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datasets_overview.R
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datasets_overview.R
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################################################################################
#' Title:
#' Author: Jorge del Pozo Lerida
#' Date: 2024-04-27
#' Description:
################################################################################
## Setup -------------------------------------------------------------------
library(tidyverse)
library(oro.nifti)
all_studies <- read_csv("../data-misc/csv/ALL_studies.csv")
# Summaries ---------------------------------------------------------------
##########
# Studies, patients
##########
# resolution, # numb CMB per patient, # size of CMB
# Summaries by dataset for all studies
summ_studies_dat_seq <- all_studies %>%
group_by(Dataset) %>%
summarise(
n_series = n_distinct(seriesUID),
n_series_cmb = n_distinct(seriesUID[healthy == "no"]),
n_patients = n_distinct(patientUID),
n_patients_cmb = n_distinct(patientUID[healthy == "no"]),
n_CMB = sum(n_CMB_new, na.rm = TRUE),
.groups = "drop"
) %>%
ungroup() %>%
# Adding totals
bind_rows(
all_studies %>%
summarise(
Dataset = "Total",
n_series = n_distinct(seriesUID),
n_series_cmb = sum(healthy == "no" & !is.na(seriesUID), na.rm = TRUE),
n_patients = n_distinct(patientUID),
n_patients_cmb = n_distinct(patientUID[healthy == "no"]),
n_CMB = sum(n_CMB_new, na.rm = TRUE)
)
)
#
# summ_studies_dat_res <- all_studies %>%
# group_by(Dataset, res_level) %>%
# summarise(
# n_patients = n_distinct(patientUID),
# n_patients_h = n_distinct(patientUID[healthy == "yes"]),
# n_series = n_distinct(seriesUID),
# n_series_h = n_distinct(seriesUID[healthy == "yes"]),
# n_CMB = sum(n_CMB_new, na.rm = TRUE),
# .groups = "drop"
# ) %>%
# ungroup() %>%
# # Adding totals
# bind_rows(
# all_studies %>%
# summarise(
# Dataset = "Total",
# res_level = "-",
# n_patients = n_distinct(patientUID),
# n_patients_h = n_distinct(patientUID[healthy == "yes"]),
# n_series = n_distinct(seriesUID),
# n_series_h = sum(healthy == "yes" & !is.na(seriesUID), na.rm = TRUE),
# n_CMB = sum(n_CMB_new, na.rm = TRUE)
# )
# )
#
# summ_studies_dat_res_seq <- all_studies %>%
# group_by(Dataset, res_level, seq_type) %>%
# summarise(
# n_patients = n_distinct(patientUID),
# n_patients_h = n_distinct(patientUID[healthy == "yes"]),
# n_series = n_distinct(seriesUID),
# n_series_h = n_distinct(seriesUID[healthy == "yes"]),
# n_CMB = sum(n_CMB_new, na.rm = TRUE),
# .groups = "drop"
# ) %>%
# ungroup() %>%
# # Adding totals
# bind_rows(
# all_studies %>%
# summarise(
# Dataset = "Total",
# res_level = "-",
# seq_type = "-",
# n_patients = n_distinct(patientUID),
# n_patients_h = n_distinct(patientUID[healthy == "yes"]),
# n_series = n_distinct(seriesUID),
# n_series_h = sum(healthy == "yes" & !is.na(seriesUID), na.rm = TRUE),
# n_CMB = sum(n_CMB_new, na.rm = TRUE)
# )
# )
# summ_studies_dat_res_seq <- all_studies %>%
# group_by(Dataset, res_level, seq_type, field_stregth) %>%
# summarise(
# n_patients = n_distinct(patientUID),
# n_patients_h = n_distinct(patientUID[healthy == "yes"]),
# n_series = n_distinct(seriesUID),
# n_series_h = n_distinct(seriesUID[healthy == "yes"]),
# n_CMB = sum(n_CMB_new, na.rm = TRUE),
# .groups = 'drop'
# ) %>%
# ungroup() %>%
# # Adding totals
# bind_rows(
# all_studies %>%
# summarise(
# Dataset = "Total",
# res_level="-",
# seq_type="-",
# field_stregth="-",
# n_patients = n_distinct(patientUID),
# n_patients_h = n_distinct(patientUID[healthy == "yes"]),
# n_series = n_distinct(seriesUID),
# n_series_h = sum(healthy == "yes" & !is.na(seriesUID), na.rm = TRUE),
# n_CMB = sum(n_CMB_new, na.rm = TRUE)
# )
# )
##########
