Reading and writing data

Code for Quiz 4

1. Load the R packages we will use

library(tidyverse)
library(here)
library(janitor)
library(skimr)

2. Download \(CO_2\) emissions per capita from Our World in Data into the directory for this post.

3. 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",
              "2022-02-22-reading-and-writing-data",
              "co-emissions-per-capita.csv")

emissions  <- read_csv(file_csv)

4. 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

5. 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: 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

6. Start with the tidy_emissions THEN use filter to extract rows with year == 2008 THEN use skim to calculate the descriptive statistics

tidy_emissions  %>% 
  filter(year == 2008)  %>% 
  skim()

Table 1: Data summary

Name	Piped data
Number of rows	229
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	229	0
code	12	0.95	3	8	0	217	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
year	0	1	2008.00	0.0	2008.00	2008.00	2008.00	2008.00	2008.00	▁▁▇▁▁
annual_co2_emissions_per_capita	0	1	5.51	6.8	0.03	0.85	3.23	8.13	44.73	▇▂▁▁▁

7. 12 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

tidy_emissions  %>% 
  filter(year == 2008, is.na(code))

# A tibble: 12 x 4
   entity                     code   year annual_co2_emissions_per_ca~
   <chr>                      <chr> <dbl>                        <dbl>
 1 Africa                     <NA>   2008                         1.20
 2 Asia                       <NA>   2008                         3.60
 3 Asia (excl. China & India) <NA>   2008                         3.75
 4 EU-27                      <NA>   2008                         8.26
 5 EU-28                      <NA>   2008                         8.33
 6 Europe                     <NA>   2008                         8.70
 7 Europe (excl. EU-27)       <NA>   2008                         9.29
 8 Europe (excl. EU-28)       <NA>   2008                         9.43
 9 North America              <NA>   2008                        13.6 
10 North America (excl. USA)  <NA>   2008                         5.57
11 Oceania                    <NA>   2008                        13.0 
12 South America              <NA>   2008                         2.62

Entities that are not countries do not have country codes.

8. 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 the emissions_2008

emissions_2008  <- tidy_emissions  %>% 
  filter(year == 2008, !is.na(code))  %>% 
  select(-year)  %>% 
  rename(country = entity)

9. Which 15 countries have the highest annual_co2_emissions_per_capita?

Start with emissions_2008 THEN use slice_max to extract the 15 rows with the annual_co2_emissions_per_capita assign the output to max_15_emitters

max_15_emitters  <- emissions_2008  %>% 
  slice_max(annual_co2_emissions_per_capita, n = 15)

10. Which 15 countries have the lowest annual_co2_emissions_per_capita?

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(annual_co2_emissions_per_capita, n=15)

11. 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)

12. Export max_min_15 to 3 file formats

max_min_15 %>% write_csv("max_min_15.csv")
max_min_15 %>% write_tsv("max_min_15.tsv")
max_min_15 %>% write_delim("max_min_15.psv", delim = "|")

13. Read the 3 file formats 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 = "|")

14. 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? There aren’t any differences.

15. 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 `annual_co2_emissions_per_capita’

max_min_15_plot_data  <- max_min_15 %>% 
  mutate(country = reorder(country, annual_co2_emissions_per_capita))

16. Plot max_min_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 2008",
       X = NULL,
       Y = NULL)

17. Save the plot directory with this post

ggsave(filename = "preview.png", 
       path = here("_posts","2022-02-22-reading-and-writing-data"))

18. Add preview.png to yaml chunk at the top of this file

preview: preview.png