The summary_table method appears to be the most popular
and widely used feature of the qwraps2
package. As such, this vignette is provided to give as much detail on
the use of the method, and the underlying qable method for
quickly building well formatted summary tables.
qable builds a formatted character matrix from inputs
and then renders a table via knitr::kable. The primary objective of this
function is to allow for easy construction of row groups.
For a simple example we will use the following data set with a grouping variable, subject id, and two variables, V2, and V3. For simplicity, we will order the data by group and id as well.
d <- data.frame(
group = sample(size = 15, paste0("grp", 1:5), replace = TRUE)
, id = sample(size = 15, x = LETTERS)
, V2 = rnorm(15)
, V3 = rep(c(1, 2, NA), times = 5)
)
d <- d[order(d$group, d$id), ]Making a simple table via kable:
| group | id | V2 | V3 |
|---|---|---|---|
| grp1 | D | 0.3584021 | 1 |
| grp1 | H | -0.9491808 | NA |
| grp1 | I | 1.7232308 | NA |
| grp1 | J | 2.1157556 | 2 |
| grp1 | O | -0.1616986 | 1 |
| grp2 | B | 0.6707038 | 2 |
| grp2 | E | 0.3024309 | 2 |
| grp2 | K | -0.7045514 | NA |
| grp2 | R | 0.9469132 | 2 |
| grp2 | V | 0.7881406 | 1 |
| grp4 | M | -0.3941145 | NA |
| grp4 | P | -0.8798365 | 1 |
| grp4 | Y | 0.0361357 | 1 |
| grp5 | A | 0.1674409 | NA |
| grp5 | C | 1.9355718 | 2 |
The group column is great for data analysis, but is not the best for
human readability. This is where qable can be useful. Start
by building a named numeric column with the name being the row group
name and the value the number of rows. For the ordered data
this is a simple call to table:
If we pass that named vector to qable as the rgroup and
with specify the id column as the row names we have the same information
but in format that is better for humans:
qable( x = d[, c("V2", "V3")]
, rgroup = c(table(d$group)) # row group
, rnames = d$id # row names
)| V2 | V3 | |
|---|---|---|
| grp1 | ||
| D | 0.358402056802064 | 1 |
| H | -0.949180809687611 | NA |
| I | 1.72323079854894 | NA |
| J | 2.11575561323695 | 2 |
| O | -0.161698647607024 | 1 |
| grp2 | ||
| B | 0.67070382675052 | 2 |
| E | 0.3024309248682 | 2 |
| K | -0.704551365955043 | NA |
| R | 0.946913174943256 | 2 |
| V | 0.788140622823556 | 1 |
| grp4 | ||
| M | -0.394114506412192 | NA |
| P | -0.879836528531105 | 1 |
| Y | 0.0361357384849679 | 1 |
| grp5 | ||
| A | 0.167440904355584 | NA |
| C | 1.93557176599585 | 2 |
The return object from qable is a character matrix.
Also, when a data.frame is passed to qable it is coerced to
a matrix before anything else, as such, any formatting of numeric values
or other strings should be done before calling qable.
To pass arguments to knitr::kable do so via the
kable_args argument.
We will build a summary table for a regression model with row groups for conceptually similar predictors.
