Mapping oil production by country in R

By Sharp Sight

(This article was first published on r-bloggers – SHARP SIGHT LABS, and kindly contributed to R-bloggers)

This week, here’s another map.

As I’ve mentioned, projects like this – a few hundred lines of code, with a variety of functions from the tidyverse – are excellent practice for intermediate data scientists.

But if you’re a beginner, don’t be intimidated. You can actually learn these tools much faster than you think.

To get to a point where you really know how this works, you’ll need a few things. You will need to know a few things about ggplot2, dplyr and the tidyverse. I recommend that you break this down, line by line. Do you know all of the functions? Do you know how it all works? If you don’t, you might have some more practice to do.

Ultimately, to be “fluent” in data science in R, you should be able to write this code (or something like it) from scratch, more or less from memory.

In any case, there’s a lot going on in this code.

In a separate post, I will explain a few details about how this works, and expand on it somewhat.

In the meantime, if you want to learn to do projects like this, go through the code below. Break it down into small pieces that you can study and practice.

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Code: mapping oil production by country


#==============
# LOAD PACKAGES
#==============

library(tidyverse)
library(sf)
library(rvest)
library(stringr)
library(scales)
#library(viridis)



#============
# SCRAPE DATA
#============

df.oil %
  html_nodes("table") %>%
  .[[1]] %>%
  html_table()



#====================
# CHANGE COLUMN NAMES 
#====================

colnames(df.oil) % mutate(rank = as.integer(rank)) 

df.oil %>% glimpse()



#---------------------------------------------------
# WRANGLE FROM CHARACTER TO NUMERIC: oil_bbl_per_day
#---------------------------------------------------

df.oil % mutate(oil_bbl_per_day = oil_bbl_per_day %>% str_replace_all(',','') %>% as.integer())


# inspect
df.oil %>% glimpse()


#===========================
#CREATE VARIABLE: 'opec_ind'
#===========================

df.oil % mutate(opec_ind = if_else(str_detect(country, 'OPEC'), 1, 0))


#=========================================================
# CLEAN UP 'country'
# - some country names are tagged as being OPEC countries
#   and this information is in the country name
# - we will strip this information out
#=========================================================

df.oil % mutate(country = country %>% str_replace(' (OPEC)', '') %>% str_replace('s{2,}',' '))


# inspect
df.oil %>% glimpse()


#------------------------------------------
# EXAMINE OPEC COUNTRIES
# - here, we'll just visually inspect
#   to make sure that the names are correct
#------------------------------------------

df.oil %>% 
  filter(opec_ind == 1) %>%
  select(country)



#==================
# REORDER VARIABLES
#==================

df.oil % select(rank, country, opec_ind, oil_bbl_per_day)

df.oil %>% glimpse()



#========
# GET MAP
#========

map.world %
  group_by(region) %>%
  summarise() %>%
  print(n = Inf)


# UK
# USA
# Democratic Republic of the Congo
# Trinidad
# Sudan
# South Sudan


df.oil %  mutate(country = recode(country, `United States` = 'USA'
                                 , `United Kingdom` = 'UK'
                                 , `Congo, Democratic Republic of the` = 'Democratic Republic of the Congo'
                                 , `Trinidad and Tobago` = 'Trinidad'
                                 , `Sudan and South Sudan` = 'Sudan'
                                 #, `Sudan and  South Sudan` = 'South Sudan'
                                 , `Congo, Republic of the` = 'Republic of Congo'
                                 )
                   )



#-----------------------
# JOIN DATASETS TOGETHER
#-----------------------

map.oil % filter(oil_bbl_per_day > 822675) %>% summarise(mean(oil_bbl_per_day))
# 3190373

df.oil %>% filter(oil_bbl_per_day % summarise(mean(oil_bbl_per_day))
# 96581.08



ggplot(map.oil, aes( x = long, y = lat, group = group )) +
  geom_polygon(aes(fill = oil_bbl_per_day)) +
  scale_fill_gradientn(colours = c('#461863','#404E88','#2A8A8C','#7FD157','#F9E53F')
                       ,values = scales::rescale(c(100,96581,822675,3190373,10000000))
                       ,labels = comma
                       ,breaks = c(100,96581,822675,3190373,10000000)
                       ) +
  guides(fill = guide_legend(reverse = T)) +
  labs(fill = 'bbl/day'
       ,title = 'Oil Production by Country'
       ,subtitle = 'Barrels per day, 2016'
       ,x = NULL
       ,y = NULL) +
  theme(text = element_text(family = 'Gill Sans', color = '#EEEEEE')
        ,plot.title = element_text(size = 28)
        ,plot.subtitle = element_text(size = 14)
        ,axis.ticks = element_blank()
        ,axis.text = element_blank()
        ,panel.grid = element_blank()
        ,panel.background = element_rect(fill = '#333333')
        ,plot.background = element_rect(fill = '#333333')
        ,legend.position = c(.18,.36)
        ,legend.background = element_blank()
        ,legend.key = element_blank()
        ) +
  annotate(geom = 'text'
           ,label = 'Source: U.S. Energy Information Administrationnhttps://en.wikipedia.org/wiki/List_of_countries_by_oil_production'
           ,x = 18, y = -55
           ,size = 3
           ,family = 'Gill Sans'
           ,color = '#CCCCCC'
           ,hjust = 'left'
           )

And here’s the finalized map that the code produces:

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The post Mapping oil production by country in R appeared first on SHARP SIGHT LABS.

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