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 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","2021-03-01-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: 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

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

Start with the ‘tidy_emissions’ THEN

use ‘filter’ to extract 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	221
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	221	0
code	13	0.94	3	8	0	208	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	2018.00	2018.00	2018.00	▁▁▇▁▁
per_capita_co2_emissions	0	1	5.05	5.79	0.03	1.09	3.51	6.65	39.27	▇▂▁▁▁

13 observations have a missing code. How are these observations 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: 13 x 4
   entity                     code   year per_capita_co2_emissions
   <chr>                      <chr> <dbl>                    <dbl>
 1 Africa                     <NA>   2018                     1.13
 2 Asia                       <NA>   2018                     4.37
 3 Asia (excl. China & India) <NA>   2018                     4.14
 4 EU-27                      <NA>   2018                     6.87
 5 EU-28                      <NA>   2018                     6.71
 6 Europe                     <NA>   2018                     7.50
 7 Europe (excl. EU-27)       <NA>   2018                     8.39
 8 Europe (excl. EU-28)       <NA>   2018                     9.16
 9 International transport    <NA>   2018                     4.62
10 North America              <NA>   2018                    11.4 
11 North America (excl. USA)  <NA>   2018                     4.75
12 Oceania                    <NA>   2018                    11.4 
13 South America              <NA>   2018                     2.59

Entities that are not countries do not have country codes. 8. Start with tidy_emissions THEN - Use ‘filter’ to extract rows with year == 2018 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_2018’

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

Which 15 countries have the highest ‘per_capita_co2_emissions’?

start with ‘emissions_2018’ 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_2018 %>% 
slice_max(per_capita_co2_emissions, n=15)

Which 15 countries have the lowest ‘per_capita_co2_emissions’?

start with ‘emissions_2018’ THEN
use ‘slice_min’ to extract the 15 rows with the lowest values

assign the output to ‘min_15_emitters’

min_15_emitters <- emissions_2018 %>% 
slice_min(per_capita_co2_emissions, 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 any differences among ‘max_min_15_csv’
```
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?

Reorder ‘country’ in ‘max_min_15’ for plotting and 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, per_capita_co2_emissions))

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 2018",
 x= NULL,
 y= NULL)

Save the plot directory with this post

ggsave(filename = "preview.png",
   path = here("_posts", "2021-03-01-reading-and-writing-data"))

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

preview: preview.png

Reading and writing data

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