Data 607 Tidyverse CREATE Assignment
Leticia Salazar
2021-10-24
Objective:
Your task here is to Create an Example. Using one or more TidyVerse packages, and any dataset from fivethirtyeight.com or Kaggle, create a programming sample “vignette” that demonstrates how to use one or more of the capabilities of the selected TidyVerse package with your selected dataset.
I am using a data set from Kaggle.com called Heart Disease Dataset. I have used this data set before but with Python Care For Your Heart. While there are plenty of Tidyverse packages to use I will be using ‘dplyr’, ‘tidyr’ and ‘ggplot2’ to demonstrate how to explore data sets in a simple but quick form.
Description of the Data set:
This data set includes dates from 1988 from four different countries: Cleveland, Hungary, Switzerland and Long Beach Virginia. Below is a description of the columns:
| Columns | Description |
|---|---|
| age | ranges from mid-20’s to late 70’s |
| sex | 1 = male; 0 = female |
| chest pain type | 4 value types from 0 - 3 |
| resting blood pressure | in mm Hg |
| serum cholesterol | in mg/dl |
| fasting blood sugar (> 120 mg/dl) | 1 = true; 0 = false |
| resting electrocardiograph results | values 0, 1, 2 |
| thalach | maximum heart rate achieved |
| exercise induced angina | 1 = yes; 0 = no |
| oldpeak | ST depression induced by exercise relative to rest |
| slope | peak exercise ST segment |
| ca | number of major vessels (0 - 3) colored by fluoroscopy |
| thal | 1 = normal; 2 = fixed defect; 3 = reversible defect |
| target | presence of heart disease in the patient: 0 = no disease; 1 = disease |
Install packages and load libraries:
# load libraries needed
library("tidyverse")## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.4 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library("dplyr")
library("tidyr")
library("ggplot2")
library("hrbrthemes")## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.## Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and## if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow# install packages if not already
# install.packages("tidyverse")
# install.packages("dplyr")
# install.packages("tidyr")
# install.packages("ggplot2")
# hrbrthemes::import_roboto_condensed()Load file into R:
# load csv file
heart_disease <- read.csv("https://raw.githubusercontent.com/letisalba/Data-607/main/Tidyverse/heart.csv", header = TRUE, na = ",")
head(heart_disease, n = 4) # by adding n = followed by any number you can select how many rows are to display## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
## 1 52 1 0 125 212 0 1 168 0 1.0 2 2 3
## 2 53 1 0 140 203 1 0 155 1 3.1 0 0 3
## 3 70 1 0 145 174 0 1 125 1 2.6 0 0 3
## 4 61 1 0 148 203 0 1 161 0 0.0 2 1 3
## target
## 1 0
## 2 0
## 3 0
## 4 0Data Exploration:
With the ‘glimpse’ function you have an overview of the columns and rows of the data. Within this data set we have 1,025 rows and 14 columns. Most columns are integer while column ‘oldpeak’ is double class since it has a decimal point.
# glimpse function
glimpse(heart_disease)## Rows: 1,025
## Columns: 14
## $ age <int> 52, 53, 70, 61, 62, 58, 58, 55, 46, 54, 71, 43, 34, 51, 52, 3…
## $ sex <int> 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1…
## $ cp <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 2, 0, 1, 2, 2…
## $ trestbps <int> 125, 140, 145, 148, 138, 100, 114, 160, 120, 122, 112, 132, 1…
## $ chol <int> 212, 203, 174, 203, 294, 248, 318, 289, 249, 286, 149, 341, 2…
## $ fbs <int> 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0…
## $ restecg <int> 1, 0, 1, 1, 1, 0, 2, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0…
## $ thalach <int> 168, 155, 125, 161, 106, 122, 140, 145, 144, 116, 125, 136, 1…
## $ exang <int> 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0…
## $ oldpeak <dbl> 1.0, 3.1, 2.6, 0.0, 1.9, 1.0, 4.4, 0.8, 0.8, 3.2, 1.6, 3.0, 0…
## $ slope <int> 2, 0, 0, 2, 1, 1, 0, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 2, 1…
## $ ca <int> 2, 0, 0, 1, 3, 0, 3, 1, 0, 2, 0, 0, 0, 3, 0, 0, 1, 1, 0, 0, 0…
## $ thal <int> 3, 3, 3, 3, 2, 2, 1, 3, 3, 2, 2, 3, 2, 3, 0, 2, 2, 3, 2, 2, 2…
## $ target <int> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0…The ‘summary’ function gives futher detail about the data such as minimum, maximum, mean, median and IQR for each column or you can do individual columns as well.
