Make a table showing numerical summary

Source: R/numSummary.R

Make a table showing numerical summary

numSummaryTable(x, ..., lang = "en", vanilla = FALSE)

Arguments

x

A grouped_df or a data.frame or a vector
...

further argument to be passed
lang

Language. choices are one of c("en","kor")
vanilla

Logical. Whether make vanilla table or not

Examples

require(moonBook)
require(magrittr)
require(dplyr)
require(rrtable)
require(webr)
numSummaryTable(acs)
#> type: regulartable object.
#> col_keys: `rowname`, `n`, `mean`, `sd`, `median`, `trimmed`, `mad`, `min`, `max`, `range`, `skew`, `kurtosis`, `se` 
#> header has 1 row(s) 
#> body has 9 row(s) 
#> original dataset sample: 
#>        rowname      n   mean    sd median trimmed   mad    min    max range
#> age        age 857.00  63.31 11.70  64.00   63.56 13.34  28.00  91.00 63.00
#> EF          EF 723.00  55.83  9.62  58.10   56.77  7.86  18.00  79.00 61.00
#> height  height 764.00 163.18  9.08 165.00  163.52  7.41 130.00 185.00 55.00
#> weight  weight 766.00  64.84 11.36  65.00   64.55 10.38  30.00 112.00 82.00
#> BMI        BMI 764.00  24.28  3.35  24.16   24.16  3.01  15.62  41.42 25.80
#>         skew kurtosis   se
#> age    -0.18    -0.57 0.40
#> EF     -0.98     1.11 0.36
#> height -0.44    -0.01 0.33
#> weight  0.34     0.44 0.41
#> BMI     0.67     2.12 0.12
numSummaryTable(acs$age)
#> type: regulartable object.
#> col_keys: `rowname`, `n`, `mean`, `sd`, `median`, `trimmed`, `mad`, `min`, `max`, `range`, `skew`, `kurtosis`, `se` 
#> header has 1 row(s) 
#> body has 1 row(s) 
#> original dataset sample: 
#>    rowname      n  mean    sd median trimmed   mad   min   max range  skew
#> X1      X1 857.00 63.31 11.70  64.00   63.56 13.34 28.00 91.00 63.00 -0.18
#>    kurtosis   se
#> X1    -0.57 0.40
acs %>% group_by(sex) %>% select(age) %>% numSummaryTable
#> Adding missing grouping variables: `sex`
#> type: regulartable object.
#> col_keys: `rowname`, `variable`, `sex`, `n`, `mean`, `sd`, `median`, `trimmed`, `mad`, `min`, `max`, `range`, `skew`, `kurtosis`, `se` 
#> header has 1 row(s) 
#> body has 2 row(s) 
#> original dataset sample: 
#>   rowname variable    sex      n  mean    sd median trimmed   mad   min   max
#> 1       1      age Female 287.00 68.68 10.73  70.00   69.43 10.38 39.00 90.00
#> 2       2      age   Male 570.00 60.61 11.23  61.00   60.65 11.86 28.00 91.00
#>   range  skew kurtosis   se
#> 1 51.00 -0.59    -0.26 0.63
#> 2 63.00 -0.01    -0.36 0.47
acs %>% group_by(sex) %>% select(age,EF) %>% numSummaryTable
#> Adding missing grouping variables: `sex`
#> type: regulartable object.
#> col_keys: `rowname`, `variable`, `sex`, `n`, `mean`, `sd`, `median`, `trimmed`, `mad`, `min`, `max`, `range`, `skew`, `kurtosis`, `se` 
#> header has 1 row(s) 
#> body has 4 row(s) 
#> original dataset sample: 
#>   rowname variable    sex      n  mean    sd median trimmed   mad   min   max
#> 1       1      age Female 287.00 68.68 10.73  70.00   69.43 10.38 39.00 90.00
#> 2       2      age   Male 570.00 60.61 11.23  61.00   60.65 11.86 28.00 91.00
#> 3       3       EF Female 240.00 56.27 10.06  59.25   57.57  7.19 18.40 75.00
#> 4       4       EF   Male 483.00 55.62  9.40  57.30   56.38  8.01 18.00 79.00
#>   range  skew kurtosis   se
#> 1 51.00 -0.59    -0.26 0.63
#> 2 63.00 -0.01    -0.36 0.47
#> 3 56.60 -1.30     1.70 0.65
#> 4 61.00 -0.79     0.76 0.43
acs %>% group_by(sex,Dx) %>% select(age,EF) %>% numSummaryTable(vanilla=FALSE)
#> Adding missing grouping variables: `sex`, `Dx`
#> type: regulartable object.
#> col_keys: `rowname`, `variable`, `sex`, `Dx`, `n`, `mean`, `sd`, `median`, `trimmed`, `mad`, `min`, `max`, `range`, `skew`, `kurtosis`, `se` 
#> header has 1 row(s) 
#> body has 12 row(s) 
#> original dataset sample: 
#>   rowname variable    sex              Dx      n  mean    sd median trimmed
#> 1       1      age Female          NSTEMI  50.00 70.88 11.35  74.50   71.88
#> 2       2      age Female           STEMI  84.00 69.11 10.36  70.00   70.04
#> 3       3      age Female Unstable Angina 153.00 67.72 10.67  70.00   68.33
#> 4       4      age   Male          NSTEMI 103.00 61.15 11.57  59.00   61.28
#> 5       5      age   Male           STEMI 220.00 59.43 11.72  59.50   59.43
#>     mad   min   max range  skew kurtosis   se
#> 1  8.90 42.00 88.00 46.00 -0.72    -0.34 1.61
#> 2 10.38 42.00 89.00 47.00 -0.65    -0.09 1.13
#> 3  8.90 39.00 90.00 51.00 -0.54    -0.34 0.86
#> 4 13.34 28.00 85.00 57.00 -0.11    -0.53 1.14
#> 5 11.12 30.00 86.00 56.00  0.00    -0.55 0.79
acs %>% group_by(sex,Dx) %>% numSummaryTable(age,EF,lang="kor")
#> type: regulartable object.
#> col_keys: `rowname`, `variable`, `sex`, `Dx`, `n`, `평균`, `표준편차`, `중앙값`, `절단평균`, `중앙값절대편차`, `최소값`, `최대값`, `범위`, `왜도`, `첨도`, `표준오차` 
#> header has 1 row(s) 
#> body has 12 row(s) 
#> original dataset sample: 
#>   rowname variable    sex              Dx      n  평균 표준편차 중앙값 절단평균
#> 1       1      age Female          NSTEMI  50.00 70.88    11.35  74.50    71.88
#> 2       2      age Female           STEMI  84.00 69.11    10.36  70.00    70.04
#> 3       3      age Female Unstable Angina 153.00 67.72    10.67  70.00    68.33
#> 4       4      age   Male          NSTEMI 103.00 61.15    11.57  59.00    61.28
#> 5       5      age   Male           STEMI 220.00 59.43    11.72  59.50    59.43
#>   중앙값절대편차 최소값 최대값  범위  왜도  첨도 표준오차
#> 1           8.90  42.00  88.00 46.00 -0.72 -0.34     1.61
#> 2          10.38  42.00  89.00 47.00 -0.65 -0.09     1.13
#> 3           8.90  39.00  90.00 51.00 -0.54 -0.34     0.86
#> 4          13.34  28.00  85.00 57.00 -0.11 -0.53     1.14
#> 5          11.12  30.00  86.00 56.00  0.00 -0.55     0.79