nci_create_aggregate_plots.R
In cukarthik/CancerTreatmentCharacterization: What the package does (short line)
#make a copy of the csv files of interest into a tmp directory
# cp *_multiple_myeloma_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv ~/Downloads/tmp-nci-results
#create aggregate file
#for f in `ls`; do awk 'NR == 1 {print $0 ",\"source\""; split(FILENAME, a, "_"); SOURCE = a[1]; next;}{print $0 ","SOURCE}' $f; done | sort -r | uniq > all_sources_lung_cancer_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv
#########################################FUNCTIONS############################################################
#This function plots the intervention by year for each cancer. It de-duplicates interventions before plotting
#NOTE: have not figured out how to
plotAggregateInterventions <- function(data, cohortName, outputFolder="~/Downloads/", fileName="test2") {
dedup_interventions <- data %>%
mutate(unique_interventions = map_chr(strsplit(data$distinct_interventions, " \\+ "),
~ toString(sort(.x)))) %>%
group_by(source, dx_year, unique_interventions, year_total) %>%
summarise(unique_total = sum(n), unique_pct = sum(pct)) %>%
mutate(filtered_labels = replace(unique_total, unique_total<=10, ""))
colourCount <- length(unique(dedup_interventions$unique_interventions))
width <- 1980
height <- 2040
outputFile <- paste0(outputFolder, "/", fileName, ".png")
print(paste0("output file:", outputFile))
png(file=outputFile, width=width, height=height)
p <- ggplot(dedup_interventions, aes(fill = unique_interventions, x = dx_year, y = unique_pct)) +
scale_y_continuous(labels = percent_format(accuracy = 1)) +
geom_bar(position = 'fill', stat = 'identity') +
# facet_wrap(~ source , ncol = 2, scales = "free" ) +
facet_wrap(~ factor(source, levels = c('CCAE', 'CUIMC', 'MDCD', 'STANFORD', 'MDCR', 'TUFTS')) , ncol = 2 , scales = "free") +
geom_text(aes(label = filtered_labels), position = position_fill(vjust = .5), size = 5) +
ggtitle(paste0("Percent Distribution of Intervention Types by Year (", cohortName, ")")) +
# theme(legend.position = 'bottom', legend.text = element_text(size = 4),
# legend.key.size = unit(.25, 'cm'), legend.title = element_text(size = 6)) +
theme(legend.position = 'bottom', legend.text = element_text(size = 12),
legend.key.size = unit(.25, 'cm'), legend.title = element_text(size = 0),
legend.spacing.x = unit(.25, "cm")) +
theme(plot.title = element_text(hjust = 0.5, size=30,face="bold"), strip.text = element_text(size = 15)) +
theme(axis.text=element_text(size=12), axis.title=element_text(size=20,face="bold")) +
labs(x = "Diagnosis Year", y = "Percentage", cex.lab = 2) +
scale_x_continuous(breaks= pretty_breaks()) +
scale_fill_manual(values = colorRampPalette(brewer.pal(12, "Set3"))(colourCount))
show(p)
dev.off()
}
#This function aggregates the plots for average drug and drug class
plotAggregateAvgDrugsAdministered <- function(mean_drugs_and_mean_classes_by_year, cohortName, outputFolder="~/Downloads/", fileName="test2") {
#1280x800
width <- 1280
height <- 800#(9/16) * width
#Breast Cancer Average number of antineoplastics agents administered plot
labels <- mean_drugs_and_mean_classes_by_year %>% filter(!is.na(dx_year)) %>% slice(1)
outputFile <- paste0(outputFolder, "/", fileName, ".png")
print(paste0("output file:", outputFile))
png(file=outputFile, width=width, height=height)
p <- ggplot(data = mean_drugs_and_mean_classes_by_year, aes(x = dx_year)) +
geom_line(aes(y = average_number_of_drugs, group = 1, colour="Avg # of Drugs")) +
geom_line(aes(y = standard_deviation, group = 1, colour="Std Dev of Drugs"), linetype=2) +
geom_line(aes(y = class_avg, group = 2, colour="Avg # of Classes of Drugs")) +
geom_line(aes(y = class_sd, group = 2, colour="Std Dev of Classes of Drugs"), linetype=2) +
facet_wrap(~ factor(source, levels = c('CCAE', 'CUIMC', 'MDCD', 'STANFORD', 'MDCR', 'TUFTS')) , ncol = 2 ) +
ggtitle(paste0("Administered Drugs by Year (", cohortName, ")")) +
theme(legend.position = 'bottom', legend.text = element_text(size = 10),
#legend.key.size = unit(.25, 'cm'),
