Fast (Weighted) Cross Tabulation
qtab.RdA versatile and computationally more efficient replacement for table. Notably, it also supports tabulations with frequency weights, and computation of a statistic over combinations of variables.
Usage
qtab(..., w = NULL, wFUN = NULL, wFUN.args = NULL,
dnn = "auto", sort = .op[["sort"]], na.exclude = TRUE,
drop = FALSE, method = "auto")
qtable(...) # Long-form. Use set_collapse(mask = "table") to replace table()Arguments
- ...
atomic vectors or factors spanning the table dimensions, (optionally) with tags for the dimension names, or a data frame / list of these. See Examples.
- w
a single vector to aggregate over the table dimensions e.g. a vector of frequency weights.
- wFUN
a function used to aggregate
wover the table dimensions. The defaultNULLcomputes the sum of the non-missing weights via an optimized internal algorithm. Fast Statistical Functions also receive vectorized execution.- wFUN.args
a list of (optional) further arguments passed to
wFUN. See Examples.- dnn
the names of the table dimensions. Either passed directly as a character vector or list (internally
unlist'ed), a function applied to the...list (e.g.names, orvlabels), or one of the following options:"auto"constructs names based on the...arguments, or callsnamesif a single list is passed as input."namlab"does the same as"auto", but also callsvlabelson the list and appends the names by the variable labels.
dnn = NULLwill return a table without dimension names.- sort, na.exclude, drop, method
arguments passed down to
qF:sort = FALSEorders table dimensions in first-appearance order of items in the data (can be more efficient if vectors are not factors already). Note that for factors this option will both recast levels in first-appearance order and drop unused levels.na.exclude = FALSEincludesNA's in the table (equivalent totable'suseNA = "ifany").drop = TRUEremoves any unused factor levels (= zero frequency rows or columns).method %in% c("radix", "hash")provides additional control over the algorithm used to convert atomic vectors to factors.
Examples
## Basic use
qtab(iris$Species)
#> iris$Species
#> setosa versicolor virginica
#> 50 50 50
with(mtcars, qtab(vs, am))
#> am
#> vs 0 1
#> 0 12 6
#> 1 7 7
qtab(mtcars[.c(vs, am)])
#> am
#> vs 0 1
#> 0 12 6
#> 1 7 7
library(magrittr)
iris %$% qtab(Sepal.Length > mean(Sepal.Length), Species)
#> Species
#> Sepal.Length > mean(Sepal.Length) setosa versicolor virginica
#> FALSE 50 24 6
#> TRUE 0 26 44
iris %$% qtab(AMSL = Sepal.Length > mean(Sepal.Length), Species)
#> Species
#> AMSL setosa versicolor virginica
#> FALSE 50 24 6
#> TRUE 0 26 44
## World after 2015
wlda15 <- wlddev |> fsubset(year >= 2015) |> collap(~ iso3c)
# Regions and income levels (country frequency)
wlda15 %$% qtab(region, income)
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 13 0 13
#> Europe & Central Asia 37 1 4
#> Latin America & Caribbean 17 1 4
#> Middle East & North Africa 8 2 5
#> North America 3 0 0
#> South Asia 0 2 4
#> Sub-Saharan Africa 1 24 17
#> income
#> region Upper middle income
#> East Asia & Pacific 10
#> Europe & Central Asia 16
#> Latin America & Caribbean 20
#> Middle East & North Africa 6
#> North America 0
#> South Asia 2
#> Sub-Saharan Africa 6
wlda15 %$% qtab(region, income, dnn = vlabels)
#> Income Level
#> Region High income Low income Lower middle income
#> East Asia & Pacific 13 0 13
#> Europe & Central Asia 37 1 4
#> Latin America & Caribbean 17 1 4
