Computes a comprehensive set of descriptive statistics for all
numeric columns in a data frame, stratified by one or more grouping
variables. Unlike compute_descriptives, this function
operates on every numeric column simultaneously and returns results in a
long format where each row corresponds to one statistic for one group
combination. Results can be exported to an Excel file.
Arguments
- df
A data frame containing the variables of interest. All numeric columns that are not listed in
ivare summarised.- iv
Integer vector of column indices identifying the grouping variables used to stratify the output.
- file
Character string naming the output Excel file (without extension). When
NULL(default) no file is written.
Value
A data frame in long format with one row per statistic per group
combination. The first column is statistic (see below), followed
by the grouping variable columns, and then one column per numeric
variable in df. The statistic column takes the following
values:
- mean
Arithmetic mean.
- SD
Standard deviation.
- median
Median.
- mad
Median absolute deviation.
- trimmed mean
50% trimmed mean.
- N
Number of non-missing observations.
- min
Minimum observed value.
- max
Maximum observed value.
- range
Difference between maximum and minimum.
- skewness
Skewness; deviations from 0 indicate departure from symmetry.
- kurtosis
Excess kurtosis; deviations from 0 indicate departure from normality.
- IQR
Interquartile range (Q0.75 - Q0.25).
- SE
Standard error of the mean.
Examples
compute_aggregate(df = mtcars, iv = 9)
#> statistic am mpg cyl disp hp drat wt qsec vs gear carb
#> 1 mean 0 17.14736842 6.9473684 290.37894737 160.26315789 3.28631579 3.7688947 18.1831579 0.3684211 3.21052632 2.7368421
#> 2 mean 1 24.39230769 5.0769231 143.53076923 126.84615385 4.05000000 2.4110000 17.3600000 0.5384615 4.38461538 2.9230769
#> 3 SD 0 3.83396639 1.5446569 110.17164678 53.90819573 0.39230389 0.7774001 1.7513076 0.4955946 0.41885391 1.1470787
#> 4 SD 1 6.16650381 1.5525001 87.20398868 84.06232425 0.36405128 0.6169816 1.7923588 0.5188745 0.50636968 2.1779784
#> 5 median 0 17.30000000 8.0000000 275.80000000 175.00000000 3.15000000 3.5200000 17.8200000 0.0000000 3.00000000 3.0000000
#> 6 median 1 22.80000000 4.0000000 120.30000000 109.00000000 4.08000000 2.3200000 17.0200000 1.0000000 4.00000000 2.0000000
#> 7 mad 0 3.11346000 0.0000000 124.83492000 77.09520000 0.22239000 0.4521930 1.1860800 0.0000000 0.00000000 1.4826000
#> 8 mad 1 6.67170000 0.0000000 58.85922000 63.75180000 0.26686800 0.6819960 2.3425080 0.0000000 0.00000000 1.4826000
#> 9 trimmed mean 0 17.30000000 8.0000000 275.80000000 175.00000000 3.15000000 3.5200000 17.8200000 0.0000000 3.00000000 3.0000000
#> 10 trimmed mean 1 22.80000000 4.0000000 120.30000000 109.00000000 4.08000000 2.3200000 17.0200000 1.0000000 4.00000000 2.0000000
#> 11 N 0 19.00000000 19.0000000 19.00000000 19.00000000 19.00000000 19.0000000 19.0000000 19.0000000 19.00000000 19.0000000
#> 12 N 1 13.00000000 13.0000000 13.00000000 13.00000000 13.00000000 13.0000000 13.0000000 13.0000000 13.00000000 13.0000000
#> 13 min 0 10.40000000 4.0000000 120.10000000 62.00000000 2.76000000 2.4650000 15.4100000 0.0000000 3.00000000 1.0000000
#> 14 min 1 15.00000000 4.0000000 71.10000000 52.00000000 3.54000000 1.5130000 14.5000000 0.0000000 4.00000000 1.0000000
#> 15 max 0 24.40000000 8.0000000 472.00000000 245.00000000 3.92000000 5.4240000 22.9000000 1.0000000 4.00000000 4.0000000
#> 16 max 1 33.90000000 8.0000000 351.00000000 335.00000000 4.93000000 3.5700000 19.9000000 1.0000000 5.00000000 8.0000000
