# Extending apa_print()

#### Frederik Aust

    library("papaja")
    #> Loading required package: tinylabels

Here, we provide a brief overview of issues to consider when implementing a new method for `apa_print()`, a convenience function to facilitate reporting of results in accordance with APA reporting guidelines. If you consider adding a new method, please review our brief contributing guidelines and code of conduct.

## Deciding on a new method

If you are reporting the results of a statistical analysis that is not yet supported by `apa_print()` you probably have a good motivation and possibly prior work to build on. If you are just looking for a way to contribute to, take a look at the open issues for inspiration.

`apa_print()` is a generic, meaning it can, in principle, work on any output object with a class that is specific enough to purposefully extract the results of the analysis. For example, objects of class `htest`, as returned by `t.test()`, `cor.test()`, `prop.test()`, etc., are named lists that follow a loose convention about the named objects they contain.

    t_test_example <- t.test(extra ~ group, data = sleep)

    class(t_test_example)
    #> [1] "htest"

    str(t_test_example)
    #> List of 10
    #>  $ statistic  : Named num -1.86
    #>   ..- attr(*, "names")= chr "t"
    #>  $ parameter  : Named num 17.8
    #>   ..- attr(*, "names")= chr "df"
    #>  $ p.value    : num 0.0794
    #>  $ conf.int   : num [1:2] -3.365 0.205
    #>   ..- attr(*, "conf.level")= num 0.95
    #>  $ estimate   : Named num [1:2] 0.75 2.33
    #>   ..- attr(*, "names")= chr [1:2] "mean in group 1" "mean in group 2"
    #>  $ null.value : Named num 0
    #>   ..- attr(*, "names")= chr "difference in means between group 1 and group 2"
    #>  $ stderr     : num 0.849
    #>  $ alternative: chr "two.sided"
    #>  $ method     : chr "Welch Two Sample t-test"
    #>  $ data.name  : chr "extra by group"
    #>  - attr(*, "class")= chr "htest"

Hence, if we pass an `htest` object to `apa_print()` the function expects there to be named elements in the list, such as `statistic`, `estimate`, or `p.value`. These expectations are reflected in the workings of the `apa_print.htest()` method. Objects of less specific classes, such as `list` or `data.frame` cannot be supported, because we cannot make any useful assumptions about their structure.

## Default structure of output

Objects returned by `apa_print()` are of class `apa_results`, a named list with four elements:

    #> $estimate
    #> NULL
    #>
    #> $statistic
    #> NULL
    #>
    #> $full_result
    #> NULL
    #>
    #> $table
    #> NULL
    #>
    #> attr(,"class")
    #> [1] "apa_results" "list"

To illustrate how `apa_results` objects are populated, let’s look at the output of `apa_print.lm()`.

    # Data from Dobson (1990), p. 9.
    ctl <- c(4.17, 5.58, 5.18, 6.11, 4.50, 4.61, 5.17, 4.53, 5.33, 5.14)
    trt <- c(4.81, 4.17, 4.41, 3.59, 5.87, 3.83, 6.03, 4.89, 4.32, 4.69)
    group <- gl(2, 10, 20, labels = c("Ctl", "Trt"))
    weight <- c(ctl, trt)
    lm_fit <- lm(weight ~ group)

    lm_fit_apa <- apa_print(lm_fit)

The `estimate` element of the returned `apa_results`-list itself contains a named list with estimated parameters—in this case regression coefficients—and corresponding confidence intervals for the model. The names of the list correspond to the names of the predictors.

    lm_fit_apa$estimate
    #> $Intercept
    #> [1] "$b = 5.03$, 95\\% CI $[4.57, 5.49]$"
    #>
    #> $groupTrt
    #> [1] "$b = -0.37$, 95\\% CI $[-1.03, 0.28]$"
    #>
    #> $modelfit
    #> $modelfit$r2
    #> [1] "$R^2 = .07$, 90\\% CI $[0.00, 0.33]$"
    #>
    #> $modelfit$r2_adj
    #> [1] "$R^2_{adj} = .02$"
    #>
    #> $modelfit$aic
    #> [1] "$\\mathrm{AIC} = 46.18$"
    #>
    #> $modelfit$bic
    #> [1] "$\\mathrm{BIC} = 49.16$"

The `estimate` list may contain additional elements, such as the list `modelfit`, that contains quantitative estimates of the model fit.

