Functions for summarising data and counting observations in data frames.
These are SciViews::R versions of tidyverse functions with standard
evaluation and formula-based non-standard evaluation (ending with underscore
_). They work with data.frame, data.table, and tibbles.
Functions:
summarise_()/summarize_()- Compute summary statistics for groupsreframe_()- Similar to summarise but always returns ungrouped datacount_()- Count observations by grouptally_()- Count total observations (wrapper around count_)add_count_()- Add count column to data frameadd_tally_()- Add total count column to data frame
summarise_(
.data = (.),
...,
.by = NULL,
.groups = "drop_last",
.keep.group_vars = TRUE,
.cols = NULL
)
summarize_(
.data = (.),
...,
.by = NULL,
.groups = "drop_last",
.keep.group_vars = TRUE,
.cols = NULL
)
reframe_(
.data,
...,
.by = NULL,
.groups = "drop",
.keep.group_vars = TRUE,
.cols = NULL
)
count_(
.data = (.),
...,
wt = NULL,
name = "n",
sort = FALSE,
decreasing = TRUE,
.drop = TRUE,
add = FALSE
)
tally_(.data = (.), wt = NULL, name = "n", sort = FALSE, decreasing = TRUE)
add_count_(
.data = (.),
...,
wt = NULL,
name = "n",
sort = FALSE,
decreasing = TRUE,
.drop = TRUE
)
add_tally_(.data = (.), wt = NULL, name = "n", sort = FALSE, decreasing = TRUE)Arguments
- .data
A data frame (data.frame, data.table, or tibble)
- ...
For
summarise_()andreframe_(): name-value pairs of summary functions. Names are column names in the output; values are expressions to compute. Use formulas for non-standard evaluation (e.g.,~mean_mpg = mean(~mpg)). Forcount_()andadd_count_(): grouping variables specified as formulas (e.g.,~cyl,~gear) or character names. Can include named expressions to create new grouping variables before counting.- .by
Optional temporary grouping variables for per-group computations. Provide as formulas (e.g.,
~group_col) or character names. Groups are temporary and not preserved in the output. Cannot be used with grouped data frames.- .groups
Control grouping of the result. Options:
"drop_last"(default) - Drop the last grouping level"drop"- Remove all grouping"keep"- Keep all grouping levels"rowwise"- Not implemented Forreframe_(), only"drop"is allowed.
- .keep.group_vars
Logical. If
TRUE(default), keep grouping variables in the result.- .cols
Optional character vector of column names to operate on. Currently only
NULL(default) is implemented.- wt
For
count_(),tally_(),add_count_(), andadd_tally_(): frequency weights. Can beNULL(default, counts rows), a numeric vector, a column name as character, or a formula (e.g.,~weight_col).- name
Character string specifying the name of the count column created in the output. Default is
"n".- sort
Logical. If
TRUE, sort the result by the count column in decreasing order (or as specified bydecreasing). Default isFALSE.- decreasing
Logical. If
TRUE(default), sort counts in decreasing order whensort = TRUE.- .drop
Logical. If
TRUE(default), drop unused factor levels. Note:.drop = FALSEis not yet implemented incount_().- add
Logical. If
TRUE, add the count column to the original data frame instead of returning a summary. Default isFALSE.
Value
summarise_()returns a data frame with one row per group (or one row if ungrouped), containing the summary statistics. Grouping depends on.groups.reframe_()returns an ungrouped data frame (can have any number of rows per group).count_()returns a data frame with one row per unique combination of grouping variables, plus a count column.tally_()returns a data frame with one row per group showing the count.add_count_()returns the original data with an additional count column.add_tally_()returns the original data with an additional count column.
Note
The summarise_() function does not support n() as does dplyr::summarise().
You can use svBase::fn() instead, but then you must give a variable name as
argument. The svBase::fn() alternative can also be used in dplyr::summarise()
for homogeneous syntax between the two.
