Imputation - New
Vu Thien
2024-05-01
1. Using airquality dataset from dlookr
library(dlookr)## Warning: package 'dlookr' was built under R version 4.2.3## Registered S3 methods overwritten by 'dlookr':
## method from
## plot.transform scales
## print.transform scales##
## Attaching package: 'dlookr'## The following object is masked from 'package:base':
##
## transformplot_na_pareto(airquality)
plot_na_pareto(airquality, only_na = T)
plot_na_intersect(airquality)
## Check missing data
library(visdat)## Warning: package 'visdat' was built under R version 4.2.1library(plotly)## Warning: package 'plotly' was built under R version 4.2.2## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.2.3##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutvis_miss(airquality)## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `gather()` instead.
## ℹ The deprecated feature was likely used in the visdat package.
## Please report the issue at <https://github.com/ropensci/visdat/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
2. Imputation by using missRanger
library(missRanger)## Warning: package 'missRanger' was built under R version 4.2.3## Generate airquality_NAset.seed(123)
airquality_NA <- generateNA(airquality) %>%
mutate(Month = factor(Month))
plot_na_pareto(airquality_NA)
plot_na_intersect(airquality_NA)
Imputation for airquality_NA
imputate_na(airquality_NA,
Ozone, Temp, method = "mean") %>%
plot()
imputate_na(airquality_NA,
Ozone, Temp, method = "rpart") %>%
plot()
imputate_na(airquality_NA,
Ozone, Temp, method = "mice") %>%
plot()##
## iter imp variable
## 1 1 Ozone Solar.R Wind Month Day
## 1 2 Ozone Solar.R Wind Month Day
## 1 3 Ozone Solar.R Wind Month Day
## 1 4 Ozone Solar.R Wind Month Day
## 1 5 Ozone Solar.R Wind Month Day
## 2 1 Ozone Solar.R Wind Month Day
## 2 2 Ozone Solar.R Wind Month Day
## 2 3 Ozone Solar.R Wind Month Day
## 2 4 Ozone Solar.R Wind Month Day
## 2 5 Ozone Solar.R Wind Month Day
## 3 1 Ozone Solar.R Wind Month Day
## 3 2 Ozone Solar.R Wind Month Day
## 3 3 Ozone Solar.R Wind Month Day
## 3 4 Ozone Solar.R Wind Month Day
## 3 5 Ozone Solar.R Wind Month Day
## 4 1 Ozone Solar.R Wind Month Day
## 4 2 Ozone Solar.R Wind Month Day
## 4 3 Ozone Solar.R Wind Month Day
## 4 4 Ozone Solar.R Wind Month Day
## 4 5 Ozone Solar.R Wind Month Day
## 5 1 Ozone Solar.R Wind Month Day
## 5 2 Ozone Solar.R Wind Month Day
## 5 3 Ozone Solar.R Wind Month Day
## 5 4 Ozone Solar.R Wind Month Day
## 5 5 Ozone Solar.R Wind Month Day
imputate_na(airquality_NA,
Ozone, Temp, method = "mice", seed = 111) %>%
plot()##
## iter imp variable
## 1 1 Ozone Solar.R Wind Month Day
## 1 2 Ozone Solar.R Wind Month Day
## 1 3 Ozone Solar.R Wind Month Day
## 1 4 Ozone Solar.R Wind Month Day
## 1 5 Ozone Solar.R Wind Month Day
## 2 1 Ozone Solar.R Wind Month Day
## 2 2 Ozone Solar.R Wind Month Day
## 2 3 Ozone Solar.R Wind Month Day
## 2 4 Ozone Solar.R Wind Month Day
## 2 5 Ozone Solar.R Wind Month Day
## 3 1 Ozone Solar.R Wind Month Day
## 3 2 Ozone Solar.R Wind Month Day
## 3 3 Ozone Solar.R Wind Month Day
## 3 4 Ozone Solar.R Wind Month Day
## 3 5 Ozone Solar.R Wind Month Day
## 4 1 Ozone Solar.R Wind Month Day
## 4 2 Ozone Solar.R Wind Month Day
## 4 3 Ozone Solar.R Wind Month Day
## 4 4 Ozone Solar.R Wind Month Day
## 4 5 Ozone Solar.R Wind Month Day
## 5 1 Ozone Solar.R Wind Month Day
## 5 2 Ozone Solar.R Wind Month Day
## 5 3 Ozone Solar.R Wind Month Day
## 5 4 Ozone Solar.R Wind Month Day
## 5 5 Ozone Solar.R Wind Month Day
airquality_impute <- missRanger(airquality_NA,
formula = .~.,
num.trees = 1000,
seed = 3)##
## Missing value imputation by random forests
##
## Variables to impute: Ozone, Solar.R, Wind, Temp, Month, Day
## Variables used to impute: Ozone, Solar.R, Wind, Temp, Month, Day
##
## iter 1
## | | | 0% | |============ | 17% | |======================= | 33% | |=================================== | 50% | |=============================================== | 67% | |========================================================== | 83% | |======================================================================| 100%
## iter 2
## | | | 0% | |============ | 17% | |======================= | 33% | |=================================== | 50% | |=============================================== | 67% | |========================================================== | 83% | |======================================================================| 100%
## iter 3
## | | | 0% | |============ | 17% | |======================= | 33% | |=================================== | 50% | |=============================================== | 67% | |========================================================== | 83% | |======================================================================| 100%# Ref: https://www.youtube.com/watch?v=Akb401i32Oc