2014 vs. 2024 Great Weapon Fighting

The new D&D Player’s Handbook (5th edition 2024) came out recently, and the Great Weapon Fighting fighting style is a bit different. But is it better, or is the old one better?

dnd
dice problems
Author

Zane Billings

Published

September 22, 2024

Show the code 
library(ggplot2)
base_pth <- here::here("posts/2024-09-22-Great-Weapon-Fighting")
source(here::here(base_pth, "enumeration.R"), echo = FALSE)

The new version of the Dungeons and Dragons 5th Edition rules came out this month, featuring some changes to features from the 2014 version of the rules. I’m getting ready to start a new campaign (as a player! which is still very fun and unusual for me) and I decided that I’m going to go out on a limb and play a fighter this time. And of course, if I’m going to be a fighter, I’m going to have a Cloud Strife, Zabuza, Guts Berserk type of sword.

An interesting statistical exercise arises from the fact that Great Weapon Fighting, an ability that makes your fighter character better at using large weapons, is one of the abilities that has been updated in the 2024 edition of the Player’s Handbook. While the change in wording might seem small from just reading the text, the damage distributions of affected weapons is changed a lot. For reference, here’s the text from the 2014 version.

Great Weapon Fighting. When you roll a 1 or 2 on a damage die for an attack you make with a melee weapon that you are wielding with two hands, you can reroll the die and must use the new roll, even if the new roll is a 1 or a 2. The weapon must have the two-handed or versatile property for you to gain this benefit. — Player’s Handbook, 2014.

And here’s the text from the 2024 version.

Great Weapon Fighting. When you roll damage for an attack you make with a Melee weapon that you are holding with two hands, you can treat any 1 or 2 on a damage die as a 3. The weapon must have the Two-Handed or Versatile property to gain this benefit. — Player’s Handbook, 2024.

In the 2014 version, you can still roll a 1 or 2, although the chance is greatly diminished. In the 2024 version, the lowest number you can roll is a 3. Now, when I first read this, my thought was “oh, clearly the 2024 version is better” which I think is an easy misinterpretation to make. While the threshold for the lowest value you can roll is raised, you are actually less likely to get high rolls with the 2024 version than with the 2014 version. That’s because those rerolls are powerful – instead of just increasing the probability of a 3, they increase the probability of rolling values that are higher than three.

Of course, as a statistical something-or-other, my inclination was to quantify how influential this effect as. And as a D&D player since 2016-ish (hard to believe it’s been 8 years…), I thought this would be a pretty fun problem to solve. It’s also not too difficult, fortunately. So let’s walk through the solution.

One major caveat is that which of the versions is better depends on the die we’re rolling. For example, if we’re rolling four-sided dice (d4’s in D&D nomenclature), bumping our lower values up to three will have a larger impact on the expected value of our rolls than if we’re rolling a d12. So I’ll work this problem out for the two most common types of damage rolls for a GWF-affected heavy weapon in 5th edition D&D: a weapon that does 1d12 damage, and a weapon that does 2d6 damage. I have, for some time, been a 2d6 fan (because rolling more dice is more fun) and it turns out the resulting distributions in the 2d6 case are more interesting, so that’s yet another reason to make a character using a greatsword, which is one of those 2d6 weapons.

How to calculate those probabilities

We’ll compare both of the GWF cases, and we’ll also include the base case of rolling without GWF at all, so we can see how the two versions compare to each other, and also to the baseline.

No GWF

The case for no GWF is simple. For the 1d12 weapon, assuming the die is fair, the distribution is just \(1/12\) for each of the values. The distribution for 2d6 is not much more complicated, and is a common problem in introductory probability courses and textbooks. I’ll use a set of helper functions that will be useful for more complex problems that I included in the enumeration code file.

Here’s the table of probabilities for a 2d6 weapon with no GWF.

Show the code 
get_distribution(6, 2, "none", FALSE) |>
  knitr::kable()
value freq rel_freq
2 1 0.0278
3 2 0.0556
4 3 0.0833
5 4 0.1111
6 5 0.1389
7 6 0.1667
8 5 0.1389
9 4 0.1111
10 3 0.0833
11 2 0.0556
12 1 0.0278

Using the normal rules for calculating the mean and standard deviation from this kind of distribution table, we can get that in summary:

  • the 1d12 weapon will have an expected value of \(6.50 ± 3.45\); and
  • the 2d6 weapon will have an expected value of \(7.00 ± 2.42\).

