wrapped pander functions
wrapped_pander.RdThese functions are wrapped versions of their analogues in the pander package. They imbue the original implementations with logger functionality.
Arguments
- x
an R object
- ...
optional parameters passed to special methods and/or raw
pandoc.*functions
Value
By default this function outputs (see: cat) the result. If you would want to catch the result instead, then call the function ending in .return.
References
John MacFarlane (2013): _Pandoc User's Guide_. https://johnmacfarlane.net/pandoc/README.html
David Hajage (2011): _ascii. Export R objects to several markup languages._ https://cran.r-project.org/package=ascii
Hlavac, Marek (2013): _stargazer: LaTeX code for well-formatted regression and summary statistics tables._ https://cran.r-project.org/package=stargazer
Examples
## Vectors
pander(1:10)
#> _1_, _2_, _3_, _4_, _5_, _6_, _7_, _8_, _9_ and _10_
pander(letters)
#> _a_, _b_, _c_, _d_, _e_, _f_, _g_, _h_, _i_, _j_, _k_, _l_, _m_, _n_, _o_, _p_, _q_, _r_, _s_, _t_, _u_, _v_, _w_, _x_, _y_ and _z_
pander(mtcars$am)
#> _1_, _1_, _1_, _0_, _0_, _0_, _0_, _0_, _0_, _0_, _0_, _0_, _0_, _0_, _0_, _0_, _0_, _1_, _1_, _1_, _0_, _0_, _0_, _0_, _0_, _1_, _1_, _1_, _1_, _1_, _1_ and _1_
pander(factor(mtcars$am))
#> _2_, _2_, _2_, _1_, _1_, _1_, _1_, _1_, _1_, _1_, _1_, _1_, _1_, _1_, _1_, _1_, _1_, _2_, _2_, _2_, _1_, _1_, _1_, _1_, _1_, _2_, _2_, _2_, _2_, _2_, _2_ and _2_
## Lists
pander(list(1, 2, 3, c(1, 2)))
#>
#>
#> * _1_
#> * _2_
#> * _3_
#> * _1_ and _2_
#>
#> <!-- end of list -->
#>
#>
pander(list(a = 1, b = 2, c = table(mtcars$am)))
#>
#>
#> * **a**: _1_
#> * **b**: _2_
#> * **c**:
#>
#> ---------
#> 0 1
#> ---- ----
#> 19 13
#> ---------
#>
#>
#> <!-- end of list -->
#>
#>
pander(list(1, 2, 3, list(1, 2)))
#>
#>
#> * _1_
#> * _2_
#> * _3_
#> *
#>
#> * _1_
#> * _2_
#>
#>
#> <!-- end of list -->
#>
#>
pander(list(a = 1, 2, 3, list(1, 2)))
#>
#>
#> * **a**: _1_
#> * _2_
#> * _3_
#> *
#>
#> * _1_
#> * _2_
#>
#>
#> <!-- end of list -->
#>
#>
pander(list('FOO', letters[1:3], list(1:5), table(mtcars$gear), list('FOOBAR', list('a', 'b'))))
#>
#>
#> * FOO
#> * _a_, _b_ and _c_
#> *
#>
#> * _1_, _2_, _3_, _4_ and _5_
#>
#> *
#>
#> -------------
#> 3 4 5
#> ---- ---- ---
#> 15 12 5
#> -------------
#>
#> *
#>
#> * FOOBAR
#> *
#>
#> * a
#> * b
#>
#>
#>
#> <!-- end of list -->
#>
#>
pander(list(a = 1, b = 2, c = table(mtcars$am), x = list(myname = 1, 2), 56))
#>
#>
#> * **a**: _1_
#> * **b**: _2_
#> * **c**:
#>
#> ---------
#> 0 1
#> ---- ----
#> 19 13
#> ---------
#>
#> * **x**:
#>
#> * **myname**: _1_
#> * _2_
#>
#> * _56_
#>
#> <!-- end of list -->
#>
#>
pander(unclass(chisq.test(table(mtcars$am, mtcars$gear))))
#> Warning: Chi-squared approximation may be incorrect
#>
#>
#> * **statistic**:
#>
#> -----------
#> X-squared
#> -----------
#> 20.94
#> -----------
#>
#> * **parameter**:
#>
#> ----
#> df
#> ----
#> 2
#> ----
#>
#> * **p.value**: _2.831e-05_
#> * **method**: Pearson's Chi-squared test
