Group data count and calculate proportions by column.
Usage
count_table(
df,
rowGroupVars,
colGroupVars,
numExpr = dplyr::n(),
denomExpr = dplyr::n(),
totalExpr = dplyr::n(),
subgroupLevel = length(rowGroupVars),
glue = list(`Count [%] (N={sprintf("%d",N)})` =
"{sprintf(\"%d/%d [%1.1f%%]\", x, n, mean*100)}"),
label_fn = label_extractor(df),
font_size = getOption("tableone.font_size", 8),
font = getOption("tableone.font", "Arial")
)Arguments
- df
a dataframe of linelist items
- rowGroupVars
the rows of the table. The last one of these is the denominator grouping
- colGroupVars
the column groupings of the table.
- numExpr
defines how the numerator is defined in the context of the column and row groups (e.g. dplyr::n())
- denomExpr
defines how the numerator is defined in the context of the column and row (ungrouped one level)
- totalExpr
defines how the column level total is defined
- subgroupLevel
defines how the numerator grouping is defined in terms of the row groupings
- glue
a named list of column value specifications.
- label_fn
(optional) a function for mapping a co-variate column name to printable label. This is by default a no-operation and the output table will contain the dataframe column names as labels. A simple alternative would be some form of dplyr::case_when lookup, or a string function such as stringr::str_to_sentence. (N.b. this function must be vectorised). Any value provided here will be overridden by the
options("tableone.labeller" = my_label_fn)which allows global setting of the labeller.- font_size
(optional) the font size for the table in points
- font
(optional) the font family for the table (which will be matched to closest on your system)
Examples
diamonds %>% count_table(dplyr::vars(cut,clarity), dplyr::vars(color), subgroupLevel = 1)
#> ─────────────────────────────────────────────────────────────────────────────
#> D E F G H
#> cut clarity Count Count Count Count Count
#> [%] [%] [%] [%] [%]
#> (N=6775) (N=9797) (N=9542) (N=11292 (N=8304)
#> )
#> ─────────────────────────────────────────────────────────────────────────────
#> Fair I1 4/163 9/224 35/312 53/314 52/303
#> [2.5%] [4.0%] [11.2%] [16.9%] [17.2%]
#> SI2 56/163 78/224 89/312 80/314 91/303
#> [34.4%] [34.8%] [28.5%] [25.5%] [30.0%]
#> SI1 58/163 65/224 83/312 69/314 75/303
#> [35.6%] [29.0%] [26.6%] [22.0%] [24.8%]
#> VS2 25/163 42/224 53/312 45/314 41/303
#> [15.3%] [18.8%] [17.0%] [14.3%] [13.5%]
#> VS1 5/163 14/224 33/312 45/314 32/303
#> [3.1%] [6.2%] [10.6%] [14.3%] [10.6%]
#> VVS2 9/163 13/224 10/312 17/314 11/303
#> [5.5%] [5.8%] [3.2%] [5.4%] [3.6%]
#> VVS1 3/163 3/224 5/312 3/314 1/303
#> [1.8%] [1.3%] [1.6%] [1.0%] [0.3%]
#> IF 3/163 — 4/312 2/314 —
#> [1.8%] [1.3%] [0.6%]
#> ─────────────────────────────────────────────────────────────────────────────
#> Good I1 8/662 23/933 19/909 19/871 14/702
#> [1.2%] [2.5%] [2.1%] [2.2%] [2.0%]
#> SI2 223/662 202/933 201/909 163/871 158/702
#> [33.7%] [21.7%] [22.1%] [18.7%] [22.5%]
#> SI1 237/662 355/933 273/909 207/871 235/702
#> [35.8%] [38.0%] [30.0%] [23.8%] [33.5%]
