Paste together information, often statistics, from two groups. There are two predefined combinations: mean(sd) and median[min,max], but user may also paste any single measure together.
paste_tbl_grp(data, vars_to_paste = "all", first_name = "Group1", second_name = "Group2", sep_val = " vs. ", na_str_out = "---", alternative = c("two.sided", "less", "greater"), digits = 0, trailing_zeros = TRUE, keep_all = TRUE, verbose = FALSE)
| data | input dataset. User must use consistent naming throughout, with an underscore to separate the group names from the measures (i.e. |
|---|---|
| vars_to_paste | vector of names of common measures to paste together. Can be the predefined 'median_min_max' or 'mean_sd', or any variable as long as they have matching columns for each group (i.e. Group1_MyMeasure and Group2_MyMeasure). Multiple measures can be requested. Default: "all" will run 'median_min_max' and 'mean_sd', as well as any pairs of columns in the proper format. |
| first_name | name of first group (string before '_') . Default is 'Group1'. |
| second_name | name of second group (string before '_'). Default is 'Group2'. |
| sep_val | value to be pasted between the two measures. Default is ' vs. '. |
| na_str_out | the character to replace missing values with. |
| alternative | a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". Will be used to determine the character to be pasted between the group names ( |
| digits | integer indicating the number of decimal places to round to before pasting for numeric variables. Default is 0. |
| trailing_zeros | logical indicating if trailing zeros should be included (i.e. 0.100 instead of 0.1). Note if set to TRUE output is a character vector. |
| keep_all | logical indicating if all remaining, unpasted variables in |
| verbose | a logical variable indicating if warnings and messages should be displayed. Default FALSE. |
data.frame with all the pasted values requested. Each name will have '_comparison' at the end of the names (i.e. mean_comparison, median_comparison, ...)
User must use consistant naming throughout, with a underscore to separate the group names from the measures (i.e. Group1_mean and Group2_mean). There also must be columns defining the group names (i.e. Group1 and Group2), which are used to form the Comparison variable.
alternative included as a parameter so the direction can easily be seen in one-sided test. If "two.sided" is selected the value to be pasted between the two group names will be set to sep_val, where "greater" will use " > " and "less" with use " < " as the pasting value.