Of course these summary metrics are not the only useful information for us, but they are a concise way to represent the amount of damage we can expect to do with one attack.

2024 GWF

Great Weapon Fighting. When you roll damage for an attack you make with a Melee weapon that you are holding with two hands, you can treat any 1 or 2 on a damage die as a 3. The weapon must have the Two-Handed or Versatile property to gain this benefit. — Player’s Handbook, 2024.

I’m doing the 2024 version first because it’s actually less interesting than the 2014 version. To compute the probabilities for one die, we can calculate all the same probabilities from the no GWF case, and then compute the probability of rolling a three as the probability of rolling a three or less. That is, \[ P(3 \mid \text{2024 GWF}) = P(1 \mid \text{no GWF}) + P(2 \mid \text{no GWF}) + P(3 \mid \text{no GWF}). \]

So for a 1d12 weapon, the probability of rolling a 3 would be \(3/12 = 1/4\), the probability of rolling a 1 or 2 is \(0\), and the probability of rolling any other number \(4, 5, \ldots, 12\) is \(1/12\) as before.

Show the code 
get_distribution(12, 1, "new", FALSE) |>
  knitr::kable()
value freq rel_freq
3 3 0.2500
4 1 0.0833
5 1 0.0833
6 1 0.0833
7 1 0.0833
8 1 0.0833
9 1 0.0833
10 1 0.0833
11 1 0.0833
12 1 0.0833

For a 2d6 weapon, we have the additional issue of having to add two dice together. So while the same formula works for one dice, like so:

Show the code 
get_distribution(6, 1, "new", FALSE) |>
  knitr::kable()
value freq rel_freq
3 3 0.5000
4 1 0.1667
5 1 0.1667
6 1 0.1667

we need to calculate the distribution of the sum. The function that I’m using does this by enumerating all of the possible combinations of the two die rolls, calculating the sum of each combination, and normalizing to get the percentages. For the 2024 version of GWF, we can get the combinations in the correct proportions by finding the combinations of \(k\) dice with the faces \(3, 3, 3, 4, 5, \ldots, n\) instead of \(1, 2, \ldots, n\). This tactic of using one die with modified faces will be crucial for the 2014 GWF probabilities so it’s nice to think about this problem that way.

Anyways, calculating the probabilities in that way gives the following table for a 2d6 weapon.

Show the code 
get_distribution(6, 2, "new", FALSE) |>
  knitr::kable()
value freq rel_freq
6 9 0.2500
7 6 0.1667
8 7 0.1944
9 8 0.2222
10 3 0.0833
11 2 0.0556
12 1 0.0278

It makes sense that 6 is the lowest value we can roll – each of the two dice has to be a 3 or greater. The dip in probability for 7 is interesting though, because 7 is famously the most common number to roll for 2d6 without any special rules. This distribution also forms an interesting asymmetrical shape with a weird dip in it.

In summary: for the 2024 GWF version, we get

  • the 1d12 weapon will have an expected value of \(6.75 ± 3.11\); and
  • the 2d6 weapon will have an expected value of \(8.00 ± 1.63\).

Interestingtly, the expected improvement is much higher for the 2d6 weapon than for the 1d12 weapon. That’s a combination of the effect of improving two die rolls instead of just one, and the fact that the new correction is better for weapons with smaller damage dice. So maybe a weapon that uses a sum of d4s (or d3s? lol) would be best with this correction. Like a greatwhip or something else that doesn’t exist.

2014 GWF

Great Weapon Fighting. When you roll a 1 or 2 on a damage die for an attack you make with a melee weapon that you are wielding with two hands, you can reroll the die and must use the new roll, even if the new roll is a 1 or a 2. The weapon must have the two-handed or versatile property for you to gain this benefit. — Player’s Handbook, 2014.

The 2014 GWF case is the hardest one to calculate analytically. The trick to this one is to stop thinking about the problem as “one die roll, and sometimes a second one”. Thinking about it that way will give you results, but it is conceptually more difficult. Instead we should try and reframe the problem as “if I were only rolling one die, what faces would that die have to have?” The die for this problem will certainly not exist in real life, so first let’s take the 1d12 case as an example.

