Generate a statistics table with the chosen statistical functions, and tests if given a "grouped" dataframe.
Usage
desctable(data, stats, tests, labels)
# S3 method for default
desctable(data, stats = stats_auto, tests, labels = NULL)
# S3 method for grouped_df
desctable(data, stats = stats_auto, tests = tests_auto, labels = NULL)Arguments
- data
The dataframe to analyze
- stats
A list of named statistics to apply to each element of the dataframe, or a function returning a list of named statistics
- tests
A list of statistical tests to use when calling desctable with a grouped_df
- labels
A named character vector of labels to use instead of variable names
Labels
labels is an option named character vector used to make the table prettier.
If given, the variable names for which there is a label will be replaced by their corresponding label.
Not all variables need to have a label, and labels for non-existing variables are ignored.
labels must be given in the form c(unquoted_variable_name = "label")
Stats
The stats can be a function which takes a dataframe and returns a list of statistical functions to use.
stats can also be a named list of statistical functions, or purrr::map like formulas.
The names will be used as column names in the resulting table. If an element of the list is a function, it will be used as-is for the stats.
Tests
The tests can be a function which takes a variable and a grouping variable, and returns an appropriate statistical test to use in that case.
tests can also be a named list of statistical test functions, associating the name of a variable in the data and a test to use specifically for that variable.
That test name must be expressed as a single-term formula (e.g. ~t.test), or a purrr::map like formula
(e.g. ~t.test(., var.equal = T)). You don't have to specify tests for all the variables: a default test for
all other variables can be defined with the name .default, and an automatic test can be defined with the name .auto.
If data is a grouped dataframe (using group_by), subtables are created and statistic tests are performed over each sub-group.
Output
The output is a desctable object, which is a list of named dataframes that can be further manipulated. Methods for printing, using in pander and DT are present. Printing reduces the object to a dataframe.
Examples
iris %>%
desctable()
#> Warning: desctable is deprecated and will be removed in 1.0.0.
#>
#> Please use the `desc_*` family of functions (`desc_table`, `desc_tests`, `desc_output`)
#> N % Min Q1 Med Mean Q3 Max sd IQR
#> 1 Sepal.Length 150 NA 4.3 5.1 5.80 5.843333 6.4 7.9 0.8280661 1.3
#> 2 Sepal.Width 150 NA 2.0 2.8 3.00 3.057333 3.3 4.4 0.4358663 0.5
#> 3 Petal.Length 150 NA 1.0 1.6 4.35 3.758000 5.1 6.9 1.7652982 3.5
#> 4 Petal.Width 150 NA 0.1 0.3 1.30 1.199333 1.8 2.5 0.7622377 1.5
#> 5 Species 150 NA NA NA NA NA NA NA NA NA
#> 6 Species: setosa 50 33.33333 NA NA NA NA NA NA NA NA
#> 7 Species: versicolor 50 33.33333 NA NA NA NA NA NA NA NA
#> 8 Species: virginica 50 33.33333 NA NA NA NA NA NA NA NA
# Does the same as stats_auto here
iris %>%
desctable(stats = list("N" = length,
"Mean" = ~ if (is.normal(.)) mean(.),
"sd" = ~ if (is.normal(.)) sd(.),
"Med" = stats::median,
"IQR" = ~ if(!is.factor(.)) IQR(.)))
#> Warning: desctable is deprecated and will be removed in 1.0.0.
#>
#> Please use the `desc_*` family of functions (`desc_table`, `desc_tests`, `desc_output`)
#> N Mean sd Med IQR
#> 1 Sepal.Length 150 NA NA 5.80 1.3
#> 2 Sepal.Width 150 3.057333 0.4358663 3.00 0.5
#> 3 Petal.Length 150 NA NA 4.35 3.5
#> 4 Petal.Width 150 NA NA 1.30 1.5
#> 5 Species 150 NA NA NA NA
#> 6 Species: setosa 50 NA NA NA NA
#> 7 Species: versicolor 50 NA NA NA NA
#> 8 Species: virginica 50 NA NA NA NA
# With labels
mtcars %>% desctable(labels = c(hp = "Horse Power",
cyl = "Cylinders",
mpg = "Miles per gallon"))
#> Warning: desctable is deprecated and will be removed in 1.0.0.