The `statistic` element of the returned `apa_results` list contains a named list with the same structure as `estimate`. Instead of parameter estimates, `statistic` contains the corresponding inferential test statistics, such as significance tests or Bayesian model comparisons.

    lm_fit_apa$statistic
    #> $Intercept
    #> [1] "$t(18) = 22.85$, $p < .001$"
    #>
    #> $groupTrt
    #> [1] "$t(18) = -1.19$, $p = .249$"
    #>
    #> $modelfit
    #> $modelfit$r2
    #> [1] "$F(1, 18) = 1.42$, $p = .249$"

Note that the `statistics` list misses elements for the information criteria `AIC` and `BIC`. Because no inferential test statistics on the information criteria are available, it is fine to simply drop those elements.

The `full_results` element is a named list that simply combines the results of `estimate` and `statistic` for convenience in reporting.

    lm_fit_apa$full_result
    #> $Intercept
    #> [1] "$b = 5.03$, 95\\% CI $[4.57, 5.49]$, $t(18) = 22.85$, $p < .001$"
    #>
    #> $groupTrt
    #> [1] "$b = -0.37$, 95\\% CI $[-1.03, 0.28]$, $t(18) = -1.19$, $p = .249$"
    #>
    #> $modelfit
    #> $modelfit$r2
    #> [1] "$R^2 = .07$, 90\\% CI $[0.00, 0.33]$, $F(1, 18) = 1.42$, $p = .249$"

Finally, the `table` element contains a `data.frame` of class `apa_results_table` that summarizes the results. In essence this is simply a regular `data.frame` that follows the column-naming conventions used in broom but allows for prettier printing of variable labels.

    lm_fit_apa$table
    #> A data.frame with 6 labelled columns:
    #>
    #>        term estimate      conf.int statistic df p.value
    #> 1 Intercept     5.03  [4.57, 5.49]     22.85 18  < .001
    #> 2  GroupTrt    -0.37 [-1.03, 0.28]     -1.19 18    .249
    #>
    #> term     : Predictor
    #> estimate : $b$
    #> conf.int : 95\\% CI
    #> statistic: $t$
    #> df       : $\\mathit{df}$
    #> p.value  : $p$

For more complex analyses `table` may contain a named list of `apa_result_table`s. We use tinylabels to set variable labels. These variable labels are attributes attached to each column and contain a typeset label for the respective column.

    # library("tinylabels")

    letters
    #>  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
    #> [20] "t" "u" "v" "w" "x" "y" "z"

    variable_label(letters) <- "Letters of the alphabet"
    variable_label(letters)
    #> [1] "Letters of the alphabet"

    letters
    #> Variable label     : Letters of the alphabet
    #>  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
    #> [20] "t" "u" "v" "w" "x" "y" "z"

    str(letters)
    #>  'tiny_labelled' chr [1:26] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" ...
    #>  - attr(*, "label")= chr "Letters of the alphabet"

    lm_fit_apa$table$statistic
    #> Variable label     : $t$
    #> [1] "22.85" "-1.19"

Variable labels are automatically used by `apa_table()` and plotting functions from the `apa_factorial_plot()`-family to create sensible default labels. If a label is enveloped in `$` it may contain LaTeX math syntax, which is automatically converted to R expressions using latex2exp for plotting.

Any new `apa_print()` method should output an object of this basic structure.

