Importing the Datasets
# Import spectrogram data into R from Raven Selection Tables.
data <- imp_raven(all.data = TRUE,
only.spectro.view = TRUE,
path = '~/Raven Pro 1.6/Selections')
# Import metadata containing body temperature and collection data
meta.data <- read_csv("~/Desktop/UTK/Tanner Lab/Analysis/Call Analysis/Peeper Analysis/Spring Peeper Data - Collection Data.csv")
## Rows: 73 Columns: 18
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): Male ID, Date, Location, Catcher, Recorder, Processer, Prevalence
## dbl (11): Body Temp, Weight (Bag + Frog), Weight (Bag), SVL, Tibia, Frog Wei...
##
## 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.
# Take the file (in the table as PC-000-24_MMDDYYYY_XX.wav) and separate into it's parts
data <- data %>% separate(`Begin File`,
c("ID", "Date", "Location.wav"),
sep = "_",
remove = FALSE
)
# New simpler column for location
data <- data %>% mutate(Location = case_when(`Location.wav`=='FR.wav' ~ 'FR',
`Location.wav`=='CV.wav' ~ 'CV'))
# Discard columns not needed for future
data <- data %>% select(-c(`View`,`Channel`,`Occupancy`,`Low Freq (Hz)`,`High Freq (Hz)`,`Location.wav`, `Frame Duration`, `Reason`))
# Calculate ICI for values based on strict row calculation.
data <- data %>% mutate(ICI = lead(`Begin Time (s)`)-`End Time (s)`)
data <- data %>% filter (ICI >= 0)
# Calculate Call_Period (Duration + ICI) and Instantaneous Rate (1/Call_Period)*60
data <- data %>% mutate(Call_Period = `Delta Time (s)`+ICI)
data <- data %>% mutate(Call_Rate_Inst = (1/Call_Period)*60)
# Output the resulting files into a CSV, gets written in a working directory. Only have to do this once if nothing before this is being done
# write.csv(data,"data.csv",row.names=FALSE)
# In the CSV file, go through each Raven file and decide which 30 consecutive calls to use. Ideally, in a calling bout, start from the 6th bout and exclude the last call. Want consecutive call numbers. Save this as a new file (e.g. edited.data and import).
import.data <- read_csv("~/Desktop/UTK/Tanner Lab/Analysis/Call Analysis/Peeper Analysis/edited.data.csv")
## Rows: 1796 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Begin File, ID, Date, selec.file, Location
## dbl (11): Selection, Begin Time (s), End Time (s), Delta Time (s), Peak Freq...
##
## 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.
# When importing data, going to go with the manually edited data.
manual.data <- left_join(import.data, meta.data, by = join_by(ID == `Male ID`))
manual.data <- manual.data %>% select(-Selection)
manual.data <- as.data.frame(manual.data)
Temperature Correction Function
Tcorr <- function(col_numbers, target_temp, data){
num_cols <- length(col_numbers)
Tcorrected_matrix <- matrix(NA, nrow = nrow(data), ncol = num_cols)
for(i in seq_along(col_numbers)){
m <- lm(data[,col_numbers[i]] ~ data$`Body Temp`, data)$coefficients[2] # Calculate m by doing a linear regression
Tcorrected_matrix[, i] <- data[,col_numbers[i]] - m * (data$`Body Temp` - target_temp) # Save a vector so I can use it outside the function
}
avg <- apply(Tcorrected_matrix, 1, mean)
stdev <- apply(Tcorrected_matrix, 1, sd)
colnames(Tcorrected_matrix) <- colnames(data[, col_numbers])
result <- list(avg = avg, stdev = stdev, Tcorrected_matrix = Tcorrected_matrix)
return(result)
}
Doing the Temperature Correction
# Set Values that you want to temperature correct for
selections <- c(3:7,13:15)