  • We can get a 1 by rolling either a 1 or 2, which will cause us to reroll, and then rolling a 1 on the second die. These are the ONLY ways we can roll a 1 for the result. So there are two ways we can get a 1.
  • We can get a 2 by rolling either a 1 or 2, triggering a reroll, then then rolling a 2. So similarly there are two ways we can get a 2 as our result.
  • We can get a 3 by rolling a 3 on the first die, or by rolling either a 1 or 2 on the first die, and then a 3 on the second die. The tricky part is changing how we think about this roll.
  • We can get a 4, 5, whatever, up to 12, in the exact same way as a 3, those numbers are all equally likely.

To think about the probability of rolling a 3, imaging that you always roll the second die. However, if the result of the first die is not a 1 or a 2, we just ignore the second die. Since we’re rolling 2d12, that means there are 144 possible outcomes, and if we were going to roll one hypothetical die, it would have to be 144 faces. (Or in general, \(n^2\) faces for an \(n\)-sided die.) From what we established above, there have to be two faces with a 1 on them, and then two faces with a 2 on them. Then we have 140 faces left to fill with 10 equally likely outcomes, so each remaining die has to get \(14\) faces.

We can also think about choosing the number of faces that show, e.g., a 3, like this. If we roll a three on the first d12, there are twelve ways we can roll the second d12 and the outcome will still be 3. However, if we roll a 1 or 2 on the first d12, and then a 3 on the second d12, that gives us 2 more ways to get a 3 overall. So we see we can get \(12 + 2 = 14\) (or in the general case, \(n+2\)) faces with a 3 on them. And the math works out the same for faces numbered \(4, \ldots, 12\).

That means the probability of rolling a given number is given by the number of faces showing that number divided by the total number of faces. So that’s \[P(1) = P(2) = 2 / 144; \quad P(3) = \ldots = P(12) = 14 / 144.\]

The table is shown below.

Show the code 
get_distribution(12, 1, "old", FALSE) |>
  knitr::kable()
value freq rel_freq
1 2 0.0139
2 2 0.0139
3 14 0.0972
4 14 0.0972
5 14 0.0972
6 14 0.0972
7 14 0.0972
8 14 0.0972
9 14 0.0972
10 14 0.0972
11 14 0.0972
12 14 0.0972

Now, that’s for just one die. If we want to roll multiple dice, say 2d6, under the 2014 GWF rules, we are actually rolling two of those special \(n^2\) faced dies we just discovered. Then we can get all of the combinations of two of those special dice and find the distribution of their sum. That’s exactly what the get_distribution() function is doing to get the following table.

Show the code 
get_distribution(6, 2, "old", FALSE) |>
  knitr::kable()
value freq rel_freq
2 4 0.0031
3 8 0.0062
4 36 0.0278
5 64 0.0494
6 128 0.0988
7 192 0.1481
8 224 0.1728
9 256 0.1975
10 192 0.1481
11 128 0.0988
12 64 0.0494

We can see that now, 9 is the mostly likely outcome instead of 7, and the low rolls of 2 and 3 are much less likely as well.

In summary: for the 2014 GWF version, we get

  • the 1d12 weapon will have an expected value of \(7.33 ± 3.00\); and
  • the 2d6 weapon will have an expected value of \(8.33 ± 2.01\).

So again, it seems that the improvement favors the 2d6 weapon instead of the 1d12 weapon. The effect of benefitting two dice instead of just one die seems to be quite strong.

Visualization and comparison

Now, these tables are not the best way to visualize the comparisons we want to make, a figure will be much more useful. So let’s make some. First we need to do some calculations and data cleaning to compute the probability for all 6 cases we’re interested in, and store them in a nice data frame. You can expand the code to see how I did that with my helper functions.

Show the code 
# Make a list that includes the function we want to call along with vectors
# specifying the arguments to map over
combos_to_run <- list(
    f = get_distribution,
    n = c(12, 12, 12, 6, 6, 6),
    k = c(1, 1, 1, 2, 2, 2),
    which_gwf = c("none", "old", "new", "none", "old", "new")
)
# use the Map function to call get_distribution() with each set of args
results <- do.call(Map, combos_to_run)

# data cleaning to get a nice data frame for ggplot
results_df <- do.call(rbind, results)
results_processed <- results_df
results_processed$weapon <- paste0(
    results_processed$k, "d", results_processed$n
)
results_processed$n <- NULL
results_processed$k <- NULL
results_processed$which_gwf <- factor(
  results_processed$which_gwf,
  levels = c("none", "old", "new"),
  labels = c("No GWF", "2014 GWF", "2024 GWF")
)

Probability of each outcome

First, we’ll look at distribution curves for both weapons under each of the conditions we just walked through. These curves will show the damage value rolled on the x-axis and the probability of rolling exactly that value on the \(y\)-axis.

