packages <- c("tidyverse", "tidymodels", "vip", "pROC", "ggplot2", "scales", "janitor", "skimr")
installed <- rownames(installed.packages())
to_install <- packages[!packages %in% installed]
if (length(to_install)) install.packages(to_install)
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.3
library(tidyverse)
## Warning: package 'readr' was built under R version 4.4.3
## Warning: package 'purrr' was built under R version 4.4.3
## Warning: package 'dplyr' was built under R version 4.4.3
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.2.1     ✔ readr     2.2.0
## ✔ forcats   1.0.1     ✔ stringr   1.6.0
## ✔ lubridate 1.9.5     ✔ tibble    3.3.1
## ✔ purrr     1.2.2     ✔ tidyr     1.3.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggrepel)
# library(tidymodels)
# library(vip)
# library(pROC)
# library(scales)
# library(janitor)
# library(skimr)

# set.seed(42) - ??

Load data

# data_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00296/dataset_diabetes'
# data_path = 'diabetic_data.csv'
# 
# if (!file.exists(data_path)) {
#   message("Downloading dataset...")
#   tmp <- tempfile(fileext = ".zip")
#   download.file(data_url, tmp, mode = "wb")
#   unzip(tmp, files = "dataset_diabetes/diabetic_data.csv", junkpaths = TRUE)
#   message("Done. File saved as: ", data_path)
# }
# 
#   raw <- read_csv(data_path, na = c("?", "Unknown", "")) |>
#   clean_names()
#   message("Rows: ", nrow(raw), " | Cols: ", ncol(raw))

diabetic_data = read.csv('diabetes+130-us+hospitals+for+years+1999-2008/diabetic_data.csv')
# diabetic_data

cat("Raw dimensions:", nrow(diabetic_data), "rows x", ncol(diabetic_data), "columns\n")
## Raw dimensions: 101766 rows x 50 columns

Race

unique_types_race = unique(diabetic_data$race)
# unique_types_race

diabetic_data$race <- factor(diabetic_data$race,
                          levels = c("Caucasian", "AfricanAmerican", "?", "Other", "Asian", "Hispanic"))

summary_race = data.frame(table(diabetic_data$race))
colnames(summary_race)[colnames(summary_race) == 'Var1'] <- 'Race'
colnames(summary_race)[colnames(summary_race) == 'Freq'] <- 'Count'
# summary_race

summary_race <- summary_race %>% 
  mutate(csum = rev(cumsum(rev(Count))), 
         pos = Count/2 + lead(csum, 1),
         pos = if_else(is.na(pos), Count/2, pos))

summary_race
##              Race Count   csum     pos
## 1       Caucasian 76099 101766 63716.5
## 2 AfricanAmerican 19210  25667 16062.0
## 3               ?  2273   6457  5320.5
## 4           Other  1506   4184  3431.0
## 5           Asian   641   2678  2357.5
## 6        Hispanic  2037   2037  1018.5

Gender

unique_types_gender = unique(diabetic_data$gender)
# unique_types_gender

diabetic_data$gender = factor(diabetic_data$gender,
                              levels = c('Female', 'Male', 'Unknown/Invalid'))

summary_gender = data.frame(table(diabetic_data$gender))
colnames(summary_gender)[colnames(summary_gender) == 'Var1'] <- 'Gender'
colnames(summary_gender)[colnames(summary_gender) == 'Freq'] <- 'Count'
# summary_gender

summary_gender <- summary_gender %>% 
  mutate(csum = rev(cumsum(rev(Count))), 
         pos = Count/2 + lead(csum, 1),
         pos = if_else(is.na(pos), Count/2, pos))

summary_gender
##            Gender Count   csum     pos
## 1          Female 54708 101766 74412.0
## 2            Male 47055  47058 23530.5
## 3 Unknown/Invalid     3      3     1.5

require(gridExtra)
## Loading required package: gridExtra
## 
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
## 
##     combine
plot1 = ggplot(summary_race, aes(x = "" , y = Count, fill = Race)) +
  geom_col(width = 1, color = 1) +
  coord_polar(theta = "y") +
  scale_fill_brewer(palette = "Pastel1") +
  geom_label_repel(data = summary_race,
                   aes(y = pos, label = paste0(Count)),
                   size = 4.5, nudge_x = 1, show.legend = FALSE) +
  guides(fill = guide_legend(title = "Race")) +
  theme_void() +
  labs(title = "Race distribution")

plot2 = ggplot(summary_gender, aes(x = "" , y = Count, fill = Gender)) +
  geom_col(width = 1, color = 1) +
  coord_polar(theta = "y") +
  scale_fill_brewer(palette = "Pastel1") +
  geom_label_repel(data = summary_gender,
                   aes(y = pos, label = paste0(Count)),
                   size = 4.5, nudge_x = 1, show.legend = FALSE) +
  guides(fill = guide_legend(title = "Gender")) +
  theme_void() +
  theme(panel.background = element_rect(fill = "#F5F5F5"))+
  labs(title = "Gender distribution")

grid.arrange(plot1, plot2, ncol = 2)

#

ggplot(summary_gender, aes(x="", y = Count, fill = Gender)) +
  #geom_bar(stat="identity", width=1) +
  geom_col(width = 1, color = 1) +
  coord_polar("y", start=0) +
  scale_fill_brewer(palette = "Pastel1") +
  geom_label_repel(data = summary_gender,
                   aes(y = Count, label = paste0(Count)), 
                   size = 4.5, nudge_x = 1, show.legend = FALSE) +
  
  theme_void()

