What Baby Names are In and Out of Fashion?

Using open data to summarize baby names over time

Published

October 19, 2025

[October 18t 2025 update - replacing arcived {RSocrata} with {socratadata}]

Open Data of Baby Names

Open Data Buffalo is a great resource and initiative to make datasets open and available to the public.

My partner works at a Children’s hospital and is convinced of trending baby names. Well, I said to her let’s see what the data says!

So I ventured out into the world wide web and found a dataset called:

Baby Names: Beginning 2007

New York State (NYS) Baby Names are aggregated and displayed by the year, county, or borough where the mother resided as stated on a New York State or New York City (NYC) birth certificate. The frequency of the baby name is listed if there are 5 or more of the same baby name in a county outside of NYC or 10 or more of the same baby name in a NYC borough.

library(jsonlite)
suppressMessages(library(tidyverse))

baby_names <- 
    socratadata::soc_read(
      url = "https://health.data.ny.gov/resource/jxy9-yhdk.json",
      page_size = 1e7
    )

ℹ Utilizing v2.1 API. `include_synthetic_cols` will be ignored. Provide an `api_key_id` and `api_key_secret` to perform a v3 request.

baby_names %>% glimpse()

Rows: 99,116
Columns: 5
$ year       <chr> "2022", "2022", "2022", "2022", "2022", "2022", "2022", "20…
$ first_name <chr> "OLIVIA", "AMELIA", "AVERY", "EMMA", "CHARLOTTE", "CHLOE", …
$ county     <chr> "Albany", "Albany", "Albany", "Albany", "Albany", "Albany",…
$ sex        <chr> "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F",…
$ name_count <dbl> 16, 15, 12, 11, 11, 11, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,…

This dataset is already tidy: One row per observation (first_name or baby name) and one column per variable (e.g. the number of observed names in a county with the given gender on the birth certificate).

I first check a few data quality characteristics such as missingness and number of unique things in each column:

purrr::map_dfr(baby_names,~{sum(is.na(.x))})

# A tibble: 1 × 5
   year first_name county   sex name_count
  <int>      <int>  <int> <int>      <int>
1     0          0      0     0          0

purrr::map_dfr(baby_names,~{n_distinct(.x)})

# A tibble: 1 × 5
   year first_name county   sex name_count
  <int>      <int>  <int> <int>      <int>
1    16       2393     61     2        244

The `baby_names` dataset requires initial preprocessing

Now we need to transform our dataset by first converting columns to the appropriate data types:

baby_names_transformed <- 
    baby_names %>% 
    mutate(
        year = as.integer(year),
        first_name = factor(first_name),
        county = factor(county),
        sex = factor(sex,levels=c("M","F"),labels=c("Male","Female")),
        name_count = as.integer(name_count)
    )

baby_names_transformed %>% summary()

      year        first_name            county          sex       
 Min.   :2007   EMMA   :  582   Kings      :11932   Male  :52728  
 1st Qu.:2010   OLIVIA :  562   Suffolk    : 9439   Female:46388  
 Median :2014   LOGAN  :  536   Nassau     : 8488                 
 Mean   :2014   LIAM   :  535   Queens     : 8038                 
 3rd Qu.:2018   MASON  :  522   Westchester: 6691                 
 Max.   :2022   NOAH   :  520   Erie       : 6160                 
                (Other):95859   (Other)    :48368                 
   name_count    
 Min.   :  5.00  
 1st Qu.:  6.00  
 Median : 11.00  
 Mean   : 17.57  
 3rd Qu.: 19.00  
 Max.   :297.00

After the data transformation, we see that counties are specified in different cases. We should revise this so county (and also first_name) are in one type of case such as title case:

baby_names_transformed <- 
    baby_names_transformed %>% 
    mutate(
        first_name = as.character(first_name) %>% stringr::str_to_title() %>% factor(),
        county = as.character(county) %>% stringr::str_to_title() %>% factor()
    )

baby_names_transformed %>% summary()

      year        first_name            county          sex       
 Min.   :2007   Emma   :  582   Kings      :11932   Male  :52728  
 1st Qu.:2010   Olivia :  562   Suffolk    : 9439   Female:46388  
 Median :2014   Logan  :  536   Nassau     : 8488                 
 Mean   :2014   Liam   :  535   Queens     : 8038                 
 3rd Qu.:2018   Mason  :  522   Westchester: 6691                 
 Max.   :2022   Noah   :  520   Erie       : 6160                 
                (Other):95859   (Other)    :48368                 
   name_count    
 Min.   :  5.00  
 1st Qu.:  6.00  
 Median : 11.00  
 Mean   : 17.57  
 3rd Qu.: 19.00  
 Max.   :297.00