Show the code 
results_processed |>
  ggplot() +
  aes(x = value, y = rel_freq, color = which_gwf, shape = which_gwf) +
  geom_point(alpha = 0.75, stroke = 1, size = 3) +
  geom_line(alpha = 0.75, linewidth = 1) +
  scale_x_continuous(
    name = "Damage result",
    breaks = seq(1, 12, 1),
    minor_breaks = NULL,
    limits = c(0.5, 12.5)
  ) +
  scale_y_continuous(
    name = "P(X = x)",
    breaks = scales::breaks_pretty(),
    labels = scales::label_percent()
  ) +
  scale_color_brewer(
    name = "Rule",
    palette = "Dark2"
  ) +
  scale_shape_manual(
    name = "Rule",
    values = 15:17
  ) +
  facet_wrap(vars(weapon)) +
  theme_minimal(base_size = 18) +
  theme(legend.position = "bottom")

By looking at the plot, we can see some interesting observations. Of course, the distributions for the 2d6 weapon damage are in general more interesting than for 1d12. For 1d12, we can see that the 2024 outcome really boosts the probability of rolling a 3 but is otherwise exactly the same as having no GWF, while the 2014 outcome slightly boosts the probability of each number 3 or larger but drastically lowers the chance of getting a 1 or 2.

For the 2d6 outcome, we can see that both corrections have an obvious impact that increases the average amount of damage, but in weird ways that are (at least to me) not incredibly intuitive. For the 2014 GWF distribution, 9 is the most common outcome, but for 2024, 9 is the second most common outcome after 6.

So, it seems that using either ability is obviously better than using neither. But it’s hard to tell whether the 2014 or the 2024 ability is better. It probably depends on our personal loss aversion bias – if you really don’t like rolling low numbers and want to more consistently reach a minimum value, then the 2024 version is better for you. However on the other hand, we can see that the 2014 version, we are more likely to roll high values. So if getting a few 1’s and 2’s is worth it for the times you get 10’s, 11’s, and 12’s, the 2014 version is for you.

We can see this more clearly if we look at the results a bit differently.

Probability of ‘at least’ some outcome

So far we’ve considered the probability that we roll exactly a specific outcome. But when we’re trying to decide which of the two options we prefer, it can be more helpful to look at the cumulative probabilities. The cumulative probability \(P(X \leq x)\) can be interpreted as “the probability that we roll at most some value \(x\)”.

For many people, and certainly for me, it is typically easier to understand \(P(X \geq x)\), “the probability that we roll at least some value \(x\)”. So that lets us answer the question “is the probability that I roll a 10 or more higher for the 2014 or 2024 GWF ability?” and other similar questions.

First I’ll calculate those at least probabilities.

Show the code 
results_cumulative <-
    results_processed |>
    dplyr::group_by(which_gwf, weapon) |>
    dplyr::mutate(
        at_most = cumsum(rel_freq),
        at_least = 1 - at_most + rel_freq
    ) |>
    dplyr::ungroup()

Now we can make a plot with the probability we roll a value of \(x\) or higher on the y-axis.

Show the code 
results_cumulative |>
  ggplot() +
  aes(x = value, y = at_least, color = which_gwf, shape = which_gwf) +
  geom_point(alpha = 0.75, stroke = 1, size = 3) +
  geom_line(alpha = 0.75, linewidth = 1) +
  scale_x_continuous(
    name = "Damage result",
    breaks = seq(1, 12, 1),
    minor_breaks = NULL,
    limits = c(0.5, 12.5)
  ) +
  scale_y_continuous(
    name = "P(X ≥ x)",
    breaks = scales::breaks_pretty(),
    labels = scales::label_percent()
  ) +
  scale_color_brewer(
    name = "Rule",
    palette = "Dark2"
  ) +
  scale_shape_manual(
    name = "Rule",
    values = 15:17
  ) +
  facet_wrap(vars(weapon)) +
  theme_minimal(base_size = 18) +
  theme(legend.position = "bottom")

Yes, I think this nicely shows my conclusion, although you might disagree with me, and that’s ok! For both weapons, we can see that the 2024 rules have a higher probability of getting at least some minimum value (3 for 1d12, 6 for 2d6), but we have a lower probability of rolling at least ANY VALUE above that threshold!

