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$Temp, data)$coefficients[2] # Calculate m by doing a linear regression
Tcorrected_matrix[, i] <- data[,col_numbers[i]] - m * (data$Temp - target_temp) # Save a vector so I can use it outside the function
}
avg <- apply(Tcorrected_matrix, 1, mean)
stdev <- apply(Tcorrected_matrix, 1, sd)
colnames(Tcorrected_matrix) <- colnames(data[, col_numbers])
result <- list(avg = avg, stdev = stdev, Tcorrected_matrix = Tcorrected_matrix)
return(result)
}
Doing the Temperature Correction
FIRST - averaging means for males
means <- manual.data %>%
group_by(ID, Location.x) %>%
summarize(Temp = mean(`Body Temp`),
Infection = mean(`Log Infection`),
Call_Rate_Inst = mean(Call_Rate_Inst),
Duration = mean(`Delta Time (s)`),
ICI = mean(ICI),
Freq = mean(`Peak Freq (Hz)`),
BW = mean(`BW 90% (Hz)`))
## `summarise()` has grouped output by 'ID'. You can override using the `.groups`
## argument.
means <- as.data.frame(means)
NEXT - run the temperature correction
# Set Values that you want to temperature correct for
selections <- c(5:9)
# Do the temperature correction to the target temp, aka 16
result <- Tcorr(selections, target_temp = 16, data = means)
# Format results as a dataframe
Tdat <- as.data.frame(result$Tcorrected_matrix)
# Add information such as ID, Location, Temp, and Log Infection
Tdat<-cbind(means[,c(1,2,3,4)], Tdat)
Plots for Checking Work
# Plots to check work - Call Rate Inst
plot(means$Call_Rate_Inst~means$Temp)
plot(Tdat$Call_Rate_Inst~means$Temp)
# Plots to check work - Call Duration
plot(means$Duration~means$Temp)
plot(Tdat$Duration~means$Temp)
# Plots to check work - ICI
plot(means$ICI~means$Temp)
plot(Tdat$ICI ~ means$Temp)
# Plots to check work - Peak Freq
plot(means$Freq~means$Temp)
plot(Tdat$Freq ~ means$Temp)
Table of Results
cvs <- manual.data %>%
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 0.111 0.0544 0.177 0.150 0.0225
## 2 PC-0… FR 0 NaN 0.179 0.0765 0.343 0.286 0.0120
## 3 PC-0… CV 0 0 0.146 0.0672 0.226 0.200 0.00874
## 4 PC-0… CV 0 0 0.175 0.0534 0.301 0.248 0.00570
## 5 PC-0… CV 0 0 0.205 0.0627 0.271 0.230 0.0187
## 6 PC-0… CV 0 NaN 0.172 0.0748 0.284 0.238 0.0120
## 7 PC-0… FR 0 NaN 0.186 0.0519 0.244 0.220 0.0136
## 8 PC-0… CV 0 NaN 0.211 0.0260 0.277 0.249 0.0111
## 9 PC-0… FR 0 0 0.127 0.0623 0.176 0.154 0
## 10 PC-0… FR 0 0 0.143 0.0374 0.206 0.173 0.0147
## # 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 0.200 0.0968
## 2 Duration 0.0707 0.0315
## 3 ICI 0.442 0.379
## 4 Period 0.393 0.352
## 5 Freq 0.0114 0.00717
## 6 Freq_5 0.00749 0.00726
## 7 Freq_95 0.00759 0.00611
## 8 BW 0.113 0.0640
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")+
scale_fill_brewer(palette = "Set2")
knitr::include_graphics("~/Desktop/UTK/Tanner Lab/Analysis/Call Analysis/Peeper Analysis/Gerhardt.png")
GRAPHS
Tdat <- Tdat %>% mutate(Prevalence = case_when(Infection == 0 ~ 'Absent',
Infection != 0 ~ 'Present'))
write.csv(Tdat,"peeper_infection_by_ID.csv",row.names=FALSE)
TdatLoad <- Tdat %>% filter(Prevalence=='Present')
ID.means.load <- TdatLoad %>%
group_by(ID, Location.x) %>%
summarize(Temp = mean(Temp),
Infection = mean(Infection),
Call_Rate_Inst = mean(Call_Rate_Inst),
Duration = mean(Duration),
ICI = mean(ICI),
Freq = mean(Freq),
BW = mean(BW))
## `summarise()` has grouped output by 'ID'. You can override using the `.groups`
## argument.
# Plots are in fact 9 Absent and 21 Present for
ggplot(Tdat,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(Tdat,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(Tdat,aes(x=Prevalence,y=Duration))+
facet_wrap(~Location.x,strip.position="bottom")+
geom_boxplot()+
geom_jitter(position=position_jitter(0.1))+
theme_classic()
ggplot(Tdat,aes(x=Prevalence,y=ICI))+
facet_wrap(~Location.x,strip.position="bottom")+
geom_boxplot()+
geom_jitter(position=position_jitter(0.1))+
theme_classic()
ggplot(Tdat,aes(x=Prevalence,y=BW))+
facet_wrap(~Location.x,strip.position="bottom")+
geom_boxplot()+
geom_jitter(position=position_jitter(0.1))+
theme_classic()
ggplot(Tdat,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
library(RColorBrewer)
# Call Rate Inst
ggplot(Tdat,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)+
scale_color_brewer(palette = "Set2")
## `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)+
scale_color_brewer(palette = "Set2")
## `geom_smooth()` using formula = 'y ~ x'
# Duration
ggplot(Tdat,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)+
scale_color_brewer(palette = "Set2")
## `geom_smooth()` using formula = 'y ~ x'
# ICI
ggplot(Tdat,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)+
scale_color_brewer(palette = "Set2")
## `geom_smooth()` using formula = 'y ~ x'
# Freq
ggplot(Tdat,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)+
scale_color_brewer(palette = "Set2")
## `geom_smooth()` using formula = 'y ~ x'
# BW
ggplot(Tdat,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)+
scale_color_brewer(palette = "Set2")
## `geom_smooth()` using formula = 'y ~ x'