#> Middle East & North Africa 8 2 5
#> North America 3 0 0
#> South Asia 0 2 4
#> Sub-Saharan Africa 1 24 17
#> Income Level
#> Region Upper middle income
#> East Asia & Pacific 10
#> Europe & Central Asia 16
#> Latin America & Caribbean 20
#> Middle East & North Africa 6
#> North America 0
#> South Asia 2
#> Sub-Saharan Africa 6
wlda15 %$% qtab(region, income, dnn = "namlab")
#> income: Income Level
#> region: Region High income Low income Lower middle income
#> East Asia & Pacific 13 0 13
#> Europe & Central Asia 37 1 4
#> Latin America & Caribbean 17 1 4
#> Middle East & North Africa 8 2 5
#> North America 3 0 0
#> South Asia 0 2 4
#> Sub-Saharan Africa 1 24 17
#> income: Income Level
#> region: Region Upper middle income
#> East Asia & Pacific 10
#> Europe & Central Asia 16
#> Latin America & Caribbean 20
#> Middle East & North Africa 6
#> North America 0
#> South Asia 2
#> Sub-Saharan Africa 6
# Population (millions)
wlda15 %$% qtab(region, income, w = POP) |> divide_by(1e6)
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 222.3763078 0.0000000 554.5787740
#> Europe & Central Asia 499.6178162 8.8839460 86.1694676
#> Latin America & Caribbean 32.1055806 10.9808262 33.3947544
#> Middle East & North Africa 64.6289084 45.1314580 148.8518602
#> North America 361.3653800 0.0000000 0.0000000
#> South Asia 0.0000000 63.9814276 1706.8229478
#> Sub-Saharan Africa 0.0956652 530.2676994 450.7138318
#> income
#> region Upper middle income
#> East Asia & Pacific 1486.9566064
#> Europe & Central Asia 319.5918390
#> Latin America & Caribbean 557.9670800
#> Middle East & North Africa 182.6471952
#> North America 0.0000000
#> South Asia 21.9126958
#> Sub-Saharan Africa 66.1922298
# Life expectancy (years)
wlda15 %$% qtab(region, income, w = LIFEEX, wFUN = fmean)
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 81.12986 69.32372
#> Europe & Central Asia 80.39692 70.63140 71.44826
#> Latin America & Caribbean 77.57964 63.26640 73.18650
#> Middle East & North Africa 78.38796 68.61450 72.73452
#> North America 80.67948
#> South Asia 67.13770 69.81250
#> Sub-Saharan Africa 73.93805 60.63336 62.37481
#> income
#> region Upper middle income
#> East Asia & Pacific 73.32000
#> Europe & Central Asia 74.39151
#> Latin America & Caribbean 74.69349
#> Middle East & North Africa 74.78043
#> North America
#> South Asia 77.48130
#> Sub-Saharan Africa 65.54593
# Life expectancy (years), weighted by population
wlda15 %$% qtab(region, income, w = LIFEEX, wFUN = fmean,
wFUN.args = list(w = POP))
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 83.47390 71.19076
#> Europe & Central Asia 81.31296 70.63140 71.46540
#> Latin America & Caribbean 78.99973 63.26640 73.01552
#> Middle East & North Africa 76.84332 68.02666 73.12402
#> North America 78.97586
#> South Asia 66.73455 69.15123
#> Sub-Saharan Africa 73.93805 62.01679 58.97093
#> income
#> region Upper middle income
#> East Asia & Pacific 76.43505
#> Europe & Central Asia 73.96995
#> Latin America & Caribbean 75.44067
#> Middle East & North Africa 74.93824
#> North America
#> South Asia 76.68486
#> Sub-Saharan Africa 63.79719
# GDP per capita (constant 2010 US$): median
wlda15 %$% qtab(region, income, w = PCGDP, wFUN = fmedian,
wFUN.args = list(na.rm = TRUE))
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 37527.1689 1863.8773
#> Europe & Central Asia 44340.1356 1026.2292 2654.3445
#> Latin America & Caribbean 16514.1173 1266.0910 2323.2438
#> Middle East & North Africa 31003.0939 677.4104 3134.9231
