report.quanti.Rdreport.quanti
Returns quantitative descriptive statistics such as mean, median, standard deviation etc...
For more examples see the website: ClinReport website
report.quanti(data, y, x1 = NULL, x2 = NULL, y.label = y, round = 2, total = F, scientific = F, digits = NULL, at.row = NULL, subjid = NULL, geomean = F, add.mad = F, default.stat = T, func.stat = NULL, stat.name = "Statistics", func.stat.name = "", drop.x1 = NULL, drop.x2 = NULL)
| data | Data.frame object |
|---|---|
| y | Character indicating a numerical vector in the data frame passed to |
| x1 | Character indicating a factor in the data (levels will be displayed in columns) |
| x2 | Character indicating a factor in the data (levels will be displayed in lines) |
| y.label | Character Indicates the label for y parameter to be displayed in the title of the table |
| round | Numeric to indicate how to round statistics |
| total | Logical to indicate if a "Total" column should be added |
| scientific | Logical Indicates if statistics should be displayed in scientific notations or not |
| digits | Numeric (used if scientific=TRUE) to indicate how many digits to use in scientific notation |
| at.row | Character Used to space the results (see examples) |
| subjid | Character Indicates the column in which there is the subject Id to add the number of subjects in the column header if x1 and x2 are not null. |
| geomean | Logical If yes geometric mean is calculated instead of arithmetic mean: |
| add.mad | Logical If yes the Median Absolute Deviance is added to the median statistics (see function |
| default.stat | Logical (default to TRUE). If FALSE you can specify your own example |
| func.stat | Function. If specified then default.stat=FALSE and only the specified statistic is reported |
| stat.name | Character. Indicates the name of the variable that report the statistics Default = "Statistics" |
| func.stat.name | Character. Used only if default.stat=FALSE. Indicates the name of specific statistic you want to report |
| drop.x1 | Character. Indicates one or several levels of the x1 factor that you want to drop in the result |
| drop.x2 | Character. Indicates one or several levels of the x2 factor that you want to drop in the result |
A desc object.
This function computes and reports quantitative statistics on y. And can gives the statistics by level of two factors (x1
in columns and/or x2 in rows).
See the example to show the results. If total=TRUE, the last column is the statistics
performed overall levels of x1 for each levels of x2.
Quantiles are calculated using type 3 (SAS presumed definition) algorithms, but even though,