See also
Examples
library(svTidy)
data(mtcars)
# Basic summarise - single summary statistic
mtcars |> summarise_(mean_mpg = ~mean(mpg))
#> mean_mpg
#> 1 20.09062
# Multiple summary statistics
mtcars |>
summarise_(
mean_mpg = ~mean(mpg),
sd_mpg = ~sd(mpg),
max_hp = ~max(hp)
)
#> mean_mpg sd_mpg max_hp
#> 1 20.09062 6.026948 335
# Summarise by groups
mtcars |>
group_by_(~cyl) |>
summarise_(
mean_mpg = ~mean(mpg),
mean_hp = ~mean(hp)
)
#> cyl mean_mpg mean_hp
#> 1 4 26.66364 82.63636
#> 2 6 19.74286 122.28571
#> 3 8 15.10000 209.21429
# Use .by for temporary grouping
mtcars |>
summarise_(
mean_mpg = ~mean(mpg),
count = ~length(mpg),
.by = 'cyl'
)
#> cyl mean_mpg count
#> 1 6 19.74286 7
#> 2 4 26.66364 11
#> 3 8 15.10000 14
# Multiple grouping variables with .by
mtcars |>
summarise_(
mean_mpg = ~mean(mpg),
.by = c('cyl', 'gear')
)
#> cyl gear mean_mpg
#> 1 6 4 19.750
#> 2 4 4 26.925
#> 3 6 3 19.750
#> 4 8 3 15.050
#> 5 4 3 21.500
#> 6 4 5 28.200
#> 7 8 5 15.400
#> 8 6 5 19.700
# Control grouping of result
mtcars |>
group_by_(~cyl, ~gear) |>
summarise_(mean_mpg = ~mean(mpg), .groups = "drop")
#> cyl gear mean_mpg
#> 1 4 3 21.500
#> 2 4 4 26.925
#> 3 4 5 28.200
#> 4 6 3 19.750
#> 5 6 4 19.750
#> 6 6 5 19.700
#> 7 8 3 15.050
#> 8 8 5 15.400
mtcars |>
group_by_(~cyl, ~gear) |>
summarise_(mean_mpg = ~mean(mpg), .groups = "keep")
#> cyl gear mean_mpg
#> 1 4 3 21.500
#> 2 4 4 26.925
#> 3 4 5 28.200
#> 4 6 3 19.750
#> 5 6 4 19.750
#> 6 6 5 19.700
#> 7 8 3 15.050
#> 8 8 5 15.400
#>
#> Grouped by: cyl, gear [8 | 1 (0)]
# Using standard evaluation (ungrouped data only)
mtcars |> summarise_(mean_mpg = mean(mtcars$mpg))
#> mean_mpg
#> 1 20.09062
# reframe_() for summaries returning multiple rows per group
mtcars |>
group_by_(~cyl) |>
reframe_(quantile_mpg = ~quantile(mpg, c(0.25, 0.5, 0.75)))
#> cyl quantile_mpg
#> 1 4 22.80
#> 2 4 26.00
#> 3 4 30.40
#> 4 6 18.65
#> 5 6 19.70
#> 6 6 21.00
#> 7 8 14.40
#> 8 8 15.20
#> 9 8 16.25
# Count observations by group
mtcars |> count_(~cyl)
#> cyl n
#> 1 4 11
#> 2 6 7
#> 3 8 14
# Count by multiple variables
mtcars |> count_(~cyl, ~gear)
#> cyl gear n
#> 1 4 3 1
#> 2 4 4 8
#> 3 4 5 2
#> 4 6 3 2
#> 5 6 4 4
#> 6 6 5 1
#> 7 8 3 12
#> 8 8 5 2
# Count with sorting
mtcars |> count_(~cyl, sort = TRUE)
#> cyl n
#> 1 8 14
#> 2 4 11
#> 3 6 7
# Count in increasing order
mtcars |> count_(~cyl, sort = TRUE, decreasing = FALSE)
#> cyl n
#> 1 6 7
#> 2 4 11
#> 3 8 14
# Count with weights
mtcars |> count_(~cyl, wt = ~mpg)
#> cyl n
#> 1 4 293.3
#> 2 6 138.2
#> 3 8 211.4
# Count with computed grouping variable
mtcars |> count_(high_mpg = ~mpg > 20)
#> high_mpg n
#> 1 FALSE 18
#> 2 TRUE 14
# Combine grouping and computation
mtcars |> count_(~cyl, high_hp = ~hp > 150)
#> cyl high_hp n
#> 1 4 FALSE 11
#> 2 6 FALSE 6
#> 3 6 TRUE 1
#> 4 8 FALSE 2
#> 5 8 TRUE 12
# tally_() - count rows (optionally by existing groups)
mtcars |> tally_()
#> n
#> Mazda RX4 32
mtcars |>
group_by_(~cyl) |>
tally_()
#> cyl n
#> 1 4 11
#> 2 6 7
#> 3 8 14
# tally with weights
mtcars |>
group_by_(~cyl) |>
tally_(wt = ~hp)
#> cyl n
#> 1 4 909
#> 2 6 856
#> 3 8 2929
# add_count_() - add count column without collapsing
mtcars |> add_count_(~cyl)
#> mpg cyl disp hp drat wt qsec vs am gear carb n
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 7
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 7
#> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 11
#> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 7
#> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 14
#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 7
#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 14
#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 11
#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 11
#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 7
#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 7
#> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 14
#> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 14
#> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 14
#> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 14
#> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 14
#> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 14
#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 11
#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 11
#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 11
#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 11
#> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 14
#> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 14
#> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 14
#> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 14
#> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 11
#> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 11
#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 11
#> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 14
#> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 7
#> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 14
#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 11
# add_count with custom column name
mtcars |> add_count_(~cyl, name = "n_cyl")
#> mpg cyl disp hp drat wt qsec vs am gear carb n_cyl
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 7
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 7
#> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 11
#> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 7
#> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 14
#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 7
#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 14
#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 11
#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 11
#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 7
#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 7
#> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 14
#> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 14
#> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 14
#> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 14
#> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 14
#> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 14
#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 11
#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 11
#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 11
#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 11
#> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 14
#> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 14
#> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 14
#> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 14
#> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 11
#> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 11
#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 11
#> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 14
#> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 7
#> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 14
#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 11
# add_count by multiple variables
mtcars |> add_count_(~cyl, ~gear)
#> mpg cyl disp hp drat wt qsec vs am gear carb n
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 4
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 4
#> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 8
#> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 2
#> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 12
#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 2
#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 12
#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 8
#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 8
#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 4
#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 4
#> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 12
#> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 12
#> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 12
#> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 12
#> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 12
#> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 12
#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 8
#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 8
#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 8
#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 1
#> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 12
#> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 12
#> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 12
#> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 12
#> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 8
#> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 2
#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 2
#> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 2
#> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 1
#> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 2
#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 8
# add_tally_() - add total count to each row
mtcars |> add_tally_()
#> mpg cyl disp hp drat wt qsec vs am gear carb n
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 32
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 32
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 32
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 32
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 32
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 32
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 32
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 32
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 32
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 32
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 32
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 32
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 32
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 32
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 32
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 32
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 32
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 32
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 32
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 32
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 32
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 32
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 32
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 32
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 32
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 32
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 32
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 32
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 32
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 32
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 32
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 32
mtcars |>
group_by_(~cyl) |>
add_tally_()
#> mpg cyl disp hp drat wt qsec vs am gear carb n
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 7
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 7
#> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 11
#> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 7
#> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 14
#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 7
#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 14
#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 11
#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 11
#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 7
#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 7
#> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 14
#> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 14
#> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 14
#> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 14
#> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 14
#> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 14
#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 11
#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 11
#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 11
#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 11
#> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 14
#> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 14
#> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 14
#> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 14
#> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 11
#> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 11
#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 11
#> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 14
#> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 7
#> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 14
#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 11
# Chain operations
mtcars |>
count_(~cyl, ~gear, sort = TRUE) |>
mutate_(pct = ~n/sum(n) * 100)
#> cyl gear n pct
#> 1 8 3 12 37.500
#> 2 4 4 8 25.000
#> 3 6 4 4 12.500
#> 4 4 5 2 6.250
#> 5 6 3 2 6.250
#> 6 8 5 2 6.250
#> 7 4 3 1 3.125
#> 8 6 5 1 3.125
# Use with filtering
mtcars |>
add_count_(~cyl) |>
filter_(~n > 10)
#> mpg cyl disp hp drat wt qsec vs am gear carb n
#> 1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 11
#> 2 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 14
#> 3 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 14
#> 4 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 11
#> 5 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 11
#> 6 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 14
#> 7 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 14
#> 8 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 14
#> 9 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 14
#> 10 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 14
#> 11 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 14
#> 12 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 11
#> 13 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 11
#> 14 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 11
#> 15 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 11
#> 16 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 14
#> 17 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 14
#> 18 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 14
#> 19 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 14
#> 20 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 11
#> 21 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 11
#> 22 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 11
#> 23 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 14
#> 24 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 14
#> 25 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 11