The broom package takes the messy output of built-in functions in R, such as lm
, nls
, or t.test
, and turns them into tidy data frames.
The concept of “tidy data”, as introduced by Hadley Wickham, offers a powerful framework for data manipulation and analysis. That paper makes a convincing statement of the problem this package tries to solve (emphasis mine):
While model inputs usually require tidy inputs, such attention to detail doesn’t carry over to model outputs. Outputs such as predictions and estimated coefficients aren’t always tidy. This makes it more difficult to combine results from multiple models. For example, in R, the default representation of model coefficients is not tidy because it does not have an explicit variable that records the variable name for each estimate, they are instead recorded as row names. In R, row names must be unique, so combining coefficients from many models (e.g., from bootstrap resamples, or subgroups) requires workarounds to avoid losing important information. This knocks you out of the flow of analysis and makes it harder to combine the results from multiple models. I’m not currently aware of any packages that resolve this problem.
broom is an attempt to bridge the gap from untidy outputs of predictions and estimations to the tidy data we want to work with. It centers around three S3 methods, each of which take common objects produced by R statistical functions (lm
, t.test
, nls
, etc) and convert them into a data frame. broom is particularly designed to work with Hadley’s dplyr package (see the broom+dplyr vignette for more).
broom should be distinguished from packages like reshape2 and tidyr, which rearrange and reshape data frames into different forms. Those packages perform critical tasks in tidy data analysis but focus on manipulating data frames in one specific format into another. In contrast, broom is designed to take format that is not in a data frame (sometimes not anywhere close) and convert it to a tidy data frame.
Tidying model outputs is not an exact science, and it’s based on a judgment of the kinds of values a data scientist typically wants out of a tidy analysis (for instance, estimates, test statistics, and p-values). You may lose some of the information in the original object that you wanted, or keep more information than you need. If you think the tidy output for a model should be changed, or if you’re missing a tidying function for an S3 class that you’d like, I strongly encourage you to open an issue or a pull request.
This package provides three S3 methods that do three distinct kinds of tidying.
tidy
: constructs a data frame that summarizes the model’s statistical findings. This includes coefficients and p-values for each term in a regression, per-cluster information in clustering applications, or per-test information for multtest
functions.augment
: add columns to the original data that was modeled. This includes predictions, residuals, and cluster assignments.glance
: construct a concise one-row summary of the model. This typically contains values such as R^2, adjusted R^2, and residual standard error that are computed once for the entire model.Note that some classes may have only one or two of these methods defined.
Consider as an illustrative example a linear fit on the built-in mtcars
dataset.
lmfit <- lm(mpg ~ wt, mtcars)
lmfit
##
## Call:
## lm(formula = mpg ~ wt, data = mtcars)
##
## Coefficients:
## (Intercept) wt
## 37.285 -5.344
summary(lmfit)
##
## Call:
## lm(formula = mpg ~ wt, data = mtcars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5432 -2.3647 -0.1252 1.4096 6.8727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.2851 1.8776 19.858 < 2e-16 ***
## wt -5.3445 0.5591 -9.559 1.29e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.046 on 30 degrees of freedom
## Multiple R-squared: 0.7528, Adjusted R-squared: 0.7446
## F-statistic: 91.38 on 1 and 30 DF, p-value: 1.294e-10
This summary output is useful enough if you just want to read it. However, converting it to a data frame that contains all the same information, so that you can combine it with other models or do further analysis, is not trivial. You have to do coef(summary(lmfit))
to get a matrix of coefficients, the terms are still stored in row names, and the column names are inconsistent with other packages (e.g. Pr(>|t|)
compared to p.value
).
Instead, you can use the tidy
function, from the broom package, on the fit:
library(broom)
tidy(lmfit)
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 37.3 1.88 19.9 8.24e-19
## 2 wt -5.34 0.559 -9.56 1.29e-10
This gives you a data.frame representation. Note that the row names have been moved into a column called term
, and the column names are simple and consistent (and can be accessed using $
).
