ascii is a R package for writing asciidoc, txt2tags, sphinx or org documents with embeded R code.

news

2009/10/28

2009/10/24

2009/10/24

2009/07/16

2009/05/11

2009/04/27

2009/04/08

short example

<<>>=
x <- matrix(1:4, 2, 2)
x
@

gives :

> x <- matrix(1:4, 2, 2)
> x
     [,1] [,2]
[1,]    1    3
[2,]    2    4
<<results=ascii,echo=FALSE>>=
ascii(x, caption = "A simple matrix", width = 30)
@

gives :

Table 1: A simple matrix

1.00

3.00

2.00

4.00

what ascii provides

ascii provided :

features/options

See ?ascii for a complete description of all arguments.

asciidoc txt2tags sphinx org

Arguments

include.rownames

yes

yes

yes

yes

include.colnames

yes

yes

yes

yes

rownames

yes

yes

yes

yes

colnames

yes

yes

yes

yes

format

yes

yes

yes

yes

digits

yes

yes

yes

yes

decimal.mark

yes

yes

yes

yes

na.print

yes

yes

yes

yes

caption

yes

yes

yes

yes

caption.level

yes

yes

yes

yes

width

yes

no

no

no

frame

yes

yes (all or none)

no

no

grid

yes

no

no

no

valign

yes

no

no

no

header

yes

yes

yes

yes

footer

yes

yes

no

no

align

yes

yes

no

no

col.width

yes

no

no

no

style

yes

yes

yes

yes

cgroup

yes

yes

yes

no

n.cgroup

yes

yes

yes

no

calign

yes

yes

no

no

cvalign

yes

no

no

no

cstyle

yes

yes

yes

no

rgroup

yes

no

yes

no

n.rgroup

yes

no

yes

no

ralign

yes

no

no

no

rvalign

yes

no

no

no

rstyle

yes

no

yes

no

list.type

yes

yes

yes

yes

condense

yes

yes

yes

yes

Output

html

yes

yes

yes

yes

docbook

yes

yes

no

yes

latex

yes (experimental)

yes

yes (col and row spans not implemented yet)

yes

Feature

syntax color

yes (but not for R…)

no

yes

yes

ascii examples

ascii provides methods for:

> methods(ascii)
 [1] ascii.anova*              ascii.aov*
 [3] ascii.aovlist*            ascii.cast_df*
 [5] ascii.character*          ascii.coxph*
 [7] ascii.data.frame*         ascii.default*
 [9] ascii.density*            ascii.describe*
[11] ascii.describe.single*    ascii.factor*
[13] ascii.ftable*             ascii.glm*
[15] ascii.htest*              ascii.integer*
[17] ascii.list*               ascii.lm*
[19] ascii.matrix*             ascii.numeric*
[21] ascii.packageDescription* ascii.prcomp*
[23] ascii.sessionInfo*        ascii.simple.list*
[25] ascii.smooth.spline*      ascii.summary.aov*
[27] ascii.summary.aovlist*    ascii.summary.glm*
[29] ascii.summary.lm*         ascii.summary.prcomp*
[31] ascii.summary.table*      ascii.survdiff*
[33] ascii.table*              ascii.ts*
[35] ascii.zoo*

   Non-visible functions are asterisked

sessionInfo

> ascii(sessionInfo())
R version::
   R version 2.10.0 (2009-10-26), i486-pc-linux-gnu
locale::
   LC_CTYPE=fr_FR.UTF-8, LC_NUMERIC=C, LC_TIME=fr_FR.UTF-8, LC_COLLATE=fr_FR.UTF-8, LC_MONETARY=C, LC_MESSAGES=fr_FR.UTF-8, LC_PAPER=fr_FR.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=fr_FR.UTF-8, LC_IDENTIFICATION=C
attached base packages::
  stats, graphics, grDevices, utils, datasets, methods, base
other attached packages::
   ascii_0.3.2, proto_0.3-8
loaded via a namespace (and not attached)::
  tools_2.10.0
R version

