John Oliver, in his very first episode on HBO’s new show, Last Week Tonight, talked about Indian elections and the near-absence of its coverage in US Television. In response to that show, Alyssa Ayres wrote in Forbes that US’s attention on India was scant. This, according to her, is also reflected in the pattern of languages studied in the US. She presented data in a tabular format from the Modern Languages Enrollment Database for 3 survey years-2002, 2006, and 2009 to show that the number of students enrolling to study Indian languages were very few in comparison to other popular languages.

Assuming that the knowledge of a language helps one understand culture in a better manner and could also potentially provide an advantage in international relations and business, it was of interest to me to see how different languages fared across a longer time frame. Here, I look at the pattern of language enrollments from MLA surveys of enrollments in US institutions of higher education using ggplot2 and rely on rCharts to facilitate comparison between any two or more languages across and within different years. This post has been generated using slidify and the code to reproduce it, along with the code and data for different charts can be found on github.

Data

Enrollment data for US higher education institutions were collected for 8 survey years spanning over two and half decades - 1983,1986,1990,1995,1998,2002,2006, and 2009. (One could go back in history further, but I chose to stop there for convenience.) Why only those years and not intermediate years? Those were the years when surveys were conducted and hence, data were provided only for those years.

Data were collected on the following languages - French, German, Italian, Japanese, Spanish, Arabic, Chinese, Korean, Portuguese, and Russian. Following Alyssa Ayres’ procedure, data on multiple Indian languages were collected and then summed for a composite Indian language score for each year. These Indian languages include Bengali, Gujarati, Hindi, Hindi-Urdu, Kannada, Malayalam, Marathi, Punjabi, Tamil, Telugu, and Urdu. For the purposes of this post, I’ll look at only the overall Indian language enrollment and not its individual components.

A look at the dataset in a tabular form

library(knitr)
lang=read.csv("languages.csv")
langmajor=lang[-c(2:12)]
kable(langmajor)
Year Indian French German Italian Japanese Spanish Arabic Chinese Korean Portuguese Russian
2009 3929 215954 96270 80672 73328 862688 35083 60311 8511 11371 26814
2006 3034 206079 94181 78091 66076 821654 23956 51286 7145 10267 24810
2002 2323 201979 91100 63899 52238 745127 10584 34153 5211 8385 23921
1998 1506 199064 89013 49287 43141 656445 5505 28456 4479 6926 23791
1995 1232 205351 96263 43760 44723 606286 4444 26471 3343 6531 24729
1990 611 273116 133594 49824 45830 534143 3683 19427 2375 6118 44476
1986 522 275132 120920 40904 23454 411376 3417 16891 875 5071 33945
1983 408 270101 128140 38672 16127 386320 3441 13178 660 4397 30453

Pattern of Enrollments across the 8 years when surveys were conducted.

Before we see the patterns, we will need to convert our dataset into the long form. This, we perform using the reshape2 package. See how the dataframe format from the earlier table is modified to the following.

library(reshape2)
langmajormelt=melt(langmajor,id="Year")
colnames(langmajormelt)=c("Year","Language","Enrollments")
kable(head(langmajormelt))
Year Language Enrollments
2009 Indian 3929
2006 Indian 3034
2002 Indian 2323
1998 Indian 1506
1995 Indian 1232
1990 Indian 611
kable(tail(langmajormelt))
id Year Language Enrollments
83 2002 Russian 23921
84 1998 Russian 23791
85 1995 Russian 24729
86 1990 Russian 44476
87 1986 Russian 33945
88 1983 Russian 30453

On to the graph(s) — ggplot2

library(ggplot2)
ggplot(langmajormelt,aes(x=Year,y=Enrollments))+geom_point()+geom_line()+facet_wrap(~Language,scales="free")+theme_bw()

plot of chunk unnamed-chunk-3

Interest in most languages has been on a rise. However, there were major drops in the interest in French, German, and Russian between 1990 and 1995.

Interactivity and Comparability Between Languages using rCharts

We could bring in interactivity to the charts by using the nvd3 JavaScript library via rCharts. In the chart below, we begin by plotting the interest in Indian languages across different survey years. You could interact with it many ways.

  1. Clicking on a dot/circle of a legend can make that language appear/disappear from the plot. Can help with the comparison of enrollments between languages. you can have as many languages in the chart as you wish.
  2. Hovering over the chart can provide information on the value during that year.
library(rCharts)
np1=nPlot(Enrollments~Year,group="Language",data=langmajormelt,type="lineChart")
np1$set(disabled = c(F, T, T, T, T, T, T, T, T, T, T))
np1$xAxis(tickValues=c(1983,1986,1990,1995,1998,2002,2006,2009))
# np1$show('inline',include_assets=TRUE, standalone=TRUE)
# np1$publish("np1",host="gist")

Enrollments in each of the 8 years- Bar chart(s) from nvd3 via rCharts

In the barchart below, we begin by comparing enrollments in Chinese and in Indian languages across all years. You could interact with it in the following ways.

  1. Clicking on a dot/circle of a legend can make that language appear/disappear from the plot. Can help with the comparison of enrollments between languages. you can have as many languages in the chart as you wish.
  2. Hovering over the chart can provide information on the value for that bar and year.
  3. You can make the comparison in either one specific year (select a year) or across all years (click on Year in the menu on the left)
np2=nPlot(Enrollments~Year,group="Language",data=langmajormelt,type="multiBarChart")
np2$set(disabled = c(F, T, T, T, T, T, T, F, T, T, T))
np2$addFilters("Year")
np2$chart(showControls = F)
#np2$publish("np2",host="gist")

If this doesn’t show up, please click on this link to interact with the chart

Interactive Charts using htmlwidgets

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