#> 17 range 0 14.00000000 4.0000000 351.90000000 183.00000000 1.16000000 2.9590000 7.4900000 1.0000000 1.00000000 3.0000000
#> 18 range 1 18.90000000 4.0000000 279.90000000 283.00000000 1.39000000 2.0570000 5.4000000 1.0000000 1.00000000 7.0000000
#> 19 skewness 0 0.01395038 -0.9456136 0.04621046 -0.01422519 0.49983246 0.9759294 0.8474103 0.5030472 1.30946959 -0.1379392
#> 20 skewness 1 0.05256118 0.8699666 1.32563029 1.35988586 0.79328982 0.2103128 -0.2291420 -0.1368460 0.42067635 0.9838681
#> 21 kurtosis 0 -0.80317826 -0.7409637 -1.26266894 -1.20969733 -1.29885730 0.1415676 0.5483468 -1.8353779 -0.29252078 -1.5741208
#> 22 kurtosis 1 -1.45535200 -0.8952805 0.40407058 0.56346347 0.20929855 -1.1737358 -1.4236598 -2.1276416 -1.95621302 -0.2116988
#> 23 IQR 0 4.25000000 2.0000000 163.70000000 76.00000000 0.62500000 0.4050000 1.9950000 1.0000000 0.00000000 2.0000000
#> 24 IQR 1 9.40000000 2.0000000 81.00000000 47.00000000 0.37000000 0.8450000 2.1500000 1.0000000 1.00000000 3.0000000
#> 25 SE 0 0.87957221 0.3543686 25.27510920 12.36738829 0.09000068 0.1783478 0.4017775 0.1136972 0.09609168 0.2631579
#> 26 SE 1 1.71028044 0.4305860 24.18603482 23.31469388 0.10096966 0.1711199 0.4971109 0.1439099 0.14044168 0.6040625
compute_aggregate(df = mtcars, iv = 9:10)
#> statistic am gear mpg cyl disp hp drat wt qsec vs carb
#> 1 mean 0 3 16.10666667 7.4666667 326.30000000 176.13333333 3.13266667 3.89260000 17.69200000 0.2000000 2.6666667
#> 2 mean 0 4 21.05000000 5.0000000 155.67500000 100.75000000 3.86250000 3.30500000 20.02500000 1.0000000 3.0000000
#> 3 mean 1 4 26.27500000 4.5000000 106.68750000 83.87500000 4.13375000 2.27250000 18.43500000 0.7500000 2.0000000
#> 4 mean 1 5 21.38000000 6.0000000 202.48000000 195.60000000 3.91600000 2.63260000 15.64000000 0.2000000 4.4000000
#> 5 SD 0 3 3.37161824 1.1872337 94.85273548 47.68927203 0.27366472 0.83299287 1.34991640 0.4140393 1.1751393
#> 6 SD 0 4 3.06974483 1.1547005 13.97888288 29.01005573 0.11500000 0.15673757 2.04185373 0.0000000 1.1547005
#> 7 SD 1 4 5.41446475 0.9258201 37.16297841 24.17458819 0.34591235 0.46081450 1.15891575 0.4629100 1.3093073
#> 8 SD 1 5 6.65897890 2.0000000 115.49063598 102.83384657 0.38952535 0.81892539 1.13048662 0.4472136 2.6076810
#> 9 median 0 3 15.50000000 8.0000000 318.00000000 180.00000000 3.08000000 3.73000000 17.42000000 0.0000000 3.0000000
#> 10 median 0 4 21.00000000 5.0000000 157.15000000 109.00000000 3.92000000 3.31500000 19.45000000 1.0000000 3.0000000
#> 11 median 1 4 25.05000000 4.0000000 93.50000000 79.50000000 4.08000000 2.26000000 18.60500000 1.0000000 1.5000000
#> 12 median 1 5 19.70000000 6.0000000 145.00000000 175.00000000 3.77000000 2.77000000 15.50000000 0.0000000 4.0000000
#> 13 mad 0 3 3.26172000 0.0000000 62.56572000 44.47800000 0.11860800 0.42995400 0.81543000 0.0000000 1.4826000
#> 14 mad 0 4 3.70650000 1.4826000 15.49317000 20.75640000 0.00000000 0.18532500 1.26021000 0.0000000 1.4826000
#> 15 mad 1 4 6.00453000 0.0000000 29.80026000 31.13460000 0.23721600 0.58192050 0.85990800 0.0000000 0.7413000
#> 16 mad 1 5 6.96822000 2.9652000 73.98174000 124.53840000 0.34099800 0.93403800 1.48260000 0.0000000 2.9652000
#> 17 trimmed mean 0 3 15.50000000 8.0000000 318.00000000 180.00000000 3.08000000 3.73000000 17.42000000 0.0000000 3.0000000
#> 18 trimmed mean 0 4 21.00000000 5.0000000 157.15000000 109.00000000 3.92000000 3.31500000 19.45000000 1.0000000 3.0000000