# Do the temperature correction to the target temp, aka 16
result <- Tcorr(selections, target_temp = 16, data = manual.data)
# Format results as a dataframe
Tdat <- as.data.frame(result$Tcorrected_matrix)
# Add information such as ID, Location, and Prevalence
Tdat<-cbind(manual.data[,c(9,10,12,18,31,32)], Tdat)
Plots for Checking Work
# Plots to check work - Call Rate Inst
plot(manual.data$Call_Rate_Inst~manual.data$`Body Temp`)
plot(Tdat$Call_Rate_Inst~manual.data$`Body Temp`)
# Plots to check work - Call Duration
plot(manual.data$`Delta Time (s)`~manual.data$`Body Temp`)
plot(Tdat$`Delta Time (s)`~manual.data$`Body Temp`)
# Plots to check work - ICI
plot(manual.data$ICI~manual.data$`Body Temp`)
plot(Tdat$ICI ~ manual.data$`Body Temp`)
# Plots to check work - Peak Freq
plot(manual.data$`Peak Freq (Hz)`~manual.data$`Body Temp`)
plot(Tdat$`Peak Freq (Hz)` ~ manual.data$`Body Temp`)
Table of Results
means <- Tdat %>%
group_by(ID, Location.x) %>%
summarize(Temp = mean(`Body Temp`),
Infection = mean(`Log Infection`),
Call_Rate_Inst = mean(Call_Rate_Inst),
Duration = mean(`Delta Time (s)`),
ICI = mean(ICI),
Freq = mean(`Peak Freq (Hz)`),
BW = mean(`BW 90% (Hz)`))
## `summarise()` has grouped output by 'ID'. You can override using the `.groups`
## argument.
means
## # A tibble: 60 × 9
## # Groups: ID [60]
## ID Location.x Temp Infection Call_Rate_Inst Duration ICI Freq BW
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 PC-002-… FR 12.8 0 98.6 0.115 0.419 2778. 265.
## 2 PC-003-… FR 12.4 0 92.7 0.116 0.498 2968. 351.
## 3 PC-004-… CV 13.1 1.01 92.8 0.0731 0.530 3052. 171.
## 4 PC-005-… CV 13.3 1.25 112. 0.0956 0.365 2802. 257.
## 5 PC-006-… CV 11.8 2.11 92.9 0.110 0.497 2760. 249.
## 6 PC-007-… CV 11.9 0 111. 0.0861 0.338 2735. 204.
## 7 PC-009-… FR 7.1 0 89.3 0.0841 0.756 3055. 312.
## 8 PC-011-… CV 10.3 0 92.4 0.0708 0.557 3033. 233.
## 9 PC-012-… FR 8.3 1.99 92.3 0.115 0.539 2964. 250.
## 10 PC-013-… FR 9.3 0.788 93.0 0.126 0.485 3081. 335.
## # ℹ 50 more rows
sds <- Tdat %>%
group_by(ID, Location.x) %>%
summarize(Temp = sd(`Body Temp`),
Infection = sd(`Log Infection`),
Call_Rate_Inst = sd(Call_Rate_Inst),
Duration = sd(`Delta Time (s)`),
ICI = sd(ICI),
Freq = sd(`Peak Freq (Hz)`),
BW = sd(`BW 90% (Hz)`))
## `summarise()` has grouped output by 'ID'. You can override using the `.groups`
## argument.
sds
## # A tibble: 60 × 9
## # Groups: ID [60]
## ID Location.x Temp Infection Call_Rate_Inst Duration ICI Freq BW
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 PC-002-… FR 0 0 9.13 0.00717 0.108 61.3 29.8
## 2 PC-003-… FR 0 0 13.3 0.0103 0.244 35.0 37.4
## 3 PC-004-… CV 0 0 11.4 0.00592 0.158 26.3 15.7
## 4 PC-005-… CV 0 0 17.3 0.00585 0.158 15.7 15.7
## 5 PC-006-… CV 0 0 14.7 0.00825 0.202 50.2 15.7
## 6 PC-007-… CV 0 0 15.6 0.00802 0.165 32.0 43.4
## 7 PC-009-… FR 0 0 8.20 0.00674 0.313 39.7 37.1
## 8 PC-011-… CV 0 0 13.4 0.00261 0.248 32.6 35.0
## 9 PC-012-… FR 0 0 6.78 0.00965 0.175 0 15.7
## 10 PC-013-… FR 0 0 8.43 0.00599 0.182 43.7 0
## # ℹ 50 more rows
cvs <- Tdat %>%
group_by(ID, Location.x) %>%
summarize(Temp = cv(`Body Temp`),
Infection = cv(`Log Infection`),
Call_Rate_Inst = cv(Call_Rate_Inst),
Duration = cv(`Delta Time (s)`),
ICI = cv(ICI),
Period = cv(Call_Period),
Freq = cv(`Peak Freq (Hz)`),
Freq_5 = cv(`Freq 5% (Hz)`),
Freq_95 = cv(`Freq 95% (Hz)`),
BW = cv(`BW 90% (Hz)`))
