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 \(C0_2\) emission 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-datea", "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 for 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 × 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 == 1993 THEN use skim to calculate the descriptive statistics

tidy_emissions %>% 
  filter(year == 1993) %>% 
  skim()

Table 1: Data summary

Name	Piped data
Number of rows	227
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	227	0
code	12	0.95	3	8	0	215	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
year	0	1	1993.00	0.00	1993.00	1993.00	1993.00	1993.00	1993.00	▁▁▇▁▁
annual_co2_emissions_per_capita	0	1	5.07	6.96	0.04	0.59	2.76	7.38	61.19	▇▁▁▁▁

7. 12 observations have a missing code. How are these observations different? Start with tidy_emissions then extract rows with year == 1993 and are missing a code

tidy_emissions %>% 
  filter(year == 1993, is.na(code))

# A tibble: 12 × 4
   entity                     code   year annual_co2_emissions_per_ca…
   <chr>                      <chr> <dbl>                        <dbl>
 1 Africa                     <NA>   1993                         1.04
 2 Asia                       <NA>   1993                         2.24
 3 Asia (excl. China & India) <NA>   1993                         3.22
 4 EU-27                      <NA>   1993                         8.52
 5 EU-28                      <NA>   1993                         8.70
 6 Europe                     <NA>   1993                         9.35
 7 Europe (excl. EU-27)       <NA>   1993                        10.5 
 8 Europe (excl. EU-28)       <NA>   1993                        10.6 
 9 North America              <NA>   1993                        14.0 
10 North America (excl. USA)  <NA>   1993                         4.97
11 Oceania                    <NA>   1993                        11.5 
12 South America              <NA>   1993                         2.09

8. Start with tidy_emissions THEN

use filter to extract rows with year == 1993 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_1993

emissions_1993 <- tidy_emissions %>% 
  filter(year == 1993, !is.na(code)) %>% 
  select(-year) %>% 
  rename(country=entity)

9. Which 15 countries have the highest annual_co2_emissions_per_capita ?

start with emissions_1993 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_1993  %>% 
  slice_max(annual_co2_emissions_per_capita, n=15)

10. Which 15 countries have the lowest annual_co2_emissions_per_capita? start with emissions_1993 THEN use slice_min to extract the 15 rows with the lowest values assign the output to min_15_emitters

min_15_emitters <- emissions_1993  %>% 
  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