some differences between SAS and R can appear on quantile values.
"geomean" compute the geometric mean defined as exp(mean(log(y))). The values below or equal 0 are removed and a message is printed to indicate how many values were deleted to calculate the geometric mean.
N returns the number of observations (including NA values)
stat.name is automatically transformed using make.names function.
data(datafake) # Quantitative statistics with no factor report.quanti(data=datafake,y="y_numeric",total=TRUE,y.label="Awesome results")#> #> ############################################ #> Quantitative descriptive statistics of: Awesome results #> ############################################ #> #> Statistics Awesome results Total #> 6 N 396 396 #> 3 Mean (SD) 2.56(2.20) 2.56(2.20) #> 4 Median 2.71 2.71 #> 2 [Q1;Q3] [1.04;4.33] [1.04;4.33] #> 1 [Min;Max] [-2.99;7.96] [-2.99;7.96] #> 5 Missing 10 10 #> #> ############################################ #>#' # Quantitative statistics with no factor with geometric mean (option geomean=TRUE) report.quanti(data=datafake,y="y_numeric",y.label="Awesome results",geomean=TRUE)#>#> #> ############################################ #> Quantitative descriptive statistics of: Awesome results #> ############################################ #> #> Statistics Awesome results #> 1 N 396 #> 3 Geo Mean (SD) 2.52(2.20) #> 6 Median 2.71 #> 4 [Q1;Q3] [1.04;4.33] #> 5 [Min;Max] [-2.99;7.96] #> 2 Missing 10 #> #> ############################################ #># Quantitative statistics with one factor report.quanti(data=datafake,y="y_numeric",x1="GROUP")#> #> ############################################ #> Quantitative descriptive statistics of: y_numeric #> ############################################ #> #> Statistics A B C #> 1 N 180 120 96 #> 3 Mean (SD) 1.46(1.50) 3.15(2.00) 3.87(2.52) #> 6 Median 1.59 3.75 4.73 #> 4 [Q1;Q3] [0.45;2.50] [2.46;4.44] [3.44;5.30] #> 5 [Min;Max] [-2.34;4.36] [-2.44;6.19] [-2.99;7.96] #> 2 Missing 4 4 2 #> #> ############################################ #># One factor with total column report.quanti(data=datafake,y="y_numeric",x1="GROUP",total=TRUE)#> #> ############################################ #> Quantitative descriptive statistics of: y_numeric #> ############################################ #> #> Statistics A B C Total #> 6 N 180 120 96 396 #> 3 Mean (SD) 1.46(1.50) 3.15(2.00) 3.87(2.52) 2.56(2.20) #> 4 Median 1.59 3.75 4.73 2.71 #> 2 [Q1;Q3] [0.45;2.50] [2.46;4.44] [3.44;5.30] [1.04;4.33] #> 1 [Min;Max] [-2.34;4.36] [-2.44;6.19] [-2.99;7.96] [-2.99;7.96] #> 5 Missing 4 4 2 10 #> #> ############################################ #># Quantitative statistics with two factors