Introduction to the ngramr.plus R package

Load the ngramr.plus package

Load the package, as well as others that we’ll use in this vignette.

library(ngramr.plus)
library(tidyverse)

Retrieving data

Running the functions is straightforward, but remember that it can take a couple minutes if your are loading one of the larger Google Books data tables. Here we return counts of zinger by year from the data tables for US English.

z_year <- google_ngram(word_forms = "zinger", variety = "us", by = "year")

Check the data:

Year	AF	Total	Per_10.6
1834	3	160777483	0.0186593
1835	3	213225556	0.0140696
1837	1	178891779	0.0055900
1857	3	426434283	0.0070351
1859	1	442579354	0.0022595
1860	2	455087035	0.004394

In current usage, zinger denotes a kind of cutting quip. Its supposed origin is as a baseball term that becomes generalized in the middle of the 20th century. Does this explanation comport with the data?

Plot the data

To partly answer such a question, we can plot the data:

  ggplot(z_year %>% filter(Year > 1799), aes(x=Year, y=Per_10.6)) +
    geom_point(size = .5) +
    geom_smooth(method = "gam", formula = y ~ s(x, bs = "cs"), size=.25) +
    labs(x="year", y = "frequency (per million words)")+ 
    theme(panel.grid.minor.x=element_blank(),
          panel.grid.major.x=element_blank()) +
    theme(panel.grid.minor.y =   element_blank(),
          panel.grid.major.y =   element_line(colour = "gray",size=0.25)) +
    theme(rect = element_blank()) +
    theme(legend.title=element_blank()) +
    theme(axis.title = element_text(family = "Arial", color="#666666", face="bold", size=10))

There does appear to be some circulation prior to the mid-20th century. To explain that, we can look at the underlying texts in Google Books.

By decade

Next, we’ll return counts of zinger and zingers by decade from the data tables for British English.

z_decade <- google_ngram(word_forms = c("zinger", "zingers"), variety = "gb", by = "decade")

Check the data:

Decade	AF	Total	Per_10.6
1780	2	374630337	0.0053386
1800	1	1302698028	0.0007676
1810	12	1802397035	0.0066578
1820	3	2558079128	0.0011728
1830	6	2954836522	0.0020306
1840	3	3547747138	0.0008456

Ploting the data

Now we can filter and plot our by-decade data:

ggplot(z_decade %>% filter(Decade > 1799), aes(x=Decade, y=Per_10.6)) +
  geom_bar(stat = "identity") +
  labs(x="decade", y = "frequency (per million words)")+ 
  theme(panel.grid.minor.x=element_blank(),
         panel.grid.major.x=element_blank()) +
  theme(panel.grid.minor.y =   element_blank(),
        panel.grid.major.y =   element_line(colour = "gray",size=0.25)) +
  theme(rect = element_blank()) +
  theme(legend.title=element_blank()) +
  theme(axis.title = element_text(family = "Arial", color="#666666", face="bold", size=10))

Bibliography

Michel, Jean-Baptiste, Yuan Kui Shen, Aviva Presser Aiden, Adrian Veres, Matthew K Gray, Joseph P Pickett, Dale Hoiberg, et al. 2011. “Quantitative Analysis of Culture Using Millions of Digitized Books.” Science 331 (6014): 176–82. https://science.sciencemag.org/content/331/6014/176.