model <-
glm(spam ~
word_freq_your + word_freq_conference + word_freq_business +
char_freq_semicolon + char_freq_exclamation_point +
capital_run_length_total + capital_run_length_longest
, data = spambase
, family = binomial()
)
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
model_summary <-
data.frame(
parameter = names(coef(model))
, odd_ratio = frmt(exp(coef(model)), digits = 3)
, lcl = frmt(exp(coef(model) + qnorm(0.025) * sqrt(diag(vcov(model)))), digits = 3)
, ucl = frmt(exp(coef(model) + qnorm(0.975) * sqrt(diag(vcov(model)))), digits = 3)
, pval = frmtp(summary(model)$coef[, 4])
)
qable(model_summary[-1, c('odd_ratio', 'lcl', 'ucl', 'pval')]
, rtitle = "Parameter"
, rgroup = c("Word Frequency" = 3, "Character Frequency" = 2, "Capital Run Length" = 2)
, rnames = c("Your", "Conference", "Business", ";", "!", "Total", "Longest")
, kable_args = list(align = "lrrrr", caption = "Regression Model Summary")
, cnames = c("Odds Ratio", "Lower Conf. Limit", "Upper Conf. Limit", "P-value")
)| Parameter | Odds Ratio | Lower Conf. Limit | Upper Conf. Limit | P-value |
|---|---|---|---|---|
| Word Frequency | ||||
| Your | 1.802 | 1.678 | 1.934 | P < 0.0001 |
| Conference | 0.001 | 0.000 | 0.012 | P < 0.0001 |
| Business | 3.573 | 2.721 | 4.693 | P < 0.0001 |
| Character Frequency | ||||
| ; | 0.325 | 0.136 | 0.779 | P = 0.0117 |
| ! | 4.148 | 3.336 | 5.158 | P < 0.0001 |
| Capital Run Length | ||||
| Total | 1.000 | 1.000 | 1.001 | P < 0.0001 |
| Longest | 1.017 | 1.014 | 1.019 | P < 0.0001 |
summary_table was developed with the primary objective
to build well formatted and easy to read data summary tables.
Conceptually, the construction of these tables start by building a
“list-of-lists” of summaries and then generating these summaries for
specific groupings of the data set.
We will use the mtcars2 data set for these examples.
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, displacement (disp), wt (weight), and gear overall and by number of cylinders and transmission type.
The use of the summary_table use to define a summary,
that is, a list-of-lists of formulas for summarizing the data.frame.
The inner lists are named formulae defining the wanted summary. 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),
"median" = ~ median(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
and rendered in Table @ref(tab:mtcars_whole).
whole <-
summary_table(
x = mtcars2
, summaries = our_summary1
, qable_args = list(kable_args = list(caption = "mtcars2 data summary"))
)
whole| mtcars2 (N = 32) | |
|---|---|
| Miles Per Gallon | |
| min | 10.4 |
| max | 33.9 |
| mean (sd) | 20.09 ± 6.03 |
| Displacement | |
| min | 71.1 |
| median | 196.3 |
| 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) |
Use the by argument to specify a grouping variable and
generate the same summary as above but for subsets of the data. When the
by column is a factor, the columns will be in the order of
the levels of the factor. In comparison, the column order is
alphabetical if the variable is just a character.
by_cylf <-
summary_table(
x = mtcars2
, summaries = our_summary1
, by = c("cyl_factor")
, qable_args = list(rtitle = "Summary Statistics"
, kable_args = list(caption = "mtcars2 data summary by cyl_factor"))
)
by_cylf| Summary Statistics | 6 cylinders (N = 7) | 4 cylinders (N = 11) | 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 | 71.1 | 275.8 |
| median | 167.6 | 108 | 350.5 |
| max | 258 | 146.7 | 472 |
| mean (sd) | 183.31 ± 41.56 | 105.14 ± 26.87 | 353.10 ± 67.77 |
| Weight (1000 lbs) | |||
| min | 2.62 | 1.513 | 3.17 |
| max | 3.46 | 3.19 | 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) |
by_cylc <-
summary_table(
x = mtcars2
, summaries = our_summary1
, by = c("cyl_character")
, qable_args = list(rtitle = "Summary Statistics"
, kable_args = list(caption = "mtcars2 data summary by cyl_character"))
)
by_cylc| Summary Statistics | 4 cylinders (N = 11) | 6 cylinders (N = 7) | 8 cylinders (N = 14) |
|---|---|---|---|
| Miles Per Gallon | |||
| min | 21.4 | 17.8 | 10.4 |
| max | 33.9 | 21.4 | 19.2 |
| mean (sd) | 26.66 ± 4.51 | 19.74 ± 1.45 | 15.10 ± 2.56 |
| Displacement | |||
| min | 71.1 | 145 | 275.8 |
| median | 108 | 167.6 | 350.5 |
| max | 146.7 | 258 | 472 |
| mean (sd) | 105.14 ± 26.87 | 183.31 ± 41.56 | 353.10 ± 67.77 |
| Weight (1000 lbs) | |||
| min | 1.513 | 2.62 | 3.17 |
| max | 3.19 | 3.46 | 5.424 |
| mean (sd) | 2.29 ± 0.57 | 3.12 ± 0.36 | 4.00 ± 0.76 |
| Forward Gears | |||
| Three | 1 (9) | 2 (29) | 12 (86) |
| Four | 8 (73) | 4 (57) | 0 (0) |
| Five | 2 (18) | 1 (14) | 2 (14) |
You are also able to generate summaries by multiple columns. For example, Table @ref(tab:mtcars2_by_cyl_transmission) reports the summary by the combination of the number of cylinders and the type of transmission.
