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 \(CO_2\) 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)