Many Baby Names Don’t Have Counts In A Year

My next data quality question is how many names have data each year?

theme_set(theme_bw(base_size = 16))

baby_names_transformed %>% 
    summarize(n_years = n_distinct(year),.by=c(first_name)) %>% 
    summarize(n_names = n_distinct(first_name),.by=n_years) %>% 
    ggplot(aes(n_years,n_names)) +
    geom_bar(color="black",fill = "gray80",stat = "identity") +
    scale_x_continuous(breaks = scales::pretty_breaks(14)) +
    scale_y_continuous(expand = c(0,0.1),
                       trans="sqrt",breaks = scales::pretty_breaks(10)) +
    labs(x="Year Data: Number of Years With Name Count Data",
         y="Number of Names With Year Data",
         title="Many Baby Names Don't Have Counts In A Year",
         subtitle="Every Name has Count Data For Atleast One Year") +
    theme(
        panel.grid.major.y = element_line(color="gray75"),
        panel.grid.minor.y = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank()
    )

Most Baby Names Are Gender-Specific

Another question is are there many names that are unisex i.e. male and female names?

tmp <- 
    baby_names_transformed %>% 
    summarize(n_sex = n_distinct(sex),
              unisex = n_sex==2,onesex = n_sex==1,
              .by=c(first_name)) %>% 
    summarize(`Unisex` = sum(unisex),`One Sex`=sum(onesex))

tmp %>% 
    pivot_longer(cols = everything()) %>% 
    mutate(label = glue::glue("{name} (N={scales::comma(value)})")) %>% 
    ggplot(aes(factor(1),value,fill=label)) +
    geom_bar(stat="identity",position = "fill") +
    scale_fill_brewer(palette = "Dark2") +
    scale_y_continuous(labels = scales::percent) +
    guides(fill=guide_legend(title=NULL)) +
    labs(x=NULL,y="Percent of Names",title="Most Baby Names Are Gender-Specific",subtitle = "There Are A Few Names That Are Unisex, However") +
    theme(
        axis.ticks.x = element_blank(),
        axis.text.x = element_blank(),
        legend.position = "top"
    )

Most Baby Names are Counted in a Few NYS Counties

My last data quality question is how many baby names have data across counties?

baby_names_transformed %>% 
    summarise(n_counties = n_distinct(county),.by=first_name) %>% 
    bind_cols(
        summarise(baby_names,total_counties = n_distinct(county))
    ) %>% 
    mutate(
        freq_counties = n_counties / total_counties
    ) %>% 
    ggplot(aes(freq_counties,y=after_stat(count))) +
    geom_density(bw="nrd",color="blue",fill="cornflowerblue") +
    scale_x_continuous(labels = scales::percent) +
    scale_y_sqrt(breaks = scales::pretty_breaks(15)) +
    labs(x="Percent of Counties",y="Number of Names With County Data",
         title="Most Names are Counted in a Few NYS Counties") +
    theme(
        panel.grid.major.y = element_line(color="gray75"),
        panel.grid.minor.y = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank()
    )

After transforming the dataset and checking data quality, we see that:

Baby names are sparsely annotated across counties
Baby names are, generally, specific to a year or are observed across all years
There are a few baby names that are not gender-specific.

