It is common for a manuscript to require a data summary table. Simple summary statistics for the whole sample and for subgroups therein. There are many tools available for easing the difficulty involved in building such tables. It is my opinion, that most of these tool exists with sufficient implicit biases and nuances imposed by the authors of the tools that other users need to not only understand the tool, but to think like the author. I find myself approaching many problems in ways that few others do. As a result, I needed my own tool for building data summary tables. I hope you like these tools and will be able to use it in your work.
This vignette presents the use of the summary_table, tab_summary, and qable functions for quickly building data summary tables. These functions implicitly use the mean_sd, median_iqr, and n_perc0 functions from qwraps2 as well.
We will use a modified version of the mtcars data set for examples throughout this vignette. The following packages are required to run the code in this vignette and to construct the mtcars2 data.frame.
The mtcars2 data frame will have three versions of the cyl vector, the original numeric values in cyl, a character version, and a factor version.
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(qwraps2)
# define the markup language we are working in.
# options(qwraps2_markup = "latex") is also supported.
options(qwraps2_markup = "markdown")
data(mtcars)
mtcars2 <-
dplyr::mutate(mtcars,
cyl_factor = factor(cyl,
levels = c(6, 4, 8),
labels = paste(c(6, 4, 8), "cylinders")),
cyl_character = paste(cyl, "cylinders"))
str(mtcars2)
## 'data.frame': 32 obs. of 13 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp : num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat : num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec : num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear : num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb : num 4 4 1 1 2 1 4 2 2 4 ...
## $ cyl_factor : Factor w/ 3 levels "6 cylinders",..: 1 1 2 1 3 1 3 2 2 1 ...
## $ cyl_character: chr "6 cylinders" "6 cylinders" "4 cylinders" "6 cylinders" ...Notice that in the construction of the cyl_factor and cyl_character vectors was done such that the coercion of cyl_character to a factor will not be the same as the cyl_factor vector, the levels are in a different order.
with(mtcars2, table(cyl_factor, cyl_character))
## cyl_character
## cyl_factor 4 cylinders 6 cylinders 8 cylinders
## 6 cylinders 0 7 0
## 4 cylinders 11 0 0
## 8 cylinders 0 0 14
with(mtcars2, all.equal(factor(cyl_character), cyl_factor))
## [1] "Attributes: < Component \"levels\": 2 string mismatches >"Let’s review some of the formatting functions provided by the qwraps2 package
mean_sd will return the (arithmetic) mean and standard deviation for numeric vector, i.e., mean_sd(mtcars2$mpg) will return the formatted string.
mean_sd(mtcars2$mpg)
## [1] "20.09 ± 6.03"
mean_sd(mtcars2$mpg, denote_sd = "paren")
## [1] "20.09 (6.03)"The default setting for mean_sd is to return the mean ± sd. In a table this default is helpful sense the default formating for counts and percentages is n (%).
The mean_sd, and other functions, are helpful for in-line text too:
The
nrow(mtcars2)vehicles in themtcarsdata set had an average fuel economy of `mean_sd(mtcars$mpg) miles per gallon.
produces
The 32 vehicles in the
mtcarsdata set had an average fuel economy of 20.09 ± 6.03 miles per gallon.
If you need the mean and a confidence interval there is mean_ci. mean_ci returns a qwraps2_mean_ci object which is a named vector with the mean, lower confidence limit, and the upper confidence limit. There printing method for qwraps2_mean_ci objects is a call to the frmtci function. You an modify the formating of printed result via adjusting the arguments pasted to frmtci
mci <- mean_ci(mtcars2$mpg)
mci
## [1] "20.09 (18.00, 22.18)"
print(mci, show_level = TRUE)
## [1] "20.09 (95% CI: 18.00, 22.18)"Similar to the mean_sd function, the median_iqr returns the median and the inner quartile range (IQR) of a data vector.
