DATA 607 Tidy Verse assignment

1.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.

Load the library

library(tidyverse)

## Warning: package 'tidyverse' was built under R version 3.5.2

## -- Attaching packages ----------------------------------------------------------------------------------------------- tidyverse 1.2.1 --

## v ggplot2 3.1.0       v purrr   0.2.5  
## v tibble  2.0.1       v dplyr   0.8.0.1
## v tidyr   0.8.2       v stringr 1.4.0  
## v readr   1.3.1       v forcats 0.4.0

## Warning: package 'ggplot2' was built under R version 3.5.2

## Warning: package 'tibble' was built under R version 3.5.2

## Warning: package 'tidyr' was built under R version 3.5.2

## Warning: package 'readr' was built under R version 3.5.2

## Warning: package 'purrr' was built under R version 3.5.2

## Warning: package 'dplyr' was built under R version 3.5.2

## Warning: package 'stringr' was built under R version 3.5.2

## Warning: package 'forcats' was built under R version 3.5.2

## -- Conflicts -------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Data source

https://www.kaggle.com/ronitf/heart-disease-uci

Read the data using readr

disease <- read_csv("heart.csv")

## Parsed with column specification:
## 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()
## )

head(disease)

## # A tibble: 6 x 14
##     age   sex    cp trestbps  chol   fbs restecg thalach exang oldpeak
##   <dbl> <dbl> <dbl>    <dbl> <dbl> <dbl>   <dbl>   <dbl> <dbl>   <dbl>
## 1    63     1     3      145   233     1       0     150     0     2.3
## 2    37     1     2      130   250     0       1     187     0     3.5
## 3    41     0     1      130   204     0       0     172     0     1.4
## 4    56     1     1      120   236     0       1     178     0     0.8
## 5    57     0     0      120   354     0       1     163     1     0.6
## 6    57     1     0      140   192     0       1     148     0     0.4
## # ... with 4 more variables: slope <dbl>, ca <dbl>, thal <dbl>,
## #   target <dbl>

The selected package I want to use id dplyr.

Capability 1.

filter capability tutorial

Description

Using filter we can select rows of the data frame matching conditions.

Usage

filter(data) ### Example

To select the people of over 20 and less than 65 we can pass the data disease and condtion age>20 and age < 65 to the function . It’ll return matching rows of heart disease.

filter(disease, age>20 & age < 65)

## # A tibble: 262 x 14
##      age   sex    cp trestbps  chol   fbs restecg thalach exang oldpeak
##    <dbl> <dbl> <dbl>    <dbl> <dbl> <dbl>   <dbl>   <dbl> <dbl>   <dbl>
##  1    63     1     3      145   233     1       0     150     0     2.3
##  2    37     1     2      130   250     0       1     187     0     3.5
##  3    41     0     1      130   204     0       0     172     0     1.4
##  4    56     1     1      120   236     0       1     178     0     0.8
##  5    57     0     0      120   354     0       1     163     1     0.6
##  6    57     1     0      140   192     0       1     148     0     0.4
##  7    56     0     1      140   294     0       0     153     0     1.3
##  8    44     1     1      120   263     0       1     173     0     0  
##  9    52     1     2      172   199     1       1     162     0     0.5
## 10    57     1     2      150   168     0       1     174     0     1.6
## # ... with 252 more rows, and 4 more variables: slope <dbl>, ca <dbl>,
## #   thal <dbl>, target <dbl>

Capability 2.

select capability tutorial

Description

Using select we can keep the selected variables

sage

select(data, …)

Example

To keep only age, sex,cp variable we can pass the data disease and age, sex,cp to the function .

df<- select(disease, c("age","sex","cp"))
head(df)

## # A tibble: 6 x 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     0

Extended by Santosh Cheruku

Using dplyr::Arrange - To change the ordering of rows

Description

Using arrange we can order the rows in an expression involving variables

Example

To arrange the rows by sex and age

df <- df %>% arrange(sex, age)
head(df)

## # A tibble: 6 x 3
##     age   sex    cp
##   <dbl> <dbl> <dbl>
## 1    34     0     1
## 2    35     0     0
## 3    37     0     2
## 4    39     0     2
## 5    39     0     2
## 6    41     0     1

tail(df)

## # A tibble: 6 x 3
##     age   sex    cp
##   <dbl> <dbl> <dbl>
## 1    69     1     2
## 2    70     1     1
## 3    70     1     0
## 4    70     1     0
## 5    70     1     2
## 6    77     1     0