Clean Data by Interpolating Missing Values
Arguments
- data
A data frame containing the dataset to be cleaned.
- n_trials
The total number of rows in the dataset.
- n_replicates
The total number of replicate columns in each row.
Details
This function cleans a dataset by interpolating missing values in the replicate columns of each row using neighboring values. If the data frame ends in null values (the last columns are nulls), it will extrapolate from the last value. If the first value is null, it will loop around and pull from the last replicate to perform the interpolation between the last replicate and the second replicate.
See also
find_next_good_datapoint for details on the interpolation process.
Examples
my_data <- matrix(
c(
1, 60, 1, 2, 3, 4, 5, # No NA values
1, 90, 9, NA, 4, NA, 2, # NA Values in row
1, 120, 3, 6, NA, NA, 9 # Consecutive NA values
),
nrow = 3,
byrow=TRUE
)
cleaned_data <- clean_data(my_data, n_trials = 3, n_replicates = 5)
print(my_data)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 1 60 1 2 3 4 5
#> [2,] 1 90 9 NA 4 NA 2
#> [3,] 1 120 3 6 NA NA 9
print(cleaned_data)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 1 60 1 2 3.0 4.00 5
#> [2,] 1 90 9 11 4.0 6.00 2
#> [3,] 1 120 3 6 10.5 15.75 9