Reading and writing data

A short description of the post.

  1. Load the R packages we will use.
  1. Download \(CO_2\) emissions per capita 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", 
                  "2021-03-01-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: 22,383 x 4
   Entity      Code   Year `Per capita CO2 emissions`
   <chr>       <chr> <dbl>                      <dbl>
 1 Afghanistan AFG    1949                    0.00191
 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                    0.0130 
 7 Afghanistan AFG    1955                    0.0186 
 8 Afghanistan AFG    1956                    0.0218 
 9 Afghanistan AFG    1957                    0.0343 
10 Afghanistan AFG    1958                    0.0380 
# … with 22,373 more rows
  1. Start with emissions data THEN
tidy_emissions   <- emissions %>% 
  clean_names()

tidy_emissions
# A tibble: 22,383 x 4
   entity      code   year per_capita_co2_emissions
   <chr>       <chr> <dbl>                    <dbl>
 1 Afghanistan AFG    1949                  0.00191
 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                  0.0130 
 7 Afghanistan AFG    1955                  0.0186 
 8 Afghanistan AFG    1956                  0.0218 
 9 Afghanistan AFG    1957                  0.0343 
10 Afghanistan AFG    1958                  0.0380 
# … with 22,373 more rows
  1. Start with the tidy_emissions THEN
tidy_emissions  %>% 
  filter(year == 2003)  %>% 
  skim()
Table 1: Data summary
Name Piped data
Number of rows 220
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 0 220 0
code 12 0.95 3 8 0 208 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
year 0 1 2003.00 0.00 2003.00 2003.00 2003.00 2003.00 2003.00 ▁▁▇▁▁
per_capita_co2_emissions 0 1 5.24 6.79 0.02 0.77 3.08 7.99 60.56 ▇▁▁▁▁
  1. 13 observations have a missing code. How are these observations different?
tidy_emissions %>% 
  filter(year == 2003, is.na(code))
# A tibble: 12 x 4
   entity                     code   year per_capita_co2_emissions
   <chr>                      <chr> <dbl>                    <dbl>
 1 Africa                     <NA>   2003                     1.12
 2 Asia                       <NA>   2003                     2.72
 3 Asia (excl. China & India) <NA>   2003                     3.47
 4 EU-27                      <NA>   2003                     8.71
 5 EU-28                      <NA>   2003                     8.82
 6 Europe                     <NA>   2003                     8.80
 7 Europe (excl. EU-27)       <NA>   2003                     8.87
 8 Europe (excl. EU-28)       <NA>   2003                     8.69
 9 North America              <NA>   2003                    14.3 
10 North America (excl. USA)  <NA>   2003                     5.52
11 Oceania                    <NA>   2003                    12.9 
12 South America              <NA>   2003                     2.29
  1. Start with tidy_emissions THEN
emissions_2003  <- tidy_emissions %>% 
  filter(year == 2003, !is.na(code)) %>% 
  select(-year) %>% 
  rename(country = entity)
  1. Which 15 countries have the highest per_capita_co2_emissions?
max_15_emitters  <- emissions_2003 %>% 
  slice_max(per_capita_co2_emissions, n = 15)
  1. Which 15 countries have the lowest per_capita_co2_emissions?
min_15_emitters  <- emissions_2003 %>% 
  slice_min(per_capita_co2_emissions, 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 the 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>,
#   per_capita_co2_emissions <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, per_capita_co2_emissions))
  1. Plot max_min_15_plot_data
ggplot(data = max_min_15_plot_data, 
       mapping = aes(x = per_capita_co2_emissions, y = country)) +
  geom_col() +
  labs(title = "The top 15 and bottom 15 per capita CO2 emissions",
       subtitle = "for 2003",
       x = NULL,
       y = NULL)

  1. Save the plot directory with this post
ggsave(filename = "preview.png",
       path = here("_posts", "2021-03-01-reading-and-writing-data"))
  1. Add preview.png to yaml chuck at the top of this file

preview: preview.png