Reading and writing data

Practice reading and writing data, more dplyr and a plot.

1. Load the R packages 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 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 × 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 outputs to ‘tidy_emissions’

• show the first 10 rows of ‘tidy_emissions’

tidy_emissions <- emissions %>% 
  clean_names()
  
tidy_emissions

# A tibble: 23,307 × 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 statisitics

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 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 × 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

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 to ‘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’?

• 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_csv.csv") #comma separated values
max_min_15 %>% write_tsv("max_min_csv.tsv") #tab separated 
max_min_15 %>% write_delim("max_min_csv.psv", delim = "|") #pipe-separated

13. Read the 3 file formats into R

max_min_15_csv <- read_csv("max_min_csv.csv") #comma separated values
max_min_15_tsv <- read_tsv("max_min_csv.tsv") #tab separated 
max_min_15_psv <- read_delim("max_min_csv.psv", delim = "|") #pipe-separated

14. Use ‘setdiff’ to check for any differences among ‘max_min_15_csv’ and ‘max_min_15_tsv’

setdiff(max_min_15_csv, max_min_15_tsv)

# A tibble: 0 × 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_plot_data

• start with ‘emissions_2008’ THEN
• use ‘mutate’ to reorder ‘country’ according to ‘annual_co2_emissions_per_capita’

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

16. Plot ‘max_min_plot_data’

ggplot(data = max_min_plot_data,
       mapping = aes(x = annual_co2_emissions_per_capita, y = country)) +
  geom_col()+
  labs(title = "The top 15 and the bottom 15 per capita CO2 emissions ", subtitle = "for 2008",
       x = NULL,
       y = NULL)

17. Save the plot directory for 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