Semester Project: The Languages of Innovation and Business
Blake Cromar
April 02, 2018
Introduction
The purpose of this report is to give some guidelines for a businessman who wants to learn a second language. This report sways in the direction of business relating to innovation. That said, if you’re hoping to do business in another sector this information could still be useful.
Top 250 Universities in the World
universities_m <- universities %>%
filter(year == 2015) %>%
slice(1:250) %>%
mutate(country = country %>% fct_infreq() %>% fct_rev())
university_count <- universities_m %>%
group_by(country) %>%
summarise(count = n()) %>%
arrange(desc(count))ggplot(universities_m, aes(country)) +
geom_bar(color = "black", aes(fill = country)) +
theme_bw() +
theme(legend.position = "none",
axis.text = element_text(color = "black")) +
labs(y = "University Count",
x = "",
title = "English represents the language of the best universities",
subtitle = "German, French, Japanese, and Dutch are the next best languages") +
scale_fill_manual(values = colorRampPalette(brewer.pal(8, "Accent"))(27)) +
coord_flip() +
geom_rect(aes(xmin = 20.5, xmax = 28, ymin = -Inf , ymax = Inf), alpha = 0.05, fill = "gray80") +
geom_bar(color = "black", aes(fill = country)) +
geom_vline(xintercept = 20.5, size = 2)
datatable(university_count, class = 'cell-border stripe',
rownames = FALSE,
options = list(dom = 't'))Commentary: The Times higher education report ranked every university in the world. This graphic examined the top 250 universities and counted the number from each country. It is worth pointing out that the data was filtered to use the most recent year (2015) of data collection. English, German, French, Japanese, and Dutch represent the most common languages of the top universities worldwide.
Years of Formal Education
development_m <- development %>%
slice(1:25) %>%
mutate(Country = as.factor(Country)) %>%
arrange(desc(`Mean Years of Education`))ggplot(data = development_m) +
geom_bar(stat = "identity",
color = "black",
aes(fill = Country,
x = reorder(Country, `Mean Years of Education`),
y = `Mean Years of Education`)) +
theme_bw() +
theme(legend.position = "none",
axis.text = element_text(color = "black")) +
labs(y = "Years of Formal Education",
x = "",
title = "People of English speaking countries have the most formal years of school.",
subtitle = "The verticle line represents high school level of education (US)\nThe horizontal line represents the countries the encompass the top 5 languages") +
scale_fill_manual(values = colorRampPalette(brewer.pal(8, "Accent"))(40)) +
coord_flip() +
scale_y_continuous(breaks = seq(1,20, 1)) +
geom_rect(aes(xmin = 15.5, xmax = 25.5, ymin = -Inf , ymax = Inf), alpha = 0.05, fill = "gray80") +
geom_bar(stat = "identity",
color = "black",
aes(fill = Country,
x = reorder(Country, `Mean Years of Education`),
y = `Mean Years of Education`)) +
geom_hline(yintercept = 12, size = 1) +
geom_vline(xintercept = 15.5, size = 2)
datatable(development, class = 'cell-border stripe',
rownames = FALSE,
options = list(dom = 't'))Commentary: This graphic shows the average years of formal education people receive in each country. The five languages of the most educated peoples are English, German, Danish, Norwegian, and Hebrew. Due to the overlap of primary languages the top 5 languages span across 10 countries. The data used to make this graphic comes from the The World Development Report 2015. It is worth pointing out that this graphic doesn’t take into the account of the quality of the education or the number of hours studying each week.
Countries with Goverment Trust
happiness_m <- happiness %>%
slice(1:25) %>%
mutate(Country = as.factor(Country))ggplot(data = happiness_m) +
geom_bar(stat = "identity",
color = "black",
aes(fill = Country,
x = reorder(Country,
Trust..Government.Corruption.),
y = Trust..Government.Corruption.)) +
theme_bw() +
theme(legend.position = "none",
axis.text = element_text(color = "black")) +
labs(y = "Government Trust Score (1 is Max)",
x = "",
title = "Northern European countries have a lot of goverment trust",
subtitle = "") +
scale_fill_manual(values = colorRampPalette(brewer.pal(8, "Accent"))(40)) +
coord_flip() +
scale_y_continuous(breaks = seq(0,20, 0.05)) +
geom_rect(aes(xmin = 20.5, xmax = 25.5, ymin = -Inf , ymax = Inf), alpha = 0.05, fill = "gray80") +
geom_bar(stat = "identity",
color = "black",
aes(fill = Country,
x = reorder(Country,
Trust..Government.Corruption.),
y = Trust..Government.Corruption.)) +
geom_vline(xintercept = 20.5, size = 2) 
datatable(development, class = 'cell-border stripe',
rownames = FALSE,
options = list(dom = 't'))Countries with Healthy Economies
ggplot(data = happiness_m) +
geom_bar(stat = "identity",
color = "black",
aes(fill = Country,
x = reorder(Country, Economy..GDP.per.Capita.),
y = Economy..GDP.per.Capita.)) +
theme_bw() +
theme(legend.position = "none",
axis.text = element_text(color = "black")) +
labs(y = "GDP per Capita (Relative Scale Between 0 - 2)",
x = "",
title = "",
subtitle = "") +
scale_fill_manual(values = colorRampPalette(brewer.pal(8, "Accent"))(40)) +
coord_flip() +
scale_y_continuous(breaks = seq(0,20, 0.25)) +
geom_rect(aes(xmin = 20.5, xmax = 25.5, ymin = -Inf , ymax = Inf), alpha = 0.05, fill = "gray80") +
geom_bar(stat = "identity",
color = "black",
aes(fill = Country,
x = reorder(Country,
Economy..GDP.per.Capita.),
y = Economy..GDP.per.Capita.)) +
geom_vline(xintercept = 20.5, size = 2) 
Commentary: An important factor of business is the healthiness of an economy. One way to measure that is by measuring GDP per PPP. The languages of the top 5 countries with this measure are Luxembourgish, Arabic, Norweigen, German, and English. It is worth pointing out that Luxembourg is a unique economy because many people from other countries travel across it’s boarder to do business. This raises the countries GDP, but the population doesn’t account for the people doing business. That is a consideration that should be taken when evaluating Luxembourg. This data also comes from the World Happiness Report 2017.