by_cyl_am <-
summary_table(
x = mtcars2
, summaries = our_summary1
, by = c("cyl_factor", "transmission")
)
by_cyl_am| 6 cylinders.Automatic (N = 4) | 4 cylinders.Automatic (N = 3) | 8 cylinders.Automatic (N = 12) | 6 cylinders.Manual (N = 3) | 4 cylinders.Manual (N = 8) | 8 cylinders.Manual (N = 2) | |
|---|---|---|---|---|---|---|
| Miles Per Gallon | ||||||
| min | 17.8 | 21.5 | 10.4 | 19.7 | 21.4 | 15 |
| max | 21.4 | 24.4 | 19.2 | 21 | 33.9 | 15.8 |
| mean (sd) | 19.12 ± 1.63 | 22.90 ± 1.45 | 15.05 ± 2.77 | 20.57 ± 0.75 | 28.07 ± 4.48 | 15.40 ± 0.57 |
| Displacement | ||||||
| min | 167.6 | 120.1 | 275.8 | 145 | 71.1 | 301 |
| median | 196.3 | 140.8 | 355 | 160 | 87.05 | 326 |
| max | 258 | 146.7 | 472 | 160 | 121 | 351 |
| mean (sd) | 204.55 ± 44.74 | 135.87 ± 13.97 | 357.62 ± 71.82 | 155.00 ± 8.66 | 93.61 ± 20.48 | 326.00 ± 35.36 |
| Weight (1000 lbs) | ||||||
| min | 3.215 | 2.465 | 3.435 | 2.62 | 1.513 | 3.17 |
| max | 3.46 | 3.19 | 5.424 | 2.875 | 2.78 | 3.57 |
| mean (sd) | 3.39 ± 0.12 | 2.94 ± 0.41 | 4.10 ± 0.77 | 2.75 ± 0.13 | 2.04 ± 0.41 | 3.37 ± 0.28 |
| Forward Gears | ||||||
| Three | 2 (50) | 1 (33) | 12 (100) | 0 (0) | 0 (0) | 0 (0) |
| Four | 2 (50) | 2 (67) | 0 (0) | 2 (67) | 6 (75) | 0 (0) |
| Five | 0 (0) | 0 (0) | 0 (0) | 1 (33) | 2 (25) | 2 (100) |
It is common that I will want to have a summary table with the first
column reporting for the whole data sets and the additional columns for
subsets of the data set. The returned objects from
summary_table can be joined together via cbind
assuming that the row groupings (summaries) are the same.
Note: the kable_args of the first item passed to
cbind will be assigned to the resulting object (Table
@ref(tab:mtcars2_cbind)). However, there is an easy way to modify the
qable_args and kable_args via the print method.
