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 from Our World in Data into the directory for this post.

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

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

-use clean_names from the janitor package to make the names easier to work with -assign the output to tidy_emissions -Show the first 10 rows of tidy_emissions

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 == 2000) %>%
  skim()
Table 1: Data summary
Name Piped data
Number of rows 219
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 0 219 0
code 12 0.95 3 8 0 207 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
year 0 1 2000.00 0.00 2e+03 2000.00 2000.00 2000.00 2000.00 ▁▁▇▁▁
per_capita_co2_emissions 0 1 5.06 6.74 2e-02 0.71 2.82 7.97 58.39 ▇▁▁▁▁
  1. 12 observations have a missing code. How are these observations different?
tidy_emissions %>%
  filter(year == 2000, is.na(code))
# A tibble: 12 x 4
   entity                     code   year per_capita_co2_emissions
   <chr>                      <chr> <dbl>                    <dbl>
 1 Africa                     <NA>   2000                     1.11
 2 Asia                       <NA>   2000                     2.40
 3 Asia (excl. China & India) <NA>   2000                     3.35
 4 EU-27                      <NA>   2000                     8.46
 5 EU-28                      <NA>   2000                     8.61
 6 Europe                     <NA>   2000                     8.48
 7 Europe (excl. EU-27)       <NA>   2000                     8.47
 8 Europe (excl. EU-28)       <NA>   2000                     8.19
 9 North America              <NA>   2000                    14.6 
10 North America (excl. USA)  <NA>   2000                     5.39
11 Oceania                    <NA>   2000                    12.6 
12 South America              <NA>   2000                     2.32

Entities that are not countries do not have country codes.

  1. Start with tidy_emissions THEN
emissions_2000 <- tidy_emissions %>%
  filter(year == 2000, !is.na(code)) %>%
  select(-year) %>%
  rename(country = entity)
  1. Which 15 countries have the highest per_capita_co2_emissions?

-start with emissions_2000 THEN -use slice_max to extract the 15 rows with the per_capita_co2_emissions -assign the output to max_15_emitters

max_15_emitters <- emissions_2000 %>%
  slice_max(per_capita_co2_emissions, n = 15)
  1. Which 15 countries have the lowest per_capita_co2_emissions?
min_15_emitters <- emissions_2000 %>%
  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 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-seperated 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()
geom_col: width = NULL, na.rm = FALSE
stat_identity: na.rm = FALSE
position_stack 
labs(title = "The top 15 and bottom 15 per capita CO2 emissions",
     subtitle = "for 2000",
     x = NULL,
     y = NULL)
$x
NULL

$y
NULL

$title
[1] "The top 15 and bottom 15 per capita CO2 emissions"

$subtitle
[1] "for 2000"

attr(,"class")
[1] "labels"
  1. Save the plot directory with this post
ggsave(filename = "preview.png",
       path = here("_posts", "2021-03-01-reading-and-writing-data"))