report.quanti(data=datafake,y="y_numeric",x1="GROUP",x2="TIMEPOINT")#> #> ############################################ #> Quantitative descriptive statistics of: y_numeric #> ############################################ #> #> TIMEPOINT Statistics A B C #> 1 D0 N 30 20 16 #> 2 D0 Mean (SD) -0.93(0.86) -0.67(1.09) -1.19(0.92) #> 3 D0 Median -0.82 -0.69 -1.26 #> 4 D0 [Q1;Q3] [-1.59;-0.16] [-1.39;-0.06] [-1.62;-0.83] #> 5 D0 [Min;Max] [-2.34;0.36] [-2.44;2.10] [-2.99;0.66] #> 6 D0 Missing 1 1 0 #> 7 #> 8 D1 N 30 20 16 #> 9 D1 Mean (SD) 1.83(1.04) 4.17(1.28) 4.98(0.69) #> 10 D1 Median 1.78 4.19 5.08 #> 11 D1 [Q1;Q3] [ 0.94; 2.54] [ 3.23; 4.92] [ 4.58; 5.46] #> 12 D1 [Min;Max] [ 0.11;3.88] [ 1.48;6.19] [ 3.80;6.23] #> 13 D1 Missing 1 0 0 #> 14 #> 15 D2 N 30 20 16 #> 16 D2 Mean (SD) 1.97(1.17) 4.04(0.89) 4.90(1.36) #> 17 D2 Median 1.66 4.19 5.06 #> 18 D2 [Q1;Q3] [ 1.23; 2.86] [ 3.62; 4.36] [ 4.34; 5.20] #> 19 D2 [Min;Max] [-0.18;4.36] [ 2.03;5.63] [ 2.39;7.96] #> 20 D2 Missing 1 1 0 #> 21 #> 22 D3 N 30 20 16 #> 23 D3 Mean (SD) 1.78(1.17) 3.81(0.94) 5.07(1.12) #> 24 D3 Median 1.78 3.63 5.22 #> 25 D3 [Q1;Q3] [ 0.93; 2.42] [ 3.13; 4.44] [ 4.11; 5.66] #> 26 D3 [Min;Max] [-0.16;3.90] [ 2.46;6.01] [ 3.16;7.37] #> 27 D3 Missing 0 1 1 #> 28 #> 29 D4 N 30 20 16 #> 30 D4 Mean (SD) 1.83(0.85) 3.80(0.95) 5.17(1.03) #> 31 D4 Median 1.67 3.83 4.88 #> 32 D4 [Q1;Q3] [ 1.26; 2.32] [ 3.12; 4.42] [ 4.69; 5.50] #> 33 D4 [Min;Max] [ 0.38;3.97] [ 2.31;5.41] [ 3.24;6.96] #> 34 D4 Missing 1 1 1 #> 35 #> 36 D5 N 30 20 16 #> 37 D5 Mean (SD) 2.27(1.20) 3.64(1.19) 4.43(0.98) #> 38 D5 Median 2.50 3.86 4.57 #> 39 D5 [Q1;Q3] [ 1.77; 3.21] [ 2.59; 4.60] [ 3.44; 4.97] #> 40 D5 [Min;Max] [-1.19;4.31] [ 0.91;5.12] [ 2.95;6.54] #> 41 D5 Missing 0 0 0 #> #> ############################################ #># Quantitative statistics with two factors and a total column report.quanti(data=datafake,y="y_numeric",x1="GROUP",x2="TIMEPOINT",total=TRUE)#> #> ############################################ #> Quantitative descriptive statistics of: y_numeric #> ############################################ #> #> TIMEPOINT Statistics A B C Total #> 1 D0 N 30 20 16 66 #> 2 D0 Mean (SD) -0.93(0.86) -0.67(1.09) -1.19(0.92) -0.92(0.95) #> 3 D0 Median -0.82 -0.69 -1.26 -0.86 #> 4 D0 [Q1;Q3] [-1.59;-0.16] [-1.39;-0.06] [-1.62;-0.83] [-1.55;-0.16] #> 5 D0 [Min;Max] [-2.34;0.36] [-2.44;2.10] [-2.99;0.66] [-2.99;2.10] #> 6 D0 Missing 1 1 0 2 #> 7 #> 8 D1 N 30 20 16 66 #> 9 D1 Mean (SD) 1.83(1.04) 4.17(1.28) 4.98(0.69) 3.33(1.73) #> 10 D1 Median 1.78 4.19 5.08 3.57 #> 11 D1 [Q1;Q3] [ 0.94; 2.54] [ 3.23; 4.92] [ 4.58; 5.46] [ 1.78; 4.91] #> 12 D1 [Min;Max] [ 0.11;3.88] [ 1.48;6.19] [ 3.80;6.23] [ 