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)
#>
#> Attaching package: 'Hmisc'
#> The following objects are masked from 'package:base':
#>
#> format.pval, round.POSIXt, trunc.POSIXt, units
tab <- tabular( (Species + 1) ~ (n=1) + Format(digits=2)*
(Sepal.Length + Sepal.Width)*(mean + sd), data=iris )
knitr::kable(tab)
#> Error in `colnames<-`(`*tmp*`, value = c("term", "term", "term", "term", : length of 'dimnames' [2] not equal to array extentThis is where pander comes in handy, as pander supports rendering for many popular classes:
methods(pander)
#> [1] pander.anova* pander.aov*
#> [3] pander.aovlist* pander.Arima*
#> [5] pander.call* pander.cast_df*
#> [7] pander.character* pander.clogit*
#> [9] pander.coxph* pander.cph*
#> [11] pander.CrossTable* pander.data.frame*
#> [13] pander.data.table* pander.Date*
#> [15] pander.default* pander.density*
#> [17] pander.describe* pander.ets*
#> [19] pander.evals* pander.factor*
#> [21] pander.formula* pander.ftable*
#> [23] pander.function* pander.glm*
#> [25] pander.Glm* pander.gtable*
#> [27] pander.htest* pander.image*
#> [29] pander.irts* pander.list*
#> [31] pander.lm* pander.lme*
#> [33] pander.logical* pander.lrm*
#> [35] pander.manova* pander.matrix*
#> [37] pander.microbenchmark* pander.mtable*
#> [39] pander.name* pander.nls*
#> [41] pander.NULL* pander.numeric*
#> [43] pander.ols* pander.orm*
#> [45] pander.polr* pander.POSIXct*
#> [47] pander.POSIXlt* pander.prcomp*
#> [49] pander.randomForest* pander.rapport*
#> [51] pander.rlm* pander.sessionInfo*
#> [53] pander.smooth.spline* pander.stat.table*
#> [55] pander.summary.aov* pander.summary.aovlist*
#> [57] pander.summary.glm* pander.summary.lm*
#> [59] pander.summary.lme* pander.summary.manova*
#> [61] pander.summary.nls* pander.summary.polr*
#> [63] pander.summary.prcomp* pander.summary.rms*
#> [65] pander.summary.survreg* pander.summary.table*
#> [67] pander.survdiff* pander.survfit*
#> [69] pander.survreg* pander.table*
#> [71] pander.tabular* pander.ts*
#> [73] 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
#> -------------------------------------------------------------------panderOptions('knitr.auto.asis', TRUE)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 |