A short description of the post.
Download \(CO_2\) emissions per capita from Our World in Data into the directory for the post.
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
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
emissions
data THENclean_names
from the janitor package to make the names easier to work withtidy_emissions
tidy_emissions
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
tidy_emissions
THENfilter
to extract rows with year == 2005
THENskim
to calculate the descriptive statisticsName | 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 | 2005.00 | 0.00 | 2005.00 | 2005.00 | 2005.00 | 2005.00 | 2005.00 | ▁▁▇▁▁ |
annual_co2_emissions_per_capita | 0 | 1 | 5.44 | 7.06 | 0.02 | 0.85 | 3.21 | 8.43 | 58.57 | ▇▁▁▁▁ |
tidy_emissions
then extract rows with year == 2005
and are missing a code# A tibble: 12 × 4
entity code year annual_co2_emissions_per_ca…
<chr> <chr> <dbl> <dbl>
1 Africa <NA> 2005 1.15
2 Asia <NA> 2005 3.15
3 Asia (excl. China & India) <NA> 2005 3.64
4 EU-27 <NA> 2005 8.60
5 EU-28 <NA> 2005 8.71
6 Europe <NA> 2005 8.78
7 Europe (excl. EU-27) <NA> 2005 9.00
8 Europe (excl. EU-28) <NA> 2005 8.89
9 North America <NA> 2005 14.4
10 North America (excl. USA) <NA> 2005 5.58
11 Oceania <NA> 2005 13.1
12 South America <NA> 2005 2.43
Entities that are not countries do not have country codes.
tidy_emissions
THENfilter
to extract rows with year == 2005 and without missing codes Thenselect
to drop the year
variable THENrename
the change the variable entity
to country
emissions_2005
annual_co2_emissions_per_capita
?emissions_2005
THENslicce_max
to extra ct the 15 rows with the annual_co2_emissions_per_capita
max_15_emitters
annual_co2_emissions_per_capita
emissions_2005
THENslice_min
to extract the 15 rows with the lowest valuesmin_15_emitters
11.Use bind_rows
to bind togheter the max_15_emitters
and min_15_emitters
- assign the output to max_min_15
max_min_15 <- bind_rows(max_15_emitters, min_15_emitters)
max_min_15
to 3 file formatsmax_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
setdiff
to check for any difference among max_min_15_csv
, max_min_15_tsv
, 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>
Are there any differences?
country
in max_min_15
for plotting and assign to max_min_15_plot_dataemissions 2005
THENmutate
to reorder coutnry
according to annual_co2_emissions_per_capita
max_min_15_plot_data
preview: preview.png