Code: mapping oil production by country<\/h2>\n\r\n\r\n#==============\r\n# LOAD PACKAGES\r\n#==============\r\n\r\nlibrary(tidyverse)\r\nlibrary(sf)\r\nlibrary(rvest)\r\nlibrary(stringr)\r\nlibrary(scales)\r\n#library(viridis)\r\n\r\n\r\n\r\n#============\r\n# SCRAPE DATA\r\n#============\r\n\r\ndf.oil <- read_html(\"https:\/\/en.wikipedia.org\/wiki\/List_of_countries_by_oil_production\") %>%\r\n html_nodes(\"table\") %>%\r\n .[[1]] %>%\r\n html_table()\r\n\r\n\r\n\r\n#====================\r\n# CHANGE COLUMN NAMES \r\n#====================\r\n\r\ncolnames(df.oil) <- c('rank', 'country', 'oil_bbl_per_day')\r\n\r\n\r\n\r\n#=============================\r\n# WRANGLE VARIABLES INTO SHAPE\r\n#=============================\r\n\r\n\r\n#----------------------------------\r\n# COERCE 'rank' VARIABLE TO INTEGER\r\n#----------------------------------\r\n\r\ndf.oil <- df.oil %>% mutate(rank = as.integer(rank)) \r\n\r\ndf.oil %>% glimpse()\r\n\r\n\r\n\r\n#---------------------------------------------------\r\n# WRANGLE FROM CHARACTER TO NUMERIC: oil_bbl_per_day\r\n#---------------------------------------------------\r\n\r\ndf.oil <- df.oil %>% mutate(oil_bbl_per_day = oil_bbl_per_day %>% str_replace_all(',','') %>% as.integer())\r\n\r\n\r\n# inspect\r\ndf.oil %>% glimpse()\r\n\r\n\r\n#===========================\r\n#CREATE VARIABLE: 'opec_ind'\r\n#===========================\r\n\r\ndf.oil <- df.oil %>% mutate(opec_ind = if_else(str_detect(country, 'OPEC'), 1, 0))\r\n\r\n\r\n#=========================================================\r\n# CLEAN UP 'country'\r\n# - some country names are tagged as being OPEC countries\r\n# and this information is in the country name\r\n# - we will strip this information out\r\n#=========================================================\r\n\r\ndf.oil <- df.oil %>% mutate(country = country %>% str_replace(' \\\\(OPEC\\\\)', '') %>% str_replace('\\\\s{2,}',' '))\r\n\r\n\r\n# inspect\r\ndf.oil %>% glimpse()\r\n\r\n\r\n#------------------------------------------\r\n# EXAMINE OPEC COUNTRIES\r\n# - here, we'll just visually inspect\r\n# to make sure that the names are correct\r\n#------------------------------------------\r\n\r\ndf.oil %>% \r\n filter(opec_ind == 1) %>%\r\n select(country)\r\n\r\n\r\n\r\n#==================\r\n# REORDER VARIABLES\r\n#==================\r\n\r\ndf.oil <- df.oil %>% select(rank, country, opec_ind, oil_bbl_per_day)\r\n\r\ndf.oil %>% glimpse()\r\n\r\n\r\n\r\n#========\r\n# GET MAP\r\n#========\r\n\r\nmap.world <- map_data('world')\r\n\r\ndf.oil\r\n\r\n\r\n#==========================\r\n# CHECK FOR JOIN MISMATCHES\r\n#==========================\r\n\r\nanti_join(df.oil, map.world, by = c('country' = 'region'))\r\n\r\n\r\n# rank country opec_ind oil_bbl_per_day\r\n# 1 67 Congo, Democratic Republic of the 0 20,000\r\n# 2 47 Trinidad and Tobago 0 60,090\r\n# 3 34 Sudan and South Sudan 0 255,000\r\n# 4 30 Congo, Republic of the 0 308,363\r\n# 5 20 United Kingdom 0 939,760\r\n# 6 3 United States 0 8,875,817\r\n\r\n\r\n\r\n#=====================\r\n# RECODE COUNTRY NAMES\r\n#=====================\r\n\r\nmap.world %>%\r\n group_by(region) %>%\r\n summarise() %>%\r\n print(n = Inf)\r\n\r\n\r\n# UK\r\n# USA\r\n# Democratic Republic of the Congo\r\n# Trinidad\r\n# Sudan\r\n# South Sudan\r\n\r\n\r\ndf.oil <- df.oil %>% mutate(country = recode(country, `United States` = 'USA'\r\n , `United Kingdom` = 'UK'\r\n , `Congo, Democratic Republic of the` = 'Democratic Republic of the Congo'\r\n , `Trinidad and Tobago` = 'Trinidad'\r\n , `Sudan and South Sudan` = 'Sudan'\r\n #, `Sudan and South Sudan` = 'South Sudan'\r\n , `Congo, Republic