Reading and writing data

A short description of the post.

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_emission show the first 10 rows of tidy_emission

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 ==1999 THEN use skim to calculate the descriptive statistics

tidy_emissions %>% 
  filter(year ==1999) %>% 
  skim()

Table 1: Data summary

Name	Piped data
Number of rows	228
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	228	0
code	12	0.95	3	8	0	216	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
year	0	1	1999.00	0.00	1999.00	1999.00	1999.00	1999.0	1999.00	▁▁▇▁▁
annual_co2_emissions_per_capita	0	1	4.75	6.01	0.03	0.71	2.83	7.5	53.91	▇▁▁▁▁

7. 13 observations have a missing code. how are these observations different? start with tidy_emissions then extract rows with year == 1999 and are missing a code

tidy_emissions %>% 
  filter(year == 1999, is.na(code))

# A tibble: 12 x 4
   entity                     code   year annual_co2_emissions_per_ca~
   <chr>                      <chr> <dbl>                        <dbl>
 1 Africa                     <NA>   1999                         1.05
 2 Asia                       <NA>   1999                         2.35
 3 Asia (excl. China & India) <NA>   1999                         3.19
 4 EU-27                      <NA>   1999                         8.42
 5 EU-28                      <NA>   1999                         8.56
 6 Europe                     <NA>   1999                         8.46
 7 Europe (excl. EU-27)       <NA>   1999                         8.48
 8 Europe (excl. EU-28)       <NA>   1999                         8.22
 9 North America              <NA>   1999                        14.4 
10 North America (excl. USA)  <NA>   1999                         5.35
11 Oceania                    <NA>   1999                        12.6 
12 South America              <NA>   1999                         2.45

Entities that are not countries do not have country codes.

8. Start with tidy_emissions THEN

   use filter to extract rows with year == 1999 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_1999

emissions_1999 <- tidy_emissions %>% 
  filter(year ==1999, !is.na(code)) %>% 
  select(-year) %>% 
  rename(country = entity)

9. Which 15 countries have the highest annual_co2_emissions_per_capita assign the output to max_15_emitters

max_15_emitters <- emissions_1999 %>% 
  slice_max(annual_co2_emissions_per_capita, n = 15)

10. Which 15 countries have the lowest annual_co2_emissions_per_capita?

start with emissions_1999 THEN use slice_min to extract the 15 rows with the lowest values assign the output to min_15_emitters

min_15_emitters <- emissions_1999 %>% 
  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 = "|") # 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>,
#   annual_co2_emissions_per_capita <dbl>

Are there any differences?
no

15. Reorder country in max_min_15 for plotting and assigning to max_min_15_plot_data

start with emission_1999 THEN use mutate to reader 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() 

geom_col: width = NULL, na.rm = FALSE
stat_identity: na.rm = FALSE
position_stack 

labs(title = "The top 15 and bottom 15 per capita CO2 emissions", subtitle = "for 1999", x = NULL, y = NULL)

$x
NULL

$y
NULL

$title
[1] "The top 15 and bottom 15 per capita CO2 emissions"

$subtitle
[1] "for 1999"

attr(,"class")
[1] "labels"

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