Skip to content

Latest commit

 

History

History
117 lines (82 loc) · 3.55 KB

readme.md

File metadata and controls

117 lines (82 loc) · 3.55 KB
Raw

Food Consumption and CO2 Emissions

The data this week comes from nu3 and was contributed by Kasia Kulma.

Kasia has put together a great guide on webscraping along with data cleaning and organization! Make sure to check out her blog post, and the raw code is duplicated as part of the cleaning script.

Get the data here

# Get the Data

food_consumption <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-18/food_consumption.csv')

# Or read in with tidytuesdayR package (https://github.com/thebioengineer/tidytuesdayR)
# PLEASE NOTE TO USE 2020 DATA YOU NEED TO USE tidytuesdayR version ? from GitHub

# Either ISO-8601 date or year/week works!

# Install via devtools::install_github("thebioengineer/tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2020-02-18')
tuesdata <- tidytuesdayR::tt_load(2020, week = 8)


food_consumption <- tuesdata$food_consumption

Data Dictionary

food_consumption.csv

variable class description
country character Country Name
food_category character Food Category
consumption double Consumption (kg/person/year)
co2_emmission double Co2 Emission (Kg CO2/person/year)

Cleaning Script

library(tidyverse)
library(janitor)
library(rvest)

# Credit to Kasia and minorly edited to create output file and test plot
# Blog post at https://r-tastic.co.uk/post/from-messy-to-tidy/

url <- "https://www.nu3.de/blogs/nutrition/food-carbon-footprint-index-2018"

# scrape the website
url_html <- read_html(url)

# extract the HTML table
whole_table <- url_html %>% 
  html_nodes('table') %>%
  html_table(fill = TRUE) %>%
  .[[1]]

table_content <- whole_table %>%
  select(-X1) %>% # remove redundant column
  filter(!dplyr::row_number() %in% 1:3) # remove redundant rows

raw_headers <- url_html %>%
  html_nodes(".thead-icon") %>%
  html_attr('title')

tidy_bottom_header <- raw_headers[28:length(raw_headers)]
tidy_bottom_header[1:10]

raw_middle_header <- raw_headers[17:27]
raw_middle_header

tidy_headers <- c(
  rep(raw_middle_header[1:7], each = 2),
  "animal_total",
  rep(raw_middle_header[8:length(raw_middle_header)], each = 2),
  "non_animal_total",
  "country_total")

tidy_headers

combined_colnames <- paste(tidy_headers, tidy_bottom_header, sep = ';')
colnames(table_content) <- c("Country", combined_colnames)
glimpse(table_content[, 1:10])

long_table <- table_content %>%
  # make column names observations of Category variable
  tidyr::pivot_longer(cols = -Country, names_to = "Category", values_to = "Values") %>%
  # separate food-related information from the metric
  tidyr::separate(col = Category, into = c("Food Category", "Metric"), sep = ';')

glimpse(long_table)

tidy_table <- long_table %>%
  tidyr::pivot_wider(names_from = Metric, values_from = Values) %>%
  janitor::clean_names('snake')

glimpse(tidy_table)

final_table <- tidy_table %>%
  rename(consumption = 3,
         co2_emmission = 4) %>%
  filter(!stringr::str_detect(food_category, "total"))

clean_table <- final_table %>% 
  mutate_at(vars(consumption, co2_emmission), parse_number)

clean_table %>% 
  write_csv(here::here("2020/2020-02-18", "food_consumption.csv"))

clean_table %>% 
  ggplot(aes(x = fct_reorder(food_category, consumption), y = consumption, color = country)) +
  geom_jitter() +
  theme(legend.position = "none") +
  coord_flip()