Final Fate Tracking Project
Avery Coble
2023-11-16
Data Used
How the raw data was wrangled
kable(head(raw.data))%>%
kable_styling(bootstrap_options = "striped")| Tag # | Date | Actual_Date | Processed | Days Since Last Record | Bleaching | 8+ Wk (56 days) Success? | Total Coral Area 1 (cm2) | Total Coral Area 2 (cm2) | Total Coral Area 3 (cm2) | Total Coral Area 4 (cm2) | Total Coral Area Sum (cm2) | Live Tissue 1 (cm2) | Live Tissue 2 (cm2) | Live Tissue 3 (cm2) | Live Tissue 4 (cm2) | Live Tissue Sum (cm2) | New SCTLD Mortality 1 (cm2) | New SCTLD Mortality 2 (cm2) | New SCTLD Mortality 3 (cm2) | New SCTLD Mortality 4 (cm2) | New SCTLD Mortality Sum (cm2) | Other Recent Mortality (cm2) | # New Lesions (count) | # Total Active Lesions (count) | Halted Lesions (count) | Greatest Lesion Expansion (cm) | Acute Lesions Present? (yes/no) | Treatment Used (mL/cc) | Change in Live Tissue (-/+ cm2) | Rate of Change in Live Tissue (cm2/day) | Lesion Progression (-/+ cm2) | Rate of Lesion Progression (cm2/day) | % New SCTLD Mort of Total | Trying %/days for rate | Notes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 901 | 2022-06-03 | 6/3/22 | Y | 0 | NA | N | 514.7 | 218.9 | 313.70000000000005 | 213.7 | 1261.0 | 413.3 | 15.6 | 216.4 | 143.19999999999999 | 788.5 | 26.5 | 1.4 | 17.899999999999999 | 2.8 | 48.6 | na | 0 | 1 | 0 | 8.69 | Yes | 6 | NA | NA | NA | NA | 3.854084 | NA | NA |
| 901 | 2022-07-20 | 7/20/22 | Y | 47 | NA | N | 531.0 | 192.5 | 242.9 | 134.69999999999999 | 1101.1 | 397.2 | 12.1 | 124 | 100.4 | 633.7 | 0.0 | 0 | 0 | 0 | 0.0 | na | 0 | 0 | 1 | 0.00 | No | 3 | -154.8 | -3.293617 | -48.6 | -1.034043 | 0.000000 | 0 | NA |
| 901 | 2022-08-11 | 8/11/22 | Y | 22 | NA | Y | 508.5 | 225.2 | 206.7 | 136.5 | 1076.9 | 343.2 | 16.5 | 104.6 | 39.700000000000003 | 504.0 | 0.0 | 0 | 0 | 0 | 0.0 | na | 0 | 0 | 1 | 0.00 | No | 0 | -129.7 | -5.895454 | 0.0 | 0.000000 | 0.000000 | 0 | NA |
| 901 | 2022-10-11 | 10/11/22 | Y | 61 | NA | Y | 346.4 | 152.30000000000001 | 88.5 | 171.9 | 759.1 | 245.8 | 7 | 40.4 | 87.9 | 381.1 | 0.0 | 0 | 0 | 0 | 0.0 | na | 0 | 0 | 1 | 0.00 | No | 0 | -122.9 | -2.014754 | 0.0 | 0.000000 | 0.000000 | 0 | NA |
| 901 | 2022-11-15 | 11/15/22 | Y | 35 | NA | Y | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA |
| 901 | 2023-01-11 | 1/11/23 | Y | 57 | NA | Y | 573.0 | 200.20000000000002 | 241.5 | 327.10000000000002 | 1341.8 | 402.8 | 12.8 | 95.9 | 175.4 | 686.9 | 0.0 | 0 | 0 | 0 | 0.0 | na | 0 | 0 | 1 | 0.00 | No | 0 | 305.8 | 5.364912 | 0.0 | 0.000000 | 0.000000 | 0 | NA |
This table is the first 10 rows of the raw data used in this project. It was full of zeros that represented unanalyzed data, as well as columns that belonged in metadata.