#> * **data.name**: table(mtcars$am, mtcars$gear)
#> * **observed**:
#>
#> ---------------------
#> 3 4 5
#> -------- ---- --- ---
#> **0** 15 4 0
#>
#> **1** 0 8 5
#> ---------------------
#>
#> * **expected**:
#>
#> --------------------------------
#> 3 4 5
#> -------- ------- ------- -------
#> **0** 8.906 7.125 2.969
#>
#> **1** 6.094 4.875 2.031
#> --------------------------------
#>
#> * **residuals**:
#>
#> -----------------------------------
#> 3 4 5
#> -------- -------- -------- --------
#> **0** 2.042 -1.171 -1.723
#>
#> **1** -2.469 1.415 2.083
#> -----------------------------------
#>
#> * **stdres**:
#>
#> -----------------------------------
#> 3 4 5
#> -------- -------- -------- --------
#> **0** 4.395 -2.323 -2.943
#>
#> **1** -4.395 2.323 2.943
#> -----------------------------------
#>
#>
#> <!-- end of list -->
#>
#>
## Arrays
pander(mtcars)
#>
#> --------------------------------------------------------------------------------
#> mpg cyl disp hp drat wt qsec vs
#> ------------------------- ------ ----- ------- ----- ------ ------- ------- ----
#> **Mazda RX4** 21 6 160 110 3.9 2.62 16.46 0
#>
#> **Mazda RX4 Wag** 21 6 160 110 3.9 2.875 17.02 0
#>
#> **Datsun 710** 22.8 4 108 93 3.85 2.32 18.61 1
#>
#> **Hornet 4 Drive** 21.4 6 258 110 3.08 3.215 19.44 1
#>
#> **Hornet Sportabout** 18.7 8 360 175 3.15 3.44 17.02 0
#>
#> **Valiant** 18.1 6 225 105 2.76 3.46 20.22 1
#>
#> **Duster 360** 14.3 8 360 245 3.21 3.57 15.84 0
#>
#> **Merc 240D** 24.4 4 146.7 62 3.69 3.19 20 1
#>
#> **Merc 230** 22.8 4 140.8 95 3.92 3.15 22.9 1
#>
#> **Merc 280** 19.2 6 167.6 123 3.92 3.44 18.3 1
#>
#> **Merc 280C** 17.8 6 167.6 123 3.92 3.44 18.9 1
#>
#> **Merc 450SE** 16.4 8 275.8 180 3.07 4.07 17.4 0
#>
#> **Merc 450SL** 17.3 8 275.8 180 3.07 3.73 17.6 0
#>
#> **Merc 450SLC** 15.2 8 275.8 180 3.07 3.78 18 0
#>
#> **Cadillac Fleetwood** 10.4 8 472 205 2.93 5.25 17.98 0
#>
#> **Lincoln Continental** 10.4 8 460 215 3 5.424 17.82 0
#>
#> **Chrysler Imperial** 14.7 8 440 230 3.23 5.345 17.42 0
#>
#> **Fiat 128** 32.4 4 78.7 66 4.08 2.2 19.47 1
#>
#> **Honda Civic** 30.4 4 75.7 52 4.93 1.615 18.52 1
#>
#> **Toyota Corolla** 33.9 4 71.1 65 4.22 1.835 19.9 1
#>
#> **Toyota Corona** 21.5 4 120.1 97 3.7 2.465 20.01 1
#>
#> **Dodge Challenger** 15.5 8 318 150 2.76 3.52 16.87 0
#>
#> **AMC Javelin** 15.2 8 304 150 3.15 3.435 17.3 0
#>
#> **Camaro Z28** 13.3 8 350 245 3.73 3.84 15.41 0
#>
#> **Pontiac Firebird** 19.2 8 400 175 3.08 3.845 17.05 0
#>
#> **Fiat X1-9** 27.3 4 79 66 4.08 1.935 18.9 1
#>
#> **Porsche 914-2** 26 4 120.3 91 4.43 2.14 16.7 0
#>
#> **Lotus Europa** 30.4 4 95.1 113 3.77 1.513 16.9 1
#>
#> **Ford Pantera L** 15.8 8 351 264 4.22 3.17 14.5 0
#>
#> **Ferrari Dino** 19.7 6 145 175 3.62 2.77 15.5 0
#>
#> **Maserati Bora** 15 8 301 335 3.54 3.57 14.6 0
#>
#> **Volvo 142E** 21.4 4 121 109 4.11 2.78 18.6 1
#> --------------------------------------------------------------------------------
#>
#> Table: Table continues below
#>
#>
