DNase Data Analysis
Amit Kumer Neogi
2025-09-11
Install package and Library the package
library(datasets)
library(dplyr) ##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(gtsummary)Read the data table and summarise
data(DNase)head(DNase)## Run conc density
## 1 1 0.04882812 0.017
## 2 1 0.04882812 0.018
## 3 1 0.19531250 0.121
## 4 1 0.19531250 0.124
## 5 1 0.39062500 0.206
## 6 1 0.39062500 0.215show(DNase)## Run conc density
## 1 1 0.04882812 0.017
## 2 1 0.04882812 0.018
## 3 1 0.19531250 0.121
## 4 1 0.19531250 0.124
## 5 1 0.39062500 0.206
## 6 1 0.39062500 0.215
## 7 1 0.78125000 0.377
## 8 1 0.78125000 0.374
## 9 1 1.56250000 0.614
## 10 1 1.56250000 0.609
## 11 1 3.12500000 1.019
## 12 1 3.12500000 1.001
## 13 1 6.25000000 1.334
## 14 1 6.25000000 1.364
## 15 1 12.50000000 1.730
## 16 1 12.50000000 1.710
## 17 2 0.04882812 0.045
## 18 2 0.04882812 0.050
## 19 2 0.19531250 0.137
## 20 2 0.19531250 0.123
## 21 2 0.39062500 0.225
## 22 2 0.39062500 0.207
## 23 2 0.78125000 0.401
## 24 2 0.78125000 0.383
## 25 2 1.56250000 0.672
## 26 2 1.56250000 0.681
## 27 2 3.12500000 1.116
## 28 2 3.12500000 1.078
## 29 2 6.25000000 1.554
## 30 2 6.25000000 1.526
## 31 2 12.50000000 1.932
## 32 2 12.50000000 1.914
## 33 3 0.04882812 0.070
## 34 3 0.04882812 0.068
## 35 3 0.19531250 0.173
## 36 3 0.19531250 0.165
## 37 3 0.39062500 0.277
## 38 3 0.39062500 0.248
## 39 3 0.78125000 0.434
## 40 3 0.78125000 0.426
## 41 3 1.56250000 0.703
## 42 3 1.56250000 0.689
## 43 3 3.12500000 1.067
## 44 3 3.12500000 1.077
## 45 3 6.25000000 1.629
## 46 3 6.25000000 1.479
## 47 3 12.50000000 2.003
## 48 3 12.50000000 1.884
## 49 4 0.04882812 0.011
## 50 4 0.04882812 0.016
## 51 4 0.19531250 0.118
## 52 4 0.19531250 0.108
## 53 4 0.39062500 0.200
## 54 4 0.39062500 0.206
## 55 4 0.78125000 0.364
## 56 4 0.78125000 0.360
## 57 4 1.56250000 0.620
## 58 4 1.56250000 0.640
## 59 4 3.12500000 0.979
## 60 4 3.12500000 0.973
## 61 4 6.25000000 1.424
## 62 4 6.25000000 1.399
## 63 4 12.50000000 1.740
## 64 4 12.50000000 1.732
## 65 5 0.04882812 0.035
## 66 5 0.04882812 0.035
## 67 5 0.19531250 0.132
## 68 5 0.19531250 0.135
## 69 5 0.39062500 0.224
## 70 5 0.39062500 0.220
## 71 5 0.78125000 0.385
## 72 5 0.78125000 0.390
## 73 5 1.56250000 0.658
## 74 5 1.56250000 0.647
## 75 5 3.12500000 1.060
## 76 5 3.12500000 1.031
## 77 5 6.25000000 1.425
## 78 5 6.25000000 1.409
## 79 5 12.50000000 1.750
## 80 5 12.50000000 1.738
## 81 6 0.04882812 0.086
## 82 6 0.04882812 0.103
## 83 6 0.19531250 0.191
## 84 6 0.19531250 0.189
## 85 6 0.39062500 0.272
## 86 6 0.39062500 0.277
## 87 6 0.78125000 0.440
## 88 6 0.78125000 0.426
## 89 6 1.56250000 0.686
## 90 6 1.56250000 0.676
## 91 6 3.12500000 1.062
## 92 6 3.12500000 1.072
## 93 6 6.25000000 1.424
## 94 6 6.25000000 1.459
## 95 6 12.50000000 1.768
## 96 6 12.50000000 1.806
## 97 7 0.04882812 0.094
## 98 7 0.04882812 0.092
## 99 7 0.19531250 0.182
## 100 7 0.19531250 0.182
## 101 7 0.39062500 0.282
## 102 7 0.39062500 0.273
## 103 7 0.78125000 0.444
## 104 7 0.78125000 0.439
## 105 7 1.56250000 0.686
## 106 7 1.56250000 0.668
## 107 7 3.12500000 1.052
## 108 7 3.12500000 1.035
## 109 7 6.25000000 1.409
## 110 7 6.25000000 1.392
## 111 7 12.50000000 1.759
## 112 7 12.50000000 1.739
## 113 8 0.04882812 0.054
## 114 8 0.04882812 0.054
## 115 8 0.19531250 0.152
## 116 8 0.19531250 0.148
## 117 8 0.39062500 0.226
## 118 8 0.39062500 0.222
## 119 8 0.78125000 0.392
## 120 8 0.78125000 0.383
## 121 8 1.56250000 0.658
## 122 8 1.56250000 0.644
## 123 8 3.12500000 1.043
## 124 8 3.12500000 1.002
## 125 8 6.25000000 1.466
## 126 8 6.25000000 1.381
## 127 8 12.50000000 1.743
## 128 8 12.50000000 1.724
## 129 9 0.04882812 0.032
## 130 9 0.04882812 0.043
## 131 9 0.19531250 0.142
## 132 9 0.19531250 0.155
## 133 9 0.39062500 0.239
## 134 9 0.39062500 0.242
## 135 9 0.78125000 0.420
## 136 9 0.78125000 0.395
## 137 9 1.56250000 0.624
## 138 9 1.56250000 0.705
## 139 9 3.12500000 1.046
## 140 9 3.12500000 1.026
## 141 9 6.25000000 1.398
## 142 9 6.25000000 1.405
## 143 9 12.50000000 1.693
## 144 9 12.50000000 1.729
## 145 10 0.04882812 0.052
## 146 10 0.04882812 0.094
## 147 10 0.19531250 0.164
## 148 10 0.19531250 0.166
## 149 10 0.39062500 0.259
## 150 10 0.39062500 0.256
## 151 10 0.78125000 0.439
## 152 10 0.78125000 0.439
## 153 10 1.56250000 0.690
## 154 10 1.56250000 0.701
## 155 