# Same examples on data.table library(data.table)#> #>#> #> #>data(exampleData_BAMA) descriptive_stats_by_group <- exampleData_BAMA[, .( Group1 = unique(group[group == 1]), Group2 = unique(group[group == 2]), Group1_n = length(magnitude[group == 1]), Group2_n = length(magnitude[group == 2]), Group1_mean = mean(magnitude[group == 1]), Group2_mean = mean(magnitude[group == 2]), Group1_sd = sd(magnitude[group == 1]), Group2_sd = sd(magnitude[group == 2]), Group1_median = median(magnitude[group == 1]), Group2_median = median(magnitude[group == 2]), Group1_min = min(magnitude[group == 1]), Group2_min = min(magnitude[group == 2]), Group1_max = max(magnitude[group == 1]), Group2_max = max(magnitude[group == 2]) ), by = .(visitno,antigen)] paste_tbl_grp(data = descriptive_stats_by_group, vars_to_paste = 'all', first_name = 'Group1', second_name = 'Group2', sep_val = " vs. ", digits = 0, keep_all = TRUE)#> visitno antigen Comparison n_comparison mean_comparison #> 1 0 A1.con.env03 140 CF 1 vs. 2 6 vs. 6 -2 vs. -5 #> 2 0 A244 D11gp120_avi 1 vs. 2 6 vs. 6 75 vs. 84 #> 3 0 B.63521_D11gp120/293F 1 vs. 2 6 vs. 6 66 vs. 12 #> 4 0 B.MN V3 gp70 1 vs. 2 6 vs. 6 -27 vs. -409 #> 5 0 B.con.env03 140 CF 1 vs. 2 6 vs. 6 21 vs. -2 #> 6 0 gp41 1 vs. 2 6 vs. 6 2017 vs. 3381 #> 7 0 p24 1 vs. 2 6 vs. 6 9010 vs. 5735 #> 8 1 A1.con.env03 140 CF 1 vs. 2 6 vs. 6 1039 vs. 551 #> 9 1 A244 D11gp120_avi 1 vs. 2 6 vs. 6 5655 vs. 1681 #> 10 1 B.63521_D11gp120/293F 1 vs. 2 6 vs. 6 1720 vs. 1577 #> 11 1 B.MN V3 gp70 1 vs. 2 6 vs. 6 140 vs. 303 #> 12 1 B.con.env03 140 CF 1 vs. 2 6 vs. 6 3150 vs. 5153 #> 13 1 gp41 1 vs. 2 6 vs. 6 16324 vs. 22559 #> 14 1 p24 1 vs. 2 6 vs. 6 6745 vs. 3880 #> 15 2 A1.con.env03 140 CF 1 vs. 2 6 vs. 6 15462 vs. 11600 #> 16 2 A244 D11gp120_avi 1 vs. 2 6 vs. 6 29700 vs. 27448 #> 17 2 B.63521_D11gp120/293F 1 vs. 2 6 vs. 6 26839 vs. 26388 #> 18 2 B.MN V3 gp70 1 vs. 2 6 vs. 6 7365 vs. 13936 #> 19 2 B.con.env03 140 CF 1 vs. 2 6 vs. 6 28914 vs. 29123 #> 20 2 gp41 1 vs. 2 6 vs. 6 10696 vs. 15868 #> 21 2 p24 1 vs. 2 6 vs. 6 11042 vs. 2717 #> sd_comparison median_comparison min_comparison max_comparison #> 1 71 vs. 48 3 vs. -8 -123 vs. -75 98 vs. 69 #> 2 31 vs. 152 79 vs. 36 40 vs. -23 112 vs. 385 #> 3 17 vs. 23 73 vs. 8 36 vs. -16 82 vs. 42 #> 4 94 vs. 658 -28 vs. -29 -152 vs. -1504 110 vs. 36 #> 5 61 vs. 67 8 vs. -9 -52 vs. -87 134 vs. 115 #> 6 1519 vs. 3572 1466 vs. 2075 874 vs. 873 4860 vs. 10472 #> 7 10467 vs. 8845 4041 vs. 1450 1880 vs. 832 28818 vs. 23381 #> 8 1210 vs. 348 648 vs. 530 10 vs. 171 3258 vs. 1040 #> 9 7413 vs. 1348 2543 vs. 1046 166 vs. 430 19183 vs. 3704 #> 10 1573 vs. 1894 1563 vs. 676 70 vs. 308 3910 vs. 5116 #> 11 158 vs. 1106 