#> 19 trimmed mean 1 4 25.05000000 4.0000000 93.50000000 79.50000000 4.08000000 2.26000000 18.60500000 1.0000000 1.5000000
#> 20 trimmed mean 1 5 19.70000000 6.0000000 145.00000000 175.00000000 3.77000000 2.77000000 15.50000000 0.0000000 4.0000000
#> 21 N 0 3 15.00000000 15.0000000 15.00000000 15.00000000 15.00000000 15.00000000 15.00000000 15.0000000 15.0000000
#> 22 N 0 4 4.00000000 4.0000000 4.00000000 4.00000000 4.00000000 4.00000000 4.00000000 4.0000000 4.0000000
#> 23 N 1 4 8.00000000 8.0000000 8.00000000 8.00000000 8.00000000 8.00000000 8.00000000 8.0000000 8.0000000
#> 24 N 1 5 5.00000000 5.0000000 5.00000000 5.00000000 5.00000000 5.00000000 5.00000000 5.0000000 5.0000000
#> 25 min 0 3 10.40000000 4.0000000 120.10000000 97.00000000 2.76000000 2.46500000 15.41000000 0.0000000 1.0000000
#> 26 min 0 4 17.80000000 4.0000000 140.80000000 62.00000000 3.69000000 3.15000000 18.30000000 1.0000000 2.0000000
#> 27 min 1 4 21.00000000 4.0000000 71.10000000 52.00000000 3.85000000 1.61500000 16.46000000 0.0000000 1.0000000
#> 28 min 1 5 15.00000000 4.0000000 95.10000000 91.00000000 3.54000000 1.51300000 14.50000000 0.0000000 2.0000000
#> 29 max 0 3 21.50000000 8.0000000 472.00000000 245.00000000 3.73000000 5.42400000 20.22000000 1.0000000 4.0000000
#> 30 max 0 4 24.40000000 6.0000000 167.60000000 123.00000000 3.92000000 3.44000000 22.90000000 1.0000000 4.0000000
#> 31 max 1 4 33.90000000 6.0000000 160.00000000 110.00000000 4.93000000 2.87500000 19.90000000 1.0000000 4.0000000
#> 32 max 1 5 30.40000000 8.0000000 351.00000000 335.00000000 4.43000000 3.57000000 16.90000000 1.0000000 8.0000000
#> 33 range 0 3 11.10000000 4.0000000 351.90000000 148.00000000 0.97000000 2.95900000 4.81000000 1.0000000 3.0000000
#> 34 range 0 4 6.60000000 2.0000000 26.80000000 61.00000000 0.23000000 0.29000000 4.60000000 0.0000000 2.0000000
#> 35 range 1 4 12.90000000 2.0000000 88.90000000 58.00000000 1.08000000 1.26000000 3.44000000 1.0000000 3.0000000
#> 36 range 1 5 15.40000000 4.0000000 255.90000000 244.00000000 0.89000000 2.05700000 2.40000000 1.0000000 6.0000000
#> 37 skewness 0 3 -0.07358952 -1.8385789 -0.24064114 -0.17699853 0.91705694 0.64365633 0.39409862 1.3525285 -0.1278075
#> 38 skewness 0 4 0.01983425 0.0000000 -0.05698706 -0.37217181 -0.75000000 -0.02103612 0.50532191 NaN 0.0000000
#> 39 skewness 1 4 0.23072681 0.9451080 0.45789515 0.01547554 1.37943579 -0.01956359 -0.49573105 -0.9451080 0.6682923
#> 40 skewness 1 5 0.26707885 0.0000000 0.29203347 0.24132430 0.27642522 -0.19717645 0.08060013 1.0733126 0.2598658
#> 41 kurtosis 0 3 -0.94417844 2.1894424 -0.59153846 -1.17927907 0.22904677 -0.52673116 -0.61466452 -0.1688889 -1.6094332
#> 42 kurtosis 0 4 -2.27194363 -2.4375000 -2.37218537 -2.03074185 -1.68750000 -2.41327550 -1.86697544 NaN -2.4375000
#> 43 kurtosis 1 4 -1.93281740 -1.2135417 -1.69418958 -2.01164191 0.61884334 -1.75471806 -1.26581589 -1.2135417 -1.4687500
#> 44 kurtosis 1 5 -2.01237681 -2.2000000 -2.13408398 -1.98781561 -2.07515954 -1.89448519 -2.18665050 -0.9200000 -1.9580623
#> 45 IQR 0 3 3.90000000 0.0000000 104.20000000 60.00000000 0.14500000 0.50750000 0.95500000 0.0000000 2.0000000
#> 46 IQR 0 4 4.35000000 2.0000000 22.37500000 36.25000000 0.05750000 0.26000000 1.97500000 0.0000000 2.0000000
#> 47 IQR 1 4 9.60000000 0.5000000 52.80000000 43.50000000 0.23750000 0.75000000 0.89750000 0.2500000 1.5000000
#> 48 IQR 1 5 10.20000000 4.0000000 180.70000000 151.00000000 0.60000000 1.03000000 2.10000000 0.0000000 4.0000000
#> 49 SE 0 3 0.87054808 0.3065424 24.49087099 12.31331709 0.07065993 0.21507783 0.34854692 0.1069045 0.3034197