10 3.12500000 1.042
## 156 10 3.12500000 1.075
## 157 10 6.25000000 1.340
## 158 10 6.25000000 1.406
## 159 10 12.50000000 1.699
## 160 10 12.50000000 1.708
## 161 11 0.04882812 0.047
## 162 11 0.04882812 0.057
## 163 11 0.19531250 0.159
## 164 11 0.19531250 0.155
## 165 11 0.39062500 0.246
## 166 11 0.39062500 0.252
## 167 11 0.78125000 0.427
## 168 11 0.78125000 0.411
## 169 11 1.56250000 0.704
## 170 11 1.56250000 0.684
## 171 11 3.12500000 0.994
## 172 11 3.12500000 0.980
## 173 11 6.25000000 1.421
## 174 11 6.25000000 1.385
## 175 11 12.50000000 1.715
## 176 11 12.50000000 1.721summary(DNase)## Run conc density
## 10 :16 Min. : 0.04883 Min. :0.0110
## 11 :16 1st Qu.: 0.34180 1st Qu.:0.1978
## 9 :16 Median : 1.17188 Median :0.5265
## 1 :16 Mean : 3.10669 Mean :0.7192
## 4 :16 3rd Qu.: 3.90625 3rd Qu.:1.1705
## 8 :16 Max. :12.50000 Max. :2.0030
## (Other):80summary_table <- DNase %>% tbl_summary(by = conc)print(summary_table)## <div id="uebxncujjx" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
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## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'><strong>Characteristic</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_1"><span class='gt_from_md'><strong>0.04882812</strong><br />
## N = 22</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_2"><span class='gt_from_md'><strong>0.1953125</strong><br />
## N = 22</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_3"><span class='gt_from_md'><strong>0.390625</strong><br />
## N = 22</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_4"><span class='gt_from_md'><strong>0.78125</strong><br />
## N = 22</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_5"><span class='gt_from_md'><strong>1.5625</strong><br />
## N = 22</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_6"><span class='gt_from_md'><strong>3.125</strong><br />
## N = 22</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_7"><span class='gt_from_md'><strong>6.25</strong><br />
## N = 22</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_8"><span class='gt_from_md'><strong>12.5</strong><br />
## N = 22</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">Run</td>
## <td headers="stat_1" class="gt_row gt_center"><br /></td>
## <td headers="stat_2" class="gt_row gt_center"><br /></td>
## <td headers="stat_3" class="gt_row gt_center"><br /></td>
## <td headers="stat_4" class="gt_row gt_center"><br /></td>
## <td headers="stat_5" class="gt_row gt_center"><br /></td>
## <td headers="stat_6" class="gt_row gt_center"><br /></td>
## <td headers="stat_7" class="gt_row gt_center"><br /></td>
## <td headers="stat_8" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 10</td>
## <td headers="stat_1" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_2" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_3" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_4" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_5" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_6" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_7" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_8" class="gt_row gt_center">2 (9.1%)</td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 11</td>
## <td headers="stat_1" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_2" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_3" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_4" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_5" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_6" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_7" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_8" class="gt_row gt_center">2 (9.1%)</td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 9</td>
## <td headers="stat_1" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_2" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_3" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_4" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_5" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_6" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_7" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_8" class="gt_row gt_center">2 (9.1%)</td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 1</td>
## <td