legend.title = element_text(size = 0),
legend.spacing.x = unit(.25, "cm")
) +
# theme(plot.title = element_text(hjust = 0.5, size=30,face="bold")) +
theme(plot.title = element_text(hjust = 0.5, size=30,face="bold"), strip.text = element_text(size = 15)) +
theme(axis.text=element_text(size=12), axis.title=element_text(size=20,face="bold")) +
labs(y="Avg Number of Drugs", x = "Diagnosis Year", cex.lab = 2) +
scale_x_continuous(breaks= pretty_breaks())
show(p)
dev.off()
}
plotAggregateAnalyses <- function(data, outputFolder="~/Downloads/", fileName="test2",
title="TITLE") {
colourCount <- length(unique(data$generic_drug_name))
width <- 1280
height <- 800
outputFile <- paste0(outputFolder, "/", fileName, ".png")
print(paste0("output file:", outputFile))
png(file=outputFile, width=width, height=height)
data <- data %>%
mutate(filtered_labels = replace(n, n<=10, ""))
p <- ggplot(data, aes(fill = generic_drug_name, x = dx_year, y = pct)) +
scale_y_continuous(labels = percent_format(accuracy = 1)) +
geom_bar(position = 'fill', stat = 'identity') +
# facet_wrap(~ source , ncol = 2, scales = "free" ) +
facet_wrap(~ factor(source, levels = c('CCAE', 'CUIMC', 'MDCD', 'STANFORD', 'MDCR', 'TUFTS')) , ncol = 2 ) +
geom_text(aes(label = filtered_labels), position = position_fill(vjust = .5), size = 5) +
ggtitle(title) +
# theme(legend.position = 'bottom', legend.text = element_text(size = 4),
# legend.key.size = unit(.25, 'cm'), legend.title = element_text(size = 6)) +
theme(legend.position = 'bottom', legend.text = element_text(size = 20),
legend.key.size = unit(.5, 'cm'), legend.title = element_text(size = 0),
legend.spacing.x = unit(.25, "cm")) +
theme(plot.title = element_text(hjust = 0.5, size=35), strip.text = element_text(size = 15)) +
theme(axis.text=element_text(size=12), axis.title=element_text(size=30)) +
labs(x = "Diagnosis Year", y = "Percentage", cex.lab = 2) +
scale_x_continuous(labels = scales::number_format(), breaks= pretty_breaks())
scale_fill_manual(values = colorRampPalette(brewer.pal(12, "Paired"))(colourCount))
show(p)
dev.off()
}
#########################################SCRIPT############################################################
library(devtools)
library(ggplot2)
library(dplyr)
library(purrr)
library(stringr)
library(RColorBrewer)
library(scales)
#ToDo: change the values for the input and output folders
inputFolder <- "~/tmp-nci-results/all/"
outputFolder <- "~/tmp-nci-results/all/"
#########################################BREAST############################################################
#breast intervention plot
data <- read.csv(paste0(inputFolder,"all_sources_breast_cancer_treatment_dataset_percent_interventions_types_per_year.csv"))
cohortName <- "Breast"
fileName <- "breast_interventions_by_year"
plotAggregateInterventions(data, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
# dedup_interventions <- data %>%
# mutate(unique_interventions = map_chr(strsplit(data$distinct_interventions, " \\+ "),
# ~ toString(sort(.x)))) %>%
# group_by(source, dx_year, unique_interventions, year_total) %>%
# summarise(unique_total = sum(n), unique_pct = sum(pct)) %>%
# mutate(filtered_labels = replace(unique_total, unique_total<=10, ""))
# ggplot(dedup_interventions, aes(fill = unique_interventions, x = dx_year, y = unique_pct)) +
# scale_y_continuous(labels = percent_format(accuracy = 1)) +
# geom_bar(position = 'fill', stat = 'identity') +
# # facet_wrap(~ source , ncol = 2, scales = "free" ) +
# facet_wrap(~ factor(source, levels = c('CCAE', 'CUIMC', 'MDCD', 'STANFORD', 'MDCR', 'TUFTS')) , ncol = 2 , scales = "free") +
# geom_text(aes(label = filtered_labels), position = position_fill(vjust = .5), size = 3) +
# ggtitle('Percent Distribution of Intervention Types by Year (Breast)') +
# # theme(legend.position = 'bottom', legend.text = element_text(size = 4),
# # legend.key.size = unit(.25, 'cm'), legend.title = element_text(size = 6)) +
# theme(legend.position = 'bottom', legend.text = element_text(size = 12),
# legend.key.size = unit(.25, 'cm'), legend.title = element_text(size = 0),
# legend.spacing.x = unit(.25, "cm")) +
# theme(plot.title = element_text(hjust = 0.5), strip.text = element_text(size = 15)) +
# labs(x = "Diagnosis Year", y = "Percentage") +
# scale_x_continuous(breaks= pretty_breaks())
# # scale_x_continuous(breaks=seq(2000, 2020, 2))
# ggsave(filename = "~/Downloads/test.png")
fileName <- "breast_avg_drug_class"
