Jiying Wang 2023-12-15
library(tidyverse)
library(dplyr)
library(MASS)
library(ggplot2)
library(corrplot)
library(leaps)
library(glmnet)
library(igraph)
library(arules)
library(caret)
library(pROC)# import data
breastcancer = read_csv("./data/Project_2_data.csv")## Rows: 4024 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): Race, Marital Status, T Stage, N Stage, 6th Stage, differentiate, ...
## dbl (5): Age, Tumor Size, Regional Node Examined, Reginol Node Positive, Su...
##
## 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.
#Data Cleaning
breastcancer_1 = breastcancer|>
janitor::clean_names()|>
mutate(
race = as_factor(race),
marital_status = factor(marital_status, levels = c("Single", "Married", "Divorced", "Separated", "Widowed")),
t_stage = factor(t_stage, levels = c("T1", "T2", "T3", "T4")),
n_stage = factor(n_stage, levels = c("N1", "N2", "N3")),
x6th_stage = factor(x6th_stage, levels = c("IIA", "IIB", "IIIA", "IIIB", "IIIC")),
differentiate = factor(differentiate, levels = c("Moderately differentiated", "Poorly differentiated", "Undifferentiated", "Well differentiated")),
grade = factor(grade, levels = c("1", "2", "3", "anaplastic; Grade IV")),
a_stage = factor(a_stage, levels = c("Distant", "Regional")),
estrogen_status = as_factor(estrogen_status),
progesterone_status = as_factor(progesterone_status),
status = ifelse(status == "Dead", 1, 0),
status = as_factor(status))
breastcancer_clean = breastcancer_1|>
mutate(node_positive_prop = reginol_node_positive/regional_node_examined,
node_positive_prop = round(node_positive_prop, 4))
breastcancer_clean## # A tibble: 4,024 × 17
## age race marital_status t_stage n_stage x6th_stage differentiate grade
## <dbl> <fct> <fct> <fct> <fct> <fct> <fct> <fct>
## 1 68 White Married T1 N1 IIA Poorly different… 3
## 2 50 White Married T2 N2 IIIA Moderately diffe… 2
## 3 58 White Divorced T3 N3 IIIC Moderately diffe… 2
## 4 58 White Married T1 N1 IIA Poorly different… 3
## 5 47 White Married T2 N1 IIB Poorly different… 3
## 6 51 White Single T1 N1 IIA Moderately diffe… 2
## 7 51 White Married T1 N1 IIA Well differentia… 1
## 8 40 White Married T2 N1 IIB Moderately diffe… 2
## 9 40 White Divorced T4 N3 IIIC Poorly different… 3
## 10 69 White Married T4 N3 IIIC Well differentia… 1
## # ℹ 4,014 more rows
## # ℹ 9 more variables: a_stage <fct>, tumor_size <dbl>, estrogen_status <fct>,
## # progesterone_status <fct>, regional_node_examined <dbl>,
## # reginol_node_positive <dbl>, survival_months <dbl>, status <fct>,
## # node_positive_prop <dbl>
# Descriptive table with summary statistics
summary(breastcancer_clean) |>
knitr::kable()| age | race | marital_status | t_stage | n_stage | x6th_stage | differentiate | grade | a_stage | tumor_size | estrogen_status | progesterone_status | regional_node_examined | reginol_node_positive | survival_months | status | node_positive_prop | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. :30.00 | White:3413 | Single : 615 | T1:1603 | N1:2732 | IIA :1305 | Moderately differentiated:2351 | 1 : 543 | Distant : 92 | Min. : 1.00 | Positive:3755 | Positive:3326 | Min. : 1.00 | Min. : 1.000 | Min. : 1.0 | 0:3408 | Min. :0.0204 | |
| 1st Qu.:47.00 | Black: 291 | Married :2643 | T2:1786 | N2: 820 | IIB :1130 | Poorly differentiated :1111 | 2 :2351 | Regional:3932 | 1st Qu.: 16.00 | Negative: 269 | Negative: 698 | 1st Qu.: 9.00 | 1st Qu.: 1.000 | 1st Qu.: 56.0 | 1: 616 | 1st Qu.:0.1034 | |
| Median :54.00 | Other: 320 | Divorced : 486 | T3: 533 | N3: 472 | IIIA:1050 | Undifferentiated : 19 | 3 :1111 | NA | Median : 25.00 | NA | NA | Median :14.00 | Median : 2.000 | Median : 73.0 | NA | Median :0.2143 | |