Conclusions

Let’s look at all the summary statistics together in one place.

Show the code 
stat_df <- data.frame(
  "Weapon" = paste0(combos_to_run$k, "d", combos_to_run$n),
  "Rule" = factor(
    combos_to_run$which_gwf,
    levels = c("none", "old", "new"),
    labels = c("No GWF", "2014 GWF", "2024 GWF")
  ),
  "Expected" = sapply(results, mean_sd_from_dist)
)
knitr::kable(stat_df)
Weapon Rule Expected
1d12 No GWF 6.50 ± 3.45
1d12 2014 GWF 7.33 ± 3.00
1d12 2024 GWF 6.75 ± 3.11
2d6 No GWF 7.00 ± 2.42
2d6 2014 GWF 8.33 ± 2.01
2d6 2024 GWF 8.00 ± 1.63

Interestingly, we can see that the 2014 GWF ability gives us the highest expected damage value for both 1d12 and 2d6 weapons. For 2d6 weapons, the 2024 GWF ability has a tighter SD, indicating that values will also tend to be more consistent in the long run – more of our rolls will be close to the mean. However, for 1d12 weapons, the 2024 GWF damage is actually a bit less consistent than the 2014 version! That’s probably good, since the mean is substantially lower than if we look at the contrast for 2d6.

For me, any guarantees of rolling above a baseline isn’t worth losing some expected high rolls, given that the means are higher for the 2014 GWF rule than for the 2024 rule. I don’t want to lose out on that extra damage just for a guarantee that I won’t get low numbers!

Notably though, the results are strongly affected by which dice we are rolling. Like I said, if your weapon requires you to roll a lot of small dice, like potentially a lot of d4’s, maybe the 2024 version would come out on top. However, I expected that as you increase the number of dice, the benefit from the 2014 version will also become more powerful, so maybe the 2024 version would be best for a weapon that does 2d4 or 3d4 damage. I didn’t do any further simulations so I’m not 100% sure right now.

All of the functions I wrote accept arbitrary integers \(n\) and \(k\) as arguments so in the future I think it would be nice to build a Shiny app or something that allows easy experimentation with that kind of thing, but we’ll see if that happens. Anyways, it was nice to think about this problem and convince myself that (by a certain metric made up by me), the 2014 GWF ability is better than the 2024 ability, even if that seems counterintuitive on a first reading of both abilities.

If you got all the way here, thank you for reading this! And please feel free to get in touch with me if you have questions about D&D dice problems.

Code

Enumeration.R 
###
# Probabilities of GWF damage by enumeration
# Zane
# 2024-09-22
###

# Probabilities for old GWF
create_die_matrix <- function(values, k) {
    perms <-
        rep(values, times = k) |>
        matrix(nrow = length(values), ncol = k) |>
        as.data.frame() |>
        expand.grid()
    
    colnames(perms) <- paste0("X", 1:ncol(perms))
    
    return(perms)
}

die_from_method <- function(which_gwf, n) {
    if (which_gwf == "none") {
        die <- seq(1, n, 1)
    } else if (which_gwf == "old") {
        die <- c(
            rep(c(1, 2), each = 2),
            rep(seq(3, n), each = n + 2)
        )
    } else if (which_gwf == "new") {
        die <- c(3, 3, seq(3, n, 1))
    } else {
        stop("'which_gwf' should be one of: 'none', 'old', or 'new'.")
    }
    
    return(die)
}

get_distribution <- function(
        n, k, which_gwf, return_args = TRUE, digits = 4, pct = FALSE
    ) {
    die <- die_from_method(which_gwf, n)
    die_matrix <- create_die_matrix(die, k)
    damage_values <- rowSums(die_matrix)
    damage_distribution <- table(damage_values, dnn = NULL)
    damage_distribution_rel <- prop.table(damage_distribution)
    
    tidy_output <- data.frame(
        value = as.integer(names(damage_distribution)),
        freq = as.integer(damage_distribution)
    )
    