of the` = 'Republic of Congo'\r\n )\r\n )\r\n\r\n\r\n\r\n#-----------------------\r\n# JOIN DATASETS TOGETHER\r\n#-----------------------\r\n\r\nmap.oil <- left_join( map.world, df.oil, by = c('region' = 'country')) \r\n\r\n\r\n\r\n#=====\r\n# PLOT\r\n#=====\r\n\r\n# BASIC (this is a first draft)\r\n\r\nggplot(map.oil, aes( x = long, y = lat, group = group )) +\r\n geom_polygon(aes(fill = oil_bbl_per_day))\r\n\r\n\r\n\r\n#=======================\r\n# FINAL, FORMATTED DRAFT\r\n#=======================\r\n\r\n\r\ndf.oil %>% filter(oil_bbl_per_day > 822675) %>% summarise(mean(oil_bbl_per_day))\r\n# 3190373\r\n\r\ndf.oil %>% filter(oil_bbl_per_day < 822675) %>% summarise(mean(oil_bbl_per_day))\r\n# 96581.08\r\n\r\n\r\n\r\nggplot(map.oil, aes( x = long, y = lat, group = group )) +\r\n geom_polygon(aes(fill = oil_bbl_per_day)) +\r\n scale_fill_gradientn(colours = c('#461863','#404E88','#2A8A8C','#7FD157','#F9E53F')\r\n ,values = scales::rescale(c(100,96581,822675,3190373,10000000))\r\n ,labels = comma\r\n ,breaks = c(100,96581,822675,3190373,10000000)\r\n ) +\r\n guides(fill = guide_legend(reverse = T)) +\r\n labs(fill = 'bbl\/day'\r\n ,title = 'Oil Production by Country'\r\n ,subtitle = 'Barrels per day, 2016'\r\n ,x = NULL\r\n ,y = NULL) +\r\n theme(text = element_text(family = 'Gill Sans', color = '#EEEEEE')\r\n ,plot.title = element_text(size = 28)\r\n ,plot.subtitle = element_text(size = 14)\r\n ,axis.ticks = element_blank()\r\n ,axis.text = element_blank()\r\n ,panel.grid = element_blank()\r\n ,panel.background = element_rect(fill = '#333333')\r\n ,plot.background = element_rect(fill = '#333333')\r\n ,legend.position = c(.18,.36)\r\n ,legend.background = element_blank()\r\n ,legend.key = element_blank()\r\n ) +\r\n annotate(geom = 'text'\r\n ,label = 'Source: U.S. Energy Information Administration\\nhttps:\/\/en.wikipedia.org\/wiki\/List_of_countries_by_oil_production'\r\n ,x = 18, y = -55\r\n ,size = 3\r\n ,family = 'Gill Sans'\r\n ,color = '#CCCCCC'\r\n ,hjust = 'left'\r\n )\r\n\r\n<\/pre>\nAnd here’s the finalized map that the code produces:<\/p>\n
![\"\"](\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2017\/12\/oil_production_bbl-per-day_2016_v2.png\")
<\/p>\n
To get more tutorials that explain how this code works, sign up for our email list.<\/p>\n","protected":false},"excerpt":{"rendered":"
This week, here’s another map: a map of world oil production that you can create using R.<\/p>\n
As I’ve mentioned, projects like this \u2013 a few hundred lines of code, with a variety of functions from the tidyverse \u2013 are excellent practice for intermediate data scientists.<\/p>\n","protected":false},"author":1,"featured_media":3194,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","om_disable_all_campaigns":false,"footnotes":""},"categories":[24,25,6,4,10],"tags":[20,38,22,39,21],"publishpress_future_action":{"enabled":false,"date":"2024-05-05 14:08:38","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category"},"_links":{"self":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/posts\/3191"}],"collection":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/comments?post=3191"}],"version-history":[{"count":0,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/posts\/3191\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/media\/3194"}],"wp:attachment":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/media?parent=3191"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/categories?post=3191"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/tags?post=3191"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}