Tools useful for building data summary tables.
Usage
summary_table(x, summaries = qsummary(x), by = NULL, qable_args = list(), ...)
qsummary(x, numeric_summaries, n_perc_args, env = parent.frame())Arguments
- x
a
data.frame.- summaries
a list of lists of formulea for summarizing the data set. See Details and examples.
- by
a character vector of variable names to generate the summary by, that is one column for each unique values of the variables specified.
- qable_args
additional values passed to
qable- ...
pass through
- numeric_summaries
a list of functions to use for summarizing numeric variables. The functions need to be provided as character strings with the single argument defined by the
%ssymbol.- n_perc_args
a list of arguments to pass to
n_percto be used withcharacterorfactorvariables withinx.- env
environment to assign to the resulting formulae
Details
summary_table can be used to generate good looking, simple tables in
LaTeX or markdown. Functions like xtables::print.xtable and Hmisc::latex
provide many more tools for formatting tables. The purpose of
summary_table is to generate good looking tables quickly within
workflow for summarizing a data set.
Creating a list-of-lists of summary functions to apply to a data set will
allow the exploration of the whole data set and grouped data sets. In the
example provided on this page we see a set of summary measures for the
mtcars data set and the construction of a table for
the whole data set and for a grouped data set.
The list-of-lists should be thought of as follows: the outer list defines row groups, the inner lists define the rows within each row group.
More detailed use of these functions can be found the "summary-statistics" vignette.
The print method for the qwraps2_summary_table objects is just
a simple wrapper for qable.
See also
qsummary for generating the summaries,
qable for marking up qwraps2_data_summary objects.
The vignette("summary-statistics", package = "qwraps2") for detailed
use of these functions and caveats.
Examples
# A list-of-lists for the summaries arg. This object is of the basic form:
#
# list("row group A" =
# list("row 1A" = ~ <summary function>,
# "row 2A" = ~ <summary function>),
# "row group B" =
# list("row 1B" = ~ <summary function>,
# "row 2B" = ~ <summary function>,
# "row 3B" = ~ <summary function>))
our_summaries <-
list("Miles Per Gallon" =
list("min" = ~ min(mpg),
"mean" = ~ mean(mpg),
"mean ± sd" = ~ qwraps2::mean_sd(mpg),
"max" = ~ max(mpg)),
"Weight" =
list("median" = ~ median(wt)),
"Cylinders" =
list("4 cyl: n (%)" = ~ qwraps2::n_perc0(cyl == 4),
"6 cyl: n (%)" = ~ qwraps2::n_perc0(cyl == 6),
"8 cyl: n (%)" = ~ qwraps2::n_perc0(cyl == 8)))
# Going to use markdown for the markup language in this example, the original
# option will be reset at the end of the example.
orig_opt <- options()$qwraps2_markup
options(qwraps2_markup = "markdown")
# The summary table for the whole mtcars data set
whole_table <- summary_table(mtcars, our_summaries)
whole_table
#>
#>
#> | |mtcars (N = 32) |
#> |:-----------------------------|:-------------------|
#> |**Miles Per Gallon** | |
#> | min |10.4 |
#> | mean |20.090625 |
#> | mean ± sd |20.09 ± 6.03 |
#> | max |33.9 |
#> |**Weight** | |
#> | median |3.325 |
#> |**Cylinders** | |
#> | 4 cyl: n (%) |11 (34) |
#> | 6 cyl: n (%) |7 (22) |
#> | 8 cyl: n (%) |14 (44) |
# The summary table for mtcars grouped by am (automatic or manual transmission)
# This will generate one column for each level of mtcars$am
grouped_by_table <-
summary_table(mtcars, our_summaries, by = "am")
grouped_by_table
#>
#>
#> | |0 (N = 19) |1 (N = 13) |
#> |:-----------------------------|:-------------------|:-------------------|
#> |**Miles Per Gallon** | | |
#> | min |10.4 |15 |
#> | mean |17.1473684210526 |24.3923076923077 |
#> | mean ± sd |17.15 ± 3.83 |24.39 ± 6.17 |
#> | max |24.4 |33.9 |
#> |**Weight** | | |
#> | median |3.52 |2.32 |
#> |**Cylinders** | | |
#> | 4 cyl: n (%) |3 (16) |8 (62) |
#> | 6 cyl: n (%) |4 (21) |3 (23) |
#> | 8 cyl: n (%) |12 (63) |2 (15) |
# an equivalent call if you are using the tidyverse:
summary_table(dplyr::group_by(mtcars, am), our_summaries)
#> Warning: grouped_df detected. Setting `by` argument to
#> c('am')
#>
#>
#> | |0 (N = 19) |1 (N = 13) |
#> |:-----------------------------|:-------------------|:-------------------|