# summary function for whole data set
summary(heart_disease)## age sex cp trestbps
## Min. :29.00 Min. :0.0000 Min. :0.0000 Min. : 94.0
## 1st Qu.:48.00 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:120.0
## Median :56.00 Median :1.0000 Median :1.0000 Median :130.0
## Mean :54.43 Mean :0.6956 Mean :0.9424 Mean :131.6
## 3rd Qu.:61.00 3rd Qu.:1.0000 3rd Qu.:2.0000 3rd Qu.:140.0
## Max. :77.00 Max. :1.0000 Max. :3.0000 Max. :200.0
## chol fbs restecg thalach
## Min. :126 Min. :0.0000 Min. :0.0000 Min. : 71.0
## 1st Qu.:211 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:132.0
## Median :240 Median :0.0000 Median :1.0000 Median :152.0
## Mean :246 Mean :0.1493 Mean :0.5298 Mean :149.1
## 3rd Qu.:275 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:166.0
## Max. :564 Max. :1.0000 Max. :2.0000 Max. :202.0
## exang oldpeak slope ca
## Min. :0.0000 Min. :0.000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:0.0000
## Median :0.0000 Median :0.800 Median :1.000 Median :0.0000
## Mean :0.3366 Mean :1.072 Mean :1.385 Mean :0.7541
## 3rd Qu.:1.0000 3rd Qu.:1.800 3rd Qu.:2.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :6.200 Max. :2.000 Max. :4.0000
## thal target
## Min. :0.000 Min. :0.0000
## 1st Qu.:2.000 1st Qu.:0.0000
## Median :2.000 Median :1.0000
## Mean :2.324 Mean :0.5132
## 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :3.000 Max. :1.0000# summary function for particular columns
summary(heart_disease$chol)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 126 211 240 246 275 564Checking for NA or missing values is crutial to assure accuracy in the data. By using colSums(is.na(df)) function you can check if there are any NA values in the data set.
# checking for any NA values in columns
colSums(is.na(heart_disease))## age sex cp trestbps chol fbs restecg thalach
## 0 0 0 0 0 0 0 0
## exang oldpeak slope ca thal target
## 0 0 0 0 0 0The ‘colnames’ function brings up the names of your columns in your data set
# get column names
colnames(heart_disease)## [1] "age" "sex" "cp" "trestbps" "chol" "fbs"
## [7] "restecg" "thalach" "exang" "oldpeak" "slope" "ca"
## [13] "thal" "target"Data Wrangling:
Using the ‘names’ function you can rename your whole data set
# rename columns in data set
names(heart_disease) <- c('Age', 'Gender', 'Chest_Pain_Type', 'Resting_Blood_Pressure', 'Cholesterol', 'Fasting_Blood_Sugar', 'Resting_ECG', 'Thalach', 'Exercise_Induced_Angina', 'Oldpeak', 'Slope', 'Num_Major_Vessels', 'Thalassemia', 'Target')
# rename one column in data set
# names(heart_disease)[1] <- 'Age' # [1] the number in the bracket is the column number you would like to change
head(heart_disease, n = 2)## Age Gender Chest_Pain_Type Resting_Blood_Pressure Cholesterol
## 1 52 1 0 125 212
## 2 53 1 0 140 203
## Fasting_Blood_Sugar Resting_ECG Thalach Exercise_Induced_Angina Oldpeak Slope
## 1 0 1 168 0 1.0 2
## 2 1 0 155 1 3.1 0
## Num_Major_Vessels Thalassemia Target
## 1 2 3 0
## 2 0 3 0# replace gender values 1 = Male and 0 = Female; Fasting_blood_sugar values 1 = True and 0 = False; Exercise_Induced_Angina values 1 = Yes and 0 = No; Thalassemia values 0 = Normal, 1 = Fixed Defect and 2 = Reversible Defect; and Target values 0 = No Disease and 1 = Disease
heart <- heart_disease %>%
mutate(Gender = replace(Gender, Gender == 1, 'Male')) %>%
