diff --git a/R/broom.R b/R/broom.R
index 4d77c764c..67d17ea98 100644
--- a/R/broom.R
+++ b/R/broom.R
@@ -9,86 +9,72 @@ generics::glance
 
 
 #' tidy helper
-#' 
+#'
 #' @noRd
 #' @export
 tidy.comparisons <- function(x, ...) {
-    insight::check_if_installed("tibble")
-    out <- tibble::as_tibble(x)
-    if (!"term" %in% names(out)) {
-        lab <- seq_len(nrow(out))
-        if ("group" %in% colnames(out) || is.character(attr(x, "by"))) {
-            tmp <- c("group", attr(x, "by"))
-            tmp <- Filter(function(j) j %in% colnames(x), tmp)
-            if (length(tmp) > 0) {
-                tmp <- do.call(paste, out[, tmp])
-                if (anyDuplicated(tmp)) {
-                    tmp <- paste(seq_len(nrow(out)), tmp)
-                }
-                lab <- tmp
-            }
-        }
-        out[["term"]] <- lab
-    }
-    return(out)
+  insight::check_if_installed("tibble")
+  out <- tibble::as_tibble(x)
+  return(out)
 }
 
 
 #' tidy helper
-#' 
+#'
 #' @noRd
 #' @export
 tidy.slopes <- tidy.comparisons
 
 
 #' tidy helper
-#' 
+#'
 #' @noRd
 #' @export
 tidy.predictions <- tidy.comparisons
 
 
 #' tidy helper
-#' 
+#'
 #' @noRd
 #' @export
 tidy.hypotheses <- tidy.comparisons
 
 
 #' tidy helper
-#' 
+#'
 #' @noRd
 #' @export
 tidy.marginalmeans <- function(x, ...) {
-    insight::check_if_installed("tibble")
-    tibble::as_tibble(x)
+  insight::check_if_installed("tibble")
+  tibble::as_tibble(x)
 }
 
 
 #' @noRd
 #' @export
 glance.slopes <- function(x, ...) {
-    insight::check_if_installed("insight")
-    insight::check_if_installed("modelsummary")
-    model <- tryCatch(attr(x, "model"), error = function(e) NULL)
-    if (is.null(model) || isTRUE(checkmate::check_string(model))) {
-        model <- tryCatch(attr(x, "call")[["model"]], error = function(e) NULL)
-    }
-    gl <- suppressMessages(suppressWarnings(try(
-        modelsummary::get_gof(model, ...), silent = TRUE)))
-    if (inherits(gl, "data.frame")) {
-        out <- data.frame(gl)
-    } else {
-        out <- NULL
-    }
-    vcov.type <- attr(x, "vcov.type")
-    if (is.null(out) && !is.null(vcov.type)) {
-        out <- data.frame("vcov.type" = vcov.type)
-    } else if (!is.null(out)) {
-        out[["vcov.type"]] <- vcov.type
-    }
-    out <- tibble::as_tibble(out)
-    return(out)
+  insight::check_if_installed("insight")
+  insight::check_if_installed("modelsummary")
+  model <- tryCatch(attr(x, "model"), error = function(e) NULL)
+  if (is.null(model) || isTRUE(checkmate::check_string(model))) {
+    model <- tryCatch(attr(x, "call")[["model"]], error = function(e) NULL)
+  }
+  gl <- suppressMessages(suppressWarnings(try(
+    modelsummary::get_gof(model, ...),
+    silent = TRUE)))
+  if (inherits(gl, "data.frame")) {
+    out <- data.frame(gl)
+  } else {
+    out <- NULL
+  }
+  vcov.type <- attr(x, "vcov.type")
+  if (is.null(out) && !is.null(vcov.type)) {
+    out <- data.frame("vcov.type" = vcov.type)
+  } else if (!is.null(out)) {
+    out[["vcov.type"]] <- vcov.type
+  }
+  out <- tibble::as_tibble(out)
+  return(out)
 }
 
