Reading and Writing Data

Comparing Country CO2 Emissions Per Capita

  1. Load the R packages we will use
  1. Download \(CO_2\) emissions per capita from Our world in data into the directory for this post.

  2. Assign the location of the file to file_csv. The data should be in the same directory as this file.

file_csv <- here("_posts",
                 "2022-02-21-reading-and-writing-data",
                 "co-emissions-per-capita (1).csv")

emissions <- read_csv(file_csv)
  1. Show the first 10 rows (observations of) emissions
emissions
# A tibble: 23,307 × 4
   Entity      Code   Year `Annual CO2 emissions (per capita)`
   <chr>       <chr> <dbl>                               <dbl>
 1 Afghanistan AFG    1949                              0.0019
 2 Afghanistan AFG    1950                              0.0109
 3 Afghanistan AFG    1951                              0.0117
 4 Afghanistan AFG    1952                              0.0115
 5 Afghanistan AFG    1953                              0.0132
 6 Afghanistan AFG    1954                              0.013 
 7 Afghanistan AFG    1955                              0.0186
 8 Afghanistan AFG    1956                              0.0218
 9 Afghanistan AFG    1957                              0.0343
10 Afghanistan AFG    1958                              0.038 
# … with 23,297 more rows
  1. Start with emissions data THEN
tidy_emissions <- emissions %>% 
  clean_names()

tidy_emissions
# A tibble: 23,307 × 4
   entity      code   year annual_co2_emissions_per_capita
   <chr>       <chr> <dbl>                           <dbl>
 1 Afghanistan AFG    1949                          0.0019
 2 Afghanistan AFG    1950                          0.0109
 3 Afghanistan AFG    1951                          0.0117
 4 Afghanistan AFG    1952                          0.0115
 5 Afghanistan AFG    1953                          0.0132
 6 Afghanistan AFG    1954                          0.013 
 7 Afghanistan AFG    1955                          0.0186
 8 Afghanistan AFG    1956                          0.0218
 9 Afghanistan AFG    1957                          0.0343
10 Afghanistan AFG    1958                          0.038 
# … with 23,297 more rows
  1. Start with tidy_emissions Then
 tidy_emissions %>% 
  filter( year== 2004) %>% 
  skim()
Table 1: Data summary
Name Piped data
Number of rows 229
Number of columns 4
_______________________
Column type frequency:
character 2
numeric 2
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
entity 0 1.00 4 32 0 229 0
code 12 0.95 3 8 0 217 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
year 0 1 2004.00 0.00 2004.00 2004.00 2004.00 2004.00 2004.0 ▁▁▇▁▁
annual_co2_emissions_per_capita 0 1 5.43 6.99 0.02 0.83 3.23 8.45 56.7 ▇▁▁▁▁
  1. 12 observations have a missing code. How are these observations different? -start with tidy_emissions then extract rows with year == 2019 and are missing a code
tidy_emissions %>% 
  filter(year == 2004, is.na(code))
# A tibble: 12 × 4
   entity                     code   year annual_co2_emissions_per_ca…
   <chr>                      <chr> <dbl>                        <dbl>
 1 Africa                     <NA>   2004                         1.16
 2 Asia                       <NA>   2004                         2.97
 3 Asia (excl. China & India) <NA>   2004                         3.61
 4 EU-27                      <NA>   2004                         8.68
 5 EU-28                      <NA>   2004                         8.79
 6 Europe                     <NA>   2004                         8.81
 7 Europe (excl. EU-27)       <NA>   2004                         8.96
 8 Europe (excl. EU-28)       <NA>   2004                         8.80
 9 North America              <NA>   2004                        14.5 
10 North America (excl. USA)  <NA>   2004                         5.53
11 Oceania                    <NA>   2004                        13.2 
12 South America              <NA>   2004                         2.36
  1. Start with tidy_emissions THEN
emissions_2004 <- tidy_emissions %>% 
  filter(year == 2004, !is.na(code)) %>% 
select(-year) %>% 
rename(country = entity)
  1. Which 15 countries have the highest annual_co2_emissions_per_capita?
max_15_emitters <- emissions_2004 %>% 
  slice_max(annual_co2_emissions_per_capita, n=15)
  1. Which 15 countries have the lowest annual_co2_emissions_per_capita?
min_15_emitters <- emissions_2004 %>% 
  slice_min(annual_co2_emissions_per_capita, n=15)
  1. Use bind_rowsto bind together the max_15_emitters and min_15_emitters
max_min_15 <- bind_rows(max_15_emitters, min_15_emitters)
  1. Export max_min_15 to 3 file formats
max_min_15 %>% write_csv("max_min_15.csv") #comma-seperated values
max_min_15 %>% write_tsv("max_min_15.tsv") #tab seperated
max_min_15 %>% write_delim("max_min_15.psv", delim = "|") #pipe-seperated
  1. Read the 3 file formats into R
max_min_15_csv <- read.csv("max_min_15.csv") #comma-seperated values
max_min_15_tsv <- read_tsv("max_min_15.tsv") #tab seperated
max_min_15_psv <- read_delim("max_min_15.psv", delim = "|") # pipe-seperated
  1. Use setdiffto check for any differences among max_min_15_csv, max_min_15_tsv and max_min_15_psv
setdiff(max_min_15_csv, max_min_15_tsv) 
# A tibble: 0 × 3
# … with 3 variables: country <chr>, code <chr>,
#   annual_co2_emissions_per_capita <dbl>
  1. Reorder country in max_min_15 for plotting and assign to max_min_15_plot_data
max_min_15_plot_data <- max_min_15 %>% 
  mutate(country = reorder(country, annual_co2_emissions_per_capita))
  1. Plot `max_min_15_plot_data
ggplot(data = max_min_15_plot_data,
       mapping = aes(x=annual_co2_emissions_per_capita, y=country)) +
geom_col() +
  labs(title = "The top 15 and bottom 15 per capita CO2 emissions",
       subtitle = "for 2004",
       x= NULL,
       y= NULL)

  1. Save the plot directory with this post
ggsave(filename = "preview.png",
       path = here("_posts", "2022-02-21-reading-and-writing-data"))
  1. Add preview.png to yaml chuck at the top of this file