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 CO2 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