0.11;6.23] #> 13 D1 Missing 1 0 0 1 #> 14 #> 15 D2 N 30 20 16 66 #> 16 D2 Mean (SD) 1.97(1.17) 4.04(0.89) 4.90(1.36) 3.32(1.70) #> 17 D2 Median 1.66 4.19 5.06 3.57 #> 18 D2 [Q1;Q3] [ 1.23; 2.86] [ 3.62; 4.36] [ 4.34; 5.20] [ 1.89; 4.44] #> 19 D2 [Min;Max] [-0.18;4.36] [ 2.03;5.63] [ 2.39;7.96] [-0.18;7.96] #> 20 D2 Missing 1 1 0 2 #> 21 #> 22 D3 N 30 20 16 66 #> 23 D3 Mean (SD) 1.78(1.17) 3.81(0.94) 5.07(1.12) 3.15(1.75) #> 24 D3 Median 1.78 3.63 5.22 3.15 #> 25 D3 [Q1;Q3] [ 0.93; 2.42] [ 3.13; 4.44] [ 4.11; 5.66] [ 1.80; 4.39] #> 26 D3 [Min;Max] [-0.16;3.90] [ 2.46;6.01] [ 3.16;7.37] [-0.16;7.37] #> 27 D3 Missing 0 1 1 2 #> 28 #> 29 D4 N 30 20 16 66 #> 30 D4 Mean (SD) 1.83(0.85) 3.80(0.95) 5.17(1.03) 3.22(1.66) #> 31 D4 Median 1.67 3.83 4.88 3.16 #> 32 D4 [Q1;Q3] [ 1.26; 2.32] [ 3.12; 4.42] [ 4.69; 5.50] [ 1.69; 4.48] #> 33 D4 [Min;Max] [ 0.38;3.97] [ 2.31;5.41] [ 3.24;6.96] [ 0.38;6.96] #> 34 D4 Missing 1 1 1 3 #> 35 #> 36 D5 N 30 20 16 66 #> 37 D5 Mean (SD) 2.27(1.20) 3.64(1.19) 4.43(0.98) 3.21(1.45) #> 38 D5 Median 2.50 3.86 4.57 3.28 #> 39 D5 [Q1;Q3] [ 1.77; 3.21] [ 2.59; 4.60] [ 3.44; 4.97] [ 2.42; 4.44] #> 40 D5 [Min;Max] [-1.19;4.31] [ 0.91;5.12] [ 2.95;6.54] [-1.19;6.54] #> 41 D5 Missing 0 0 0 0 #> #> ############################################ #># Add median absolute deviance to the median statistics report.quanti(data=datafake,y="y_numeric",x1="GROUP",x2="TIMEPOINT",total=TRUE,add.mad=TRUE)#> #> ############################################ #> Quantitative descriptive statistics of: y_numeric #> ############################################ #> #> TIMEPOINT Statistics A B C #> 1 D0 N 30 20 16 #> 2 D0 Mean (SD) -0.93(0.86) -0.67(1.09) -1.19(0.92) #> 3 D0 Median (MAD) -0.82(1.00) -0.69(0.97) -1.26(0.59) #> 4 D0 [Q1;Q3] [-1.59;-0.16] [-1.39;-0.06] [-1.62;-0.83] #> 5 D0 [Min;Max] [-2.34;0.36] [-2.44;2.10] [-2.99;0.66] #> 6 D0 Missing 1 1 0 #> 7 #> 8 D1 N 30 20 16 #> 9 D1 Mean (SD) 1.83(1.04) 4.17(1.28) 4.98(0.69) #> 10 D1 Median (MAD) 1.78(1.12) 4.19(1.25) 5.08(0.66) #> 11 D1 [Q1;Q3] [ 0.94; 2.54] [ 3.23; 4.92] [ 4.58; 5.46] #> 12 D1 [Min;Max] [ 0.11;3.88] [ 1.48;6.19] [ 3.80;6.23] #> 13 D1 Missing 1 0 0 #> 14 #> 15 D2 N 30 20 16 #> 16 D2 Mean (SD) 1.97(1.17) 4.04(0.89) 4.90(1.36) #> 17 D2 Median (MAD) 1.66(1.08) 4.19(0.50) 5.06(0.67) #> 18 D2 [Q1;Q3] [ 1.23; 2.86] [ 3.62; 4.36] [ 4.34; 5.20] #> 19 D2 [Min;Max] [-0.18;4.36] [ 2.03;5.63] [ 2.39;7.96] #> 20 D2 Missing 1 1 0 #> 21 #> 22 D3 N 30 20 16 #> 23 D3 Mean (SD) 1.78(1.17) 3.81(0.94) 5.07(1.12) #> 24 D3 Median (MAD) 1.78(1.23) 3.63(1.20) 5.22(1.25) #> 25 D3 [Q1;Q3] [ 0.93; 2.42] [ 3.13; 4.44] [ 4.11; 5.66] #> 26 D3 [Min;Max] [-0.16;3.90] [ 2.46;6.01] [ 