## load packages
library(tidyverse)
library(tidytext)
library(here)

Let’s read in the Pam-bigram data (which you can access for your own amusement here).

## read csv file
pam_bigrams <- read_csv("data/pam_bigrams.csv")

Now we’ll use the sample_n() function from dplyr to get 15 (though you could use any number) observations/rows from our pamgram data. Note that the freq variable contains the number of times a given bigram occurred in the “PAM” corpus (which, as of this writing, includes all of Archer, seasons 1 - 7).

sample_n(pam_bigrams, 15)
## # A tibble: 15 x 3
##                bigram  freq speaker
##                 <chr> <int>   <chr>
##  1  hypnotized people     1     PAM
##  2      wooden stakes     1     PAM
##  3            pop pow     1     PAM
##  4   good's happening     1     PAM
##  5             wh hey     2     PAM
##  6    chuckling fluid     1     PAM
##  7       equal shares     1     PAM
##  8        life advice     1     PAM
##  9        people fill     1     PAM
## 10          holy shit     7     PAM
## 11        bionic legs     1     PAM
## 12 sterile conditions     1     PAM
## 13        coca fields     1     PAM
## 14      fister roboto     1     PAM
## 15           hunh unh     1     PAM

Tables of Pam

Since pretty much every pair of words Pam utters is priceless, let’s check out some of the different ways you can display data frames as tables in R Markdown. If you’re looking at this in markdown (.md file), what you see may not match the descriptions. This makes sense, and is worth paying attention to for things like GitHub’s README.md files, and the like.

kable

First, we might as well make use of the knitr::kable function, since we’re knitting this document anyhow. Note that the chunk option results='asis' needs to be set so that our output isn’t further processed by knitr when we pull our document together.1

library(knitr)
kable(sample_n(pam_bigrams, 15), caption = "kable pamgrams")
kable pamgrams
bigram freq speaker
gummi bears 1 PAM
ike turner.i 1 PAM
god hates 1 PAM
boom ahem 1 PAM
frickin zod 1 PAM
suckin bullshit 1 PAM
tinker toy 1 PAM
claws rowr 1 PAM
inspiring story 1 PAM
leavin yer 1 PAM
gal’s love 1 PAM
pinky swear 1 PAM
yeah whatcha 1 PAM
ha ha 6 PAM
heard coming 1 PAM

Ok, we’ve got bold headers, and lines between rows – basically, things are looking a little more table-y than the did before. The kable() function can take other formatting arguments, but we’re just gonna leave it at the defaults here.

pander

pander is another R Pandoc writer. It supports four Pandoc formats for tables (which can be specified using pandoc.table2), and you may or may not want to add some information about that in your frontmatter/YAML. You can also make more adjustments to your table formatting using panderOptions().

library(pander)
## multiline table by default
pandoc.table(sample_n(pam_bigrams, 5))
## 
## ------------------------------------
##       bigram         freq   speaker 
## ------------------- ------ ---------
##      pam's hot        1       PAM   
## 
##  separate sessions    1       PAM   
## 
##  dicknuts hrmmmff     1       PAM   
## 
##   kill decepticon     1       PAM   
## 
##      dump time        1       PAM   
## ------------------------------------
## simple tables
pandoc.table(sample_n(pam_bigrams, 5), style = "simple")
## 
## 
##       bigram        freq   speaker 
## ------------------ ------ ---------
##   kills jermaine     1       PAM   
##    bodied woman      1       PAM   
##    dirty floor       1       PAM   
##   frazier's dumb     1       PAM   
##  explain compound    1       PAM
## grid tables
pandoc.table(sample_n(pam_bigrams, 5), style = "grid")
## 
## 
## +-----------------+------+---------+
## |     bigram      | freq | speaker |
## +=================+======+=========+
## |   blow pam's    |  1   |   PAM   |
## +-----------------+------+---------+
## |  wan attorney   |  1   |   PAM   |
## +-----------------+------+---------+
## |  ass drowning   |  1   |   PAM   |
## +-----------------+------+---------+
## |    clock yer    |  1   |   PAM   |
## +-----------------+------+---------+
## | happenin sunday |  1   |   PAM   |
## +-----------------+------+---------+
## rmarkdown tables (aka pipe table format)
pandoc.table(sample_n(pam_bigrams, 5), style = "rmarkdown")
## 
## 
## |    bigram     | freq | speaker |
## |:-------------:|:----:|:-------:|
## | quality dump  |  1   |   PAM   |
## | ingrown hairs |  1   |   PAM   |
## | pacman jones  |  2   |   PAM   |
## |   ekblad jr   |  1   |   PAM   |
## | wilfrid noyce |  1   |   PAM   |

There are a bunch of other options you can play around with as well, such as captions, cell highlighting, cell alignment, and width.

xtable

The xtable package has been around for a while, and it has a ton of formatting options for both LaTeX and HTML output. (Not to be confused with a tonne of options, which – how does one even measure formatting options in kilos?) The output defaults to latex, and, as with kable, we’ll need to be sure to include results="asis" in our chunk options.

library(xtable)
xpam <- xtable(sample_n(pam_bigrams, 15))
print(xpam, type="html")
bigram freq speaker
1 thousand divided 1 PAM
2 flow toilets 1 PAM
3 marley chuckles 1 PAM
4 cb handle 1 PAM
5 forfeit league 1 PAM
6 whatcha waiting 1 PAM
7 dump time 1 PAM
8 werewolf aw 1 PAM
9 krieger’s selling 1 PAM
10 cocaine online 1 PAM
11 balls taste 1 PAM
12 low flow 1 PAM
13 skinny lying 1 PAM
14 phuh phuh 3 PAM
15 downstairs neighbors 1 PAM