mutate(Gender = replace(Gender, Gender == 0, 'Female')) %>%
mutate(Fasting_Blood_Sugar = replace(Fasting_Blood_Sugar, Fasting_Blood_Sugar == 1, 'True')) %>%
mutate(Fasting_Blood_Sugar = replace(Fasting_Blood_Sugar, Fasting_Blood_Sugar == 0, 'False')) %>%
mutate(Exercise_Induced_Angina = replace(Exercise_Induced_Angina, Exercise_Induced_Angina == 1, 'Yes')) %>%
mutate(Exercise_Induced_Angina = replace(Exercise_Induced_Angina, Exercise_Induced_Angina == 0, 'No')) %>%
mutate(Thalassemia = replace(Thalassemia, Thalassemia == 1, 'Normal')) %>%
mutate(Thalassemia = replace(Thalassemia, Thalassemia == 2, 'Fixed Defect')) %>%
mutate(Thalassemia = replace(Thalassemia, Thalassemia == 3, 'Reversible Defect')) %>%
mutate(Target = replace(Target, Target == 0, 'No Disease')) %>%
mutate(Target = replace(Target, Target == 1, 'Disease'))
head(heart, n = 4)## Age Gender Chest_Pain_Type Resting_Blood_Pressure Cholesterol
## 1 52 Male 0 125 212
## 2 53 Male 0 140 203
## 3 70 Male 0 145 174
## 4 61 Male 0 148 203
## Fasting_Blood_Sugar Resting_ECG Thalach Exercise_Induced_Angina Oldpeak Slope
## 1 False 1 168 No 1.0 2
## 2 True 0 155 Yes 3.1 0
## 3 False 1 125 Yes 2.6 0
## 4 False 1 161 No 0.0 2
## Num_Major_Vessels Thalassemia Target
## 1 2 Reversible Defect No Disease
## 2 0 Reversible Defect No Disease
## 3 0 Reversible Defect No Disease
## 4 1 Reversible Defect No DiseaseData Analysis:
When it comes to graphing I like to use r-graph-gallery for my graphing selection.
Graph 1 shows the range of ages in this data set that start from mid-20’s to late 70’s. Majority of the population is centered around the ages of 50’s - 60’s.
# Age Analysis histogram
age_histogram <- heart %>%
ggplot(aes(x = Age)) +
geom_histogram( binwidth=3, fill="#69b3a2", color="#e9ecef", alpha=0.9) +
ggtitle("Age Analysis Histogram") +
theme_ipsum() +
theme(
plot.title = element_text(size=15)
)
age_histogram
Graph 2 shows the relation between fasting blood sugar and heart disease. Those who do not have a fasting blood sugar tend to have heart disease than those who have fasted.
# Age and Chest Pain Type histogram
barplot <- heart %>%
ggplot(aes(x = Fasting_Blood_Sugar, fill = Target)) +
geom_bar() +
ggtitle("Fasting Blood Sugar in Relation to Heart Disease Histogram") +
xlab("Fasting Blood Sugar") +
ylab("Count") +
theme_ipsum() +
theme(legend.position="right")
barplot
Graph 3 shows the distribution of heart disease by gender based on chest pain type. From this we see that men have the highest count on chest pain type compared to females. Therefore, men are more likely to be targets of having heart disease.
# side by side bar plot
plot <- heart %>%
ggplot(aes(x= Chest_Pain_Type,fill= Target)) +
theme_bw() +
geom_bar() +
facet_wrap(~Gender) +
labs(x = "Chest Pain Type",
y = "Count",
title = "Distribution of Target by Gender based on Chest Pain Type")
plot
Lastly, graph 4 is a scatterplot between resting blood pressure and cholesterol.There is a positive relationship between both of these values as the linear regression line moves slightly upward from left to right.
# Blood Pressure and Cholesterol scatter plot
scatterplot <- heart %>%
ggplot(aes(x = Resting_Blood_Pressure, y = Cholesterol)) +
geom_point(color = "#69b3a2") +
geom_smooth(method = lm , color="red", se = FALSE) +
theme_ipsum()
scatterplot## `geom_smooth()` using formula 'y ~ x'