 
@@ -109,4 +95,5 @@ glance.hypotheses <- glance.slopes
 
 #' @noRd
 #' @export
-glance.marginalmeans <- glance.slopes
\ No newline at end of file
+glance.marginalmeans <- glance.slopes
+
diff --git a/inst/tinytest/test-by.R b/inst/tinytest/test-by.R
index fded001c3..8eebe4898 100644
--- a/inst/tinytest/test-by.R
+++ b/inst/tinytest/test-by.R
@@ -66,9 +66,10 @@ mod <- lm(mpg ~ factor(cyl) * hp + wt, data = dat)
 mar <- margins(mod, at = list(cyl = unique(dat$cyl)))
 mar <- data.frame(summary(mar))
 mfx <- slopes(
-    mod,
-    by = "cyl",
-    newdata = datagrid(cyl = c(4, 6, 8), grid_type = "counterfactual"))
+  mod,
+  by = "cyl",
+  newdata = datagrid(cyl = c(4, 6, 8), grid_type = "counterfactual"))
+mfx <- mfx[order(mfx$term, mfx$contrast), ]
 expect_equivalent(mfx$estimate, mar$AME)
 expect_equivalent(mfx$std.error, mar$SE, tolerance = 1e6)
 
@@ -77,9 +78,9 @@ expect_equivalent(mfx$std.error, mar$SE, tolerance = 1e6)
 # issue #434 by with character precitors
 dat <- read.csv("https://vincentarelbundock.github.io/Rdatasets/csv/AER/Affairs.csv")
 mod <- glm(
-    affairs ~ children + gender + yearsmarried,
-    family = poisson,
-    data = dat)
+  affairs ~ children + gender + yearsmarried,
+  family = poisson,
+  data = dat)
 p <- predictions(mod, by = "children")
 expect_equivalent(nrow(p), 2)
 expect_false(anyNA(p$estimate))
@@ -97,8 +98,8 @@ cmp <- comparisons(mod, type = "probs", by = "group")
 expect_equivalent(nrow(cmp), 9)
 
 by <- data.frame(
-    group = c("3", "4", "5"),
-    by = c("(3,4)", "(3,4)", "(5)"))
+  group = c("3", "4", "5"),
+  by = c("(3,4)", "(3,4)", "(5)"))
 p1 <- predictions(mod, type = "probs")
 p2 <- predictions(mod, type = "probs", by = by)
 p3 <- predictions(mod, type = "probs", by = by, hypothesis = "sequential")
@@ -113,26 +114,26 @@ cmp <- comparisons(mod, type = "probs", by = "am")
 expect_equivalent(nrow(cmp), 18)
 
 cmp <- comparisons(
-    mod,
-    variables = "am",
-    by = by,
-    type = "probs")
+  mod,
+  variables = "am",
+  by = by,
+  type = "probs")
 expect_equivalent(nrow(cmp), 2)
 
 cmp <- comparisons(
-    mod,
-    variables = "am",
-    by = by,
-    hypothesis = "sequential",
-    type = "probs")
+  mod,
+  variables = "am",
+  by = by,
+  hypothesis = "sequential",
+  type = "probs")
 expect_equivalent(nrow(cmp), 1)
 
 
 # Issue #481: warning on missing by categories
 mod <- nnet::multinom(factor(gear) ~ mpg + am * vs, data = mtcars, trace = FALSE)
 by <- data.frame(
-    by = c("4", "5"),
-    group = 4:5)
+  by = c("4", "5"),
+  group = 4:5)
 expect_warning(comparisons(mod, variables = "mpg", newdata = "mean", by = by))
 expect_warning(predictions(mod, newdata = "mean", by = by))
 
