After tidying up your analyses with the broom package, go ahead and
grab the pixiedust. Customize your table output and write it to
markdown, HTML, LaTeX, or even just the console. pixiedust makes it
easy to customize the appearance of your tables in all of these formats
by adding any number of “sprinkles”, much in the same way you can add
layers to a ggplot.
fit <- lm(mpg ~ qsec + factor(am) + wt + factor(gear), data = mtcars)
library(pixiedust)
dust(fit) %>%
sprinkle(col = 2:4, round = 3) %>%
sprinkle(col = 5, fn = quote(pvalString(value))) %>%
sprinkle_colnames(term = "Term",
estimate = "Estimate",
std.error = "SE",
statistic = "T-statistic",
p.value = "P-value") %>%
sprinkle_print_method("console")
#> Term Estimate SE T-statistic P-value
#> 1 (Intercept) 9.365 8.373 1.118 0.27
#> 2 qsec 1.245 0.383 3.252 0.003
#> 3 factor(am)1 3.151 1.941 1.624 0.12
#> 4 wt -3.926 0.743 -5.286 < 0.001
#> 5 factor(gear)4 -0.268 1.655 -0.162 0.87
#> 6 factor(gear)5 -0.27 2.063 -0.131 0.9Tables can be customized by row, column, or even by a single cell by
adding sprinkles to the dust object. The table below shows the
currently planned and implemented sprinkles. In the “implemented”
column, an ‘x’ indicates a customization that has been implemented,
while a blank cell suggests that the customization is planned but has
not yet been implemented. In the remaining columns, an ‘x’ indicates
that the sprinkle is already implemented for the output format; an ‘o’
indicates that implementation is planned but not yet completed; and a
blank cell indicates that the sprinkle will not be implemented (usually
because the output format doesn’t support the option).
| sprinkle | implemented | console | markdown | html | latex |
|---|---|---|---|---|---|
| bg | x | x | x | ||
| bg_pattern | x | x | x | ||
| bg_pattern_by | x | x | x | ||
| bold | x | x | x | x | x |
| bookdown | x | x | |||
| border_collapse | x | x | x | ||
| border | x | x | x | ||
| border_thickness | x | x | x | ||
| border_units | x | x | x | ||
| border_style | x | x | x | ||
| border_color | x | x | x | ||
| caption | x | x | x | x | x |
| colnames | x | x | x | x | x |
| discrete | x | x | x | ||
| discrete_colors | x | x | x | ||
| float | x | x | |||
| fn | x | x | x | x | x |
| font_color | x | x | x | ||
| font_family | x | x | |||
| font_size | x | x | x | ||
| font_size_units | x | x | x | ||
| gradient | x | x | x | ||
| gradient_colors | x | x | x | ||
| gradient_cut | x | x | x | ||
| gradient_n | x | x | x | ||
| gradient_na | x | x | x | ||
| halign | x | x | x | ||
| height | x | x | x | ||
| height_units | x | x | x | ||
| hhline | x | x | |||
| italic | x | x | x | x | x |
| justify | x | x | x | ||
| label | x | x | x | ||
| longtable | x | x | x | x | x |
| merge | x | x | x | x | x |
| na_string | x | x | x | x | x |
| padding | x | x | |||
| replace | x | x | x | x | x |
| round | x | x | x | x | x |
| rotate_degree | x | x | x | ||
| sanitize | x | ||||
| sanitize_args | x | ||||
| tabcolsep | x | ||||
| valign | x | x | x | ||
| width | x | x | x | ||
| width_units | x | x | x |
To demonstrate, let’s look at a simple linear model. We build the model and generate the standard summary.
fit <- lm(mpg ~ qsec + factor(am) + wt + factor(gear), data = mtcars)
summary(fit)
#>
#> Call:
#> lm(formula = mpg ~ qsec + factor(am) + wt + factor(gear), data = mtcars)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -3.5064 -1.5220 -0.7517 1.3841 4.6345
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 9.3650 8.3730 1.118 0.27359
#> qsec 1.2449 0.3828 3.252 0.00317 **
#> factor(am)1 3.1505 1.9405 1.624 0.11654
#> wt -3.9263 0.7428 -5.286 1.58e-05 ***
#> factor(gear)4 -0.2682 1.6555 -0.162 0.87257
#> factor(gear)5 -0.2697 2.0632 -0.131 0.89698
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 2.55 on 26 degrees of freedom
#> Multiple R-squared: 0.8498, Adjusted R-squared: 0.8209
#> F-statistic: 29.43 on 5 and 26 DF, p-value: 6.379e-10While the summary is informative and useful, it is full of “stats-speak”
and isn’t necessarily in a format that is suitable for publication or
submission to a client. The broom package provides the summary in tidy
format that, serendipitously, it a lot closer to what we would want for
formal reports.
