Filter an HD object based on a variable, either in the data or metadata component.
Arguments
- hd_obj
An HD object to be filtered.
- variable
The name of the variable to filter on.
- values
The values to filter on (for categorical variables) or the value to compare to (for continuous variables).
- flag
The type of filter to apply (see details).
- verbose
A logical indicating whether to print messages during filtering. Default is TRUE.
Details
The flag argument can take the following values:
"k" : Keep only rows where the variable matches the values (categorical)
"r" : Remove rows where the variable matches the values (categorical)
"=" : Keep only rows where the variable equals the value (continuous)
"<" : Keep only rows where the variable is less than the value (continuous)
"<=" : Keep only rows where the variable is less than or equal to the value (continuous)
">" : Keep only rows where the variable is greater than the value (continuous)
">=" : Keep only rows where the variable is greater than or equal to the value (continuous)
"!=" : Keep only rows where the variable is not equal to the value (continuous)
Examples
# Create the HDAnalyzeR object providing the data and metadata
hd_obj <- hd_initialize(example_data, example_metadata)
hd_obj
#> $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>, …
#>
#> $metadata
#> # A tibble: 586 × 9
#> DAid Sample Disease Stage Grade Sex Age BMI Cohort
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
#> 1 DA00001 AML_syn_1 AML 2 NA F 42 22.7 UCAN
#> 2 DA00002 AML_syn_2 AML Unknown NA M 69 33.1 UCAN
#> 3 DA00003 AML_syn_3 AML 2 NA F 61 26.2 UCAN
#> 4 DA00004 AML_syn_4 AML Unknown NA M 54 28.1 UCAN
#> 5 DA00005 AML_syn_5 AML 2 NA F 57 21.4 UCAN
#> 6 DA00006 AML_syn_6 AML Unknown NA M 86 33.9 UCAN
#> 7 DA00007 AML_syn_7 AML 1 NA F 85 28.7 UCAN
#> 8 DA00008 AML_syn_8 AML 3 NA F 88 32.6 UCAN
#> 9 DA00009 AML_syn_9 AML Unknown NA M 80 26.1 UCAN
#> 10 DA00010 AML_syn_10 AML 3 NA M 48 33.8 UCAN
#> # ℹ 576 more rows
#>
#> $sample_id
#> [1] "DAid"
#>
#> $var_name
#> [1] "Assay"
#>
#> $value_name
#> [1] "NPX"
#>
#> attr(,"class")
#> [1] "HDAnalyzeR"
# Filter by categorical variable
hd_filter(hd_obj, variable = "Sex", values = "F", flag = "k")
#> Variable Sex is categorical
#> Filtering complete. Rows remaining: 366
#> $data
#> # A tibble: 366 × 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… NA NA NA 0.989 NA 0.330 1.37 NA NA NA
#> 3 DA00… 5.01 5.05 0.128 0.401 -0.933 -0.584 0.0265 1.16 2.73 0.350
#> 4 DA00… NA NA 3.96 0.682 3.14 2.62 1.47 2.25 2.01 0.170
#> 5 DA00… 2.78 0.812 -0.552 0.982 -0.101 -0.304 0.376 -0.826 1.52 -0.597
#> 6 DA00… 3.48 4.96 3.50 -0.338 4.48 1.26 2.18 1.62 1.79 0.233
#> 7 DA00… 4.31 0.710 -1.44 -0.218 -0.469 -0.361 -0.0714 -1.30 2.86 0.129
#> 8 DA00… 1.79 1.36 0.106 -0.372 3.40 -1.19 1.77 1.07 2.00 0.980
