Omit missing values

hd_omit_na() removes rows with missing values from a dataset. It allows the user to specify the columns to consider for the removal of missing values. If no columns are provided, the function removes rows with missing values in any column.

Usage

hd_omit_na(dat, columns = NULL)

Arguments

dat

An HDAnalyzeR object or a dataset in wide format and sample ID as its first column.

columns

The columns to consider for the removal of missing values.

Value

The dataset without the rows containing missing values.

Examples

# Create the HDAnalyzeR object providing the data and metadata
hd_object <- hd_initialize(example_data, example_metadata)
hd_object$data
#> # A tibble: 586 × 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 576 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 removing missing values
res <- hd_omit_na(hd_object)
res$data
#> # A tibble: 442 × 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 DA00005   5.01 5.05   0.128  0.401  -0.933  -0.584  0.0265  1.16   2.73 
#>  4 DA00008   2.78 0.812 -0.552  0.982  -0.101  -0.304  0.376  -0.826  1.52 
#>  5 DA00009   4.39 3.34  -0.452 -0.868   0.395   1.71   1.49   -0.0285 0.200
#>  6 DA00010   1.83 1.21  -0.912 -1.04   -0.0918 -0.304  1.69    0.0920 2.04 
#>  7 DA00011   3.48 4.96   3.50  -0.338   4.48    1.26   2.18    1.62   1.79 
#>  8 DA00012   4.31 0.710 -1.44  -0.218  -0.469  -0.361 -0.0714 -1.30   2.86 
#>  9 DA00013   1.31 2.52   1.11   0.997   4.56   -1.35   0.833   2.33   3.57 
#> 10 DA00014   6.34 7.25   5.12   0.0193  1.29    0.370 -0.382   0.830  3.89 
#> # ℹ 432 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 removing missing values in specific columns
res <- hd_omit_na(hd_object, columns = "AARSD1")
res$data
#> # A tibble: 552 × 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 DA00004   3.41 3.38   1.69  NA       1.52   NA      0.841   0.582  1.70 
#>  4 DA00005   5.01 5.05   0.128  0.401  -0.933  -0.584  0.0265  1.16   2.73 
#>  5 DA00006   6.83 1.18  -1.74  -0.156   1.53   -0.721  0.620   0.527  0.772
#>  6 DA00008   2.78 0.812 -0.552  0.982  -0.101  -0.304  0.376  -0.826  1.52 
#>  7 DA00009   4.39 3.34  -0.452 -0.868   0.395   1.71   1.49   -0.0285 0.200
#>  8 DA00010   1.83 1.21  -0.912 -1.04   -0.0918 -0.304  1.69    0.0920 2.04 
#>  9 DA00011   3.48 4.96   3.50  -0.338   4.48    1.26   2.18    1.62   1.79 
#> 10 DA00012   4.31 0.710 -1.44  -0.218  -0.469  -0.361 -0.0714 -1.30   2.86 
#> # ℹ 542 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>, …