Reading and Writing Data

A short description of the post.

  1. Load the packages we’ll use.
library(tidyverse)
#helps navigate folders
library(here)
#clean name of variables
library(janitor)
#calc descriptive statistics
library(skimr)

  1. Download \(CO_2\) emmisions per capita from Our world in Data into the directory for this post.

  2. Assign the location of file to file_csv. The data should be in the same directory as the file.

    Read the data into R and assign the emissions.

file_csv <- here("_posts", 
                 "2021-03-01-reading-and-writing-data",
                 "co-emissions-per-capita-new.csv")

emissions <- read_csv(file_csv)
  1. 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
  1. Start with emissions data then

-use clean_names from the janitor package to make the name easier to work with -assign the output to tidy_emissions -show the first 10 rows of tidy_emmissions

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
  1. Start with tidy_emissions then
-use filter to extract rows with year == 2019 then -use skim to calculate the descrptive statistics 
tidy_emissions %>% 
  #quiz year will change, so change this
  filter(year == 2019) %>% 
  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 2019 0.00 2019.00 2019.00 2019.00 2019.00 2019.00 ▁▁▇▁▁
per_capita_co2_emissions 0 1 5 5.71 0.03 1.12 3.53 6.56 38.61 ▇▂▁▁▁
  1. 13 observations have a missing code. How are these observations different? -start with tidy_emissions then extract rows with year == 2019 and are missing a code 
    tidy_emissions %>% 
  filter(year == 2019, is.na(code))

# A tibble: 13 x 4
   entity                     code   year per_capita_co2_emissions
   <chr>                      <chr> <dbl>                    <dbl>
 1 Africa                     <NA>   2019                     1.12
 2 Asia                       <NA>   2019                     4.40
 3 Asia (excl. China & India) <NA>   2019                     4.14
 4 EU-27                      <NA>   2019                     6.56
 5 EU-28                      <NA>   2019                     6.41
 6 Europe                     <NA>   2019                     7.28
 7 Europe (excl. EU-27)       <NA>   2019                     8.33
 8 Europe (excl. EU-28)       <NA>   2019                     9.14
 9 International transport    <NA>   2019                     4.58
10 North America              <NA>   2019                    11.0 
11 North America (excl. USA)  <NA>   2019                     4.63
12 Oceania                    <NA>   2019                    11.2 
13 South America              <NA>   2019                     2.54

Entities that are not countries do not have country codes. 8. Start with tidy_emissions then

-use filter to extract rows with year == 2019 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_2019

emissions_2019 <- tidy_emissions %>% 
  filter(year == 2019, !is.na(code)) %>% 
  select(-year) %>% 
  rename(country = entity)
  1. Which 15 countries have the highest per_capita_co2_emissions? -start with emissions_2019 -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_2019 %>% 
  slice_max(per_capita_co2_emissions, n=15)
  1. Which 15 countries have the lowest per_capita_co2_emissions?

-start with emissions_2019 -use slice_min to extract the 15 rows with the lowest value -assign the output to 15_min_emitters

min_15_emitters <- emissions_2019 %>% 
  slice_min(per_capita_co2_emissions, n=15)
  1. Use bind_rows to bind together the max_15_emitters and min_15_emitters -assign the out to max_min_15
max_min_15 <- bind_rows(max_15_emitters, min_15_emitters)
  1. Export max_min_15 to 3 file formats. 
    max_min_15 %>% write_csv("max_min_15.csv") #comma seperated values
max_min_15 %>% write_tsv("max_min_15.tsv") #tab seperated
max_min_15 %>% write_delim("max_min_15.psv", delim = "|") #pipe-seperated
  1. 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 = "|")
  1. 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?

  1. Reorder country in max_min_15 for plotting and assign to max_min_15_plot_data

-start with emissions_2019 -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))
  1. 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 2019", 
   x = NULL,
   y = NULL)
  1. Save the plot directory with this post
ggsave(filename = "preview.png",
       path = here("_posts", "2021-03-01-reading-and-writing-data"))
  1. Add preview.png to yaml chuck at the top of this file
preview:preview.png