We can estimate baby name trends using generalized additive models

Let’s do a small example first. My subquestion is, is the baby name Charlotte trending over time irrespective of other factors?

library(mgcv)

Loading required package: nlme


Attaching package: 'nlme'

The following object is masked from 'package:dplyr':

    collapse

This is mgcv 1.9-3. For overview type 'help("mgcv-package")'.

tmp <- 
    baby_names_transformed %>% 
    filter(first_name=="Charlotte")

form <- as.formula(glue::glue("name_count ~ s(year) + county:year"))
fit <- mgcv::gam(form,
                family="poisson",
                data=tmp,method = "GACV.Cp")

tmp %>% 
    bind_cols(.pred = fit$fitted.values) %>% 
    ggplot(aes(name_count,.pred)) +
    geom_point(shape=21) +
    geom_smooth(formula = 'y ~ x',method="lm") +
    labs(x="Annotated Counts",y="Predicted Counts",title="Model Predictions Look Pretty Accurate With Not Much Bias")

coefs_ <- fit$coefficients[str_detect(names(fit$coefficients),"s\\(year\\)")] %>% unname()
tibble(
    year = seq_along(coefs_),
    coef = coefs_
) %>% 
    ggplot(aes(year,coef)) +
    geom_line(linewidth=2) +
    labs(x="Time",y="Weight",title="Charlotte is predicted as falling in and out of fashion")

GAMs allow for estimating trends given “random” naming patterns across NYS counties over time

If we want to consider all names, we need to specify the name as a random effect and the trend line as a random slope between the name and the year:

(form <- as.formula(glue::glue("name_count ~ s(first_name, bs = 're') + s(first_name, year, bs = 're') + county:year + sex:year")))

name_count ~ s(first_name, bs = "re") + s(first_name, year, bs = "re") + 
    county:year + sex:year

We can use this formula in our model to get the trends of the baby names over time (Note: we switch from gam to bam so that we can fit the model faster using method = ‘fREML’):

tmp <- 
    baby_names_transformed

if(file.exists(paste0(here::here('baby_name_gam_full.rds')))){
    fit <- readr::read_rds(paste0(here::here(),"/baby_name_gam_full.rds"))
}else{
    system.time(fit <- mgcv::bam(form,
                 family = "poisson",
                 data = tmp,
                 discrete = TRUE))
    readr::write_rds(fit,paste0(here::here('baby_name_gam_full.rds')))
}

Now we combine the predicted baby name counts to the original data:

(data_pred <- 
        bind_cols(tmp,
                  as.data.frame(predict(fit,
                                        new_data = tmp,
                                        se.fit = TRUE))) %>% 
        tibble())

# A tibble: 99,116 × 7
    year first_name county sex    name_count   fit se.fit
   <int> <fct>      <fct>  <fct>       <int> <dbl>  <dbl>
 1  2022 Olivia     Albany Female         16 2.74  0.0112
 2  2022 Amelia     Albany Female         15 2.07  0.0141
 3  2022 Avery      Albany Female         12 1.36  0.0164
 4  2022 Emma       Albany Female         11 2.70  0.0113
 5  2022 Charlotte  Albany Female         11 2.23  0.0136
 6  2022 Chloe      Albany Female         11 2.14  0.0139
 7  2022 Sophia     Albany Female          8 2.77  0.0112
 8  2022 Cora       Albany Female          8 0.979 0.0391
 9  2022 Mia        Albany Female          7 2.59  0.0120
10  2022 Luna       Albany Female          7 1.61  0.0203
# ℹ 99,106 more rows

And we can now plot predicted baby name trends over time:

pred_baby_names <- 
    data_pred %>% 
    summarize(avg_pred = mean(fit),.by=c(first_name,year))

pred_baby_names %>% 
    ggplot(aes(year,avg_pred,group=first_name)) +
    geom_line(show.legend = F,color="gray80") +
    scale_x_continuous(breaks = scales::pretty_breaks(14)) +
    labs(x="Year",y="Average Prediction Across Counties and Sex") +
    theme(
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.major.x = element_line(color="gray95"),
        panel.grid.minor.x = element_blank()
    )

Now we have trend estimates for baby names over time considering the naming variability across counties, gender, and years.

GAM estimates offer robust baby name trends, but still need to consider data missingness

We can now ask the question what are the top trending baby names in NY?

(top_10_baby_names <- 
    pred_baby_names %>% 
    summarise(cor = cor(year,avg_pred,method="spearman"),.by = first_name) %>% 
    slice_max(order_by = cor,n = 10,with_ties = F))

# A tibble: 10 × 2
   first_name   cor
   <fct>      <dbl>
 1 Kailani        1
 2 Mirha          1
 3 Sonny          1
 4 Lennon         1
 5 Aitana         1
 6 Matthias       1
 7 Reed           1
 8 Eithan         1
 9 Octavia        1
10 Romy           1

pred_baby_names %>% 
    filter(first_name %in% top_10_baby_names$first_name) %>% 
    ggplot(aes(year,avg_pred,color=first_name)) +
    geom_line(linewidth=1) +
    scale_x_continuous(breaks = scales::pretty_breaks(14)) +
    labs(x="Year",y="Average Prediction",title="Missing Data Cast Doubt On Baby Name Trends") +
    guides(color=guide_legend(title=NULL)) +
    theme(
        legend.position = "bottom"
    )

Looks like I need to add a variable for the name attributing how many years worth of count data it has as well as if it was a name in 2019 or 2020.