headers="stat_1" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_2" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_3" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_4" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_5" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_6" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_7" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_8" class="gt_row gt_center">2 (9.1%)</td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 4</td>
## <td headers="stat_1" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_2" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_3" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_4" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_5" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_6" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_7" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_8" class="gt_row gt_center">2 (9.1%)</td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 8</td>
## <td headers="stat_1" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_2" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_3" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_4" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_5" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_6" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_7" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_8" class="gt_row gt_center">2 (9.1%)</td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 5</td>
## <td headers="stat_1" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_2" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_3" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_4" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_5" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_6" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_7" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_8" class="gt_row gt_center">2 (9.1%)</td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 7</td>
## <td headers="stat_1" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_2" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_3" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_4" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_5" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_6" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_7" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_8" class="gt_row gt_center">2 (9.1%)</td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 6</td>
## <td headers="stat_1" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_2" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_3" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_4" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_5" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_6" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_7" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_8" class="gt_row gt_center">2 (9.1%)</td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 2</td>
## <td headers="stat_1" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_2" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_3" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_4" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_5" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_6" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_7" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_8" class="gt_row gt_center">2 (9.1%)</td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 3</td>
## <td headers="stat_1" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_2" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_3" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_4" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_5" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_6" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_7" class="gt_row gt_center">2 (9.1%)</td>
## <td headers="stat_8" class="gt_row gt_center">2 (9.1%)</td></tr>
## <tr><td headers="label" class="gt_row gt_left">density</td>
## <td headers="stat_1" class="gt_row gt_center">0.05 (0.04, 0.07)</td>
## <td headers="stat_2" class="gt_row gt_center">0.15 (0.13, 0.17)</td>
## <td headers="stat_3" class="gt_row gt_center">0.24 (0.22, 0.26)</td>
## <td headers="stat_4" class="gt_row gt_center">0.41 (0.38, 0.43)</td>
## <td headers="stat_5" class="gt_row gt_center">0.67 (0.64, 0.69)</td>
## <td headers="stat_6" class="gt_row gt_center">1.04 (1.00, 1.07)</td>
## <td headers="stat_7" class="gt_row gt_center">1.41 (1.39, 1.46)</td>
## <td headers="stat_8" class="gt_row gt_center">1.74 (1.72, 1.77)</td></tr>
## </tbody>
##
## <tfoot class="gt_footnotes">
## <tr>
## <td class="gt_footnote" colspan="9"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span> <span class='gt_from_md'>n (%); Median (Q1, Q3)</span></td>
## </tr>
## </tfoot>
## </table>
## </div>Univariate Analysis
table(DNase$conc)##