data <- read.csv(paste0(outputFolder,"all_sources_breast_cancer_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv"))
plotAggregateAvgDrugsAdministered(data, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
#1280x800
# width = 1280
# height = 800#(9/16) * width
# #Breast Cancer Average number of antineoplastics agents administered plot
# mean_drugs_and_mean_classes_by_year <- read.csv(paste0(outputFolder,"all_sources_breast_cancer_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv"))
# labels <- mean_drugs_and_mean_classes_by_year %>% filter(!is.na(dx_year)) %>% slice(1)
# ggplot(data = mean_drugs_and_mean_classes_by_year, aes(x = dx_year)) +
# geom_line(aes(y = average_number_of_drugs, group = 1, colour="Avg # of Drugs")) +
# geom_line(aes(y = standard_deviation, group = 1, colour="Std Dev of Drugs"), linetype=2) +
# geom_line(aes(y = class_avg, group = 2, colour="Avg # of Classes of Drugs")) +
# geom_line(aes(y = class_sd, group = 2, colour="Std Dev of Classes of Drugs"), linetype=2) +
# facet_wrap(~ factor(source, levels = c('CCAE', 'CUIMC', 'MDCD', 'STANFORD', 'MDCR', 'TUFTS')) , ncol = 2 ) +
# ggtitle('Administered Drugs by Year (Breast)') +
# theme(legend.position = 'bottom', legend.text = element_text(size = .1/.pt),
# #legend.key.size = unit(.25, 'cm'),
# legend.title = element_text(size = 0),
# legend.spacing.x = unit(.25, "cm")
# ) +
# theme(plot.title = element_text(hjust = 0.5)) +
# theme(plot.title = element_text(hjust = 0.5), strip.text = element_text(size = 15/.pt)) +
# labs(y="Avg Number of Drugs", x = "Diagnosis Year")
# coord_fixed(ratio = (width/height))
#ggsave(filename = "~/Downloads/test2.png", width = 10.16, height = 6.35, units = "cm")
#code to generate aggregate data file
# for f in `ls | grep breast | grep chemotherapy_neoadjuvant.csv`; do awk 'NR == 1 {print $0 ",\"source\""; split(FILENAME, a, "_"); SOURCE = a[1]; next;}{print $0 ","SOURCE}' $f; done | sort -r | uniq > all/all_sources_breast_cancer_chemotherapy_neoadjuvant.csv
fileName <- "breast_chemotherapy_neoadjuvant"
title <- "First-line Chemotherapy in the Neoadjuvant Setting (Breast)"
data <- read.csv(paste0(outputFolder,"all_sources_breast_cancer_chemotherapy_neoadjuvant.csv"))
fieldName <- "generic_drug_name" #field in data that will be plotted
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
#########################################PROSTATE############################################################
data <- read.csv(paste0(inputFolder,"all_sources_prostate_cancer_treatment_dataset_percent_interventions_types_per_year.csv"))
cohortName <- "Prostate"
fileName <- "prostate_interventions_by_year"
plotAggregateInterventions(data, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
#Prostate Cancer Average number of antineoplastics agents administered plot
mean_drugs_and_mean_classes_by_year <- read.csv(paste0(outputFolder,"all_sources_prostate_cancer_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv"))
fileName <- "prostate_avg_drug_class"
plotAggregateAvgDrugsAdministered(mean_drugs_and_mean_classes_by_year, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
fileName <- "prostate_chemotherapy_neoadjuvant"
title <- "Percent Distribution of First-line Chemotherapy Neoadjuvant (Prostate)"
data <- read.csv(paste0(outputFolder,"all_sources_prostate_cancer_chemotherapy_neoadjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
fileName <- "prostate_chemotherapy_adjuvant"
title <- "Percent Distribution of First-line Chemotherapy Adjuvant (Prostate)"
data <- read.csv(paste0(outputFolder,"all_sources_prostate_cancer_chemotherapy_adjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
