cleanerR (version 0.1.1)

CompleteDataset: CompleteDataset Asks for a dataframe, a vector of collumn indices and the goal collumn and returns the data frame with the values filled

Description

CompleteDataset Asks for a dataframe, a vector of collumn indices and the goal collumn and returns the data frame with the values filled

Usage

CompleteDataset(df, rows, goal)

Arguments

df

A dataframe with the missing values you wish to fill

rows

The collumns you wish to use to predict the missing values

goal

The collum with the missing values you wish to fill

Examples

# NOT RUN {
#The CompleteDataset Function shall do the following
#Take a dataframe and a goal collumn to predict
#Takes a set of vectors to use for prediction
#Use this set to predict with accuracy given by MeanAccuracy function
#Then to run some experiments first lets build a dataframe
e=sample(1:5,1e4,replace=TRUE)
e1=sample(1:5,1e4,replace=TRUE)
e2=sample(1:5,1e4,replace=TRUE)
e=data.frame(e,e1,e2,paste(LETTERS[e],LETTERS[e1]),paste(LETTERS[e],LETTERS[e1],LETTERS[e2])   )
#Now we got a dataframe lets create a copy of it
ce=e
ce[sample(1:nrow(e),0.3*nrow(e)),5]=NA
#So 30 percent of the data is now missing
#Lets try to recover it then with CompleteDataset
#First we must choose a set of vectors to use
#Lets first try with BestVector
vector_c=BestVector(ce,5,4,nrow(ce),1)
ce1=CompleteDataset(ce,rows=vector_c,goal=5)
#We can see how many values are still missing with NA_VALUES
print(NA_VALUES(ce1) )
#And check how many we got wrong by
print(sum(ce1[,5]!=e[,5]) )
#If the user wanted he of course could choose a set of his own, for example
user_set=c(1,3)
ce1=CompleteDataset(ce,rows=user_set,goal=5)
#We can see how many values are still missing with NA_VALUES
print(NA_VALUES(ce1) )
#And check how many we got wrong by
print(sum(ce1[,5]!=e[,5]) )
#But we can see that is not the best solution
#To see how to check the best sets take a look at generate_candidates
# The process could be done for the 4 collum as well
ce=e
ce[sample(1:nrow(e),0.5*nrow(e)),4]=NA
#So 50 percent of the data is now missing
#Lets try to recover it then with CompleteDataset
vector_c=BestVector(ce,4,4,nrow(ce),1)
ce1=CompleteDataset(ce,rows=vector_c,goal=4)
#We can see how many values are still missing with NA_VALUES
print(NA_VALUES(ce1) )
#And check how many we got wrong by
print(sum(ce1[,4]!=e[,4]) )
#Here we can easily see e holds the original data
#ce1 is the recovered data
# }