## Typesetting numeric information

`apa_results` do not contain numeric information. Rather the numeric information has been processed for printing in accordance with APA guidelines. There are several papaja-functions to facilitate the typesetting. `apa_num()` is a flexible general purpose function that wraps `formatC()` and can be used to round, set decimal as well as thousands separators, or remove leading zeros.

    x <- rnorm(3) * 1e4
    apa_num(x)
    #> [1] "12,175.75" "-64.68"    "-5,156.08"

    apa_num(x, digits = 3, big.mark = ".", decimal.mark = ",")
    #> [1] "12.175,747" "-64,679"    "-5.156,079"

    apa_num(Inf)
    #> [1] "$\\infty$"

`apa_p()` is a wrapper for `apa_num()` that sets appropriate defaults to report p values in accordance with APA guidelines.

    apa_p(c(0.0001, 0.05, 0.99999))
    #> [1] "< .001" ".050"   "> .999"

The internal function `apa_df()` is geared towards typesetting degrees of freedom.

    apa_df(c(12, 12.485))
    #> [1] "12"    "12.48"

    apa_df(12L)
    #> [1] "12"

Finally, `apa_interval()` can be used to typeset interval estimates.

    apa_interval(rnorm(2), conf.int = 0.95, interval_type = "CI")
    #> [1] "95\\% CI [1.87, 0.20]"

Again, there are two wrappers that set appropriate defaults to typeset frequentist confidence intervals and Bayesian highest-density intervals.

    apa_confint(rnorm(2), conf.int = 0.95)
    #> [1] "95\\% CI [-0.91, 0.06]"

    apa_hdint(rnorm(2), conf.int = 0.95)
    #> [1] "95\\% HDI [0.74, 0.84]"

## Typesetting model terms

When creating named lists from terms, these terms names should use `_` as separator, and be valid R names. Adhering to these conventions ensures that `apa_results` can conveniently be indexed using the `$` operator.

To facilitate the generation of list names, papaja provides the internal function `sanitize_terms()`.

    mod_terms <- c("(Intercept)", "Factor A", "Factor B",
                   "Factor A:Factor B", "scale(Factor A)")
    sanitize_terms(mod_terms, standardized = TRUE)
    #> [1] "Intercept"         "Factor_A"          "Factor_B"
    #> [4] "Factor_A_Factor_B" "z_Factor_A"

While these sanitized terms are well suited to name R objects, they are not ideal for reporting. To facilitate typesetting term names for reporting, there is another internal function `beautify_terms()`.

    beautify_terms(mod_terms, standardized = TRUE)
    #> [1] "Intercept"                   "Factor A"
    #> [3] "Factor B"                    "Factor A $\\times$ Factor B"
    #> [5] "Factor A"

## Internal workflow

### Method dispatch

As with `lm` objects, it is often the case that the objects, as returned by the analysis function, may not contain all information necessary to populate the lists described above. For example, to obtain inferential statistics it may be necessary to call `summary()`.

    npk_aov <- aov(yield ~ block + N * P * K, npk)
    npk_aov
    #> Call:
    #>    aov(formula = yield ~ block + N * P * K, data = npk)
    #>
    #> Terms:
    #>                    block        N        P        K      N:P      N:K      P:K
    #> Sum of Squares  343.2950 189.2817   8.4017  95.2017  21.2817  33.1350   0.4817
    #> Deg. of Freedom        5        1        1        1        1        1        1
    #>                 Residuals
    #> Sum of Squares   185.2867
    #> Deg. of Freedom        12
    #>
    #> Residual standard error: 3.929447
    #> 1 out of 13 effects not estimable
    #> Estimated effects may be unbalanced

    summary(npk_aov)
    #>             Df Sum Sq Mean Sq F value  Pr(>F)
    #> block        5  343.3   68.66   4.447 0.01594 *
    #> N            1  189.3  189.28  12.259 0.00437 **
    #> P            1    8.4    8.40   0.544 0.47490
    #> K            1   95.2   95.20   6.166 0.02880 *
    #> N:P          1   21.3   21.28   1.378 0.26317
    #> N:K          1   33.1   33.14   2.146 0.16865
    #> P:K          1    0.5    0.48   0.031 0.86275
    #> Residuals   12  185.3   15.44
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