117 vs. 14 -36 vs. -619 329 vs. 2425 #> 12 2365 vs. 4686 2819 vs. 4898 191 vs. 590 5990 vs. 13738 #> 13 8023 vs. 10658 14378 vs. 25038 5381 vs. 8504 26022 vs. 32447 #> 14 4760 vs. 3180 6688 vs. 3266 1728 vs. 964 13559 vs. 9886 #> 15 9629 vs. 5538 15420 vs. 11001 5002 vs. 4682 26700 vs. 20801 #> 16 2184 vs. 4584 29637 vs. 28282 26267 vs. 19268 32274 vs. 31747 #> 17 3533 vs. 6004 26034 vs. 27921 23352 vs. 14839 31956 vs. 31820 #> 18 5782 vs. 9896 6556 vs. 12932 2166 vs. 2752 15885 vs. 25838 #> 19 2163 vs. 3911 28240 vs. 30191 26292 vs. 21395 31628 vs. 32295 #> 20 6993 vs. 10463 10310 vs. 13112 2048 vs. 6414 20089 vs. 29466 #> 21 9775 vs. 1583 10763 vs. 2222 1548 vs. 1102 21449 vs. 4961 #> median_min_max_comparison mean_sd_comparison #> 1 3 [-123, 98] vs. -8 [-75, 69] -2 (71) vs. -5 (48) #> 2 79 [40, 112] vs. 36 [-23, 385] 75 (31) vs. 84 (152) #> 3 73 [36, 82] vs. 8 [-16, 42] 66 (17) vs. 12 (23) #> 4 -28 [-152, 110] vs. -29 [-1504, 36] -27 (94) vs. -409 (658) #> 5 8 [-52, 134] vs. -9 [-87, 115] 21 (61) vs. -2 (67) #> 6 1466 [874, 4860] vs. 2075 [873, 10472] 2017 (1519) vs. 3381 (3572) #> 7 4041 [1880, 28818] vs. 1450 [832, 23381] 9010 (10467) vs. 5735 (8845) #> 8 648 [10, 3258] vs. 530 [171, 1040] 1039 (1210) vs. 551 (348) #> 9 2543 [166, 19183] vs. 1046 [430, 3704] 5655 (7413) vs. 1681 (1348) #> 10 1563 [70, 3910] vs. 676 [308, 5116] 1720 (1573) vs. 1577 (1894) #> 11 117 [-36, 329] vs. 14 [-619, 2425] 140 (158) vs. 303 (1106) #> 12 2819 [191, 5990] vs. 4898 [590, 13738] 3150 (2365) vs. 5153 (4686) #> 13 14378 [5381, 26022] vs. 25038 [8504, 32447] 16324 (8023) vs. 22559 (10658) #> 14 6688 [1728, 13559] vs. 3266 [964, 9886] 6745 (4760) vs. 3880 (3180) #> 15 15420 [5002, 26700] vs. 11001 [4682, 20801] 15462 (9629) vs. 11600 (5538) #> 16 29637 [26267, 32274] vs. 28282 [19268, 31747] 29700 (2184) vs. 27448 (4584) #> 17 26034 [23352, 31956] vs. 27921 [14839, 31820] 26839 (3533) vs. 26388 (6004) #> 18 6556 [2166, 15885] vs. 12932 [2752, 25838] 7365 (5782) vs. 13936 (9896) #> 19 28240 [26292, 31628] vs. 30191 [21395, 32295] 28914 (2163) vs. 29123 (3911) #> 20 10310 [2048, 20089] vs. 13112 [6414, 29466] 10696 (6993) vs. 15868 (10463) #> 21 10763 [1548, 21449] vs. 2222 [1102, 4961] 11042 (9775) vs. 2717 (1583)paste_tbl_grp(data = descriptive_stats_by_group, vars_to_paste = c("mean", "median_min_max"), alternative= "less", keep_all = FALSE)#> Comparison mean_comparison median_min_max_comparison #> 1 1 < 2 -2 vs. -5 3 [-123, 98] vs. -8 [-75, 69] #> 2 1 < 2 75 vs. 84 79 [40, 112] vs. 36 [-23, 385] #> 3 1 < 2 66 vs. 12 73 [36, 82] vs. 8 [-16, 42] #> 4 1 < 2 -27 vs. -409 -28 [-152, 110] vs. -29 [-1504, 36] #> 5 1 < 2 21 vs. -2 8 [-52, 134] vs. -9 [-87, 115] #> 6 1 < 2 2017 vs. 3381 1466 [874, 4860] vs. 2075 [873, 10472] #> 7 1 < 2 9010 vs. 5735 4041 [1880, 28818] vs. 1450 [832, 23381] #> 8 1 < 2 1039 vs. 551 648 [10, 3258] vs. 530 [171, 1040] #> 9 1 < 2 5655 vs. 1681 2543 [166, 19183] vs. 1046 [430, 3704] #> 10 1 < 2 1720 vs. 1577 1563 [70, 3910] vs. 676 [308, 5116] #> 11 1 < 2 140 vs. 303 117 [-36, 329] vs. 14 [-619, 2425] #> 12 1 < 2 3150 vs. 5153 2819 [191, 5990] vs. 4898 [590, 13738] #> 13 1 < 2 16324 vs. 22559 14378 [5381, 26022] vs. 25038 [8504, 32447] #> 14 1 < 2 6745 vs. 3880 6688 [1728, 13559] vs. 3266 [964, 9886] #> 15 1 < 2 15462 vs. 11600 15420 [5002, 26700] vs. 11001 [4682, 20801] #> 16 1 < 2 29700 vs. 27448 29637 [26267, 32274] vs. 28282 [19268, 31747] #> 17 1 < 2 26839 vs. 26388 26034 [23352, 31956] vs. 27921 [14839, 31820] #> 18 1 < 2 7365 vs. 13936 6556 [2166, 15885] vs. 12932 [2752, 25838] #> 19 1 < 2 28914 vs. 29123 28240 [26292, 31628] vs. 30191 [21395, 32295] #> 20 1 < 2 10696 vs. 15868 10310 [2048, 20089] vs. 13112 [6414, 29466] #> 21 1 < 2 11042 vs. 2717 10763 [1548, 21449] vs. 2222 [1102, 4961]paste_tbl_grp(data = descriptive_stats_by_group, vars_to_paste = 'all', first_name = 'Group1', second_name = 'Group2', sep_val = " vs. ", alternative = 'less', digits = 5, keep_all = FALSE)#> Comparison n_comparison mean_comparison #> 1 1 < 2 6 vs. 6 -1.79167 vs. -5.16667 #> 2 1 < 2 6 vs. 6 74.95833 vs. 83.58333 #> 3 1 < 2 6 vs. 6 65.79167 vs. 12.08333 #> 4 1 < 2 6 vs. 6 -26.75000 vs. -409.16667 #> 5 1 < 2 6 vs. 6 21.25000 vs. -2.12500 #> 6 1 < 2 6 vs. 6 2017.41667 vs. 3380.62500 #> 7 1 < 2 6 vs. 6 9010.16667 vs. 5735.33333 #> 8 1 < 2 6 vs. 6 1038.58333 vs. 550.91667 #> 9 1 < 2 6 vs. 6 5655.16667 vs. 1680.70833 #> 10 1 < 2 6 vs. 6 1719.87500 vs. 1577.16667 #> 11 1 < 2 6 vs. 6 140.04167 vs. 302.70833 #> 12 1 < 2 6 vs. 6 3149.50000 vs. 5152.87500 #> 13 1 < 2 6 vs. 6 16323.66667 vs. 22558.66667 #> 14 1 < 2 6 vs. 6 6745.00000 vs. 3880.45833 #> 15 1 < 2 6 vs. 6 15462.20833 vs. 11599.91667 #> 16 1 < 2 6 vs. 6 29699.79167 vs. 27448.16667 #> 17 1 < 2 6 vs. 6 26839.12500 vs. 26387.54167 #> 18 1 < 2 6 vs. 6 7364.91667 vs. 13935.75000 #> 19 1 < 2 6 vs. 6 28914.29167 vs. 29123.45833 #> 20 1 < 2 6 vs. 6 10695.62500 vs. 15868.20833 #> 21 1 < 2 6 vs. 6 11041.70833 vs. 2716.79167 #> sd_comparison median_comparison #> 1 70.52401 vs. 48.24486 2.62500 vs. -8.12500 #> 2 31.40717 vs. 151.55376 78.87500 vs. 36.50000 #> 3 17.06855 vs. 22.77151 73.00000 vs. 7.75000 #> 4 93.98191 vs. 657.60959 -28.00000 vs. -29.12500 #> 5 61.48069 vs. 67.36148 7.50000 vs. -8.87500 #> 6 1519.19892 vs. 