| mtcars2 (N = 32) | 6 cylinders (N = 7) | 4 cylinders (N = 11) | 8 cylinders (N = 14) | |
|---|---|---|---|---|
| Miles Per Gallon | ||||
| min | 10.4 | 17.8 | 21.4 | 10.4 |
| max | 33.9 | 21.4 | 33.9 | 19.2 |
| mean (sd) | 20.09 ± 6.03 | 19.74 ± 1.45 | 26.66 ± 4.51 | 15.10 ± 2.56 |
| Displacement | ||||
| min | 71.1 | 145 | 71.1 | 275.8 |
| median | 196.3 | 167.6 | 108 | 350.5 |
| max | 472 | 258 | 146.7 | 472 |
| mean (sd) | 230.72 ± 123.94 | 183.31 ± 41.56 | 105.14 ± 26.87 | 353.10 ± 67.77 |
| Weight (1000 lbs) | ||||
| min | 1.513 | 2.62 | 1.513 | 3.17 |
| max | 5.424 | 3.46 | 3.19 | 5.424 |
| mean (sd) | 3.22 ± 0.98 | 3.12 ± 0.36 | 2.29 ± 0.57 | 4.00 ± 0.76 |
| Forward Gears | ||||
| Three | 15 (47) | 2 (29) | 1 (9) | 12 (86) |
| Four | 12 (38) | 4 (57) | 8 (73) | 0 (0) |
| Five | 5 (16) | 1 (14) | 2 (18) | 2 (14) |
If you want to update how a summary table is printed, you can do so
by calling the print method explicitly while passing a new set of
qable_args, see Table @ref(tab:updated_both).
print(both,
qable_args = list(
rtitle = "ROW-TITLE",
cnames = c("Col 0", "Col 1", "Col 2", "Col 3"),
kable_args = list(
align = "lcrcr",
caption = "mtcars2 data summary - new caption"
)
))| ROW-TITLE | Col 0 | Col 1 | Col 2 | Col 3 |
|---|---|---|---|---|
| Miles Per Gallon | ||||
| min | 10.4 | 17.8 | 21.4 | 10.4 |
| max | 33.9 | 21.4 | 33.9 | 19.2 |
| mean (sd) | 20.09 ± 6.03 | 19.74 ± 1.45 | 26.66 ± 4.51 | 15.10 ± 2.56 |
| Displacement | ||||
| min | 71.1 | 145 | 71.1 | 275.8 |
| median | 196.3 | 167.6 | 108 | 350.5 |
| max | 472 | 258 | 146.7 | 472 |
| mean (sd) | 230.72 ± 123.94 | 183.31 ± 41.56 | 105.14 ± 26.87 | 353.10 ± 67.77 |
| Weight (1000 lbs) | ||||
| min | 1.513 | 2.62 | 1.513 | 3.17 |
| max | 5.424 | 3.46 | 3.19 | 5.424 |
| mean (sd) | 3.22 ± 0.98 | 3.12 ± 0.36 | 2.29 ± 0.57 | 4.00 ± 0.76 |
| Forward Gears | ||||
| Three | 15 (47) | 2 (29) | 1 (9) | 12 (86) |
| Four | 12 (38) | 4 (57) | 8 (73) | 0 (0) |
| Five | 5 (16) | 1 (14) | 2 (18) | 2 (14) |
There are many different ways to format data summary tables. Adding p-values to a table is just one thing that can be done in more than one way. For example, if a row group reports the counts and percentages for each level of a categorical variable across multiple (column) groups, then I would argue that the p-value resulting from a chi square test or a Fisher exact test would be best placed on the line of the table labeling the row group. However, say we reported the minimum, median, mean, and maximum with in a row group for one variable. The p-value from a t-test, or other meaningful test for the difference in mean, I would suggest should be reported on the line of the summary table for the mean, not the row group itself.
With so many possibilities I have reserved construction of a p-value column to be ad hoc. Perhaps an additional column wouldn’t be used and the p-values are edited into row group labels, for example.
If you want to add a p-value column, or any other column(s) to a
qwraps2_summary_table object you can with some degree of
ease. Note that qwraps2_summary_table objects are just
character matrices with additional attributes.
str(both)
## 'qwraps2_summary_table' chr [1:17, 1:5] "**Miles Per Gallon**" ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:5] "" "mtcars2 (N = 32)" "6 cylinders (N = 7)" "4 cylinders (N = 11)" ...