#> VS2 104/662 160/933 184/909 192/871 138/702
#> [15.7%] [17.1%] [20.2%] [22.0%] [19.7%]
#> VS1 43/662 89/933 132/909 152/871 77/702
#> [6.5%] [9.5%] [14.5%] [17.5%] [11.0%]
#> VVS2 25/662 52/933 50/909 75/871 45/702
#> [3.8%] [5.6%] [5.5%] [8.6%] [6.4%]
#> VVS1 13/662 43/933 35/909 41/871 31/702
#> [2.0%] [4.6%] [3.9%] [4.7%] [4.4%]
#> IF 9/662 9/933 15/909 22/871 4/702
#> [1.4%] [1.0%] [1.7%] [2.5%] [0.6%]
#> ─────────────────────────────────────────────────────────────────────────────
#> Very I1 5/1513 22/2400 13/2164 16/2299 12/1824
#> Good [0.3%] [0.9%] [0.6%] [0.7%] [0.7%]
#> SI2 314/1513 445/2400 343/2164 327/2299 343/1824
#> [20.8%] [18.5%] [15.9%] [14.2%] [18.8%]
#> SI1 494/1513 626/2400 559/2164 474/2299 547/1824
#> [32.7%] [26.1%] [25.8%] [20.6%] [30.0%]
#> VS2 309/1513 503/2400 466/2164 479/2299 376/1824
#> [20.4%] [21.0%] [21.5%] [20.8%] [20.6%]
#> VS1 175/1513 293/2400 293/2164 432/2299 257/1824
#> [11.6%] [12.2%] [13.5%] [18.8%] [14.1%]
#> VVS2 141/1513 298/2400 249/2164 302/2299 145/1824
#> [9.3%] [12.4%] [11.5%] [13.1%] [7.9%]
#> VVS1 52/1513 170/2400 174/2164 190/2299 115/1824
#> [3.4%] [7.1%] [8.0%] [8.3%] [6.3%]
#> IF 23/1513 43/2400 67/2164 79/2299 29/1824
#> [1.5%] [1.8%] [3.1%] [3.4%] [1.6%]
#> ─────────────────────────────────────────────────────────────────────────────
#> Premium I1 12/1603 30/2337 34/2331 46/2924 46/2360
#> [0.7%] [1.3%] [1.5%] [1.6%] [1.9%]
#> SI2 421/1603 519/2337 523/2331 492/2924 521/2360
#> [26.3%] [22.2%] [22.4%] [16.8%] [22.1%]
#> SI1 556/1603 614/2337 608/2331 566/2924 655/2360
#> [34.7%] [26.3%] [26.1%] [19.4%] [27.8%]
#> VS2 339/1603 629/2337 619/2331 721/2924 532/2360
#> [21.1%] [26.9%] [26.6%] [24.7%] [22.5%]
#> VS1 131/1603 292/2337 290/2331 566/2924 336/2360
#> [8.2%] [12.5%] [12.4%] [19.4%] [14.2%]
#> VVS2 94/1603 121/2337 146/2331 275/2924 118/2360
#> [5.9%] [5.2%] [6.3%] [9.4%] [5.0%]
#> VVS1 40/1603 105/2337 80/2331 171/2924 112/2360
#> [2.5%] [4.5%] [3.4%] [5.8%] [4.7%]
#> IF 10/1603 27/2337 31/2331 87/2924 40/2360
#> [0.6%] [1.2%] [1.3%] [3.0%] [1.7%]
#> ─────────────────────────────────────────────────────────────────────────────
#> Ideal I1 13/2834 18/3903 42/3826 16/4884 38/3115
#> [0.5%] [0.5%] [1.1%] [0.3%] [1.2%]
#> SI2 356/2834 469/3903 453/3826 486/4884 450/3115
#> [12.6%] [12.0%] [11.8%] [10.0%] [14.4%]
#> SI1 738/2834 766/3903 608/3826 660/4884 763/3115
#> [26.0%] [19.6%] [15.9%] [13.5%] [24.5%]
#> VS2 920/2834 1136/390 879/3826 910/4884 556/3115
#> [32.5%] 3 [23.0%] [18.6%] [17.8%]
#> [29.1%]
#> VS1 351/2834 593/3903 616/3826 953/4884 467/3115
#> [12.4%] [15.2%] [16.1%] [19.5%] [15.0%]
#> VVS2 284/2834 507/3903 520/3826 774/4884 289/3115
#> [10.0%] [13.0%] [13.6%] [15.8%] [9.3%]
#> VVS1 144/2834 335/3903 440/3826 594/4884 326/3115
#> [5.1%] [8.6%] [11.5%] [12.2%] [10.5%]
#> IF 28/2834 79/3903 268/3826 491/4884 226/3115
#> [1.0%] [2.0%] [7.0%] [10.1%] [7.3%]
#> ─────────────────────────────────────────────────────────────────────────────
#>
#> Column names: cut, clarity, 1, 2, 3, 4, 5, 6, 7
#>
#> 7/9 columns shown.