- Load the R packages that 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 ‘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",
"2021-02-25-reading-and-writing-data",
"co-emissions-per-capita.csv")
emissions <- read_csv(file_csv)
- Show the first 10 rows (observation 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 ‘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
Table 1: Data summary Name 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 ▇▂▁▁▁ - 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: 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- Start with ‘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’
- Which 15 countries have the highest ‘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)- Which 15 countries have the lowest ‘per_capita_co2_emissions’?
min_15_emitters <- emissions_2019 %>% slice_min(per_capita_co2_emissions, n = 15)- 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_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 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, 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? No.
- Reorder ‘country’ in ‘max_min_15’ for plotting and assign to max_min_15_plot_data
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 2019", x = NULL, y = NULL)
- Save the plot directory with the post.
ggsave(filename = "preview.png", path = here("_posts", "2021-02-25-reading-and-writing-data"))- Add preview.png to yaml chuck at the top of this file
preview: preview.png