impute_knn() imputes missing values in a dataset using the k-nearest neighbors method.
It allows the user to exclude certain columns from imputation and can also display the
percentage of missing values in each column before imputation. The user can also specify
the number of neighbors to consider for imputation.
Usage
impute_knn(
olink_data,
wide = TRUE,
k = 5,
exclude_cols = c("DAid", "Disease"),
show_na_percentage = TRUE
)Examples
# Data before imputation
test_data <- example_data |>
dplyr::select(DAid, Assay, NPX) |>
tidyr::pivot_wider(names_from = "Assay", values_from = "NPX") |>
dplyr::slice_head(n = 100)
test_data
#> # A tibble: 100 × 101
#> DAid AARSD1 ABL1 ACAA1 ACAN ACE2 ACOX1 ACP5 ACP6 ACTA2
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 DA00001 3.39 2.76 1.71 0.0333 1.76 -0.919 1.54 2.15 2.81
#> 2 DA00002 1.42 1.25 -0.816 -0.459 0.826 -0.902 0.647 1.30 0.798
#> 3 DA00003 NA NA NA 0.989 NA 0.330 1.37 NA NA
#> 4 DA00004 3.41 3.38 1.69 NA 1.52 NA 0.841 0.582 1.70
#> 5 DA00005 5.01 5.05 0.128 0.401 -0.933 -0.584 0.0265 1.16 2.73
#> 6 DA00006 6.83 1.18 -1.74 -0.156 1.53 -0.721 0.620 0.527 0.772
#> 7 DA00007 NA NA 3.96 0.682 3.14 2.62 1.47 2.25 2.01
#> 8 DA00008 2.78 0.812 -0.552 0.982 -0.101 -0.304 0.376 -0.826 1.52
#> 9 DA00009 4.39 3.34 -0.452 -0.868 0.395 1.71 1.49 -0.0285 0.200
#> 10 DA00010 1.83 1.21 -0.912 -1.04 -0.0918 -0.304 1.69 0.0920 2.04
#> # ℹ 90 more rows
#> # ℹ 91 more variables: ACTN4 <dbl>, ACY1 <dbl>, ADA <dbl>, ADA2 <dbl>,
#> # ADAM15 <dbl>, ADAM23 <dbl>, ADAM8 <dbl>, ADAMTS13 <dbl>, ADAMTS15 <dbl>,
#> # ADAMTS16 <dbl>, ADAMTS8 <dbl>, ADCYAP1R1 <dbl>, ADGRE2 <dbl>, ADGRE5 <dbl>,
#> # ADGRG1 <dbl>, ADGRG2 <dbl>, ADH4 <dbl>, ADM <dbl>, AGER <dbl>, AGR2 <dbl>,
#> # AGR3 <dbl>, AGRN <dbl>, AGRP <dbl>, AGXT <dbl>, AHCY <dbl>, AHSP <dbl>,
#> # AIF1 <dbl>, AIFM1 <dbl>, AK1 <dbl>, AKR1B1 <dbl>, AKR1C4 <dbl>, …
# Data after imputation
impute_knn(test_data, k = 3)
#> # A tibble: 88 × 2
#> column na_percentage
#> <chr> <dbl>
#> 1 ADA2 7
#> 2 ANG 7
#> 3 ANGPTL3 7
#> 4 ANPEP 7
#> 5 AOC3 7
#> 6 APOM 7
#> 7 ART3 7
#> 8 AXL 7
#> 9 ADAMTS8 6
#> 10 AHSP 6
#> # ℹ 78 more rows
#> # A tibble: 100 × 101
#> DAid AARSD1 ABL1 ACAA1 ACAN ACE2 ACOX1 ACP5 ACP6 ACTA2 ACTN4
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 DA00… 3.39 2.76 1.71 0.0333 1.76 -0.919 1.54 2.15 2.81 0.742
#> 2 DA00… 1.42 1.25 -0.816 -0.459 0.826 -0.902 0.647 1.30 0.798 -0.0659
#> 3 DA00… 3.73 1.66 1.48 0.989 0.533 0.330 1.37 1.16 1.06 0.388
#> 4 DA00… 3.41 3.38 1.69 0.988 1.52 1.18 0.841 0.582 1.70 0.108
#> 5 DA00… 5.01 5.05 0.128 0.401 -0.933 -0.584 0.0265 1.16 2.73 0.350
#> 6 DA00… 6.83 1.18 -1.74 -0.156 1.53 -0.721 0.620 0.527 0.772 -0.184
#> 7 DA00… 4.05 5.47 3.96 0.682 3.14 2.62 1.47 2.25 2.01 0.170
#> 8 DA00… 2.78 0.812 -0.552 0.982 -0.101 -0.304 0.376 -0.826 1.52 -0.597
#> 9 DA00… 4.39 3.34 -0.452 -0.868 0.395 1.71 1.49 -0.0285 0.200 -0.532
#> 10 DA00… 1.83 1.21 -0.912 -1.04 -0.0918 -0.304 1.69 0.0920 2.04 0.501
#> # ℹ 90 more rows
#> # ℹ 90 more variables: ACY1 <dbl>, ADA <dbl>, ADA2 <dbl>, ADAM15 <dbl>,
#> # ADAM23 <dbl>, ADAM8 <dbl>, ADAMTS13 <dbl>, ADAMTS15 <dbl>, ADAMTS16 <dbl>,
#> # ADAMTS8 <dbl>, ADCYAP1R1 <dbl>, ADGRE2 <dbl>, ADGRE5 <dbl>, ADGRG1 <dbl>,
#> # ADGRG2 <dbl>, ADH4 <dbl>, ADM <dbl>, AGER <dbl>, AGR2 <dbl>, AGR3 <dbl>,
#> # AGRN <dbl>, AGRP <dbl>, AGXT <dbl>, AHCY <dbl>, AHSP <dbl>, AIF1 <dbl>,
#> # AIFM1 <dbl>, AK1 <dbl>, AKR1B1 <dbl>, AKR1C4 <dbl>, AKT1S1 <dbl>, …