#> --------------------------------------------
#> am gear carb
#> ------------------------- ---- ------ ------
#> **Mazda RX4** 1 4 4
#>
#> **Mazda RX4 Wag** 1 4 4
#>
#> **Datsun 710** 1 4 1
#>
#> **Hornet 4 Drive** 0 3 1
#>
#> **Hornet Sportabout** 0 3 2
#>
#> **Valiant** 0 3 1
#>
#> **Duster 360** 0 3 4
#>
#> **Merc 240D** 0 4 2
#>
#> **Merc 230** 0 4 2
#>
#> **Merc 280** 0 4 4
#>
#> **Merc 280C** 0 4 4
#>
#> **Merc 450SE** 0 3 3
#>
#> **Merc 450SL** 0 3 3
#>
#> **Merc 450SLC** 0 3 3
#>
#> **Cadillac Fleetwood** 0 3 4
#>
#> **Lincoln Continental** 0 3 4
#>
#> **Chrysler Imperial** 0 3 4
#>
#> **Fiat 128** 1 4 1
#>
#> **Honda Civic** 1 4 2
#>
#> **Toyota Corolla** 1 4 1
#>
#> **Toyota Corona** 0 3 1
#>
#> **Dodge Challenger** 0 3 2
#>
#> **AMC Javelin** 0 3 2
#>
#> **Camaro Z28** 0 3 4
#>
#> **Pontiac Firebird** 0 3 2
#>
#> **Fiat X1-9** 1 4 1
#>
#> **Porsche 914-2** 1 5 2
#>
#> **Lotus Europa** 1 5 2
#>
#> **Ford Pantera L** 1 5 4
#>
#> **Ferrari Dino** 1 5 6
#>
#> **Maserati Bora** 1 5 8
#>
#> **Volvo 142E** 1 4 2
#> --------------------------------------------
#>
pander(table(mtcars$am))
#>
#> ---------
#> 0 1
#> ---- ----
#> 19 13
#> ---------
#>
pander(table(mtcars$am, mtcars$gear))
#>
#> ---------------------
#> 3 4 5
#> -------- ---- --- ---
#> **0** 15 4 0
#>
#> **1** 0 8 5
#> ---------------------
#>
## Tests
pander(ks.test(runif(50), runif(50)))
#>
#> ---------------------------------------------------
#> Test statistic P value Alternative hypothesis
#> ---------------- --------- ------------------------
#> 0.22 0.1786 two-sided
#> ---------------------------------------------------
#>
#> Table: Exact two-sample Kolmogorov-Smirnov test: `runif(50)` and `runif(50)`
#>
pander(chisq.test(table(mtcars$am, mtcars$gear)))
#> Warning: Chi-squared approximation may be incorrect
#>
#> ---------------------------------------
#> Test statistic df P value
#> ---------------- ---- -----------------
#> 20.94 2 2.831e-05 * * *
#> ---------------------------------------
#>
#> Table: Pearson's Chi-squared test: `table(mtcars$am, mtcars$gear)`
#>
pander(t.test(extra ~ group, data = sleep))
#>
#> -----------------------------------------------------------------------------
#> Test statistic df P value Alternative hypothesis mean in group 1
#> ---------------- ------- --------- ------------------------ -----------------
#> -1.861 17.78 0.07939 two.sided 0.75
#> -----------------------------------------------------------------------------
#>
#> Table: Welch Two Sample t-test: `extra` by `group` (continued below)
#>
#>
#> -----------------
#> mean in group 2
#> -----------------
#> 2.33
#> -----------------
#>
## Models
ml <- with(lm(mpg ~ hp + wt), data = mtcars)
pander(ml)
#>
#> ---------------------------------------------------------------
#> Estimate Std. Error t value Pr(>|t|)
#> ----------------- ---------- ------------ --------- -----------
#> **(Intercept)** 37.23 1.599 23.28 2.565e-20
#>
#> **hp** -0.03177 0.00903 -3.519 0.001451
#>
#> **wt** -3.878 0.6327 -6.129 1.12e-06