#> |**Miles Per Gallon** | | |
#> | min |10.4 |15 |
#> | mean |17.1473684210526 |24.3923076923077 |
#> | mean ± sd |17.15 ± 3.83 |24.39 ± 6.17 |
#> | max |24.4 |33.9 |
#> |**Weight** | | |
#> | median |3.52 |2.32 |
#> |**Cylinders** | | |
#> | 4 cyl: n (%) |3 (16) |8 (62) |
#> | 6 cyl: n (%) |4 (21) |3 (23) |
#> | 8 cyl: n (%) |12 (63) |2 (15) |
# To build a table with a column for the whole data set and each of the am
# levels
cbind(whole_table, grouped_by_table)
#>
#>
#> | |mtcars (N = 32) |0 (N = 19) |1 (N = 13) |
#> |:-----------------------------|:-------------------|:-------------------|:-------------------|
#> |**Miles Per Gallon** | | | |
#> | min |10.4 |10.4 |15 |
#> | mean |20.090625 |17.1473684210526 |24.3923076923077 |
#> | mean ± sd |20.09 ± 6.03 |17.15 ± 3.83 |24.39 ± 6.17 |
#> | max |33.9 |24.4 |33.9 |
#> |**Weight** | | | |
#> | median |3.325 |3.52 |2.32 |
#> |**Cylinders** | | | |
#> | 4 cyl: n (%) |11 (34) |3 (16) |8 (62) |
#> | 6 cyl: n (%) |7 (22) |4 (21) |3 (23) |
#> | 8 cyl: n (%) |14 (44) |12 (63) |2 (15) |
# Adding a caption for a LaTeX table
print(whole_table, caption = "Hello world", markup = "latex")
#>
#>
#> Table: Hello world
#>
#> | |mtcars (N = 32) |
#> |:-----------------------------|:-------------------|
#> |**Miles Per Gallon** | |
#> | min |10.4 |
#> | mean |20.090625 |
#> | mean ± sd |20.09 ± 6.03 |
#> | max |33.9 |
#> |**Weight** | |
#> | median |3.325 |
#> |**Cylinders** | |
#> | 4 cyl: n (%) |11 (34) |
#> | 6 cyl: n (%) |7 (22) |
#> | 8 cyl: n (%) |14 (44) |
# A **warning** about grouped_df objects.
# If you use dplyr::group_by or
# dplyr::rowwise to manipulate a data set and fail to use dplyr::ungroup you
# might find a table that takes a long time to create and does not summarize the
# data as expected. For example, let's build a data set with twenty subjects
# and injury severity scores for head and face injuries. We'll clean the data
# by finding the max ISS score for each subject and then reporting summary
# statistics there of.
set.seed(42)
dat <- data.frame(id = letters[1:20],
head_iss = sample(1:6, 20, replace = TRUE, prob = 10 * (6:1)),
face_iss = sample(1:6, 20, replace = TRUE, prob = 10 * (6:1)))
dat <- dplyr::group_by(dat, id)
dat <- dplyr::mutate(dat, iss = max(head_iss, face_iss))
iss_summary <-
list("Head ISS" =
list("min" = ~ min(head_iss),
"median" = ~ median(head_iss),
"max" = ~ max(head_iss)),
"Face ISS" =
list("min" = ~ min(face_iss),
"median" = ~ median(face_iss),
"max" = ~ max(face_iss)),
"Max ISS" =
list("min" = ~ min(iss),
"median" = ~ median(iss),
"max" = ~ max(iss)))
# Want: a table with one column for all subjects with nine rows divided up into
# three row groups. However, the following call will create a table with 20
# columns, one for each subject because dat is a grouped_df
summary_table(dat, iss_summary)
#> Warning: grouped_df detected. Setting `by` argument to
#> c('id')
#>
#>
#> | |a (N = 1) |b (N = 1) |c (N = 1) |d (N = 1) |e (N = 1) |f (N = 1) |g (N = 1) |h (N = 1) |i (N = 1) |j (N = 1) |k (N = 1) |l (N = 1) |m (N = 1) |n (N = 1) |o (N = 1) |p (N = 1) |q (N = 1) |r (N = 1) |s (N = 1) |t (N = 1) |
#> |:-------------------|:------------|:------------|:------------|:------------|:------------|:------------|:------------|:------------|:------------|:------------|:------------|:------------|:------------|:------------|:------------|:------------|:------------|:------------|:------------|:------------|
#> |**Head ISS** | | | | | | | | | | | | | | | | | | | | |
#> | min |5 |5 |2 |4 |3 |2 |4 |1 |3 |3 |2 |4 |5 |1 |2 |5 |6 |1 |2 |3 |
#> | median |5 |5 |2 |4 |3 |2 |4 |1 |3 |3 |2 |4 |5 |1 |2 |5 |6 |1 |2 |3 |
#> | max |5 |5 |2 |4 |3 |2 |4 |1 |3 |3 |2 |4 |5 |1 |2 |5 |6 |1 |2 |3 |
#> |**Face ISS** | | | | | | | | | | | | | | | | | | | | |
#> | min |5 |1 |6 |5 |1 |2 |2 |5 |2 |4 |4 |4 |2 |3 |1 |4 |1 |1 |5 |3 |
#> | median |5 |1 |6 |5 |1 |2 |2 |5 |2 |4 |4 |4 |2 |3 |1 |4 |1 |1 |5 |3 |
#> | max |5 |1 |6 |5 |1 |2 |2 |5 |2 |4 |4 |4 |2 |3 |1 |4 |1 |1 |5 |3 |
#> |**Max ISS** | | | | | | | | | | | | | | | | | | | | |
#> | min |5 |5 |6 |5 |3 |2 |4 |5 |3 |4 |4 |4 |5 |3 |2 |5 |6 |1 |5 |3 |
#> | median |5 |5 |6 |5 |3 |2 |4 |5 |3 |4 |4 |4 |5 |3 |2 |5 |6 |1 |5 |3 |
#> | max |5 |5 |6 |5 |3 |2 |4 |5 |3 |4 |4 |4 |5 |3 |2 |5 |6 |1 |5 |3 |
# Ungroup the data.frame to get the correct output
summary_table(dplyr::ungroup(dat), iss_summary)