#> North America 53790.1303
#> South Asia 677.2265 1558.9751
#> Sub-Saharan Africa 14057.6452 635.8458 1657.2758
#> income
#> region Upper middle income
#> East Asia & Pacific 5364.0199
#> Europe & Central Asia 6552.2411
#> Latin America & Caribbean 6824.2245
#> Middle East & North Africa 5920.2114
#> North America
#> South Asia 5883.8904
#> Sub-Saharan Africa 8578.7116
# GDP per capita (constant 2010 US$): median, weighted by population
wlda15 %$% qtab(region, income, w = PCGDP, wFUN = fmedian,
wFUN.args = list(w = POP))
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 48194.0408 3038.6464
#> Europe & Central Asia 42912.4250 1026.2292 3010.4318
#> Latin America & Caribbean 14888.7707 1266.0910 2483.3969
#> Middle East & North Africa 20945.0874 677.4104 2840.9586
#> North America 53790.1303
#> South Asia 570.9192 1970.4413
#> Sub-Saharan Africa 14057.6452 543.6803 2430.1368
#> income
#> region Upper middle income
#> East Asia & Pacific 7360.8953
#> Europe & Central Asia 11623.6264
#> Latin America & Caribbean 10231.2777
#> Middle East & North Africa 6255.4776
#> North America
#> South Asia 3846.9157
#> Sub-Saharan Africa 7457.1928
# Including OECD membership
tab <- wlda15 %$% qtab(region, income, OECD)
tab
#> , , OECD = FALSE
#>
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 9 0 13
#> Europe & Central Asia 11 1 4
#> Latin America & Caribbean 16 1 4
#> Middle East & North Africa 7 2 5
#> North America 1 0 0
#> South Asia 0 2 4
#> Sub-Saharan Africa 1 24 17
#> income
#> region Upper middle income
#> East Asia & Pacific 10
#> Europe & Central Asia 15
#> Latin America & Caribbean 19
#> Middle East & North Africa 6
#> North America 0
#> South Asia 2
#> Sub-Saharan Africa 6
#>
#> , , OECD = TRUE
#>
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 4 0 0
#> Europe & Central Asia 26 0 0
#> Latin America & Caribbean 1 0 0
#> Middle East & North Africa 1 0 0
#> North America 2 0 0
#> South Asia 0 0 0
#> Sub-Saharan Africa 0 0 0
#> income
#> region Upper middle income
#> East Asia & Pacific 0
#> Europe & Central Asia 1
#> Latin America & Caribbean 1
#> Middle East & North Africa 0
#> North America 0
#> South Asia 0
#> Sub-Saharan Africa 0
#>
# Various 'table' methods
tab |> addmargins()
#> , , OECD = FALSE
#>
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 9 0 13
#> Europe & Central Asia 11 1 4
#> Latin America & Caribbean 16 1 4
#> Middle East & North Africa 7 2 5
#> North America 1 0 0
#> South Asia 0 2 4
#> Sub-Saharan Africa 1 24 17
#> Sum 45 30 47
#> income
#> region Upper middle income Sum
#> East Asia & Pacific 10 32
#> Europe & Central Asia 15 31
#> Latin America & Caribbean 19 40
#> Middle East & North Africa 6 20
#> North America 0 1
#> South Asia 2 8
#> Sub-Saharan Africa 6 48
#> Sum 58 180
#>
#> , , OECD = TRUE
#>
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 4 0 0
#> Europe & Central Asia 26 0 0
#> Latin America & Caribbean 1 0 0
#> Middle East & North Africa 1 0 0
#> North America 2 0 0
#> South Asia 0 0 0
#> income
#> region Upper middle income Sum
#> East Asia & Pacific 0 4
#> Europe & Central Asia 1 27
#> Latin America & Caribbean 1 2
#> Middle East & North Africa 0 1
#> North America 0 2
#> South Asia 0 0
#>
#> [ reached getOption("max.print") -- omitted 2 row(s) and 1 matrix slice(s) ]
tab |> marginSums(margin = c("region", "income"))
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 13 0 13
#> Europe & Central Asia 37 1 4
#> Latin America & Caribbean 17 1 4
