Reading And Writing Data

A short description of the post.

1. Load the R Packages we will use

library(tidyverse)
library(here)
library(janitor) # make sure to install
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", 
                 "2021-02-26-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 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

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: 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

6. Start with the tidy_emissions THEN

• use filter to extract rows with year == 1994 THEN
• use skim to calculate the descriptive statistics

tidy_emissions %>%
  filter(year == 1994) %>%
  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	1994.00	0.00	1994.00	1994.00	1994.00	1994.00	1994.00	▁▁▇▁▁
per_capita_co2_emissions	0	1	4.89	6.82	0.02	0.56	2.66	7.26	60.56	▇▁▁▁▁

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

• start with tidy_emissions then extract rows with year == 1994 and are missing a code

tidy_emissions %>%
  filter(year == 1994, is.na(code))

# A tibble: 12 x 4
   entity                     code   year per_capita_co2_emissions
   <chr>                      <chr> <dbl>                    <dbl>
 1 Africa                     <NA>   1994                     1.04
 2 Asia                       <NA>   1994                     2.27
 3 Asia (excl. China & India) <NA>   1994                     3.23
 4 EU-27                      <NA>   1994                     8.48
 5 EU-28                      <NA>   1994                     8.66
 6 Europe                     <NA>   1994                     8.87
 7 Europe (excl. EU-27)       <NA>   1994                     9.36
 8 Europe (excl. EU-28)       <NA>   1994                     9.22
 9 North America              <NA>   1994                    14.1 
10 North America (excl. USA)  <NA>   1994                     4.98
11 Oceania                    <NA>   1994                    11.5 
12 South America              <NA>   1994                     2.06

Entities that are not countries do not have country codes.

8. start with tidy_emissions THEN

• use filter to extract rows with year == 1994 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_1994

emissions_1994 <- tidy_emissions %>%
  filter(year == 1994, !is.na(code)) %>%
  select(-year) %>%
  rename(country = entity)

9. Which 15 countries have the highest per_capita_co2_emissions?

• start with emissions_1994 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_1994 %>%
  slice_max(per_capita_co2_emissions, n = 15)

10. Which 15 countries have the lowest per_capita_co2_emissions?

• start with emissions_1994 THEN
• use slice_min to extract the 15 rows with the per_capita_co2_emissions
• assign the output to min_15_emitters

min_15_emitters <- emissions_1994 %>%
  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 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_1994 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 bottom 15 per capita CO2 emission",
       subtitle = "for 1994",
       x = NULL,
       y = NULL)

17. Save the plot directory with this post

ggsave(filename = "preview.png", 
       path = here("_posts", "2021-02-26-reading-and-writing-data"))

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

preview: preview.png