3.16;7.37] #> 27 D3 Missing 0 1 1 #> 28 #> 29 D4 N 30 20 16 #> 30 D4 Mean (SD) 1.83(0.85) 3.80(0.95) 5.17(1.03) #> 31 D4 Median (MAD) 1.67(0.62) 3.83(0.96) 4.88(0.82) #> 32 D4 [Q1;Q3] [ 1.26; 2.32] [ 3.12; 4.42] [ 4.69; 5.50] #> 33 D4 [Min;Max] [ 0.38;3.97] [ 2.31;5.41] [ 3.24;6.96] #> 34 D4 Missing 1 1 1 #> 35 #> 36 D5 N 30 20 16 #> 37 D5 Mean (SD) 2.27(1.20) 3.64(1.19) 4.43(0.98) #> 38 D5 Median (MAD) 2.50(1.09) 3.86(1.18) 4.57(0.95) #> 39 D5 [Q1;Q3] [ 1.77; 3.21] [ 2.59; 4.60] [ 3.44; 4.97] #> 40 D5 [Min;Max] [-1.19;4.31] [ 0.91;5.12] [ 2.95;6.54] #> 41 D5 Missing 0 0 0 #> Total #> 1 66 #> 2 -0.92(0.95) #> 3 -0.86(1.03) #> 4 [-1.55;-0.16] #> 5 [-2.99;2.10] #> 6 2 #> 7 #> 8 66 #> 9 3.33(1.73) #> 10 3.57(2.14) #> 11 [ 1.78; 4.91] #> 12 [ 0.11;6.23] #> 13 1 #> 14 #> 15 66 #> 16 3.32(1.70) #> 17 3.57(1.89) #> 18 [ 1.89; 4.44] #> 19 [-0.18;7.96] #> 20 2 #> 21 #> 22 66 #> 23 3.15(1.75) #> 24 3.15(1.88) #> 25 [ 1.80; 4.39] #> 26 [-0.16;7.37] #> 27 2 #> 28 #> 29 66 #> 30 3.22(1.66) #> 31 3.16(2.14) #> 32 [ 1.69; 4.48] #> 33 [ 0.38;6.96] #> 34 3 #> 35 #> 36 66 #> 37 3.21(1.45) #> 38 3.28(1.48) #> 39 [ 2.42; 4.44] #> 40 [-1.19;6.54] #> 41 0 #> #> ############################################ #># Quantitative statistics with spacing rows (option at.row) report.quanti(data=datafake,y="y_numeric",x1="GROUP", x2="TIMEPOINT",total=TRUE,at.row="TIMEPOINT")#> #> ############################################ #> Quantitative descriptive statistics of: y_numeric #> ############################################ #> #> TIMEPOINT Statistics A B C Total #> 1 D0 N 30 20 16 66 #> 2 D0 Mean (SD) -0.93(0.86) -0.67(1.09) -1.19(0.92) -0.92(0.95) #> 3 D0 Median -0.82 -0.69 -1.26 -0.86 #> 4 D0 [Q1;Q3] [-1.59;-0.16] [-1.39;-0.06] [-1.62;-0.83] [-1.55;-0.16] #> 5 D0 [Min;Max] [-2.34;0.36] [-2.44;2.10] [-2.99;0.66] [-2.99;2.10] #> 6 D0 Missing 1 1 0 2 #> 7 #> 8 D1 N 30 20 16 66 #> 9 D1 Mean (SD) 1.83(1.04) 4.17(1.28) 4.98(0.69) 3.33(1.73) #> 10 D1 Median 1.78 4.19 5.08 3.57 #> 11 D1 [Q1;Q3] [ 0.94; 2.54] [ 3.23; 4.92] [ 4.58; 5.46] [ 1.78; 4.91] #> 12 D1 [Min;Max] [ 0.11;3.88] [ 1.48;6.19] [ 3.80;6.23] [ 0.11;6.23] #> 13 D1 Missing 1 0 0 1 #> 14 #> 15 D2 N 30 20 16 66 #> 16 D2 Mean (SD) 1.97(1.17) 4.04(0.89) 4.90(1.36) 3.32(1.70) #> 17 D2 Median 1.66 4.19 5.06 3.57 #> 18 D2 [Q1;Q3] [ 1.23; 2.86] [ 3.62; 4.36] [ 4.34; 5.20] [ 1.89; 4.44] #> 19 D2 [Min;Max] [-0.18;4.36] [ 2.03;5.63] [ 2.39;7.96] [-0.18;7.96] #> 20 D2 Missing 1 1 0 2 #> 21 #> 22 D3 N 30 20 16 66 #> 23 D3 Mean (SD) 1.78(1.17) 3.81(0.94) 5.07(1.12) 3.15(1.75) #> 24 D3 Median 1.78 3.63 5.22 3.15 #> 25 D3 [Q1;Q3] [ 0.93; 2.42] [ 3.13; 4.44] [ 4.11; 5.66] [ 1.80; 4.39] #> 26 D3 [Min;Max] [-0.16;3.90] [ 