#########################################Lung############################################################
#Lung Cancer Average number of antineoplastics agents administered plot
data <- read.csv(paste0(inputFolder,"all_sources_lung_cancer_treatment_dataset_percent_interventions_types_per_year.csv"))
cohortName = "Lung"
fileName = "lung_interventions_by_year"
#fix data by merging Total Lobectomy and Lobectomy into one
data$distinct_interventions_orig <- data$distinct_interventions
data <- data %>%
mutate(tmp_col = str_replace(data$distinct_interventions, "Total Lobectomy", "Lobectomy")) %>%
mutate(distinct_interventions = sapply(strsplit(tmp_col, " \\+ "),
function(x) paste(unique(x), collapse = " + ")))
plotAggregateInterventions(data, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
mean_drugs_and_mean_classes_by_year <- read.csv(paste0(outputFolder,"all_sources_lung_cancer_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv"))
fileName <- "lung_avg_drug_class"
plotAggregateAvgDrugsAdministered(mean_drugs_and_mean_classes_by_year, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
####Lung Chemo
fileName <- "lung_chemotherapy_neoadjuvant"
title <- "Percent Distribution of First-line Chemotherapy Neoadjuvant (Lung)"
data <- read.csv(paste0(outputFolder,"all_sources_lung_cancer_chemotherapy_neoadjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
fileName <- "lung_chemotherapy_adjuvant"
title <- "Percent Distribution of First-line Chemotherapy Adjuvant (Lung)"
data <- read.csv(paste0(outputFolder,"all_sources_lung_cancer_chemotherapy_adjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
####Lung EGFR
fileName <- "lung_egfr_neoadjuvant"
title <- "Percent Distribution of First-line EGFR Therapy Neoadjuvant (Lung)"
data <- read.csv(paste0(outputFolder,"all_sources_lung_cancer_egfr_therapy_neoadjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
fileName <- "lung_egfr_adjuvant"
title <- "Percent Distribution of First-line EGFR Therapy Adjuvant (Lung)"
data <- read.csv(paste0(outputFolder,"all_sources_lung_cancer_egfr_therapy_adjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
####Lung VEGF
fileName <- "lung_vegf_neoadjuvant"
title <- "Percent Distribution of First-line VEGF Therapy Neoadjuvant (Lung)"
data <- read.csv(paste0(outputFolder,"all_sources_lung_cancer_vegf_therapy_neoadjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
fileName <- "lung_vegf_adjuvant"
title <- "Percent Distribution of First-line VEGF Therapy Adjuvant (Lung)"
data <- read.csv(paste0(outputFolder,"all_sources_lung_cancer_vegf_therapy_adjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
#########################################Multiple Myeloma############################################################
#for f in `ls | grep multiple | grep interventions_types_per_year.csv`; do awk 'NR == 1 {print $0 ",\"source\""; split(FILENAME, a, "_"); SOURCE = a[1]; next;}{print $0 ","SOURCE}' $f; done | sort -r | uniq > all/all_sources_multiple_myeloma_treatment_dataset_interventions_types_per_year.csv
data <- read.csv(paste0(inputFolder,"all_sources_multiple_myeloma_treatment_dataset_percent_interventions_types_per_year.csv"))
cohortName = "Multiple Myeloma"
fileName = "mm_interventions_by_year"
plotAggregateInterventions(data, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
#MM Average number of antineoplastics agents administered plot
#for f in `ls | grep multiple | grep mean_drugs_and_mean_classes_by_year.csv`; do awk 'NR == 1 {print $0 ",\"source\""; split(FILENAME, a, "_"); SOURCE = a[1]; next;}{print $0 ","SOURCE}' $f; done | sort -r | uniq > all/all_sources_multiple_myeloma_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv
mean_drugs_and_mean_classes_by_year <- read.csv(paste0(outputFolder,"all_sources_multiple_myeloma_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv"))
fileName <- "mm_avg_drug_class"
plotAggregateAvgDrugsAdministered(mean_drugs_and_mean_classes_by_year, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
cukarthik/CancerTreatmentCharacterization documentation built on Dec. 19, 2021, 7:03 p.m.