#> 50 SE 0 4 1.53487242 0.5773503 6.98944144 14.50502786 0.05750000 0.07836879 1.02092687 0.0000000 0.5773503
#> 51 SE 1 4 1.91430237 0.3273268 13.13909702 8.54700762 0.12229849 0.16292253 0.40973859 0.1636634 0.4629100
#> 52 SE 1 5 2.97798590 0.8944272 51.64898257 45.98869426 0.17420103 0.36623457 0.50556899 0.2000000 1.1661904
compute_aggregate(df = mtcars, iv = 11)
#> statistic carb mpg cyl disp hp drat wt qsec vs am gear
#> 1 mean 1 25.342857143 4.5714286 134.27142857 86.00000000 3.6814286 2.4900000 19.5071429 1.0000000 0.5714286 3.5714286
#> 2 mean 2 22.400000000 5.6000000 208.16000000 117.20000000 3.6990000 2.8628000 18.1860000 0.5000000 0.4000000 3.8000000
#> 3 mean 3 16.300000000 8.0000000 275.80000000 180.00000000 3.0700000 3.8600000 17.6666667 0.0000000 0.0000000 3.0000000
#> 4 mean 4 15.790000000 7.2000000 308.82000000 187.00000000 3.5960000 3.8974000 16.9650000 0.2000000 0.3000000 3.6000000
#> 5 mean 6 19.700000000 6.0000000 145.00000000 175.00000000 3.6200000 2.7700000 15.5000000 0.0000000 1.0000000 5.0000000
#> 6 mean 8 15.000000000 8.0000000 301.00000000 335.00000000 3.5400000 3.5700000 14.6000000 0.0000000 1.0000000 5.0000000
#> 7 SD 1 6.001349055 0.9759001 75.90036702 19.78214683 0.5548702 0.6214499 0.5908670 0.0000000 0.5345225 0.5345225
#> 8 SD 2 5.472151720 2.0655911 122.50499491 43.96412679 0.6782568 0.8269761 1.9670689 0.5270463 0.5163978 0.7888106
#> 9 SD 3 1.053565375 0.0000000 0.00000000 0.00000000 0.0000000 0.1835756 0.3055050 0.0000000 0.0000000 0.0000000
#> 10 SD 4 3.911081124 1.0327956 132.06336695 62.94971538 0.4575102 1.0536001 1.3949373 0.4216370 0.4830459 0.6992059
#> 11 SD 6 NA NA NA NA NA NA NA NA NA NA
#> 12 SD 8 NA NA NA NA NA NA NA NA NA NA
#> 13 median 1 22.800000000 4.0000000 108.00000000 93.00000000 3.8500000 2.3200000 19.4700000 1.0000000 1.0000000 4.0000000
#> 14 median 2 22.100000000 4.0000000 143.75000000 111.00000000 3.7300000 3.1700000 17.1750000 0.5000000 0.0000000 4.0000000
#> 15 median 3 16.400000000 8.0000000 275.80000000 180.00000000 3.0700000 3.7800000 17.6000000 0.0000000 0.0000000 3.0000000
#> 16 median 4 15.250000000 8.0000000 350.50000000 210.00000000 3.8150000 3.5050000 17.2200000 0.0000000 0.0000000 3.5000000
#> 17 median 6 19.700000000 6.0000000 145.00000000 175.00000000 3.6200000 2.7700000 15.5000000 0.0000000 1.0000000 5.0000000
#> 18 median 8 15.000000000 8.0000000 301.00000000 335.00000000 3.5400000 3.5700000 14.6000000 0.0000000 1.0000000 5.0000000
#> 19 mad 1 6.671700000 0.0000000 43.44018000 25.20420000 0.3409980 0.5708010 0.8006040 0.0000000 0.0000000 0.0000000
#> 20 mad 2 5.411490000 0.0000000 86.50971000 57.82140000 0.8599080 0.5485620 0.5782140 0.7413000 0.0000000 1.4826000
#> 21 mad 3 1.334340000 0.0000000 0.00000000 0.00000000 0.0000000 0.0741300 0.2965200 0.0000000 0.0000000 0.0000000
#> 22 mad 4 4.818450000 0.0000000 171.24030000 65.97570000 0.3780630 0.7153545 1.3639920 0.0000000 0.0000000 0.7413000
#> 23 mad 6 0.000000000 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 24 mad 8 0.000000000 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 25 trimmed mean 1 22.800000000 4.0000000 108.00000000 93.00000000 3.8500000 2.3200000 19.4700000 1.0000000 1.0000000 4.0000000
#> 26 trimmed mean 2 22.100000000 4.0000000 143.75000000 111.00000000 3.7300000 3.1700000 17.1750000 0.5000000 0.0000000 4.0000000
#> 27 trimmed mean 3 16.400000000 8.0000000 275.80000000 180.00000000 3.0700000 3.7800000 17.6000000 0.0000000 0.0000000 3.0000000