    mult <- ifelse(isTRUE(pct), 100, 1)
    tidy_output$rel_freq <- round(
        as.numeric(damage_distribution_rel) * mult,
        digits = ifelse(isTRUE(pct), digits - 2, digits)
    )
    
    if (isTRUE(return_args)) {
        tidy_output$n <- n
        tidy_output$k <- k
        tidy_output$which_gwf <- which_gwf
    }
    
    return(tidy_output)
}

mean_sd_from_dist <- function(dist_res, format = TRUE, pct_input = FALSE) {
    mult <- ifelse(isTRUE(pct_input), 0.01, 1)
    x <- dist_res$value
    wt <- dist_res$rel_freq * mult
    wm <- sum(x * wt)
    wv <- sum(wt * (x - wm)^2)
    wsd <- sqrt(wv)
    
    if (isTRUE(format)) {
        out <- sprintf("%.2f ± %.2f", wm, wsd)
    } else {
        out <- c("mean" = wm, "sd" = wsd)
    }
    
    return(out)
}
Simulation.R 
###
# Simulating GWF damage
# Zane
# 2024-09-22
###

N_sims <- 1e6

validate_args_as_integer <- function(...) {
    mc <- match.call(expand.dots = FALSE)
    dots <- list(...)
    names(dots) <- mc$...
    
    for (i in seq_along(dots)) {
        it <- dots[[i]]
        it_name <- names(dots)[[i]]
        if (!is.numeric(it) || (it < 1) || (it %% 1 != 0)) {
            stop(it_name, " should be an integer ≥ 1")
        }
    }
    
    invisible(TRUE)
}

# Old method
simulation_old_gwf <- function(n, k, n_sims) {
    
    validate_args_as_integer(n, k, n_sims)
    
    one_die <- function() {
        result <- sample.int(n, size = n_sims, replace = TRUE)
        rerolls <- (result %in% c(1, 2))
        result[rerolls] <- sample.int(n, size = sum(rerolls), replace = TRUE)
        return(result)
    }
    
    sim_dice <-
        lapply(1:k, \(x) one_die()) |> 
        simplify2array(except = NULL)
    
    damage <- rowSums(sim_dice)
    
    return(damage)
}

set.seed(375)
old_sim <- simulation_old_gwf(6, 2, 1e6)
old_sim_counts <- old_sim |> table()
old_sim_props <- old_sim_counts |> prop.table()
barplot(old_sim_props)

# New method

simulation_new_gwf <- function(n, k, n_sims) {
    
    validate_args_as_integer(n, k, n_sims)
    
    one_die <- function() {
        result <- sample.int(n, size = n_sims, replace = TRUE)
        result[result < 3] <- 3
        return(result)
    }
    
    sim_dice <-
        lapply(1:k, \(x) one_die()) |> 
        simplify2array(except = NULL)
    
    damage <- rowSums(sim_dice)
    
    return(damage)
}

set.seed(375)
new_sim <- simulation_new_gwf(6, 2, 1e6)
new_sim_counts <- new_sim |> table()
new_sim_props <- new_sim_counts |> prop.table()
barplot(new_sim_props)