## - attr(*, "qable_args")=List of 6
## ..$ rtitle : chr ""
## ..$ rgroup : Named int [1:4] 3 4 3 3
## .. ..- attr(*, "names")= chr [1:4] "Miles Per Gallon" "Displacement" "Weight (1000 lbs)" "Forward Gears"
## ..$ rnames : chr [1:13] "min" "max" "mean (sd)" "min" ...
## ..$ cnames : chr [1:5] "" "mtcars2 (N = 32)" "6 cylinders (N = 7)" "4 cylinders (N = 11)" ...
## ..$ markup : chr "markdown"
## ..$ kable_args:List of 1
## .. ..$ caption: chr "mtcars2 data summary"For this example, we will added p-values for testing the difference in the mean between the three cylinder groups and the distribution of forward gears by cylinder groups.
# difference in means
mpvals <-
sapply(
list(mpg = lm(mpg ~ cyl_factor, data = mtcars2),
disp = lm(disp ~ cyl_factor, data = mtcars2),
wt = lm(wt ~ cyl_factor, data = mtcars2)),
extract_fpvalue)
# Fisher test
fpval <- frmtp(fisher.test(table(mtcars2$gear, mtcars2$cyl_factor))$p.value)In this case, adding the p-value column, is done by creating a empty column and then writing in the needed p-value on the wanted rows. This could be within a row group (tests for means) or for a row group (Fisher test).
both <- cbind(both, "P-value" = "")
both[grepl("mean \\(sd\\)", both[, 1]), "P-value"] <- mpvals
both[grepl("Forward Gears", both[, 1]), "P-value"] <- fpval| mtcars2 (N = 32) | 6 cylinders (N = 7) | 4 cylinders (N = 11) | 8 cylinders (N = 14) | P-value | |
|---|---|---|---|---|---|
| Miles Per Gallon | |||||
| min | 10.4 | 17.8 | 21.4 | 10.4 | |
| max | 33.9 | 21.4 | 33.9 | 19.2 | |
| mean (sd) | 20.09 ± 6.03 | 19.74 ± 1.45 | 26.66 ± 4.51 | 15.10 ± 2.56 | P < 0.0001 |
| Displacement | |||||
| min | 71.1 | 145 | 71.1 | 275.8 | |
| median | 196.3 | 167.6 | 108 | 350.5 | |
| max | 472 | 258 | 146.7 | 472 | |
| mean (sd) | 230.72 ± 123.94 | 183.31 ± 41.56 | 105.14 ± 26.87 | 353.10 ± 67.77 | P < 0.0001 |
| Weight (1000 lbs) | |||||
| min | 1.513 | 2.62 | 1.513 | 3.17 | |
| max | 5.424 | 3.46 | 3.19 | 5.424 | |
| mean (sd) | 3.22 ± 0.98 | 3.12 ± 0.36 | 2.29 ± 0.57 | 4.00 ± 0.76 | P < 0.0001 |
| Forward Gears | P < 0.0001 | ||||
| Three | 15 (47) | 2 (29) | 1 (9) | 12 (86) | |
| Four | 12 (38) | 4 (57) | 8 (73) | 0 (0) | |
| Five | 5 (16) | 1 (14) | 2 (18) | 2 (14) |
Another option you might consider is to have the p-value in the row group name. Consider the following construction. The p-values are added to the names of the row groups when building the summary table.
gear_summary <-
list("Forward Gears" =
list("Three" = ~ qwraps2::n_perc0(gear == 3),
"Four" = ~ qwraps2::n_perc0(gear == 4),
"Five" = ~ qwraps2::n_perc0(gear == 5)),
"Transmission" =
list("Automatic" = ~ qwraps2::n_perc0(am == 0),
"Manual" = ~ qwraps2::n_perc0(am == 1))
)
gear_summary <-
setNames(gear_summary,
c(
paste("Forward Gears: ", frmtp(fisher.test(xtabs( ~ gear + cyl_factor, data = mtcars2))$p.value)),
paste("Transmission: ", frmtp(fisher.test(xtabs( ~ am + cyl_factor, data = mtcars2))$p.value)))
)
summary_table(mtcars2, gear_summary, by = "cyl_factor")| 6 cylinders (N = 7) | 4 cylinders (N = 11) | 8 cylinders (N = 14) | |
|---|---|---|---|
| Forward Gears: P < 0.0001 | |||
| Three | 2 (29) | 1 (9) | 12 (86) |
| Four | 4 (57) | 8 (73) | 0 (0) |
| Five | 1 (14) | 2 (18) | 2 (14) |
| Transmission: P = 0.0091 | |||
| Automatic | 4 (57) | 3 (27) | 12 (86) |
| Manual | 3 (43) | 8 (73) | 2 (14) |
There is a rbind method of summary tables. This can be useful when
building a large a table in smaller sections would be advantageous. For
example, it might be helpful to add p-values to a summary table with
just one row group and then rbind all the tables together for printing.