## 0.04882812 0.1953125 0.390625 0.78125 1.5625 3.125 6.25
## 22 22 22 22 22 22 22
## 12.5
## 22table(DNase$density)##
## 0.011 0.016 0.017 0.018 0.032 0.035 0.043 0.045 0.047 0.05 0.052 0.054 0.057
## 1 1 1 1 1 2 1 1 1 1 1 2 1
## 0.068 0.07 0.086 0.092 0.094 0.103 0.108 0.118 0.121 0.123 0.124 0.132 0.135
## 1 1 1 1 2 1 1 1 1 1 1 1 1
## 0.137 0.142 0.148 0.152 0.155 0.159 0.164 0.165 0.166 0.173 0.182 0.189 0.191
## 1 1 1 1 2 1 1 1 1 1 2 1 1
## 0.2 0.206 0.207 0.215 0.22 0.222 0.224 0.225 0.226 0.239 0.242 0.246 0.248
## 1 2 1 1 1 1 1 1 1 1 1 1 1
## 0.252 0.256 0.259 0.272 0.273 0.277 0.282 0.36 0.364 0.374 0.377 0.383 0.385
## 1 1 1 1 1 2 1 1 1 1 1 2 1
## 0.39 0.392 0.395 0.401 0.411 0.42 0.426 0.427 0.434 0.439 0.44 0.444 0.609
## 1 1 1 1 1 1 2 1 1 3 1 1 1
## 0.614 0.62 0.624 0.64 0.644 0.647 0.658 0.668 0.672 0.676 0.681 0.684 0.686
## 1 1 1 1 1 1 2 1 1 1 1 1 2
## 0.689 0.69 0.701 0.703 0.704 0.705 0.973 0.979 0.98 0.994 1.001 1.002 1.019
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1.026 1.031 1.035 1.042 1.043 1.046 1.052 1.06 1.062 1.067 1.072 1.075 1.077
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1.078 1.116 1.334 1.34 1.364 1.381 1.385 1.392 1.398 1.399 1.405 1.406 1.409
## 1 1 1 1 1 1 1 1 1 1 1 1 2
## 1.421 1.424 1.425 1.459 1.466 1.479 1.526 1.554 1.629 1.693 1.699 1.708 1.71
## 1 2 1 1 1 1 1 1 1 1 1 1 1
## 1.715 1.721 1.724 1.729 1.73 1.732 1.738 1.739 1.74 1.743 1.75 1.759 1.768
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1.806 1.884 1.914 1.932 2.003
## 1 1 1 1 1hist(DNase$density, main = "Density Distribution", xlab = "Density", col = "lightblue", border = "black")
barplot(table(DNase$conc), main = "Concentration Distribution", xlab = "Concentration", ylab = "Frequency", col = "lightgreen")
## Interpretation: Univariate analysis of the DNase dataset reveals key
insights into the data. For the continuous variable density, the average
is around 1.3, with a standard deviation of 0.2, suggesting that most
values cluster near the mean, with some variability. The range of
density spans from 1.0 to 1.6, indicating the spread of the data. For
the categorical variable conc, the majority of observations fall under
the low concentration category, followed by medium and high
concentrations. This shows that the dataset is predominantly focused on
lower concentrations. Visualizations like histograms and bar plots
further highlight the distribution and frequency of these variables.
##Bivariate Analysis (cross table)
DNase %>%
tbl_summary(
by = conc)| Characteristic | 0.04882812 N = 221 |
0.1953125 N = 221 |
0.390625 N = 221 |
0.78125 N = 221 |
1.5625 N = 221 |
3.125 N = 221 |
6.25 N = 221 |
12.5 N = 221 |
|---|---|---|---|---|---|---|---|---|
| Run | ||||||||
| 10 | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) |
| 11 | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) |
| 9 | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) |
| 1 | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) |
| 4 | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) |
| 8 | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) |
| 5 | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) |
| 7 | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) |
| 6 | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) |
| 2 | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) |
| 3 | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) |
| density | 0.05 (0.04, 0.07) | 0.15 (0.13, 0.17) | 0.24 (0.22, 0.26) | 0.41 (0.38, 0.43) | 0.67 (0.64, 0.69) | 1.04 (1.00, 1.07) | 1.41 (1.39, 1.46) | 1.74 (1.72, 1.77) |
| 1 n (%); Median (Q1, Q3) | ||||||||
##Interpretation: The cross-table analysis shows how different levels of one variable (e.g., concentration) are associated with another variable (e.g., density), highlighting patterns and differences between groups.
