Reading and writting Data

A short description of the post.
  1. Load the r 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 this post.

3.) assign the location of the file to ‘file_csv’. The data should be in the same directory as the file

read the data into r and assign it to emissions

file_csv <- here("_posts",
                  "2021-02-26-reading-and-writting-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
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 year == 1994 THEN use skim to calculate the descriptive statistics

tidy_emissions %>% 
  filter(year == 1994) %>% #grab rows where year is 2019
  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 ▇▁▁▁▁

7.)13 observations have a missing code. How are these observations different? Start with tidy_emissions then extract rows with year == 1994 and are missing a code.

tidy_emissions %>%
  filter (year ==1994, is.na(code)) #missing a value with no 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

Step 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== 1994, !is.na(code))%>%
  select(-year) %>%
  rename(country =entity)

9.) Which countries have the highest per_capita_c02_emissions ?

max_15_emitters <- emissions_2019 %>%
  slice_max(per_capita_co2_emissions, n=15)

10.) Which countries have the lowest per_capita_co2_emissions ?

min_15_emitters <- emissions_2019 %>%
  slice_min(per_capita_co2_emissions, n=15)

11.) use bind_rows to bind together the max_15_emitters -assign the output to max_15_emitters

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

13.) REad the 3 files into r format

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 = "|")

14.) use setdif 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>

15.) reorder country in max_min_15 for plotting and assign to max_min_15_plot_data start with emissions_2019 THEN use mutate to reorder country according to `per_capita

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

16.) plot max_min_15_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 per capita CO2 emissions",
     subtitle = "for 1994",
     x= NULL,
     Y=NULL)

Footnotes