| Mean :53.97 | NA | Separated: 45 | T4: 102 | NA | IIIB: 67 | Well differentiated : 543 | anaplastic; Grade IV: 19 | NA | Mean : 30.47 | NA | NA | Mean :14.36 | Mean : 4.158 | Mean : 71.3 | NA | Mean :0.3265 | |
| 3rd Qu.:61.00 | NA | Widowed : 235 | NA | NA | IIIC: 472 | NA | NA | NA | 3rd Qu.: 38.00 | NA | NA | 3rd Qu.:19.00 | 3rd Qu.: 5.000 | 3rd Qu.: 90.0 | NA | 3rd Qu.:0.5000 | |
| Max. :69.00 | NA | NA | NA | NA | NA | NA | NA | NA | Max. :140.00 | NA | NA | Max. :61.00 | Max. :46.000 | Max. :107.0 | NA | Max. :1.0000 |
breastcancer_clean |>
summarise(across(where(is.numeric), list(mean = ~mean(., na.rm = TRUE), sd = ~sd(., na.rm = TRUE)))) |>
knitr::kable()| age_mean | age_sd | tumor_size_mean | tumor_size_sd | regional_node_examined_mean | regional_node_examined_sd | reginol_node_positive_mean | reginol_node_positive_sd | survival_months_mean | survival_months_sd | node_positive_prop_mean | node_positive_prop_sd |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 53.97217 | 8.963134 | 30.47366 | 21.1197 | 14.35711 | 8.099675 | 4.158052 | 5.109331 | 71.29796 | 22.92143 | 0.3264658 | 0.2870226 |
prop.table(table(breastcancer_clean$status)) |>
knitr::kable()| Var1 | Freq |
|---|---|
| 0 | 0.8469185 |
| 1 | 0.1530815 |
# Correlation matrix for all variables
cor_matrix = breastcancer_clean |>
select_if(is.numeric) |>
cor()
corrplot::corrplot(cor_matrix, type = "upper", diag = FALSE)
# Heat map
corrplot::corrplot(cor_matrix, method = "color")
# Scatter plot matrix for all variables
pairs(breastcancer_clean)
# Histograms and density plots
breastcancer_clean |>
ggplot(aes(x = age, y = ..density.., fill = "purple", alpha = 0.5)) +
geom_histogram(binwidth = 0.3, colour = "lightblue", alpha = 0.1) +
geom_density(alpha = 0.4) +
labs(
x = "Age",
y = "Density") +
scale_fill_viridis_d("") +
theme_classic() +
theme(legend.position = "none")## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
breastcancer_clean |>
ggplot(aes(x = tumor_size, y = ..density.., fill = "purple", alpha = 0.5)) +
geom_histogram(binwidth = 0.3, colour = "lightblue", alpha = 0.1) +
geom_density(alpha = 0.4) +
labs(
x = "Age",
y = "Density") +
scale_fill_viridis_d("") +
theme_classic() +
theme(legend.position = "none")
breastcancer_clean |>
ggplot(aes(x = regional_node_examined, y = ..density.., fill = "purple", alpha = 0.5)) +
geom_histogram(binwidth = 0.3, colour = "lightblue", alpha = 0.1) +
geom_density(alpha = 0.4) +
labs(
x = "Age",
y = "Density") +
scale_fill_viridis_d("") +
theme_classic() +
theme(legend.position = "none")
breastcancer_clean |>
ggplot(aes(x = reginol_node_positive, y = ..density.., fill = "purple", alpha = 0.5)) +
geom_histogram(binwidth = 0.3, colour = "lightblue", alpha = 0.1) +
geom_density(alpha = 0.4) +
labs(
x = "Age",
y = "Density") +
scale_fill_viridis_d("") +
theme_classic() +
theme(legend.position = "none")
breastcancer_clean |>
ggplot(aes(x = node_positive_prop, y = ..density.., fill = "purple", alpha = 0.5)) +
geom_histogram(binwidth = 0.3, colour = "lightblue", alpha = 0.1) +
geom_density(alpha = 0.4) +
labs(
x = "Age",
y = "Density") +
scale_fill_viridis_d("") +
theme_classic() +
theme(legend.position = "none")
# Boxplots
par(mfrow=c(2,3))
boxplot(breastcancer_clean$age, main='Age')
boxplot(breastcancer_clean$tumor_size, main='Tumor Size')
boxplot(breastcancer_clean$regional_node_examined,main='Node Examined' )
boxplot(breastcancer_clean$reginol_node_positive, main='Positive Node')
boxplot(breastcancer_clean$node_positive_prop, main='Proportion of Positive Nodes')
par(mfrow = c(2,3))
boxplot(age ~ status, breastcancer_clean)
boxplot(tumor_size ~ status, breastcancer_clean)