#>
#>
#> | |dplyr::ungroup(dat) (N = 20) |
#> |:-------------------|:----------------------------|
#> |**Head ISS** | |
#> | min |1 |
#> | median |3 |
#> | max |6 |
#> |**Face ISS** | |
#> | min |1 |
#> | median |3 |
#> | max |6 |
#> |**Max ISS** | |
#> | min |1 |
#> | median |4 |
#> | max |6 |
################################################################################
# The Default call will work with non-syntactically valid names and will
# generate a table with statistics defined by the qsummary call.
summary_table(mtcars, by = "cyl")
#>
#>
#> | |4 (N = 11) |6 (N = 7) |8 (N = 14) |
#> |:-------------------------|:----------------------|:-----------------------|:-----------------------|
#> |**mpg** | | | |
#> | minimum |21.40 |17.80 |10.40 |
#> | median (IQR) |26.00 (22.80, 30.40) |19.70 (18.65, 21.00) |15.20 (14.40, 16.25) |
#> | mean (sd) |26.66 ± 4.51 |19.74 ± 1.45 |15.10 ± 2.56 |
#> | maximum |33.90 |21.40 |19.20 |
#> |**cyl** | | | |
#> | minimum |4.00 |6.00 |8.00 |
#> | median (IQR) |4.00 (4.00, 4.00) |6.00 (6.00, 6.00) |8.00 (8.00, 8.00) |
#> | mean (sd) |4.00 ± 0.00 |6.00 ± 0.00 |8.00 ± 0.00 |
#> | maximum |4.00 |6.00 |8.00 |
#> |**disp** | | | |
#> | minimum |71.10 |145.00 |275.80 |
#> | median (IQR) |108.00 (78.85, 120.65) |167.60 (160.00, 196.30) |350.50 (301.75, 390.00) |
#> | mean (sd) |105.14 ± 26.87 |183.31 ± 41.56 |353.10 ± 67.77 |
#> | maximum |146.70 |258.00 |472.00 |
#> |**hp** | | | |
#> | minimum |52.00 |105.00 |150.00 |
#> | median (IQR) |91.00 (65.50, 96.00) |110.00 (110.00, 123.00) |192.50 (176.25, 241.25) |
#> | mean (sd) |82.64 ± 20.93 |122.29 ± 24.26 |209.21 ± 50.98 |
#> | maximum |113.00 |175.00 |335.00 |
#> |**drat** | | | |
#> | minimum |3.69 |2.76 |2.76 |
#> | median (IQR) |4.08 (3.81, 4.17) |3.90 (3.35, 3.91) |3.12 (3.07, 3.22) |
#> | mean (sd) |4.07 ± 0.37 |3.59 ± 0.48 |3.23 ± 0.37 |
#> | maximum |4.93 |3.92 |4.22 |
#> |**wt** | | | |
#> | minimum |1.51 |2.62 |3.17 |
#> | median (IQR) |2.20 (1.89, 2.62) |3.21 (2.82, 3.44) |3.75 (3.53, 4.01) |
#> | mean (sd) |2.29 ± 0.57 |3.12 ± 0.36 |4.00 ± 0.76 |
#> | maximum |3.19 |3.46 |5.42 |
#> |**qsec** | | | |
#> | minimum |16.70 |15.50 |14.50 |
#> | median (IQR) |18.90 (18.56, 19.95) |18.30 (16.74, 19.17) |17.18 (16.10, 17.55) |
#> | mean (sd) |19.14 ± 1.68 |17.98 ± 1.71 |16.77 ± 1.20 |
#> | maximum |22.90 |20.22 |18.00 |
#> |**vs** | | | |
#> | minimum |0.00 |0.00 |0.00 |
#> | median (IQR) |1.00 (1.00, 1.00) |1.00 (0.00, 1.00) |0.00 (0.00, 0.00) |
#> | mean (sd) |0.91 ± 0.30 |0.57 ± 0.53 |0.00 ± 0.00 |
#> | maximum |1.00 |1.00 |0.00 |
#> |**am** | | | |
#> | minimum |0.00 |0.00 |0.00 |
#> | median (IQR) |1.00 (0.50, 1.00) |0.00 (0.00, 1.00) |0.00 (0.00, 0.00) |
#> | mean (sd) |0.73 ± 0.47 |0.43 ± 0.53 |0.14 ± 0.36 |
#> | maximum |1.00 |1.00 |1.00 |
#> |**gear** | | | |
#> | minimum |3.00 |3.00 |3.00 |
#> | median (IQR) |4.00 (4.00, 4.00) |4.00 (3.50, 4.00) |3.00 (3.00, 3.00) |
#> | mean (sd) |4.09 ± 0.54 |3.86 ± 0.69 |3.29 ± 0.73 |
#> | maximum |5.00 |5.00 |5.00 |
#> |**carb** | | | |
#> | minimum |1.00 |1.00 |2.00 |
#> | median (IQR) |2.00 (1.00, 2.00) |4.00 (2.50, 4.00) |3.50 (2.25, 4.00) |
#> | mean (sd) |1.55 ± 0.52 |3.43 ± 1.81 |3.50 ± 1.56 |
#> | maximum |2.00 |6.00 |8.00 |
# Another example from the diamonds data
data("diamonds", package = "ggplot2")
diamonds["The Price"] <- diamonds$price
diamonds["A Logical"] <- sample(c(TRUE, FALSE), size = nrow(diamonds), replace = TRUE)