Consider that in the above example for adding p-values we have made an
assumption that the order of the summary and the mpvals
will be static. Remembering to make the sequence changes in more than
one location can be more difficult than we would like to admit. Writing
code to be robust to such changes is preferable.
t_mpg <- summary_table(mtcars2, summaries = our_summary1["Miles Per Gallon"], by = "cyl_factor")
t_disp <- summary_table(mtcars2, summaries = our_summary1["Displacement"], by = "cyl_factor")
t_wt <- summary_table(mtcars2, summaries = our_summary1["Weight (1000 lbs)"], by = "cyl_factor")
t_mpg <- cbind(t_mpg, "pvalue" = "")
t_disp <- cbind(t_disp, "pvalue" = "")
t_wt <- cbind(t_wt, "pvalue" = "")
t_mpg[ grepl("mean", t_mpg[, 1]), "pvalue"] <- "mpg-pvalue"
t_disp[grepl("mean", t_disp[, 1]), "pvalue"] <- "disp-pvalue"
t_wt[ grepl("mean", t_wt[, 1]), "pvalue"] <- "wt-pvalue"Calling rbind now will let us have the table in different sequences without having to worry about the alignment of rows between different elements:
rbind(t_mpg, t_disp, t_wt)
##
##
## | |6 cylinders (N = 7) |4 cylinders (N = 11) |8 cylinders (N = 14) |pvalue |
## |:----------------------|:---------------------|:---------------------|:---------------------|:-----------|
## |**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 |mpg-pvalue |
## |**Displacement** | | | | |
## | min |145 |71.1 |275.8 | |
## | median |167.6 |108 |350.5 | |
## | max |258 |146.7 |472 | |
## | mean (sd) |183.31 ± 41.56 |105.14 ± 26.87 |353.10 ± 67.77 |disp-pvalue |
## |**Weight (1000 lbs)** | | | | |
## | min |2.62 |1.513 |3.17 | |
## | max |3.46 |3.19 |5.424 | |
## | mean (sd) |3.12 ± 0.36 |2.29 ± 0.57 |4.00 ± 0.76 |wt-pvalue |
rbind(t_wt, t_disp, t_mpg)
##
##
## | |6 cylinders (N = 7) |4 cylinders (N = 11) |8 cylinders (N = 14) |pvalue |
## |:----------------------|:---------------------|:---------------------|:---------------------|:-----------|
## |**Weight (1000 lbs)** | | | | |
## | min |2.62 |1.513 |3.17 | |
## | max |3.46 |3.19 |5.424 | |
## | mean (sd) |3.12 ± 0.36 |2.29 ± 0.57 |4.00 ± 0.76 |wt-pvalue |
## |**Displacement** | | | | |
## | min |145 |71.1 |275.8 | |
## | median |167.6 |108 |350.5 | |
## | max |258 |146.7 |472 | |
## | mean (sd) |183.31 ± 41.56 |105.14 ± 26.87 |353.10 ± 67.77 |disp-pvalue |
## |**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 |mpg-pvalue |Some data management paradigms will use attributes to keep a label associated with a variable in a data.frame. Notable examples are the Hmisc and sjPlot. If you associate a label with a variable in the data frame the that label will be used when building a summary table. This feature was suggested https://github.com/dewittpe/qwraps2/issues/74 and implemented thusly:
new_data_frame <-
data.frame(age = c(18, 20, 24, 17, 43),
edu = c(1, 3, 1, 5, 2),
rt = c(0.01, 0.04, 0.02, 0.10, 0.06))
# Set a label for the variables
attr(new_data_frame$age, "label") <- "Age in years"
attr(new_data_frame$rt, "label") <- "Reaction time"
# mistakenly set the attribute to name instead of label
attr(new_data_frame$edu, "name") <- "Education"When calling qsummary the provide labels for the age and
rt variables will be used. Since the attribute “label” does not exist
for the edu variable, edu will be used in the output.
