Reading and writing Data

A short description of the post.

  1. Load the packages that we will use
library(tidyverse)
library(here)
library(janitor)
library(skimr)

  1. Download \(CO_2\) emissions per capita from Our world in data in the directory for this post.

  2. 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",
                 "2021-02-22-reading-and-writing-data",
                 "co-emissions-per-capita.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 the 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
  1. Start with the tidy_emissions THEN use filter to extract the rows with year==1988 THEN use skim to calculate the descriptive statistics
tidy_emissions %>% 
  filter(year==1988) %>% 
  skim()
Table 1: Data summary
Name Piped data
Number of rows 209
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 0 209 0
code 12 0.94 3 8 0 197 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
year 0 1 1988.00 0.00 1988.00 1988.00 1988.00 1988.00 1988.00 ▁▁▇▁▁
per_capita_co2_emissions 0 1 5.07 5.86 0.01 0.54 2.82 8.11 29.56 ▇▃▁▁▁
  1. 13 observations have a missing code.How are these observations different? Start with tidy_emissions then extract rows with year == 1988 and are missing a code.
tidy_emissions %>% 
  filter(year==1988,is.na(code)) 

# A tibble: 12 x 4
   entity                     code   year per_capita_co2_emissions
   <chr>                      <chr> <dbl>                    <dbl>
 1 Africa                     <NA>   1988                     1.23
 2 Asia                       <NA>   1988                     1.98
 3 Asia (excl. China & India) <NA>   1988                     2.94
 4 EU-27                      <NA>   1988                     9.07
 5 EU-28                      <NA>   1988                     9.18
 6 Europe                     <NA>   1988                    10.9 
 7 Europe (excl. EU-27)       <NA>   1988                    13.4 
 8 Europe (excl. EU-28)       <NA>   1988                    14.2 
 9 North America              <NA>   1988                    13.8 
10 North America (excl. USA)  <NA>   1988                     5.06
11 Oceania                    <NA>   1988                    11.2 
12 South America              <NA>   1988                     2.04
  1. Start with the tidy_emissions THEN

use filterto extract the rows with the year == 1988 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_1988

emissions_1988 <- tidy_emissions %>% 
  filter(year==1988,!is.na(code)) %>% 
  select(-year) %>% 
  rename(country=entity)
  1. Which of the countries have the highest per_capita_co2_emissions ?

start with emissions_1988 THEN use slice_max to extract the 15 rows from the per_capita_co2_emissions assign the output to max_15_emitters

max_15_emitters <- emissions_1988 %>% 
  slice_max(per_capita_co2_emissions,n=15)
  1. Which 15 companies have the lowest per_capita_co2_emissions?

start with emissions_1988 THEN use slice_min to extract the 15 rows with the lowest values assign the output to min_15_emitters

min_15_emitters <- emissions_1988 %>% 
  slice_min(per_capita_co2_emissions, n=15)
  1. Use bind_rows to bind together the max_15_emitters and min_15_emitters assign output to max_min_15
max_min_15 <- bind_rows(max_15_emitters,min_15_emitters)
  1. Export max_min_15into 3 diffrent 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
  1. 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
  1. Use setdiff to check for any differences among max_min_15_csv, max_min_15_tsv and 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>
  1. Reorder country in max_min_15 for plotting and assign to max_min_15_plot_data

start with emissions_1988 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))
  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 1988", 
       x = NULL, 
       y = NULL)

  1. Save the plot directory with this post 
    ggsave(filename = "preview.png", 
   path = here("_posts", "2021-02-22-reading-and-writing-data"))
  1. Add preview.png to yaml chuck at the top of this file

preview:preview.png