# the next two lines are equivalent.
summary_table(diamonds)
#>
#>
#> | |diamonds (N = 53,940) |
#> |:-------------------------|:---------------------------|
#> |**carat** | |
#> | minimum |0.20 |
#> | median (IQR) |0.70 (0.40, 1.04) |
#> | mean (sd) |0.80 ± 0.47 |
#> | maximum |5.01 |
#> |**cut** | |
#> | Fair |1,610 (3) |
#> | Good |4,906 (9) |
#> | Very Good |12,082 (22) |
#> | Premium |13,791 (26) |
#> | Ideal |21,551 (40) |
#> |**color** | |
#> | D |6,775 (13) |
#> | E |9,797 (18) |
#> | F |9,542 (18) |
#> | G |11,292 (21) |
#> | H |8,304 (15) |
#> | I |5,422 (10) |
#> | J |2,808 (5) |
#> |**clarity** | |
#> | I1 |741 (1) |
#> | SI2 |9,194 (17) |
#> | SI1 |13,065 (24) |
#> | VS2 |12,258 (23) |
#> | VS1 |8,171 (15) |
#> | VVS2 |5,066 (9) |
#> | VVS1 |3,655 (7) |
#> | IF |1,790 (3) |
#> |**depth** | |
#> | minimum |43.00 |
#> | median (IQR) |61.80 (61.00, 62.50) |
#> | mean (sd) |61.75 ± 1.43 |
#> | maximum |79.00 |
#> |**table** | |
#> | minimum |43.00 |
#> | median (IQR) |57.00 (56.00, 59.00) |
#> | mean (sd) |57.46 ± 2.23 |
#> | maximum |95.00 |
#> |**price** | |
#> | minimum |326 |
#> | median (IQR) |2,401.00 (950.00, 5,324.25) |
#> | mean (sd) |3,932.80 ± 3,989.44 |
#> | maximum |18,823 |
#> |**x** | |
#> | minimum |0.00 |
#> | median (IQR) |5.70 (4.71, 6.54) |
#> | mean (sd) |5.73 ± 1.12 |
#> | maximum |10.74 |
#> |**y** | |
#> | minimum |0.00 |
#> | median (IQR) |5.71 (4.72, 6.54) |
#> | mean (sd) |5.73 ± 1.14 |
#> | maximum |58.90 |
#> |**z** | |
#> | minimum |0.00 |
#> | median (IQR) |3.53 (2.91, 4.04) |
#> | mean (sd) |3.54 ± 0.71 |
#> | maximum |31.80 |
#> |**The Price** | |
#> | minimum |326 |
#> | median (IQR) |2,401.00 (950.00, 5,324.25) |
#> | mean (sd) |3,932.80 ± 3,989.44 |
#> | maximum |18,823 |
#> |**A Logical** | |
#> | |27,214 (50) |
summary_table(diamonds, qsummary(diamonds))
#>
#>
#> | |diamonds (N = 53,940) |
#> |:-------------------------|:---------------------------|
#> |**carat** | |
#> | minimum |0.20 |
#> | median (IQR) |0.70 (0.40, 1.04) |
#> | mean (sd) |0.80 ± 0.47 |
#> | maximum |5.01 |
#> |**cut** | |
#> | Fair |1,610 (3) |
#> | Good |4,906 (9) |
#> | Very Good |12,082 (22) |
#> | Premium |13,791 (26) |
#> | Ideal |21,551 (40) |
#> |**color** | |
#> | D |6,775 (13) |
#> | E |9,797 (18) |
#> | F |9,542 (18) |
#> | G |11,292 (21) |
#> | H |8,304 (15) |
#> | I |5,422 (10) |
#> | J |2,808 (5) |
#> |**clarity** | |
#> | I1 |741 (1) |
#> | SI2 |9,194 (17) |
#> | SI1 |13,065 (24) |
#> | VS2 |12,258 (23) |
#> | VS1 |8,171 (15) |
#> | VVS2 |5,066 (9) |
#> | VVS1 |3,655 (7) |
#> | IF |1,790 (3) |
#> |**depth** | |
#> | minimum |43.00 |
#> | median (IQR) |61.80 (61.00, 62.50) |
#> | mean (sd) |61.75 ± 1.43 |
#> | maximum |79.00 |
#> |**table** | |
#> | minimum |43.00 |
