R/CompFlow.R
In HHayman/FlowR: Carries out a selected workflow
Defines functions
CompFlow
Documented in
CompFlow
# Bland-Altman
# Shade ICCC red/amber/green
# Output; % points above and below confidence limits, whether difference increases with magnitude and the spread of distance from the mean
# Select two measures or select to automatically detect number of measures and run for all possible pairs
#'Compflow
#' @author Hayman, \email{l.hayman.1@@research.gla.ac.uk}
#' This function takes in data for two sets of measurements and produces a comparison.
#' @import ggplot2
#' @import plyr
#' @import irr
#' @import ggpubr
#' @import ggplotify
#' @import gridExtra
#' @param Subdirectory A subdirectory of your working directory, in which you store your dataset CSV files. Required.
#' @param TrainingData The name of your training dataset, which is used to generate cutpoints. Required.
#' @param CutPointStatus The survival status you wish to use to define your cutpoints. Required.
#' @param CutPointTime The time variable you wish to use to define your cutpoints. Required.
#' @param minprop The minimum proportion of cases to be included in a group. Default is 0.1 if none is provided.
#' @param Variables A list of all variables you wish to be included. Names must match and be identical across datasets. Required.
#' @param Palette Use 'Greyscale' if a greyscale plot is wanted. Otherwise, red/blue is the default palette.
#' @return CompFlow returns a scatter plot, a Bland-Altman plot and an accompanying ICC.
#' @examples CutFlow(Subdirectory = "CutFlowData", TrainingData = "Glasgow", CutPointStatus = "CSS", CutPointTime = "CSS_2017", minprop = 0.1, Variables = c("GD_PercPositiveCellsinHealthyStroma", "GD_PercPositiveCellsinHealthyEpithelium", "GD_PercPositiveCellsinHealthyTissue", "GD_PercPositiveCellsinTumourStroma", "GD_PercPositiveCellsinTumourEpithelium", "GD_PercPositiveCellsinTumourTissue", "CD8_PercPositiveCellsinHealthyStroma", "CD8_PercPositiveCellsinHealthyEpithelium", "CD8_PercPositiveCellsinHealthyTissue", "CD8_PercPositiveCellsinTumourStroma", "CD8_PercPositiveCellsinTumourEpithelium", "CD8_PercPositiveCellsinTumourTissue"))
#' @export
# library(ggplot2)
# library(ggpubr)
# library(plyr)
# library(irr)
# library(ggplotify)
# library(gridExtra)
# Data <- read.csv(file.choose(), fileEncoding = 'UTF-8-BOM')
# CompFlow(Data, ICCModel = "twoway", ICCType = "agreement", ICCUnit = "single", Density = "Yes")
#Function
CompFlow <-
function(Data,
ICCModel = NULL,
ICCType = NULL,
ICCUnit = NULL,
Density = "No") {
#### Read data and check for errors ####
# Save original working directory and set to restore on exit
WD <- toString(getwd())
on.exit(setwd(WD), add = TRUE)
# Check that there are two columns
if (ncol(Data) != 2 | length(Data[, 1]) != length(Data[, 2]))
stop("Data should contain two columns of equal length")
#### Create a subdirectory for output ####
# Define and check subdirectory name
Number <- 1
OutputDirectory <-
paste("CompFlow", Sys.Date(), Number, sep = '_')
while (file.exists(OutputDirectory))
{
Number <- Number + 1
OutputDirectory <-
paste("CompFlow", Sys.Date(), Number, sep = '_')
}
# Create the subdirectory
dir.create(OutputDirectory)
setwd(OutputDirectory)
OD <- toString(getwd())
#### Deposit raw datasets into a folder for record keeping ####
# Create and set a subdirectory
dir.create("0.OriginalData")