#>
#> Please use the `desc_*` family of functions (`desc_table`, `desc_tests`, `desc_output`)
#> Min Q1 Med Mean Q3 Max
#> 1 Miles per gallon 10.400 15.42500 19.200 20.090625 22.80 33.900
#> 2 Cylinders 4.000 4.00000 6.000 6.187500 8.00 8.000
#> 3 disp 71.100 120.82500 196.300 230.721875 326.00 472.000
#> 4 Horse Power 52.000 96.50000 123.000 146.687500 180.00 335.000
#> 5 drat 2.760 3.08000 3.695 3.596563 3.92 4.930
#> 6 wt 1.513 2.58125 3.325 3.217250 3.61 5.424
#> 7 qsec 14.500 16.89250 17.710 17.848750 18.90 22.900
#> 8 vs 0.000 0.00000 0.000 0.437500 1.00 1.000
#> 9 am 0.000 0.00000 0.000 0.406250 1.00 1.000
#> 10 gear 3.000 3.00000 4.000 3.687500 4.00 5.000
#> 11 carb 1.000 2.00000 2.000 2.812500 4.00 8.000
#> sd IQR
#> 1 6.0269481 7.37500
#> 2 1.7859216 4.00000
#> 3 123.9386938 205.17500
#> 4 68.5628685 83.50000
#> 5 0.5346787 0.84000
#> 6 0.9784574 1.02875
#> 7 1.7869432 2.00750
#> 8 0.5040161 1.00000
#> 9 0.4989909 1.00000
#> 10 0.7378041 1.00000
#> 11 1.6152000 2.00000
# With grouping on a factor
iris %>%
group_by(Species) %>%
desctable(stats = stats_default)
#> Warning: desctable is deprecated and will be removed in 1.0.0.
#>
#> Please use the `desc_*` family of functions (`desc_table`, `desc_tests`, `desc_output`)
#> Species: setosa (n=50) / N Species: setosa (n=50) / %
#> 1 Sepal.Length 50 NA
#> 2 Sepal.Width 50 NA
#> 3 Petal.Length 50 NA
#> 4 Petal.Width 50 NA
#> Species: setosa (n=50) / Mean Species: setosa (n=50) / sd
#> 1 5.006 0.3524897
#> 2 3.428 0.3790644
#> 3 NA NA
#> 4 NA NA
#> Species: setosa (n=50) / Med Species: setosa (n=50) / IQR
#> 1 5.0 0.400
#> 2 3.4 0.475
#> 3 1.5 0.175
#> 4 0.2 0.100
#> Species: versicolor (n=50) / N Species: versicolor (n=50) / %
#> 1 50 NA
#> 2 50 NA
#> 3 50 NA
#> 4 50 NA
#> Species: versicolor (n=50) / Mean Species: versicolor (n=50) / sd
#> 1 5.936 0.5161711
#> 2 2.770 0.3137983
#> 3 4.260 0.4699110
#> 4 NA NA
#> Species: versicolor (n=50) / Med Species: versicolor (n=50) / IQR
#> 1 5.90 0.700
#> 2 2.80 0.475
#> 3 4.35 0.600
#> 4 1.30 0.300
#> Species: virginica (n=50) / N Species: virginica (n=50) / %
#> 1 50 NA
#> 2 50 NA
#> 3 50 NA
#> 4 50 NA
#> Species: virginica (n=50) / Mean Species: virginica (n=50) / sd
#> 1 6.588 0.6358796
#> 2 2.974 0.3224966
#> 3 5.552 0.5518947
#> 4 NA NA
#> Species: virginica (n=50) / Med Species: virginica (n=50) / IQR tests / p
#> 1 6.50 0.675 8.918734e-22
#> 2 3.00 0.375 1.569282e-14
#> 3 5.55 0.775 4.803974e-29
#> 4 2.00 0.500 3.261796e-29
#> tests / test
#> 1 kruskal.test
#> 2 kruskal.test
#> 3 kruskal.test
#> 4 kruskal.test
# With nested grouping, on arbitrary variables
mtcars %>%
group_by(vs, cyl) %>%
desctable()
#> Warning: desctable is deprecated and will be removed in 1.0.0.