This is why there are usually multiple `apa_print()`-methods that are called subsequently to make the function both flexible and convenient. For convenience, `apa_print.aov()` calls `summary()` with its default arguments and passes the result onto `apa_print.summary.aov()`.

    papaja:::apa_print.aov
    #> function (x, estimate = getOption("papaja.estimate_anova", "ges"),
    #>     observed = NULL, intercept = FALSE, mse = getOption("papaja.mse",
    #>         TRUE), in_paren = FALSE, ...)
    #> {
    #>     apa_print(summary(x), .x = x, intercept = intercept, estimate = estimate,
    #>         mse = mse, observed = observed, in_paren = in_paren,
    #>         ...)
    #> }
    #> <bytecode: 0x7d1c31818>
    #> <environment: namespace:papaja>

This approach also ensures that a variety of object types are supported while minimizing code redundancy.

### Restructuring results

The internals of `apa_print()` heavily rely on broom, a package to conveniently restructure the output of analysis functions into tidy `data.frame`s. The objects are often processed using `broom::tidy()`, and `broom::glance()` if necessary, before being modified further to create the contents of the `table` element.

Once the results table has been assembled, numeric values have been typeset, and variable labels have been assigned `glue_apa_results()` can be used to create an `apa_results` object according to the above specifications. Consider the following example of an `lm`-object. First we `tidy()` and `glance()` the object to obtain tidy results. We than typeset all “special” numerical results, that is, all results that would not be typeset appropriately by applying `apa_num()` with its default settings. Moreover, we combine the separate columns for lower and upper confidence interval bounds into one column `conf.int` which contains the complete confidence interval.

    lm_fit <- lm(mpg ~ cyl + wt, mtcars)

    # Tidy and typeset output
    library("broom")
    tidy_lm_fit <- tidy(lm_fit, conf.int = TRUE)
    tidy_lm_fit$p.value <- apa_p(tidy_lm_fit$p.value)
    tidy_lm_fit$conf.int <- unlist(apa_confint(tidy_lm_fit[, c("conf.low", "conf.high")]))

    str(tidy_lm_fit)
    #> tibble [3 × 8] (S3: tbl_df/tbl/data.frame)
    #>  $ term     : chr [1:3] "(Intercept)" "cyl" "wt"
    #>  $ estimate : num [1:3] 39.69 -1.51 -3.19
    #>  $ std.error: num [1:3] 1.715 0.415 0.757
    #>  $ statistic: num [1:3] 23.14 -3.64 -4.22
    #>  $ p.value  : chr [1:3] "< .001" ".001" "< .001"
    #>  $ conf.low : num [1:3] 36.18 -2.36 -4.74
    #>  $ conf.high: num [1:3] 43.19 -0.66 -1.64
    #>  $ conf.int : chr [1:3] "[36.18, 43.19]" "[-2.36, -0.66]" "[-4.74, -1.64]"

    glance_lm_fit <- glance(lm_fit)
    glance_lm_fit$r.squared <- apa_num(glance_lm_fit$r.squared, gt1 = FALSE)
    glance_lm_fit$p.value <- apa_p(glance_lm_fit$p.value)
    glance_lm_fit$df <- apa_df(glance_lm_fit$df)
    glance_lm_fit$df.residual <- apa_df(glance_lm_fit$df.residual)

    str(glance_lm_fit)
    #> tibble [1 × 12] (S3: tbl_df/tbl/data.frame)
    #>  $ r.squared    : chr ".83"
    #>  $ adj.r.squared: num 0.819
    #>  $ sigma        : num 2.57
    #>  $ statistic    : Named num 70.9
    #>   ..- attr(*, "names")= chr "value"
    #>  $ p.value      : chr "< .001"
    #>  $ df           : chr "2"
    #>  $ logLik       : num -74
    #>  $ AIC          : num 156
    #>  $ BIC          : num 162
    #>  $ deviance     : num 191
    #>  $ df.residual  : chr "29"
    #>  $ nobs         : int 32

