transpose.RdUsed to get the statistics in column for example.
For more examples see the website: ClinReport website
transpose(desc, ...) # S3 method for desc transpose(desc, ...)
| desc | a desc object |
|---|---|
| ... | Not used |
A desc object
None
library(reshape2) data(datafake) desc=report.quanti(data=datafake,y="y_numeric",x1="GROUP", x2="TIMEPOINT",total=TRUE,at.row="TIMEPOINT",subjid="SUBJID") transpose(desc)#> #> ############################################ #> Quantitative descriptive statistics of: y_numeric #> ############################################ #> #> TIMEPOINT GROUP N Mean (SD) Median [Q1;Q3] [Min;Max] #> 1 D0 A (N=30) 30 -0.93(0.86) -0.82 [-1.59;-0.16] [-2.34;0.36] #> 2 D0 B (N=21) 20 -0.67(1.09) -0.69 [-1.39;-0.06] [-2.44;2.10] #> 3 D0 C (N=17) 16 -1.19(0.92) -1.26 [-1.62;-0.83] [-2.99;0.66] #> 4 D0 Total (N=68) 66 -0.92(0.95) -0.86 [-1.55;-0.16] [-2.99;2.10] #> 5 #> 6 D1 A (N=30) 30 1.83(1.04) 1.78 [ 0.94; 2.54] [ 0.11;3.88] #> 7 D1 B (N=21) 20 4.17(1.28) 4.19 [ 3.23; 4.92] [ 1.48;6.19] #> 8 D1 C (N=17) 16 4.98(0.69) 5.08 [ 4.58; 5.46] [ 3.80;6.23] #> 9 D1 Total (N=68) 66 3.33(1.73) 3.57 [ 1.78; 4.91] [ 0.11;6.23] #> 10 #> 11 D2 A (N=30) 30 1.97(1.17) 1.66 [ 1.23; 2.86] [-0.18;4.36] #> 12 D2 B (N=21) 20 4.04(0.89) 4.19 [ 3.62; 4.36] [ 2.03;5.63] #> 13 D2 C (N=17) 16 4.90(1.36) 5.06 [ 4.34; 5.20] [ 2.39;7.96] #> 14 D2 Total (N=68) 66 3.32(1.70) 3.57 [ 1.89; 4.44] [-0.18;7.96] #> 15 #> 16 D3 A (N=30) 30 1.78(1.17) 1.78 [ 0.93; 2.42] [-0.16;3.90] #> 17 D3 B (N=21) 20 3.81(0.94) 3.63 [ 3.13; 4.44] [ 2.46;6.01] #> 18 D3 C (N=17) 16 5.07(1.12) 5.22 [ 4.11; 5.66] [ 3.16;7.37] #> 19 D3 Total (N=68) 66 3.15(1.75) 3.15 [ 1.80; 4.39] [-0.16;7.37] #> 20 #> 21 D4 A (N=30) 30 1.83(0.85) 1.67 [ 1.26; 2.32] [ 0.38;3.97] #> 22 D4 B (N=21) 20 3.80(0.95) 3.83 [ 3.12; 4.42] [ 2.31;5.41] #> 23 D4 C (N=17) 16 5.17(1.03) 4.88 [ 4.69; 5.50] [ 3.24;6.96] #> 24 D4 Total (N=68) 66 3.22(1.66) 3.16 [ 1.69; 4.48] [ 0.38;6.96] #> 25 #> 26 D5 A (N=30) 30 2.27(1.20) 2.50 [ 1.77; 3.21] [-1.19;4.31] #> 27 D5 B (N=21) 20 3.64(1.19) 3.86 [ 2.59; 4.60] [ 0.91;5.12] #> 28 D5 C (N=17) 16 4.43(0.98) 4.57 [ 3.44; 4.97] [ 2.95;6.54] #> 29 D5 Total (N=68) 66 3.21(1.45) 3.28 [ 2.42; 4.44] [-1.19;6.54] #> Missing #> 1 1 #> 2 1 #> 3 0 #> 4 2 #> 5 #> 6 1 #> 7 0 #> 8 0 #> 9 1 #> 10 #> 11 1 #> 12 1 #> 13 0 #> 14 2 #> 15 #> 16 0 #> 17 1 #> 18 1 #> 19 2 #> 20 #> 21 1 #> 22 1 #> 23 1 #> 24 3 #> 25 #> 26 0 #> 27 0 #> 28 0 #> 29 0 #> #> ############################################ #>#> #> ############################################ #> Quantitative descriptive statistics of: y_numeric #> ############################################ #> #> GROUP N Mean (SD) Median [Q1;Q3] [Min;Max] Missing #> 1 A 180 1.46(1.50) 1.59 [0.45;2.50] [-2.34;4.36] 4 #> 2 B 120 3.15(2.00) 3.75 [2.46;4.44] [-2.44;6.19] 4 #> 3 C 96 3.87(2.52) 4.73 [3.44;5.30] [-2.99;7.96] 2 #> #> ############################################ #>#> #> ############################################ #> Quantitative descriptive statistics of: y_numeric #> ############################################ #> #> N Mean (SD) Median [Q1;Q3] [Min;Max] Missing #> 1 396 2.56(2.20) 2.71 [1.04;4.33] [-2.99;7.96] 10 #> #> ############################################ #>#> #> ############################################ #> Qualitative descriptive statistics of : y_logistic #> ############################################ #> #> GROUP Levels n (GROUP%) Missing n(%) #> 1 A 4(2.22%) #> 2 A 0 79(43.89%) #> 3 A 1 97(53.89%) #> 4 B 1(0.83%) #> 5 B 0 60(50.00%) #> 6 B 1 59(49.17%) #> 7 C 5(5.21%) #> 8 C 0 47(48.96%) #> 9 C 1 44(45.83%) #> #> ############################################ #>#> #> ############################################ #> Qualitative descriptive statistics of : y_logistic #> ############################################ #> #> TIMEPOINT GROUP Levels n (GROUP%) Missing n(%) #> 1 D0 A 1(3.33%) #> 2 D0 A 0 11(36.67%) #> 3 D0 A 1 18(60.00%) #> 4 D0 B 1(5.00%) #> 5 D0 B 0 11(55.00%) #> 6 D0 B 1 8(40.00%) #> 7 D0 C 2(12.50%) #> 8 D0 C 0 7(43.75%) #> 9 D0 C 1 7(43.75%) #> 10 #> 11 D1 A 2(6.67%) #> 12 D1 A 0 7(23.33%) #> 13 D1 A 1 21(70.00%) #> 14 D1 B 0(0%) #> 15 D1 B 0 13(65.00%) #> 16 D1 B 1 7(35.00%) #> 17 D1 C 1(6.25%) #> 18 D1 C 0 8(50.00%) #> 19 D1 C 1 7(43.75%) #> 20 #> 21 D2 A 0(0%) #> 22 D2 A 0 18(60.00%) #> 23 D2 A 1 12(40.00%) #> 24 D2 B 0(0%) #> 25 D2 B 0 7(35.00%) #> 26 D2 B 1 13(65.00%) #> 27 D2 C 0(0%) #> 28 D2 C 0 11(68.75%) #> 29 D2 C 1 5(31.25%) #> 30 #> 31 D3 A 0(0%) #> 32 D3 A 0 11(36.67%) #> 33 D3 A 1 19(63.33%) #> 34 D3 B 0(0%) #> 35 D3 B 0 10(50.00%) #> 36 D3 B 1 10(50.00%) #> 37 D3 C 0(0%) #> 38 D3 C 0 7(43.75%) #> 39 D3 C 1 9(56.25%) #> 40 #> 41 D4 A 0(0%) #> 42 D4 A 0 18(60.00%) #> 43 D4 A 1 12(40.00%) #> 44 D4 B 0(0%) #> 45 D4 B 0 12(60.00%) #> 46 D4 B 1 8(40.00%) #> 47 D4 C 2(12.50%) #> 48 D4 C 0 6(37.50%) #> 49 D4 C 1 8(50.00%) #> 50 #> 51 D5 A 1(3.33%) #> 52 D5 A 0 14(46.67%) #> 53 D5 A 1 15(50.00%) #> 54 D5 B 0(0%) #> 55 D5 B 0 7(35.00%) #> 56 D5 B 1 13(65.00%) #> 57 D5 C 0(0%) #> 58 D5 C 0 8(50.00%) #> 59 D5 C 1 8(50.00%) #> #> ############################################ #>#> #> ############################################ #> Qualitative descriptive statistics of : y_logistic #> ############################################ #> #> Levels n (%) Missing n(%) #> 1 10(2.53%) #> 2 0 186(46.97%) #> 3 1 200(50.51%) #> #> ############################################ #>