#> Middle East & North Africa 8 2 5
#> North America 3 0 0
#> South Asia 0 2 4
#> Sub-Saharan Africa 1 24 17
#> income
#> region Upper middle income
#> East Asia & Pacific 10
#> Europe & Central Asia 16
#> Latin America & Caribbean 20
#> Middle East & North Africa 6
#> North America 0
#> South Asia 2
#> Sub-Saharan Africa 6
tab |> proportions()
#> , , OECD = FALSE
#>
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 0.041666667 0.000000000 0.060185185
#> Europe & Central Asia 0.050925926 0.004629630 0.018518519
#> Latin America & Caribbean 0.074074074 0.004629630 0.018518519
#> Middle East & North Africa 0.032407407 0.009259259 0.023148148
#> North America 0.004629630 0.000000000 0.000000000
#> South Asia 0.000000000 0.009259259 0.018518519
#> Sub-Saharan Africa 0.004629630 0.111111111 0.078703704
#> income
#> region Upper middle income
#> East Asia & Pacific 0.046296296
#> Europe & Central Asia 0.069444444
#> Latin America & Caribbean 0.087962963
#> Middle East & North Africa 0.027777778
#> North America 0.000000000
#> South Asia 0.009259259
#> Sub-Saharan Africa 0.027777778
#>
#> , , OECD = TRUE
#>
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 0.018518519 0.000000000 0.000000000
#> Europe & Central Asia 0.120370370 0.000000000 0.000000000
#> Latin America & Caribbean 0.004629630 0.000000000 0.000000000
#> Middle East & North Africa 0.004629630 0.000000000 0.000000000
#> North America 0.009259259 0.000000000 0.000000000
#> South Asia 0.000000000 0.000000000 0.000000000
#> Sub-Saharan Africa 0.000000000 0.000000000 0.000000000
#> income
#> region Upper middle income
#> East Asia & Pacific 0.000000000
#> Europe & Central Asia 0.004629630
#> Latin America & Caribbean 0.004629630
#> Middle East & North Africa 0.000000000
#> North America 0.000000000
#> South Asia 0.000000000
#> Sub-Saharan Africa 0.000000000
#>
tab |> proportions(margin = "income")
#> , , OECD = FALSE
#>
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 0.11392405 0.00000000 0.27659574
#> Europe & Central Asia 0.13924051 0.03333333 0.08510638
#> Latin America & Caribbean 0.20253165 0.03333333 0.08510638
#> Middle East & North Africa 0.08860759 0.06666667 0.10638298
#> North America 0.01265823 0.00000000 0.00000000
#> South Asia 0.00000000 0.06666667 0.08510638
#> Sub-Saharan Africa 0.01265823 0.80000000 0.36170213
#> income
#> region Upper middle income
#> East Asia & Pacific 0.16666667
#> Europe & Central Asia 0.25000000
#> Latin America & Caribbean 0.31666667
#> Middle East & North Africa 0.10000000
#> North America 0.00000000
#> South Asia 0.03333333
#> Sub-Saharan Africa 0.10000000
#>
#> , , OECD = TRUE
#>
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 0.05063291 0.00000000 0.00000000
#> Europe & Central Asia 0.32911392 0.00000000 0.00000000
#> Latin America & Caribbean 0.01265823 0.00000000 0.00000000
#> Middle East & North Africa 0.01265823 0.00000000 0.00000000
#> North America 0.02531646 0.00000000 0.00000000
#> South Asia 0.00000000 0.00000000 0.00000000
#> Sub-Saharan Africa 0.00000000 0.00000000 0.00000000
#> income
#> region Upper middle income
#> East Asia & Pacific 0.00000000
#> Europe & Central Asia 0.01666667
#> Latin America & Caribbean 0.01666667
#> Middle East & North Africa 0.00000000
#> North America 0.00000000
#> South Asia 0.00000000
#> Sub-Saharan Africa 0.00000000
#>
as.data.frame(tab) |> head(10)
#> region income OECD Freq
#> 1 East Asia & Pacific High income FALSE 9