#> 9 DA00… 3.59 3.38 1.79 -0.303 1.59 0.604 1.71 -0.837 1.65 1.13
#> 10 DA00… 1.80 1.70 2.77 -1.04 1.33 -0.0247 1.02 0.112 2.58 1.14
#> # ℹ 356 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>, …
#>
#> $metadata
#> # A tibble: 366 × 9
#> DAid Sample Disease Stage Grade Sex Age BMI Cohort
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
#> 1 DA00001 AML_syn_1 AML 2 NA F 42 22.7 UCAN
#> 2 DA00003 AML_syn_3 AML 2 NA F 61 26.2 UCAN
#> 3 DA00005 AML_syn_5 AML 2 NA F 57 21.4 UCAN
#> 4 DA00007 AML_syn_7 AML 1 NA F 85 28.7 UCAN
#> 5 DA00008 AML_syn_8 AML 3 NA F 88 32.6 UCAN
#> 6 DA00011 AML_syn_11 AML 3 NA F 54 34.7 UCAN
#> 7 DA00012 AML_syn_12 AML 3 NA F 78 21.4 UCAN
#> 8 DA00016 AML_syn_16 AML 3 NA F 78 25.4 UCAN
#> 9 DA00019 AML_syn_19 AML 1 NA F 81 22.9 UCAN
#> 10 DA00020 AML_syn_20 AML 3 NA F 65 24.1 UCAN
#> # ℹ 356 more rows
#>
#> $sample_id
#> [1] "DAid"
#>
#> $var_name
#> [1] "Assay"
#>
#> $value_name
#> [1] "NPX"
#>
#> attr(,"class")
#> [1] "HDAnalyzeR"
# Filter by continuous variable
hd_filter(hd_obj, variable = "Age", values = 80, flag = ">")
#> Variable Age is continuous
#> Filtering complete. Rows remaining: 142
#> $data
#> # A tibble: 142 × 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… 6.83 1.18 -1.74 -0.156 1.53 -0.721 0.620 0.527 0.772 NA
#> 2 DA00… NA NA 3.96 0.682 3.14 2.62 1.47 2.25 2.01 0.170
#> 3 DA00… 2.78 0.812 -0.552 0.982 -0.101 -0.304 0.376 -0.826 1.52 -0.597
#> 4 DA00… 1.31 2.52 1.11 0.997 4.56 -1.35 0.833 2.33 3.57 1.37
#> 5 DA00… 3.59 3.38 1.79 -0.303 1.59 0.604 1.71 -0.837 1.65 1.13
#> 6 DA00… 2.40 3.50 2.47 -0.0788 2.25 -0.0102 0.581 1.28 1.45 0.320
#> 7 DA00… 2.53 3.00 0.166 0.707 -0.00699 1.05 0.898 1.53 2.02 0.127
#> 8 DA00… 2.93 1.55 -0.126 0.868 0.963 0.249 1.15 1.16 1.32 -0.753
#> 9 DA00… 4.29 1.66 4.13 1.56 3.66 -1.06 2.09 1.26 2.78 0.786
#> 10 DA00… 1.33 0.406 0.934 0.253 0.133 0.117 0.289 0.878 1.43 1.34
#> # ℹ 132 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>, …
#>
#> $metadata
#> # A tibble: 142 × 9
#> DAid Sample Disease Stage Grade Sex Age BMI Cohort
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
#> 1 DA00006 AML_syn_6 AML Unknown NA M 86 33.9 UCAN
#> 2 DA00007 AML_syn_7 AML 1 NA F 85 28.7 UCAN
#> 3 DA00008 AML_syn_8 AML 3 NA F 88 32.6 UCAN
#> 4 DA00013 AML_syn_13 AML Unknown NA M 81 20.9 UCAN
#> 5 DA00019 AML_syn_19 AML 1 NA F 81 22.9 UCAN
#> 6 DA00031 AML_syn_31 AML 3 NA M 85 22 UCAN
#> 7 DA00051 BRC_syn_1 BRC 2 NA F 82 30.1 Gender_specific
#> 8 DA00053 BRC_syn_3 BRC 2 NA F 82 33 Gender_specific
#> 9 DA00059 BRC_syn_9 BRC 2 NA F 81 33.3 Gender_specific
#> 10 DA00060 BRC_syn_10 BRC 2 NA F 90 26.2 Gender_specific
#> # ℹ 132 more rows
#>
#> $sample_id
#> [1] "DAid"
#>
#> $var_name
#> [1] "Assay"
#>
#> $value_name
#> [1] "NPX"
#>
#> attr(,"class")
#> [1] "HDAnalyzeR"