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My first workspace
My first workspace
In this workspace, we'll be exploring the relationship between total funding a company receives and its valuation.
Data frameas
SELECT * FROM companies INNER JOIN funding USING(company_id)library(plotly)
plot_ly(data = df, x=~funding, y=~valuation, text = ~company, type = "scatter", mode = "markers")Existe una cierta relación entre...
Data frameas
select continent, count(company) as number_companies, sum(valuation)/1000000000 as valor
from companies INNER JOIN funding USING(company_id)
where valuation > 0
group by continent
order by number_companies DESClibrary(plotly)
plot_ly(data = df2, x=~continent, y = ~valor, text = ~valor, type = "bar")Data frameas
select year_founded, count(company_id) as companies_per_year
from dates INNER JOIN companies USING(company_id)
group by year_founded
order by companies_per_year DESC
Data frameas
ggplot(data = df3 , aes(x = year_founded, y = companies_per_year, group=continent)) +
geom_line(aes(color=continent)) + facet_wrap(~continent)
ggplot(data = df3 , aes(x = year_founded, y = companies_per_year)) +
geom_col(linewidth=2, color="black", fill="blue") +
scale_x_continuous(breaks=seq(1910,2021,5)) +
scale_y_continuous(breaks=seq(0,170,10)) +
labs(x="Year of foundation", y="Number of companies founded per year")+
theme_bw()Data frameas
select *
from product_emissions
limit 100;Data frameas
select country, sum(carbon_footprint_pcf)/10000 as cfp
from product_emissions
group by country
order by cfp DESCData frameas
select industry_group, sum(carbon_footprint_pcf)/10000 as cfp
from product_emissions
where carbon_footprint_pcf > 10000
group by industry_group
order by cfp DESC