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#make a copy of the csv files of interest into a tmp directory
# cp *_multiple_myeloma_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv ~/Downloads/tmp-nci-results
#create aggregate file
#for f in `ls`; do awk 'NR == 1 {print $0 ",\"source\""; split(FILENAME, a, "_"); SOURCE = a[1]; next;}{print $0 ","SOURCE}' $f; done | sort -r | uniq > all_sources_lung_cancer_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv
#########################################FUNCTIONS############################################################
#This function plots the intervention by year for each cancer. It de-duplicates interventions before plotting
#NOTE: have not figured out how to
plotAggregateInterventions <- function(data, cohortName, outputFolder="~/Downloads/", fileName="test2") {
dedup_interventions <- data %>%
mutate(unique_interventions = map_chr(strsplit(data$distinct_interventions, " \\+ "),
~ toString(sort(.x)))) %>%
group_by(source, dx_year, unique_interventions, year_total) %>%
summarise(unique_total = sum(n), unique_pct = sum(pct)) %>%
mutate(filtered_labels = replace(unique_total, unique_total<=10, ""))
colourCount <- length(unique(dedup_interventions$unique_interventions))
width <- 1980
height <- 2040
outputFile <- paste0(outputFolder, "/", fileName, ".png")
print(paste0("output file:", outputFile))
png(file=outputFile, width=width, height=height)
p <- ggplot(dedup_interventions, aes(fill = unique_interventions, x = dx_year, y = unique_pct)) +
scale_y_continuous(labels = percent_format(accuracy = 1)) +
geom_bar(position = 'fill', stat = 'identity') +
# facet_wrap(~ source , ncol = 2, scales = "free" ) +
facet_wrap(~ factor(source, levels = c('CCAE', 'CUIMC', 'MDCD', 'STANFORD', 'MDCR', 'TUFTS')) , ncol = 2 , scales = "free") +
geom_text(aes(label = filtered_labels), position = position_fill(vjust = .5), size = 5) +
ggtitle(paste0("Percent Distribution of Intervention Types by Year (", cohortName, ")")) +
# theme(legend.position = 'bottom', legend.text = element_text(size = 4),
# legend.key.size = unit(.25, 'cm'), legend.title = element_text(size = 6)) +
theme(legend.position = 'bottom', legend.text = element_text(size = 12),
legend.key.size = unit(.25, 'cm'), legend.title = element_text(size = 0),
legend.spacing.x = unit(.25, "cm")) +
theme(plot.title = element_text(hjust = 0.5, size=30,face="bold"), strip.text = element_text(size = 15)) +
theme(axis.text=element_text(size=12), axis.title=element_text(size=20,face="bold")) +
labs(x = "Diagnosis Year", y = "Percentage", cex.lab = 2) +
scale_x_continuous(breaks= pretty_breaks()) +
scale_fill_manual(values = colorRampPalette(brewer.pal(12, "Set3"))(colourCount))
show(p)
dev.off()
}
#This function aggregates the plots for average drug and drug class
plotAggregateAvgDrugsAdministered <- function(mean_drugs_and_mean_classes_by_year, cohortName, outputFolder="~/Downloads/", fileName="test2") {
#1280x800
width <- 1280
height <- 800#(9/16) * width
#Breast Cancer Average number of antineoplastics agents administered plot
labels <- mean_drugs_and_mean_classes_by_year %>% filter(!is.na(dx_year)) %>% slice(1)
outputFile <- paste0(outputFolder, "/", fileName, ".png")
print(paste0("output file:", outputFile))
png(file=outputFile, width=width, height=height)
p <- ggplot(data = mean_drugs_and_mean_classes_by_year, aes(x = dx_year)) +
geom_line(aes(y = average_number_of_drugs, group = 1, colour="Avg # of Drugs")) +
geom_line(aes(y = standard_deviation, group = 1, colour="Std Dev of Drugs"), linetype=2) +
geom_line(aes(y = class_avg, group = 2, colour="Avg # of Classes of Drugs")) +
geom_line(aes(y = class_sd, group = 2, colour="Std Dev of Classes of Drugs"), linetype=2) +
facet_wrap(~ factor(source, levels = c('CCAE', 'CUIMC', 'MDCD', 'STANFORD', 'MDCR', 'TUFTS')) , ncol = 2 ) +
ggtitle(paste0("Administered Drugs by Year (", cohortName, ")")) +
theme(legend.position = 'bottom', legend.text = element_text(size = 10),
#legend.key.size = unit(.25, 'cm'),
legend.title = element_text(size = 0),
