Reading and writting data

A short description of the post.

1.Load packages we will use

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

  1. download co2 emissions per capita from Our world in Data into the directory for the 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 emissions

file_csv <- here("_posts","2021-02-16-reading-and-writting-data","co-emissions-per-capita.csv")

emissions <- read_csv(file_csv)
  1. show the first 10 rows (observation 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 easier to work with - assign the output totidy_emissions - show the first 10 rows of tidy_emission

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 years == 2000 -use skim to calculate the descriptive statistics

tidy_emissions %>% 
  filter(year==2000) %>% 
  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 2000.00 0.00 2e+03 2000.00 2000.00 2000.00 2000.00 ▁▁▇▁▁
per_capita_co2_emissions 0 1 5.06 6.74 2e-02 0.71 2.82 7.97 58.39 ▇▁▁▁▁
  1. 13 observations have a missing code. How are these observations different? -start with tidy_emissions then extract rows with year==2000 and are missing a code 
    tidy_emissions %>% 
  filter(year==2000,is.na(code))

# A tibble: 12 x 4
   entity                     code   year per_capita_co2_emissions
   <chr>                      <chr> <dbl>                    <dbl>
 1 Africa                     <NA>   2000                     1.11
 2 Asia                       <NA>   2000                     2.40
 3 Asia (excl. China & India) <NA>   2000                     3.35
 4 EU-27                      <NA>   2000                     8.46
 5 EU-28                      <NA>   2000                     8.61
 6 Europe                     <NA>   2000                     8.48
 7 Europe (excl. EU-27)       <NA>   2000                     8.47
 8 Europe (excl. EU-28)       <NA>   2000                     8.19
 9 North America              <NA>   2000                    14.6 
10 North America (excl. USA)  <NA>   2000                     5.39
11 Oceania                    <NA>   2000                    12.6 
12 South America              <NA>   2000                     2.32
  1. Start with the tidy_emissions THEN -use filter to extract rows with year==2000 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_2000 
    emissions_2000 <- tidy_emissions %>% 
  filter(year==2000, !is.na(code)) %>% 
  select(-year) %>% 
  rename(country=entity)
  1. Which 15 countries have the highest per_capita_co2_emissions? -start with emissions_2000 THEN -use slice_max to extract then 15 rows with the per_caita_co2_emissions assign the output to max_15_emitters 
    max_15_emitters <- emissions_2000 %>% 
  slice_max(per_capita_co2_emissions,n=15)
  1. which 15 countries have the lowest per_capita_co2_emissions? -start with emissions_2000 THEN -use slice_min to extract the 15 rows with the lowest values -assign the output min_15_emitters 
    min_15_emitters <- emissions_2000 %>% 
  slice_min(per_capita_co2_emissions,n=15)

11.Use bind_rowsto 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)
  1. 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 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.
14 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>

Are there any differences?

15 reader country in max_min_151 for plotting and assign to max_min_15_plot_data -start with emissions_2000 THEN -use mutate to reorder country according 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(per_capita_co2_emissions, country)) +
  geom_col() +
  labs(title = "the top 15 and bottom 15 per capita CO2 emissions", 
   x=NULL,
   Y=NULL)
  1. Save the plot directory with this post 
    ggsave(filename = "preview.png",
   path = here("_posts","2021-02-16-reading-and-writting-data"))

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

preview: preview.png