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Bonus Slides: Making pretty regression tables with pipes

autosize: true author: Tristan Mahr, @tjmahr date: March 18, 2015 css: assets/custom.css

Madison R Users Group

Repository for this talk: https://github.com/tjmahr/MadR_Pipelines

Plan

Earlier, I covered pipelines.

Then, I used pipelines with dplyr.

Now, I will demonstrate off how I use pipelines to make pretty-printed regression tables.

library("magrittr")
library("dplyr")
library("broom")
library("stringr")
library("knitr")

linear regression

title: false

I want to print a regression summary table.

model <- lm(Sepal.Length ~ Sepal.Width, iris)
summary(model)

Call:
lm(formula = Sepal.Length ~ Sepal.Width, data = iris)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5561 -0.6333 -0.1120  0.5579  2.2226 

Coefficients:
            Estimate Std. Error t value
(Intercept)   6.5262     0.4789   13.63
Sepal.Width  -0.2234     0.1551   -1.44
            Pr(>|t|)    
(Intercept)   <2e-16 ***
Sepal.Width    0.152    
---
Signif. codes:  
  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1  ' ' 1

Residual standard error: 0.8251 on 148 degrees of freedom
Multiple R-squared:  0.01382,	Adjusted R-squared:  0.007159 
F-statistic: 2.074 on 1 and 148 DF,  p-value: 0.1519

broom::tidy

broom's tidy function extracts summary values into a data-frame.

m_table <- tidy(model)
m_table %>% print(digits = 3)
         term estimate std.error statistic
1 (Intercept)    6.526     0.479     13.63
2 Sepal.Width   -0.223     0.155     -1.44
   p.value
1 6.47e-28
2 1.52e-01

knitr::kable

knitr's kable function prints a data-frame as a Markdown table, which rmarkdown/pandoc can convert to HTML or Word.

kable(m_table, digits = 3,
      col.names = c("Param", "B", "SE", "t", "p"))

Raw Markdown output



|Param       |      B|    SE|      t|     p|
|:-----------|------:|-----:|------:|-----:|
|(Intercept) |  6.526| 0.479| 13.628| 0.000|
|Sepal.Width | -0.223| 0.155| -1.440| 0.152|

======================================================== title: false

Which renders as:

kable(m_table, digits = 3,
      col.names = c("Param", "B", "SE", "t", "p"))
Param B SE t p
(Intercept) 6.526 0.479 13.628 0.000
Sepal.Width -0.223 0.155 -1.440 0.152

Almost there

incremental: true

Two more things that I want.

  1. APA style for printing numbers
    • Two digits of precision for estimates, errors and statistics.
    • Three digits for p-values.
    • Bounded values (correlations, p-values) don't need leading zero.
    • Tiny p-values should not be 0.000. Use < .001 instead.
  2. Flexibility for many related models. (I don't know the final in-text model beforehand.)

Approach

incremental: true

  • Build a set of generic, reusable functions for formatting numbers.
  • Make a custom pipeline to format the term column.
  • Assemble a pipeline to convert a model into a pretty table.

Digit Formatters

The formatC function is big and confusing, but after I figured out something that works well enough, I extracted a function. Now I don't have to worry about solving that problem again.

# Print with n digits of precision
fixed_digits <- function(xs, n = 2) {
  formatC(xs, digits = n, format = "f")
}

# Want .100 to print with two digits
c(1000.012, 0.0001, 0.100) %>% fixed_digits(2)
[1] "1000.01" "0.00"    "0.10"

Remove leading zeros

Here I do a sanity check to see if this operation is necessary.

# Don't print leading zero on bounded numbers.
remove_leading_zero <- function(xs) {
  # Problem if any value is greater than 1.0
  digit_matters <- xs %>% as.numeric %>%
    abs %>% is_greater_than(1)
  if (any(digit_matters)) {
    warning("Non-zero leading digit")
  }
  str_replace(xs, "^(-?)0", "\\1")
}
c(1.00, -0.12,  0.87, 0.82) %>% remove_leading_zero
[1] "1"    "-.12" ".87"  ".82"

p-value formatter

I format p-values with the previous two functions and overwrite the really small ones with the < .001 template.

# Print three digits of a p-value, but use
# the "< .001" notation on tiny values.
format_pval <- function(ps, html = FALSE) {
  tiny <- ifelse(html, "&lt;&nbsp;.001", "< .001")
  ps_chr <- ps %>% fixed_digits(3) %>%
    remove_leading_zero
  ps_chr[ps < 0.001] <- tiny
  ps_chr
}
m_table$p.value %>% format_pval
[1] "< .001" ".152"

Renaming variables

A few packages already solve the pretty-table problem--stargazer, texreg, xtable--and they do amazing things. But their functions usually rely on an argument where you can specify custom labels for the regression predictors. This is fine for one-off tables.