setwd("0.OriginalData")
# Write datasets to csv files
write.csv(Data, paste("RawData.csv", sep = ""), row.names = FALSE)
#Revert to output subdirectory
setwd(OD)
#### Configure Data ####
# Create and set a subdirectory
dir.create("1.ConfiguredData")
setwd("1.ConfiguredData")
# Calculate difference and mean
DataForICC <- Data
Data$Difference <- Data[, 1] - Data[, 2]
Data$Mean <- (Data[, 1] + Data[, 2]) / 2
DataDifference <- Data$Difference
DifferenceMean <- mean(DataDifference, na.rm = T)
DifferenceStDev <- sd(DataDifference, na.rm = T)
DifferenceUpperLimit <-
DifferenceMean + (DifferenceStDev * 1.96)
DifferenceLowerLimit <-
DifferenceMean - (DifferenceStDev * 1.96)
write.csv(Data, paste("DataConfigured.csv", sep = ""), row.names = FALSE)
ScatterValues <-
data.frame(DifferenceMean,
DifferenceStDev,
DifferenceLowerLimit,
DifferenceUpperLimit)
write.csv(ScatterValues,
paste("ScatterValues.csv", sep = ""),
row.names = FALSE)
xAxisLabel <- colnames(Data)[1]
yAxisLabel <- colnames(Data)[2]
colnames(Data)[1] <- "Variable1"
colnames(Data)[2] <- "Variable2"
# Calculate ICC
ICCModelList <- list("oneway", "twoway")
ICCTypeList <- list("consistency", "agreement")
ICCUnitList <- list("single", "average")
if (ICCModel %in% ICCModelList &
ICCType %in% ICCTypeList & ICCUnit %in% ICCUnitList) {
ICCData <-
icc(
DataForICC,
model = ICCModel,
type = ICCType,
unit = ICCUnit,
conf.level = 0.95
)
}
ICC <- round(ICCData$value, digits = 3)
ICCText <- paste("ICCC = ", ICC, sep = "")
if (ICC < 0.5) {
SubtitleColour <- "red"
}
if (ICC >= 0.5 & ICC < 0.75) {
SubtitleColour <- "orange"
}
if (ICC >= 0.75) {
SubtitleColour <- "green"
}
theme_SC <- function() {
theme_classic() %+replace%
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
}
theme_BA <- function() {
theme_classic() %+replace%
theme(
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5, colour = SubtitleColour)
)
}
# Revert to output subdirectory
setwd(OD)
#### Generate Plots ####
# Create and set a subdirectory
dir.create("2.Plots")
setwd("2.Plots")
## Scatter plots
# Generate scatter plot
Scatter <-
ggplot(Data, aes(x = Variable1, y = Variable2)) +
geom_point() +
labs(title = "Scatter Plot", x = xAxisLabel, y = yAxisLabel) +
theme_SC()
# Generate scatter plot with density
ScatterDensity <-
Scatter +
stat_density_2d(aes(fill = ..level..), geom = "polygon") +
scale_fill_gradient(low = "blue", high = "red")
# # Export scatter plot
# FileName <- paste("Scatter", ".tiff", sep = "")
# tiff(
# FileName,
# width = 1500,
# height = 1500,
# units = "px",
# bg = "transparent",
# res = 300,
# compression = "lzw"
# )
#
# print(Scatter)
# dev.off()
#
# # Export scatter plot with density
# if (Density == "Yes") {
# FileName <- paste("Scatter_Density", ".tiff", sep = "")
# tiff(
# FileName,
# width = 1500,
# height = 1500,
# units = "px",
# bg = "transparent",
# res = 300,
# compression = "lzw"
# )
# print(ScatterDensity)
# dev.off()
# }
## Bland-Altman
AbsDiffMax <- max(abs(DataDifference), na.rm = T)
yLimMax <- AbsDiffMax
yLimMin <- AbsDiffMax * (-1)
Data$LimitStatus <- "In"
Data$LimitStatus[Data$Difference > DifferenceUpperLimit |
Data$Difference < DifferenceLowerLimit] <-
"Out"
PercOut <-
round((sum(Data$LimitStatus == "Out") / nrow(Data) * 100), digits = 1)
PercOutText <-
paste(PercOut, "% of points outside of confidence limits", sep = "")
ShapiroResult1 <- shapiro.test(Data[, 1])