@@ -159,13 +160,13 @@ expect_equivalent(nrow(pre2), 96)
 dat <- mtcars
 mod <- glm(gear ~ cyl + am, family = poisson, data = dat)
 mfx <- avg_slopes(
-    mod,
-    by = c("cyl", "am"),
-    newdata = datagrid(
-        cyl = unique,
-        am = unique,
-        grid_type = "counterfactual")) |>
-    dplyr::arrange(term, cyl, am)
+  mod,
+  by = c("cyl", "am"),
+  newdata = datagrid(
+    cyl = unique,
+    am = unique,
+    grid_type = "counterfactual")) |>
+  dplyr::arrange(term, cyl, am)
 mar <- margins(mod, at = list(cyl = unique(dat$cyl), am = unique(dat$am)))
 mar <- summary(mar)
 # margins doesn't treat the binary am as binary automatically
@@ -179,16 +180,16 @@ dat$cyl <- factor(dat$cyl)
 dat$am <- as.logical(dat$am)
 mod <- glm(gear ~ cyl + am, family = poisson, data = dat)
 mfx <- comparisons(
-    mod,
-    by = c("cyl", "am"),
-    newdata = datagrid(
-        cyl = unique,
-        am = unique,
-        grid_type = "counterfactual"))
+  mod,
+  by = c("cyl", "am"),
+  newdata = datagrid(
+    cyl = unique,
+    am = unique,
+    grid_type = "counterfactual"))
 
 mfx <- tidy(mfx)
 
-mfx <- mfx[order(mfx$term, mfx$contrast, mfx$cyl, mfx$am),]
+mfx <- mfx[order(mfx$term, mfx$contrast, mfx$cyl, mfx$am), ]
 mar <- margins(mod, at = list(cyl = unique(dat$cyl), am = unique(dat$am)))
 mar <- summary(mar)
 expect_equivalent(mfx$estimate, mar$AME, tolerance = tol)
@@ -200,9 +201,9 @@ dat <- transform(mtcars, vs = vs, am = as.factor(am), cyl = as.factor(cyl))
 mod <- lm(mpg ~ qsec + am + cyl, dat)
 fun <- \(hi, lo) mean(hi) / mean(lo)
 cmp1 <- comparisons(mod, variables = "cyl", comparison = fun, by = "am") |>
-    dplyr::arrange(am, contrast)
+  dplyr::arrange(am, contrast)
 cmp2 <- comparisons(mod, variables = "cyl", comparison = "ratioavg", by = "am") |>
-    dplyr::arrange(am, contrast)
+  dplyr::arrange(am, contrast)
 expect_equivalent(cmp1$estimate, cmp2$estimate)
 expect_equivalent(cmp1$std.error, cmp2$std.error)
 expect_equal(nrow(cmp1), 4)
@@ -218,18 +219,18 @@ cmp2 <- comparisons(mod, variables = "am") %>%
   dplyr::group_by(cyl) %>%
   dplyr::summarize(estimate = mean(estimate), .groups = "keep") |>
   dplyr::ungroup()
-cmp3 <- predictions(mod)  |>
-    aggregate(estimate ~ am + cyl, FUN = mean) |>
-    aggregate(estimate ~ cyl, FUN = diff)
+cmp3 <- predictions(mod) |>
+  aggregate(estimate ~ am + cyl, FUN = mean) |>
+  aggregate(estimate ~ cyl, FUN = diff)
 expect_equivalent(cmp1$estimate, cmp2$estimate)
 expect_equivalent(cmp1$estimate, cmp3$estimate)
 
 
 # Issue #1058
 tmp <- mtcars
-tmp <- tmp[c('mpg', 'cyl', 'hp')]
+tmp <- tmp[c("mpg", "cyl", "hp")]
 tmp$cyl <- as.factor(tmp$cyl) # 3 levels
-tmp$hp  <- as.factor(tmp$hp)
+tmp$hp <- as.factor(tmp$hp)
 bygrid <- datagrid(newdata = tmp, by = "cyl", hp = unique)
 expect_equivalent(nrow(bygrid), 23)