3572.37247 1466.00000 vs. 2075.00000 #> 7 10466.69376 vs. 8845.37576 4041.12500 vs. 1449.62500 #> 8 1209.99794 vs. 347.74372 648.00000 vs. 529.75000 #> 9 7413.16625 vs. 1348.19803 2542.87500 vs. 1046.12500 #> 10 1572.95930 vs. 1894.47811 1562.87500 vs. 675.87500 #> 11 158.30488 vs. 1105.90485 117.37500 vs. 14.12500 #> 12 2364.74946 vs. 4685.93171 2819.25000 vs. 4897.62500 #> 13 8023.22147 vs. 10658.00931 14377.75000 vs. 25038.50000 #> 14 4760.00364 vs. 3180.21395 6688.12500 vs. 3265.50000 #> 15 9628.93196 vs. 5537.86394 15419.75000 vs. 11000.62500 #> 16 2184.43202 vs. 4583.82720 29636.75000 vs. 28282.00000 #> 17 3532.87984 vs. 6003.76938 26033.62500 vs. 27920.87500 #> 18 5782.41369 vs. 9896.06080 6555.87500 vs. 12931.50000 #> 19 2162.59904 vs. 3910.76717 28240.00000 vs. 30191.12500 #> 20 6992.66463 vs. 10462.80168 10309.87500 vs. 13111.75000 #> 21 9775.01390 vs. 1583.32778 10762.87500 vs. 2222.50000 #> min_comparison max_comparison #> 1 -122.75000 vs. -74.75000 98.00000 vs. 68.75000 #> 2 39.50000 vs. -23.25000 112.00000 vs. 385.00000 #> 3 35.75000 vs. -16.00000 81.75000 vs. 41.50000 #> 4 -152.25000 vs. -1504.50000 110.50000 vs. 36.25000 #> 5 -52.50000 vs. -87.25000 133.50000 vs. 115.00000 #> 6 873.75000 vs. 873.25000 4860.50000 vs. 10472.50000 #> 7 1879.50000 vs. 831.50000 28817.50000 vs. 23381.00000 #> 8 9.75000 vs. 171.00000 3258.50000 vs. 1040.25000 #> 9 166.25000 vs. 430.00000 19183.25000 vs. 3704.00000 #> 10 69.50000 vs. 307.75000 3910.50000 vs. 5115.50000 #> 11 -36.25000 vs. -618.75000 329.00000 vs. 2425.00000 #> 12 191.25000 vs. 589.75000 5989.75000 vs. 13738.25000 #> 13 5380.75000 vs. 8504.50000 26021.75000 vs. 32447.00000 #> 14 1728.00000 vs. 963.50000 13559.00000 vs. 9886.25000 #> 15 5002.25000 vs. 4681.75000 26699.75000 vs. 20800.75000 #> 16 26267.00000 vs. 19268.50000 32273.50000 vs. 31747.25000 #> 17 23351.75000 vs. 14839.25000 31956.00000 vs. 31820.00000 #> 18 2166.25000 vs. 2752.50000 15885.25000 vs. 25838.25000 #> 19 26292.50000 vs. 21395.25000 31627.50000 vs. 32295.00000 #> 20 2047.75000 vs. 6414.25000 20088.75000 vs. 29466.50000 #> 21 1548.25000 vs. 1101.75000 21448.75000 vs. 4961.00000 #> median_min_max_comparison #> 1 2.62500 [-122.75000, 98.00000] vs. -8.12500 [-74.75000, 68.75000] #> 2 78.87500 [39.50000, 112.00000] vs. 36.50000 [-23.25000, 385.00000] #> 3 73.00000 [35.75000, 81.75000] vs. 7.75000 [-16.00000, 41.50000] #> 4 -28.00000 [-152.25000, 110.50000] vs. -29.12500 [-1504.50000, 36.25000] #> 5 7.50000 [-52.50000, 133.50000] vs. -8.87500 [-87.25000, 115.00000] #> 6 1466.00000 [873.75000, 4860.50000] vs. 2075.00000 [873.25000, 10472.50000] #> 7 4041.12500 [1879.50000, 28817.50000] vs. 1449.62500 [831.50000, 23381.00000] #> 8 648.00000 [9.75000, 3258.50000] vs. 529.75000 [171.00000, 1040.25000] #> 9 2542.87500 [166.25000, 19183.25000] vs. 1046.12500 [430.00000, 3704.00000] #> 10 1562.87500 [69.50000, 3910.50000] vs. 675.87500 [307.75000, 5115.50000] #> 11 117.37500 [-36.25000, 329.00000] vs. 14.12500 [-618.75000, 2425.00000] #> 12 2819.25000 [191.25000, 5989.75000] vs. 4897.62500 [589.75000, 13738.25000] #> 13 14377.75000 [5380.75000, 26021.75000] vs. 25038.50000 [8504.50000, 32447.00000] #> 14 6688.12500 [1728.00000, 13559.00000] vs. 3265.50000 [963.50000, 9886.25000] #> 15 15419.75000 [5002.25000, 26699.75000] vs. 11000.62500 [4681.75000, 20800.75000] #> 16 29636.75000 [26267.00000, 32273.50000] vs. 28282.00000 [19268.50000, 31747.25000] #> 17 26033.62500 [23351.75000, 31956.00000] vs. 27920.87500 [14839.25000, 31820.00000] #> 18 6555.87500 [2166.25000, 15885.25000] vs. 12931.50000 [2752.50000, 25838.25000] #> 19 28240.00000 [26292.50000, 31627.50000] vs. 30191.12500 [21395.25000, 32295.00000] #> 20 10309.87500 [2047.75000, 20088.75000] vs. 13111.75000 [6414.25000, 29466.50000] #> 21 10762.87500 [1548.25000, 21448.75000] vs. 2222.50000 [1101.75000, 4961.00000] #> mean_sd_comparison #> 1 -1.79167 (70.52401) vs. -5.16667 (48.24486) #> 2 74.95833 (31.40717) vs. 83.58333 (151.55376) #> 3 65.79167 (17.06855) vs. 12.08333 (22.77151) #> 4 -26.75000 (93.98191) vs. -409.16667 (657.60959) #> 5 21.25000 (61.48069) vs. -2.12500 (67.36148) #> 6 2017.41667 (1519.19892) vs. 3380.62500 (3572.37247) #> 7 9010.16667 (10466.69376) vs. 5735.33333 (8845.37576) #> 8 1038.58333 (1209.99794) vs. 550.91667 (347.74372) #> 9 5655.16667 (7413.16625) vs. 1680.70833 (1348.19803) #> 10 1719.87500 (1572.95930) vs. 1577.16667 (1894.47811) #> 11 140.04167 (158.30488) vs. 302.70833 (1105.90485) #> 12 3149.50000 (2364.74946) vs. 5152.87500 (4685.93171) #> 13 16323.66667 (8023.22147) vs. 22558.66667 (10658.00931) #> 14 6745.00000 (4760.00364) vs. 3880.45833 (3180.21395) #> 15 15462.20833 (9628.93196) vs. 11599.91667 (5537.86394) #> 16 29699.79167 (2184.43202) vs. 27448.16667 (4583.82720) #> 17 26839.12500 (3532.87984) vs. 26387.54167 (6003.76938) #> 18 7364.91667 (5782.41369) vs. 13935.75000 (9896.06080) #> 19 28914.29167 (2162.59904) vs. 29123.45833 (3910.76717) #> 20 10695.62500 (6992.66463) vs. 15868.20833 (10462.80168) #> 21 11041.70833 (9775.01390) vs. 2716.79167 (1583.32778)# Same example with tidyverse (dplyr+tidyr) with some custom functions library(dplyr) library(tidyr) q95_fun = function(x) quantile(x, 0.95) N = function(x) length(x) exampleData_BAMA %>% mutate(group = paste0("Group", group)) %>% group_by(group, visitno, antigen) %>% summarise_at("magnitude", funs(N, mean, sd, median, min, max, q95_fun)) %>% gather(variable, value, -(group:antigen)) %>% # these three chains create a wide dataset unite(temp, group, variable) %>% spread(temp, value) %>% mutate(Group1 = "Group 1", Group2 = "Group 2") %>% paste_tbl_grp()#> Warning: funs() is soft deprecated as of dplyr 0.8.0 #> please use list() instead #> #> # Before: #> funs(name = f(.)) #> #> # After: #> list(name = ~f(.)) #> This warning is displayed once per session.#> visitno antigen Comparison max_comparison #> 1 0 A1.con.env03 140 CF Group 1 vs. Group 2 98 vs. 69 #> 2 0 A244 D11gp120_avi Group 1 vs. Group 2 112 vs. 385 #> 3 0 B.63521_D11gp120/293F Group 1 vs. Group 2 82 vs. 42 #> 4 0 B.con.env03 140 CF Group 1 vs. Group 2 134 vs. 115 #> 5 0 B.MN V3 gp70 Group 1 vs. Group 2 110 vs. 36 #> 6 0 gp41 Group 1 vs. Group 2 4860 vs. 10472 #> 7 0 p24 Group 1 vs. Group 2 28818 vs. 23381 #> 8 1 A1.con.env03 140 CF Group 1 vs. Group 2 3258 vs. 1040 #> 9 1 A244 D11gp120_avi Group 1 vs. Group 2 19183 vs. 3704 #> 10 1 B.63521_D11gp120/293F Group 1 vs. Group 2 3910 vs. 5116 #> 11 1 B.con.env03 140 CF Group 1 vs. Group 2 5990 vs. 13738 #> 12 1 B.MN V3 gp70 Group 1 vs. Group 2 329 vs. 2425 #> 13 1 gp41 Group 1 vs. Group 2 26022 vs. 32447 #> 14 1 p24 Group 1 vs. Group 2 13559 vs. 9886 #> 15 2 A1.con.env03 140 CF Group 1 vs. Group 2 26700 vs. 20801 #> 16 2 A244 D11gp120_avi Group 1 vs. Group 2 32274 vs. 31747 #> 17 2 B.63521_D11gp120/293F Group 1 vs. Group 2 31956 vs. 31820 #> 18 2 B.con.env03 140 CF Group 1 vs. Group 2 31628 vs. 32295 #> 19 2 B.MN V3 gp70 Group 1 vs. Group 2 15885 vs. 25838 #> 20 2 gp41 Group 1 vs. Group 2 20089 vs. 29466 #> 21 2 p24 Group 1 vs. Group 2 21449 vs. 4961 #> mean_comparison median_comparison min_comparison N_comparison #> 1 -2 vs. -5 3 vs. -8 -123 vs. -75 6 vs. 6 #> 2 75 vs. 84 79 vs. 36 40 vs. -23 6 vs. 6 #> 3 66 vs. 12 73 vs. 8 36 vs. -16 6 vs. 6 #> 4 21 vs. -2 8 vs. -9 -52 vs. -87 6 vs. 6 #> 5 -27 vs. -409 -28 vs. -29 -152 vs. -1504 6 vs. 6 #> 6 2017 vs. 3381 1466 vs. 2075 874 vs. 873 6 vs. 6 #> 7 9010 vs. 5735 4041 vs. 1450 1880 vs. 832 6 vs. 6 #> 8 1039 vs. 551 648 vs. 530 10 vs. 171 6 vs. 6 #> 9 5655 vs. 1681 2543 vs. 1046 166 vs. 430 6 vs. 6 #> 10 1720 vs. 1577 1563 vs. 676 70 vs. 308 6 vs. 6 #> 11 3150 vs. 5153 2819 vs. 4898 191 vs. 590 6 vs. 6 #> 12 140 vs. 303 117 vs. 14 -36 vs. -619 6 vs. 6 #> 13 16324 vs. 22559 14378 vs. 25038 5381 vs. 8504 6 vs. 6 #> 14 6745 vs. 3880 6688 vs. 3266 1728 vs. 964 6 vs. 6 #> 15 15462 vs. 11600 15420 vs. 11001 5002 vs. 4682 6 vs. 6 #> 