Next, we typeset the remaining numeric columns and assign informative variable labels:

    tidy_lm_fit <- apa_num(tidy_lm_fit)

    variable_labels(tidy_lm_fit) <- c(
      term = "Term"
      , estimate = "$b$"
      , statistic = paste0("$t(", glance_lm_fit$df.residual, ")")
      , p.value = "$p$"
      , conf.int = "95% CI"
    )

    glance_lm_fit <- apa_num(glance_lm_fit)

    variable_labels(glance_lm_fit) <- c(
      r.squared = "$R^2$"
      , statistic = "$F$"
      , p.value = "$p$"
      , AIC = "$\\mathrm{AIC}$"
    )

Now we can use `glue_apa_results()` to create the output object. In doing so, we use the internal function `construct_glue()` to automatically determine the correct “glue” of the reporting string. Let’s first examine the glue.

    papaja:::construct_glue(tidy_lm_fit, "estimate")
    #> [1] "$<<svl(estimate)>> = <<estimate>>$, <<svl(conf.int, use_math = TRUE)>> $<<conf.int>>$"

The character string contains a combination of text and to-be-evaluated R code enveloped in `<<` and `>>`. All variable names (e.g. `estimate`) are assumed to be columns of `x` (here `tidy_lm_fit`) or any additional object passed to `glue_apa_results()` via `...`. `svl()` is a function that returns a column variable label but, by default, remove the math-environment tags (`$`) as these are not needed here.

    lm_results <- glue_apa_results(
        x = tidy_lm_fit
        , est_glue = papaja:::construct_glue(tidy_lm_fit, "estimate")
        , stat_glue = papaja:::construct_glue(tidy_lm_fit, "statistic")
        , term_names = sanitize_terms(tidy_lm_fit$term)
    )

    lm_results
    #> $estimate
    #> $estimate$Intercept
    #> [1] "$b = 39.69$, 95% CI $[36.18, 43.19]$"
    #>
    #> $estimate$cyl
    #> [1] "$b = -1.51$, 95% CI $[-2.36, -0.66]$"
    #>
    #> $estimate$wt
    #> [1] "$b = -3.19$, 95% CI $[-4.74, -1.64]$"
    #>
    #>
    #> $statistic
    #> $statistic$Intercept
    #> [1] "$t(29) = 23.14$, $p < .001$"
    #>
    #> $statistic$cyl
    #> [1] "$t(29) = -3.64$, $p = .001$"
    #>
    #> $statistic$wt
    #> [1] "$t(29) = -4.22$, $p < .001$"
    #>
    #>
    #> $full_result
    #> $full_result$Intercept
    #> [1] "$b = 39.69$, 95% CI $[36.18, 43.19]$, $t(29) = 23.14$, $p < .001$"
    #>
    #> $full_result$cyl
    #> [1] "$b = -1.51$, 95% CI $[-2.36, -0.66]$, $t(29) = -3.64$, $p = .001$"
    #>
    #> $full_result$wt
    #> [1] "$b = -3.19$, 95% CI $[-4.74, -1.64]$, $t(29) = -4.22$, $p < .001$"
    #>
    #>
    #> $table
    #>        term estimate std.error statistic p.value conf.low conf.high
    #> 1 Intercept    39.69      1.71     23.14  < .001    36.18     43.19
    #> 2       Cyl    -1.51      0.41     -3.64    .001    -2.36     -0.66
    #> 3        Wt    -3.19      0.76     -4.22  < .001    -4.74     -1.64
    #>         conf.int
    #> 1 [36.18, 43.19]
    #> 2 [-2.36, -0.66]
    #> 3 [-4.74, -1.64]
    #>
    #> attr(,"class")
    #> [1] "apa_results" "list"