Details

  • Another common tactic I use for this types of problems, which I call the lazy way, is just to write a simulation that replicates the behavior of interest a million times and look at the empirical probabilities. This is often a great strategy, but for this example the analytical computation is simple enough that it’s not worth doing a simulation. However, the more dice you start rolling, the more RAM it takes to enumerate combinations and the more worthwhile it becomes to just do a simulation. You can see my example simulation code in the code links.
  • A much simpler way to do this is to use the specialized web app AnyDice by Jasper Flick. Implementing an AnyDice program for this comparison takes only 3 lines of code, although I’ve modified this so that \(n\), the number of faces on the dice, and \(k\), the number of dice to roll, are variables that you can easily change all at once.
  • Information from the Player’s Handbook is not owned by me, and is included here under fair use for educational purposes (although it seems prudent to mention that all of the information included here is also licensed under the Open Game License Version 1.0a and is included in the Basic Rules). The CC-BY-NC-SA licensed under which my content is not distributed does not extend to information which is owned by Wizards of the Coast or Hasbro.
  • This document was last updated at 2024-09-23 00:16:56.274673. The complete R session information is reproduced below.
Show the code 
sessioninfo::session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14 ucrt)
 os       Windows 10 x64 (build 19045)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  English_United States.utf8
 ctype    English_United States.utf8
 tz       America/New_York
 date     2024-09-23
 pandoc   3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package      * version date (UTC) lib source
 cli            3.6.3   2024-06-21 [1] CRAN (R 4.4.1)
 colorspace     2.1-0   2023-01-23 [1] CRAN (R 4.4.1)
 digest         0.6.36  2024-06-23 [1] CRAN (R 4.4.1)
 dplyr          1.1.4   2023-11-17 [1] CRAN (R 4.4.1)
 evaluate       0.24.0  2024-06-10 [1] CRAN (R 4.4.1)
 fansi          1.0.6   2023-12-08 [1] CRAN (R 4.4.1)
 farver         2.1.2   2024-05-13 [1] CRAN (R 4.4.1)
 fastmap        1.2.0   2024-05-15 [1] CRAN (R 4.4.1)
 generics       0.1.3   2022-07-05 [1] CRAN (R 4.4.1)
 ggplot2      * 3.5.1   2024-04-23 [1] CRAN (R 4.4.1)
 glue           1.7.0   2024-01-09 [1] CRAN (R 4.4.1)
 gtable         0.3.5   2024-04-22 [1] CRAN (R 4.4.1)
 here           1.0.1   2020-12-13 [1] CRAN (R 4.4.1)
 htmltools      0.5.8.1 2024-04-04 [1] CRAN (R 4.4.1)
 jsonlite       1.8.8   2023-12-04 [1] CRAN (R 4.4.1)
 knitr          1.48    2024-07-07 [1] RSPM
 lifecycle      1.0.4   2023-11-07 [1] CRAN (R 4.4.1)
 magrittr       2.0.3   2022-03-30 [1] CRAN (R 4.4.1)
 munsell        0.5.1   2024-04-01 [1] CRAN (R 4.4.1)
 pillar         1.9.0   2023-03-22 [1] CRAN (R 4.4.1)
 pkgconfig      2.0.3   2019-09-22 [1] CRAN (R 4.4.1)
 R6             2.5.1   2021-08-19 [1] CRAN (R 4.4.1)
 RColorBrewer   1.1-3   2022-04-03 [1] CRAN (R 4.4.0)
 renv           1.0.7   2024-04-11 [1] CRAN (R 4.4.1)
 rlang          1.1.4   2024-06-04 [1] CRAN (R 4.4.1)
 rmarkdown      2.27    2024-05-17 [1] CRAN (R 4.4.1)
 rprojroot      2.0.4   2023-11-05 [1] CRAN (R 4.4.1)
 rstudioapi     0.16.0  2024-03-24 [1] CRAN (R 4.4.1)
 scales         1.3.0   2023-11-28 [1] CRAN (R 4.4.1)
 sessioninfo    1.2.2   2021-12-06 [1] CRAN (R 4.4.1)
 tibble         3.2.1   2023-03-20 [1] CRAN (R 4.4.1)
 tidyselect     1.2.1   2024-03-11 [1] CRAN (R 4.4.1)
 utf8           1.2.4   2023-10-22 [1] CRAN (R 4.4.1)
 vctrs          0.6.5   2023-12-01 [1] CRAN (R 4.4.1)
 withr          3.0.0   2024-01-16 [1] CRAN (R 4.4.1)
 xfun           0.45    2024-06-16 [1] CRAN (R 4.4.1)
 yaml           2.3.9   2024-07-05 [1] RSPM

 [1] D:/proj/quarto-website/renv/library/windows/R-4.4/x86_64-w64-mingw32
 [2] C:/Users/Zane/AppData/Local/R/cache/R/renv/sandbox/windows/R-4.4/x86_64-w64-mingw32/e0da0d43

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Reuse

Citation

BibTeX citation:
@online{billings2024,
  author = {Billings, Zane},
  title = {2014 Vs. 2024 {Great} {Weapon} {Fighting}},
  date = {2024-09-22},
  url = {https://wzbillings.com/posts/2024-09-22-Great-Weapon-Fighting},
  langid = {en}
}
For attribution, please cite this work as:
Billings, Zane. 2024. “2014 Vs. 2024 Great Weapon Fighting.” September 22, 2024. https://wzbillings.com/posts/2024-09-22-Great-Weapon-Fighting.