HAZAL GUNDUZ
Tidyverse Assignment
The 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 selected dataset.
Columns meanings:
The meaning of some of the column headers are not obvious. Here’s what they mean: • age: The person’s age in years • sex: The person’s sex (1 = male, 0 = female) • cp: The chest pain experienced (Value 1: typical angina, Value 2: atypical angina, Value 3: non-angina pain, Value 4: asymptomatic angina) • trestbps: The person’s resting blood pressure (mm Hg on admission to the hospital) • chol: The person’s cholesterol measurement in mg/dl • fbs: The person’s fasting blood sugar (> 120 mg/dl, 1 = true; 0 = false) • restecg: Resting electrocardiographic measurement (0 = normal, 1 = having ST-T wave abnormality, 2 = showing probable or definite left ventricular hypertrophy by Estes’ criteria) • thalach: The person’s maximum heart rate achieved • exang: Exercise induced angina (1 = yes; 0 = no) • oldpeak: ST depression induced by exercise relative to rest (‘ST’ relates to positions on the ECG plot. See more here) • slope: the slope of the peak exercise ST segment (Value 1: upsloping, Value 2: flat, Value 3: downsloping) • ca: The number of major vessels (0-3) • thal: A blood disorder called thalassemia (3 = normal; 6 = fixed defect; 7 = reversable defect) • target: Heart disease (0 = no, 1 = yes)
Load the library
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.5 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.0.2 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggplot2)
library(dplyr)Data source
https://www.kaggle.com/ronitf/heart-disease-uci
heart <- read_csv("heart.csv")## Rows: 303 Columns: 14## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (14): age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpea...##
## 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.names (heart) <- c("age", "sex", "cp", "trestbps", "chol", "fbs", "restecg", "thalach", "exang", "oldpeak", "slope", "ca", "thal", "target")
head(heart)## # A tibble: 6 × 14
## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 63 1 3 145 233 1 0 150 0 2.3 0
## 2 37 1 2 130 250 0 1 187 0 3.5 0
## 3 41 0 1 130 204 0 0 172 0 1.4 2
## 4 56 1 1 120 236 0 1 178 0 0.8 2
## 5 57 0 0 120 354 0 1 163 1 0.6 2
## 6 57 1 0 140 192 0 1 148 0 0.4 1
## # … with 3 more variables: ca <dbl>, thal <dbl>, target <dbl>dim(heart)## [1] 303 14str(heart)## spec_tbl_df [303 × 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ age : num [1:303] 63 37 41 56 57 57 56 44 52 57 ...
