Code and text for Quiz 4.
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.
emissions
file_csv <- here("_posts",
"2021-02-16-reading-and-writing-data",
"co-emissions-per-capita.csv")
emissions <- read_csv(file_csv)
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==1985
THEN
use skim
to calculate the descriptive statistics
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 | 1985.00 | 0.00 | 1985.00 | 1985.00 | 1985.00 | 1985.00 | 1985.00 | ▁▁▇▁▁ |
per_capita_co2_emissions | 0 | 1 | 5.53 | 8.94 | 0.04 | 0.51 | 2.65 | 7.62 | 83.83 | ▇▁▁▁▁ |
12 observations have a missing code. How are these observations different?
tidy_emissions
then extract rows with year == 1985
and are missing a code# A tibble: 12 x 4
entity code year per_capita_co2_emissions
<chr> <chr> <dbl> <dbl>
1 Africa <NA> 1985 1.23
2 Asia <NA> 1985 1.81
3 Asia (excl. China & India) <NA> 1985 2.76
4 EU-27 <NA> 1985 9.19
5 EU-28 <NA> 1985 9.28
6 Europe <NA> 1985 10.9
7 Europe (excl. EU-27) <NA> 1985 13.3
8 Europe (excl. EU-28) <NA> 1985 14.1
9 North America <NA> 1985 13.2
10 North America (excl. USA) <NA> 1985 5.01
11 Oceania <NA> 1985 10.8
12 South America <NA> 1985 1.87
Entities that are not countries do not have country codes.
Start with tidy_emissions
THEN
use filter
to extract rows with year==1985
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_1985
Which 15 countries have the highest per_capita_co2_emissions
?
start with emissions_1985 THEN
use slice_max
to extract the 15 rows with the highest values
assign the output to max_15_emitters
max_15_emitters <- emissions_1985 %>%
slice_max(per_capita_co2_emissions, n=15)
Which 15 countries have the lowest per_capita_co2_emissions
?
start with emissions_1985 THEN
use slice_min
to extract the 15 rows with the lowest values
assign the output to min_15_emitters
min_15_emitters <- emissions_1985 %>%
slice_min(per_capita_co2_emissions, n=15)
Use bind_rows
to bind together the max_15_emitters
and min_15_emitters
max_min_15
max_min_15 <- bind_rows(max_15_emitters, min_15_emitters)
max_min_15
to 3 file formatsmax_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
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
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?
Reorder country
in max_min_15
for plotting and assign to max_min_15_plot_data
start with emissions_1985 THEN
use mutate
to reorder country
according to per_capital_co2_emissions
max_min_15_plot_data <- max_min_15 %>%
mutate(country=reorder(country, per_capita_co2_emissions))
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 1985",
x=NULL,
y=NULL)
ggsave(filename = "preview.png",
path=here("_posts", "2021-02-16-reading-and-writing-data"))
preview:preview.png