Plastic Pollution 🚮
This week’s CorrelAid TidyTuesday Coding Hangout included a lot of casual chats and discussions about various #rstats topics, so not a lot of us managed to create a visualization (which is ok!). We still learned a lot and - most importantly during the current times - had fun and engaged with each other. We discussed different the advantages and disadvantages of the two most prominent blogging frameworks in R - blogdown and distill - and learned how to control the spacing between the dots in dotted lines: https://ggplot2.tidyverse.org/reference/aes_linetype_size_shape.html . 🤓
Here are the two contributions from this week:
A bubble chart
by Andreas Neumann
library(tidyverse) library(ggforce) library(scales) library(glue) plastics <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-01-26/plastics.csv')
###load data and transform into long data set### b<-subset(plastics, year==2019 & parent_company=="Grand Total") tall <- b %>% gather(key = total, value = cat, empty:pvc) tall$id <- group_indices(tall, country)
## Warning: The `...` argument of `group_keys()` is deprecated as of dplyr 1.0.0. ## Please `group_by()` first ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_warnings()` to see where this warning was generated.
tall<-tall%>% mutate_if(is.numeric, ~replace(., is.na(.), 0)) ###add percentages+additional data wrangling### tall<- tall %>% dplyr::group_by(id) %>% dplyr::mutate(percent = cat/sum(cat)) tall[which(tall$country=="Cote D_ivoire"),1] <- "Côte d'Ivoire" tall[which(tall$country=="Taiwan_ Republic of China (ROC)"),1] <- "Taiwan" tall[which(tall$country=="NIGERIA"),1] <- "Nigeria" tall[which(tall$country=="ECUADOR"),1] <- "Ecuador" tall[which(tall$country=="United States of America"),1] <- "United States" ###Plot### tall%>% dplyr::filter(total!="empty")%>% dplyr::filter(country!="EMPTY")%>% #subset(percent!=1.0)%>% #subset(percent!=0.0)%>% dplyr::arrange(country,percent, .by_group = TRUE)%>% ggplot() + geom_circle(aes(x0=0, y0 =percent/2, r =percent/2,color=total),alpha=5)+ facet_wrap(~country)+ scale_y_continuous(labels = percent,name="%-share")+ scale_x_continuous(breaks=NULL)+ scale_colour_manual(name="Category",values = c("red","black","lightblue","green3","yellow","orange","pink"), labels = c("High density\npolyethylene", "Low density\npolyethylene", "other plastic","Polyester plastic","Polypropylene count","Polystyrene count","PVC plastic"))+ labs(title = "Plastic not so fantastic\n",subtitle = "Each bubble represents the percentage share of a plastic type\ncollected in a specific country in 2019\n",caption = glue("Data source: Break free from plastic\nGraphics: Andreas Neumann"))+ theme(plot.title = element_text(color="white", size=14,hjust = 0.5, face="bold.italic"), axis.title.y = element_text(color = "white"), axis.title.x = element_blank(), axis.text.y = element_text(color = "white", size = 8), axis.ticks.y = element_blank(), axis.text.x = element_blank(), panel.grid.major = element_line(linetype = "blank"), panel.grid.minor = element_blank(), strip.background =element_rect(fill="gray48"), strip.text = element_text(colour = 'white'), panel.background = element_rect(fill = "gray48", color = NA), plot.background = element_rect(fill = "gray48", color = NA), plot.subtitle=element_text(size=10, hjust=0.5, face="italic", color="white"), legend.background = element_rect(fill = "gray48", color = NA), legend.text = element_text(color = "white"), legend.title = element_text(color = "white"), legend.key = element_rect(fill = "gray48"), title = element_text(colour = "white"))
Using CSS grids for Rmd layout
by Ihaddaden M. EL Fodil
My contribution to this week \#TidyTuesday using ggplot2 and CSS Grid for the layout. \#RStats pic.twitter.com/N6xRAXLO81— Ihaddaden M. EL Fodil, Ph.D (@moh\_fodil) January 26, 2021
Check out the full page here and the source code in this GitHub repository.