alisaad / agebyname Goto Github PK

This R package is inspired by Nate Silver's recent article on How to Tell Someone's Age When All you Know is Her Name. It allows one to (almost) replicate the analysis done in the article, and provides more extensive features.

To get started, you can install the package from github using devtools. You will also need to install the latest version of dplyr, also from github.

library(devtools)
install_github("hadley/dplyr")
install_github("ramnathv/agebyname")

There are two main functions in this package that allow you to carry out interesting analysis. The plot_name function allows you to plot the distribution of births by name, sex and state, while the estimate_age function provides the age estimate, given the same set of input arguments.

Let us start by plotting the distribution for the name Joseph.

library(agebyname)
plot_name('Joseph')

Since the sex argument is not specified, plot_name guesses it based on the modal sex for the given name.

One can also plot the distribution for names from a given state. For example, we can plot

library(agebyname)
plot_name('Violet', state_ = "MA")

Let us now use the estimate_age function to plot the age distribution for the 25 most common female names.

library(dplyr, warn.conflicts = FALSE)

top_100_names = bnames %>%
  group_by(name, sex) %>%
  summarize(n = sum(n)) %>%
  arrange(desc(n)) %>%
  head(100)

estimates = plyr::ldply(top_100_names$name, estimate_age)

library(ggplot2)
estimates %>%
  left_join(top_100_names) %>%
  filter(sex == "F") %>%
  arrange(desc(n)) %>%
  head(25) %>%
  ggplot(aes(x = reorder(name, -q50), y = q50)) +
    geom_point() +
    geom_linerange(aes(ymin = q25, ymax = q75)) + 
    coord_flip()

## Joining by: "name"

You can also use this package to generate an interactive Shiny application that allows people to explore the age distribution of names. An example app is available in the package.

app = system.file('example1', package = 'agebyname')
shiny::runApp(app)

One can also use the rMaps package to generate an interactive choropleth map of the relative popularity of a name.

library(rMaps)
bnames_by_state %>%
  group_by(state, year, sex) %>%
  mutate(prop = n/sum(n)) %>%
  filter(year %in% 2000:2005, name == 'Anna', sex == 'F') %>%
  ichoropleth(prop ~ state, data = ., animate = 'year')

This package uses four primary datasets.

1. Babynames
2. Babynames by State
3. Cohort Life Tables
4. Census Live Births Data

All data was downloaded from the above sources, processed using the R scripts in the rawdata folder, and saved as .rdata files in the data folder. Some extrapolation was done on the raw data to correct for the fact that not all births were recorded by SSA till around 1930, since it wasn't mandatory. Note that there might be some differences in the way I have extrapolated the data, as compared to Nate Silver. If you find any discrepancies in my data cleaning process, please feel free to file an issue or a pull-request.