Babynames

An exploration of first letters

First, let’s import Kaggle’s ‘State Names’ dataset:

library(readr)
StateNames <- read_csv("~/Desktop/StateNames.csv", 
    col_types = cols(Year = col_number(), 
        Gender = col_character()))

This report will be an exploration of the first letter of babynames. This report will explore which first letters are most popular for females and males. Are they the same for both genders? Which letters are more common now and what were in the past? Does the popularity have to do with specific time periods? If so why?

library(babynames)
library(tidyverse)
library(ggthemes)

Let’s look at the name George over time:

babynames %>% filter(name %in% "Rachel" & sex =="F") %>%
  arrange(desc(prop)) %>% 
  head(20) %>% 
  knitr::kable()

year	sex	name	n	prop
1985	F	Rachel	16358	0.0088618
1984	F	Rachel	15837	0.0087842
1996	F	Rachel	16115	0.0084069
1986	F	Rachel	15452	0.0083749
1995	F	Rachel	16044	0.0083509
1987	F	Rachel	15643	0.0083481
1983	F	Rachel	14592	0.0081549
1993	F	Rachel	15971	0.0081023
1991	F	Rachel	16343	0.0080384
1988	F	Rachel	15344	0.0079809
1994	F	Rachel	15486	0.0079455
1992	F	Rachel	15837	0.0079015
1989	F	Rachel	15356	0.0077093
1990	F	Rachel	15705	0.0076462
1982	F	Rachel	13869	0.0076456
1997	F	Rachel	13787	0.0072228
1981	F	Rachel	12586	0.0070382
1980	F	Rachel	11622	0.0065279
1998	F	Rachel	12201	0.0062956
1999	F	Rachel	11624	0.0059727

babynames %>% filter(name %in% "George" & sex =="M") %>%
  arrange(desc(prop)) %>% 
  head(20) %>% 
  ggplot(aes(year, prop)) + geom_line() +
  theme_economist()