R version 2.10.0 (2009-10-26), i486-pc-linux-gnu

locale

LC_CTYPE=fr_FR.UTF-8, LC_NUMERIC=C, LC_TIME=fr_FR.UTF-8, LC_COLLATE=fr_FR.UTF-8, LC_MONETARY=C, LC_MESSAGES=fr_FR.UTF-8, LC_PAPER=fr_FR.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=fr_FR.UTF-8, LC_IDENTIFICATION=C

attached base packages

stats, graphics, grDevices, utils, datasets, methods, base

other attached packages

ascii_0.3.2, proto_0.3-8

loaded via a namespace (and not attached)

tools_2.10.0

vector

> ascii(1:4)
|==========================
| 1.00 | 2.00 | 3.00 | 4.00
|==========================

1.00

2.00

3.00

4.00

matrix

> ascii(VADeaths, include.rownames = T, include.colnames = T, caption = "VADeaths",
+     header = T, col.width = c(1, 2, 2, 2, 2, 2), valign = "middle",
+     align = "lrrrr", frame = "topbot")
.VADeaths
[frame="topbot",valign="middle",options="header",cols="1,2,2,2,2"]
|=============================================================================
<.^|       >.^| Rural Male >.^| Rural Female >.^| Urban Male >.^| Urban Female
<.^| 50-54 >.^| 11.70      >.^| 8.70         >.^| 15.40      >.^| 8.40
<.^| 55-59 >.^| 18.10      >.^| 11.70        >.^| 24.30      >.^| 13.60
<.^| 60-64 >.^| 26.90      >.^| 20.30        >.^| 37.00      >.^| 19.30
<.^| 65-69 >.^| 41.00      >.^| 30.90        >.^| 54.60      >.^| 35.10
<.^| 70-74 >.^| 66.00      >.^| 54.30        >.^| 71.10      >.^| 50.00
|=============================================================================
Table 2: VADeaths
Rural Male Rural Female Urban Male Urban Female

50-54

11.70

8.70

15.40

8.40

55-59

18.10

11.70

24.30

13.60

60-64

26.90

20.30

37.00

19.30

65-69

41.00

30.90

54.60

35.10

70-74

66.00

54.30

71.10

50.00

data.frame

> ascii(iris[1:10, ], include.rownames = F, caption = "iris", width = 75,
+     align = "c", valign = "bottom", frame = "topbot", grid = "none")
.iris
[frame="topbot",grid="none",valign="bottom",options="header",width="75%"]
|=================================================================================
^.>| Sepal.Length ^.>| Sepal.Width ^.>| Petal.Length ^.>| Petal.Width ^.>| Species
^.>| 5.10         ^.>| 3.50        ^.>| 1.40         ^.>| 0.20        ^.>| setosa
^.>| 4.90         ^.>| 3.00        ^.>| 1.40         ^.>| 0.20        ^.>| setosa
^.>| 4.70         ^.>| 3.20        ^.>| 1.30         ^.>| 0.20        ^.>| setosa
^.>| 4.60         ^.>| 3.10        ^.>| 1.50         ^.>| 0.20        ^.>| setosa
^.>| 5.00         ^.>| 3.60        ^.>| 1.40         ^.>| 0.20        ^.>| setosa
^.>| 5.40         ^.>| 3.90        ^.>| 1.70         ^.>| 0.40        ^.>| setosa
^.>| 4.60         ^.>| 3.40        ^.>| 1.40         ^.>| 0.30        ^.>| setosa
^.>| 5.00         ^.>| 3.40        ^.>| 1.50         ^.>| 0.20        ^.>| setosa
^.>| 4.40         ^.>| 2.90        ^.>| 1.40         ^.>| 0.20        ^.>| setosa
^.>| 4.90         ^.>| 3.10        ^.>| 1.50         ^.>| 0.10        ^.>| setosa
|=================================================================================
Table 3: iris
Sepal.Length Sepal.Width Petal.Length Petal.Width Species