What is the Deal with People Who Speak Germanic Languages?
happiness_germanic <- happiness %>%
mutate(
language = case_when(
Country %in% c("Germany", "Switzerland", "United Kingdom", "Luxembourg", "Netherlands", "United States", "Canada", "Austria", "New Zealand", "Australia", "Singapore", "Ireland", "Belgium") ~ "Germanic-West",
Country %in% c("Denmark", "Sweden", "Norway", "Iceland") ~ "Germanic-North",
TRUE ~ "Other"
)
)ggplot(happiness_germanic, aes(x = Economy..GDP.per.Capita., y = Happiness.Score)) +
geom_point(aes(fill = language),
pch = 21,
color = "black",
size = 2) +
geom_smooth(se = FALSE,
color = "black") +
theme_bw() +
labs(y = "Happiness Score",
x = "GDP per Capita (Scaled Between 0-2)",
fill = "Primary Language\nFamily of Country",
title = "Countries that historically speak germanic languages are doing well") +
geom_rect(aes(xmin = 1.38, xmax = 1.8, ymin = 6.5 , ymax = 7.6), alpha = 0.001, color = "black") +
theme(axis.text = element_text(color = "black"))
# Data for Labels
happiness_germanic_slice <- happiness_germanic %>%
filter(Happiness.Score >= 6.5,
Economy..GDP.per.Capita. >= 1.4)
ggplot(happiness_germanic, aes(x = Economy..GDP.per.Capita., y = Happiness.Score)) +
geom_point(aes(fill = language),
pch = 21,
color = "black",
size = 2) +
geom_smooth(se = FALSE,
color = "black") +
theme_bw() +
labs(y = "Happiness Score",
x = "GDP per Capita (Scaled Between 0-2)",
fill = "Primary Language\nFamily of Country",
title = "Highlighted region of previous graphic") +
theme(axis.text = element_text(color = "black")) +
coord_cartesian(xlim = c(1.4, 1.8), ylim = c(6.5, 7.6)) +
ggrepel::geom_label_repel(aes(label = Country, color = language),
data = happiness_germanic_slice,
segment.color = "black") +
guides(color = FALSE)
germanic_sumamry_h <- happiness_germanic %>%
group_by(language) %>%
summarise(`Mean of Happiness Score` = mean(Happiness.Score)) %>%
rename(`Language Family` = language)
germanic_sumamry_g <- happiness_germanic %>%
group_by(language) %>%
summarise(`Mean of GDP per PPP (Relative)` = mean(Economy..GDP.per.Capita.)) %>%
rename(`Language Family` = language)
combinded <- germanic_sumamry_h %>%
left_join(germanic_sumamry_g)
pander(combinded)| Language Family | Mean of Happiness Score | Mean of GDP per PPP (Relative) |
|---|---|---|
| Germanic-North | 7.462 | 1.518 |
| Germanic-West | 7.058 | 1.526 |
| Other | 5.132 | 0.9183 |
Commentary: While working with the data I noticed a specific pattern. Countries that speak a Germanic language as their primary language are doing well. Generally speaking, all of those countries were in the top 15 in every category. These two graphics compare Happiness Score against GDP per Capita. Red includes countries that speak Danish, Swedish, Icelandic, and Norwegian. Green includes countries that speak English, German, Dutch, and Luxembourgish. You will notice that all those countries have a high GDP per capita and have relatively happy people. If you’re a businessman, a good rule of thumb might be to invest in a country where a Germanic language is spoken. These populations seem to be doing well and have a lot of money to invest.
Note: As of 2015, English is the most well known language in Singapore. It is also an official language of that country. Also, the languages of Belgium are Dutch, French, and German with Dutch being the most spoken language.
Final Remarks
If you are an international businessman, who is hoping to learn a language, English is crucial. If you already speak English a good second language is a hard choice. It really comes down to what you want to emphasize within your business. This project can give you some good guidelines, but your own intuition is important too. Ultimately, if you want to do business in a specific country you’re going to want to learn that language.