While the report generation functionality of
pander and knitr do overlap, we feel that the
most powerful way to use R/knitr/pander for report
generation is to utilize them together. This short vignette aims to
explain how to embed pander output in reports generated by
knitr. If you are not aware of knitr, be sure
to check out the project’s homepage for extensive documentation
and examples.
One of knitr’s most useful features is the ability to
convert tables to output format on the fly. For example:
head(iris)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
knitr::kable(head(iris))| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species |
|---|---|---|---|---|
| 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 5.0 | 3.6 | 1.4 | 0.2 | setosa |
| 5.4 | 3.9 | 1.7 | 0.4 | setosa |
However, kable table generator is simple by design, and
does not capture all the variety of classes that R has to
offer. For example, CrossTable and tabular are
not supported:
library(descr, quietly = TRUE)
ct <- CrossTable(mtcars$gear, mtcars$cyl)
#> Warning in chisq.test(tab, correct = FALSE, ...): Chi-squared approximation may
#> be incorrect
knitr::kable(ct)
#> Error in as.data.frame.default(x): cannot coerce class '"CrossTable"' to a data.frame
library(tables, quietly = TRUE)
tab <- tabular( (Species + 1) ~ (n=1) + Format(digits=2)*
(Sepal.Length + Sepal.Width)*(mean + sd), data=iris )
knitr::kable(tab)| term | term | term | term | term |
|---|---|---|---|---|
| 50 | 5.006 | 0.352489687213451 | 3.428 | 0.379064369096289 |
| 50 | 5.936 | 0.516171147063863 | 2.77 | 0.313798323378411 |
| 50 | 6.588 | 0.635879593274432 | 2.974 | 0.322496638172637 |
| 150 | 5.84333333333333 | 0.828066127977863 | 3.05733333333333 | 0.435866284936698 |
This is where pander comes in handy, as
pander supports rendering for many popular classes:
methods(pander)
#> [1] pander.Arima* pander.CrossTable* pander.Date*
#> [4] pander.Glm* pander.NULL* pander.POSIXct*
#> [7] pander.POSIXlt* pander.anova* pander.aov*
#> [10] pander.aovlist* pander.call* pander.cast_df*
#> [13] pander.character* pander.clogit* pander.coxph*
#> [16] pander.cph* pander.data.frame* pander.data.table*
#> [19] pander.default* pander.density* pander.describe*
#> [22] pander.ets* pander.evals* pander.factor*
#> [25] pander.formula* pander.ftable* pander.function*
#> [28] pander.glm* pander.gtable* pander.htest*
#> [31] pander.image* pander.irts* pander.list*
#> [34] pander.lm* pander.lme* pander.logical*
#> [37] pander.lrm* pander.manova* pander.matrix*
#> [40] pander.microbenchmark* pander.name* pander.nls*
#> [43] pander.numeric* pander.ols* pander.orm*
#> [46] pander.polr* pander.prcomp* pander.randomForest*
#> [49] pander.rapport* pander.rlm* pander.sessionInfo*
#> [52] pander.smooth.spline* pander.stat.table* pander.summary.aov*
#> [55] pander.summary.aovlist* pander.summary.glm* pander.summary.lm*
#> [58] pander.summary.lme* pander.summary.manova* pander.summary.nls*
#> [61] pander.summary.polr* pander.summary.prcomp* pander.summary.rms*
#> [64] pander.summary.survreg* pander.summary.table* pander.survdiff*
#> [67] pander.survfit* pander.survreg* pander.table*
#> [70] pander.tabular* pander.ts* pander.zoo*
#> see '?methods' for accessing help and source codeAlso, pander is integrated with knitr by
default. pander simply identifies if knitr is
running in the background, and if so, it uses
capture.output to return the resulting string as an
knit_asis object, meaning that you do not need to specify
the results='asis' option in your knitr chunk:
library(descr, quietly = TRUE)
pander(CrossTable(mtcars$gear, mtcars$cyl))
#> Warning in chisq.test(tab, correct = FALSE, ...): Chi-squared approximation may
#> be incorrect| mtcars$gear |
mtcars$cyl 4 |
6 |
8 |
Total |
|---|---|---|---|---|
| 3 N Chi-square Row(%) Column(%) Total(%) |
1 3.3502 6.6667% 9.0909% 3.125% |
2 0.5003 13.3333% 28.5714% 6.250% |
12 4.5054 80.0000% 85.7143% 37.500% |
15 46.8750% |
| 4 N Chi-square Row(%) Column(%) Total(%) |
8 3.6402 66.6667% 72.7273% 25.000% |
4 0.7202 33.3333% 57.1429% 12.500% |
0 5.2500 0.0000% 0.0000% 0.000% |
12 37.5000% |
| 5 N Chi-square Row(%) Column(%) Total(%) |
2 0.0460 40.0000% 18.1818% 6.250% |
1 0.0080 20.0000% 14.2857% 3.125% |
2 0.0161 40.0000% 14.2857% 6.250% |
5 15.6250% |
| Total |
11 34.375% |
7 21.875% |
14 43.75% |
32 |
library(tables, quietly = TRUE)
tab <- tabular( (Species + 1) ~ (n=1) + Format(digits=2)*
(Sepal.Length + Sepal.Width)*(mean + sd), data=iris )
pander(tab)Species |
n |
Sepal.Length mean |
sd |
Sepal.Width mean |
sd |
|---|---|---|---|---|---|
| setosa | 50 | 5.01 | 0.35 | 3.43 | 0.38 |
| versicolor | 50 | 5.94 | 0.52 | 2.77 | 0.31 |
| virginica | 50 | 6.59 | 0.64 | 2.97 | 0.32 |
| All | 150 | 5.84 | 0.83 | 3.06 | 0.44 |
In a nutshell, this is achieved by modification that whenever you
call pander inside of a knitr document,
instead of returning the markdown text to the standard output (the
default behavior), pander returns a knit_asis
class object, which renders correctly in the resulting document —
without the double comment chars, thus properly rendering the tables in
HTML, PDF, or other document formats.