5.10

3.50

1.40

0.20

setosa

4.90

3.00

1.40

0.20

setosa

4.70

3.20

1.30

0.20

setosa

4.60

3.10

1.50

0.20

setosa

5.00

3.60

1.40

0.20

setosa

5.40

3.90

1.70

0.40

setosa

4.60

3.40

1.40

0.30

setosa

5.00

3.40

1.50

0.20

setosa

4.40

2.90

1.40

0.20

setosa

4.90

3.10

1.50

0.10

setosa

row (and col) headings

> library(reshape)
> ff_d <- melt(french_fries, id = 1:4, na.rm = TRUE)
> toto <- cast(ff_d, treatment + subject ~ variable, mean, margins = "treatment")
> ascii(toto[, -1], rgroup = c("Treatment", paste("Treatment:",
+     as.character(unique(toto[, 1])))), n.rgroup = c(1, table(toto[,
+     1])), rstyle = "s", rvalign = "middle")
[options="header"]
|============================================================================
.1+.^s| Treatment | subject | potato | buttery | grassy | rancid | painty
.13+.^s| Treatment: 1 | 3       | 6.22   | 0.37    | 0.19   | 2.11   | 3.11
| 10      | 9.96   | 6.75    | 0.58   | 4.02   | 1.38
| 15      | 3.36   | 0.72    | 0.42   | 3.96   | 3.26
| 16      | 6.50   | 3.26    | 0.76   | 4.12   | 1.23
| 19      | 9.38   | 3.06    | 2.02   | 5.36   | 2.77
| 31      | 8.84   | 0.44    | 0.09   | 5.94   | 3.21
| 51      | 10.68  | 2.64    | 1.05   | 5.15   | 1.96
| 52      | 5.06   | 0.81    | 0.88   | 4.29   | 2.65
| 63      | 6.78   | 0.03    | 0.00   | 6.05   | 3.85
| 78      | 3.62   | 0.73    | 0.54   | 1.50   | 3.49
| 79      | 8.06   | 0.28    | 0.34   | 0.57   | 0.00
| 86      | 4.18   | 1.77    | 0.81   | 5.49   | 4.11
| (all)   | 6.89   | 1.78    | 0.65   | 4.07   | 2.58
.13+.^s| Treatment: 2 | 3       | 6.74   | 0.59    | 0.11   | 3.14   | 2.48
| 10      | 9.99   | 6.98    | 0.47   | 2.15   | 0.82
| 15      | 4.41   | 1.31    | 0.34   | 2.29   | 2.06
| 16      | 6.45   | 3.37    | 1.05   | 3.40   | 0.46
| 19      | 8.64   | 2.45    | 1.14   | 5.41   | 4.16
| 31      | 8.03   | 0.62    | 0.16   | 6.05   | 5.06
| 51      | 9.98   | 3.79    | 1.57   | 4.67   | 2.25
| 52      | 5.51   | 1.02    | 1.18   | 4.22   | 2.19
| 63      | 8.41   | 0.10    | 0.01   | 5.09   | 4.36
| 78      | 3.78   | 0.29    | 0.76   | 1.55   | 2.73
| 79      | 7.94   | 0.69    | 0.26   | 1.03   | 0.00
| 86      | 3.99   | 2.06    | 0.78   | 4.52   | 2.84
| (all)   | 7.00   | 1.97    | 0.66   | 3.62   | 2.46
.13+.^s| Treatment: 3 | 3       | 5.29   | 0.77    | 0.09   | 2.86   | 2.87
| 10      | 10.03  | 6.45    | 0.14   | 3.11   | 0.69
| 15      | 3.96   | 0.99    | 0.44   | 2.55   | 2.37
| 16      | 6.86   | 2.70    | 1.12   | 3.20   | 0.56
| 19      | 8.74   | 1.73    | 2.07   | 7.24   | 3.90
| 31      | 9.03   | 0.65    | 0.17   | 6.58   | 5.13
| 51      | 10.22  | 3.13    | 1.35   | 4.92   | 2.54
| 52      | 5.47   | 0.86    | 0.77   | 3.16   | 2.66
| 63      | 8.06   | 0.07    | 0.12   | 6.18   | 3.10
| 78      | 4.00   | 0.70    | 0.67   | 1.19   | 3.52
| 79      | 7.73   | 0.57    | 0.12   | 1.18   | 0.03
| 86      | 3.87   | 1.63    | 0.94   | 4.11   | 3.03
| (all)   | 6.97   | 1.72    | 0.68   | 3.87   | 2.53
|============================================================================
Treatment subject potato buttery grassy rancid painty