2.46;6.01] [ 3.16;7.37] [-0.16;7.37] #> 27 D3 Missing 0 1 1 2 #> 28 #> 29 D4 N 30 20 16 66 #> 30 D4 Mean (SD) 1.83(0.85) 3.80(0.95) 5.17(1.03) 3.22(1.66) #> 31 D4 Median 1.67 3.83 4.88 3.16 #> 32 D4 [Q1;Q3] [ 1.26; 2.32] [ 3.12; 4.42] [ 4.69; 5.50] [ 1.69; 4.48] #> 33 D4 [Min;Max] [ 0.38;3.97] [ 2.31;5.41] [ 3.24;6.96] [ 0.38;6.96] #> 34 D4 Missing 1 1 1 3 #> 35 #> 36 D5 N 30 20 16 66 #> 37 D5 Mean (SD) 2.27(1.20) 3.64(1.19) 4.43(0.98) 3.21(1.45) #> 38 D5 Median 2.50 3.86 4.57 3.28 #> 39 D5 [Q1;Q3] [ 1.77; 3.21] [ 2.59; 4.60] [ 3.44; 4.97] [ 2.42; 4.44] #> 40 D5 [Min;Max] [-1.19;4.31] [ 0.91;5.12] [ 2.95;6.54] [-1.19;6.54] #> 41 D5 Missing 0 0 0 0 #> #> ############################################ #># Add number of subjects in headers (option subjid) tab=report.quanti(data=datafake,y="y_numeric",x1="GROUP", x2="TIMEPOINT",total=TRUE,at.row="TIMEPOINT",subjid="SUBJID") # Print tab output tab#> #> ############################################ #> Quantitative descriptive statistics of: y_numeric #> ############################################ #> #> TIMEPOINT Statistics A (N=30) B (N=21) C (N=17) Total (N=68) #> 1 D0 N 30 20 16 66 #> 2 D0 Mean (SD) -0.93(0.86) -0.67(1.09) -1.19(0.92) -0.92(0.95) #> 3 D0 Median -0.82 -0.69 -1.26 -0.86 #> 4 D0 [Q1;Q3] [-1.59;-0.16] [-1.39;-0.06] [-1.62;-0.83] [-1.55;-0.16] #> 5 D0 [Min;Max] [-2.34;0.36] [-2.44;2.10] [-2.99;0.66] [-2.99;2.10] #> 6 D0 Missing 1 1 0 2 #> 7 #> 8 D1 N 30 20 16 66 #> 9 D1 Mean (SD) 1.83(1.04) 4.17(1.28) 4.98(0.69) 3.33(1.73) #> 10 D1 Median 1.78 4.19 5.08 3.57 #> 11 D1 [Q1;Q3] [ 0.94; 2.54] [ 3.23; 4.92] [ 4.58; 5.46] [ 1.78; 4.91] #> 12 D1 [Min;Max] [ 0.11;3.88] [ 1.48;6.19] [ 3.80;6.23] [ 0.11;6.23] #> 13 D1 Missing 1 0 0 1 #> 14 #> 15 D2 N 30 20 16 66 #> 16 D2 Mean (SD) 1.97(1.17) 4.04(0.89) 4.90(1.36) 3.32(1.70) #> 17 D2 Median 1.66 4.19 5.06 3.57 #> 18 D2 [Q1;Q3] [ 1.23; 2.86] [ 3.62; 4.36] [ 4.34; 5.20] [ 1.89; 4.44] #> 19 D2 [Min;Max] [-0.18;4.36] [ 2.03;5.63] [ 2.39;7.96] [-0.18;7.96] #> 20 D2 Missing 1 1 0 2 #> 21 #> 22 D3 N 30 20 16 66 #> 23 D3 Mean (SD) 1.78(1.17) 3.81(0.94) 5.07(1.12) 3.15(1.75) #> 24 D3 Median 1.78 3.63 5.22 3.15 #> 25 D3 [Q1;Q3] [ 0.93; 2.42] [ 3.13; 4.44] [ 4.11; 5.66] [ 1.80; 4.39] #> 26 D3 [Min;Max] [-0.16;3.90] [ 2.46;6.01] [ 3.16;7.37] [-0.16;7.37] #> 27 D3 Missing 0 1 1 2 #> 28 #> 29 D4 N 30 20 16 66 #> 30 D4 Mean (SD) 1.83(0.85) 3.80(0.95) 5.17(1.03) 3.22(1.66) #> 31 D4 Median 1.67 3.83 4.88 3.16 #> 32 D4 [Q1;Q3] [ 1.26; 2.32] [ 3.12; 4.42] [ 4.69; 5.50] [ 1.69; 4.48] #> 33 D4 [Min;Max] [ 0.38;3.97] [ 2.31;5.41] [ 3.24;6.96] [ 0.38;6.96] #> 34 D4 Missing 1 1 1 3 #> 35 #> 36 D5 N 30 20 16 66 #> 37 D5 Mean (SD) 