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https://github.com/tjmahr/printy

Tools for pretty-printing numbers in RMarkdown reports
https://github.com/tjmahr/printy

formatted-text r rmarkdown

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Tools for pretty-printing numbers in RMarkdown reports

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README 

        
---

output: 

  github_document: 

    default

---



```{r, include = FALSE}

library(dplyr, warn.conflicts = FALSE)

library(lme4)



knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "fig/README-"

)

```



# printy



Over the years, I've written a lot of one-off functions for formatting numbers

in RMarkdown documents. This packages collects them in a single location.



## Installation 📚



You can install printy from github with:



```{r gh-installation, eval = FALSE}

# install.packages("remotes")

remotes::install_github("tjmahr/printy")

```



## Formatters ✍ 



`fmt_fix_digits()` prints a number with n digits of precision. R numbers lose

precision when converted to strings. This function converts the numbers to

strings and keeps precision. (It's a wrapper for `sprintf()`.)



```{r fix-digits}

library(dplyr)

library(printy)

test_cor <- cor(mtcars[, 1:4]) 



# Typical loss of trailing zeroes

test_cor[1:4, 3] |> round(2) |> as.character()



test_cor[1:4, 3] |> fmt_fix_digits(2)

```



`fmt_leading_zero()` removes a leading zero on numbers that are bounded between

−1 and 1, such as correlations or *p*-values.



```{r leading-zero}

fmt_leading_zero(c(-0.3, 0.4, 1))

```



`fmt_minus_sign()` formats negative numbers with a minus sign.



```{r minus-sign}

fmt_minus_sign(c(1, 2, -3, -0.4, -pi))

```



Putting it all together: Print a correlation matrix with 2 digits, no leading

zero and with minus signs.



```{r}

fmt_correlation <- function(xs, digits = 2) {

  xs |> fmt_fix_digits(digits) |> fmt_leading_zero() |> fmt_minus_sign()

}



test_cor |> 

  as.data.frame() |> 

  tibble::rownames_to_column(".rowname") |> 

  tibble::as_tibble() |> 

  mutate(

    across(-.rowname, fmt_correlation)

  ) |> 

  rename(` ` = .rowname) |> 

  knitr::kable(align = "lrrrr")

```



### *p*-values 🎣



`fmt_p_value()` formats *p*-values with *n* digits of precision, with no leading

zero, and with very small values being printed with a `<` sign.



```{r}

p <- c(1, 0.1, 0.01, 0.001, 0.0001)

fmt_p_value(p, digits = 2)

fmt_p_value(p, digits = 3)

```



`fmt_p_value_md()` formats *p*-values in markdown with nice defaults. 



* Use 3 digits of precision for values less than .06

* Otherwise, use 2 digits of precision.

* Include *p* in markdown



```{r}

p <- c(1, 0.1, 0.06, 0.059, 0.051, 0.01, 0.001, 0.0001)

fmt_p_value_md(p)

```



These render as: `r paste0(fmt_p_value_md(p), collapse = ", ")`.



### Experimental formatters  🧪



`fmt_effect_md()` is an experimental function for getting model effects

formatted in markdown. You give the function a model, an effect and a string

listing the quantities you want.



```{r}

model <- lm(breaks ~ wool * tension, warpbreaks) 

summary(model)

```



```{r}

# default to: b (beta), e (error), s (statistic), p (p value)

fmt_effect_md(model, "woolB", "besp")

```



`r fmt_effect_md(model, "woolB", "besp")`



```{r}

# Just a subset of them

fmt_effect_md(model, "woolB", terms = "bp")

```



`r fmt_effect_md(model, "woolB", terms = "bp")`



```{r}

# B for labeled b

fmt_effect_md(model, "woolB", terms = "Bp", b_lab = "Wool B")

```



`r  fmt_effect_md(model, "woolB", terms = "Bp", b_lab = "Wool B")`



```{r bi}

# i for interval

fmt_effect_md(model, "woolB", terms = "bi")

```



`r fmt_effect_md(model, "woolB", terms = "bi")`



```{r bSp}

# S for statistic with df

fmt_effect_md(model, "woolB", terms = "bSp")