#> | median (IQR) |57.00 (56.00, 59.00) |
#> | mean (sd) |57.46 ± 2.23 |
#> | maximum |95.00 |
#> |**price** | |
#> | minimum |326 |
#> | median (IQR) |2,401.00 (950.00, 5,324.25) |
#> | mean (sd) |3,932.80 ± 3,989.44 |
#> | maximum |18,823 |
#> |**x** | |
#> | minimum |0.00 |
#> | median (IQR) |5.70 (4.71, 6.54) |
#> | mean (sd) |5.73 ± 1.12 |
#> | maximum |10.74 |
#> |**y** | |
#> | minimum |0.00 |
#> | median (IQR) |5.71 (4.72, 6.54) |
#> | mean (sd) |5.73 ± 1.14 |
#> | maximum |58.90 |
#> |**z** | |
#> | minimum |0.00 |
#> | median (IQR) |3.53 (2.91, 4.04) |
#> | mean (sd) |3.54 ± 0.71 |
#> | maximum |31.80 |
#> |**The Price** | |
#> | minimum |326 |
#> | median (IQR) |2,401.00 (950.00, 5,324.25) |
#> | mean (sd) |3,932.80 ± 3,989.44 |
#> | maximum |18,823 |
#> |**A Logical** | |
#> | |27,214 (50) |
summary_table(diamonds, by = "cut")
#>
#>
#> | |Fair (N = 1610) |Good (N = 4906) |Very Good (N = 12082) |Premium (N = 13791) |Ideal (N = 21551) |
#> |:-------------------------|:-----------------------------|:-----------------------------|:---------------------------|:--------------------------|:--------------------------|
#> |**carat** | | | | | |
#> | minimum |0.22 |0.23 |0.20 |0.20 |0.20 |
#> | median (IQR) |1.00 (0.70, 1.20) |0.82 (0.50, 1.01) |0.71 (0.41, 1.02) |0.86 (0.41, 1.20) |0.54 (0.35, 1.01) |
#> | mean (sd) |1.05 ± 0.52 |0.85 ± 0.45 |0.81 ± 0.46 |0.89 ± 0.52 |0.70 ± 0.43 |
#> | maximum |5.01 |3.01 |4.00 |4.01 |3.50 |
#> |**cut** | | | | | |
#> | Fair |1,610 (100) |0 (0) |0 (0) |0 (0) |0 (0) |
#> | Good |0 (0) |4,906 (100) |0 (0) |0 (0) |0 (0) |
#> | Very Good |0 (0) |0 (0) |12,082 (100) |0 (0) |0 (0) |
#> | Premium |0 (0) |0 (0) |0 (0) |13,791 (100) |0 (0) |
#> | Ideal |0 (0) |0 (0) |0 (0) |0 (0) |21,551 (100) |
#> |**color** | | | | | |
#> | D |163 (10) |662 (13) |1,513 (13) |1,603 (12) |2,834 (13) |
#> | E |224 (14) |933 (19) |2,400 (20) |2,337 (17) |3,903 (18) |
#> | F |312 (19) |909 (19) |2,164 (18) |2,331 (17) |3,826 (18) |
#> | G |314 (20) |871 (18) |2,299 (19) |2,924 (21) |4,884 (23) |
#> | H |303 (19) |702 (14) |1,824 (15) |2,360 (17) |3,115 (14) |
#> | I |175 (11) |522 (11) |1,204 (10) |1,428 (10) |2,093 (10) |
#> | J |119 (7) |307 (6) |678 (6) |808 (6) |896 (4) |
#> |**clarity** | | | | | |
#> | I1 |210 (13) |96 (2) |84 (1) |205 (1) |146 (1) |
#> | SI2 |466 (29) |1,081 (22) |2,100 (17) |2,949 (21) |2,598 (12) |
#> | SI1 |408 (25) |1,560 (32) |3,240 (27) |3,575 (26) |4,282 (20) |
#> | VS2 |261 (16) |978 (20) |2,591 (21) |3,357 (24) |5,071 (24) |
#> | VS1 |170 (11) |648 (13) |1,775 (15) |1,989 (14) |3,589 (17) |
#> | VVS2 |69 (4) |286 (6) |1,235 (10) |870 (6) |2,606 (12) |
#> | VVS1 |17 (1) |186 (4) |789 (7) |616 (4) |2,047 (9) |
#> | IF |9 (1) |71 (1) |268 (2) |230 (2) |1,212 (6) |