boxplot(regional_node_examined ~ status, breastcancer_clean)
boxplot(reginol_node_positive ~ status, breastcancer_clean)
boxplot(node_positive_prop ~ status, breastcancer_clean)
# Barplots for categorical variables
ggplot(breastcancer_clean, aes(x = race, fill = status)) +
geom_bar(position = "dodge")
ggplot(breastcancer_clean, aes(x = marital_status, fill = status)) +
geom_bar(position = "dodge")
ggplot(breastcancer_clean, aes(x = t_stage, fill = status)) +
geom_bar(position = "dodge")
ggplot(breastcancer_clean, aes(x = n_stage, fill = status)) +
geom_bar(position = "dodge")
ggplot(breastcancer_clean, aes(x = x6th_stage, fill = status)) +
geom_bar(position = "dodge")
ggplot(breastcancer_clean, aes(x = differentiate, fill = status)) +
geom_bar(position = "dodge")
ggplot(breastcancer_clean, aes(x = grade, fill = status)) +
geom_bar(position = "dodge")
ggplot(breastcancer_clean, aes(x = a_stage, fill = status)) +
geom_bar(position = "dodge")
ggplot(breastcancer_clean, aes(x = estrogen_status, fill = status)) +
geom_bar(position = "dodge")
ggplot(breastcancer_clean, aes(x = progesterone_status, fill = status)) +
geom_bar(position = "dodge")
# Violin plots
ggplot(breastcancer_clean, aes(x = status, y = race)) +
geom_violin(aes(fill = race), alpha = .5, trim = FALSE) 
ggplot(breastcancer_clean, aes(x = status, y = marital_status)) +
geom_violin(aes(fill = marital_status), alpha = .5, trim = FALSE)
ggplot(breastcancer_clean, aes(x = status, y = t_stage)) +
geom_violin(aes(fill = t_stage), alpha = .5, trim = FALSE) 
ggplot(breastcancer_clean, aes(x = status, y = n_stage)) +
geom_violin(aes(fill = n_stage), alpha = .5, trim = FALSE)
ggplot(breastcancer_clean, aes(x = status, y = x6th_stage)) +
geom_violin(aes(fill = x6th_stage), alpha = .5, trim = FALSE) 
ggplot(breastcancer_clean, aes(x = status, y = differentiate)) +
geom_violin(aes(fill = differentiate), alpha = .5, trim = FALSE) 
ggplot(breastcancer_clean, aes(x = status, y = grade)) +
geom_violin(aes(fill = grade), alpha = .5, trim = FALSE) 
ggplot(breastcancer_clean, aes(x = status, y = a_stage)) +
geom_violin(aes(fill = a_stage), alpha = .5, trim = FALSE) 
ggplot(breastcancer_clean, aes(x = status, y = estrogen_status)) +
geom_violin(aes(fill = estrogen_status), alpha = .5, trim = FALSE) 
ggplot(breastcancer_clean, aes(x = status, y = progesterone_status)) +
geom_violin(aes(fill = progesterone_status), alpha = .5, trim = FALSE) 
# Mosaic Plots
par(mfrow = c(3,4))
mosaicplot(table(breastcancer_clean$race, breastcancer_clean$status), main="Race vs Status", color = TRUE)
mosaicplot(table(breastcancer_clean$marital_status, breastcancer_clean$status), main="Marital Status vs Status", color = TRUE)
mosaicplot(table(breastcancer_clean$t_stage, breastcancer_clean$status), main="T-stage vs Status", color = TRUE)
mosaicplot(table(breastcancer_clean$n_stage, breastcancer_clean$status), main="N-stage vs Status", color = TRUE)
mosaicplot(table(breastcancer_clean$x6th_stage, breastcancer_clean$status), main="X6th Stage vs Status", color = TRUE)
mosaicplot(table(breastcancer_clean$differentiate, breastcancer_clean$status), main="Differentiate vs Status", color = TRUE)
mosaicplot(table(breastcancer_clean$grade, breastcancer_clean$status), main="Grade vs Status", color = TRUE)
mosaicplot(table(breastcancer_clean$a_stage, breastcancer_clean$status), main="A-stage vs Status", color = TRUE)
mosaicplot(table(breastcancer_clean$estrogen_status, breastcancer_clean$status), main="Estrogen Status vs Status", color = TRUE)
mosaicplot(table(breastcancer_clean$progesterone_status, breastcancer_clean$status), main="Progesterone Status vs Status", color = TRUE)