Treatment: 1

3

6.22

0.37

0.19

2.11

3.11

10

9.96

6.75

0.58

4.02

1.38

15

3.36

0.72

0.42

3.96

3.26

16

6.50

3.26

0.76

4.12

1.23

19

9.38

3.06

2.02

5.36

2.77

31

8.84

0.44

0.09

5.94

3.21

51

10.68

2.64

1.05

5.15

1.96

52

5.06

0.81

0.88

4.29

2.65

63

6.78

0.03

0.00

6.05

3.85

78

3.62

0.73

0.54

1.50

3.49

79

8.06

0.28

0.34

0.57

0.00

86

4.18

1.77

0.81

5.49

4.11

(all)

6.89

1.78

0.65

4.07

2.58

Treatment: 2

3

6.74

0.59

0.11

3.14

2.48

10

9.99

6.98

0.47

2.15

0.82

15

4.41

1.31

0.34

2.29

2.06

16

6.45

3.37

1.05

3.40

0.46

19

8.64

2.45

1.14

5.41

4.16

31

8.03

0.62

0.16

6.05

5.06

51

9.98

3.79

1.57

4.67

2.25

52

5.51

1.02

1.18

4.22

2.19

63

8.41

0.10

0.01

5.09

4.36

78

3.78

0.29

0.76

1.55

2.73

79

7.94

0.69

0.26

1.03

0.00

86

3.99

2.06

0.78

4.52

2.84

(all)

7.00

1.97

0.66

3.62

2.46

Treatment: 3

3

5.29

0.77

0.09

2.86

2.87

10

10.03

6.45

0.14

3.11

0.69

15

3.96

0.99

0.44

2.55

2.37

16

6.86

2.70

1.12

3.20

0.56

19

8.74

1.73

2.07

7.24

3.90

31

9.03

0.65

0.17

6.58

5.13

51

10.22

3.13

1.35

4.92

2.54

52

5.47

0.86

0.77

3.16

2.66

63

8.06

0.07

0.12

6.18

3.10

78

4.00

0.70

0.67

1.19

3.52

79

7.73

0.57

0.12

1.18

0.03

86

3.87

1.63

0.94

4.11

3.03

(all)

6.97

1.72

0.68

3.87

2.53

summary.table

> ascii(summary(table(1:4, 1:4)))
* Number of cases in table: 4
* Number of factors: 2
* Test for independence of all factors:
** Chisq = 12, df = 9, p-value = 0.2133
** Chi-squared approximation may be incorrect

labeled list

> ascii(version)
platform::
   i486-pc-linux-gnu
arch::
  i486
os::
   linux-gnu
system::
  i486, linux-gnu
status::

major::
   2
minor::
   10.0
year::
   2009
month::
   10
day::
  26
svn rev::
  50208
language::
   R
version.string::
   R version 2.10.0 (2009-10-26)
platform

i486-pc-linux-gnu

arch

i486

os

linux-gnu

system

i486, linux-gnu

status
major

2

minor

10.0

year

2009

month

10

day

26

svn rev

50208

language

R

version.string

R version 2.10.0 (2009-10-26)

glm

> counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
> outcome <- gl(3, 1, 9)
> treatment <- gl(3, 3)
> d.AD <- data.frame(treatment, outcome, counts)
> glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
> glm.D93
Call:  glm(formula = counts ~ outcome + treatment, family = poisson())

Coefficients:
(Intercept)     outcome2     outcome3   treatment2   treatment3
  3.045e+00   -4.543e-01   -2.930e-01    8.717e-16    4.557e-16

Degrees of Freedom: 8 Total (i.e. Null);  4 Residual
Null Deviance:      10.58
Residual Deviance: 5.129        AIC: 56.76
> ascii(glm.D93, caption = "glm.D93")
.glm.D93
[options="header"]
|===========================================================
|             | Estimate | Std. Error | z value | Pr(>\|z\|)
| (Intercept) | 3.04     | 0.17       | 17.81   | 0.00
| outcome2    | -0.45    | 0.20       | -2.25   | 0.02
| outcome3    | -0.29    | 0.19       | -1.52   | 0.13
| treatment2  | 0.00     | 0.20       | 0.00    | 1.00
| treatment3  | 0.00     | 0.20       | 0.00    | 1.00
|===========================================================
> ascii(anova(glm.D93), caption = "anova glm.D93", include.rownames = T)
.anova glm.D93
[options="header"]
|=====================================================
|           | Df   | Deviance | Resid. Df | Resid. Dev
| NULL      |      |          | 8.00      | 10.58
| outcome   | 2.00 | 5.45     | 6.00      | 5.13
| treatment | 2.00 | 0.00     | 4.00      | 5.13
|=====================================================
Table 4: glm.D93
Estimate Std. Error z value Pr(>|z|)