#>
#> Please use the `desc_*` family of functions (`desc_table`, `desc_tests`, `desc_output`)
#> Warning: cannot compute exact p-value with ties
#> Warning: cannot compute exact p-value with ties
#> Warning: cannot compute exact p-value with ties
#> Warning: cannot compute exact p-value with ties
#> Warning: cannot compute exact p-value with ties
#> Warning: cannot compute exact p-value with ties
#> Warning: cannot compute exact p-value with ties
#> Warning: cannot compute exact p-value with ties
#> Warning: cannot compute exact p-value with ties
#> vs: 0 (n=18) / cyl: 4 (n=1) / Min vs: 0 (n=18) / / Q1
#> 1 mpg 26.00 26.00
#> 2 disp 120.30 120.30
#> 3 hp 91.00 91.00
#> 4 drat 4.43 4.43
#> 5 wt 2.14 2.14
#> 6 qsec 16.70 16.70
#> 7 am 1.00 1.00
#> 8 gear 5.00 5.00
#> 9 carb 2.00 2.00
#> vs: 0 (n=18) / / Med vs: 0 (n=18) / / Mean vs: 0 (n=18) / / Q3
#> 1 26.00 26.00 26.00
#> 2 120.30 120.30 120.30
#> 3 91.00 91.00 91.00
#> 4 4.43 4.43 4.43
#> 5 2.14 2.14 2.14
#> 6 16.70 16.70 16.70
#> 7 1.00 1.00 1.00
#> 8 5.00 5.00 5.00
#> 9 2.00 2.00 2.00
#> vs: 0 (n=18) / / Max vs: 0 (n=18) / / sd vs: 0 (n=18) / / IQR
#> 1 26.00 NA 0
#> 2 120.30 NA 0
#> 3 91.00 NA 0
#> 4 4.43 NA 0
#> 5 2.14 NA 0
#> 6 16.70 NA 0
#> 7 1.00 NA 0
#> 8 5.00 NA 0
#> 9 2.00 NA 0
#> vs: 0 (n=18) / cyl: 6 (n=3) / Min vs: 0 (n=18) / / Q1 vs: 0 (n=18) / / Med
#> 1 19.70 20.350 21.00
#> 2 145.00 152.500 160.00
#> 3 110.00 110.000 110.00
#> 4 3.62 3.760 3.90
#> 5 2.62 2.695 2.77
#> 6 15.50 15.980 16.46
#> 7 1.00 1.000 1.00
#> 8 4.00 4.000 4.00
#> 9 4.00 4.000 4.00
#> vs: 0 (n=18) / / Mean vs: 0 (n=18) / / Q3 vs: 0 (n=18) / / Max
#> 1 20.566667 21.0000 21.000
#> 2 155.000000 160.0000 160.000
#> 3 131.666667 142.5000 175.000
#> 4 3.806667 3.9000 3.900
#> 5 2.755000 2.8225 2.875
#> 6 16.326667 16.7400 17.020
#> 7 1.000000 1.0000 1.000
#> 8 4.333333 4.5000 5.000
#> 9 4.666667 5.0000 6.000
#> vs: 0 (n=18) / / sd vs: 0 (n=18) / / IQR vs: 0 (n=18) / cyl: 8 (n=14) / Min
#> 1 0.7505553 0.6500 10.40
#> 2 8.6602540 7.5000 275.80
#> 3 37.5277675 32.5000 150.00
#> 4 0.1616581 0.1400 2.76
#> 5 0.1281601 0.1275 3.17
#> 6 0.7687219 0.7600 14.50
#> 7 0.0000000 0.0000 0.00
#> 8 0.5773503 0.5000 3.00
#> 9 1.1547005 1.0000 2.00
#> vs: 0 (n=18) / / Q1 vs: 0 (n=18) / / Med vs: 0 (n=18) / / Mean
#> 1 14.4000 15.200 15.1000000
#> 2 301.7500 350.500 353.1000000
#> 3 176.2500 192.500 209.2142857
#> 4 3.0700 3.115 3.2292857
#> 5 3.5325 3.755 3.9992143
#> 6 16.0975 17.175 16.7721429
#> 7 0.0000 0.000 0.1428571
#> 8 3.0000 3.000 3.2857143
#> 9 2.2500 3.500 3.5000000
#> vs: 0 (n=18) / / Q3 vs: 0 (n=18) / / Max vs: 0 (n=18) / / sd
#> 1 16.25000 19.200 2.5600481
#> 2 390.00000 472.000 67.7713236
#> 3 241.25000 335.000 50.9768855
#> 4 3.22500 4.220 0.3723618
#> 5 4.01375 5.424 0.7594047