(pred_baby_names <- 
    pred_baby_names %>% 
    left_join(
        data_pred %>% 
        summarise(n_years = n_distinct(year),prop_years = n_years/14,
                  recent = any(year %in% c(2019,2020)),.by=first_name),
        by = "first_name"
    ))

# A tibble: 18,406 × 6
   first_name  year avg_pred n_years prop_years recent
   <fct>      <int>    <dbl>   <int>      <dbl> <lgl> 
 1 Olivia      2022     3.04      16      1.14  TRUE  
 2 Amelia      2022     2.43      16      1.14  TRUE  
 3 Avery       2022     2.37      16      1.14  TRUE  
 4 Emma        2022     3.16      16      1.14  TRUE  
 5 Charlotte   2022     2.49      16      1.14  TRUE  
 6 Chloe       2022     3.09      16      1.14  TRUE  
 7 Sophia      2022     3.18      16      1.14  TRUE  
 8 Cora        2022     1.97      12      0.857 TRUE  
 9 Mia         2022     3.43      16      1.14  TRUE  
10 Luna        2022     2.23      16      1.14  TRUE  
# ℹ 18,396 more rows

Filtering our GAM estimates can give more reliable top trending baby names in NYS

Now we can ask what are the top 10 trending baby names that are recently popular?

(top_10_recent_baby_names <- 
    pred_baby_names %>% 
    filter(recent & prop_years>.5) %>% 
    summarise(cor = cor(year,avg_pred,method="spearman"),.by = first_name) %>% 
    slice_max(order_by = cor,n = 10,with_ties = F))

# A tibble: 10 × 2
   first_name   cor
   <fct>      <dbl>
 1 Tyler      0.962
 2 Joshua     0.959
 3 Aiden      0.932
 4 Zachary    0.926
 5 Andrew     0.924
 6 Jacob      0.921
 7 Matthew    0.918
 8 Nicholas   0.918
 9 Emily      0.912
10 Ethan      0.912

pred_baby_names %>% 
    filter(first_name %in% top_10_recent_baby_names$first_name) %>% 
    mutate(first_name = factor(first_name,top_10_recent_baby_names$first_name)) %>% 
    ggplot(aes(year,avg_pred,color=first_name)) +
    geom_line(linewidth=1) +
    scale_x_continuous(breaks = scales::pretty_breaks(14)) +
    labs(x="Year",y="Average Prediction",title="Top 10 Trending Names") +
    guides(color=guide_legend(title=NULL)) +
    theme(
        legend.position = "top"
    )

pred_baby_names %>% 
    filter(first_name %in% top_10_recent_baby_names$first_name) %>% 
    mutate(first_name = factor(first_name,top_10_recent_baby_names$first_name)) %>% 
    ggplot(aes(year,avg_pred,color=first_name)) +
    geom_line(linewidth=1,show.legend = FALSE) +
    scale_x_continuous(breaks = scales::pretty_breaks(14)) +
    facet_wrap(~first_name,ncol=3) +
    labs(x="Year",y="Average Prediction",title="Top 10 Baby Trending Names") +
    theme(
        axis.text.x = element_text(angle=90,vjust=1,hjust=1)
    )

So there we go! This seems accurate, as a top trending baby name is Nicholas and my nephew’s name (born in 2018) is Aiden 🤓

And here is a summary table with the trending baby names in order:

pred_baby_names %>% 
    summarise(cor = cor(year,avg_pred,method="spearman"),
              .by = c(first_name,recent,prop_years)) %>% 
    arrange(desc(recent),desc(prop_years),desc(cor)) %>% 
    DT::datatable()

This was a fun post and exploration of trending baby names in NYS. If you made it this far, I hope you enjoyed reading 😁