#> ---------------------------------------------------------------
#>
#> Table: Fitting linear model: mpg ~ hp + wt
#>
pander(anova(ml))
#>
#> -------------------------------------------------------------
#> Df Sum Sq Mean Sq F value Pr(>F)
#> --------------- ---- -------- --------- --------- -----------
#> **hp** 1 678.4 678.4 100.9 5.987e-11
#>
#> **wt** 1 252.6 252.6 37.56 1.12e-06
#>
#> **Residuals** 29 195 6.726 NA NA
#> -------------------------------------------------------------
#>
#> Table: Analysis of Variance Table
#>
pander(aov(ml))
#>
#> -------------------------------------------------------------
#> Df Sum Sq Mean Sq F value Pr(>F)
#> --------------- ---- -------- --------- --------- -----------
#> **hp** 1 678.4 678.4 100.9 5.987e-11
#>
#> **wt** 1 252.6 252.6 37.56 1.12e-06
#>
#> **Residuals** 29 195 6.726 NA NA
#> -------------------------------------------------------------
#>
#> Table: Analysis of Variance Model
#>
## Dobson (1990) Page 93: Randomized Controlled Trial (examples from: ?glm)
counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome <- gl(3, 1, 9)
treatment <- gl(3, 3)
m <- glm(counts ~ outcome + treatment, family = poisson())
pander(m)
#>
#> ------------------------------------------------------------------
#> Estimate Std. Error z value Pr(>|z|)
#> ----------------- ----------- ------------ ----------- -----------
#> **(Intercept)** 3.045 0.1709 17.81 5.427e-71
#>
#> **outcome2** -0.4543 0.2022 -2.247 0.02465
#>
#> **outcome3** -0.293 0.1927 -1.52 0.1285
#>
#> **treatment2** 1.218e-15 0.2 6.088e-15 1
#>
#> **treatment3** 8.438e-16 0.2 4.219e-15 1
#> ------------------------------------------------------------------
#>
#> Table: Fitting generalized (poisson/log) linear model: counts ~ outcome + treatment
#>
pander(anova(m))
#>
#> --------------------------------------------------------------------
#> Df Deviance Resid. Df Resid. Dev Pr(>Chi)
#> --------------- ---- ----------- ----------- ------------ ----------
#> **NULL** NA NA 8 10.58 NA
#>
#> **outcome** 2 5.452 6 5.129 0.06547
#>
#> **treatment** 2 6.217e-15 4 5.129 1
#> --------------------------------------------------------------------
#>
#> Table: Analysis of Deviance Table
#>
pander(aov(m))
#>
#> ----------------------------------------------------------------
#> Df Sum Sq Mean Sq F value Pr(>F)
#> --------------- ---- ----------- ---------- ----------- --------
#> **outcome** 2 92.67 46.33 2.224 0.2242
#>
#> **treatment** 2 9.861e-31 4.93e-31 2.367e-32 1
#>
#> **Residuals** 4 83.33 20.83 NA NA
#> ----------------------------------------------------------------
#>
#> Table: Analysis of Variance Model
#>
## overwriting labels
pander(lm(Sepal.Width ~ Species, data = iris), covariate.labels = c('Versicolor', 'Virginica'))
#>
#> ---------------------------------------------------------------------
#> Estimate Std. Error t value Pr(>|t|)
#> ---------------------- ---------- ------------ --------- ------------
#> **Versicolor** 3.428 0.04804 71.36 5.708e-116