ShapiroResult2 <- shapiro.test(Data[, 2])
ShapiroP1 <- ShapiroResult1$p.value
ShapiroP2 <- ShapiroResult2$p.value
ifelse (
ShapiroP1 > 0.05 &
ShapiroP2 > 0.05,
PearsonResult <-
cor.test(Data$Difference, Data$Mean),
PearsonResult <- NA
)
if (!is.na(PearsonResult)) {
PearsonP <- PearsonResult$p.value
PearsonP <-
format(round(PearsonP, 3),
nsmall = 3,
scientific = FALSE)
PropBiasText <-
paste("Proportional bias (Pearson) - p = ", PearsonP, sep = "")
}
if (is.na(PearsonResult)) {
PropBiasText <-
paste("Proportional bias (Pearson) - p = ", PearsonResult, sep = "")
}
lay2 <- rbind(c(1, 2))
lay3 <- rbind(c(1, 2, 3))
BlandAltman <-
ggplot(Data, aes(x = Mean, y = Difference, colour = LimitStatus)) +
ylim(yLimMin, yLimMax) +
scale_color_manual(breaks = c("In", "Out"),
values = c("green", "red")) +
geom_point(size = 2, shape = 23) +
geom_hline(yintercept = DifferenceMean,
linetype = "dashed",
color = "green") +
geom_hline(yintercept = DifferenceLowerLimit,
linetype = "dashed",
color = "red") +
geom_hline(yintercept = DifferenceUpperLimit,
linetype = "dashed",
color = "red") +
labs(title = "Bland-Altman Plot") +
theme_BA()
BlandAltmanDensity <-
BlandAltman + stat_density_2d(aes(fill = ..level..), geom = "polygon") +
scale_fill_gradient(low = "blue", high = "red")
# # Export Bland Altman
# FileName <- paste("Bland-Altman", ".tiff", sep = "")
# tiff(
# FileName,
# width = 1500,
# height = 1500,
# units = "px",
# bg = "transparent",
# res = 300,
# compression = "lzw"
# )
# print(BlandAltman)
# dev.off()
#
# # Export Bland Altman with density
# if (Density == "Yes") {
# FileName <- paste("Bland-Altman_Density", ".tiff", sep = "")
# tiff(
# FileName,
# width = 1500,
# height = 1500,
# units = "px",
# bg = "transparent",
# res = 300,
# compression = "lzw"
# )
# print(BlandAltmanDensity)
# dev.off()
# }
## Composite plots
# Horizontal Scatter plot + Bland-Altman
FileName <-
paste("ScatterPlusBlandAltmanHorizontal", ".tiff", sep = "")
tiff(
FileName,
width = 3000,
height = 1500,
units = "px",
bg = "transparent",
res = 300,
compression = "lzw"
)
ScatterGrob <- as.grob(Scatter)
BlandAltmanGrob <- as.grob(BlandAltman)
ICCGrob <- text_grob(ICCText,
just = "centre")
OutGrob <- text_grob(PercOutText,
just = "centre")
PropBiasGrob <- text_grob(PropBiasText,
just = "centre")
GrobPlots <-
arrangeGrob(grobs = list(ScatterGrob, BlandAltmanGrob),
layout_matrix = lay2)
GrobLabels <-
arrangeGrob(grobs = list(OutGrob, ICCGrob, PropBiasGrob),
layout_matrix = lay3)
grid.arrange(GrobPlots,
GrobLabels,
nrow = 2,
heights = c(10, 1))
dev.off()
# Horizontal Scatter plot + Bland-Altman with density
if (Density == "Yes") {
FileName <-
paste("ScatterPlusBlandAltmanDensityHorizontal", ".tiff", sep = "")
tiff(
FileName,
width = 3000,
height = 1500,
units = "px",
bg = "transparent",
res = 300,
compression = "lzw"
)
ScatterGrob <- as.grob(ScatterDensity)
BlandAltmanGrob <- as.grob(BlandAltmanDensity)
ICCGrob <- text_grob(ICCText,
just = "centre")
OutGrob <- text_grob(PercOutText,
just = "centre")
PropBiasGrob <- text_grob(PropBiasText,
just = "centre")
GrobPlots <-
arrangeGrob(grobs = list(ScatterGrob, BlandAltmanGrob),
layout_matrix = lay2)
GrobLabels <-
arrangeGrob(grobs = list(OutGrob, ICCGrob, PropBiasGrob),
layout_matrix = lay3)
grid.arrange(GrobPlots,
GrobLabels,
nrow = 2,
heights = c(10, 1))
dev.off()
}
setwd(WD)
}
HHayman/FlowR documentation built on April 13, 2022, 11:31 p.m.