```



`r fmt_effect_md(model, "woolB", terms = "bSp")`



```{r}

# extra digits (except for p-values; those go through `fmt_p_value_md()`)

fmt_effect_md(model, "woolB", terms = "bep", digits = 6)

```



`r fmt_effect_md(model, "woolB", terms = "bep", digits = 6)`



These are the currently supported models:



  - `lm()`

  - `lme4::lmer()` 



For lme4 models, Wald confidence intervals are provided. For *p*-values, the

Kenwood--Roger approximation for the degrees of freedom is used by default. We

can also choose a [method supported by the parameters

package](https://easystats.github.io/parameters/reference/p_value.lmerMod.html).



```{r}

library(lme4)

data(Machines, package = "nlme")



m <- lmer(score ~ 1 + Machine + (Machine | Worker), data = Machines)



# Default is Kenward

fmt_effect_md(m, "MachineB", terms = "beSp")

fmt_effect_md(m, "MachineB", terms = "beSp", p_value_method = "kenward")



# Note residual degrees of freedom for Wald

fmt_effect_md(m, "MachineB", terms = "beSp", p_value_method = "wald")



# This example doesn't find differences between Satterthwaite and Kenward

fmt_effect_md(m, "MachineB", terms = "beSp", p_value_method = "satterthwaite")

```



We can also format effects from `glmer()` models. `"S"` is not supported because

the model summary uses *z* statistics, not *t* statistics.



```{r, error = TRUE}

gm1 <- glmer(

  cbind(incidence, size - incidence) ~ period + (1 | herd),

  data = cbpp, 

  family = binomial

)



round(coef(summary(gm1)), 3)



fmt_effect_md(gm1, "period2", terms = "bespi")



# Don't use S here

fmt_effect_md(gm1, "period2", terms = "beSp")

```



Skeletons 🦴

-----------------------------------------------------------------------



I use `fmt_` for formatting functions. The other convention in the package is

`skel_` to plug values into a formatting skeleton. 



`skel_conf_interval_pair()` creates a confidence interval from two numbers.



```{r}

skel_conf_interval_pair(c(1, 2))

```



`skel_conf_interval()` is the vectorized version. It is suitable for working

on columns of numbers.



```{r}

model <- lm(breaks ~ wool * tension, warpbreaks) 



ci_starts <- confint(model)[, 1] |> 

  fmt_fix_digits(2) |> 

  fmt_minus_sign()



ci_ends <- confint(model)[, 2] |> 

  fmt_fix_digits(2) |> 

  fmt_minus_sign()



skel_conf_interval(ci_starts, ci_ends)

```



`skel_stat_n_value_pair()` creates *t*-test-like or correlation-like statistic

from a vector of two numbers.



```{r}

skel_stat_n_value_pair(c("20", "2.0"))

skel_stat_n_value_pair(c("39", ".98"), stat = "*r*")

```



`skel_se()` and `skel_ci()` are shorthand functions to help with inline

reporting.



```{r}

skel_se(c(10, 4))



skel_ci(c("[1, 2]"))



skel_ci(c("[1, 2]"), ci_width = 90)

```



## Formatting tables from lme4 models 🖇



One thing I've had to do a lot is summarize mixed effects models fit with lme4.

This package provides `pretty_lme4_ranefs()` which creates a dataframe random

effect variances and covariances like those printed by `summary()`.



For example, we can fit the model.



```{r}

library(lme4)

model <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)

summary(model)

```



`pretty_lme4_ranefs()` creates the following dataframe.



```{r}

pretty_lme4_ranefs(model)

```



Which in markdown renders as



```{r}

knitr::kable(

  pretty_lme4_ranefs(model), 

  align = c("l", "l", "r", "r", "r")

)

```



Here's a dumb model with a lot going on in the random effects.



```{r, warning = FALSE}

model <- lmer(mpg ~ wt * hp + (drat | gear) + (hp * cyl | am), mtcars)

model



knitr::kable(

  pretty_lme4_ranefs(model), 

  align = c("l", "l", "r", "r", "r", "r", "r", "r", "r")

)

```