#> 28 trimmed mean 4 15.250000000 8.0000000 350.50000000 210.00000000 3.8150000 3.5050000 17.2200000 0.0000000 0.0000000 3.5000000
#> 29 trimmed mean 6 19.700000000 6.0000000 145.00000000 175.00000000 3.6200000 2.7700000 15.5000000 0.0000000 1.0000000 5.0000000
#> 30 trimmed mean 8 15.000000000 8.0000000 301.00000000 335.00000000 3.5400000 3.5700000 14.6000000 0.0000000 1.0000000 5.0000000
#> 31 N 1 7.000000000 7.0000000 7.00000000 7.00000000 7.0000000 7.0000000 7.0000000 7.0000000 7.0000000 7.0000000
#> 32 N 2 10.000000000 10.0000000 10.00000000 10.00000000 10.0000000 10.0000000 10.0000000 10.0000000 10.0000000 10.0000000
#> 33 N 3 3.000000000 3.0000000 3.00000000 3.00000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000
#> 34 N 4 10.000000000 10.0000000 10.00000000 10.00000000 10.0000000 10.0000000 10.0000000 10.0000000 10.0000000 10.0000000
#> 35 N 6 1.000000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
#> 36 N 8 1.000000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
#> 37 min 1 18.100000000 4.0000000 71.10000000 65.00000000 2.7600000 1.8350000 18.6100000 1.0000000 0.0000000 3.0000000
#> 38 min 2 15.200000000 4.0000000 75.70000000 52.00000000 2.7600000 1.5130000 16.7000000 0.0000000 0.0000000 3.0000000
#> 39 min 3 15.200000000 8.0000000 275.80000000 180.00000000 3.0700000 3.7300000 17.4000000 0.0000000 0.0000000 3.0000000
#> 40 min 4 10.400000000 6.0000000 160.00000000 110.00000000 2.9300000 2.6200000 14.5000000 0.0000000 0.0000000 3.0000000
#> 41 min 6 19.700000000 6.0000000 145.00000000 175.00000000 3.6200000 2.7700000 15.5000000 0.0000000 1.0000000 5.0000000
#> 42 min 8 15.000000000 8.0000000 301.00000000 335.00000000 3.5400000 3.5700000 14.6000000 0.0000000 1.0000000 5.0000000
#> 43 max 1 33.900000000 6.0000000 258.00000000 110.00000000 4.2200000 3.4600000 20.2200000 1.0000000 1.0000000 4.0000000
#> 44 max 2 30.400000000 8.0000000 400.00000000 175.00000000 4.9300000 3.8450000 22.9000000 1.0000000 1.0000000 5.0000000
#> 45 max 3 17.300000000 8.0000000 275.80000000 180.00000000 3.0700000 4.0700000 18.0000000 0.0000000 0.0000000 3.0000000
#> 46 max 4 21.000000000 8.0000000 472.00000000 264.00000000 4.2200000 5.4240000 18.9000000 1.0000000 1.0000000 5.0000000
#> 47 max 6 19.700000000 6.0000000 145.00000000 175.00000000 3.6200000 2.7700000 15.5000000 0.0000000 1.0000000 5.0000000
#> 48 max 8 15.000000000 8.0000000 301.00000000 335.00000000 3.5400000 3.5700000 14.6000000 0.0000000 1.0000000 5.0000000
#> 49 range 1 15.800000000 2.0000000 186.90000000 45.00000000 1.4600000 1.6250000 1.6100000 0.0000000 1.0000000 1.0000000
#> 50 range 2 15.200000000 4.0000000 324.30000000 123.00000000 2.1700000 2.3320000 6.2000000 1.0000000 1.0000000 2.0000000
#> 51 range 3 2.100000000 0.0000000 0.00000000 0.00000000 0.0000000 0.3400000 0.6000000 0.0000000 0.0000000 0.0000000
#> 52 range 4 10.600000000 2.0000000 312.00000000 154.00000000 1.2900000 2.8040000 4.4000000 1.0000000 1.0000000 2.0000000
#> 53 range 6 0.000000000 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 54 range 8 0.000000000 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 55 skewness 1 0.311376251 0.7528372 0.66239754 -0.05358933 -0.5955824 0.4836041 -0.2743787 NaN -0.2290811 -0.2290811
#> 56 skewness 2 0.178531082 0.3485685 0.38722010 -0.02134659 0.3014403 -0.5499922 1.3059123 0.0000000 0.3485685 0.2933893
#> 57 skewness 3 -0.094060702 NaN NaN NaN NaN 0.3530269 0.2078266 NaN NaN NaN
#> 58 skewness 4 -0.004360841 -0.3485685 -0.08196907 -0.19374963 -0.2548633 0.4532610 -0.3279298 1.2807225 0.7452708 0.5616761