# n CMB
##########
filtered_data <- all_studies %>%
filter(healthy_all!=T) %>%
filter(Dataset != "sMOMENI") %>%
distinct(Dataset, patientUID, nCMB_avg, CMB_level)
# Create a histogram of 'n_CMB_new', grouped by 'PATIENTiD'
ggplot(filtered_data, aes(x = nCMB_avg)) +
geom_histogram(binwidth = 1, fill = "skyblue", color = "black") + # You can adjust the binwidth as needed
theme_minimal() +
labs(title = "Histogram of CMB by patientUID",
x = "CMB",
y = "Frequency") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Improves readability of x-axis labels if needed
summ_nCMB <- all_studies %>%
distinct(seriesUID, .keep_all = T) %>%
select(seriesUID, n_CMB_new) %>%
mutate(numeric_var = replace_na(n_CMB_new, 0)) %>%
filter(numeric_var != 0) %>%
summarise(
Count = n(),
Mean = mean(numeric_var, na.rm = TRUE),
Median = median(numeric_var, na.rm = TRUE),
Std_Deviation = sd(numeric_var, na.rm = TRUE),
Min = min(numeric_var, na.rm = TRUE),
Max = max(numeric_var, na.rm = TRUE),
Range = max(numeric_var, na.rm = TRUE) - min(numeric_var, na.rm = TRUE),
IQR = IQR(numeric_var, na.rm = TRUE)
)
# summ_nCMB_real <- all_studies_real %>%
# distinct(seriesUID, .keep_all = T) %>%
# select(seriesUID, n_CMB_new) %>%
# mutate(numeric_var = replace_na(n_CMB_new, 0)) %>%
# filter(numeric_var != 0) %>%
# summarise(
# Count = n(),
# Mean = mean(numeric_var, na.rm = TRUE),
# Median = median(numeric_var, na.rm = TRUE),
# Std_Deviation = sd(numeric_var, na.rm = TRUE),
# Min = min(numeric_var, na.rm = TRUE),
# Max = max(numeric_var, na.rm = TRUE),
# Range = max(numeric_var, na.rm = TRUE) - min(numeric_var, na.rm = TRUE),
# IQR = IQR(numeric_var, na.rm = TRUE)
# )
#
# ##########
# # size and radius CMB
# ##########
# summ_sizeCMB <- cmb_new %>%
# distinct(subject, Dataset, .keep_all = T) %>%
# mutate(numeric_var = replace_na(size, 0)) %>%
# filter(numeric_var != 0) %>%
# summarise(
# Count = n(),
# Mean = mean(numeric_var, na.rm = TRUE),
# Median = median(numeric_var, na.rm = TRUE),
# Std_Deviation = sd(numeric_var, na.rm = TRUE),
# Min = min(numeric_var, na.rm = TRUE),
# Max = max(numeric_var, na.rm = TRUE),
# Range = max(numeric_var, na.rm = TRUE) - min(numeric_var, na.rm = TRUE),
# IQR = IQR(numeric_var, na.rm = TRUE)
# )
# summ_sizeCMB_real <- cmb_new %>%
# filter(Dataset != "pMOMENI_synth") %>%
# distinct(subject, Dataset, .keep_all = T) %>%
# mutate(numeric_var = replace_na(size, 0)) %>%
# filter(numeric_var != 0) %>%
# summarise(
# Count = n(),
# Mean = mean(numeric_var, na.rm = TRUE),
# Median = median(numeric_var, na.rm = TRUE),
# Std_Deviation = sd(numeric_var, na.rm = TRUE),
# Min = min(numeric_var, na.rm = TRUE),
# Max = max(numeric_var, na.rm = TRUE),
# Range = max(numeric_var, na.rm = TRUE) - min(numeric_var, na.rm = TRUE),
# IQR = IQR(numeric_var, na.rm = TRUE)
# )
#
# summ_radCMB <- cmb_new %>%
# distinct(subject, Dataset, .keep_all = T) %>%
# mutate(numeric_var = replace_na(radius, 0)) %>%
# filter(numeric_var != 0) %>%
# summarise(
# Count = n(),
# Mean = mean(numeric_var, na.rm = TRUE),
# Median = median(numeric_var, na.rm = TRUE),
# Std_Deviation = sd(numeric_var, na.rm = TRUE),
# Min = min(numeric_var, na.rm = TRUE),
# Max = max(numeric_var, na.rm = TRUE),
# Range = max(numeric_var, na.rm = TRUE) - min(numeric_var, na.rm = TRUE),
# IQR = IQR(numeric_var, na.rm = TRUE)
# )
# summ_radCMB_real <- cmb_new %>%
# filter(Dataset != "pMOMENI_synth") %>%
# distinct(subject, Dataset, .keep_all = T) %>%
# mutate(numeric_var = replace_na(radius, 0)) %>%
