3:Reading and Writing Data

Practice reading and writing data, more dplyr and a plot.

1. Load the R packages that we will use.

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

2. Download \(C0_2\) emissions 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_cvs <- here("_posts", "2021-02-22-reading-and-writing-data", "co-emissions-per-capita.csv")

emissions <- read_csv(file_cvs)

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 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: 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 == 1997 THEN

-Use skim to calculate the descriptive statistics.

tidy_emissions %>% 
  filter(year == 1997) %>% 
  skim()

Table 1: Data summary

Name	Piped data
Number of rows	219
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	219	0
code	12	0.95	3	8	0	207	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
year	0	1	1997.00	0.00	1997.00	1997.00	1997.0	1997.00	1997.00	▁▁▇▁▁
per_capita_co2_emissions	0	1	4.91	6.95	0.04	0.65	2.9	7.06	69.89	▇▁▁▁▁

7. 12 observations have a missing code. How are these observations different?

-Start with tidy_emissions then extract rows with year == 1997 and are missing a code.

tidy_emissions %>% 
  filter(year == 1997, is.na(code))

# A tibble: 12 x 4
   entity                     code   year per_capita_co2_emissions
   <chr>                      <chr> <dbl>                    <dbl>
 1 Africa                     <NA>   1997                     1.09
 2 Asia                       <NA>   1997                     2.39
 3 Asia (excl. China & India) <NA>   1997                     3.23
 4 EU-27                      <NA>   1997                     8.62
 5 EU-28                      <NA>   1997                     8.74
 6 Europe                     <NA>   1997                     8.62
 7 Europe (excl. EU-27)       <NA>   1997                     8.59
 8 Europe (excl. EU-28)       <NA>   1997                     8.34
 9 North America              <NA>   1997                    14.4 
10 North America (excl. USA)  <NA>   1997                     5.16
11 Oceania                    <NA>   1997                    12.0 
12 South America              <NA>   1997                     2.28

Entities that are not countries do not have country codes.

8. Start with tidy_emissions THEN

• Use filter to extract rows with year == 1997 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_1997

emissions_1997 <- tidy_emissions %>% 
  filter(year == 1997, !is.na(code)) %>% 
  select(-year) %>% 
  rename(country = entity)

9. Which 15 countries have the highest per_capita_co2_emissions?

-Start with emissions_1997 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_1997 %>% 
  slice_max(per_capita_co2_emissions, n = 15)

10. Which 15 countries have the lowest per_capita_co2_emissions?

-Start with emissions_1997 THEN

-Use slice_min to extract the 15 rows with the lowest values

-Assign the output to min_15_emitters

min_15_emitters <- emissions_1997 %>% 
  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 files 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, 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? Answer: No

15. Reorder country in max_min_15 for plotting and assign to `max_min_15_plot_data

-Start with emissions_1997 THEN

-Use mutate to reorder country according to per_capitol_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 1997", x = NULL, y = NULL)

17. Save the plot directory with this post

ggsave(filename = "preview.png", path = here("_posts", "2021-02-22-reading-and-writing-data"))

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

preview: preview.png