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:

# 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().)

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

# Typical loss of trailing zeroes
test_cor[1:4, 3] |> round(2) |> as.character()
#> [1] "-0.85" "0.9"   "1"     "0.79"

test_cor[1:4, 3] |> fmt_fix_digits(2)
#> [1] "-0.85" "0.90"  "1.00"  "0.79"

fmt_leading_zero() removes a leading zero on numbers that are bounded between −1 and 1, such as correlations or p-values.

fmt_leading_zero(c(-0.3, 0.4, 1))
#> [1] "-.3" ".4"  "1"

fmt_minus_sign() formats negative numbers with a minus sign.

fmt_minus_sign(c(1, 2, -3, -0.4, -pi))
#> [1] "1"                       "2"                      
#> [3] "−3"                "−0.4"             
#> [5] "−3.14159265358979"

Putting it all together: Print a correlation matrix with 2 digits, no leading zero and with minus signs.

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.

p <- c(1, 0.1, 0.01, 0.001, 0.0001)
fmt_p_value(p, digits = 2)
#> [1] "1.00"  ".10"   ".01"   "< .01" "< .01"
fmt_p_value(p, digits = 3)
#> [1] "1.000"  ".100"   ".010"   ".001"   "< .001"

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

p <- c(1, 0.1, 0.06, 0.059, 0.051, 0.01, 0.001, 0.0001)
fmt_p_value_md(p)
#> [1] "*p*&nbsp;> .99"  "*p*&nbsp;= .10"  "*p*&nbsp;= .06"  "*p*&nbsp;= .059"
#> [5] "*p*&nbsp;= .051" "*p*&nbsp;= .010" "*p*&nbsp;= .001" "*p*&nbsp;< .001"

These render as: p > .99, p = .10, p = .06, p = .059, p = .051, p = .010, p = .001, p < .001.

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.

model <- lm(breaks ~ wool * tension, warpbreaks) 
summary(model)
#> 
#> Call:
#> lm(formula = breaks ~ wool * tension, data = warpbreaks)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -19.5556  -6.8889  -0.6667   7.1944  25.4444 
#> 
#> Coefficients:
#>                Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)      44.556      3.647  12.218 2.43e-16 ***
#> woolB           -16.333      5.157  -3.167 0.002677 ** 
#> tensionM        -20.556      5.157  -3.986 0.000228 ***
#> tensionH        -20.000      5.157  -3.878 0.000320 ***
#> woolB:tensionM   21.111      7.294   2.895 0.005698 ** 
#> woolB:tensionH   10.556      7.294   1.447 0.154327    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 10.94 on 48 degrees of freedom
#> Multiple R-squared:  0.3778, Adjusted R-squared:  0.3129 
#> F-statistic: 5.828 on 5 and 48 DF,  p-value: 0.0002772

# default to: b (beta), e (error), s (statistic), p (p value)
fmt_effect_md(model, "woolB", "besp")
#> [1] "*b* = −16.33, SE = 5.16, *t* = −3.17, *p* = .003"

b = −16.33, SE = 5.16, t = −3.17, p = .003

# Just a subset of them
fmt_effect_md(model, "woolB", terms = "bp")
#> [1] "*b* = −16.33, *p* = .003"

b = −16.33, p = .003

# B for labeled b
fmt_effect_md(model, "woolB", terms = "Bp", b_lab = "Wool B")
#> [1] "*b*<sub>Wool B</sub>&nbsp;= &minus;16.33, *p*&nbsp;= .003"

b_{Wool B} = −16.33, p = .003

# i for interval
fmt_effect_md(model, "woolB", terms = "bi")
#> [1] "*b* = −16.33, 95% CI = [−26.70, −5.96]"

b = −16.33, 95% CI = [−26.70, −5.96]

# S for statistic with df
fmt_effect_md(model, "woolB", terms = "bSp")
#> [1] "*b* = −16.33, *t*(48) = −3.17, *p* = .003"

b = −16.33, t(48) = −3.17, p = .003

# extra digits (except for p-values; those go through `fmt_p_value_md()`)
fmt_effect_md(model, "woolB", terms = "bep", digits = 6)
#> [1] "*b* = −16.333333, SE = 5.157299, *p* = .003"

b = −16.333333, SE = 5.157299, p = .003

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.