If we need to add additional information to this output, we can use `add_glue_to_apa_results()`. This function takes an existing output and adds new strings to a specific sublist. So, let’s add some model fit information to the output.

    add_glue_to_apa_results(
      .x = glance_lm_fit
      , container = lm_results
      , sublist = "modelfit"
      , est_glue = c(
          r2 = "$<<svl(r.squared)>> = <<r.squared>>$"
          , aic = ""
      )
      , stat_glue = c(
          r2 = papaja:::construct_glue(glance_lm_fit, "statistic")
          , aic = "$<<svl(AIC)>> = <<AIC>>$"
      )
    )
    #> $estimate
    #> $estimate$Intercept
    #> [1] "$b = 39.69$, 95% CI $[36.18, 43.19]$"
    #>
    #> $estimate$cyl
    #> [1] "$b = -1.51$, 95% CI $[-2.36, -0.66]$"
    #>
    #> $estimate$wt
    #> [1] "$b = -3.19$, 95% CI $[-4.74, -1.64]$"
    #>
    #> $estimate$modelfit
    #>            r2
    #> "$R^2 = .83$"
    #>
    #>
    #> $statistic
    #> $statistic$Intercept
    #> [1] "$t(29) = 23.14$, $p < .001$"
    #>
    #> $statistic$cyl
    #> [1] "$t(29) = -3.64$, $p = .001$"
    #>
    #> $statistic$wt
    #> [1] "$t(29) = -4.22$, $p < .001$"
    #>
    #> $statistic$modelfit
    #> $statistic$modelfit$r2
    #> [1] "$F(2, 29) = 70.91$, $p < .001$"
    #>
    #> $statistic$modelfit$aic
    #> [1] "$\\mathrm{AIC} = 156.01$"
    #>
    #>
    #>
    #> $full_result
    #> $full_result$Intercept
    #> [1] "$b = 39.69$, 95% CI $[36.18, 43.19]$, $t(29) = 23.14$, $p < .001$"
    #>
    #> $full_result$cyl
    #> [1] "$b = -1.51$, 95% CI $[-2.36, -0.66]$, $t(29) = -3.64$, $p = .001$"
    #>
    #> $full_result$wt
    #> [1] "$b = -3.19$, 95% CI $[-4.74, -1.64]$, $t(29) = -4.22$, $p < .001$"
    #>
    #> $full_result$modelfit
    #> $full_result$modelfit$r2
    #> [1] "$R^2 = .83$, $F(2, 29) = 70.91$, $p < .001$"
    #>
    #> $full_result$modelfit$aic
    #> [1] "$\\mathrm{AIC} = 156.01$"
    #>
    #>
    #>
    #> $table
    #>        term estimate std.error statistic p.value conf.low conf.high
    #> 1 Intercept    39.69      1.71     23.14  < .001    36.18     43.19
    #> 2       Cyl    -1.51      0.41     -3.64    .001    -2.36     -0.66
    #> 3        Wt    -3.19      0.76     -4.22  < .001    -4.74     -1.64
    #>         conf.int
    #> 1 [36.18, 43.19]
    #> 2 [-2.36, -0.66]
    #> 3 [-4.74, -1.64]
    #>
    #> attr(,"class")
    #> [1] "apa_results" "list"

## User interface

A final issue to consider is that users may pass inappropriate input to `apa_print()`. To ensure that we return correct output or informative error messages, we need input validation. Currently, papaja relies on the internal function `validate()` for this.

    in_paren <- TRUE
    papaja:::validate(in_paren, check_class = "logical", check_length = 1)
    #> [1] TRUE

Please use either `validate()` or perform input validation using the assertthat package.