(Intercept)

3.04

0.17

17.81

0.00

outcome2

-0.45

0.20

-2.25

0.02

outcome3

-0.29

0.19

-1.52

0.13

treatment2

0.00

0.20

0.00

1.00

treatment3

0.00

0.20

0.00

1.00
Table 5: anova glm.D93
Df Deviance Resid. Df Resid. Dev

NULL

8.00

10.58

outcome

2.00

5.45

6.00

5.13

treatment

2.00

0.00

4.00

5.13

describe

> library(Hmisc)
> label(esoph$agegp) <- "Age group"
> label(esoph$alcgp) <- "Alcohol group"
> label(esoph$tobgp) <- "Tobacco group"
> label(esoph$ncontrols) <- "Number of control"
> label(esoph$age) <- "Age"
> units(esoph$age) <- "Years"
> ascii(describe(esoph))
.esoph
* 6 Variable
* 88 Observations

*agegp : Age group*

|======================
| n  | missing | unique
| 88 | 0       | 6
|======================

|========================================================
|           | 25-34 | 35-44 | 45-54 | 55-64 | 65-74 | 75+
| Frequency | 15    | 15    | 16    | 16    | 15    | 11
| %         | 17    | 17    | 18    | 18    | 17    | 12
|========================================================

*alcgp : Alcohol group*

|======================
| n  | missing | unique
| 88 | 0       | 4
|======================

0-39g/day (23, 26%), 40-79 (23, 26%), 80-119 (21, 24%), 120+ (21, 24%)

*tobgp : Tobacco group*

|======================
| n  | missing | unique
| 88 | 0       | 4
|======================

0-9g/day (24, 27%), 10-19 (24, 27%), 20-29 (20, 23%), 30+ (20, 23%)

*ncases*

|========================================================================
| n  | missing | unique | Mean  | .05 | .10 | .25 | .50 | .75 | .90 | .95
| 88 | 0       | 10     | 2.273 | 0.0 | 0.0 | 0.0 | 1.0 | 4.0 | 5.3 | 6.0
|========================================================================

|=======================================================
|           | 0  | 1  | 2  | 3  | 4 | 5 | 6 | 8 | 9 | 17
| Frequency | 29 | 16 | 11 | 9  | 8 | 6 | 5 | 1 | 2 | 1
| %         | 33 | 18 | 12 | 10 | 9 | 7 | 6 | 1 | 2 | 1
|=======================================================

*ncontrols : Number of control*

|===============================================================================
| n  | missing | unique | Mean  | .05  | .10  | .25  | .50  | .75  | .90  | .95
| 88 | 0       | 30     | 11.08 |  1.0 |  1.0 |  3.0 |  6.0 | 14.0 | 29.1 | 40.0
|===============================================================================

lowest:  1  2  3  4  5, highest: 40 46 48 49 60

*age : Age [Years]*

|======================
| n  | missing | unique
| 88 | 0       | 6
|======================

|========================================================
|           | 25-34 | 35-44 | 45-54 | 55-64 | 65-74 | 75+
| Frequency | 15    | 15    | 16    | 16    | 15    | 11
| %         | 17    | 17    | 18    | 18    | 17    | 12
|========================================================
esoph

agegp : Age group

n

missing

unique

88

0

6

25-34

35-44

45-54

55-64

65-74

75+

Frequency

15

15

16

16

15

11

%

17

17

18

18

17

12

alcgp : Alcohol group

n

missing

unique

88

0

4

0-39g/day (23, 26%), 40-79 (23, 26%), 80-119 (21, 24%), 120+ (21, 24%)

tobgp : Tobacco group

n

missing

unique

88

0

4

0-9g/day (24, 27%), 10-19 (24, 27%), 20-29 (20, 23%), 30+ (20, 23%)

