Reading and Writing Data

Reading and writing data

1. Load the R packages we will use.

library(tidyverse)
library(here)
library(janitor)
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",
                "2022-02-21-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 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 == 2011’ THEN
• use ‘skim’ to calculate the descriptive statistics

tidy_emissions  %>% 
  filter(year == 2011)  %>%
  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	2011.00	0.00	2011.00	2011.00	2011.00	2011.00	2011.00	▁▁▇▁▁
annual_co2_emissions_per_capita	0	1	5.28	6.26	0.04	0.85	3.27	7.53	39.12	▇▂▁▁▁

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

• start with ‘tidy_emissions’ then extract rows with ‘year == 2011’ and are missing a code

tidy_emissions  %>%  
  filter(year == 2011, is.na(code))

# A tibble: 12 x 4
   entity                     code   year annual_co2_emissions_per_ca~
   <chr>                      <chr> <dbl>                        <dbl>
 1 Africa                     <NA>   2011                         1.18
 2 Asia                       <NA>   2011                         4.17
 3 Asia (excl. China & India) <NA>   2011                         3.96
 4 EU-27                      <NA>   2011                         7.56
 5 EU-28                      <NA>   2011                         7.53
 6 Europe                     <NA>   2011                         8.16
 7 Europe (excl. EU-27)       <NA>   2011                         9.00
 8 Europe (excl. EU-28)       <NA>   2011                         9.45
 9 North America              <NA>   2011                        12.4 
10 North America (excl. USA)  <NA>   2011                         5.30
11 Oceania                    <NA>   2011                        12.2 
12 South America              <NA>   2011                         2.78

Entities that are not countries do not have country codes.

8. Start with tidy_emissions THEN

• use ‘filter’ to extract rows with year == 2011 and without missing codes THEN
• use ‘select’ to drag the ‘year’ variable THEN
• use ‘rename’ to change the variable ‘entity’ to ‘country’
• assign the output to ‘emissions_2011’

emissions_2011  <- tidy_emissions  %>%
  filter(year == 2011, !is.na(code))   %>%
  select(-year)  %>% 
  rename(country = entity)

9. Which 15 countries have the highest ‘annual_co2_emissions_per_capita’?

• start with ‘emissions_2011’
• 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_2011  %>%
  slice_max(annual_co2_emissions_per_capita, n = 15)

10. Which 15 countries have the lowest ‘annual_co2_emissions_per_capita’?

• start with ‘emissions_2011’ THEN
• use ‘slice_min’ to extract the 15 rows with the lowest values
• assign the output to ‘min_15_emitters’

min_15_emitters  <- emissions_2011  %>%
  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_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 = "l") # 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 = "l") # 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)

# 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_2011’ 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 2011", 
       x = NULL, 
       y = NULL)

17. Save the plot directory with this post

ggsave(filename = "preview.png",
       path = here("_posts", "2022-02-21-reading-and-writing-data"))

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

preview: preview.png