#> 6 17.55500 18.000 1.1960138
#> 7 0.00000 1.000 0.3631365
#> 8 3.00000 5.000 0.7262730
#> 9 4.00000 8.000 1.5566236
#> vs: 0 (n=18) / / IQR vs: 0 (n=18) / tests / p vs: 0 (n=18) / / test
#> 1 1.85000 0.01124786 kruskal.test
#> 2 88.25000 0.01109171 kruskal.test
#> 3 65.00000 0.02510507 kruskal.test
#> 4 0.15500 0.03286104 kruskal.test
#> 5 0.48125 0.01135263 kruskal.test
#> 6 1.45750 0.47244498 kruskal.test
#> 7 0.00000 0.00777266 kruskal.test
#> 8 0.00000 0.01810659 kruskal.test
#> 9 1.75000 0.12211556 kruskal.test
#> vs: 1 (n=14) / cyl: 4 (n=10) / Min vs: 1 (n=14) / / Q1 vs: 1 (n=14) / / Med
#> 1 21.400 22.8000 25.850
#> 2 71.100 78.7750 101.550
#> 3 52.000 65.2500 79.500
#> 4 3.690 3.7900 4.000
#> 5 1.513 1.8600 2.260
#> 6 16.900 18.6025 19.185
#> 7 0.000 0.2500 1.000
#> 8 3.000 4.0000 4.000
#> 9 1.000 1.0000 1.500
#> vs: 1 (n=14) / / Mean vs: 1 (n=14) / / Q3 vs: 1 (n=14) / / Max
#> 1 26.7300 30.40000 33.90
#> 2 103.6200 120.77500 146.70
#> 3 81.8000 96.50000 113.00
#> 4 4.0350 4.10250 4.93
#> 5 2.3003 2.70125 3.19
#> 6 19.3810 19.97500 22.90
#> 7 0.7000 1.00000 1.00
#> 8 4.0000 4.00000 5.00
#> 9 1.5000 2.00000 2.00
#> vs: 1 (n=14) / / sd vs: 1 (n=14) / / IQR vs: 1 (n=14) / cyl: 6 (n=4) / Min
#> 1 4.7481107 7.60000 17.800
#> 2 27.8246414 42.00000 167.600
#> 3 21.8723570 31.25000 105.000
#> 4 0.3642115 0.31250 2.760
#> 5 0.5982073 0.84125 3.215
#> 6 1.5553453 1.37250 18.300
#> 7 0.4830459 0.75000 0.000
#> 8 0.4714045 0.00000 3.000
#> 9 0.5270463 1.00000 1.000
#> vs: 1 (n=14) / / Q1 vs: 1 (n=14) / / Med vs: 1 (n=14) / / Mean
#> 1 18.02500 18.65 19.12500
#> 2 167.60000 196.30 204.55000
#> 3 108.75000 116.50 115.25000
#> 4 3.00000 3.50 3.42000
#> 5 3.38375 3.44 3.38875
#> 6 18.75000 19.17 19.21500
#> 7 0.00000 0.00 0.00000
#> 8 3.00000 3.50 3.50000
#> 9 1.00000 2.50 2.50000
#> vs: 1 (n=14) / / Q3 vs: 1 (n=14) / / Max vs: 1 (n=14) / / sd
#> 1 19.750 21.40 1.6317169
#> 2 233.250 258.00 44.7426344
#> 3 123.000 123.00 9.1787799
#> 4 3.920 3.92 0.5919459
#> 5 3.445 3.46 0.1162164
#> 6 19.635 20.22 0.8159044
#> 7 0.000 0.00 0.0000000
#> 8 4.000 4.00 0.5773503
#> 9 4.000 4.00 1.7320508
#> vs: 1 (n=14) / / IQR vs: 1 (n=14) / tests / p vs: 1 (n=14) / / test
#> 1 1.72500 0.007019704 wilcox.test
#> 2 65.65000 0.005766846 wilcox.test
#> 3 14.25000 0.019356258 wilcox.test
#> 4 0.92000 0.135397657 wilcox.test
#> 5 0.06125 0.005766846 wilcox.test
#> 6 0.88500 1.000000000 wilcox.test
#> 7 0.00000 0.027884340 wilcox.test
#> 8 1.00000 0.129468688 wilcox.test
#> 9 3.00000 0.485044622 wilcox.test
# With grouping on a condition, and choice of tests
iris %>%
group_by(Petal.Length > 5) %>%
desctable(tests = list(.auto = tests_auto, Species = ~chisq.test))
#> Warning: desctable is deprecated and will be removed in 1.0.0.