legend.spacing.x = unit(.25, "cm")
) +
# theme(plot.title = element_text(hjust = 0.5, size=30,face="bold")) +
theme(plot.title = element_text(hjust = 0.5, size=30,face="bold"), strip.text = element_text(size = 15)) +
theme(axis.text=element_text(size=12), axis.title=element_text(size=20,face="bold")) +
labs(y="Avg Number of Drugs", x = "Diagnosis Year", cex.lab = 2) +
scale_x_continuous(breaks= pretty_breaks())
show(p)
dev.off()
}
plotAggregateAnalyses <- function(data, outputFolder="~/Downloads/", fileName="test2",
title="TITLE") {
colourCount <- length(unique(data$generic_drug_name))
width <- 1280
height <- 800
outputFile <- paste0(outputFolder, "/", fileName, ".png")
print(paste0("output file:", outputFile))
png(file=outputFile, width=width, height=height)
data <- data %>%
mutate(filtered_labels = replace(n, n<=10, ""))
p <- ggplot(data, aes(fill = generic_drug_name, x = dx_year, y = pct)) +
scale_y_continuous(labels = percent_format(accuracy = 1)) +
geom_bar(position = 'fill', stat = 'identity') +
# facet_wrap(~ source , ncol = 2, scales = "free" ) +
facet_wrap(~ factor(source, levels = c('CCAE', 'CUIMC', 'MDCD', 'STANFORD', 'MDCR', 'TUFTS')) , ncol = 2 ) +
geom_text(aes(label = filtered_labels), position = position_fill(vjust = .5), size = 5) +
ggtitle(title) +
# theme(legend.position = 'bottom', legend.text = element_text(size = 4),
# legend.key.size = unit(.25, 'cm'), legend.title = element_text(size = 6)) +
theme(legend.position = 'bottom', legend.text = element_text(size = 20),
legend.key.size = unit(.5, 'cm'), legend.title = element_text(size = 0),
legend.spacing.x = unit(.25, "cm")) +
theme(plot.title = element_text(hjust = 0.5, size=35), strip.text = element_text(size = 15)) +
theme(axis.text=element_text(size=12), axis.title=element_text(size=30)) +
labs(x = "Diagnosis Year", y = "Percentage", cex.lab = 2) +
scale_x_continuous(labels = scales::number_format(), breaks= pretty_breaks())
scale_fill_manual(values = colorRampPalette(brewer.pal(12, "Paired"))(colourCount))
show(p)
dev.off()
}
#########################################SCRIPT############################################################
library(devtools)
library(ggplot2)
library(dplyr)
library(purrr)
library(stringr)
library(RColorBrewer)
library(scales)
#ToDo: change the values for the input and output folders
inputFolder <- "~/tmp-nci-results/all/"
outputFolder <- "~/tmp-nci-results/all/"
#########################################BREAST############################################################
#breast intervention plot
data <- read.csv(paste0(inputFolder,"all_sources_breast_cancer_treatment_dataset_percent_interventions_types_per_year.csv"))
cohortName <- "Breast"
fileName <- "breast_interventions_by_year"
plotAggregateInterventions(data, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
# dedup_interventions <- data %>%
# mutate(unique_interventions = map_chr(strsplit(data$distinct_interventions, " \\+ "),
# ~ toString(sort(.x)))) %>%
# group_by(source, dx_year, unique_interventions, year_total) %>%
# summarise(unique_total = sum(n), unique_pct = sum(pct)) %>%
# mutate(filtered_labels = replace(unique_total, unique_total<=10, ""))
# ggplot(dedup_interventions, aes(fill = unique_interventions, x = dx_year, y = unique_pct)) +
# scale_y_continuous(labels = percent_format(accuracy = 1)) +
# geom_bar(position = 'fill', stat = 'identity') +
# # facet_wrap(~ source , ncol = 2, scales = "free" ) +
# facet_wrap(~ factor(source, levels = c('CCAE', 'CUIMC', 'MDCD', 'STANFORD', 'MDCR', 'TUFTS')) , ncol = 2 , scales = "free") +
# geom_text(aes(label = filtered_labels), position = position_fill(vjust = .5), size = 3) +
# ggtitle('Percent Distribution of Intervention Types by Year (Breast)') +
# # theme(legend.position = 'bottom', legend.text = element_text(size = 4),
# # legend.key.size = unit(.25, 'cm'), legend.title = element_text(size = 6)) +
# theme(legend.position = 'bottom', legend.text = element_text(size = 12),
# legend.key.size = unit(.25, 'cm'), legend.title = element_text(size = 0),
# legend.spacing.x = unit(.25, "cm")) +
# theme(plot.title = element_text(hjust = 0.5), strip.text = element_text(size = 15)) +
# labs(x = "Diagnosis Year", y = "Percentage") +
# scale_x_continuous(breaks= pretty_breaks())
# # scale_x_continuous(breaks=seq(2000, 2020, 2))
# ggsave(filename = "~/Downloads/test.png")
fileName <- "breast_avg_drug_class"
