, …Check that the calculations we’ve done here match the data in the data table.
checkcalc <-
vertmeasdata %>%
rename_with(~ str_c(.x, "_orig"), ends_with("raw")) %>%
vertmeasurements() %>%
mutate(alpha_Ant_raw_orig = alpha_Ant_raw_orig * 180/pi,
alpha_Pos_raw_orig = alpha_Pos_raw_orig * 180/pi) %>%
rename_with(~ str_c(.x, "_orig"), starts_with("Pt")) %>%
group_by(Species, Indiv) %>%
mutate(across(starts_with("Pt") & ends_with("x_orig"), ~ .x - Pt5x_orig),
across(starts_with("Pt") & ends_with("y_orig"), ~ .x - Pt5y_orig)) %>%
reversemeasurements()checkcalc %>%
ggplot(aes(x = alpha_Ant_raw_orig, y = alpha_Ant_raw)) +
geom_point()
checkcalc %>%
ggplot(aes(x = CBL_raw_orig, y = CBL2_raw)) +
geom_point() +
geom_point(aes(y = CBL_raw), color="red")
They match!
checkcalc %>%
ggplot(aes(x = Pt7y_orig, y = Pt7y)) +
geom_point()
Now merge the Cymatogaster data in, and save the data file.
vertmeasdata <-
bind_rows(vertmeasdata, coordswide)write_csv(vertmeasdata, here('output/MasterVert_Measurements.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 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[5] purrr_0.3.4 readr_2.0.1 tidyr_1.1.3 tibble_3.1.4
[9] ggplot2_3.3.5 tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 lubridate_1.7.10 assertthat_0.2.1 rprojroot_2.0.2
[5] digest_0.6.27 utf8_1.2.2 R6_2.5.1 cellranger_1.1.0
[9] backports_1.2.1 reprex_2.0.1 evaluate_0.14 highr_0.9
[13] httr_1.4.2 pillar_1.6.2 rlang_0.4.11 readxl_1.3.1
[17] rstudioapi_0.13 whisker_0.4 rmarkdown_2.10 labeling_0.4.2
[21] bit_4.0.4 munsell_0.5.0 broom_0.7.9 compiler_4.1.2
[25] httpuv_1.6.4 modelr_0.1.8 xfun_0.25 pkgconfig_2.0.3
[29] htmltools_0.5.2 tidyselect_1.1.1 workflowr_1.7.0 fansi_0.5.0
[33] crayon_1.4.1 tzdb_0.1.2 dbplyr_2.1.1 withr_2.4.2
[37] later_1.3.0 grid_4.1.2 jsonlite_1.7.2 gtable_0.3.0
[41] lifecycle_1.0.0 DBI_1.1.1 git2r_0.29.0 magrittr_2.0.1
[45] scales_1.1.1 vroom_1.5.4 cli_3.0.1 stringi_1.7.4
[49] farver_2.1.0 renv_0.14.0 fs_1.5.0 promises_1.2.0.1
[53] xml2_1.3.2 ellipsis_0.3.2 generics_0.1.0 vctrs_0.3.8
[57] tools_4.1.2 bit64_4.0.5 glue_1.4.2 hms_1.1.0
[61] parallel_4.1.2 fastmap_1.1.0 yaml_2.2.1 colorspace_2.0-2
[65] rvest_1.0.1 knitr_1.34 haven_2.4.3