# filter(numeric_var != 0) %>%
# summarise(
# Count = n(),
# Mean = mean(numeric_var, na.rm = TRUE),
# Median = median(numeric_var, na.rm = TRUE),
# Std_Deviation = sd(numeric_var, na.rm = TRUE),
# Min = min(numeric_var, na.rm = TRUE),
# Max = max(numeric_var, na.rm = TRUE),
# Range = max(numeric_var, na.rm = TRUE) - min(numeric_var, na.rm = TRUE),
# IQR = IQR(numeric_var, na.rm = TRUE)
# )
#
#
# ##########
# # resolutions, scan params, scanner
# ##########
# # Function to calculate percentages and format output
# calc_summary <- function(x) {
# freq <- table(x)
# percent <- round(100 * freq / sum(freq), 2)
#
# # Check if only one category exists
# if (length(freq) == 1) {
# return(names(freq))
# } else {
# return(paste(paste0(round(percent), "%"), names(freq), sep = ": ", collapse = ", "))
# }
# }
#
# summ_reso <- all_studies %>%
# summarise(
# # # Demographics = calc_summary(Demographics),
# # Location = calc_summary(Location),
# # `Scanner Type` = calc_summary(`Scanner Type`),
# # `Scanner Model` = calc_summary(`Scanner Model`),
# # `Seq. Type` = calc_summary(`Seq. Type`),
# # `TR/TE (ms)` = calc_summary(`TR/TE (ms)`),
# # # `TR (ms)` = calc_summary(`TR (ms)`),
# # # `TE (ms)` = calc_summary(`TE (ms)`),
# # `Flip Angle` = calc_summary(`Flip Angle`),
# Resolution = calc_summary(new_shape),
# Resolution_old = calc_summary(old_shape),
# `Voxel Size (mm3)` = calc_summary(new_voxel_dim),
# `Voxel Size (mm3) - OLD` = calc_summary(old_voxel_dim),
# `# patients` = n()
# )
#
# summ_reso_dat <- all_studies %>%
# group_by(Dataset) %>%
# summarise(
# # # Demographics = calc_summary(Demographics),
# # Location = calc_summary(Location),
# # `Scanner Type` = calc_summary(`Scanner Type`),
# # `Scanner Model` = calc_summary(`Scanner Model`),
# # `Seq. Type` = calc_summary(`Seq. Type`),
# # `TR/TE (ms)` = calc_summary(`TR/TE (ms)`),
# # # `TR (ms)` = calc_summary(`TR (ms)`),
# # # `TE (ms)` = calc_summary(`TE (ms)`),
# # `Flip Angle` = calc_summary(`Flip Angle`),
# Resolution = calc_summary(new_shape),
# Resolution_old = calc_summary(old_shape),
# `Voxel Size (mm3)` = calc_summary(new_voxel_dim),
# `Voxel Size (mm3) - OLD` = calc_summary(old_voxel_dim),
# `# patients` = n()
# )
#
# summ_reso_real <- all_studies_real %>%
# summarise(
# # # Demographics = calc_summary(Demographics),
# # Location = calc_summary(Location),
# # `Scanner Type` = calc_summary(`Scanner Type`),
# # `Scanner Model` = calc_summary(`Scanner Model`),
# # `Seq. Type` = calc_summary(`Seq. Type`),
# # `TR/TE (ms)` = calc_summary(`TR/TE (ms)`),
# # # `TR (ms)` = calc_summary(`TR (ms)`),
# # # `TE (ms)` = calc_summary(`TE (ms)`),
# # `Flip Angle` = calc_summary(`Flip Angle`),
# Resolution = calc_summary(new_shape),
# Resolution_old = calc_summary(old_shape),
# `Voxel Size (mm3)` = calc_summary(new_voxel_dim),
# `Voxel Size (mm3) - OLD` = calc_summary(old_voxel_dim),
# `# patients` = n()
# )
#
# summ_reso_dat_real <- all_studies_real %>%
# group_by(Dataset) %>%
# summarise(
# # # Demographics = calc_summary(Demographics),
# # Location = calc_summary(Location),
# # `Scanner Type` = calc_summary(`Scanner Type`),
# # `Scanner Model` = calc_summary(`Scanner Model`),
# # `Seq. Type` = calc_summary(`Seq. Type`),
# # `TR/TE (ms)` = calc_summary(`TR/TE (ms)`),
# # # `TR (ms)` = calc_summary(`TR (ms)`),
# # # `TE (ms)` = calc_summary(`TE (ms)`),
# # `Flip Angle` = calc_summary(`Flip Angle`),
# Resolution = calc_summary(new_shape),
# Resolution_old = calc_summary(old_shape),
# `Voxel Size (mm3)` = calc_summary(new_voxel_dim),
# `Voxel Size (mm3) - OLD` = calc_summary(old_voxel_dim),
# `# patients` = n()
# )