- Load the r packages we will use.
max 2. 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 file
Read data 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)
- 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 ‘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==2008’ THEN use ‘skim’ to calculate the descriptive statistics.
| Name | Piped data |
| Number of rows | 220 |
| 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 | 220 | 0 |
| code | 12 | 0.95 | 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 | 2008.00 | 0.00 | 2008.00 | 2008.00 | 2008.0 | 2008.00 | 2008.00 | ▁▁▇▁▁ |
| per_capita_co2_emissions | 0 | 1 | 5.42 | 6.52 | 0.03 | 0.78 | 3.2 | 8.19 | 44.72 | ▇▂▁▁▁ |
- 13 observations have a missing code. How are these observations different? start with tidy_emissions then extract rows with year==2008 and are missing a code
# A tibble: 12 x 4
entity code year per_capita_co2_emissions
<chr> <chr> <dbl> <dbl>
1 Africa <NA> 2008 1.22
2 Asia <NA> 2008 3.57
3 Asia (excl. China & India) <NA> 2008 3.76
4 EU-27 <NA> 2008 8.30
5 EU-28 <NA> 2008 8.35
6 Europe <NA> 2008 8.72
7 Europe (excl. EU-27) <NA> 2008 9.30
8 Europe (excl. EU-28) <NA> 2008 9.44
9 North America <NA> 2008 13.5
10 North America (excl. USA) <NA> 2008 5.45
11 Oceania <NA> 2008 12.9
12 South America <NA> 2008 2.54Entities that are not countries do not have country codes
- Start with tidy_emissions THEN use ‘filter’ to extract rows with year==2008 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_2008’
- Which 15 countries have the highest ‘per_capita_co2_emissions’?
start with ‘emissions_2008’ THEN use ‘slice_max’ to extract 15 rows with the ‘per_capita_co2_emissions’ assign output to ‘max_15_emitters’
max_15_emitters <- emissions_2008 %>%
slice_max(per_capita_co2_emissions,n=15)
- Which 15 countries have the lowest ‘per_capita-co2_emissions’?
start with ‘emissions_2008’ THEN use ‘slice_min’ to extract the 15 rows with the lowest values assign the output to ‘min_15_emitters’
min_15_emitters <- emissions_2008 %>%
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 seperated
- Read the 3 file format into R
max_min_15_csv <- read_csv("max_min_15.csv")
max_min_15_tsv <- read_tsv("max_min_15.tsv")
max_min_15_psv <- read_delim("max_min_15.psv", delim="|")
- 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>- Reorder ‘country’ in ‘max_min_15’ for plotting and assign to max_min_15_plot_data
start with ‘emissions_2008’ 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 C02 emissions",
subtitle="for 2008",
x= NULL,
y= NULL)
- Save the plot directory with this post.
ggsave(filename="preview.png",
path=here("_posts", "2021-03-01-reading-and-writing-data"))
- Add preveiw.png to yaml chunk at the top
preview: preview.png