#>
#> Please use the `desc_*` family of functions (`desc_table`, `desc_tests`, `desc_output`)
#> Petal.Length > 5: FALSE (n=108) / N
#> 1 Sepal.Length 108
#> 2 Sepal.Width 108
#> 3 Petal.Length 108
#> 4 Petal.Width 108
#> 5 Species 108
#> 6 Species: setosa 50
#> 7 Species: versicolor 49
#> 8 Species: virginica 9
#> Petal.Length > 5: FALSE (n=108) / % Petal.Length > 5: FALSE (n=108) / Min
#> 1 NA 4.3
#> 2 NA 2.0
#> 3 NA 1.0
#> 4 NA 0.1
#> 5 NA NA
#> 6 46.296296 NA
#> 7 45.370370 NA
#> 8 8.333333 NA
#> Petal.Length > 5: FALSE (n=108) / Q1 Petal.Length > 5: FALSE (n=108) / Med
#> 1 5.0 5.5
#> 2 2.8 3.0
#> 3 1.5 3.5
#> 4 0.2 1.0
#> 5 NA NA
#> 6 NA NA
#> 7 NA NA
#> 8 NA NA
#> Petal.Length > 5: FALSE (n=108) / Mean Petal.Length > 5: FALSE (n=108) / Q3
#> 1 5.5018519 6.0
#> 2 3.0666667 3.4
#> 3 3.0074074 4.5
#> 4 0.8638889 1.4
#> 5 NA NA
#> 6 NA NA
#> 7 NA NA
#> 8 NA NA
#> Petal.Length > 5: FALSE (n=108) / Max Petal.Length > 5: FALSE (n=108) / sd
#> 1 7.0 0.6386290
#> 2 4.4 0.4800701
#> 3 5.0 1.4885673
#> 4 2.0 0.6110292
#> 5 NA NA
#> 6 NA NA
#> 7 NA NA
#> 8 NA NA
#> Petal.Length > 5: FALSE (n=108) / IQR Petal.Length > 5: TRUE (n=42) / N
#> 1 1.0 42
#> 2 0.6 42
#> 3 3.0 42
#> 4 1.2 42
#> 5 NA 42
#> 6 NA 0
#> 7 NA 1
#> 8 NA 41
#> Petal.Length > 5: TRUE (n=42) / % Petal.Length > 5: TRUE (n=42) / Min
#> 1 NA 5.8
#> 2 NA 2.5
#> 3 NA 5.1
#> 4 NA 1.4
#> 5 NA NA
#> 6 0.000000 NA
#> 7 2.380952 NA
#> 8 97.619048 NA
#> Petal.Length > 5: TRUE (n=42) / Q1 Petal.Length > 5: TRUE (n=42) / Med
#> 1 6.325 6.7
#> 2 2.800 3.0
#> 3 5.300 5.6
#> 4 1.825 2.1
#> 5 NA NA
#> 6 NA NA
#> 7 NA NA
#> 8 NA NA
#> Petal.Length > 5: TRUE (n=42) / Mean Petal.Length > 5: TRUE (n=42) / Q3
#> 1 6.721429 7.175
#> 2 3.033333 3.200
#> 3 5.688095 5.975
#> 4 2.061905 2.300
#> 5 NA NA
#> 6 NA NA
#> 7 NA NA
#> 8 NA NA
#> Petal.Length > 5: TRUE (n=42) / Max Petal.Length > 5: TRUE (n=42) / sd
#> 1 7.9 0.5748958
#> 2 3.8 0.2968671
#> 3 6.9 0.4919857
#> 4 2.5 0.2802023
#> 5 NA NA
#> 6 NA NA
#> 7 NA NA
#> 8 NA NA
#> Petal.Length > 5: TRUE (n=42) / IQR tests / p tests / test
#> 1 0.850 1.553676e-15 wilcox.test
#> 2 0.400 6.927432e-01 wilcox.test
#> 3 0.675 2.076978e-21 wilcox.test
#> 4 0.475 1.577769e-19 wilcox.test
#> 5 NA 2.707889e-24 chisq.test
#> 6 NA NA <NA>
#> 7 NA NA <NA>
#> 8 NA NA <NA>