## Abstract

As a final post in the baby-names-the-data-scientist’s-way series, we use the US Social Security Administration 1910-2015 data to space-time visualize for each state the most popular baby name for girls and boys, respectively. The code uses in parts the simple features package (sf) in order to to get some first experience with the new approach for handling spatial maps.

## Introduction

After a series of posts on naming uncertainty, name collisions in classrooms and illustrating these name collisions over time, it is time to leave onomatology for now. However, the availability of the US social security baby name data at state level requires one last effort: visualizing the top names per state for the years 1910-2015. Creating a map-based visualization also provides a nice opportunity to experiment with the new sf (simple features) package for spatial visualization.

## Data Dancing

We download the US social security data, which consists of a zip file containing a bunch of 51 text files - one for each state.

We then read these individual text files and bind them together into one large data.frame:

##Get list of all file names containing state specific baby name data
fList <- list.files(path=file.path(filePath,"namesbystate"), pattern=".TXT")

##Read complete name list of each state
names <- purrr::map_df(fList, .f=function(f) {
read_csv(file=file.path(filePath,"namesbystate",f), col_names=c("State","Sex","Year","Name","Count"),col_types=cols(col_character(), col_factor(c("M","F")), col_integer(), col_character(), col_integer()))
})

##Show result
head(names, n=4)
## # A tibble: 4 × 5
##   State    Sex  Year     Name Count
##   <chr> <fctr> <int>    <chr> <int>
## 1    AK      F  1910     Mary    14
## 2    AK      F  1910    Annie    12
## 3    AK      F  1910     Anna    10
## 4    AK      F  1910 Margaret     8

With the complete data in place, it’s easy to compute the top boy and girl name per state and year. For later use we convert this information into long-format.

##Find top-1 names for each state by gender. Data are already sorted.
topnames <- names %>% group_by(Year,State,Sex) %>% do({
}) %>% spread(Sex, Name)
## Source: local data frame [4 x 4]
## Groups: Year, State [4]
##
##    Year State     M     F
##   <int> <chr> <chr> <chr>
## 1  1910    AK  John  Mary
## 2  1910    AL James  Mary
## 3  1910    AR James  Mary
## 4  1910    AZ  John  Mary

## Map Massaging

For the map visualization we use an US map from the R package fiftystater where Alaska and Hawaii have been re-located as map-insets. The process for doing the necessary transforms sp-style are described in the package vignette. We store the output of this transformation as a shapefile usa.shp with appropriate support files. Furthermore, a lines.shp shapefile was created which contains information on where to put the text labels for each state. This was easily edited interactively in QGIS.

We then use the sf package for loading these two shapefiles back into R:

suppressMessages(library("sf"))
textplacement <- st_read(file.path(filePath, "maps", "lines.shp"))

The textplacement information is converted to a data.frame where each row contains the state name and the coordinates of the start and endpoint of each line-segment - this corresponds to text location and geographical centroid of the region, respectively.

location <- textplacement %>% split(.$State) %>% purrr::map_df(.f = function(x) { pos <- st_geometry(x)[[1]] data.frame(State=x$State, x1.loc=pos[1,1], x2.loc=pos[1,2], x1.center=pos[2,1],x2.center=pos[2,2])
}) %>% ungroup

(Note: Is there a fancier way to extract the coordinates for the geometry of the sf objects while keeping the data.frame part alongside?)

## State-Time Visualization

By using the animation::saveGIF function we create an animation of the the top girl and boy name for each state for the sequence of years 1910-2015.

## State-Time Cartogram

We use the Rcartogram and getcartr packages to create an analogous cartogram - see the previous Cartograms with R post for further details. The total number of births per state in a given year is used as scaling variable for the cartogram.

Its amazing to observe how births go west in the US during the considered time period.