week_3_challenge
YI WANG
9.20.2022
What is the correlation between GDP, and total food of waste per year?
#View Data
library(readr)## Warning: ³Ì¼°ü'readr'ÊÇÓÃR°æ±¾4.1.3 À´½¨ÔìµÄcountry_level <- read_csv("C:/Users/ywang/Desktop/country_level_data_0.csv")## Rows: 217 Columns: 51
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (10): iso3c, region_id, country_name, income_id, other_information_infor...
## dbl (41): gdp, composition_food_organic_waste_percent, composition_glass_per...
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.View(country_level)names(country_level)## [1] "iso3c"
## [2] "region_id"
## [3] "country_name"
## [4] "income_id"
## [5] "gdp"
## [6] "composition_food_organic_waste_percent"
## [7] "composition_glass_percent"
## [8] "composition_metal_percent"
## [9] "composition_other_percent"
## [10] "composition_paper_cardboard_percent"
## [11] "composition_plastic_percent"
## [12] "composition_rubber_leather_percent"
## [13] "composition_wood_percent"
## [14] "composition_yard_garden_green_waste_percent"
## [15] "other_information_information_system_for_solid_waste_management"
## [16] "other_information_national_agency_to_enforce_solid_waste_laws_and_regulations"
## [17] "other_information_national_law_governing_solid_waste_management_in_the_country"
## [18] "other_information_ppp_rules_and_regulations"
## [19] "other_information_summary_of_key_solid_waste_information_made_available_to_the_public"
## [20] "population_population_number_of_people"
## [21] "special_waste_agricultural_waste_tons_year"
## [22] "special_waste_construction_and_demolition_waste_tons_year"
## [23] "special_waste_e_waste_tons_year"
## [24] "special_waste_hazardous_waste_tons_year"
## [25] "special_waste_industrial_waste_tons_year"
## [26] "special_waste_medical_waste_tons_year"
## [27] "total_msw_total_msw_generated_tons_year"
## [28] "waste_collection_coverage_rural_percent_of_geographic_area"
## [29] "waste_collection_coverage_rural_percent_of_households"
## [30] "waste_collection_coverage_rural_percent_of_population"
## [31] "waste_collection_coverage_rural_percent_of_waste"
## [32] "waste_collection_coverage_total_percent_of_geographic_area"
## [33] "waste_collection_coverage_total_percent_of_households"
## [34] "waste_collection_coverage_total_percent_of_population"
## [35] "waste_collection_coverage_total_percent_of_waste"
## [36] "waste_collection_coverage_urban_percent_of_geographic_area"
## [37] "waste_collection_coverage_urban_percent_of_households"
## [38] "waste_collection_coverage_urban_percent_of_population"
## [39] "waste_collection_coverage_urban_percent_of_waste"
## [40] "waste_treatment_anaerobic_digestion_percent"
## [41] "waste_treatment_compost_percent"
## [42] "waste_treatment_controlled_landfill_percent"
## [43] "waste_treatment_incineration_percent"
## [44] "waste_treatment_landfill_unspecified_percent"
## [45] "waste_treatment_open_dump_percent"
## [46] "waste_treatment_other_percent"
## [47] "waste_treatment_recycling_percent"
## [48] "waste_treatment_sanitary_landfill_landfill_gas_system_percent"
## [49] "waste_treatment_unaccounted_for_percent"
## [50] "waste_treatment_waterways_marine_percent"
## [51] "where_where_is_this_data_measured"dim(country_level)## [1] 217 51library(dplyr)## Warning: ³Ì¼°ü'dplyr'ÊÇÓÃR°æ±¾4.1.3 À´½¨ÔìµÄ##
## 载入程辑包：'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unioncountry_level_waste <-summarize(country_level,country_level$country_name,country_level$composition_food_organic_waste_percent,country_level$gdp)
summarize(country_level,country_level$country_name,country_level$composition_food_organic_waste_percent,country_level$gdp) ## # A tibble: 217 x 3
## `country_level$country_name` country_level$composition_food_organic~1 count~2
## <chr> <dbl> <dbl>
## 1 Aruba NA 35563.
## 2 Afghanistan NA 2057.
## 3 Angola 51.8 8037.
## 4 Albania 51.4 13724.
## 5 Andorra 31.2 43712.
## 6 United Arab Emirates 39 67119.
## 7 Argentina 38.7 23550.
## 8 Armenia 57 11020.
## 9 American Samoa 19.7 11113.
## 10 Antigua and Barbuda 46 17966.
## # ... with 207 more rows, and abbreviated variable names
## # 1: `country_level$composition_food_organic_waste_percent`,
## # 2: `country_level$gdp`colnames(country_level_waste) <- c("Country","Food of Waste Per Year","GDP")#Count: Total Food of Waste vs. GDP
country_level_waste %>% count(country_level_waste$`Food of Waste Per Year`,country_level_waste$GDP)## # A tibble: 217 x 3
## `country_level_waste$\`Food of Waste Per Year\`` country_level_waste$~1 n
## <dbl> <dbl> <int>
## 1 3.1 117336. 1
## 2 4.8 4784. 1
## 3 5.35 58806. 1
## 4 6.5 24216. 1
## 5 8 3629. 1
## 6 8.1 14126. 1
## 7 10 55274. 1
## 8 10.5 97341. 1
## 9 10.9 66207. 1
## 10 12 3068. 1
## # ... with 207 more rows, and abbreviated variable name
## # 1: `country_level_waste$GDP`#Correlation between Total food of Waste and GDP
country_level_waste %>% group_by(country_level_waste$country_level$`Food of Waste Per Year`)## Warning: Unknown or uninitialised column: `country_level`.## # A tibble: 217 x 3
## Country `Food of Waste Per Year` GDP
## <chr> <dbl> <dbl>
## 1 Aruba NA 35563.
## 2 Afghanistan NA 2057.
## 3 Angola 51.8 8037.
## 4 Albania 51.4 13724.
## 5 Andorra 31.2 43712.
## 6 United Arab Emirates 39 67119.
## 7 Argentina 38.7 23550.
## 8 Armenia 57 11020.
## 9 American Samoa 19.7 11113.
## 10 Antigua and Barbuda 46 17966.
## # ... with 207 more rowsplot(country_level_waste$`Food of Waste Per Year`,country_level_waste$"GDP",main="Food of Waste vs GDP",xlab='Waste in Food',ylab="GDP")
cor(country_level_waste$`Food of Waste Per Year`,country_level_waste$GDP,method=c("pearson"),use = "complete.obs")## [1] -0.4366114##Summary: There is a correlation between Food of Waste vs GDP (-0.43)