#> 59 skewness 6 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
#> 60 skewness 8 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
#> 61 kurtosis 1 -1.776408282 -1.6040816 -1.54752064 -2.07310914 -1.5123521 -1.6126905 -1.6759381 NaN -2.2040816 -2.2040816
#> 62 kurtosis 2 -1.480671557 -2.0550000 -1.78374034 -1.58903667 -1.2428099 -1.4241175 0.4692786 -2.1900000 -2.0550000 -1.5039796
#> 63 kurtosis 3 -2.333333333 NaN NaN NaN NaN -2.3333333 -2.3333333 NaN NaN NaN
#> 64 kurtosis 4 -1.564875649 -2.0550000 -1.90955853 -1.94422810 -1.7352572 -1.5957526 -1.3290326 -0.3675000 -1.5728571 -1.0787603
#> 65 kurtosis 6 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
#> 66 kurtosis 8 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
#> 67 IQR 1 8.400000000 1.0000000 93.70000000 35.00000000 0.6900000 0.7725000 0.7850000 0.0000000 1.0000000 1.0000000
#> 68 IQR 2 6.775000000 4.0000000 194.02500000 58.00000000 0.9125000 1.1387500 1.6500000 1.0000000 1.0000000 1.0000000
#> 69 IQR 3 1.050000000 0.0000000 0.00000000 0.00000000 0.0000000 0.1700000 0.3000000 0.0000000 0.0000000 0.0000000
#> 70 IQR 4 5.300000000 2.0000000 252.40000000 118.25000000 0.7000000 1.6600000 1.9450000 0.0000000 0.7500000 1.0000000
#> 71 IQR 6 0.000000000 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 72 IQR 8 0.000000000 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 73 SE 1 2.268296733 0.3688556 28.68764222 7.47694870 0.2097212 0.2348860 0.2233267 0.0000000 0.2020305 0.2020305
#> 74 SE 2 1.730446314 0.6531973 38.73948087 13.90267760 0.2144836 0.2615128 0.6220418 0.1666667 0.1632993 0.2494438
#> 75 SE 3 0.608276253 0.0000000 0.00000000 0.00000000 0.0000000 0.1059874 0.1763834 0.0000000 0.0000000 0.0000000
#> 76 SE 4 1.236792446 0.3265986 41.76210350 19.90644787 0.1446774 0.3331776 0.4411179 0.1333333 0.1527525 0.2211083
#> 77 SE 6 NA NA NA NA NA NA NA NA NA NA
#> 78 SE 8 NA NA NA NA NA NA NA NA NA NA
compute_aggregate(df = mtcars, iv = 11, file = "descriptives")
#> statistic carb mpg cyl disp hp drat wt qsec vs am gear
#> 1 mean 1 25.342857143 4.5714286 134.27142857 86.00000000 3.6814286 2.4900000 19.5071429 1.0000000 0.5714286 3.5714286
#> 2 mean 2 22.400000000 5.6000000 208.16000000 117.20000000 3.6990000 2.8628000 18.1860000 0.5000000 0.4000000 3.8000000
#> 3 mean 3 16.300000000 8.0000000 275.80000000 180.00000000 3.0700000 3.8600000 17.6666667 0.0000000 0.0000000 3.0000000
#> 4 mean 4 15.790000000 7.2000000 308.82000000 187.00000000 3.5960000 3.8974000 16.9650000 0.2000000 0.3000000 3.6000000
#> 5 mean 6 19.700000000 6.0000000 145.00000000 175.00000000 3.6200000 2.7700000 15.5000000 0.0000000 1.0000000 5.0000000
#> 6 mean 8 15.000000000 8.0000000 301.00000000 335.00000000 3.5400000 3.5700000 14.6000000 0.0000000 1.0000000 5.0000000
#> 7 SD 1 6.001349055 0.9759001 75.90036702 19.78214683 0.5548702 0.6214499 0.5908670 0.0000000 0.5345225 0.5345225
#> 8 SD 2 5.472151720 2.0655911 122.50499491 43.96412679 0.6782568 0.8269761 1.9670689 0.5270463 0.5163978 0.7888106
#> 9 SD 3 1.053565375 0.0000000 0.00000000 0.00000000 0.0000000 0.1835756 0.3055050 0.0000000 0.0000000 0.0000000
#> 10 SD 4 3.911081124 1.0327956 132.06336695 62.94971538 0.4575102 1.0536001 1.3949373 0.4216370 0.4830459 0.6992059
#> 11 SD 6 NA NA NA NA NA NA NA NA NA NA
#> 12 SD 8 NA NA NA NA NA NA NA NA NA NA
#> 13 median 1 22.800000000 4.0000000 108.00000000 93.00000000 3.8500000 2.3200000 19.4700000 1.0000000 1.0000000 4.0000000
#> 14 median 2 22.100000000 4.0000000 143.75000000 111.00000000 3.7300000 3.1700000 17.1750000 0.5000000 0.0000000 4.0000000
