Reading and writing data

Code for project 4

Load the R packages

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

Download \(CO_2\) emissions per capita from Our world in Data into directory.

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 emmisions

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 emissions data THEN

-use clean_names from the janitor package -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 extracr rows with year == 2018 THEN -use skim to calculate the descriptive statistics

tidy_emissions %>%
  filter(year == 2018) %>%
  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	2018.00	0.00	2018.00	2018.00	2018.0	2018.00	2018.00	▁▁▇▁▁
annual_co2_emissions_per_capita	0	1	5.03	5.63	0.03	0.99	3.5	6.85	38.44	▇▂▁▁▁

12observations have a missing code, how are these different? -start with tidy_emissions then extract rows with year == 2018 and are missing a code

tidy_emissions %>%
  filter(year == 2018, is.na(code))

# A tibble: 12 x 4
   entity                     code   year annual_co2_emissions_per_ca~
   <chr>                      <chr> <dbl>                        <dbl>
 1 Africa                     <NA>   2018                         1.09
 2 Asia                       <NA>   2018                         4.44
 3 Asia (excl. China & India) <NA>   2018                         4.14
 4 EU-27                      <NA>   2018                         6.85
 5 EU-28                      <NA>   2018                         6.70
 6 Europe                     <NA>   2018                         7.48
 7 Europe (excl. EU-27)       <NA>   2018                         8.39
 8 Europe (excl. EU-28)       <NA>   2018                         9.15
 9 North America              <NA>   2018                        11.4 
10 North America (excl. USA)  <NA>   2018                         4.80
11 Oceania                    <NA>   2018                        11.4 
12 South America              <NA>   2018                         2.58

Start with tidy_emissions THEN -use filter to extract rows with year == 2019 and without missing codes THEN -useselect to drop the year variable -use rename to change the variable entity to country -assign the output to emissions_2019

emissions_2018 <- tidy_emissions %>%
  filter(year == 2018, !is.na(code)) %>%
  select(-year) %>%
  rename(country = entity)

Which 15 countries have the highestper_capita_co2_emissions? -Start with emissions_2019 THEN -use slice_max to extract the 15 rows with the per_capita_co2_emissions -assign output to max_15_emitters

max_15_emitters <- emissions_2018 %>%
  slice_max(annual_co2_emissions_per_capita, n = 15)

Which 15 countries have the lowest per_capita_co2_emissions? -start with emissions_2019 THEN -use slice_min to extract the 15 rows with lowest values -assign the output to min_15_emitters

min_15_emitters <- emissions_2018 %>%
  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")
max_min_15 %>% write_tsv("max_min_15.tsv")
max_min_15 %>% write_delim("max_min_15.psv", delim = "|")

Read the 3 file formats into R

max_min_15_csv <- read_csv("max_min_15.csv")
max_min_15_tsv <- read_tsv("max_min_15.tsv")
max_min_15_psv <- read_delim("max_min_15.psv", delim = "|")

Use setdiff to check for differences in max_min_, max_min_15_csv, 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>

Any differences?

15. Reorder country in max_min_15 for plotting and to assign to max_min_15_plot_data -start with emissions_2018 THEN -use mutate to reorder country according to per_capital_co2_emissions

    max_min_15_plot_data <- max_min_15 %>%
      mutate(country = reorder(country, annual_co2_emissions_per_capita))