median_iqr(mtcars2$mpg)
## [1] "19.20 (15.43, 22.80)"The n_perc function is the workhorse, but n_perc0 is also provided for ease of use in the same way that base R has paste and paste0. n_perc returns the n (%) with the percentage sign in the string, n_perc0 omits the percentage sign from the string. The latter is good for table, the former for in-line text.
n_perc(mtcars2$cyl == 4)
## [1] "11 (34.38%)"
n_perc0(mtcars2$cyl == 4)
## [1] "11 (34)"
n_perc(mtcars2$cyl_factor == 4) # this returns 0 (0.00%)
## [1] "0 (0.00%)"
n_perc(mtcars2$cyl_factor == "4 cylinders")
## [1] "11 (34.38%)"
n_perc(mtcars2$cyl_factor == levels(mtcars2$cyl_factor)[2])
## [1] "11 (34.38%)"
# The count and percentage of 4 or 6 cylinders vehicles in the data set is
n_perc(mtcars2$cyl %in% c(4, 6))
## [1] "18 (56.25%)"Objective: build a table reporting summary statistics for some of the variables in the mtcars2 data.frame overall, and within subgroups. We’ll start with something very simple and build up to something bigger.
Let’s report the min, max, and mean (sd) for continuous variables and n (%) for categorical variables. We will report mpg, disp, wt, and gear overall and by number of cylinders.
The function summary_table, along with some dplyr functions will do the work for us. summary_table takes two arguments:
.data a (grouped_df) data.framesummaries a list of summaries. This is a list-of-lists. The outer list defines the row groups and the inner lists define the specif summaries.args(summary_table)
## function (.data, summaries)
## NULLLet’s build a list-of-lists to pass to the summaries argument of summary_table. The inner lists are named formulae defining the wanted summary. These formulae are passed through dplyr::summarize_ to generate the table. The names are important, as they are used to label row groups and row names in the table.
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)),
"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))
) Building the table is done with a call to summary_table:
summary_table(mtcars2, our_summary1)| mtcars2 (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(mtcars2, our_summary1)| mtcars2 (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(dplyr::group_by(mtcars2, cyl_factor), our_summary1)| cyl_factor: 6 cylinders (N = 7) | cyl_factor: 4 cylinders (N = 11) | cyl_factor: 8 cylinders (N = 14) | |
|---|---|---|---|
| Miles Per Gallon | |||
| min | 17.8 | 21.4 | 10.4 |
| max | 21.4 | 33.9 | 19.2 |
| mean (sd) | 19.74 ± 1.45 | 26.66 ± 4.51 | 15.10 ± 2.56 |
| Displacement | |||
| min | 145.0 | 71.1 | 275.8 |
| max | 258.0 | 146.7 | 472.0 |
| mean (sd) | 183.31 ± 41.56 | 105.14 ± 26.87 | 353.10 ± 67.77 |
| Weight (1000 lbs) | |||
| min | 2.620 | 1.513 | 3.170 |
| max | 3.460 | 3.190 | 5.424 |
| mean (sd) | 3.12 ± 0.36 | 2.29 ± 0.57 | 4.00 ± 0.76 |
| Forward Gears | |||
| Three | 2 (29) | 1 (9) | 12 (86) |
| Four | 4 (57) | 8 (73) | 0 (0) |
| Five | 1 (14) | 2 (18) | 2 (14) |
If you want to change the column names do so via the cnames argument to qable via the print method for qwraps2_summary_table objects. Any argument that you want to send to qable can be sent there when explicitly using the print method for qwraps2_summary_table objects.