R Package Documentation
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Defines functions CompFlow
Documented in CompFlow
# Bland-Altman
# Shade ICCC red/amber/green
# Output; % points above and below confidence limits, whether difference increases with magnitude and the spread of distance from the mean
# Select two measures or select to automatically detect number of measures and run for all possible pairs
#'Compflow
#' @author Hayman, \email{l.hayman.1@@research.gla.ac.uk}
#' This function takes in data for two sets of measurements and produces a comparison.
#' @import ggplot2
#' @import plyr
#' @import irr
#' @import ggpubr
#' @import ggplotify
#' @import gridExtra
#' @param Subdirectory A subdirectory of your working directory, in which you store your dataset CSV files. Required.
#' @param TrainingData The name of your training dataset, which is used to generate cutpoints. Required.
#' @param CutPointStatus The survival status you wish to use to define your cutpoints. Required.
#' @param CutPointTime The time variable you wish to use to define your cutpoints. Required.
#' @param minprop The minimum proportion of cases to be included in a group. Default is 0.1 if none is provided.
#' @param Variables A list of all variables you wish to be included. Names must match and be identical across datasets. Required.
#' @param Palette Use 'Greyscale' if a greyscale plot is wanted. Otherwise, red/blue is the default palette.
#' @return CompFlow returns a scatter plot, a Bland-Altman plot and an accompanying ICC.
#' @examples CutFlow(Subdirectory = "CutFlowData", TrainingData = "Glasgow", CutPointStatus = "CSS", CutPointTime = "CSS_2017", minprop = 0.1, Variables = c("GD_PercPositiveCellsinHealthyStroma", "GD_PercPositiveCellsinHealthyEpithelium", "GD_PercPositiveCellsinHealthyTissue", "GD_PercPositiveCellsinTumourStroma", "GD_PercPositiveCellsinTumourEpithelium", "GD_PercPositiveCellsinTumourTissue", "CD8_PercPositiveCellsinHealthyStroma", "CD8_PercPositiveCellsinHealthyEpithelium", "CD8_PercPositiveCellsinHealthyTissue", "CD8_PercPositiveCellsinTumourStroma", "CD8_PercPositiveCellsinTumourEpithelium", "CD8_PercPositiveCellsinTumourTissue"))
#' @export
# library(ggplot2)
# library(ggpubr)
# library(plyr)
# library(irr)
# library(ggplotify)
# library(gridExtra)
# Data <- read.csv(file.choose(), fileEncoding = 'UTF-8-BOM')
# CompFlow(Data, ICCModel = "twoway", ICCType = "agreement", ICCUnit = "single", Density = "Yes")
#Function
CompFlow <-
function(Data,
ICCModel = NULL,
ICCType = NULL,
ICCUnit = NULL,
Density = "No") {
#### Read data and check for errors ####
# Save original working directory and set to restore on exit
WD <- toString(getwd())
on.exit(setwd(WD), add = TRUE)
# Check that there are two columns
if (ncol(Data) != 2 | length(Data[, 1]) != length(Data[, 2]))
stop("Data should contain two columns of equal length")
#### Create a subdirectory for output ####
# Define and check subdirectory name
Number <- 1
OutputDirectory <-
paste("CompFlow", Sys.Date(), Number, sep = '_')
while (file.exists(OutputDirectory))
{
Number <- Number + 1
OutputDirectory <-
paste("CompFlow", Sys.Date(), Number, sep = '_')
}
# Create the subdirectory
dir.create(OutputDirectory)
setwd(OutputDirectory)
OD <- toString(getwd())
#### Deposit raw datasets into a folder for record keeping ####
# Create and set a subdirectory
dir.create("0.OriginalData")