Instead of viewing the coefficients, you might be interested in the fitted values and residuals for each of the original points in the regression. For this, use augment
, which augments the original data with information from the model:
augment(lmfit)
## # A tibble: 32 x 10
## .rownames mpg wt .fitted .se.fit .resid .hat .sigma .cooksd
## * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Mazda RX4 21 2.62 23.3 0.634 -2.28 0.0433 3.07 1.33e-2
## 2 Mazda RX… 21 2.88 21.9 0.571 -0.920 0.0352 3.09 1.72e-3
## 3 Datsun 7… 22.8 2.32 24.9 0.736 -2.09 0.0584 3.07 1.54e-2
## 4 Hornet 4… 21.4 3.22 20.1 0.538 1.30 0.0313 3.09 3.02e-3
## 5 Hornet S… 18.7 3.44 18.9 0.553 -0.200 0.0329 3.10 7.60e-5
## 6 Valiant 18.1 3.46 18.8 0.555 -0.693 0.0332 3.10 9.21e-4
## 7 Duster 3… 14.3 3.57 18.2 0.573 -3.91 0.0354 3.01 3.13e-2
## 8 Merc 240D 24.4 3.19 20.2 0.539 4.16 0.0313 3.00 3.11e-2
## 9 Merc 230 22.8 3.15 20.5 0.540 2.35 0.0314 3.07 9.96e-3
## 10 Merc 280 19.2 3.44 18.9 0.553 0.300 0.0329 3.10 1.71e-4
## # ... with 22 more rows, and 1 more variable: .std.resid <dbl>
Note that each of the new columns begins with a .
(to avoid overwriting any of the original columns).
Finally, several summary statistics are computed for the entire regression, such as R^2 and the F-statistic. These can be accessed with the glance
function:
glance(lmfit)
## # A tibble: 1 x 11
## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
## * <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.753 0.745 3.05 91.4 1.29e-10 2 -80.0 166. 170.
## # ... with 2 more variables: deviance <dbl>, df.residual <int>
This distinction between the tidy
, augment
and glance
functions is explored in a different context in the k-means vignette.
These functions apply equally well to the output from glm
:
glmfit <- glm(am ~ wt, mtcars, family="binomial")
tidy(glmfit)
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 12.0 4.51 2.67 0.00759
## 2 wt -4.02 1.44 -2.80 0.00509
augment(glmfit)
## # A tibble: 32 x 10
## .rownames am wt .fitted .se.fit .resid .hat .sigma .cooksd
## * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Mazda RX4 1 2.62 1.50 0.918 0.635 0.126 0.803 0.0184
## 2 Mazda RX… 1 2.88 0.471 0.676 0.985 0.108 0.790 0.0424
## 3 Datsun 7… 1 2.32 2.70 1.28 0.360 0.0963 0.810 0.00394
## 4 Hornet 4… 0 3.22 -0.897 0.601 -0.827 0.0744 0.797 0.0177
## 5 Hornet S… 0 3.44 -1.80 0.749 -0.553 0.0681 0.806 0.00647
## 6 Valiant 0 3.46 -1.88 0.767 -0.532 0.0674 0.807 0.00590
## 7 Duster 3… 0 3.57 -2.33 0.878 -0.432 0.0625 0.809 0.00348
## 8 Merc 240D 0 3.19 -0.796 0.593 -0.863 0.0755 0.796 0.0199
## 9 Merc 230 0 3.15 -0.635 0.586 -0.922 0.0776 0.793 0.0242
## 10 Merc 280 0 3.44 -1.80 0.749 -0.553 0.0681 0.806 0.00647
## # ... with 22 more rows, and 1 more variable: .std.resid <dbl>
glance(glmfit)
## # A tibble: 1 x 7
## null.deviance df.null logLik AIC BIC deviance df.residual
## <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int>
## 1 43.2 31 -9.59 23.2 26.1 19.2 30
Note that the statistics computed by glance
are different for glm
objects than for lm
(e.g. deviance rather than R^2):
These functions also work on other fits, such as nonlinear models (nls
):
nlsfit <- nls(mpg ~ k / wt + b, mtcars, start=list(k=1, b=0))
tidy(nlsfit)
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 k 45.8 4.25 10.8 7.64e-12
## 2 b 4.39 1.54 2.85 7.74e- 3
augment(nlsfit, mtcars)
## # A tibble: 32 x 14
## .rownames mpg cyl disp hp drat wt qsec vs am gear
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4
## 2 Mazda RX… 21 6 160 110 3.9 2.88 17.0 0 1 4
## 3 Datsun 7… 22.8 4 108 93 3.85 2.32 18.6 1 1 4
## 4 Hornet 4… 21.4 6 258 110 3.08 3.22 19.4 1 0 3
## 5 Hornet S… 18.7 8 360 175 3.15 3.44 17.0 0 0 3
## 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3
## 7 Duster 3… 14.3 8 360 245 3.21 3.57 15.8 0 0 3
## 8 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4
## 9 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4
## 10 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 1 0 4
## # ... with 22 more rows, and 3 more variables: carb <dbl>, .fitted <dbl>,
## # .resid <dbl>
glance(nlsfit)
## # A tibble: 1 x 8
## sigma isConv finTol logLik AIC BIC deviance df.residual
## <dbl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 2.77 TRUE 0.0000000288 -77.0 160. 164. 231. 30
The tidy
function can also be applied to htest
objects, such as those output by popular built-in functions like t.test
, cor.test
, and wilcox.test
.
