Reading and writing data

A short description of the post.

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

Download \(CO_2\) emissions per capita from [Our World In Data] https://ourworldindata.org/co2/country/united-states?country=~USA#per-capita-how-much-co2-does-the-average-person-emit

file_csv <- here("_posts",
                 "2021-03-02-reading-and-writing-data",
                 "co-emissions-per-capita.csv")
emissions <- read_csv(file_csv)

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

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

tidy_emissions %>% 
  filter(year == 1994) %>% 
  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	1994.00	0.00	1994.00	1994.00	1994.00	1994.00	1994.00	▁▁▇▁▁
per_capita_co2_emissions	0	1	4.89	6.82	0.02	0.56	2.66	7.26	60.56	▇▁▁▁▁

tidy_emissions %>% 
  filter(year == 1994, is.na(code))

# A tibble: 12 x 4
   entity                     code   year per_capita_co2_emissions
   <chr>                      <chr> <dbl>                    <dbl>
 1 Africa                     <NA>   1994                     1.04
 2 Asia                       <NA>   1994                     2.27
 3 Asia (excl. China & India) <NA>   1994                     3.23
 4 EU-27                      <NA>   1994                     8.48
 5 EU-28                      <NA>   1994                     8.66
 6 Europe                     <NA>   1994                     8.87
 7 Europe (excl. EU-27)       <NA>   1994                     9.36
 8 Europe (excl. EU-28)       <NA>   1994                     9.22
 9 North America              <NA>   1994                    14.1 
10 North America (excl. USA)  <NA>   1994                     4.98
11 Oceania                    <NA>   1994                    11.5 
12 South America              <NA>   1994                     2.06

emissions_1994 <- tidy_emissions %>% 
  filter(year == 1994, !is.na(code)) %>% 
  select(-year) %>% 
  rename(country = entity)

max_15_emitters <- emissions_1994 %>% 
  slice_max(per_capita_co2_emissions, n=15)

min_15_emitters <- emissions_1994 %>% 
  slice_min(per_capita_co2_emissions, n=15)

max_min_15 <- bind_rows(max_15_emitters, min_15_emitters)

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 = "|")

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 ="|")

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>

max_min_15_plot_data <- max_min_15 %>% 
  mutate(country = reorder(country, per_capita_co2_emissions))

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 1994",
     x = NULL,
     y = NULL)

ggsave(filename="preview.png",
    path = here("_posts", "2021-03-02-reading-and-writing-data"))

preview: preview.png