Reading and writing data

Code and text 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 Out 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", 
                  "2021-02-23-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: 22,383 x 4
   Entity Code   Year `Per capita CO2 emissions`
   <chr>  <chr> <dbl>                      <dbl>
 1 Aruba  ABW    1950                      10.8 
 2 Aruba  ABW    1951                      17.0 
 3 Aruba  ABW    1952                       9.56
 4 Aruba  ABW    1953                      10.8 
 5 Aruba  ABW    1954                      20.8 
 6 Aruba  ABW    1955                      16.4 
 7 Aruba  ABW    1956                      20.3 
 8 Aruba  ABW    1957                      13.3 
 9 Aruba  ABW    1958                      11.0 
10 Aruba  ABW    1959                      13.6 
# ... with 22,373 more rows

5. Start with emissions data THEN

• use clean_names from the janitor package to make 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 Aruba  ABW    1950                    10.8 
 2 Aruba  ABW    1951                    17.0 
 3 Aruba  ABW    1952                     9.56
 4 Aruba  ABW    1953                    10.8 
 5 Aruba  ABW    1954                    20.8 
 6 Aruba  ABW    1955                    16.4 
 7 Aruba  ABW    1956                    20.3 
 8 Aruba  ABW    1957                    13.3 
 9 Aruba  ABW    1958                    11.0 
10 Aruba  ABW    1959                    13.6 
# ... with 22,373 more rows

6. Start with the tidy_emissions THEN

• use filter to extract rows with year == 2000 THEN
• use skim to calculate the descriptive statistics.

tidy_emissions  %>%
  filter(year == 2000)  %>% 
  skim()

Table 1: Data summary

Name	Piped data
Number of rows	219
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	219	0
code	12	0.95	3	8	0	207	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
year	0	1	2000.00	0.00	2e+03	2000.00	2000.00	2000.00	2000.00	▁▁▇▁▁
per_capita_co2_emissions	0	1	5.06	6.74	2e-02	0.71	2.82	7.97	58.39	▇▁▁▁▁

7. 13 observations have a missing code. How are these observations different?

• start with tidy_emissions then extract rows with year == 2000 and are missing a code.

tidy_emissions  %>% 
  filter(year == 2000, is.na(code))

# A tibble: 12 x 4
   entity                     code   year per_capita_co2_emissions
   <chr>                      <chr> <dbl>                    <dbl>
 1 Africa                     <NA>   2000                     1.11
 2 Asia                       <NA>   2000                     2.40
 3 Asia (excl. China & India) <NA>   2000                     3.35
 4 EU-27                      <NA>   2000                     8.46
 5 EU-28                      <NA>   2000                     8.61
 6 Europe                     <NA>   2000                     8.48
 7 Europe (excl. EU-27)       <NA>   2000                     8.47
 8 Europe (excl. EU-28)       <NA>   2000                     8.19
 9 North America              <NA>   2000                    14.6 
10 North America (excl. USA)  <NA>   2000                     5.39
11 Oceania                    <NA>   2000                    12.6 
12 South America              <NA>   2000                     2.32

Entities that are not countries do not have country codes.

8. Start with tidy_emissions THEN

• use filter to extract rows with year == 2000 and without missing code THEN
• use select to drop the year variable THEN
• use rename to change the variable entity to country
• assign the output to emissions_2000

emissions_2000  <- tidy_emissions %>% 
  filter(year == 2000, !is.na(code))  %>% 
  select(-year) %>% 
  rename(country = entity)

9. Which 15 countries have the highest per_capita_co2_emissions?

• start with emissions_2000 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_2000  %>% 
  slice_max(per_capita_co2_emissions, n = 15)

10. Which countries have the lowest per_capita_co2_emissions?

• start with emissions_2000 THEN
• use slice_min to extract the 15 rows with the lowest values
• assign the output to min_15_emitters

min_15_emitters <- emissions_2000  %>% 
  slice_min(per_capita_co2_emissions, 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 this 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

13. 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

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, 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?

15. Reorder country in max_min_15 for plotting and assign to max_min_15_plot_data

• start with emissions_2000 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))

16. 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 bottem 15 per capita CO2 emissions",
       subtitle = "for 2000",
       x = NULL,
       y = NULL)

17. Save the plot directory with this post.

ggsave(filename = "preview.png",
       path = here("_posts", "2021-02-23-reading-and-writing-data"))

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

preview: preview.png