#>
#> **Virginica** -0.658 0.06794 -9.685 1.832e-17
#>
#> **Speciesvirginica** -0.454 0.06794 -6.683 4.539e-10
#> ---------------------------------------------------------------------
#>
#> Table: Fitting linear model: Sepal.Width ~ Species
#>
## Prcomp
pander(prcomp(USArrests))
#>
#> ---------------------------------------------------------
#> PC1 PC2 PC3 PC4
#> -------------- --------- ---------- ---------- ----------
#> **Murder** 0.0417 -0.04482 0.07989 -0.9949
#>
#> **Assault** 0.9952 -0.05876 -0.06757 0.03894
#>
#> **UrbanPop** 0.04634 0.9769 -0.2005 -0.05817
#>
#> **Rape** 0.07516 0.2007 0.9741 0.07233
#> ---------------------------------------------------------
#>
#> Table: Principal Components Analysis
#>
## Others
pander(density(runif(10)))
#>
#> --------------------------------------------
#> Coordinates Density values
#> ------------- ------------- ----------------
#> **Min.** -0.133 0.003805
#>
#> **1st Qu.** 0.197 0.1455
#>
#> **Median** 0.527 0.7132
#>
#> **Mean** 0.527 0.7558
#>
#> **3rd Qu.** 0.857 1.428
#>
#> **Max.** 1.187 1.492
#> --------------------------------------------
#>
#> Table: Kernel density of *runif(10)* (bandwidth: 0.1171332)
#>
pander(density(mtcars$hp))
#>
#> --------------------------------------------
#> Coordinates Density values
#> ------------- ------------- ----------------
#> **Min.** -32.12 4.956e-06
#>
#> **1st Qu.** 80.69 0.0004066
#>
#> **Median** 193.5 0.001663
#>
#> **Mean** 193.5 0.002212
#>
#> **3rd Qu.** 306.3 0.004087
#>
#> **Max.** 419.1 0.006048
#> --------------------------------------------
#>
#> Table: Kernel density of *mtcars$hp* (bandwidth: 28.04104)
#>
## default method
x <- chisq.test(table(mtcars$am, mtcars$gear))
#> Warning: Chi-squared approximation may be incorrect
class(x) <- 'I heave never heard of!'
pander(x)
#> Warning: No pander.method for "I heave never heard of!", reverting to default.
#>
#>
#> * **statistic**:
#>
#> -----------
#> X-squared
#> -----------
#> 20.94
#> -----------
#>
#> * **parameter**:
#>
#> ----
#> df
#> ----
#> 2
#> ----
#>
#> * **p.value**: _2.831e-05_
#> * **method**: Pearson's Chi-squared test
#> * **data.name**: table(mtcars$am, mtcars$gear)
#> * **observed**:
#>
#> ---------------------
#> 3 4 5
#> -------- ---- --- ---
#> **0** 15 4 0
#>
#> **1** 0 8 5
#> ---------------------
#>
#> * **expected**:
#>
#> --------------------------------
#> 3 4 5
#> -------- ------- ------- -------
#> **0** 8.906 7.125 2.969
#>
#> **1** 6.094 4.875 2.031
#> --------------------------------
#>
#> * **residuals**:
#>
#> -----------------------------------
#> 3 4 5
#> -------- -------- -------- --------
#> **0** 2.042 -1.171 -1.723
#>
#> **1** -2.469 1.415 2.083
#> -----------------------------------
#>
#> * **stdres**:
#>
#> -----------------------------------
#> 3 4 5
#> -------- -------- -------- --------
#> **0** 4.395 -2.323 -2.943
#>
#> **1** -4.395 2.323 2.943
#> -----------------------------------
#>
#>
#> <!-- end of list -->
#>
#>