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 tibbles.

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
tibble. 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 tabular data format
(sometimes not anywhere close) and convert it to a tidy tibble.

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 tibble 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.

```
<- lm(mpg ~ wt, mtcars)
lmfit 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 tabular data 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 × 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 tabular data 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 × 9
## .rownames mpg wt .fitted .resid .hat .sigma .cooksd .std.resid
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Mazda RX4 21 2.62 23.3 -2.28 0.0433 3.07 1.33e-2 -0.766
## 2 Mazda RX4 Wag 21 2.88 21.9 -0.920 0.0352 3.09 1.72e-3 -0.307
## 3 Datsun 710 22.8 2.32 24.9 -2.09 0.0584 3.07 1.54e-2 -0.706
## 4 Hornet 4 Drive 21.4 3.22 20.1 1.30 0.0313 3.09 3.02e-3 0.433
## 5 Hornet Sportabout 18.7 3.44 18.9 -0.200 0.0329 3.10 7.60e-5 -0.0668
## 6 Valiant 18.1 3.46 18.8 -0.693 0.0332 3.10 9.21e-4 -0.231
## 7 Duster 360 14.3 3.57 18.2 -3.91 0.0354 3.01 3.13e-2 -1.31
## 8 Merc 240D 24.4 3.19 20.2 4.16 0.0313 3.00 3.11e-2 1.39
## 9 Merc 230 22.8 3.15 20.5 2.35 0.0314 3.07 9.96e-3 0.784
## 10 Merc 280 19.2 3.44 18.9 0.300 0.0329 3.10 1.71e-4 0.100
## # ℹ 22 more rows
```

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 × 12
## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.753 0.745 3.05 91.4 1.29e-10 1 -80.0 166. 170.
## # ℹ 3 more variables: deviance <dbl>, df.residual <int>, nobs <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`

:

```
<- glm(am ~ wt, mtcars, family = "binomial")
glmfit tidy(glmfit)
```

```
## # A tibble: 2 × 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 × 9
## .rownames am wt .fitted .resid .hat .sigma .cooksd .std.resid
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Mazda RX4 1 2.62 1.50 0.635 0.126 0.803 0.0184 0.680
## 2 Mazda RX4 Wag 1 2.88 0.471 0.985 0.108 0.790 0.0424 1.04
## 3 Datsun 710 1 2.32 2.70 0.360 0.0963 0.810 0.00394 0.379
## 4 Hornet 4 Drive 0 3.22 -0.897 -0.827 0.0744 0.797 0.0177 -0.860
## 5 Hornet Sportabout 0 3.44 -1.80 -0.553 0.0681 0.806 0.00647 -0.572
## 6 Valiant 0 3.46 -1.88 -0.532 0.0674 0.807 0.00590 -0.551
## 7 Duster 360 0 3.57 -2.33 -0.432 0.0625 0.809 0.00348 -0.446
## 8 Merc 240D 0 3.19 -0.796 -0.863 0.0755 0.796 0.0199 -0.897
## 9 Merc 230 0 3.15 -0.635 -0.922 0.0776 0.793 0.0242 -0.960
## 10 Merc 280 0 3.44 -1.80 -0.553 0.0681 0.806 0.00647 -0.572
## # ℹ 22 more rows
```

`glance(glmfit)`

```
## # A tibble: 1 × 8
## null.deviance df.null logLik AIC BIC deviance df.residual nobs
## <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int> <int>
## 1 43.2 31 -9.59 23.2 26.1 19.2 30 32
```

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`

):

```
<- nls(mpg ~ k / wt + b, mtcars, start = list(k = 1, b = 0))
nlsfit tidy(nlsfit)
```

```
## # A tibble: 2 × 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 × 14
## .rownames mpg cyl disp hp drat wt qsec vs am gear carb
## <chr> <dbl> <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 4
## 2 Mazda RX4 … 21 6 160 110 3.9 2.88 17.0 0 1 4 4
## 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 Hornet 4 D… 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 Hornet Spo… 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## 7 Duster 360 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
## 8 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 9 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
## 10 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
## # ℹ 22 more rows
## # ℹ 2 more variables: .fitted <dbl>, .resid <dbl>
```