percentages <- raw.data %>%
select("Tag #",
"Actual_Date",
"Total Coral Area Sum (cm2)",
"Live Tissue Sum (cm2)",
"New SCTLD Mortality Sum (cm2)",
"Treatment Used (mL/cc)")%>%
rename("Date" = "Actual_Date",
"Tag" = "Tag #",
"Total_Coral_Area" = "Total Coral Area Sum (cm2)",
"Live_Tissue" = "Live Tissue Sum (cm2)",
"New_SCTLD_Mortality" = "New SCTLD Mortality Sum (cm2)",
"Treatment" = "Treatment Used (mL/cc)")%>%
na.omit()%>%
mutate(Date = mdy(Date),
Percent_Live = Live_Tissue / Total_Coral_Area,
Percent_NewMort = New_SCTLD_Mortality / Total_Coral_Area,
Tag = as.factor(Tag))
kable(head(percentages))%>%
kable_styling(bootstrap_options = "striped")| Tag | Date | Total_Coral_Area | Live_Tissue | New_SCTLD_Mortality | Treatment | Percent_Live | Percent_NewMort |
|---|---|---|---|---|---|---|---|
| 901 | 2022-06-03 | 1261.0 | 788.5 | 48.6 | 6 | 0.6252974 | 0.0385408 |
| 901 | 2022-07-20 | 1101.1 | 633.7 | 0.0 | 3 | 0.5755154 | 0.0000000 |
| 901 | 2022-08-11 | 1076.9 | 504.0 | 0.0 | 0 | 0.4680100 | 0.0000000 |
| 901 | 2022-10-11 | 759.1 | 381.1 | 0.0 | 0 | 0.5020419 | 0.0000000 |
| 901 | 2023-01-11 | 1341.8 | 686.9 | 0.0 | 0 | 0.5119243 | 0.0000000 |
| 901 | 2023-06-20 | 1147.0 | 617.7 | 0.0 | 0 | 0.5385353 | 0.0000000 |
This table was created to analyze the rate of change in each colonies live tissue and active mortality over time.
counts <- raw.data %>%
select("Tag #",
"Actual_Date",
"# New Lesions (count)",
"# Total Active Lesions (count)",
"Halted Lesions (count)",
"Treatment Used (mL/cc)")%>%
rename("Date" = "Actual_Date",
"Tag" = "Tag #",
"New_Lesions" = "# New Lesions (count)",
"Total_Active_Lesions" = "# Total Active Lesions (count)",
"Halted_Lesions" = "Halted Lesions (count)",
"Treatment_Used" = "Treatment Used (mL/cc)")%>%
na.omit()%>%
mutate(Date = mdy(Date),
Tag = as.factor(Tag))
kable(head(counts))%>%
kable_styling(bootstrap_options = "striped")| Tag | Date | New_Lesions | Total_Active_Lesions | Halted_Lesions | Treatment_Used |
|---|---|---|---|---|---|
| 901 | 2022-06-03 | 0 | 1 | 0 | 6 |
| 901 | 2022-07-20 | 0 | 0 | 1 | 3 |
| 901 | 2022-08-11 | 0 | 0 | 1 | 0 |
| 901 | 2022-10-11 | 0 | 0 | 1 | 0 |
| 901 | 2023-01-11 | 0 | 0 | 1 | 0 |
| 901 | 2023-06-20 | 0 | 0 | 1 | 0 |
This table was created to show the changes in number of active and halted diseased lesion on each colony over time.
Visualization
Graph showing live tissue and new mortality
Colony_Tag_Number <- hcl.colors(10, palette = "Roma", alpha = NULL)
plots <- function(data, x, y, z){
p <- ggplot(data, aes({{x}},{{y}}, color = {{z}})) +
geom_point() +
geom_line() +
scale_color_carto_d("Colony_Tag_Number")
return(p)
}
LiveT <- plots(percentages, Date, Percent_Live, Tag)
NewM <- plots(percentages, Date, Percent_NewMort, Tag)
NewL <- plots(counts, Date, New_Lesions, Tag)
ActiveL <- plots(counts, Date, Total_Active_Lesions, Tag)
HaltedL <- plots(counts, Date, Halted_Lesions, Tag)
Treatment <- plots(percentages, Date, Treatment, Tag)
tissue <- LiveT + NewM + Treatment + plot_layout(guides = "collect")
tissue 
Visualization
Graph showing live tissue and new mortality
lesions <- ActiveL + HaltedL + Treatment + plot_layout(guides = "collect")
print(lesions)