A short description of the post.
Download \(C0_2\) 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 same directory as this file
Read the date 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)
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 <- 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
Name | Piped data |
Number of rows | 209 |
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 | 209 | 0 |
code | 12 | 0.94 | 3 | 8 | 0 | 197 | 0 |
Variable type: numeric
skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
---|---|---|---|---|---|---|---|---|---|---|
year | 0 | 1 | 1988.00 | 0.00 | 1988.00 | 1988.00 | 1988.00 | 1988.00 | 1988.00 | ▁▁▇▁▁ |
per_capita_co2_emissions | 0 | 1 | 5.07 | 5.86 | 0.01 | 0.54 | 2.82 | 8.11 | 29.56 | ▇▃▁▁▁ |
# A tibble: 12 x 4
entity code year per_capita_co2_emissions
<chr> <chr> <dbl> <dbl>
1 Africa <NA> 1988 1.23
2 Asia <NA> 1988 1.98
3 Asia (excl. China & India) <NA> 1988 2.94
4 EU-27 <NA> 1988 9.07
5 EU-28 <NA> 1988 9.18
6 Europe <NA> 1988 10.9
7 Europe (excl. EU-27) <NA> 1988 13.4
8 Europe (excl. EU-28) <NA> 1988 14.2
9 North America <NA> 1988 13.8
10 North America (excl. USA) <NA> 1988 5.06
11 Oceania <NA> 1988 11.2
12 South America <NA> 1988 2.04
Entities that are not countries do not have country codes 8. Start with tiddy_emissions THEN
max_15_emitters <- emissions_2019 %>%
slice_max(per_capita_co2_emissions, n = 15)
min_15_emitters <- emissions_2019 %>%
slice_min(per_capita_co2_emissions, n = 15)
max_min_15 <- bind_rows(max_15_emitters , min_15_emitters)
max_min_15 %>% write_csv("max_min_15.csv") # comma-seperated values
max_min_15 %>% write_tsv("max_min_15.tsv") # tab seperated
max_min_15 %>% write_delim("max_min_15.psv", delim = "l") # pipe-seperated
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 = "l") # pipe-separated
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>
max_min_15_plot_data <- max_min_15 %>%
mutate(country = reorder(country, per_capita_co2_emissions))
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 1988",
x = NULL,
y = NULL)
ggsave(filename = "preview.png",
path = here("_posts", "2021-03-01-reading-and-writing-data"))
preview: preview.png