Plot a slope chart comparing values across two categories derived from the diabetes dataset.
we first load the ggplot package , which is used for data visualization. ggrepel automatically adjusts labels so they don’t overlap.
library(ggplot2)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(tidyr)
library(ggrepel)Warning: package 'ggrepel' was built under R version 4.5.3data <- read.csv("C:/Users/rnaya/Downloads/diabetes (2).csv")After loading display the first few rows and also the type of the data we are using
str(data)'data.frame': 768 obs. of 9 variables:
$ Pregnancies : int 6 1 8 1 0 5 3 10 2 8 ...
$ Glucose : int 148 85 183 89 137 116 78 115 197 125 ...
$ BloodPressure : int 72 66 64 66 40 74 50 0 70 96 ...
$ SkinThickness : int 35 29 0 23 35 0 32 0 45 0 ...
$ Insulin : int 0 0 0 94 168 0 88 0 543 0 ...
$ BMI : num 33.6 26.6 23.3 28.1 43.1 25.6 31 35.3 30.5 0 ...
$ DiabetesPedigreeFunction: num 0.627 0.351 0.672 0.167 2.288 ...
$ Age : int 50 31 32 21 33 30 26 29 53 54 ...
$ Outcome : int 1 0 1 0 1 0 1 0 1 1 ...head(data) Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
1 6 148 72 35 0 33.6
2 1 85 66 29 0 26.6
3 8 183 64 0 0 23.3
4 1 89 66 23 94 28.1
5 0 137 40 35 168 43.1
6 5 116 74 0 0 25.6
DiabetesPedigreeFunction Age Outcome
1 0.627 50 1
2 0.351 31 0
3 0.672 32 1
4 0.167 21 0
5 2.288 33 1
6 0.201 30 0Since our dataset is medical, we convert it into comparison form.
summary_data <- data %>%
group_by(Outcome) %>%
summarise(
Glucose = mean(Glucose, na.rm = TRUE),
BMI = mean(BMI, na.rm = TRUE),
Age = mean(Age, na.rm = TRUE),
Insulin = mean(Insulin, na.rm = TRUE),
BloodPressure = mean(BloodPressure, na.rm = TRUE)
)long_data <- summary_data %>%
pivot_longer(cols = -Outcome,
names_to = "Variable",
values_to = "Value")long_data$Outcome <- ifelse(long_data$Outcome == 0,
"Non-Diabetic",
"Diabetic")long_data$Outcome <- factor(long_data$Outcome,
levels = c("Non-Diabetic", "Diabetic"))ggplot(long_data, aes(x = Outcome, y = Value, group = Variable, color = Variable))
ggplot(long_data, aes(x = Outcome, y = Value, group = Variable, color = Variable)) +
geom_line(size = 1.2) +
geom_point(size = 3) +
geom_text(data = long_data %>% filter(Outcome == "Non-Diabetic"),
aes(label = Variable),
hjust = 1.2) +
geom_text(data = long_data %>% filter(Outcome == "Diabetic"),
aes(label = Variable),
hjust = -0.2)Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
ggplot(long_data, aes(x = Outcome, y = Value, group = Variable, color = Variable)) +
geom_line(size = 1.2) +
geom_point(size = 3) +
geom_text_repel(data = long_data %>% filter(Outcome == "Non-Diabetic"),
aes(label = Variable),
nudge_x = -0.2,
direction = "y") +
geom_text_repel(data = long_data %>% filter(Outcome == "Diabetic"),
aes(label = Variable),
nudge_x = 0.2,
direction = "y") +
labs(
title = "Slope Chart Comparing Health Factors (Diabetic vs Non-Diabetic)",
x = "Category",
y = "Average Values"
) +
theme_minimal() +
theme(legend.position = "top")
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