If you don’t want the results of pander to be converted
automatically, just set knitr.auto.asis to
FALSE using panderOptions:
panderOptions('knitr.auto.asis', FALSE)
pander(head(iris))
#>
#> -------------------------------------------------------------------
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> -------------- ------------- -------------- ------------- ---------
#> 5.1 3.5 1.4 0.2 setosa
#>
#> 4.9 3 1.4 0.2 setosa
#>
#> 4.7 3.2 1.3 0.2 setosa
#>
#> 4.6 3.1 1.5 0.2 setosa
#>
#> 5 3.6 1.4 0.2 setosa
#>
#> 5.4 3.9 1.7 0.4 setosa
#> -------------------------------------------------------------------One frequenly asked question is how to use pander with
knitr in a loop or vectorized function. For example, we
have 3 tables that we want to render using lapply:
dfs <- list(mtcars[1:3, 1:4], mtcars[4:6, 1:4], mtcars[7:9, 1:4])
lapply(dfs, pander)
#> [[1]]
#> [1] "\n---------------------------------------------\n mpg cyl disp hp \n------------------- ------ ----- ------ -----\n **Mazda RX4** 21 6 160 110 \n\n **Mazda RX4 Wag** 21 6 160 110 \n\n **Datsun 710** 22.8 4 108 93 \n---------------------------------------------\n\n"
#> attr(,"class")
#> [1] "knit_asis"
#> attr(,"knit_cacheable")
#> [1] NA
#>
#> [[2]]
#> [1] "\n-------------------------------------------------\n mpg cyl disp hp \n----------------------- ------ ----- ------ -----\n **Hornet 4 Drive** 21.4 6 258 110 \n\n **Hornet Sportabout** 18.7 8 360 175 \n\n **Valiant** 18.1 6 225 105 \n-------------------------------------------------\n\n"
#> attr(,"class")
#> [1] "knit_asis"
#> attr(,"knit_cacheable")
#> [1] NA
#>
#> [[3]]
#> [1] "\n-------------------------------------------\n mpg cyl disp hp \n---------------- ------ ----- ------- -----\n **Duster 360** 14.3 8 360 245 \n\n **Merc 240D** 24.4 4 146.7 62 \n\n **Merc 230** 22.8 4 140.8 95 \n-------------------------------------------\n\n"
#> attr(,"class")
#> [1] "knit_asis"
#> attr(,"knit_cacheable")
#> [1] NAAs you can see, this doesn’t work correctly because
pander tries to return a knit_asis class
object when run inside knitr, but for loops/vectorized
functions this results in incorrect output. The recommended way to solve
this is to disable this behavior by setting knitr.auto.asis
to FALSE using panderOptions. However, we also
need to tell knitr to convert the table on the fly by
specifying results='asis' in the chunk options:
panderOptions('knitr.auto.asis', FALSE)
dfs <- list(mtcars[1:3, 1:4], mtcars[4:6, 1:4], mtcars[7:9, 1:4])
invisible(lapply(dfs, pander))| mpg | cyl | disp | hp | |
|---|---|---|---|---|
| Mazda RX4 | 21 | 6 | 160 | 110 |
| Mazda RX4 Wag | 21 | 6 | 160 | 110 |
| Datsun 710 | 22.8 | 4 | 108 | 93 |
| mpg | cyl | disp | hp | |
|---|---|---|---|---|
| Hornet 4 Drive | 21.4 | 6 | 258 | 110 |
| Hornet Sportabout | 18.7 | 8 | 360 | 175 |
| Valiant | 18.1 | 6 | 225 | 105 |
| mpg | cyl | disp | hp | |
|---|---|---|---|---|
| Duster 360 | 14.3 | 8 | 360 | 245 |
| Merc 240D | 24.4 | 4 | 146.7 | 62 |
| Merc 230 | 22.8 | 4 | 140.8 | 95 |