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 Our World in Data into the directory of 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",
                 "2021-03-02-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 emmisions 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 == 2008' THEN useskim` to calculate the descriptive statistics

tidy_emissions %>% 
  filter(year == 2008) %>% 
  skim()

Table 1: Data summary

Name	Piped data
Number of rows	220
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	220	0
code	12	0.95	3	8	0	208	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
year	0	1	2008.00	0.00	2008.00	2008.00	2008.0	2008.00	2008.00	▁▁▇▁▁
per_capita_co2_emissions	0	1	5.42	6.52	0.03	0.78	3.2	8.19	44.72	▇▂▁▁▁

7. 12 observations have a 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 x 4
   entity                     code   year per_capita_co2_emissions
   <chr>                      <chr> <dbl>                    <dbl>
 1 Africa                     <NA>   2008                     1.22
 2 Asia                       <NA>   2008                     3.57
 3 Asia (excl. China & India) <NA>   2008                     3.76
 4 EU-27                      <NA>   2008                     8.30
 5 EU-28                      <NA>   2008                     8.35
 6 Europe                     <NA>   2008                     8.72
 7 Europe (excl. EU-27)       <NA>   2008                     9.30
 8 Europe (excl. EU-28)       <NA>   2008                     9.44
 9 North America              <NA>   2008                    13.5 
10 North America (excl. USA)  <NA>   2008                     5.45
11 Oceania                    <NA>   2008                    12.9 
12 South America              <NA>   2008                     2.54

Entities that are not countries do not have country codes

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_2019

emissions_2008 <- tidy_emissions %>% 
  filter(year == 2008, !is.na(code)) %>% 
  select(-year) %>% 
  rename(country = entity)

9.Which 15 countries have the highest per_capita_co2_emissions?

start with emissions_2008 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_2008 %>% 
  slice_max(per_capita_co2_emissions, n = 15)

10. Which 15 countries have the lowest ‘per_capita_co2_emissions’?

start with emissions_2008 THEN use slice_min to extract the 15 rows with the lowest values assign the output the min_15_emmitters

min_15_emitters <- emissions_2008 %>% 
  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_plot_data

start with emissions_2008 THEN use mutate to reorder country according to per_capital_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 emissions",
    subtitle = "for 2008",
    x = NULL,
    y = NULL)

17. Save the plot directory with this post

ggsave(filename = "preview.png",
       path = here("_posts", "2021-03-02-reading-and-writing-data"))

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

preview: preview.png