library(broom)
tidy(fit)
#> # A tibble: 6 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 9.37 8.37 1.12 0.274
#> 2 qsec 1.24 0.383 3.25 0.00317
#> 3 factor(am)1 3.15 1.94 1.62 0.117
#> 4 wt -3.93 0.743 -5.29 0.0000158
#> 5 factor(gear)4 -0.268 1.66 -0.162 0.873
#> 6 factor(gear)5 -0.270 2.06 -0.131 0.897It has been observed by some, however, that even this summary isn’t
quite ready for publication. There are too many decimal places, the
p-value employ scientific notation, and column titles like “statistic”
don’t specify what type of statistic. These kinds of details aren’t the
purview of broom, however, as broom is focused on tidying the
results of a model for further analysis (particularly with respect to
comparing slightly varying models).
The pixiedust package diverts from broom’s mission here and provides
the ability to customize the broom output for presentation. The
initial dust object returns a table that is similar to the broom
output.
library(pixiedust)
dust(fit) %>%
sprinkle_print_method("console")
#> term estimate std.error statistic p.value
#> 1 (Intercept) 9.3650443 8.3730161 1.1184792 0.2735903
#> 2 qsec 1.2449212 0.3828479 3.2517387 0.0031681
#> 3 factor(am)1 3.1505178 1.9405171 1.6235455 0.1165367
#> 4 wt -3.9263022 0.7427562 -5.2861251 1.58e-05
#> 5 factor(gear)4 -0.268163 1.6554617 -0.1619868 0.8725685
#> 6 factor(gear)5 -0.2697468 2.0631829 -0.130743 0.896985Where pixiedust shows its strength is the ease of which these tables
can be customized. The code below rounds the columns estimate,
std.error, and statistic to three decimal places each, and then
formats the p.value into a format that happens to be one that I like.
x <- dust(fit) %>%
sprinkle(col = 2:4, round = 3) %>%
sprinkle(col = 5, fn = quote(pvalString(value))) %>%
sprinkle_print_method("console")
x
#> term estimate std.error statistic p.value
#> 1 (Intercept) 9.365 8.373 1.118 0.27
#> 2 qsec 1.245 0.383 3.252 0.003
#> 3 factor(am)1 3.151 1.941 1.624 0.12
#> 4 wt -3.926 0.743 -5.286 < 0.001
#> 5 factor(gear)4 -0.268 1.655 -0.162 0.87
#> 6 factor(gear)5 -0.27 2.063 -0.131 0.9Now we’re almost there! Let’s change up the column names, and while we’re add it, let’s add some “bold” markers to the statistically significant terms in order to make them stand out some (I say “bold” because the console output doesn’t show up in bold, but with the markdown tags for bold text. In a rendered table, the text would actually be rendered in bold).
x <- x %>%
sprinkle(col = c("estimate", "p.value"),
row = c(2, 4),
bold = TRUE) %>%
sprinkle_colnames(term = "Term",
estimate = "Estimate",
std.error = "SE",
statistic = "T-statistic",
p.value = "P-value") %>%
sprinkle_print_method("console")
x
#> Term Estimate SE T-statistic P-value
#> 1 (Intercept) 9.365 8.373 1.118 0.27
#> 2 qsec **1.245** 0.383 3.252 **0.003**
#> 3 factor(am)1 3.151 1.941 1.624 0.12
#> 4 wt **-3.926** 0.743 -5.286 **< 0.001**
#> 5 factor(gear)4 -0.268 1.655 -0.162 0.87
#> 6 factor(gear)5 -0.27 2.063 -0.131 0.9The markdown output from pixiedust is somewhat limited due to the
limitations of Rmarkdown itself. If/when more features become
available for Rmarkdown output, I’ll be sure to include them. But what
can you do if you really want all of the flexibility of the HTML
tables but need the MS Word document?
With a little help from the Gmisc package, you can have the best of
both worlds. Gmisc isn’t available on CRAN yet, but if you’re willing
to install it from GitHub, you can render a docx file. Install Gmisc
with
install.packages("Gmisc")
Then use in your YAML header
---
output: Gmisc::docx_document
---
When you knit your document, it knits as an HTML file, but I’ve had no problems with the rendering when I right-click the file and open with MS Word.
Read more at
http://gforge.se/2014/07/fast-track-publishing-using-rmarkdown/ (but
note that this blog post was written about the Grmd package before it
was moved into the Gmisc package).