Titanic competition from Kaggle. Part 2. Create dummy variables to factor variables using Caret package.
Part 1 is https://rpubs.com/Minxing2046/395349
library(tidyverse)
library(DataExplorer)
library(lubridate)
library(pander)
library(data.table)
library(grid)
library(gridExtra)
library(mice)
library(caret)the dataset is generated from part 1: https://rpubs.com/Minxing2046/395349
## 'data.frame': 1309 obs. of 7 variables:
## $ PassengerId: int 892 893 894 895 896 897 898 899 900 901 ...
## $ Pclass : int 3 3 2 3 3 3 3 2 3 3 ...
## $ Sex : Factor w/ 2 levels "female","male": 2 1 2 2 1 2 1 2 1 2 ...
## $ Age : num 34.5 47 62 27 22 14 30 26 18 21 ...
## $ SibSp : int 0 1 0 0 1 0 0 1 0 2 ...
## $ Parch : int 0 0 0 0 1 0 0 1 0 0 ...
## $ Survived : int NA NA NA NA NA NA NA NA NA NA ...Set Pclass as factor
Introduction is here https://www.machinelearningplus.com/machine-learning/caret-package/
create dummy variables to factor variables using dummyVars function in the Caret package
dummyVars takes two steps
dummyVars function.predict function, then convert into data.framedummyVariable <- dummyVars(~., data = titanic.mice)
titanic.mice.dummy <- data.frame(predict(dummyVariable,newdata = titanic.mice))## 'data.frame': 1309 obs. of 10 variables:
## $ PassengerId: num 892 893 894 895 896 897 898 899 900 901 ...
## $ Pclass.1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Pclass.2 : num 0 0 1 0 0 0 0 1 0 0 ...
## $ Pclass.3 : num 1 1 0 1 1 1 1 0 1 1 ...
## $ Sex.female : num 0 1 0 0 1 0 1 0 1 0 ...
## $ Sex.male : num 1 0 1 1 0 1 0 1 0 1 ...
## $ Age : num 34.5 47 62 27 22 14 30 26 18 21 ...
## $ SibSp : num 0 1 0 0 1 0 0 1 0 2 ...
## $ Parch : num 0 0 0 0 1 0 0 1 0 0 ...
## $ Survived : num NA NA NA NA NA NA NA NA NA NA ...## [1] 418Export dataset