Reading and writing data

A short description of the post.

Load the R packages we will use

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

Download \(CO_2\) emissions per capita form Our World in Data into the directory for this post.
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)

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

Start with emiaaions 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

Start with the tidy_emissions THEN

use filter to extract rows with year == 1988 THEN
use skim to calculate the descriptive statistics

tidy_emissions %>% 
  filter(year == 1988) %>% 
  skim()

Table 1: Data summary
Name	Piped data
Number of rows	217
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	217	0
code	12	0.94	3	8	0	205	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
year	0	1	1988.00	0.00	1988.00	1988.00	1988.00	1988.00	1988.00	▁▁▇▁▁
annual_co2_emissions_per_capita	0	1	5.06	5.87	0.01	0.56	2.76	8.11	29.56	▇▂▁▁▁

12 observations have a missing code. How are these observations diffrent?

start with tidy_emissions then extract rows with year == 1988 and are missing a code

tidy_emissions %>% 
  filter(year == 1988, is.na(code))

# A tibble: 12 x 4
   entity                     code   year annual_co2_emissions_per_ca~
   <chr>                      <chr> <dbl>                        <dbl>
 1 Africa                     <NA>   1988                         1.23
 2 Asia                       <NA>   1988                         2.00
 3 Asia (excl. China & India) <NA>   1988                         2.92
 4 EU-27                      <NA>   1988                         9.08
 5 EU-28                      <NA>   1988                         9.20
 6 Europe                     <NA>   1988                        11.0 
 7 Europe (excl. EU-27)       <NA>   1988                        13.5 
 8 Europe (excl. EU-28)       <NA>   1988                        14.3 
 9 North America              <NA>   1988                        14.3 
10 North America (excl. USA)  <NA>   1988                         5.17
11 Oceania                    <NA>   1988                        11.2 
12 South America              <NA>   1988                         2.04

Start with tidy_emissions THEN -use filter to extract rows with year == 1988 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_1988

emissions_1988 <- tidy_emissions %>% 
filter(year == 1988, !is.na(code)) %>% 
select(-year) %>% 
rename(country = entity)

Which 15 countries have the highest per_capita_co2_emissions?

-start with emissions_1988 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_1988 %>% 
  slice_max(annual_co2_emissions_per_capita, n = 15)

Which 15 countries have the lowest annual_co2_emissions_per_capita?

start with emissions_1988 then -use slice_min to extract the 15 rows with the lowest values assign the output to min_15_emitters

min_15_emitters <- emissions_1988 %>% 
  slice_min(annual_co2_emissions_per_capita, n = 15)

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)

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

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

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_psv, max_min_15_tsv)

# A tibble: 0 x 3
# ... with 3 variables: country <chr>, code <chr>,
#   annual_co2_emissions_per_capita <dbl>

Reorder country in max_min_15 for plotting and assign to max_min_15_plot_data -start with emissions_2019 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))

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 1988",
   x = NULL,
   y = NULL)

Save the plot dirextory with this post

ggsave(filename = "preview.png",
   path = here("_posts", "2022-02-22-reading-and-writing-data"))

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

preview: preview.png