setwd("0.OriginalData")
# Write datasets to csv files
write.csv(Data, paste("RawData.csv", sep = ""), row.names = FALSE)
#Revert to output subdirectory
setwd(OD)
#### Configure Data ####
# Create and set a subdirectory
dir.create("1.ConfiguredData")
setwd("1.ConfiguredData")
# Calculate difference and mean
DataForICC <- Data
Data$Difference <- Data[, 1] - Data[, 2]
Data$Mean <- (Data[, 1] + Data[, 2]) / 2
DataDifference <- Data$Difference
DifferenceMean <- mean(DataDifference, na.rm = T)
DifferenceStDev <- sd(DataDifference, na.rm = T)
DifferenceUpperLimit <-
DifferenceMean + (DifferenceStDev * 1.96)
DifferenceLowerLimit <-
DifferenceMean - (DifferenceStDev * 1.96)
write.csv(Data, paste("DataConfigured.csv", sep = ""), row.names = FALSE)
ScatterValues <-
data.frame(DifferenceMean,
DifferenceStDev,
DifferenceLowerLimit,
DifferenceUpperLimit)
write.csv(ScatterValues,
paste("ScatterValues.csv", sep = ""),
row.names = FALSE)
xAxisLabel <- colnames(Data)[1]
yAxisLabel <- colnames(Data)[2]
colnames(Data)[1] <- "Variable1"
colnames(Data)[2] <- "Variable2"
# Calculate ICC
ICCModelList <- list("oneway", "twoway")
ICCTypeList <- list("consistency", "agreement")
ICCUnitList <- list("single", "average")
if (ICCModel %in% ICCModelList &
ICCType %in% ICCTypeList & ICCUnit %in% ICCUnitList) {
ICCData <-
icc(
DataForICC,
model = ICCModel,
type = ICCType,
unit = ICCUnit,
conf.level = 0.95
)
}
ICC <- round(ICCData$value, digits = 3)
ICCText <- paste("ICCC = ", ICC, sep = "")
if (ICC < 0.5) {
SubtitleColour <- "red"
}
if (ICC >= 0.5 & ICC < 0.75) {
SubtitleColour <- "orange"
}
if (ICC >= 0.75) {
SubtitleColour <- "green"
}
theme_SC <- function() {
theme_classic() %+replace%
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
}
theme_BA <- function() {
theme_classic() %+replace%
theme(
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5, colour = SubtitleColour)
)
}
# Revert to output subdirectory
setwd(OD)
#### Generate Plots ####
# Create and set a subdirectory
dir.create("2.Plots")
setwd("2.Plots")
## Scatter plots
# Generate scatter plot
Scatter <-
ggplot(Data, aes(x = Variable1, y = Variable2)) +
geom_point() +
labs(title = "Scatter Plot", x = xAxisLabel, y = yAxisLabel) +
theme_SC()
# Generate scatter plot with density
ScatterDensity <-
Scatter +
stat_density_2d(aes(fill = ..level..), geom = "polygon") +
scale_fill_gradient(low = "blue", high = "red")
# # Export scatter plot
# FileName <- paste("Scatter", ".tiff", sep = "")
# tiff(
# FileName,
# width = 1500,
# height = 1500,
# units = "px",
# bg = "transparent",
# res = 300,
# compression = "lzw"
# )
#
# print(Scatter)
# dev.off()
#
# # Export scatter plot with density
# if (Density == "Yes") {
# FileName <- paste("Scatter_Density", ".tiff", sep = "")
# tiff(
# FileName,
# width = 1500,
# height = 1500,
# units = "px",
# bg = "transparent",
# res = 300,
# compression = "lzw"
# )
# print(ScatterDensity)
# dev.off()
# }
## Bland-Altman
AbsDiffMax <- max(abs(DataDifference), na.rm = T)
yLimMax <- AbsDiffMax
yLimMin <- AbsDiffMax * (-1)
Data$LimitStatus <- "In"
Data$LimitStatus[Data$Difference > DifferenceUpperLimit |
Data$Difference < DifferenceLowerLimit] <-
"Out"
PercOut <-
round((sum(Data$LimitStatus == "Out") / nrow(Data) * 100), digits = 1)
PercOutText <-
paste(PercOut, "% of points outside of confidence limits", sep = "")
ShapiroResult1 <- shapiro.test(Data[, 1])