Yeah, so, the default doesn’t look so awesome right now. However, this is really just an HTML table, so it can be easily styled as such (in addition to using the xtable parameters).

huxtable

Like xtable, huxtable deals in both LaTeX and HTML, and has a ton of properties you can adjust to your liking (you can even use the magrittr-style pipe operator, %>%, to do so). The number format defaults to two decimal places, so we’ll set that manually here. In order for the column names to show up in your huxtable, they need to be added to the data frame itself. If you’re working in R Markdown, it will look like they show up twice, but that shouldn’t be the case once you’ve knit your document.

library(huxtable)
hpam <- hux(sample_n(pam_bigrams, 15)) %>%
  huxtable::add_colnames()
number_format(hpam) <- 0
hpam
bigram freq speaker
ringing happy 1 PAM
krieger shuckin 1 PAM
steering wheel's 1 PAM
list uh 1 PAM
phone call 1 PAM
pretty sad 1 PAM
paper stuck 1 PAM
amazing driver 1 PAM
frickin starving 1 PAM
pam hangs 1 PAM
stayin alive 1 PAM
hai hai 2 PAM
monster truck 1 PAM
wait till 2 PAM
guido sardoochbag 1 PAM

Though it’s a bit clunky for such simple output, there are more options, such as conditional formatting, as well as some very fine-tuned controls that may make huxtable a good choice for you.

printr

Yihui Xie, the author of knitr, and a bevy of other useful R packages (including most of the ones ending in “-down”), also created the printr package as a “companion” to knitr. And, as far as data frames, tibbles, and the like go, the output will look the same as it does using kable() without printr attached. However, it will make a difference in terms of the way things like matrices, or lists of vignettes, and datasets are displayed.

library(printr)
kable(sample_n(pam_bigrams, 15), caption = "printr pamgrams")
printr pamgrams
bigram freq speaker
smelly ass 1 PAM
sister edie 3 PAM
bailed triflin 1 PAM
heh head 1 PAM
follow kenny 1 PAM
matter ya 1 PAM
whatcha waiting 1 PAM
snacks enable 1 PAM
legally binding 1 PAM
horn honks 1 PAM
disease means 1 PAM
hunh unh 1 PAM
quick learner 1 PAM
yagoat raping 1 PAM
masturbate til 1 PAM 
vignette(package = "printr")
Vignettes in printr
Item Title
printr An Introduction to the printr Package (source, html)

DT

DT, from RStudio, provides “an R interface to the DataTables library.” It’s different from the other options discussed here, as it creates an HTML widget in which it displays the data. This means that it can be interactive– so, explore all the great Pam-ness, below.

library(DT)
datatable(pam_bigrams)

RJSplot

RJSplot is new to me, but while perusing their gallery of some pretty cool interactive graphics, I happened upon their data table interface. Though this generates its own, separate DataTable page, it gives viewers the ability to download their data in several formats (as shown in the image of the generated DataTable page).

library(RJSplot)
# Create a data table
tables_rjs(pam_bigrams)
## The graph has been generated in the DataTable folder.
sessionInfo()
## R version 3.4.0 (2017-04-21)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.5
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] RJSplot_2.1          DT_0.2.12            printr_0.1          
##  [4] huxtable_0.3.0       xtable_1.8-2         pander_0.6.0        
##  [7] knitr_1.16           here_0.0-6           tidytext_0.1.2.900  
## [10] forcats_0.2.0        stringr_1.2.0        dplyr_0.5.0         
## [13] purrr_0.2.2.2        readr_1.1.1          tidyr_0.6.3         
## [16] tibble_1.3.1         ggplot2_2.2.1        tidyverse_1.1.1.9000
## 
## loaded via a namespace (and not attached):
##  [1] reshape2_1.4.2.9000   haven_1.0.0           lattice_0.20-35      
##  [4] colorspace_1.3-2      htmltools_0.3.6       SnowballC_0.5.1      
##  [7] yaml_2.1.14           rlang_0.1.1.9000      foreign_0.8-68       
## [10] DBI_0.6-1             modelr_0.1.0          readxl_1.0.0         
## [13] plyr_1.8.4            munsell_0.4.3         gtable_0.2.0         
## [16] cellranger_1.1.0      rvest_0.3.2.9000      htmlwidgets_0.8      
## [19] psych_1.7.5           evaluate_0.10         httpuv_1.3.3         
## [22] crosstalk_1.0.0       parallel_3.4.0        highr_0.6            
## [25] broom_0.4.2           tokenizers_0.1.4.9000 Rcpp_0.12.11         
## [28] scales_0.4.1.9000     backports_1.1.0       jsonlite_1.4         
## [31] mime_0.5              mnormt_1.5-5          hms_0.3              
## [34] digest_0.6.12         stringi_1.1.5         shiny_1.0.3.9000     
## [37] grid_3.4.0            rprojroot_1.2         tools_3.4.0          
## [40] magrittr_1.5          lazyeval_0.2.0.9000   janeaustenr_0.1.4    
## [43] Matrix_1.2-10         xml2_1.1.1            lubridate_1.6.0      
## [46] assertthat_0.2.0      rmarkdown_1.5.9000    httr_1.2.1.9000      
## [49] rstudioapi_0.6        R6_2.2.1              nlme_3.1-131         
## [52] compiler_3.4.0

  1. See RStudio’s R Markdown – Tables documentation for more on this.↩

  2. By default, it will use mulitiline format, but there’s not much in the way of visuals for that with these bigrams.↩