What is prodNA() Function in R

The prodNA() in R is a function that artificially introduces missing values into a given dataframe. It deletes some entries completely at random up to a specified percentage. For example, if you have a dataframe with 10 rows and 5 columns and use the prodNA(df, noNA = 0.2) function will delete 10% of the entries in each column.

Syntax

prodNA(x, noNA = 0.1)

Parameters

x: It is a dataframe subjected to missing value introduction.

noNA: It is a proportion of missing values for the number of entries of ‘x’.

Example

We will use the missForest package for this example, and to install optimx, you can use the install.packages function in R with the ‘missForest’ package as an argument.

install.packages('missForest')

To load the package before using it, you can use the library() function.

library(missForest)

We will write a complete R program of the prodNA() function.

# Load the missForest package
library(missForest)

# Create a dataframe with 10 rows and 5 columns
df <- data.frame(
  x1 = rnorm(10), x2 = runif(10),
  x3 = rpois(10, 2), x4 = sample(letters, 10),
  x5 = factor(sample(c("yes", "no"), 10, replace = TRUE))
)

# Print the original dataframe
df

# Introduce missing values with prodNA()
df_na <- prodNA(df, noNA = 0.3)

cat("After using prodNA() function", "\n")

# Print the dataframe with missing values
df_na

Output

      x1         x2         x3   x4   x5
1   -0.8807456  0.27571685  0    w    yes
2   -0.8153316  0.79504456  1    i    no
3   -1.0094884  0.24994839  3    v    yes
4   -2.0131791  0.04208713  0    y    no
5   0.6224654   0.61558290  3    b    no
6   1.8538188   0.21143796  2    j    yes
7   1.2067980   0.58401559  2    g    yes
8   0.6956096   0.42331752  1    c    yes
9   -1.1648995  0.35927637  6    q    no
10  -1.2445996  0.90805339  2    d    no

After using prodNA() function
 
      x1            x2        x3    x4   x5
1    -0.8807456  0.27571685   NA    w    yes
2     NA         0.79504456   1     i    no
3    -1.0094884     NA        3     v    yes
4    -2.0131791  0.04208713   NA    y    no
5    0.6224654   0.61558290   3    <NA>  <NA>
6    1.8538188   0.21143796   NA    j    yes
7    NA          0.58401559   2    <NA>  yes
8    NA          0.42331752   1    <NA>  yes
9    NA          0.35927637   6     q    <NA>
10   -1.2445996     NA        2     d    <NA>

In the above code, we first created a data frame “df” with 10 rows and 5 columns, where the columns contain random values of different data types.

In the next step, we used the “prodNA()” function from the “missForest” package to create a new data frame, “df_na” with imputed missing values.

The “noNA” parameter is set to 0.3, which means that any columns with more than 30% missing values will not be imputed and will be set to NA.

Finally, you print the resulting “df_na” data frame using thecat() function. The “df_na” data frame should have the same dimensions as the original “df” data frame but with some missing values imputed by the “prodNA()” function.

That’s it.

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