#

df3 <- summary_gender %>% 
  mutate(csum = rev(cumsum(rev(Count))), 
         pos = Count/2 + lead(csum, 1),
         pos = if_else(is.na(pos), Count/2, pos))

df3
##            Gender Count   csum     pos
## 1          Female 54708 101766 74412.0
## 2            Male 47055  47058 23530.5
## 3 Unknown/Invalid     3      3     1.5
ggplot(df3, aes(x = "" , y = Count, fill = Gender)) +
  geom_col(width = 1, color = 1) +
  coord_polar(theta = "y") +
  scale_fill_brewer(palette = "Pastel1") +
  geom_label_repel(data = df3,
                   aes(y = pos, label = paste0(Count)),
                   size = 4.5, nudge_x = 1, show.legend = FALSE) +
  guides(fill = guide_legend(title = "Group")) +
  theme_void()

str(diabetic_data)
## 'data.frame':    101766 obs. of  50 variables:
##  $ encounter_id            : int  2278392 149190 64410 500364 16680 35754 55842 63768 12522 15738 ...
##  $ patient_nbr             : int  8222157 55629189 86047875 82442376 42519267 82637451 84259809 114882984 48330783 63555939 ...
##  $ race                    : Factor w/ 6 levels "Caucasian","AfricanAmerican",..: 1 1 2 1 1 1 1 1 1 1 ...
##  $ gender                  : Factor w/ 3 levels "Female","Male",..: 1 1 1 2 2 2 2 2 1 1 ...
##  $ age                     : chr  "[0-10)" "[10-20)" "[20-30)" "[30-40)" ...
##  $ weight                  : chr  "?" "?" "?" "?" ...
##  $ admission_type_id       : int  6 1 1 1 1 2 3 1 2 3 ...
##  $ discharge_disposition_id: int  25 1 1 1 1 1 1 1 1 3 ...
##  $ admission_source_id     : int  1 7 7 7 7 2 2 7 4 4 ...
##  $ time_in_hospital        : int  1 3 2 2 1 3 4 5 13 12 ...
##  $ payer_code              : chr  "?" "?" "?" "?" ...
##  $ medical_specialty       : chr  "Pediatrics-Endocrinology" "?" "?" "?" ...
##  $ num_lab_procedures      : int  41 59 11 44 51 31 70 73 68 33 ...
##  $ num_procedures          : int  0 0 5 1 0 6 1 0 2 3 ...
##  $ num_medications         : int  1 18 13 16 8 16 21 12 28 18 ...
##  $ number_outpatient       : int  0 0 2 0 0 0 0 0 0 0 ...
##  $ number_emergency        : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ number_inpatient        : int  0 0 1 0 0 0 0 0 0 0 ...
##  $ diag_1                  : chr  "250.83" "276" "648" "8" ...
##  $ diag_2                  : chr  "?" "250.01" "250" "250.43" ...
##  $ diag_3                  : chr  "?" "255" "V27" "403" ...
##  $ number_diagnoses        : int  1 9 6 7 5 9 7 8 8 8 ...
##  $ max_glu_serum           : chr  "None" "None" "None" "None" ...
##  $ A1Cresult               : chr  "None" "None" "None" "None" ...
##  $ metformin               : chr  "No" "No" "No" "No" ...
##  $ repaglinide             : chr  "No" "No" "No" "No" ...
##  $ nateglinide             : chr  "No" "No" "No" "No" ...
##  $ chlorpropamide          : chr  "No" "No" "No" "No" ...
##  $ glimepiride             : chr  "No" "No" "No" "No" ...
##  $ acetohexamide           : chr  "No" "No" "No" "No" ...
##  $ glipizide               : chr  "No" "No" "Steady" "No" ...
##  $ glyburide               : chr  "No" "No" "No" "No" ...
##  $ tolbutamide             : chr  "No" "No" "No" "No" ...
##  $ pioglitazone            : chr  "No" "No" "No" "No" ...
##  $ rosiglitazone           : chr  "No" "No" "No" "No" ...
##  $ acarbose                : chr  "No" "No" "No" "No" ...
##  $ miglitol                : chr  "No" "No" "No" "No" ...
##  $ troglitazone            : chr  "No" "No" "No" "No" ...
##  $ tolazamide              : chr  "No" "No" "No" "No" ...
##  $ examide                 : chr  "No" "No" "No" "No" ...
##  $ citoglipton             : chr  "No" "No" "No" "No" ...
##  $ insulin                 : chr  "No" "Up" "No" "Up" ...
##  $ glyburide.metformin     : chr  "No" "No" "No" "No" ...
##  $ glipizide.metformin     : chr  "No" "No" "No" "No" ...
##  $ glimepiride.pioglitazone: chr  "No" "No" "No" "No" ...
##  $ metformin.rosiglitazone : chr  "No" "No" "No" "No" ...
##  $ metformin.pioglitazone  : chr  "No" "No" "No" "No" ...
##  $ change                  : chr  "No" "Ch" "No" "Ch" ...
##  $ diabetesMed             : chr  "No" "Yes" "Yes" "Yes" ...
##  $ readmitted              : chr  "NO" ">30" "NO" "NO" ...

# na_count = sapply(diabetic_data_raw, function(y) sum(length(which(is.na(y)))))
# na_count = data.frame(na_count)
# na_count
# data$epc_label <- factor(data$epc_label,
#                           levels = c("A", "B", "C", "D", "E", "F"))

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