#> |**depth** | | | | | |
#> | minimum |43.00 |54.30 |56.80 |58.00 |43.00 |
#> | median (IQR) |65.00 (64.40, 65.90) |63.40 (61.30, 63.80) |62.10 (60.90, 62.90) |61.40 (60.50, 62.20) |61.80 (61.30, 62.20) |
#> | mean (sd) |64.04 ± 3.64 |62.37 ± 2.17 |61.82 ± 1.38 |61.26 ± 1.16 |61.71 ± 0.72 |
#> | maximum |79.00 |67.00 |64.90 |63.00 |66.70 |
#> |**table** | | | | | |
#> | minimum |49.00 |51.00 |44.00 |51.00 |43.00 |
#> | median (IQR) |58.00 (56.00, 61.00) |58.00 (56.00, 61.00) |58.00 (56.00, 59.00) |59.00 (58.00, 60.00) |56.00 (55.00, 57.00) |
#> | mean (sd) |59.05 ± 3.95 |58.69 ± 2.85 |57.96 ± 2.12 |58.75 ± 1.48 |55.95 ± 1.25 |
#> | maximum |95.00 |66.00 |66.00 |62.00 |63.00 |
#> |**price** | | | | | |
#> | minimum |337 |327 |336 |326 |326 |
#> | median (IQR) |3,282.00 (2,050.25, 5,205.50) |3,050.50 (1,145.00, 5,028.00) |2,648.00 (912.00, 5,372.75) |3,185 (1,046.00, 6,296.00) |1,810 (878.00, 4,678.50) |
#> | mean (sd) |4,358.76 ± 3,560.39 |3,928.86 ± 3,681.59 |3,981.76 ± 3,935.86 |4,584.26 ± 4,349.20 |3,457.54 ± 3,808.40 |
#> | maximum |18,574 |18,788 |18,818 |18,823 |18,806 |
#> |**x** | | | | | |
#> | minimum |0.00 |0.00 |0.00 |0.00 |0.00 |
#> | median (IQR) |6.17 (5.63, 6.70) |5.98 (5.02, 6.42) |5.74 (4.75, 6.47) |6.11 (4.80, 6.80) |5.25 (4.54, 6.44) |
#> | mean (sd) |6.25 ± 0.96 |5.84 ± 1.06 |5.74 ± 1.10 |5.97 ± 1.19 |5.51 ± 1.06 |
#> | maximum |10.74 |9.44 |10.01 |10.14 |9.65 |
#> |**y** | | | | | |
#> | minimum |0.00 |0.00 |0.00 |0.00 |0.00 |
#> | median (IQR) |6.10 (5.57, 6.64) |5.99 (5.02, 6.44) |5.77 (4.77, 6.51) |6.06 (4.79, 6.76) |5.26 (4.55, 6.45) |
#> | mean (sd) |6.18 ± 0.96 |5.85 ± 1.05 |5.77 ± 1.10 |5.94 ± 1.26 |5.52 ± 1.07 |
#> | maximum |10.54 |9.38 |9.94 |58.90 |31.80 |
#> |**z** | | | | | |
#> | minimum |0.00 |0.00 |0.00 |0.00 |0.00 |
#> | median (IQR) |3.97 (3.61, 4.28) |3.70 (3.07, 4.03) |3.56 (2.95, 4.02) |3.72 (2.94, 4.16) |3.23 (2.80, 3.98) |
#> | mean (sd) |3.98 ± 0.65 |3.64 ± 0.65 |3.56 ± 0.73 |3.65 ± 0.73 |3.40 ± 0.66 |
#> | maximum |6.98 |5.79 |31.80 |8.06 |6.03 |
#> |**The Price** | | | | | |
#> | minimum |337 |327 |336 |326 |326 |
#> | median (IQR) |3,282.00 (2,050.25, 5,205.50) |3,050.50 (1,145.00, 5,028.00) |2,648.00 (912.00, 5,372.75) |3,185 (1,046.00, 6,296.00) |1,810 (878.00, 4,678.50) |
#> | mean (sd) |4,358.76 ± 3,560.39 |3,928.86 ± 3,681.59 |3,981.76 ± 3,935.86 |4,584.26 ± 4,349.20 |3,457.54 ± 3,808.40 |
#> | maximum |18,574 |18,788 |18,818 |18,823 |18,806 |
#> |**A Logical** | | | | | |
#> | |785 (49) |2,490 (51) |6,102 (51) |6,981 (51) |10,856 (50) |
summary_table(diamonds,
summaries =
list("My Summary of Price" =
list("min price" = ~ min(price),
"IQR" = ~ stats::IQR(price))),
by = "cut")
#>
#>
#> | |Fair (N = 1610) |Good (N = 4906) |Very Good (N = 12082) |Premium (N = 13791) |Ideal (N = 21551) |