print(summary_table(dplyr::group_by(mtcars2, cyl_factor), our_summary1),
rtitle = "Summary Statistics",
cnames = c("Col 1", "Col 2", "Col 3"))| Summary Statistics | Col 1 | Col 2 | Col 3 |
|---|---|---|---|
| Miles Per Gallon | |||
| min | 17.8 | 21.4 | 10.4 |
| max | 21.4 | 33.9 | 19.2 |
| mean (sd) | 19.74 ± 1.45 | 26.66 ± 4.51 | 15.10 ± 2.56 |
| Displacement | |||
| min | 145.0 | 71.1 | 275.8 |
| max | 258.0 | 146.7 | 472.0 |
| mean (sd) | 183.31 ± 41.56 | 105.14 ± 26.87 | 353.10 ± 67.77 |
| Weight (1000 lbs) | |||
| min | 2.620 | 1.513 | 3.170 |
| max | 3.460 | 3.190 | 5.424 |
| mean (sd) | 3.12 ± 0.36 | 2.29 ± 0.57 | 4.00 ± 0.76 |
| Forward Gears | |||
| Three | 2 (29) | 1 (9) | 12 (86) |
| Four | 4 (57) | 8 (73) | 0 (0) |
| Five | 1 (14) | 2 (18) | 2 (14) |
The task building the summaries list-of-lists can be tedious. tab_summary is provided to help with that. For numeric variable, tab_summary will provide the formulae for the min, median (iqr), mean (sd), and max. factor and character vectors will have calls to qwraps2::n_perc for all levels provided.
For version 0.2.3.9000 or beyond, arguments have been added to tab_summary to help control some of the formatting of counts and percentages. The original behavior of tab_summary used n_perc0 to format the summary of categorical variables. Now, n_perc is called and the end user can specify formatting options via a list passed via the n_perc_args argument. The default settings for tab_summary is below.
args(tab_summary)
## function (x, n_perc_args = list(digits = 0, show_symbol = FALSE),
## envir = parent.frame())
## NULLThese options will make the output look as if n_perc0 had been called instead of n_perc. More importantly, these defaults will not honor the options()$qwraps2_frmt_digits.
Examples for tab_summary follow:
tab_summary(mtcars2$mpg)
## $min
## ~min(mtcars2$mpg)
##
## $`median (IQR)`
## ~qwraps2::median_iqr(mtcars2$mpg)
##
## $`mean (sd)`
## ~qwraps2::mean_sd(mtcars2$mpg)
##
## $max
## ~max(mtcars2$mpg)
tab_summary(mtcars2$gear) # gear is a numeric vector!
## $min
## ~min(mtcars2$gear)
##
## $`median (IQR)`
## ~qwraps2::median_iqr(mtcars2$gear)
##
## $`mean (sd)`
## ~qwraps2::mean_sd(mtcars2$gear)
##
## $max
## ~max(mtcars2$gear)
tab_summary(factor(mtcars2$gear))
## $`3`
## ~qwraps2::n_perc(factor(mtcars2$gear) == "3", digits = 0, show_symbol = FALSE)
##
## $`4`
## ~qwraps2::n_perc(factor(mtcars2$gear) == "4", digits = 0, show_symbol = FALSE)
##
## $`5`
## ~qwraps2::n_perc(factor(mtcars2$gear) == "5", digits = 0, show_symbol = FALSE)The our_summary1 object can be recreated as follows. Some additional row groups are provided to show default behavior of tab_summary. Important: Note that the tab_summary are made while using with. Further explanation for this follows.
