- Load the R packages we will use.
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", "2021-02-26-reading-and-writing-data", "co-emissions-per-capita.csv")
emissions <- read_csv(file_csv)
- 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- Start with the ‘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- Start with the ‘tidy_emissions’ THEN - use ‘filter’ to extract rows with ‘year == 2019’ THEN - use ‘skim’ to calculate the descriptive statistics
| 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 | 1994.00 | 0.00 | 1994.00 | 1994.00 | 1994.00 | 1994.00 | 1994.00 | ▁▁▇▁▁ |
| per_capita_co2_emissions | 0 | 1 | 4.89 | 6.82 | 0.02 | 0.56 | 2.66 | 7.26 | 60.56 | ▇▁▁▁▁ |
- 13 observations have a missing code. How are these observations different? - start with ‘tidy_emissions’ then extract rows with year == 2019 and are missing a code
# A tibble: 12 x 4
entity code year per_capita_co2_emissions
<chr> <chr> <dbl> <dbl>
1 Africa <NA> 1994 1.04
2 Asia <NA> 1994 2.27
3 Asia (excl. China & India) <NA> 1994 3.23
4 EU-27 <NA> 1994 8.48
5 EU-28 <NA> 1994 8.66
6 Europe <NA> 1994 8.87
7 Europe (excl. EU-27) <NA> 1994 9.36
8 Europe (excl. EU-28) <NA> 1994 9.22
9 North America <NA> 1994 14.1
10 North America (excl. USA) <NA> 1994 4.98
11 Oceania <NA> 1994 11.5
12 South America <NA> 1994 2.06Entities that are not countries do not have country codes
- start with tidy_emissions THEN - use ‘filter’ to extract rows with year == 1994 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’
- Which 15 countries have the highest ‘per_capita_co2_emissions’? - start with ‘emissions_1994’ THEN - use ‘slice_max’to extract the 15 rows with the ’per_capita_co2_emissions’ - assign output to ‘max_15_emitters’
max_15_emitters <- emissions_1994 %>%
slice_max(per_capita_co2_emissions, n = 15)
- Which 15 countries have the lowest ‘per_capita_co2_emissions’? - start with ‘emissions_2019’ THEN - use ‘slice_min’to extract the 15 rows with the lowest values - assign output to ’min_15_emitters’
min_15_emitters <- emissions_1994 %>%
slice_min(per_capita_co2_emissions, n = 15)
- Use ‘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)
- 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 values
max_min_15 %>% write_delim("max_min_15.psv", delim = "|") # pipe separated
- 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 values
max_min_15_psv <- read_delim("max_min_15.psv", delim = "|") # pipe-separated values
- 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? Nope! There are no differences.
- Reorder ‘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_capita_co2_emissions’
max_min_15_plot_data <- max_min_15 %>%
mutate(country = reorder(country, per_capita_co2_emissions))
- 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 1994",
x = NULL,
y = NULL)
- Save the plot directory with this post
ggsave(filename = "preview.png",
path = here("_posts", "2021-02-26-reading-and-writing-data"))
- Add preview.png to yaml at the top of this file
preview: preview.png