## `summarise()` has grouped output by 'ID'. You can override using the `.groups`
## argument.
cvs
## # A tibble: 60 × 12
## # Groups: ID [60]
## ID Location.x Temp Infection Call_Rate_Inst Duration ICI Period Freq
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 PC-0… FR 0 NaN 0.0925 0.0622 0.257 0.207 0.0220
## 2 PC-0… FR 0 NaN 0.144 0.0888 0.490 0.394 0.0118
## 3 PC-0… CV 0 0 0.123 0.0809 0.299 0.262 0.00861
## 4 PC-0… CV 0 0 0.154 0.0612 0.433 0.341 0.00561
## 5 PC-0… CV 0 0 0.158 0.0751 0.406 0.332 0.0182
## 6 PC-0… CV 0 NaN 0.140 0.0932 0.487 0.386 0.0117
## 7 PC-0… FR 0 NaN 0.0918 0.0802 0.413 0.370 0.0130
## 8 PC-0… CV 0 NaN 0.145 0.0368 0.445 0.395 0.0108
## 9 PC-0… FR 0 0 0.0735 0.0837 0.325 0.270 0
## 10 PC-0… FR 0 0 0.0907 0.0476 0.375 0.295 0.0142
## # ℹ 50 more rows
## # ℹ 3 more variables: Freq_5 <dbl>, Freq_95 <dbl>, BW <dbl>
write.csv(cvs,"peeper_infection_cvs.csv",row.names=FALSE)
CV Table
# Calculated and formatted in Excel
cv.table <- read_xlsx("~/Desktop/UTK/Tanner Lab/Analysis/Call Analysis/Peeper Analysis/Peeper_CVs_Means_SDs.xlsx")
cv.table
## # A tibble: 8 × 3
## Variable Means SDs
## <chr> <dbl> <dbl>
## 1 Call_Rate_Inst 0.185 0.0964
## 2 Duration 0.0747 0.0379
## 3 ICI 0.472 0.385
## 4 Period 0.419 0.359
## 5 Freq 0.0113 0.00717
## 6 Freq_5 0.00744 0.00720
## 7 Freq_95 0.00756 0.00610
## 8 BW 0.113 0.0642
ggplot(cv.table, aes(x=reorder(Variable, Means), y=Means,fill=Variable)) +
geom_bar(stat="identity")+
theme_classic()+
theme(axis.text.x = element_text(angle = 50, vjust = 0.5, hjust=0.5),
legend.position = "none")+
geom_errorbar( aes(ymin = Means-SDs, ymax = Means+SDs),
data = cv.table, width = 0.2, alpha = 0.3)+
geom_text(aes(label=round(Means,4)), vjust=-0.3, size=3.5, alpha=1)+
labs(x = "Variable",
y = "CV",
title = "Variables ordered by CV")
knitr::include_graphics("~/Desktop/UTK/Tanner Lab/Analysis/Call Analysis/Peeper Analysis/Gerhardt.png")
GRAPHS
TdatLoad <- Tdat %>% filter(Prevalence=='Present')
ID.means <- Tdat %>%
group_by(ID, Location.x,Prevalence) %>%
summarize(Temp = mean(`Body Temp`),
Infection = mean(`Log Infection`),
Call_Rate_Inst = mean(Call_Rate_Inst),
Duration = mean(`Delta Time (s)`),
ICI = mean(ICI),
Freq = mean(`Peak Freq (Hz)`),
BW = mean(`BW 90% (Hz)`))
## `summarise()` has grouped output by 'ID', 'Location.x'. You can override using
## the `.groups` argument.
write.csv(ID.means,"peeper_infection_by_ID.csv",row.names=FALSE)
ID.means.load <- TdatLoad %>%
group_by(ID, Location.x) %>%
summarize(Temp = mean(`Body Temp`),
Infection = mean(`Log Infection`),
Call_Rate_Inst = mean(Call_Rate_Inst),
Duration = mean(`Delta Time (s)`),
ICI = mean(ICI),
Freq = mean(`Peak Freq (Hz)`),
BW = mean(`BW 90% (Hz)`))