data <- read.csv(paste0(outputFolder,"all_sources_breast_cancer_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv"))
plotAggregateAvgDrugsAdministered(data, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
#1280x800
# width = 1280
# height = 800#(9/16) * width
# #Breast Cancer Average number of antineoplastics agents administered plot
# mean_drugs_and_mean_classes_by_year <- read.csv(paste0(outputFolder,"all_sources_breast_cancer_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv"))
# labels <- mean_drugs_and_mean_classes_by_year %>% filter(!is.na(dx_year)) %>% slice(1)
# ggplot(data = mean_drugs_and_mean_classes_by_year, aes(x = dx_year)) +
# geom_line(aes(y = average_number_of_drugs, group = 1, colour="Avg # of Drugs")) +
# geom_line(aes(y = standard_deviation, group = 1, colour="Std Dev of Drugs"), linetype=2) +
# geom_line(aes(y = class_avg, group = 2, colour="Avg # of Classes of Drugs")) +
# geom_line(aes(y = class_sd, group = 2, colour="Std Dev of Classes of Drugs"), linetype=2) +
# facet_wrap(~ factor(source, levels = c('CCAE', 'CUIMC', 'MDCD', 'STANFORD', 'MDCR', 'TUFTS')) , ncol = 2 ) +
# ggtitle('Administered Drugs by Year (Breast)') +
# theme(legend.position = 'bottom', legend.text = element_text(size = .1/.pt),
# #legend.key.size = unit(.25, 'cm'),
# legend.title = element_text(size = 0),
# legend.spacing.x = unit(.25, "cm")
# ) +
# theme(plot.title = element_text(hjust = 0.5)) +
# theme(plot.title = element_text(hjust = 0.5), strip.text = element_text(size = 15/.pt)) +
# labs(y="Avg Number of Drugs", x = "Diagnosis Year")
# coord_fixed(ratio = (width/height))
#ggsave(filename = "~/Downloads/test2.png", width = 10.16, height = 6.35, units = "cm")
#code to generate aggregate data file
# for f in `ls | grep breast | grep chemotherapy_neoadjuvant.csv`; do awk 'NR == 1 {print $0 ",\"source\""; split(FILENAME, a, "_"); SOURCE = a[1]; next;}{print $0 ","SOURCE}' $f; done | sort -r | uniq > all/all_sources_breast_cancer_chemotherapy_neoadjuvant.csv
fileName <- "breast_chemotherapy_neoadjuvant"
title <- "First-line Chemotherapy in the Neoadjuvant Setting (Breast)"
data <- read.csv(paste0(outputFolder,"all_sources_breast_cancer_chemotherapy_neoadjuvant.csv"))
fieldName <- "generic_drug_name" #field in data that will be plotted
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
#########################################PROSTATE############################################################
data <- read.csv(paste0(inputFolder,"all_sources_prostate_cancer_treatment_dataset_percent_interventions_types_per_year.csv"))
cohortName <- "Prostate"
fileName <- "prostate_interventions_by_year"
plotAggregateInterventions(data, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
#Prostate Cancer Average number of antineoplastics agents administered plot
mean_drugs_and_mean_classes_by_year <- read.csv(paste0(outputFolder,"all_sources_prostate_cancer_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv"))
fileName <- "prostate_avg_drug_class"
plotAggregateAvgDrugsAdministered(mean_drugs_and_mean_classes_by_year, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
fileName <- "prostate_chemotherapy_neoadjuvant"
title <- "Percent Distribution of First-line Chemotherapy Neoadjuvant (Prostate)"
data <- read.csv(paste0(outputFolder,"all_sources_prostate_cancer_chemotherapy_neoadjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
fileName <- "prostate_chemotherapy_adjuvant"
title <- "Percent Distribution of First-line Chemotherapy Adjuvant (Prostate)"
data <- read.csv(paste0(outputFolder,"all_sources_prostate_cancer_chemotherapy_adjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
#########################################Lung############################################################