#> 15 median 3 16.400000000 8.0000000 275.80000000 180.00000000 3.0700000 3.7800000 17.6000000 0.0000000 0.0000000 3.0000000
#> 16 median 4 15.250000000 8.0000000 350.50000000 210.00000000 3.8150000 3.5050000 17.2200000 0.0000000 0.0000000 3.5000000
#> 17 median 6 19.700000000 6.0000000 145.00000000 175.00000000 3.6200000 2.7700000 15.5000000 0.0000000 1.0000000 5.0000000
#> 18 median 8 15.000000000 8.0000000 301.00000000 335.00000000 3.5400000 3.5700000 14.6000000 0.0000000 1.0000000 5.0000000
#> 19 mad 1 6.671700000 0.0000000 43.44018000 25.20420000 0.3409980 0.5708010 0.8006040 0.0000000 0.0000000 0.0000000
#> 20 mad 2 5.411490000 0.0000000 86.50971000 57.82140000 0.8599080 0.5485620 0.5782140 0.7413000 0.0000000 1.4826000
#> 21 mad 3 1.334340000 0.0000000 0.00000000 0.00000000 0.0000000 0.0741300 0.2965200 0.0000000 0.0000000 0.0000000
#> 22 mad 4 4.818450000 0.0000000 171.24030000 65.97570000 0.3780630 0.7153545 1.3639920 0.0000000 0.0000000 0.7413000
#> 23 mad 6 0.000000000 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 24 mad 8 0.000000000 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 25 trimmed mean 1 22.800000000 4.0000000 108.00000000 93.00000000 3.8500000 2.3200000 19.4700000 1.0000000 1.0000000 4.0000000
#> 26 trimmed mean 2 22.100000000 4.0000000 143.75000000 111.00000000 3.7300000 3.1700000 17.1750000 0.5000000 0.0000000 4.0000000
#> 27 trimmed mean 3 16.400000000 8.0000000 275.80000000 180.00000000 3.0700000 3.7800000 17.6000000 0.0000000 0.0000000 3.0000000
#> 28 trimmed mean 4 15.250000000 8.0000000 350.50000000 210.00000000 3.8150000 3.5050000 17.2200000 0.0000000 0.0000000 3.5000000
#> 29 trimmed mean 6 19.700000000 6.0000000 145.00000000 175.00000000 3.6200000 2.7700000 15.5000000 0.0000000 1.0000000 5.0000000
#> 30 trimmed mean 8 15.000000000 8.0000000 301.00000000 335.00000000 3.5400000 3.5700000 14.6000000 0.0000000 1.0000000 5.0000000
#> 31 N 1 7.000000000 7.0000000 7.00000000 7.00000000 7.0000000 7.0000000 7.0000000 7.0000000 7.0000000 7.0000000
#> 32 N 2 10.000000000 10.0000000 10.00000000 10.00000000 10.0000000 10.0000000 10.0000000 10.0000000 10.0000000 10.0000000
#> 33 N 3 3.000000000 3.0000000 3.00000000 3.00000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000
#> 34 N 4 10.000000000 10.0000000 10.00000000 10.00000000 10.0000000 10.0000000 10.0000000 10.0000000 10.0000000 10.0000000
#> 35 N 6 1.000000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
#> 36 N 8 1.000000000 1.0000000 1.00000000 1.00000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
#> 37 min 1 18.100000000 4.0000000 71.10000000 65.00000000 2.7600000 1.8350000 18.6100000 1.0000000 0.0000000 3.0000000
#> 38 min 2 15.200000000 4.0000000 75.70000000 52.00000000 2.7600000 1.5130000 16.7000000 0.0000000 0.0000000 3.0000000
#> 39 min 3 15.200000000 8.0000000 275.80000000 180.00000000 3.0700000 3.7300000 17.4000000 0.0000000 0.0000000 3.0000000
#> 40 min 4 10.400000000 6.0000000 160.00000000 110.00000000 2.9300000 2.6200000 14.5000000 0.0000000 0.0000000 3.0000000
#> 41 min 6 19.700000000 6.0000000 145.00000000 175.00000000 3.6200000 2.7700000 15.5000000 0.0000000 1.0000000 5.0000000
#> 42 min 8 15.000000000 8.0000000 301.00000000 335.00000000 3.5400000 3.5700000 14.6000000 0.0000000 1.0000000 5.0000000
#> 43 max 1 33.900000000 6.0000000 258.00000000 110.00000000 4.2200000 3.4600000 20.2200000 1.0000000 1.0000000 4.0000000
#> 44 max 2 30.400000000 8.0000000 400.00000000 175.00000000 4.9300000 3.8450000 22.9000000 1.0000000 1.0000000 5.0000000