our_summary2 <-
with(mtcars2,
list("Miles Per Gallon" = tab_summary(mpg)[c(1, 4, 3)],
"Displacement (default summary)" = tab_summary(disp),
"Displacement" = c(tab_summary(disp)[c(1, 4, 3)],
"mean (95% CI)" = ~ frmtci(qwraps2::mean_ci(disp))),
"Weight (1000 lbs)" = tab_summary(wt)[c(1, 4, 3)],
"Forward Gears" = tab_summary(as.character(gear))
))whole <- summary_table(mtcars2, our_summary2)
whole| mtcars2 (N = 32) | |
|---|---|
| Miles Per Gallon | |
| min | 10.4 |
| max | 33.9 |
| mean (sd) | 20.09 ± 6.03 |
| Displacement (default summary) | |
| min | 71.1 |
| median (IQR) | 196.30 (120.83, 326.00) |
| mean (sd) | 230.72 ± 123.94 |
| max | 472 |
| Displacement | |
| min | 71.1 |
| max | 472 |
| mean (sd) | 230.72 ± 123.94 |
| mean (95% CI) | 230.72 (187.78, 273.66) |
| Weight (1000 lbs) | |
| min | 1.513 |
| max | 5.424 |
| mean (sd) | 3.22 ± 0.98 |
| Forward Gears | |
| 3 | 15 (47) |
| 4 | 12 (38) |
| 5 | 5 (16) |
Group by multiple factors:
grouped <- summary_table(dplyr::group_by(mtcars2, am, vs), our_summary2)
grouped| am: 0 vs: 0 (N = 12) | am: 0 vs: 1 (N = 7) | am: 1 vs: 0 (N = 6) | am: 1 vs: 1 (N = 7) | |
|---|---|---|---|---|
| Miles Per Gallon | ||||
| min | 10.4 | 17.8 | 15.0 | 21.4 |
| max | 19.2 | 24.4 | 26.0 | 33.9 |
| mean (sd) | 15.05 ± 2.77 | 20.74 ± 2.47 | 19.75 ± 4.01 | 28.37 ± 4.76 |
| Displacement (default summary) | ||||
| min | 275.8 | 120.1 | 120.3 | 71.1 |
| median (IQR) | 355.00 (296.95, 410.00) | 167.60 (143.75, 196.30) | 160.00 (148.75, 265.75) | 79.00 (77.20, 101.55) |
| mean (sd) | 357.62 ± 71.82 | 175.11 ± 49.13 | 206.22 ± 95.23 | 89.80 ± 18.80 |
| max | 472 | 258 | 351 | 121 |
| Displacement | ||||
| min | 275.8 | 120.1 | 120.3 | 71.1 |
| max | 472 | 258 | 351 | 121 |
| mean (sd) | 357.62 ± 71.82 | 175.11 ± 49.13 | 206.22 ± 95.23 | 89.80 ± 18.80 |
| mean (95% CI) | 357.62 (316.98, 398.25) | 175.11 (138.72, 211.51) | 206.22 (130.02, 282.42) | 89.80 (75.87, 103.73) |
| Weight (1000 lbs) | ||||
| min | 3.435 | 2.465 | 2.140 | 1.513 |
| max | 5.424 | 3.460 | 3.570 | 2.780 |
| mean (sd) | 4.10 ± 0.77 | 3.19 ± 0.35 | 2.86 ± 0.49 | 2.03 ± 0.44 |
| Forward Gears | ||||
| 3 | 12 (100) | 3 (43) | 0 (0) | 0 (0) |
| 4 | 0 (0) | 4 (57) | 2 (33) | 6 (86) |
| 5 | 0 (0) | 0 (0) | 4 (67) | 1 (14) |
As one table:
both <- cbind(whole, grouped)
both| mtcars2 (N = 32) | am: 0 vs: 0 (N = 12) | am: 0 vs: 1 (N = 7) | am: 1 vs: 0 (N = 6) | am: 1 vs: 1 (N = 7) | |
|---|---|---|---|---|---|
| Miles Per Gallon | |||||
| min | 10.4 | 10.4 | 17.8 | 15.0 | 21.4 |
| max | 33.9 | 19.2 | 24.4 | 26.0 | 33.9 |
| mean (sd) | 20.09 ± 6.03 | 15.05 ± 2.77 | 20.74 ± 2.47 | 19.75 ± 4.01 | 28.37 ± 4.76 |
| Displacement (default summary) | |||||
| min | 71.1 | 275.8 | 120.1 | 120.3 | 71.1 |
| median (IQR) | 196.30 (120.83, 326.00) | 355.00 (296.95, 410.00) | 167.60 (143.75, 196.30) | 160.00 (148.75, 265.75) | 79.00 (77.20, 101.55) |
| mean (sd) | 230.72 ± 123.94 | 357.62 ± 71.82 | 175.11 ± 49.13 | 206.22 ± 95.23 | 89.80 ± 18.80 |
| max | 472 | 472 | 258 | 351 | 121 |
| Displacement | |||||
