My Blog: Reading and writing data

Load the R packages we will use.

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

Download \(CO_2\) emissions per capita from 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",
                                                                    "2022-02-20-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 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

6.Start with the tidy_emissions THEN -use filter to extract rows with year==2000 THEN -use skim to calculate the descriptive statistics

tidy_emissions %>% 
  filter(year==2000) %>% 
  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	2000.00	0.00	2e+03	2000.00	2000.00	2000.00	2000.00	▁▁▇▁▁
annual_co2_emissions_per_capita	0	1	5.15	6.93	2e-02	0.74	2.97	7.85	57.41	▇▁▁▁▁

12 observations have a missing code. How are these observations different? -start with tidy_emissions then extract rows with year==2000 and are missing a code

tidy_emissions %>% 
  filter(year==2000, is.na(code))

# A tibble: 12 x 4
   entity                     code   year annual_co2_emissions_per_ca~
   <chr>                      <chr> <dbl>                        <dbl>
 1 Africa                     <NA>   2000                         1.09
 2 Asia                       <NA>   2000                         2.43
 3 Asia (excl. China & India) <NA>   2000                         3.36
 4 EU-27                      <NA>   2000                         8.44
 5 EU-28                      <NA>   2000                         8.58
 6 Europe                     <NA>   2000                         8.47
 7 Europe (excl. EU-27)       <NA>   2000                         8.47
 8 Europe (excl. EU-28)       <NA>   2000                         8.19
 9 North America              <NA>   2000                        14.7 
10 North America (excl. USA)  <NA>   2000                         5.48
11 Oceania                    <NA>   2000                        12.6 
12 South America              <NA>   2000                         2.36

Entities that are not countries do not have country codes.

Start with tidy_emissions THEN

-use filter to extract rows with year == 2000 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_2000

emissions_2000 <- tidy_emissions %>% 
  filter(year==2000, !is.na(code)) %>%
  select(-year) %>% 
  rename(country = entity)

9.Which 15 countries have the highest annual_co2_emissions_per_capita?

-start with emissions_2000 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_2000 %>% 
  slice_max(annual_co2_emissions_per_capita, n=15)

10.Which 15 countries have the lowest annual_co2_emissions_per_capita?

-start with emissions_2000 THEN -use slice_min to extract the 15 rows with the lowest values -assign the output to min_15_emitters

min_15_emitters <- emissions_2000 %>% 
  slice_min(annual_co2_emissions_per_capita, n=15)

11.Use bind_rows to bind together the max_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 33 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_tv 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>

Are there any differences?

15.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))

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()+
  labs(title = "the top 15 and bottom 15 per capita CO2 emissions",
       subtitle = "for 2000",
       x = NULL,
       y = NULL)

17.Save the plot directory with this post

ggsave(filename = "preview.png",
       path = here("_posts", "2022-02-20-reading-and-writing-data"))

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

preview