A short description of the post.
Download \(CO_2\) emissions per capita from Our World in Data into the directory for this 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
file_csv <- here("_posts",
"2021-02-19-reading-and-writing-data",
"co-emissions-per-capita.csv")
emissions <- read_csv(file_csv)
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
emissions
data THENuse 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
tidy_emissions
THEN use filter
to extract rows with year == 2019
THEN use skim
to calculate the descriptive statisticsName | Piped data |
Number of rows | 221 |
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 | 221 | 0 |
code | 13 | 0.94 | 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 | 2019 | 0.00 | 2019.00 | 2019.00 | 2019.00 | 2019.00 | 2019.00 | ▁▁▇▁▁ |
per_capita_co2_emissions | 0 | 1 | 5 | 5.71 | 0.03 | 1.12 | 3.53 | 6.56 | 38.61 | ▇▂▁▁▁ |
tidy_emissions
then extract rows with year == 2019
and are missing a code# A tibble: 13 x 4
entity code year per_capita_co2_emissions
<chr> <chr> <dbl> <dbl>
1 Africa <NA> 2019 1.12
2 Asia <NA> 2019 4.40
3 Asia (excl. China & India) <NA> 2019 4.14
4 EU-27 <NA> 2019 6.56
5 EU-28 <NA> 2019 6.41
6 Europe <NA> 2019 7.28
7 Europe (excl. EU-27) <NA> 2019 8.33
8 Europe (excl. EU-28) <NA> 2019 9.14
9 International transport <NA> 2019 4.58
10 North America <NA> 2019 11.0
11 North America (excl. USA) <NA> 2019 4.63
12 Oceania <NA> 2019 11.2
13 South America <NA> 2019 2.54
Entities that are not coutries do not have country codes.
Tidy_emissions
THEN -use filter
to extract rows with year == 2019 and without missing codes THEN -use select
to drop the year
variable THEN -use rename
to change the variable entity
to country
-assign the output to emissions_2019
per_capita_co2_emissions
?-start with emissions_2019
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_2019 %>%
slice_max(per_capita_co2_emissions, n =15)
per_capita_co2_emissions
?-start with emissions_2019
THEN -use slice_min
to extract the 15 rows with the lowest values -assign the output to min_5_emitters
min_15_emitters<-emissions_2019 %>%
slice_min(per_capita_co2_emissions, n =15)
bind_rows
to bind together the max_15_emitters
and min_15_emitters
-assign 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%>%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 = "l") #pipe-separated
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 = "l") #pipe-separated
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?
country
in max_min_15
for plotting and assign to max_min_15_plot_data-start with emissions_2019
THEN -use mutate
to reorder country
according to per_capital_co2_emissions
max_min_15_plot_data<-max_min_15 %>%
mutate(country = reorder(country, per_capita_co2_emissions))
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 15 per capita CO2 emissions",
subtitle = "for 2019",
x = NULL,
y = NULL)
ggsave(filename = "preview.png",
path = here("_posts" , "2021-02-19-reading-and-writing-data"))
preview: preview.png