ncases

n

missing

unique

Mean

.05

.10

.25

.50

.75

.90

.95

88

0

10

2.273

0.0

0.0

0.0

1.0

4.0

5.3

6.0

0

1

2

3

4

5

6

8

9

17

Frequency

29

16

11

9

8

6

5

1

2

1

%

33

18

12

10

9

7

6

1

2

1

ncontrols : Number of control

n

missing

unique

Mean

.05

.10

.25

.50

.75

.90

.95

88

0

30

11.08

1.0

1.0

3.0

6.0

14.0

29.1

40.0

lowest: 1 2 3 4 5, highest: 40 46 48 49 60

age : Age [Years]

n

missing

unique

88

0

6

25-34

35-44

45-54

55-64

65-74

75+

Frequency

15

15

16

16

15

11

%

17

17

18

18

17

12

plot

> library(ggplot2)
> p <- qplot(price, ..density.., data = diamonds, geom = "freqpoly",
+     binwidth = 1000, colour = color)
> print(p)
index-ggplot2.jpg

other outputs

> library(reshape)
> names(airquality) <- tolower(names(airquality))
> aqm <- melt(airquality, id = c("month", "day"), na.rm = TRUE)
> res <- cast(aqm, month ~ variable, mean, margins = "grand_row")
> res
  month    ozone  solar.r      wind     temp
1     5 23.61538 181.2963 11.622581 65.54839
2     6 29.44444 190.1667 10.266667 79.10000
3     7 59.11538 216.4839  8.941935 83.90323
4     8 59.96154 171.8571  8.793548 83.96774
5     9 31.44828 167.4333 10.180000 76.90000
6 (all) 42.12931 185.9315  9.957516 77.88235
> print(ascii(res), "t2t")
|| month | ozone | solar.r | wind  | temp  |
| 5     | 23.62 | 181.30  | 11.62 | 65.55 |
| 6     | 29.44 | 190.17  | 10.27 | 79.10 |
| 7     | 59.12 | 216.48  | 8.94  | 83.90 |
| 8     | 59.96 | 171.86  | 8.79  | 83.97 |
| 9     | 31.45 | 167.43  | 10.18 | 76.90 |
| (all) | 42.13 | 185.93  | 9.96  | 77.88 |
> print(ascii(res), "sphinx")
+-------+-------+---------+-------+-------+
| month | ozone | solar.r | wind  | temp  |
+=======+=======+=========+=======+=======+
| 5     | 23.62 | 181.30  | 11.62 | 65.55 |
+-------+-------+---------+-------+-------+
| 6     | 29.44 | 190.17  | 10.27 | 79.10 |
+-------+-------+---------+-------+-------+
| 7     | 59.12 | 216.48  | 8.94  | 83.90 |
+-------+-------+---------+-------+-------+
| 8     | 59.96 | 171.86  | 8.79  | 83.97 |
+-------+-------+---------+-------+-------+
| 9     | 31.45 | 167.43  | 10.18 | 76.90 |
+-------+-------+---------+-------+-------+
| (all) | 42.13 | 185.93  | 9.96  | 77.88 |
+-------+-------+---------+-------+-------+
> print(ascii(res), "org")
|-------+-------+---------+-------+-------+
| month | ozone | solar.r | wind  | temp  |
|-------+-------+---------+-------+-------+
| 5     | 23.62 | 181.30  | 11.62 | 65.55 |
| 6     | 29.44 | 190.17  | 10.27 | 79.10 |
| 7     | 59.12 | 216.48  | 8.94  | 83.90 |
| 8     | 59.96 | 171.86  | 8.79  | 83.97 |
| 9     | 31.45 | 167.43  | 10.18 | 76.90 |
| (all) | 42.13 | 185.93  | 9.96  | 77.88 |
|-------+-------+---------+-------+-------+

Follow those links to see real examples:

convert

Sweave process creates a yourdocument.xxx file from yourdocument.Rnw.

Sweave("youdocument.Rnw", RweaveXxx)

You can convert it to html format with the following command:

asciidoc yourdocument.txt
or
txt2tags -t html yourdocument.t2t
or
sphinx-build -b html . yourdocument # need a conf.py file
or
Alt-X org-export-as-html

or to other formats…

For example, you can see the source of this documentation, the file generated by Sweave, the same file in docbook format, the same file converted to pdf with dblatex, and the same file converted to odt with docbook2odf.

more informations

There is another way to create org documents with embedded R code : org-babel. Thanks to Erik Iverson for informing me about org-mode and org-babel.

ascii for real