The most popular Animal Crossing villagers


January 7, 2022

Gif showing a blue card on a green background. The card has a picture of a villager from Nintendo's Animal Crossing game with their name, personality type, species and hobby. A mouse cursor swipes the card to the right, meaning 'like', and another card appears with a different character. The card is swiped to the left, meaning 'dislike'.


I once wrote an R Shiny app to run a popularity contest for Animal Crossing villagers. Surprise: cute ones are favourites.

Swiping {shinyswipe} code

A while back I wrote a Shiny app (site, source, blogpost) for TidyTuesday to replicate a Tinder-like experience using villagers from Nintendo’s Animal Crossing New Horizons game. It uses the swipe mechanic from Nick Strayer’s {shinysense} package to gauge popularity: left for a ‘dislike’, right for a ‘like’.

After exceeding 3000 total swipes, it’s time to take a look at the results.


I re-rendered this post in July 2023 when there were about 6000 swipes(!).

Oh sheet

Data from each swipe in the app is automatically appended to a public Google Sheets sheet that can be read with {googlesheets4}. Public sheets don’t require authentication to download, so run gs4_deauth() before read_sheet() to prevent it.


raw <- read_sheet(
  ss = "1kMbmav6XvYqnTO202deyZQh37JeWtTK4ThIXdxGmEbs",
  col_types = "Tcc"  # datetime, char, char
✔ Reading from "acnh-swipe_results".
✔ Range 'Sheet1'.

First thing is to isolate the left and right swipes only. The {shinysense} package also allows for up and down swipes by default and I wasn’t sure how to remove this capability from my app (and was too lazy to work it out).

dat <- raw[raw$swipe %in% c("left", "right"), ]
dat[sample(rownames(dat), 5), ]  # random sample
# A tibble: 5 × 3
  date                name     swipe
  <dttm>              <chr>    <chr>
1 2021-12-01 01:09:52 Wart Jr. left 
2 2022-12-03 00:41:01 Dora     left 
3 2022-01-09 22:38:01 Pango    left 
4 2021-05-14 22:58:52 Bertha   right
5 2022-12-03 00:41:42 Rosie    left 

The data are one row per swipe, with columns for date (datetime of when the swipe happened), name (the villager’s name) and swipe (the swipe direction).

But what we’re really after is a grouped table with a row per villager, plus new columns for the total number of swipes, the difference between right and left swipes and the percentage of swipes that were to the right (pc_right). These will let us better rank the characters.

df <- with(dat, table(name, swipe)) |>  # like dplyr::count() = "n") |>
  reshape(  # like tidyr::pivot_*()
    v.names   = "n",      # values_from
    idvar     = "name",   # id_cols
    timevar   = "swipe",  # names_from
    direction = "wide",   # i.e. pivot_wider()
    sep       = "_"       # names_sep
  ) |> 
  transform(  # like dplyr::mutate()
    total    = n_left + n_right,
    diff     = n_right - n_left,
    pc_right = 100 * round(n_right / (n_right + n_left), 2)

     name n_left n_right total diff pc_right
1 Admiral     14       4    18  -10       22
2 Agent S     10       4    14   -6       29
3   Agnes     14       8    22   -6       36
4      Al     13       3    16  -10       19
5 Alfonso      6       7    13    1       54
6   Alice      8       8    16    0       50
Click to expand code explanation

I think most readers of this blog are probably {tidyverse} users, so I’ll explain some of the base R approach I took here:

  • I’ve used the base pipe (|>) introduced in R v4.1 to chain the functions, which is analogous to {magrittr}’s pipe (%>%) in this example
  • with() allows the bare column names in table() to be evaluated as columns of dat, which means you only write the name of the data object once
  • a table() coerced with is equivalent to dplyr::count(), basically
  • reshape() can be used like tidyr::pivot_wider() (I’ve added comments in the code block above to show how the arguments are used)
  • turns out that transform() can be used like dplyr::mutate() to create new columns, thought the help files say it should only be used for interactive and that ‘you deserve whatever you get!’

We can also bring in some additional villager data collected for TidyTuesday and join it to the swipe data. This will come in useful later.

tt <- read.csv(

df <- merge(df, tt, by = "name")

New Horizons scanning

There are 391 villagers represented in these data, with a combined total of 5950 legitimate swipes.

The total swipes per villager ranged from 7 to 29, with a mean of 15.2±3.9, so some characters didn’t really get enough swipes for proper assessment. You’d better go to the app and add some more swipes, eh?

par(bg = "lightgreen")
  main = "Distribution of total swipes per villager",
  xlab = "Total swipes",
  col = "lightblue",
  las = 1

Histogram of total swipes per villager. It's roughly normally distributed between 5 and 10 swipes, but slightly left-skewed with a tail going beyond 15 swipes.

What if we look now at right swipes (i.e. ‘likes’), adjusted for the total swipes per character?

par(bg = "lightgreen")
  main = "Distribution of right swipes per villager",
  xlab = "Right swipes (%)",
  col = "lightblue",
  las = 1

Histogram of the percentage of right swipes (likes) per villager. Nearly normal, with a slight skew to the left.

