- Load the R packages we will use.
Download CO2 emissions per capita from Our World in Data intro 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",
"2022-02-21-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: 23,307 x 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- Start with ‘emissions’ data THEN
-use ‘clean_names’ from the janitor to make the names earier to mark with -assign the output to ‘tidy_emissions’ -show the first 10 rows of ‘tidy_emissions’
tidy_emissions <- emissions %>%
clean_names()
tidy_emissions
# A tibble: 23,307 x 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- Start with the ‘tidy_emissions’ THEN -use ‘filter’ to extract rows with ‘year == 1994’ THEN -use ‘skim’ to calculate the descriptive statistics
| Name | Piped data |
| Number of rows | 228 |
| 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 | 228 | 0 |
| code | 12 | 0.95 | 3 | 8 | 0 | 216 | 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 | ▁▁▇▁▁ |
| annual_co2_emissions_per_capita | 0 | 1 | 4.99 | 6.92 | 0.02 | 0.57 | 2.73 | 7.36 | 59.77 | ▇▁▁▁▁ |
- 12 observations have a missing code. How are these observations different? -Start with ‘tidy_emissions’ then extract rows with ‘year == 1994’ and are missing a code.
# A tibble: 12 x 4
entity code year annual_co2_emissions_per_ca~
<chr> <chr> <dbl> <dbl>
1 Africa <NA> 1994 1.03
2 Asia <NA> 1994 2.31
3 Asia (excl. China & India) <NA> 1994 3.25
4 EU-27 <NA> 1994 8.46
5 EU-28 <NA> 1994 8.63
6 Europe <NA> 1994 8.85
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 5.07
11 Oceania <NA> 1994 11.7
12 South America <NA> 1994 2.11Entities 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_1994’
emissions_1994 <- tidy_emissions %>%
filter(year == 1994, !is.na(code)) %>%
select(-year) %>%
rename(country = entity)
- Which 15 countries have the highest ‘annual_co2_emissions_per_capita’?
start with ‘emissions_1994’ THEN use ‘slice_max’ to extract the 15 rows with the ‘annual_co2_emissions_per_capita’ assign the output to ‘max_15_emitters’
- Which 15 countries have the lowest ‘annual_co2_emissions_per_capita’?
start with ‘emissions_1994’ THEN use ‘slice_min’ to extract the 15 rows with the lowest annual_co2_emissions_per_capita’ assign the output to ‘min_15_emitters’
- Use ‘bind_rows’ to bind together 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)
- Export
max_min_15to 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
- 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
max_min_15_psv <- read_delim("max_min_15.psv", delim = "!") #.pipe separated
- 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)
# A tibble: 0 x 3
# ... with 3 variables: country <chr>, code <chr>,
# annual_co2_emissions_per_capita <dbl>Are there any differences?
- Reorder ‘country’ in ‘max_min_15’ for plotting and assign to max_min_15_plot_data
-Start with ‘emissions_1994’ THEN -use ‘mutate’ to reorder ‘country’ according to ‘annual_co2_emissions_per_capita’
max_min_15_plot_data <- max_min_15 %>%
mutate(country = reorder(country, annual_co2_emissions_per_capita))
- Plot ‘max_min_15_plot_data’
ggplot(data = max_min_15_plot_data,
mapping = aes(x= annual_co2_emissions_per_capita, 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 ths post
- Add preview.png to yml check at the top of this file
preview: preview.png