#Lung Cancer Average number of antineoplastics agents administered plot
data <- read.csv(paste0(inputFolder,"all_sources_lung_cancer_treatment_dataset_percent_interventions_types_per_year.csv"))
cohortName = "Lung"
fileName = "lung_interventions_by_year"
#fix data by merging Total Lobectomy and Lobectomy into one
data$distinct_interventions_orig <- data$distinct_interventions
data <- data %>%
mutate(tmp_col = str_replace(data$distinct_interventions, "Total Lobectomy", "Lobectomy")) %>%
mutate(distinct_interventions = sapply(strsplit(tmp_col, " \\+ "),
function(x) paste(unique(x), collapse = " + ")))
plotAggregateInterventions(data, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
mean_drugs_and_mean_classes_by_year <- read.csv(paste0(outputFolder,"all_sources_lung_cancer_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv"))
fileName <- "lung_avg_drug_class"
plotAggregateAvgDrugsAdministered(mean_drugs_and_mean_classes_by_year, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
####Lung Chemo
fileName <- "lung_chemotherapy_neoadjuvant"
title <- "Percent Distribution of First-line Chemotherapy Neoadjuvant (Lung)"
data <- read.csv(paste0(outputFolder,"all_sources_lung_cancer_chemotherapy_neoadjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
fileName <- "lung_chemotherapy_adjuvant"
title <- "Percent Distribution of First-line Chemotherapy Adjuvant (Lung)"
data <- read.csv(paste0(outputFolder,"all_sources_lung_cancer_chemotherapy_adjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
####Lung EGFR
fileName <- "lung_egfr_neoadjuvant"
title <- "Percent Distribution of First-line EGFR Therapy Neoadjuvant (Lung)"
data <- read.csv(paste0(outputFolder,"all_sources_lung_cancer_egfr_therapy_neoadjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
fileName <- "lung_egfr_adjuvant"
title <- "Percent Distribution of First-line EGFR Therapy Adjuvant (Lung)"
data <- read.csv(paste0(outputFolder,"all_sources_lung_cancer_egfr_therapy_adjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
####Lung VEGF
fileName <- "lung_vegf_neoadjuvant"
title <- "Percent Distribution of First-line VEGF Therapy Neoadjuvant (Lung)"
data <- read.csv(paste0(outputFolder,"all_sources_lung_cancer_vegf_therapy_neoadjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
fileName <- "lung_vegf_adjuvant"
title <- "Percent Distribution of First-line VEGF Therapy Adjuvant (Lung)"
data <- read.csv(paste0(outputFolder,"all_sources_lung_cancer_vegf_therapy_adjuvant.csv"))
plotAggregateAnalyses(data, outputFolder = outputFolder, fileName = fileName, title = title)
#########################################Multiple Myeloma############################################################
#for f in `ls | grep multiple | grep interventions_types_per_year.csv`; do awk 'NR == 1 {print $0 ",\"source\""; split(FILENAME, a, "_"); SOURCE = a[1]; next;}{print $0 ","SOURCE}' $f; done | sort -r | uniq > all/all_sources_multiple_myeloma_treatment_dataset_interventions_types_per_year.csv
data <- read.csv(paste0(inputFolder,"all_sources_multiple_myeloma_treatment_dataset_percent_interventions_types_per_year.csv"))
cohortName = "Multiple Myeloma"
fileName = "mm_interventions_by_year"
plotAggregateInterventions(data, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
#MM Average number of antineoplastics agents administered plot
#for f in `ls | grep multiple | grep mean_drugs_and_mean_classes_by_year.csv`; do awk 'NR == 1 {print $0 ",\"source\""; split(FILENAME, a, "_"); SOURCE = a[1]; next;}{print $0 ","SOURCE}' $f; done | sort -r | uniq > all/all_sources_multiple_myeloma_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv
mean_drugs_and_mean_classes_by_year <- read.csv(paste0(outputFolder,"all_sources_multiple_myeloma_treatment_dataset_mean_drugs_and_mean_classes_by_year.csv"))
fileName <- "mm_avg_drug_class"
plotAggregateAvgDrugsAdministered(mean_drugs_and_mean_classes_by_year, cohortName = cohortName, outputFolder = outputFolder, fileName = fileName)
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