R Statistics Cookbook

How to do it...

The imputeTS package is incredibly powerful and easy to use; it can be used to impute missing values for time series. In this recipe, we will load a dataset containing biodiesel production figures for Argentina (this is a monthly dataset from 2008 to 2018 containing monthly production values):

1. First, we load the imputeTS package and the necessary dataset:

library(imputeTS) library(ggplot2) biodisel_prod = read.csv("./biodiesel.csv") biodisel_prod$indice_tiempo = as.Date(biodisel_prod$indice_tiempo,"%Y-%m-%d") 

2. We will remove six observations and use the Kalman filter approach to replace the missing values. There are multiple ways of imputing these missing values:

biodisel_prod$indice_tiempo = as.Date(biodisel_prod$indice_tiempo,"%Y-%m-%d") ...