Reading and Writing data

Practice reading and writing data, more dplyr and a plot

1. Load the packages that we will use

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

2. Download CO2 emissions per capita from Our World in Data into the directory for this post.

3. Assign the location of the file 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", 
                 "2022-02-19-reading-and-writing-data",   "co-emissions-per-capita.csv")

emissions <- read_csv(file_csv)

4. Show 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 == 2004
• use skim to calculate the descriptive statistics

tidy_emissions %>% 
  filter(year == 2004) %>% 
  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	2004.00	0.00	2004.00	2004.00	2004.00	2004.00	2004.0	▁▁▇▁▁
annual_co2_emissions_per_capita	0	1	5.43	6.99	0.02	0.83	3.23	8.45	56.7	▇▁▁▁▁

7. 12 Observations have a missing code. How are these observations different?

• Start with tidy_emissions then,
• extract rows with year == 2004 and are missing a code.

tidy_emissions %>% 
  filter(year == 2004, is.na(code))

# A tibble: 12 x 4
   entity                     code   year annual_co2_emissions_per_ca~
   <chr>                      <chr> <dbl>                        <dbl>
 1 Africa                     <NA>   2004                         1.16
 2 Asia                       <NA>   2004                         2.97
 3 Asia (excl. China & India) <NA>   2004                         3.61
 4 EU-27                      <NA>   2004                         8.68
 5 EU-28                      <NA>   2004                         8.79
 6 Europe                     <NA>   2004                         8.81
 7 Europe (excl. EU-27)       <NA>   2004                         8.96
 8 Europe (excl. EU-28)       <NA>   2004                         8.80
 9 North America              <NA>   2004                        14.5 
10 North America (excl. USA)  <NA>   2004                         5.53
11 Oceania                    <NA>   2004                        13.2 
12 South America              <NA>   2004                         2.36

• Entities that are not countries do not have country codes.

8. Start with tidy_emissions THEN

• use filter to extract rows with year == 2004 and
• without missing codes THEN,
• use select to drop the year variable THEN,
• use the rename to change the variable entity to country.

emissions_2004 <- tidy_emissions %>% 
  filter(year == 2004, !is.na(code)) %>% 
  select(-year) %>% 
  rename(country = entity)

emissions_2004

# A tibble: 217 x 3
   country             code  annual_co2_emissions_per_capita
   <chr>               <chr>                           <dbl>
 1 Afghanistan         AFG                             0.036
 2 Albania             ALB                             1.32 
 3 Algeria             DZA                             2.69 
 4 Andorra             AND                             7.35 
 5 Angola              AGO                             0.997
 6 Anguilla            AIA                             9.89 
 7 Antigua and Barbuda ATG                             4.56 
 8 Argentina           ARG                             4.08 
 9 Armenia             ARM                             1.23 
10 Aruba               ABW                            26.5  
# ... with 207 more rows

9. Which 15 countries have the highest annual_co2_emissions_per_capita?

• start with emissions_2004 THEN,
• use slice_max to extract the 15 rows with annual_co2_emissions_per_capita,
• assign the output to max_15_emitters

max_15_emitters <- emissions_2004 %>% 
  slice_max(annual_co2_emissions_per_capita, n = 15)

10. Which 15 countries have the lowest annual_co2_emissions_per_capita?

• start with emissions_2004 THEN,
• use slice_min to extract the 15 rows with the lowest values
• assign the output to min_15_emitters.

min_15_emitters <- emissions_2004 %>% 
  slice_min(annual_co2_emissions_per_capita, n =15)

11. Use bind_rows to bind together the max_15_emitter and min_15_emitters assign the outpout to max_min_15.

max_min_15 <- bind_rows(max_15_emitters, min_15_emitters)

12. Export max_min_15 to 3 files 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 value
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_psv)

# A tibble: 0 x 3
# ... with 3 variables: country <chr>, code <chr>,
#   annual_co2_emissions_per_capita <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_2004 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_15_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 2004",
       x = NULL,
       y = NULL)

17. Save the plot directory with this post

ggsave(filename = "preview.png", path = here("_posts", "2022-02-19-reading-and-writing-data"))

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

preview: preview.png