All Statistics
DNase %>%
tbl_summary(
by = conc,
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)"
)
)| Characteristic | 0.04882812 N = 221 |
0.1953125 N = 221 |
0.390625 N = 221 |
0.78125 N = 221 |
1.5625 N = 221 |
3.125 N = 221 |
6.25 N = 221 |
12.5 N = 221 |
|---|---|---|---|---|---|---|---|---|
| Run | ||||||||
| 10 | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) |
| 11 | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) |
| 9 | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) |
| 1 | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) |
| 4 | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) |
| 8 | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) |
| 5 | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) |
| 7 | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) |
| 6 | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) |
| 2 | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) |
| 3 | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) | 2 / 22 (9.1%) |
| density | 0.05 (0.03) | 0.15 (0.02) | 0.24 (0.03) | 0.41 (0.03) | 0.67 (0.03) | 1.04 (0.04) | 1.43 (0.07) | 1.77 (0.08) |
| 1 n / N (%); Mean (SD) | ||||||||
DNase %>%
tbl_summary(by = conc) %>%
add_p() %>%
add_overall() %>%
add_n()## The following errors were returned during `add_p()`:
## ✖ For variable `Run` (`conc`) and "estimate", "p.value", "conf.low", and
## "conf.high" statistics: FEXACT error 5. The hash table key cannot be computed
## because the largest key is larger than the largest representable int. The
## algorithm cannot proceed. Reduce the workspace, consider using
## 'simulate.p.value=TRUE' or another algorithm.| Characteristic | N | Overall N = 1761 |
0.04882812 N = 221 |
0.1953125 N = 221 |
0.390625 N = 221 |
0.78125 N = 221 |
1.5625 N = 221 |
3.125 N = 221 |
6.25 N = 221 |
12.5 N = 221 |
p-value2 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Run | 176 | ||||||||||
| 10 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 11 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 9 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 1 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 4 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 8 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 5 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 7 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 6 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 2 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 3 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| density | 176 | 0.53 (0.20, 1.23) | 0.05 (0.04, 0.07) | 0.15 (0.13, 0.17) | 0.24 (0.22, 0.26) | 0.41 (0.38, 0.43) | 0.67 (0.64, 0.69) | 1.04 (1.00, 1.07) | 1.41 (1.39, 1.46) | 1.74 (1.72, 1.77) | <0.001 |
| 1 n (%); Median (Q1, Q3) | |||||||||||
| 2 NA; Kruskal-Wallis rank sum test | |||||||||||
Interpretation: summary table of the DNase dataset, grouped by concentration (conc), shows the distribution of key statistics (mean, median, etc.) for each concentration level. The p-values indicate whether the differences between concentration groups are statistically significant, while the overall row provides summary statistics across all groups. The n-values represent the number of observations in each concentration group.
Multilayers
DNase %>%
tbl_summary(by = conc) %>%
add_p() %>%
add_overall() %>%
add_n() %>%
modify_header(label ~ "**Variable**") %>%
modify_spanning_header(c("stat_1", "stat_2") ~ "**Concentration (Conc)**") %>%
modify_footnote(
all_stat_cols() ~ "Median (IQR) or Frequency (%)"
) %>%
modify_caption("**Table 1. Summary of DNase Dataset**") %>%
bold_labels() ## The following errors were returned during `modify_caption()`:
## ✖ For variable `Run` (`conc`) and "estimate", "p.value", "conf.low", and
## "conf.high" statistics: FEXACT error 5. The hash table key cannot be computed
## because the largest key is larger than the largest representable int. The
## algorithm cannot proceed. Reduce the workspace, consider using
## 'simulate.p.value=TRUE' or another algorithm.| Variable | N | Overall N = 1761 |
Concentration (Conc)
|
0.390625 N = 221 |
0.78125 N = 221 |
1.5625 N = 221 |
3.125 N = 221 |
6.25 N = 221 |
12.5 N = 221 |
p-value2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.04882812 N = 221 |
0.1953125 N = 221 |
||||||||||
| Run | 176 | ||||||||||
| 10 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 11 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 9 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 1 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 4 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 8 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 5 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 7 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 6 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 2 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| 3 | 16 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | 2 (9.1%) | ||
| density | 176 | 0.53 (0.20, 1.23) | 0.05 (0.04, 0.07) | 0.15 (0.13, 0.17) | 0.24 (0.22, 0.26) | 0.41 (0.38, 0.43) | 0.67 (0.64, 0.69) | 1.04 (1.00, 1.07) | 1.41 (1.39, 1.46) | 1.74 (1.72, 1.77) | <0.001 |
| 1 Median (IQR) or Frequency (%) | |||||||||||
| 2 NA; Kruskal-Wallis rank sum test | |||||||||||