## `summarise()` has grouped output by 'ID'. You can override using the `.groups`
## argument.
# Plots are in fact 9 Absent and 21 Present for
ggplot(ID.means,aes(x = Prevalence))+
geom_histogram(stat="count")+
facet_wrap(~Location.x,strip.position="bottom")+
theme_classic()+
theme(aspect.ratio = 1,
strip.background = element_blank(),
strip.placement = "outside")+
labs(title = "Prevalence across sites")
## Warning in geom_histogram(stat = "count"): Ignoring unknown parameters:
## `binwidth`, `bins`, and `pad`
Prevalence Graphs
ggplot(ID.means,aes(x=Prevalence,y=Call_Rate_Inst))+
facet_wrap(~Location.x,strip.position="bottom")+
geom_boxplot()+
geom_jitter(position=position_jitter(0.1))+
theme_classic()
ggplot(ID.means,aes(x=Prevalence,y=Duration))+
facet_wrap(~Location.x,strip.position="bottom")+
geom_boxplot()+
geom_jitter(position=position_jitter(0.1))+
theme_classic()
ggplot(ID.means,aes(x=Prevalence,y=ICI))+
facet_wrap(~Location.x,strip.position="bottom")+
geom_boxplot()+
geom_jitter(position=position_jitter(0.1))+
theme_classic()
ggplot(ID.means,aes(x=Prevalence,y=BW))+
facet_wrap(~Location.x,strip.position="bottom")+
geom_boxplot()+
geom_jitter(position=position_jitter(0.1))+
theme_classic()
ggplot(ID.means,aes(x=Prevalence,y=Freq))+
facet_wrap(~Location.x,strip.position="bottom")+
geom_boxplot()+
geom_jitter(position=position_jitter(0.1))+
theme_classic()
Fungal Load Graphs
# Call Rate Inst
ggplot(ID.means,aes(x =Infection,y = Call_Rate_Inst,group=Location.x))+
theme_classic()+
geom_point(aes(color=Location.x))+
geom_smooth(aes(color=Location.x),
method = 'lm',
se = FALSE)
## `geom_smooth()` using formula = 'y ~ x'
ggplot(ID.means.load,aes(x =Infection,y = Call_Rate_Inst,group=Location.x))+
theme_classic()+
geom_point(aes(color=Location.x))+
geom_smooth(aes(color=Location.x),
method = 'lm',
se = FALSE)
## `geom_smooth()` using formula = 'y ~ x'
# Duration
ggplot(ID.means,aes(x =Infection,y = Duration,group=Location.x))+
theme_classic()+
geom_point(aes(color=Location.x))+
geom_smooth(aes(color=Location.x),
method = 'lm',
se = FALSE)
## `geom_smooth()` using formula = 'y ~ x'
# ICI
ggplot(ID.means,aes(x =Infection,y = ICI,group=Location.x))+
theme_classic()+
geom_point(aes(color=Location.x))+
geom_smooth(aes(color=Location.x),
method = 'lm',
se = FALSE)
## `geom_smooth()` using formula = 'y ~ x'
# Freq
ggplot(ID.means,aes(x =Infection,y = Freq,group=Location.x))+
theme_classic()+
geom_point(aes(color=Location.x))+
geom_smooth(aes(color=Location.x),
method = 'lm',
se = FALSE)
## `geom_smooth()` using formula = 'y ~ x'
# BW
ggplot(ID.means,aes(x =Infection,y = BW,group=Location.x))+
theme_classic()+
geom_point(aes(color=Location.x))+
geom_smooth(aes(color=Location.x),
method = 'lm',
se = FALSE)
## `geom_smooth()` using formula = 'y ~ x'