## $ sex : num [1:303] 1 1 0 1 0 1 0 1 1 1 ...
## $ cp : num [1:303] 3 2 1 1 0 0 1 1 2 2 ...
## $ trestbps: num [1:303] 145 130 130 120 120 140 140 120 172 150 ...
## $ chol : num [1:303] 233 250 204 236 354 192 294 263 199 168 ...
## $ fbs : num [1:303] 1 0 0 0 0 0 0 0 1 0 ...
## $ restecg : num [1:303] 0 1 0 1 1 1 0 1 1 1 ...
## $ thalach : num [1:303] 150 187 172 178 163 148 153 173 162 174 ...
## $ exang : num [1:303] 0 0 0 0 1 0 0 0 0 0 ...
## $ oldpeak : num [1:303] 2.3 3.5 1.4 0.8 0.6 0.4 1.3 0 0.5 1.6 ...
## $ slope : num [1:303] 0 0 2 2 2 1 1 2 2 2 ...
## $ ca : num [1:303] 0 0 0 0 0 0 0 0 0 0 ...
## $ thal : num [1:303] 1 2 2 2 2 1 2 3 3 2 ...
## $ target : num [1:303] 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "spec")=
## .. cols(
## .. age = col_double(),
## .. sex = col_double(),
## .. cp = col_double(),
## .. trestbps = col_double(),
## .. chol = col_double(),
## .. fbs = col_double(),
## .. restecg = col_double(),
## .. thalach = col_double(),
## .. exang = col_double(),
## .. oldpeak = col_double(),
## .. slope = col_double(),
## .. ca = col_double(),
## .. thal = col_double(),
## .. target = col_double()
## .. )
## - attr(*, "problems")=<externalptr>sum(is.na(heart))## [1] 0summary(heart)## age sex cp trestbps
## Min. :29.00 Min. :0.0000 Min. :0.000 Min. : 94.0
## 1st Qu.:47.50 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:120.0
## Median :55.00 Median :1.0000 Median :1.000 Median :130.0
## Mean :54.37 Mean :0.6832 Mean :0.967 Mean :131.6
## 3rd Qu.:61.00 3rd Qu.:1.0000 3rd Qu.:2.000 3rd Qu.:140.0
## Max. :77.00 Max. :1.0000 Max. :3.000 Max. :200.0
## chol fbs restecg thalach
## Min. :126.0 Min. :0.0000 Min. :0.0000 Min. : 71.0
## 1st Qu.:211.0 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:133.5
## Median :240.0 Median :0.0000 Median :1.0000 Median :153.0
## Mean :246.3 Mean :0.1485 Mean :0.5281 Mean :149.6
## 3rd Qu.:274.5 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:166.0
## Max. :564.0 Max. :1.0000 Max. :2.0000 Max. :202.0
## exang oldpeak slope ca
## Min. :0.0000 Min. :0.00 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.00 1st Qu.:1.000 1st Qu.:0.0000
## Median :0.0000 Median :0.80 Median :1.000 Median :0.0000
## Mean :0.3267 Mean :1.04 Mean :1.399 Mean :0.7294
## 3rd Qu.:1.0000 3rd Qu.:1.60 3rd Qu.:2.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :6.20 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 :2.314 Mean :0.5446
## 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :3.000 Max. :1.0000heart ## # A tibble: 303 × 14
## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 63 1 3 145 233 1 0 150 0 2.3 0
## 2 37 1 2 130 250 0 1 187 0 3.5 0
## 3 41 0 1 130 204 0 0 172 0 1.4 2
## 4 56 1 1 120 236 0 1 178 0 0.8 2
## 5 57 0 0 120 354 0 1 163 1 0.6 2
## 6 57 1 0 140 192 0 1 148 0 0.4 1
## 7 56 0 1 140 294 0 0 153 0 1.3 1
## 8 44 1 1 120 263 0 1 173 0 0 2
## 9 52 1 2 172 199 1 1 162 0 0.5 2
## 10 57 1 2 150 168 0 1 174 0 1.6 2
## # … with 293 more rows, and 3 more variables: ca <dbl>, thal <dbl>,
## # target <dbl>urlGet <- ("https://github.com/Gunduzhazal/heart")df<- select(heart, c("age","sex","cp"))
head(df)## # A tibble: 6 × 3
## age sex cp
## <dbl> <dbl> <dbl>
## 1 63 1 3
## 2 37 1 2
## 3 41 0 1
## 4 56 1 1
## 5 57 0 0
## 6 57 1 0df <- df %>% arrange(age, sex, cp)
head(df)## # A tibble: 6 × 3
## age sex cp
## <dbl> <dbl> <dbl>
## 1 29 1 1
## 2 34 0 1
## 3 34 1 3
## 4 35 0 0
## 5 35 1 0
## 6 35 1 0tail(df)## # A tibble: 6 × 3
## age sex cp
## <dbl> <dbl> <dbl>
## 1 71 0 0
## 2 71 0 1
## 3 71 0 2
## 4 74 0 1
## 5 76 0 2
## 6 77 1 0Conclusion
Heart disease is a major health concern and there are factors that people should be aware of.
Github => https://github.com/Gunduzhazal/heart
Rpubs => https://rpubs.com/gunduzhazal/826400