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")
#> [1] "*b*&nbsp;= 7.97, SE&nbsp;= 2.42, *t*(5)&nbsp;= 3.29, *p*&nbsp;= .022"
fmt_effect_md(m, "MachineB", terms = "beSp", p_value_method = "kenward")
#> [1] "*b*&nbsp;= 7.97, SE&nbsp;= 2.42, *t*(5)&nbsp;= 3.29, *p*&nbsp;= .022"

# Note residual degrees of freedom for Wald
fmt_effect_md(m, "MachineB", terms = "beSp", p_value_method = "wald")
#> [1] "*b*&nbsp;= 7.97, SE&nbsp;= 2.42, *t*(44)&nbsp;= 3.29, *p*&nbsp;= .002"

# This example doesn't find differences between Satterthwaite and Kenward
fmt_effect_md(m, "MachineB", terms = "beSp", p_value_method = "satterthwaite")
#> [1] "*b*&nbsp;= 7.97, SE&nbsp;= 2.42, *t*(5)&nbsp;= 3.29, *p*&nbsp;= .022"

We can also format effects from glmer() models. "S" is not supported because the model summary uses z statistics, not t statistics.

gm1 <- glmer(
  cbind(incidence, size - incidence) ~ period + (1 | herd),
  data = cbpp, 
  family = binomial
)

round(coef(summary(gm1)), 3)
#>             Estimate Std. Error z value Pr(>|z|)
#> (Intercept)   -1.398      0.231  -6.048    0.000
#> period2       -0.992      0.303  -3.272    0.001
#> period3       -1.128      0.323  -3.495    0.000
#> period4       -1.580      0.422  -3.743    0.000

fmt_effect_md(gm1, "period2", terms = "bespi")
#> [1] "*b*&nbsp;= &minus;0.99, SE&nbsp;= 0.30, *z*&nbsp;= &minus;3.27, *p*&nbsp;= .001, 95% CI&nbsp;= [&minus;1.59, &minus;0.40]"

# Don't use S here
fmt_effect_md(gm1, "period2", terms = "beSp")
#> Error in get_terms.glmerMod(model, effect, terms, ci_width = ci_width, : S is not supported for glmer models

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.

skel_conf_interval_pair(c(1, 2))
#> [1] "[1, 2]"

skel_conf_interval() is the vectorized version. It is suitable for working on columns of numbers.

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)
#> [1] "[37.22, 51.89]"               "[&minus;26.70, &minus;5.96]" 
#> [3] "[&minus;30.93, &minus;10.19]" "[&minus;30.37, &minus;9.63]" 
#> [5] "[6.45, 35.78]"                "[&minus;4.11, 25.22]"

skel_stat_n_value_pair() creates t-test-like or correlation-like statistic from a vector of two numbers.

skel_stat_n_value_pair(c("20", "2.0"))
#> [1] "t(20) = 2.0"
skel_stat_n_value_pair(c("39", ".98"), stat = "*r*")
#> [1] "*r*(39) = .98"

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

skel_se(c(10, 4))
#> [1] "SE = 10" "SE = 4"

skel_ci(c("[1, 2]"))
#> [1] "95% CI = [1, 2]"

skel_ci(c("[1, 2]"), ci_width = 90)
#> [1] "90% CI = [1, 2]"

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.

library(lme4)
model <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
summary(model)
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: Reaction ~ Days + (Days | Subject)
#>    Data: sleepstudy
#> 
#> REML criterion at convergence: 1743.6
#> 
#> Scaled residuals: 
#>     Min      1Q  Median      3Q     Max 
#> -3.9536 -0.4634  0.0231  0.4634  5.1793 
#> 
#> Random effects:
#>  Groups   Name        Variance Std.Dev. Corr
#>  Subject  (Intercept) 612.10   24.741       
#>           Days         35.07    5.922   0.07
#>  Residual             654.94   25.592       
#> Number of obs: 180, groups:  Subject, 18
#> 
#> Fixed effects:
#>             Estimate Std. Error t value
#> (Intercept)  251.405      6.825  36.838
#> Days          10.467      1.546   6.771
#> 
#> Correlation of Fixed Effects:
#>      (Intr)
#> Days -0.138

pretty_lme4_ranefs() creates the following dataframe.

pretty_lme4_ranefs(model)
#>      Group   Parameter Variance    SD Correlations  
#> 1  Subject (Intercept)   612.10 24.74         1.00  
#> 2            Days    35.07  5.92          .07   1.00
#> 3 Residual          654.94 25.59          

Which in markdown renders as

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.

model <- lmer(mpg ~ wt * hp + (drat | gear) + (hp * cyl | am), mtcars)
#> boundary (singular) fit: see help('isSingular')
model
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: mpg ~ wt * hp + (drat | gear) + (hp * cyl | am)
#>    Data: mtcars
#> REML criterion at convergence: 152.7432
#> Random effects:
#>  Groups   Name        Std.Dev. Corr             
#>  gear     (Intercept) 1.556809                  
#>           drat        0.166292 -1.00            
#>  am       (Intercept) 1.940271                  
#>           hp          0.004055 -0.96            
#>           cyl         0.456219 -0.98  0.93      
#>           hp:cyl      0.001508  0.95 -0.94 -0.99
#>  Residual             2.113554                  
#> Number of obs: 32, groups:  gear, 3; am, 2
#> Fixed Effects:
#> (Intercept)           wt           hp        wt:hp  
#>    48.98745     -7.80904     -0.12118      0.02737  
#> optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warnings

knitr::kable(
  pretty_lme4_ranefs(model), 
  align = c("l", "l", "r", "r", "r", "r", "r", "r", "r")
)