Reading and Writing Data

Code and text for Quiz 4 Regarding CO2 emissions

  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

Read the data into R and assign it to emissions

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

emissions  <- read_csv(file_csv)
  1. Show the first 10 rows (observations of) emissions
emissions
# A tibble: 23,307 x 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 x 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 the tidy_emissions then,
tidy_emissions  %>% 
  filter(year == 1993)  %>% 
  skim()
Table 1: Data summary
Name Piped data
Number of rows 227
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 0 227 0
code 12 0.95 3 8 0 215 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
year 0 1 1993.00 0.00 1993.00 1993.00 1993.00 1993.00 1993.00 ▁▁▇▁▁
annual_co2_emissions_per_capita 0 1 5.07 6.96 0.04 0.59 2.76 7.38 61.19 ▇▁▁▁▁
  1. 13 observations have a missing code. How are these observations different?
tidy_emissions  %>% 
  filter(year == 1993, is.na(code))  
# A tibble: 12 x 4
   entity                     code   year annual_co2_emissions_per_ca~
   <chr>                      <chr> <dbl>                        <dbl>
 1 Africa                     <NA>   1993                         1.04
 2 Asia                       <NA>   1993                         2.24
 3 Asia (excl. China & India) <NA>   1993                         3.22
 4 EU-27                      <NA>   1993                         8.52
 5 EU-28                      <NA>   1993                         8.70
 6 Europe                     <NA>   1993                         9.35
 7 Europe (excl. EU-27)       <NA>   1993                        10.5 
 8 Europe (excl. EU-28)       <NA>   1993                        10.6 
 9 North America              <NA>   1993                        14.0 
10 North America (excl. USA)  <NA>   1993                         4.97
11 Oceania                    <NA>   1993                        11.5 
12 South America              <NA>   1993                         2.09

Note: Entities that are not countries do not have country codes.

  1. Start with tidy_emissions then,
emissions_1993  <- tidy_emissions  %>% 
  filter(year == 1993, !is.na(code))   %>% 
  select(-year)  %>% 
  rename(country = entity)
  1. Which 15 countries have the highest annual_co2_emissions_per_capita?
max_15_emitters  <- emissions_1993  %>% 
  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_1993  %>% 
  slice_min(annual_co2_emissions_per_capita, n = 15)
  1. Use bind_rows to 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-separated values
max_min_15  %>% write_tsv("max_min_15.tsv")  # tab separated
max_min_15  %>% write_delim("max_min_15.psv", delim = "|") # pipe-separated
  1. Read the 3 file formats into R
max_min_15_csv <-  read_csv("max_min_15.csv") # comma-separated values
max_min_15_tsv <-  read_tsv("max_min_15.tsv")  # tab separated
max_min_15_psv <-  read_delim("max_min_15.psv", delim = "|") # pipe-separated
  1. Use setdiff to 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, max_min_15_psv)
# A tibble: 0 x 3
# ... with 3 variables: country <chr>, code <chr>,
#   annual_co2_emissions_per_capita <dbl>

Are there any differences?

  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. Step 16 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 1993", 
       x = NULL, 
       y = NULL)  

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