power.r

library(tidyverse)
data = read_csv("https://www.ofgem.gov.uk/node/98510/revisions/321984/csv") %>% separate(Date, into=c("q", "y")) %>% gather(-q, -y, key="type", value="perc")
data %>% group_by(y, type) %>% summarize(p = mean(perc)) %>% filter(type %in% c("Gas", "Coal", "Nuclear", "Bioenergy", "Wind (onshore and offshore) and Solar")) %>% 
  ggplot() + geom_line(aes(x=as.numeric(y), y=p, colour=type)) + ggthemes::theme_economist() + xlab("Year") + ylab("% of electricity generated")

                       
data %>% group_by(y, type) %>% summarize(p = mean(perc)) %>%
  mutate(type=case_when(type %in% c("Gas", "Coal") ~ "Gas & Coal", type %in% c("Bioenergy", "Wind (onshore and offshore) and Solar") ~ "Wind & Solar & Bio",  T ~ "other")) %>% 
  group_by(y, type) %>% summarize(p = sum(p)) %>%
  ggplot() + geom_line(aes(x=as.numeric(y), y=p, colour=type)) + ggthemes::theme_economist() + xlab("Year") + ylab("% of electricity generated")