qsummary(new_data_frame)
## $`Age in years`
## $`Age in years`$minimum
## ~qwraps2::frmt(min(age))
##
## $`Age in years`$`median (IQR)`
## ~qwraps2::median_iqr(age)
##
## $`Age in years`$`mean (sd)`
## ~qwraps2::mean_sd(age)
##
## $`Age in years`$maximum
## ~qwraps2::frmt(max(age))
##
##
## $edu
## $edu$minimum
## ~qwraps2::frmt(min(edu))
##
## $edu$`median (IQR)`
## ~qwraps2::median_iqr(edu)
##
## $edu$`mean (sd)`
## ~qwraps2::mean_sd(edu)
##
## $edu$maximum
## ~qwraps2::frmt(max(edu))
##
##
## $`Reaction time`
## $`Reaction time`$minimum
## ~qwraps2::frmt(min(rt))
##
## $`Reaction time`$`median (IQR)`
## ~qwraps2::median_iqr(rt)
##
## $`Reaction time`$`mean (sd)`
## ~qwraps2::mean_sd(rt)
##
## $`Reaction time`$maximum
## ~qwraps2::frmt(max(rt))This behavior is also seen with the summary_table
call.
| new_data_frame (N = 5) | |
|---|---|
| Age in years | |
| minimum | 17.00 |
| median (IQR) | 20.00 (18.00, 24.00) |
| mean (sd) | 24.40 ± 10.74 |
| maximum | 43.00 |
| edu | |
| minimum | 1.00 |
| median (IQR) | 2.00 (1.00, 3.00) |
| mean (sd) | 2.40 ± 1.67 |
| maximum | 5.00 |
| Reaction time | |
| minimum | 0.01 |
| median (IQR) | 0.04 (0.02, 0.06) |
| mean (sd) | 0.05 ± 0.04 |
| maximum | 0.10 |
The task of building the summaries list-of-lists can be
tedious. The function qummaries is designed to make it
easier. qummaries will use a set of predefined functions to
summarize numeric columns of a data.frame, a set of arguments to pass to
n_perc for categorical (character and factor)
variables.
By default, calling summary_table will use the default
summary metrics defined by qsummary. The purpose of
qsummary is to provide the same summary for all numeric
variables within a data.frame and a single style of summary for
categorical variables within the data.frame. For example, the default
summary for a set of variables from the mtcars2 data set
is
qsummary(mtcars2[, c("mpg", "cyl_factor", "wt")])
## $mpg
## $mpg$minimum
## ~qwraps2::frmt(min(mpg))
##
## $mpg$`median (IQR)`
## ~qwraps2::median_iqr(mpg)
##
## $mpg$`mean (sd)`
## ~qwraps2::mean_sd(mpg)
##
## $mpg$maximum
## ~qwraps2::frmt(max(mpg))
##
##
## $cyl_factor
## $cyl_factor$`6 cylinders`
## ~qwraps2::n_perc(cyl_factor == "6 cylinders", digits = 0, show_symbol = FALSE)
##
## $cyl_factor$`4 cylinders`
## ~qwraps2::n_perc(cyl_factor == "4 cylinders", digits = 0, show_symbol = FALSE)
##
## $cyl_factor$`8 cylinders`
## ~qwraps2::n_perc(cyl_factor == "8 cylinders", digits = 0, show_symbol = FALSE)
##
##
## $wt
## $wt$minimum
## ~qwraps2::frmt(min(wt))
##
## $wt$`median (IQR)`
## ~qwraps2::median_iqr(wt)
##
## $wt$`mean (sd)`
## ~qwraps2::mean_sd(wt)
##
## $wt$maximum
## ~qwraps2::frmt(max(wt))That default summary is used for a table as follows:
| mtcars2[, c(“mpg”, “cyl_factor”, “wt”)] (N = 32) | |
|---|---|
| mpg | |
| minimum | 10.40 |
| median (IQR) | 19.20 (15.43, 22.80) |
| mean (sd) | 20.09 ± 6.03 |
| maximum | 33.90 |
| cyl_factor | |
| 6 cylinders | 7 (22) |
| 4 cylinders | 11 (34) |
| 8 cylinders | 14 (44) |
| wt | |
| minimum | 1.51 |
| median (IQR) | 3.33 (2.58, 3.61) |
| mean (sd) | 3.22 ± 0.98 |
| maximum | 5.42 |
Now, say we want to only report the minimum and maximum for each of
the numeric variables and for the categorical variables we want two show
the denominator for each category and for the percentage, to one digit
with the percent symbol in the table. Note that when defining the list
of numeric_summaries that the argument place holder is the
%s% character.