tt <- t.test(wt ~ am, mtcars)
tidy(tt)
## # A tibble: 1 x 10
## estimate estimate1 estimate2 statistic p.value parameter conf.low
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.36 3.77 2.41 5.49 6.27e-6 29.2 0.853
## # ... with 3 more variables: conf.high <dbl>, method <chr>,
## # alternative <chr>
Some cases might have fewer columns (for example, no confidence interval):
wt <- wilcox.test(wt ~ am, mtcars)
tidy(wt)
## # A tibble: 1 x 4
## statistic p.value method alternative
## <dbl> <dbl> <chr> <chr>
## 1 230. 0.0000435 Wilcoxon rank sum test with continuity … two.sided
Since the tidy
output is already only one row, glance
returns the same output:
glance(tt)
## # A tibble: 1 x 10
## estimate estimate1 estimate2 statistic p.value parameter conf.low
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.36 3.77 2.41 5.49 6.27e-6 29.2 0.853
## # ... with 3 more variables: conf.high <dbl>, method <chr>,
## # alternative <chr>
glance(wt)
## # A tibble: 1 x 4
## statistic p.value method alternative
## <dbl> <dbl> <chr> <chr>
## 1 230. 0.0000435 Wilcoxon rank sum test with continuity … two.sided
augment
method is defined only for chi-squared tests, since there is no meaningful sense, for other tests, in which a hypothesis test produces output about each initial data point.
chit <- chisq.test(xtabs(Freq ~ Sex + Class, data = as.data.frame(Titanic)))
tidy(chit)
## # A tibble: 1 x 4
## statistic p.value parameter method
## <dbl> <dbl> <int> <chr>
## 1 350. 1.56e-75 3 Pearson's Chi-squared test
augment(chit)
## # A tibble: 8 x 9
## Sex Class .observed .prop .row.prop .col.prop .expected .residuals
## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Male 1st 180 0.0818 0.104 0.554 256. -4.73
## 2 Fema… 1st 145 0.0659 0.309 0.446 69.4 9.07
## 3 Male 2nd 179 0.0813 0.103 0.628 224. -3.02
## 4 Fema… 2nd 106 0.0482 0.226 0.372 60.9 5.79
## 5 Male 3rd 510 0.232 0.295 0.722 555. -1.92
## 6 Fema… 3rd 196 0.0891 0.417 0.278 151. 3.68
## 7 Male Crew 862 0.392 0.498 0.974 696. 6.29
## 8 Fema… Crew 23 0.0104 0.0489 0.0260 189. -12.1
## # ... with 1 more variable: .stdres <dbl>
In order to maintain consistency, we attempt to follow some conventions regarding the structure of returned data.
tidy
, augment
and glance
functions is always a data frame.pval
or PValue
?” every time). The examples below are not all the possible column names, nor will all tidy output contain all or even any of these columns.tidy
output typically represents some well-defined concept, such as one term in a regression, one test, or one cluster/class. This meaning varies across models but is usually self-evident. The one thing each row cannot represent is a point in the initial data (for that, use the augment
method).term
“” the term in a regression or model that is being estimated.p.value
: this spelling was chosen (over common alternatives such as pvalue
, PValue
, or pval
) to be consistent with functions in R’s built-in stats
packagestatistic
a test statistic, usually the one used to compute the p-value. Combining these across many sub-groups is a reliable way to perform (e.g.) bootstrap hypothesis testingestimate
conf.low
the low end of a confidence interval on the estimate
conf.high
the high end of a confidence interval on the estimate
df
degrees of freedomaugment(model, data)
adds columns to the original data.
data
argument is missing, augment
attempts to reconstruct the data from the model (note that this may not always be possible, and usually won’t contain columns not used in the model).augment
output matches the corresponding row in the original data.augment
turns them into a column called .rownames
..
to avoid overwriting columns in the original data..fitted
: the predicted values, on the same scale as the data..resid
: residuals: the actual y values minus the fitted values.cluster
: cluster assignmentsglance
always returns a one-row data frame.
glance(NULL)
returns an empty data frame.glm
glance output does not need to contain a field for family
, since that is decided by the user calling glm
rather than the modeling function itself.r.squared
the fraction of variance explained by the modeladj.r.squared
R^2 adjusted based on the degrees of freedomsigma
the square root of the estimated variance of the residuals