When I have a bunch of related models, I prefer to use a string-manipulation pipeline to convert R names into long-hand names.

Renaming variables

fix_names <- . %>%
  str_replace(".Intercept.", "Intercept") %>%
  str_replace("Species", "") %>%
  # Capitalize species names
  str_replace("setosa", "Setosa") %>%
  str_replace("versicolor", "Versicolor") %>%
  str_replace("virginica", "Virginica") %>%
  # Clean up special characters
  str_replace_all(".Width", " Width") %>%
  str_replace_all(".Length", " Length") %>%
  str_replace_all(":", " x ")

Formatting pipeline

  1. Format the numbers
  2. Rename the terms
  3. Rename the column headings
two_digits <- . %>% fixed_digits(2)
table_names <- c("Parameter", "Estimate", "SE",
                 "_t_", "_p_")

format_model_table <- . %>%
  mutate_each(funs(two_digits),
              -term, -p.value) %>%
  mutate(term = fix_names(term),
         p.value = format_pval(p.value)) %>%
  set_colnames(table_names)

Overall pipeline

  1. Fit a model
  2. Extract values for summary table
  3. Format table
  4. Print as markdown
lm(..., iris) %>%
  tidy %>%
  format_model_table %>%
  kable

======================================================== title: false

lm(Sepal.Length ~ Sepal.Width, iris) %>%
  tidy %>%
  format_model_table %>%
  kable(align = "r")
Parameter Estimate SE t p
Intercept 6.53 0.48 13.63 < .001
Sepal Width -0.22 0.16 -1.44 .152

======================================================== title: false

lm(Sepal.Length ~ Species * Sepal.Width, iris) %>%
  tidy %>%
  format_model_table %>%
  kable(align = "r")
Parameter Estimate SE t p
Intercept 2.64 0.57 4.62 < .001
Versicolor 0.90 0.80 1.13 .261
Virginica 1.27 0.82 1.55 .123
Sepal Width 0.69 0.17 4.17 < .001
Versicolor x Sepal Width 0.17 0.26 0.67 .503
Virginica x Sepal Width 0.21 0.26 0.83 .411

======================================================== title: false

lm(Sepal.Length ~ Petal.Length * Species, iris) %>%
  tidy %>%
  format_model_table %>%
  kable(align = "r")
Parameter Estimate SE t p
Intercept 4.21 0.41 10.34 < .001
Petal Length 0.54 0.28 1.96 .052
Versicolor -1.81 0.60 -3.02 .003
Virginica -3.15 0.63 -4.97 < .001
Petal Length x Versicolor 0.29 0.30 0.97 .334
Petal Length x Virginica 0.45 0.29 1.56 .120

======================================================== title: false

lm(Sepal.Length ~ Species * Sepal.Width + Petal.Length, iris) %>%
  tidy %>%
  format_model_table %>%
  kable(align = "r")
Parameter Estimate SE t p
Intercept 1.67 0.41 4.11 < .001
Versicolor 0.28 0.56 0.51 .614
Virginica -0.65 0.59 -1.10 .274
Sepal Width 0.62 0.12 5.40 < .001
Petal Length 0.82 0.07 12.42 < .001
Versicolor x Sepal Width -0.45 0.19 -2.39 .018
Virginica x Sepal Width -0.29 0.18 -1.57 .119

======================================================== title: false

lm(Sepal.Length ~ Species * Sepal.Width * Petal.Length, iris) %>%
  tidy %>%
  format_model_table %>%
  kable(align = "r")
Parameter Estimate SE t p
Intercept -1.37 3.91 -0.35 .727
Versicolor 3.17 5.50 0.58 .565
Virginica -0.99 5.27 -0.19 .851
Sepal Width 1.72 1.12 1.54 .126
Petal Length 2.88 2.75 1.05 .297
Versicolor x Sepal Width -1.36 1.86 -0.73 .466
Virginica x Sepal Width -0.43 1.66 -0.26 .797
Versicolor x Petal Length -2.07 2.90 -0.71 .476
Virginica x Petal Length -1.42 2.82 -0.51 .614
Sepal Width x Petal Length -0.74 0.79 -0.95 .345
Versicolor x Sepal Width x Petal Length 0.72 0.86 0.84 .405
Virginica x Sepal Width x Petal Length 0.56 0.81 0.69 .488