#> |:-----------------------|:---------------|:---------------|:---------------------|:-------------------|:-----------------|
#> |**My Summary of Price** | | | | | |
#> | min price |337 |327 |336 |326 |326 |
#> | IQR |3155.25 |3883 |4460.75 |5250 |3800.5 |
################################################################################
# Data sets with missing values
temp <- mtcars
temp$cyl[5] <- NA
temp$am[c(1, 5, 10)] <- NA
temp$am <- factor(temp$am, levels = 0:1, labels = c("Automatic", "Manual"))
temp$vs <- as.logical(temp$vs)
temp$vs[c(2, 6)] <- NA
qsummary(temp[, c("cyl", "am", "vs")])
#> $cyl
#> $cyl$minimum
#> ~qwraps2::frmt(min(na.omit(cyl)))
#> <environment: 0x555e7adc65c8>
#>
#> $cyl$`median (IQR)`
#> ~qwraps2::median_iqr(na.omit(cyl))
#> <environment: 0x555e7adc65c8>
#>
#> $cyl$`mean (sd)`
#> ~qwraps2::mean_sd(na.omit(cyl))
#> <environment: 0x555e7adc65c8>
#>
#> $cyl$maximum
#> ~qwraps2::frmt(max(na.omit(cyl)))
#> <environment: 0x555e7adc65c8>
#>
#> $cyl$`Unknown/Missing`
#> ~qwraps2::n_perc(is.na(cyl))
#> <environment: 0x555e7adc65c8>
#>
#>
#> $am
#> $am$Automatic
#> ~qwraps2::n_perc(na.omit(am) == "Automatic", digits = 0, show_symbol = FALSE)
#> <environment: 0x555e7adc65c8>
#>
#> $am$Manual
#> ~qwraps2::n_perc(na.omit(am) == "Manual", digits = 0, show_symbol = FALSE)
#> <environment: 0x555e7adc65c8>
#>
#> $am$`Unknown/Missing`
#> ~qwraps2::n_perc(is.na(am))
#> <environment: 0x555e7adc65c8>
#>
#>
#> $vs
#> $vs[[1]]
#> ~qwraps2::n_perc(na.omit(vs), digits = 0, show_symbol = FALSE)
#> <environment: 0x555e7adc65c8>
#>
#> $vs$`Unknown/Missing`
#> ~qwraps2::n_perc(is.na(vs))
#> <environment: 0x555e7adc65c8>
#>
#>
summary_table(temp[, c("cyl", "am", "vs")])
#>
#>
#> | |temp[, c("cyl", "am", "vs")] (N = 32) |
#> |:----------------------------|:-------------------------------------|
#> |**cyl** | |
#> | minimum |4.00 |
#> | median (IQR) |6.00 (4.00, 8.00) |
#> | mean (sd) |6.13 ± 1.78 |
#> | maximum |8.00 |
#> | Unknown/Missing |1 (3.12%) |
#> |**am** | |
#> | Automatic |17 (59) |
#> | Manual |12 (41) |
#> | Unknown/Missing |3 (9.38%) |
#> |**vs** | |
#> | |13 (43) |
#> | Unknown/Missing |2 (6.25%) |
################################################################################
# Group by Multiple Variables
temp <- mtcars
temp$trans <- factor(temp$am, 0:1, c("Manual", "Auto"))
temp$engine <- factor(temp$vs, 0:1, c("V-Shaped", "Straight"))
summary_table(temp, our_summaries, by = c("trans", "engine"))
#>
#>
#> | |Manual.V-Shaped (N = 12) |Auto.V-Shaped (N = 6) |Manual.Straight (N = 7) |Auto.Straight (N = 7) |
#> |:-----------------------------|:------------------------|:---------------------|:-----------------------|:---------------------|