2.27(1.20) 3.64(1.19) 4.43(0.98) 3.21(1.45) #> 38 D5 Median 2.50 3.86 4.57 3.28 #> 39 D5 [Q1;Q3] [ 1.77; 3.21] [ 2.59; 4.60] [ 3.44; 4.97] [ 2.42; 4.44] #> 40 D5 [Min;Max] [-1.19;4.31] [ 0.91;5.12] [ 2.95;6.54] [-1.19;6.54] #> 41 D5 Missing 0 0 0 0 #> #> ############################################ #>#Getting a specific statistic and not the default ones mystat=function(x) quantile(x,0.99,na.rm=TRUE) tab=report.quanti(data=datafake,y="y_numeric",x1="GROUP", x2="TIMEPOINT",total=TRUE,subjid="SUBJID", func.stat=mystat,func.stat.name="99% quantile")#> Error in mystat(y_numeric): impossible de trouver la fonction "mystat"tab#> #> ############################################ #> Quantitative descriptive statistics of: y_numeric #> ############################################ #> #> TIMEPOINT Statistics A (N=30) B (N=21) C (N=17) Total (N=68) #> 1 D0 N 30 20 16 66 #> 2 D0 Mean (SD) -0.93(0.86) -0.67(1.09) -1.19(0.92) -0.92(0.95) #> 3 D0 Median -0.82 -0.69 -1.26 -0.86 #> 4 D0 [Q1;Q3] [-1.59;-0.16] [-1.39;-0.06] [-1.62;-0.83] [-1.55;-0.16] #> 5 D0 [Min;Max] [-2.34;0.36] [-2.44;2.10] [-2.99;0.66] [-2.99;2.10] #> 6 D0 Missing 1 1 0 2 #> 7 #> 8 D1 N 30 20 16 66 #> 9 D1 Mean (SD) 1.83(1.04) 4.17(1.28) 4.98(0.69) 3.33(1.73) #> 10 D1 Median 1.78 4.19 5.08 3.57 #> 11 D1 [Q1;Q3] [ 0.94; 2.54] [ 3.23; 4.92] [ 4.58; 5.46] [ 1.78; 4.91] #> 12 D1 [Min;Max] [ 0.11;3.88] [ 1.48;6.19] [ 3.80;6.23] [ 0.11;6.23] #> 13 D1 Missing 1 0 0 1 #> 14 #> 15 D2 N 30 20 16 66 #> 16 D2 Mean (SD) 1.97(1.17) 4.04(0.89) 4.90(1.36) 3.32(1.70) #> 17 D2 Median 1.66 4.19 5.06 3.57 #> 18 D2 [Q1;Q3] [ 1.23; 2.86] [ 3.62; 4.36] [ 4.34; 5.20] [ 1.89; 4.44] #> 19 D2 [Min;Max] [-0.18;4.36] [ 2.03;5.63] [ 2.39;7.96] [-0.18;7.96] #> 20 D2 Missing 1 1 0 2 #> 21 #> 22 D3 N 30 20 16 66 #> 23 D3 Mean (SD) 1.78(1.17) 3.81(0.94) 5.07(1.12) 3.15(1.75) #> 24 D3 Median 1.78 3.63 5.22 3.15 #> 25 D3 [Q1;Q3] [ 0.93; 2.42] [ 3.13; 4.44] [ 4.11; 5.66] [ 1.80; 4.39] #> 26 D3 [Min;Max] [-0.16;3.90] [ 2.46;6.01] [ 3.16;7.37] [-0.16;7.37] #> 27 D3 Missing 0 1 1 2 #> 28 #> 29 D4 N 30 20 16 66 #> 30 D4 Mean (SD) 1.83(0.85) 3.80(0.95) 5.17(1.03) 3.22(1.66) #> 31 D4 Median 1.67 3.83 4.88 3.16 #> 32 D4 [Q1;Q3] [ 1.26; 2.32] [ 3.12; 4.42] [ 4.69; 5.50] [ 1.69; 4.48] #> 33 D4 [Min;Max] [ 0.38;3.97] [ 2.31;5.41] [ 3.24;6.96] [ 0.38;6.96] #> 34 D4 Missing 1 1 1 3 #> 35 #> 36 D5 N 30 20 16 66 #> 37 D5 Mean (SD) 2.27(1.20) 3.64(1.19) 4.43(0.98) 3.21(1.45) #> 38 D5 Median 2.50 3.86 4.57 3.28 #> 39 D5 [Q1;Q3] [ 1.77; 3.21] [ 2.59; 4.60] [ 3.44; 4.97] [ 2.42; 4.44] #> 40 D5 [Min;Max] [-1.19;4.31] [ 0.91;5.12] [ 2.95;6.54] [-1.19;6.54] #> 41 D5 Missing 0 0 0 0 #> #> ############################################ #>mystat2=function(x) mean(x,na.rm=TRUE)/sd(x,na.rm=TRUE) tab=report.quanti(data=datafake,y="y_numeric",x1="GROUP", total=TRUE,subjid="SUBJID",func.stat=mystat2, func.stat.name="Coefficient of variation")#> Error in mystat2(y_numeric): impossible de trouver la fonction "mystat2"tab#> #> ############################################ #> Quantitative descriptive statistics of: y_numeric #> ############################################ #> #> TIMEPOINT Statistics A (N=30) B (N=21) C (N=17) Total (N=68) #> 1 D0 N 30 20 16 66 #> 2 D0 Mean (SD) -0.93(0.86) -0.67(1.09) -1.19(0.92) -0.92(0.95) #> 3 D0 Median -0.82 -0.69 -1.26 -0.86 #> 4 D0 [Q1;Q3] [-1.59;-0.16] [-1.39;-0.06] [-1.62;-0.83] [-1.55;-0.16] #> 5 D0 [Min;Max] [-2.34;0.36] [-2.44;2.10] [-2.99;0.66] [-2.99;2.10] #> 6 D0 Missing 1 1 0 2 #> 7 #> 8 D1 N 30 20 16 66 #> 9 D1 Mean (SD) 1.83(1.04) 4.17(1.28) 4.98(0.69) 3.33(1.73) #> 10 D1 Median 1.78 4.19 5.08 3.57 #> 11 D1 [Q1;Q3] [ 0.94; 2.54] [ 3.23; 4.92] [ 4.58; 5.46] [ 1.78; 4.91] #> 12 D1 [Min;Max] [ 0.11;3.88] [ 1.48;6.19] [ 3.80;6.23] [ 0.11;6.23] #> 13 D1 Missing 1 0 0 1 #> 14 #> 15 D2 N 30 20 16 66 #> 16 D2 Mean (SD) 1.97(1.17) 4.04(0.89) 4.90(1.36) 3.32(1.70) #> 17 D2 Median 1.66 4.19 5.06 3.57 #> 18 D2 [Q1;Q3] [ 1.23; 2.86] [ 3.62; 4.36] [ 4.34; 5.20] [ 1.89; 4.44] #> 19 D2 [Min;Max] [-0.18;4.36] [ 2.03;5.63] [ 2.39;7.96] [-0.18;7.96] #> 20 D2 Missing 1 1 0 2 #> 21 #> 22 D3 N 30 20 16 66 #> 23 D3 Mean (SD) 1.78(1.17) 3.81(0.94) 5.07(1.12) 3.15(1.75) #> 24 D3 Median 1.78 3.63 5.22 3.15 #> 25 D3 [Q1;Q3] [ 0.93; 2.42] [ 3.13; 4.44] [ 4.11; 5.66] [ 1.80; 4.39] #> 26 D3 [Min;Max] [-0.16;3.90] [ 2.46;6.01] [ 3.16;7.37] [-0.16;7.37] #> 27 D3 Missing 0 1 1 2 #> 28 #> 29 D4 N 30 20 16 66 #> 30 D4 Mean (SD) 1.83(0.85) 3.80(0.95) 5.17(1.03) 3.22(1.66) #> 31 D4 Median 1.67 3.83 4.88 3.16 #> 32 D4 [Q1;Q3] [ 1.26; 2.32] [ 3.12; 4.42] [ 4.69; 5.50] [ 1.69; 4.48] #> 33 D4 [Min;Max] [ 0.38;3.97] [ 2.31;5.41] [ 3.24;6.96] [ 0.38;6.96] #> 34 D4 Missing 1 1 1 3 #> 35 #> 36 D5 N 30 20 16 66 #> 37 D5 Mean (SD) 2.27(1.20) 3.64(1.19) 4.43(0.98) 3.21(1.45) #> 38 D5 Median 2.50 3.86 4.57 3.28 #> 39 D5 [Q1;Q3] [ 1.77; 3.21] [ 2.59; 4.60] [ 3.44; 4.97] [ 2.42; 4.44] #> 40 D5 [Min;Max] [-1.19;4.31] [ 0.91;5.12] [ 2.95;6.54] [-1.19;6.54] #> 41 D5 Missing 0 0 0 0 #> #> ############################################ #>mode=function(x) { x=na.omit(x) ux <- unique(x) ux[which.max(tabulate(match(x, ux)))] } tab=report.quanti(data=datafake,y="y_numeric", func.stat=mode,func.stat.name="Mode") #Getting raw output tab$raw.output#> y_numeric #> 1 numeric#Getting a data.frame version of the output tab$output#> Statistics y_numeric #> 1 Mode numeric