16 29700 vs. 27448 29637 vs. 28282 26267 vs. 19268 6 vs. 6 #> 17 26839 vs. 26388 26034 vs. 27921 23352 vs. 14839 6 vs. 6 #> 18 28914 vs. 29123 28240 vs. 30191 26292 vs. 21395 6 vs. 6 #> 19 7365 vs. 13936 6556 vs. 12932 2166 vs. 2752 6 vs. 6 #> 20 10696 vs. 15868 10310 vs. 13112 2048 vs. 6414 6 vs. 6 #> 21 11042 vs. 2717 10763 vs. 2222 1548 vs. 1102 6 vs. 6 #> q95_fun_comparison sd_comparison #> 1 76 vs. 57 71 vs. 48 #> 2 109 vs. 307 31 vs. 152 #> 3 80 vs. 40 17 vs. 23 #> 4 108 vs. 90 61 vs. 67 #> 5 89 vs. 32 94 vs. 658 #> 6 4279 vs. 8669 1519 vs. 3572 #> 7 24778 vs. 19017 10467 vs. 8845 #> 8 2802 vs. 989 1210 vs. 348 #> 9 16618 vs. 3538 7413 vs. 1348 #> 10 3649 vs. 4420 1573 vs. 1894 #> 11 5948 vs. 11682 2365 vs. 4686 #> 12 322 vs. 1927 158 vs. 1106 #> 13 25861 vs. 32234 8023 vs. 10658 #> 14 12531 vs. 8440 4760 vs. 3180 #> 15 26055 vs. 19145 9629 vs. 5538 #> 16 32146 vs. 31556 2184 vs. 4584 #> 17 31428 vs. 31230 3533 vs. 6004 #> 18 31589 vs. 32016 2163 vs. 3911 #> 19 14619 vs. 25611 5782 vs. 9896 #> 20 19322 vs. 28684 6993 vs. 10463 #> 21 21080 vs. 4811 9775 vs. 1583 #> median_min_max_comparison mean_sd_comparison #> 1 3 [-123, 98] vs. -8 [-75, 69] -2 (71) vs. -5 (48) #> 2 79 [40, 112] vs. 36 [-23, 385] 75 (31) vs. 84 (152) #> 3 73 [36, 82] vs. 8 [-16, 42] 66 (17) vs. 12 (23) #> 4 8 [-52, 134] vs. -9 [-87, 115] 21 (61) vs. -2 (67) #> 5 -28 [-152, 110] vs. -29 [-1504, 36] -27 (94) vs. -409 (658) #> 6 1466 [874, 4860] vs. 2075 [873, 10472] 2017 (1519) vs. 3381 (3572) #> 7 4041 [1880, 28818] vs. 1450 [832, 23381] 9010 (10467) vs. 5735 (8845) #> 8 648 [10, 3258] vs. 530 [171, 1040] 1039 (1210) vs. 551 (348) #> 9 2543 [166, 19183] vs. 1046 [430, 3704] 5655 (7413) vs. 1681 (1348) #> 10 1563 [70, 3910] vs. 676 [308, 5116] 1720 (1573) vs. 1577 (1894) #> 11 2819 [191, 5990] vs. 4898 [590, 13738] 3150 (2365) vs. 5153 (4686) #> 12 117 [-36, 329] vs. 14 [-619, 2425] 140 (158) vs. 303 (1106) #> 13 14378 [5381, 26022] vs. 25038 [8504, 32447] 16324 (8023) vs. 22559 (10658) #> 14 6688 [1728, 13559] vs. 3266 [964, 9886] 6745 (4760) vs. 3880 (3180) #> 15 15420 [5002, 26700] vs. 11001 [4682, 20801] 15462 (9629) vs. 11600 (5538) #> 16 29637 [26267, 32274] vs. 28282 [19268, 31747] 29700 (2184) vs. 27448 (4584) #> 17 26034 [23352, 31956] vs. 27921 [14839, 31820] 26839 (3533) vs. 26388 (6004) #> 18 28240 [26292, 31628] vs. 30191 [21395, 32295] 28914 (2163) vs. 29123 (3911) #> 19 6556 [2166, 15885] vs. 12932 [2752, 25838] 7365 (5782) vs. 13936 (9896) #> 20 10310 [2048, 20089] vs. 13112 [6414, 29466] 10696 (6993) vs. 15868 (10463) #> 21 10763 [1548, 21449] vs. 2222 [1102, 4961] 11042 (9775) vs. 2717 (1583)