#> 45 max 3 17.300000000 8.0000000 275.80000000 180.00000000 3.0700000 4.0700000 18.0000000 0.0000000 0.0000000 3.0000000
#> 46 max 4 21.000000000 8.0000000 472.00000000 264.00000000 4.2200000 5.4240000 18.9000000 1.0000000 1.0000000 5.0000000
#> 47 max 6 19.700000000 6.0000000 145.00000000 175.00000000 3.6200000 2.7700000 15.5000000 0.0000000 1.0000000 5.0000000
#> 48 max 8 15.000000000 8.0000000 301.00000000 335.00000000 3.5400000 3.5700000 14.6000000 0.0000000 1.0000000 5.0000000
#> 49 range 1 15.800000000 2.0000000 186.90000000 45.00000000 1.4600000 1.6250000 1.6100000 0.0000000 1.0000000 1.0000000
#> 50 range 2 15.200000000 4.0000000 324.30000000 123.00000000 2.1700000 2.3320000 6.2000000 1.0000000 1.0000000 2.0000000
#> 51 range 3 2.100000000 0.0000000 0.00000000 0.00000000 0.0000000 0.3400000 0.6000000 0.0000000 0.0000000 0.0000000
#> 52 range 4 10.600000000 2.0000000 312.00000000 154.00000000 1.2900000 2.8040000 4.4000000 1.0000000 1.0000000 2.0000000
#> 53 range 6 0.000000000 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 54 range 8 0.000000000 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 55 skewness 1 0.311376251 0.7528372 0.66239754 -0.05358933 -0.5955824 0.4836041 -0.2743787 NaN -0.2290811 -0.2290811
#> 56 skewness 2 0.178531082 0.3485685 0.38722010 -0.02134659 0.3014403 -0.5499922 1.3059123 0.0000000 0.3485685 0.2933893
#> 57 skewness 3 -0.094060702 NaN NaN NaN NaN 0.3530269 0.2078266 NaN NaN NaN
#> 58 skewness 4 -0.004360841 -0.3485685 -0.08196907 -0.19374963 -0.2548633 0.4532610 -0.3279298 1.2807225 0.7452708 0.5616761
#> 59 skewness 6 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
#> 60 skewness 8 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
#> 61 kurtosis 1 -1.776408282 -1.6040816 -1.54752064 -2.07310914 -1.5123521 -1.6126905 -1.6759381 NaN -2.2040816 -2.2040816
#> 62 kurtosis 2 -1.480671557 -2.0550000 -1.78374034 -1.58903667 -1.2428099 -1.4241175 0.4692786 -2.1900000 -2.0550000 -1.5039796
#> 63 kurtosis 3 -2.333333333 NaN NaN NaN NaN -2.3333333 -2.3333333 NaN NaN NaN
#> 64 kurtosis 4 -1.564875649 -2.0550000 -1.90955853 -1.94422810 -1.7352572 -1.5957526 -1.3290326 -0.3675000 -1.5728571 -1.0787603
#> 65 kurtosis 6 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
#> 66 kurtosis 8 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
#> 67 IQR 1 8.400000000 1.0000000 93.70000000 35.00000000 0.6900000 0.7725000 0.7850000 0.0000000 1.0000000 1.0000000
#> 68 IQR 2 6.775000000 4.0000000 194.02500000 58.00000000 0.9125000 1.1387500 1.6500000 1.0000000 1.0000000 1.0000000
#> 69 IQR 3 1.050000000 0.0000000 0.00000000 0.00000000 0.0000000 0.1700000 0.3000000 0.0000000 0.0000000 0.0000000
#> 70 IQR 4 5.300000000 2.0000000 252.40000000 118.25000000 0.7000000 1.6600000 1.9450000 0.0000000 0.7500000 1.0000000
#> 71 IQR 6 0.000000000 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 72 IQR 8 0.000000000 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 73 SE 1 2.268296733 0.3688556 28.68764222 7.47694870 0.2097212 0.2348860 0.2233267 0.0000000 0.2020305 0.2020305
#> 74 SE 2 1.730446314 0.6531973 38.73948087 13.90267760 0.2144836 0.2615128 0.6220418 0.1666667 0.1632993 0.2494438
#> 75 SE 3 0.608276253 0.0000000 0.00000000 0.00000000 0.0000000 0.1059874 0.1763834 0.0000000 0.0000000 0.0000000
#> 76 SE 4 1.236792446 0.3265986 41.76210350 19.90644787 0.1446774 0.3331776 0.4411179 0.1333333 0.1527525 0.2211083
#> 77 SE 6 NA NA NA NA NA NA NA NA NA NA
#> 78 SE 8 NA NA NA NA NA NA NA NA NA NA