#> 2 Europe & Central Asia High income FALSE 11
#> 3 Latin America & Caribbean High income FALSE 16
#> 4 Middle East & North Africa High income FALSE 7
#> 5 North America High income FALSE 1
#> 6 South Asia High income FALSE 0
#> 7 Sub-Saharan Africa High income FALSE 1
#> 8 East Asia & Pacific Low income FALSE 0
#> 9 Europe & Central Asia Low income FALSE 1
#> 10 Latin America & Caribbean Low income FALSE 1
ftable(tab, row.vars = c("region", "OECD"))
#> income High income Low income Lower middle income Upper middle income
#> region OECD
#> East Asia & Pacific FALSE 9 0 13 10
#> TRUE 4 0 0 0
#> Europe & Central Asia FALSE 11 1 4 15
#> TRUE 26 0 0 1
#> Latin America & Caribbean FALSE 16 1 4 19
#> TRUE 1 0 0 1
#> Middle East & North Africa FALSE 7 2 5 6
#> TRUE 1 0 0 0
#> North America FALSE 1 0 0 0
#> TRUE 2 0 0 0
#> South Asia FALSE 0 2 4 2
#> TRUE 0 0 0 0
#> Sub-Saharan Africa FALSE 1 24 17 6
#> TRUE 0 0 0 0
# Other options
tab |> fsum(TRA = "%") # Percentage table (on a matrix use fsum.default)
#> , , OECD = FALSE
#>
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 4.1666667 0.0000000 6.0185185
#> Europe & Central Asia 5.0925926 0.4629630 1.8518519
#> Latin America & Caribbean 7.4074074 0.4629630 1.8518519
#> Middle East & North Africa 3.2407407 0.9259259 2.3148148
#> North America 0.4629630 0.0000000 0.0000000
#> South Asia 0.0000000 0.9259259 1.8518519
#> Sub-Saharan Africa 0.4629630 11.1111111 7.8703704
#> income
#> region Upper middle income
#> East Asia & Pacific 4.6296296
#> Europe & Central Asia 6.9444444
#> Latin America & Caribbean 8.7962963
#> Middle East & North Africa 2.7777778
#> North America 0.0000000
#> South Asia 0.9259259
#> Sub-Saharan Africa 2.7777778
#>
#> , , OECD = TRUE
#>
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 1.8518519 0.0000000 0.0000000
#> Europe & Central Asia 12.0370370 0.0000000 0.0000000
#> Latin America & Caribbean 0.4629630 0.0000000 0.0000000
#> Middle East & North Africa 0.4629630 0.0000000 0.0000000
#> North America 0.9259259 0.0000000 0.0000000
#> South Asia 0.0000000 0.0000000 0.0000000
#> Sub-Saharan Africa 0.0000000 0.0000000 0.0000000
#> income
#> region Upper middle income
#> East Asia & Pacific 0.0000000
#> Europe & Central Asia 0.4629630
#> Latin America & Caribbean 0.4629630
#> Middle East & North Africa 0.0000000
#> North America 0.0000000
#> South Asia 0.0000000
#> Sub-Saharan Africa 0.0000000
#>
tab %/=% (sum(tab)/100) # Another way (division by reference, preserves integers)
tab
#> , , OECD = FALSE
#>
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 4 0 6
#> Europe & Central Asia 5 0 2
#> Latin America & Caribbean 8 0 2
#> Middle East & North Africa 3 1 2
#> North America 0 0 0
#> South Asia 0 1 2
#> Sub-Saharan Africa 0 12 8
#> income
#> region Upper middle income
#> East Asia & Pacific 5
#> Europe & Central Asia 7
#> Latin America & Caribbean 9
#> Middle East & North Africa 3
#> North America 0
#> South Asia 1
#> Sub-Saharan Africa 3
#>
#> , , OECD = TRUE
#>
#> income
#> region High income Low income Lower middle income
#> East Asia & Pacific 2 0 0
#> Europe & Central Asia 13 0 0
#> Latin America & Caribbean 0 0 0
#> Middle East & North Africa 0 0 0
#> North America 1 0 0
#> South Asia 0 0 0
#> Sub-Saharan Africa 0 0 0
#> income
#> region Upper middle income
#> East Asia & Pacific 0
#> Europe & Central Asia 0
#> Latin America & Caribbean 0
#> Middle East & North Africa 0
#> North America 0
#> South Asia 0
#> Sub-Saharan Africa 0
#>
rm(tab, wlda15)