ShapiroResult2 <- shapiro.test(Data[, 2])
ShapiroP1 <- ShapiroResult1$p.value
ShapiroP2 <- ShapiroResult2$p.value
ifelse (
ShapiroP1 > 0.05 &
ShapiroP2 > 0.05,
PearsonResult <-
cor.test(Data$Difference, Data$Mean),
PearsonResult <- NA
)
if (!is.na(PearsonResult)) {
PearsonP <- PearsonResult$p.value
PearsonP <-
format(round(PearsonP, 3),
nsmall = 3,
scientific = FALSE)
PropBiasText <-
paste("Proportional bias (Pearson) - p = ", PearsonP, sep = "")
}
if (is.na(PearsonResult)) {
PropBiasText <-
paste("Proportional bias (Pearson) - p = ", PearsonResult, sep = "")
}
lay2 <- rbind(c(1, 2))
lay3 <- rbind(c(1, 2, 3))
BlandAltman <-
ggplot(Data, aes(x = Mean, y = Difference, colour = LimitStatus)) +
ylim(yLimMin, yLimMax) +
scale_color_manual(breaks = c("In", "Out"),
values = c("green", "red")) +
geom_point(size = 2, shape = 23) +
geom_hline(yintercept = DifferenceMean,
linetype = "dashed",
color = "green") +
geom_hline(yintercept = DifferenceLowerLimit,
linetype = "dashed",
color = "red") +
geom_hline(yintercept = DifferenceUpperLimit,
linetype = "dashed",
color = "red") +
labs(title = "Bland-Altman Plot") +
theme_BA()
BlandAltmanDensity <-
BlandAltman + stat_density_2d(aes(fill = ..level..), geom = "polygon") +
scale_fill_gradient(low = "blue", high = "red")
# # Export Bland Altman
# FileName <- paste("Bland-Altman", ".tiff", sep = "")
# tiff(
# FileName,
# width = 1500,
# height = 1500,
# units = "px",
# bg = "transparent",
# res = 300,
# compression = "lzw"
# )
# print(BlandAltman)
# dev.off()
#
# # Export Bland Altman with density
# if (Density == "Yes") {
# FileName <- paste("Bland-Altman_Density", ".tiff", sep = "")
# tiff(
# FileName,
# width = 1500,
# height = 1500,
# units = "px",
# bg = "transparent",
# res = 300,
# compression = "lzw"
# )
# print(BlandAltmanDensity)
# dev.off()
# }
## Composite plots
# Horizontal Scatter plot + Bland-Altman
FileName <-
paste("ScatterPlusBlandAltmanHorizontal", ".tiff", sep = "")
tiff(
FileName,
width = 3000,
height = 1500,
units = "px",
bg = "transparent",
res = 300,
compression = "lzw"
)
ScatterGrob <- as.grob(Scatter)
BlandAltmanGrob <- as.grob(BlandAltman)
ICCGrob <- text_grob(ICCText,
just = "centre")
OutGrob <- text_grob(PercOutText,
just = "centre")
PropBiasGrob <- text_grob(PropBiasText,
just = "centre")
GrobPlots <-
arrangeGrob(grobs = list(ScatterGrob, BlandAltmanGrob),
layout_matrix = lay2)
GrobLabels <-
arrangeGrob(grobs = list(OutGrob, ICCGrob, PropBiasGrob),
layout_matrix = lay3)
grid.arrange(GrobPlots,
GrobLabels,
nrow = 2,
heights = c(10, 1))
dev.off()
# Horizontal Scatter plot + Bland-Altman with density
if (Density == "Yes") {
FileName <-
paste("ScatterPlusBlandAltmanDensityHorizontal", ".tiff", sep = "")
tiff(
FileName,
width = 3000,
height = 1500,
units = "px",
bg = "transparent",
res = 300,
compression = "lzw"
)
ScatterGrob <- as.grob(ScatterDensity)
BlandAltmanGrob <- as.grob(BlandAltmanDensity)
ICCGrob <- text_grob(ICCText,
just = "centre")
OutGrob <- text_grob(PercOutText,
just = "centre")
PropBiasGrob <- text_grob(PropBiasText,
just = "centre")
GrobPlots <-
arrangeGrob(grobs = list(ScatterGrob, BlandAltmanGrob),
layout_matrix = lay2)
GrobLabels <-
arrangeGrob(grobs = list(OutGrob, ICCGrob, PropBiasGrob),
layout_matrix = lay3)
grid.arrange(GrobPlots,
GrobLabels,
nrow = 2,
heights = c(10, 1))
dev.off()
}
setwd(WD)
}
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