#> |**Miles Per Gallon** | | | | |
#> | min |10.4 |15 |17.8 |21.4 |
#> | mean |15.05 |19.75 |20.7428571428571 |28.3714285714286 |
#> | mean ± sd |15.05 ± 2.77 |19.75 ± 4.01 |20.74 ± 2.47 |28.37 ± 4.76 |
#> | max |19.2 |26 |24.4 |33.9 |
#> |**Weight** | | | | |
#> | median |3.81 |2.8225 |3.215 |1.935 |
#> |**Cylinders** | | | | |
#> | 4 cyl: n (%) |0 (0) |1 (17) |3 (43) |7 (100) |
#> | 6 cyl: n (%) |0 (0) |3 (50) |4 (57) |0 (0) |
#> | 8 cyl: n (%) |12 (100) |2 (33) |0 (0) |0 (0) |
################################################################################
# binding tables together. The original design and expected use of
# summary_table did not require a rbind, as all rows are defined in the
# summaries argument. That said, here are examples of using cbind and rbind to
# build several different tables.
our_summary1 <-
list("Miles Per Gallon" =
list("min" = ~ min(mpg),
"max" = ~ max(mpg),
"mean (sd)" = ~ qwraps2::mean_sd(mpg)),
"Displacement" =
list("min" = ~ min(disp),
"max" = ~ max(disp),
"mean (sd)" = ~ qwraps2::mean_sd(disp)))
our_summary2 <-
list(
"Weight (1000 lbs)" =
list("min" = ~ min(wt),
"max" = ~ max(wt),
"mean (sd)" = ~ qwraps2::mean_sd(wt)),
"Forward Gears" =
list("Three" = ~ qwraps2::n_perc0(gear == 3),
"Four" = ~ qwraps2::n_perc0(gear == 4),
"Five" = ~ qwraps2::n_perc0(gear == 5))
)
tab1 <- summary_table(mtcars, our_summary1)
tab2 <- summary_table(dplyr::group_by(mtcars, am), our_summary1)
#> Warning: grouped_df detected. Setting `by` argument to
#> c('am')
tab3 <- summary_table(dplyr::group_by(mtcars, vs), our_summary1)
#> Warning: grouped_df detected. Setting `by` argument to
#> c('vs')
tab4 <- summary_table(mtcars, our_summary2)
tab5 <- summary_table(dplyr::group_by(mtcars, am), our_summary2)
#> Warning: grouped_df detected. Setting `by` argument to
#> c('am')
tab6 <- summary_table(dplyr::group_by(mtcars, vs), our_summary2)
#> Warning: grouped_df detected. Setting `by` argument to
#> c('vs')
cbind(tab1, tab2, tab3)
#>
#>
#> | |mtcars (N = 32) |0 (N = 19) |1 (N = 13) |0 (N = 18) |1 (N = 14) |
#> |:----------------------|:----------------------|:----------------------|:---------------------|:----------------------|:---------------------|
#> |**Miles Per Gallon** | | | | | |
#> | min |10.4 |10.4 |15 |10.4 |17.8 |
#> | max |33.9 |24.4 |33.9 |26 |33.9 |
#> | mean (sd) |20.09 ± 6.03 |17.15 ± 3.83 |24.39 ± 6.17 |16.62 ± 3.86 |24.56 ± 5.38 |
#> |**Displacement** | | | | | |
#> | min |71.1 |120.1 |71.1 |120.3 |71.1 |
#> | max |472 |472 |351 |472 |258 |
#> | mean (sd) |230.72 ± 123.94 |290.38 ± 110.17 |143.53 ± 87.20 |307.15 ± 106.77 |132.46 ± 56.89 |
cbind(tab4, tab5, tab6)
#>
#>
#> | |mtcars (N = 32) |0 (N = 19) |1 (N = 13) |0 (N = 18) |1 (N = 14) |
#> |:----------------------|:------------------|:------------------|:------------------|:------------------|:------------------|
#> |**Weight (1000 lbs)** | | | | | |
#> | min |1.513 |2.465 |1.513 |2.14 |1.513 |
#> | max |5.424 |5.424 |3.57 |5.424 |3.46 |
#> | mean (sd) |3.22 ± 0.98 |3.77 ± 0.78 |2.41 ± 0.62 |3.69 ± 0.90 |2.61 ± 0.72 |
#> |**Forward Gears** | | | | | |
#> | Three |15 (47) |15 (79) |0 (0) |12 (67) |3 (21) |
#> | Four |12 (38) |4 (21) |8 (62) |2 (11) |10 (71) |
#> | Five |5 (16) |0 (0) |5 (38) |4 (22) |1 (7) |
# row bind is possible, but it is recommended to extend the summary instead.
rbind(tab1, tab4)
#>
#>
#> | |mtcars (N = 32) |
#> |:----------------------|:----------------------|
#> |**Miles Per Gallon** | |
#> | min |10.4 |
#> | max |33.9 |
#> | mean (sd) |20.09 ± 6.03 |
#> |**Displacement** | |
#> | min |71.1 |
#> | max |472 |
#> | mean (sd) |230.72 ± 123.94 |
#> |**Weight (1000 lbs)** | |
#> | min |1.513 |
#> | max |5.424 |
#> | mean (sd) |3.22 ± 0.98 |
#> |**Forward Gears** | |
#> | Three |15 (47) |
#> | Four |12 (38) |
#> | Five |5 (16) |
summary_table(mtcars, summaries = c(our_summary1, our_summary2))
#>
#>
#> | |mtcars (N = 32) |
#> |:----------------------|:----------------------|
#> |**Miles Per Gallon** | |
#> | min |10.4 |
#> | max |33.9 |
#> | mean (sd) |20.09 ± 6.03 |
#> |**Displacement** | |
#> | min |71.1 |
#> | max |472 |
#> | mean (sd) |230.72 ± 123.94 |
#> |**Weight (1000 lbs)** | |
#> | min |1.513 |
#> | max |5.424 |
#> | mean (sd) |3.22 ± 0.98 |
#> |**Forward Gears** | |
#> | Three |15 (47) |
#> | Four |12 (38) |
#> | Five |5 (16) |
if (FALSE) { # \dontrun{
cbind(tab1, tab4) # error because rows are not the same
rbind(tab1, tab2) # error because columns are not the same
} # }
################################################################################
# reset the original markup option that was used before this example was
# evaluated.
options(qwraps2_markup = orig_opt)
# Detailed examples in the vignette
# vignette("summary-statistics", package = "qwraps2")