| min | 71.1 | 275.8 | 120.1 | 120.3 | 71.1 |
| max | 472 | 472 | 258 | 351 | 121 |
| mean (sd) | 230.72 ± 123.94 | 357.62 ± 71.82 | 175.11 ± 49.13 | 206.22 ± 95.23 | 89.80 ± 18.80 |
| mean (95% CI) | 230.72 (187.78, 273.66) | 357.62 (316.98, 398.25) | 175.11 (138.72, 211.51) | 206.22 (130.02, 282.42) | 89.80 (75.87, 103.73) |
| Weight (1000 lbs) | |||||
| min | 1.513 | 3.435 | 2.465 | 2.140 | 1.513 |
| max | 5.424 | 5.424 | 3.460 | 3.570 | 2.780 |
| mean (sd) | 3.22 ± 0.98 | 4.10 ± 0.77 | 3.19 ± 0.35 | 2.86 ± 0.49 | 2.03 ± 0.44 |
| Forward Gears | |||||
| 3 | 15 (47) | 12 (100) | 3 (43) | 0 (0) | 0 (0) |
| 4 | 12 (38) | 0 (0) | 4 (57) | 2 (33) | 6 (86) |
| 5 | 5 (16) | 0 (0) | 0 (0) | 4 (67) | 1 (14) |
with with tab_summary?tab_summary was written to help construct formulae to save the end user key strokes. There are plenty of reasons for summary_table to be used without tab_summary. However, when it is helpful to use tab_summary make sure you understand the results.
For example, let’s look at a simple summary of the miles per gallon.
# tab_summary(mpg)[[3]] ## this errors
tab_summary(mtcars$mpg)[[3]]
## ~qwraps2::mean_sd(mtcars$mpg)
with(mtcars, tab_summary(mpg))[[3]]
## ~qwraps2::mean_sd(mpg)
## <environment: 0x440f808>The first call errors because mpg is not in the global environment. The difference between the second and third calls is subtle. The second call generates a formula with mtcars$mpg as an argument whereas the third call generates a formula with only mpg as the argument. The difference will be seen in the summary tables if the .data is sub setted.
# The same tables:
summary_table(mtcars, list("MPG 1" = with(mtcars, tab_summary(mpg)[[3]])))| mtcars (N = 32) | |
|---|---|
| MPG 1 | |
| qwraps2::mean_sd(mpg) | 20.09 ± 6.03 |
summary_table(mtcars, list("MPG 2" = tab_summary(mtcars$mpg)[[3]]))| mtcars (N = 32) | |
|---|---|
| MPG 2 | |
| qwraps2::mean_sd(mtcars$mpg) | 20.09 ± 6.03 |
These two calls generate the same table because the .data and the implied data within the second call are the same.
# Different tables
summary_table(dplyr::filter(mtcars, am == 0), list("MPG 3" = with(mtcars, tab_summary(mpg)[[3]])))| dplyr::filter(mtcars, am == 0) (N = 19) | |
|---|---|
| MPG 3 | |
| qwraps2::mean_sd(mpg) | 17.15 ± 3.83 |
summary_table(dplyr::filter(mtcars, am == 0), list("MPG 4" = tab_summary(mtcars$mpg)[[3]]))| dplyr::filter(mtcars, am == 0) (N = 19) | |
|---|---|
| MPG 4 | |
| qwraps2::mean_sd(mtcars$mpg) | 20.09 ± 6.03 |
Now, the result of the second call above is not correct, it is the same as for the first two calls. This is because mtcars$ is part of the formula and the .data is ignored. The correct result is in the table with MPG 3.
It is critical that you use tab_summary only as a took to help save you key strokes. I strongly encourage you, the end user, to use summary_table a lot, and use tab_summary as a quick tool for generating a script. It might be best if you use tab_summary to generate a template of the formulae you will want, copy the template into your script and editing accordingly.