You can see that the distribution isn’t quite normal. The frequency of swipes below 50% is 297 and above 50% is 83. This implies that the majority of characters were disliked in a binary sense.

The bins at 0 and 100% tell you that there were some characters that were met with universal disapproval and approval, while the bin at 50% tells us that same characters split people’s opinions. Which were they?

Drumroll, please

So, onto the villager rankings.

I’ve written a little function to output an HTML table where each character’s name links to their profile on the Animal Crossing Wiki and exposes their photo from VillagerDB.

entable <- function(df) {
  df$url <- paste0(
    "<img src='", df$url, "' ",
    "width=50 ",
    "alt='Animal Crossing villager ", df$name,"'>"
  df$name <- paste0(
    "<a href='",
    df$name, "'>", df$name, "</a>"
  df <- df[, c("name", "url", "pc_right", "total")]
  names(df) <- c("Name", "Picture", "Right swipes (%)", "Total swipes")
  rownames(df) <- NULL

Most polarising

To build even more tension, let’s look at the characters who had a 50:50 ratio of likes to dislikes.

meh <- subset(df[order(-df$total), ], diff == 0) |> head()
Name Picture Right swipes (%) Total swipes
Alice Animal Crossing villager Alice 50 16
Hopkins Animal Crossing villager Hopkins 50 16
Hornsby Animal Crossing villager Hornsby 50 16
Melba Animal Crossing villager Melba 50 16
Cherry Animal Crossing villager Cherry 50 14
Goose Animal Crossing villager Goose 50 14

I’m not sure why these villagers are so controversial Perhaps they’re too ‘plain’ for some people?


I know what you’re thinking: the results are on a villager-by-villager basis, but which species are the most popular? We can aggregate swipes and take a look.

sp_l <- aggregate(n_left ~ species, sum, data = df)
sp_r <- aggregate(n_right ~ species, sum, data = df)
sp_n <- with(df, table(species)) |> = "n_villagers")

sp <- sp_n |> 
  merge(sp_l, by = "species") |> 
  merge(sp_r, by = "species") |> 
    total = n_right + n_left,
    pc_right = 100 * round(n_right / (n_right + n_left), 2)
Click to expand code explanation

A couple more base functions here for those not used to them:

  • aggregate() is like dplyr::group_by() followed by dplyr::summarise() and it allows for compact ‘formula syntax’, so we can say ‘aggregate y by x’ with y ~ x
  • merge() is just like the dplyr::*_join() family

So, firstly, the species ranked by lowest proportion of right swipes.

sp_bot <- sp[order(sp$pc_right, -sp$n_left), ]
rownames(sp_bot) <- NULL
   species n_villagers n_left n_right total pc_right
1    mouse          15    172      24   196       12
2    hippo           7     86      19   105       18
3   monkey           8    110      26   136       19
4 kangaroo           8    114      29   143       20
5      pig          15    178      46   224       21
6     bear          15    172      47   219       21

I can see how monkeys and hippos might not be that ‘cute’, per se, but what about the mice? Although ‘cute’ is probably not the best term for the cranky mouse Limberg (sorry Limberg).

What about the most liked species?

sp_top <- sp[order(-sp$pc_right, sp$n_right), ]
rownames(sp_top) <- NULL
  species n_villagers n_left n_right total pc_right
1    deer          10     85      96   181       53
2     dog          16    104     115   219       53
3 octopus           3     22      24    46       52
4     cat          23    190     191   381       50
5 ostrich          10     79      72   151       48
6     cub          16    133     104   237       44

Deer (all-around solid designs) and dogs (generally friend-shaped) top the table.

Octopuses are up there too, although there’s relatively few octopus villagers. Personally, I like Zucker, an octopus who looks like takoyaki and therefore delicious.

This wasn’t meant to be about villager tastiness, was it? We may need a new app to rank by apparent edibility…


Session info
Last rendered: 2023-07-17 18:18:03 BST
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.2.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/London
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] googlesheets4_1.1.1

loaded via a namespace (and not attached):
 [1] vctrs_0.6.3       httr_1.4.6        cli_3.6.1         knitr_1.43.1     
 [5] rlang_1.1.1       xfun_0.39         purrr_1.0.1       generics_0.1.3   
 [9] jsonlite_1.8.7    glue_1.6.2        gargle_1.5.1      htmltools_0.5.5  
[13] fansi_1.0.4       rmarkdown_2.23    cellranger_1.1.0  evaluate_0.21    
[17] tibble_3.2.1      fontawesome_0.5.1 fastmap_1.1.1     yaml_2.3.7       
[21] lifecycle_1.0.3   compiler_4.3.1    dplyr_1.1.2       fs_1.6.2         
[25] pkgconfig_2.0.3   htmlwidgets_1.6.2 rstudioapi_0.15.0 digest_0.6.31    
[29] R6_2.5.1          tidyselect_1.2.0  utf8_1.2.3        curl_5.0.1       
[33] pillar_1.9.0      magrittr_2.0.3    tools_4.3.1       googledrive_2.1.1