new_summary <-
qsummary(mtcars2[, c("mpg", "cyl_factor", "wt")],
numeric_summaries = list("Minimum" = "~ min(%s)",
"Maximum" = "~ max(%s)"),
n_perc_args = list(digits = 1, show_symbol = TRUE, show_denom = "always"))
str(new_summary)
## List of 3
## $ mpg :List of 2
## ..$ Minimum:Class 'formula' language ~min(mpg)
## .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## ..$ Maximum:Class 'formula' language ~max(mpg)
## .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## $ cyl_factor:List of 3
## ..$ 6 cylinders:Class 'formula' language ~qwraps2::n_perc(cyl_factor == "6 cylinders", digits = 1, show_symbol = TRUE, show_denom = "always")
## .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## ..$ 4 cylinders:Class 'formula' language ~qwraps2::n_perc(cyl_factor == "4 cylinders", digits = 1, show_symbol = TRUE, show_denom = "always")
## .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## ..$ 8 cylinders:Class 'formula' language ~qwraps2::n_perc(cyl_factor == "8 cylinders", digits = 1, show_symbol = TRUE, show_denom = "always")
## .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## $ wt :List of 2
## ..$ Minimum:Class 'formula' language ~min(wt)
## .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## ..$ Maximum:Class 'formula' language ~max(wt)
## .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>The resulting table is:
| mtcars2 (N = 32) | |
|---|---|
| mpg | |
| Minimum | 10.4 |
| Maximum | 33.9 |
| cyl_factor | |
| 6 cylinders | 7/32 (21.9%) |
| 4 cylinders | 11/32 (34.4%) |
| 8 cylinders | 14/32 (43.8%) |
| wt | |
| Minimum | 1.513 |
| Maximum | 5.424 |
print(sessionInfo(), local = FALSE)
## R version 4.3.2 (2023-10-31)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] survival_3.5-7 qwraps2_0.6.0
##
## loaded via a namespace (and not attached):
## [1] glmnet_4.1-8 Matrix_1.6-2 gtable_0.3.4 jsonlite_1.8.7
## [5] dplyr_1.1.3 compiler_4.3.2 highr_0.10 tidyselect_1.2.0
## [9] Rcpp_1.0.11 jquerylib_0.1.4 splines_4.3.2 scales_1.2.1
## [13] yaml_2.3.7 fastmap_1.1.1 lattice_0.22-5 ggplot2_3.4.4
## [17] R6_2.5.1 labeling_0.4.3 generics_0.1.3 shape_1.4.6
## [21] knitr_1.45 iterators_1.0.14 tibble_3.2.1 munsell_0.5.0
## [25] bslib_0.5.1 pillar_1.9.0 rlang_1.1.2 utf8_1.2.4
## [29] cachem_1.0.8 xfun_0.41 sass_0.4.7 cli_3.6.1
## [33] withr_2.5.2 magrittr_2.0.3 foreach_1.5.2 digest_0.6.33
## [37] grid_4.3.2 lifecycle_1.0.4 vctrs_0.6.4 evaluate_0.23
## [41] glue_1.6.2 farver_2.1.1 codetools_0.2-19 fansi_1.0.5
## [45] colorspace_2.1-0 rmarkdown_2.25 tools_4.3.2 pkgconfig_2.0.3
## [49] htmltools_0.5.7