`glance(nlsfit)`

```
## # A tibble: 1 × 9
## sigma isConv finTol logLik AIC BIC deviance df.residual nobs
## <dbl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
## 1 2.77 TRUE 0.0000000188 -77.0 160. 164. 231. 30 32
```

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`

.

```
<- t.test(wt ~ am, mtcars)
tt tidy(tt)
```

```
## # A tibble: 1 × 10
## estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.36 3.77 2.41 5.49 0.00000627 29.2 0.853 1.86
## # ℹ 2 more variables: method <chr>, alternative <chr>
```

Some cases might have fewer columns (for example, no confidence interval):

```
<- wilcox.test(wt ~ am, mtcars)
wt tidy(wt)
```

```
## # A tibble: 1 × 4
## statistic p.value method alternative
## <dbl> <dbl> <chr> <chr>
## 1 230. 0.0000435 Wilcoxon rank sum test with continuity correc… two.sided
```

Since the `tidy`

output is already only one row,
`glance`

returns the same output:

`glance(tt)`

```
## # A tibble: 1 × 10
## estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.36 3.77 2.41 5.49 0.00000627 29.2 0.853 1.86
## # ℹ 2 more variables: method <chr>, alternative <chr>
```

`glance(wt)`

```
## # A tibble: 1 × 4
## statistic p.value method alternative
## <dbl> <dbl> <chr> <chr>
## 1 230. 0.0000435 Wilcoxon rank sum test with continuity correc… 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.

```
<- chisq.test(xtabs(Freq ~ Sex + Class,
chit data = as.data.frame(Titanic)
))tidy(chit)
```

```
## # A tibble: 1 × 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 × 9
## Sex Class .observed .prop .row.prop .col.prop .expected .resid .std.resid
## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Male 1st 180 0.0818 0.104 0.554 256. -4.73 -11.1
## 2 Female 1st 145 0.0659 0.309 0.446 69.4 9.07 11.1
## 3 Male 2nd 179 0.0813 0.103 0.628 224. -3.02 -6.99
## 4 Female 2nd 106 0.0482 0.226 0.372 60.9 5.79 6.99
## 5 Male 3rd 510 0.232 0.295 0.722 555. -1.92 -5.04
## 6 Female 3rd 196 0.0891 0.417 0.278 151. 3.68 5.04
## 7 Male Crew 862 0.392 0.498 0.974 696. 6.29 17.6
## 8 Female Crew 23 0.0104 0.0489 0.0260 189. -12.1 -17.6
```

In order to maintain consistency, we attempt to follow some conventions regarding the structure of returned data.

- The output of the
`tidy`

,`augment`

and`glance`

functions is*always*a tibble. - The output never has rownames. This ensures that you can combine it with other tidy outputs without fear of losing information (since rownames in R cannot contain duplicates).
- Some column names are kept consistent, so that they can be combined
across different models and so that you know what to expect (in contrast
to asking “is it
`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.

- Each row in a
`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). - Common column names include:
`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`

package`statistic`

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 testing`estimate`

`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 freedom

`augment(model, data)`

adds columns to the original data.- If the
`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).

- If the
- Each row in an
`augment`

output matches the corresponding row in the original data. - If the original data contained rownames,
`augment`

turns them into a column called`.rownames`

. - Newly added column names begin with
`.`

to avoid overwriting columns in the original data. - Common column names include:
`.fitted`

: the predicted values, on the same scale as the data.`.resid`

: residuals: the actual y values minus the fitted values`.cluster`

: cluster assignments

`glance`

always returns a one-row tibble.- The only exception is that
`glance(NULL)`

returns an empty tibble.

- The only exception is that
- We avoid including arguments that were
*given*to the modeling function. For example, a`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. - Common column names include:
`r.squared`

the fraction of variance explained by the model`adj.r.squared`

\(R^2\) adjusted based on the degrees of freedom`sigma`

the square root of the estimated variance of the residuals