library(rio)#Importando todas las datas
library(readr)
Broadband_fixed_subscriptions <- read_csv("Broadband - fixed subscriptions.csv")## Rows: 214 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): name, slug, region
## dbl (2): date_of_information, ranking
## num (1): value
##
## 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(Broadband_fixed_subscriptions)Carbon_dioxide_emissions <- read_csv("Carbon dioxide emissions.csv")## Rows: 218 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): name, slug, region
## dbl (2): date_of_information, ranking
## num (1): metric tonnes of CO2
##
## 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(Carbon_dioxide_emissions)
Debt_external <- read_csv("Debt - external.csv")## Rows: 207 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): name, slug, value, date_of_information, region
## dbl (1): ranking
##
## 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(Debt_external)
Electricity_installed_generating_capacity <- read_csv("Electricity - installed generating capacity.csv")## Rows: 213 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): name, slug, region
## dbl (2): date_of_information, ranking
## num (1): kW
##
## 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(Electricity_installed_generating_capacity)
Energy_consumption_per_capita <- read_csv("Energy consumption per capita.csv")## Rows: 212 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): name, slug, region
## dbl (2): date_of_information, ranking
## num (1): Btu/person
##
## 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(Energy_consumption_per_capita)
Inflation_rate_consumer_prices_ <- read_csv("Inflation rate (consumer prices).csv")## Rows: 221 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): name, slug, date_of_information, region
## dbl (1): ranking
## num (1): %
##
## 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(Inflation_rate_consumer_prices_)
Public_debt <- read_csv("Public debt.csv")## Rows: 210 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): name, slug, date_of_information, region
## dbl (2): % of GDP, ranking
##
## 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(Public_debt)
Refined_petroleum_products_production <- read_csv("Refined petroleum products - production.csv")## Rows: 216 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): name, slug, region
## dbl (2): date_of_information, ranking
## num (1): bbl/day
##
## 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(Refined_petroleum_products_production)
Telephones_fixed_lines <- read_csv("Telephones - fixed lines.csv")## Rows: 224 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): name, slug, region
## dbl (2): date_of_information, ranking
## num (1): value
##
## 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(Telephones_fixed_lines)
Telephones_mobile_cellular <- read_csv("Telephones - mobile cellular.csv")## Rows: 225 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): name, slug, region
## dbl (2): date_of_information, ranking
## num (1): value
##
## 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(Telephones_mobile_cellular)
Youth_unemployment_rate_ages_15_24_ <- read_csv("Youth unemployment rate (ages 15-24).csv")## Rows: 203 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): name, slug, region
## dbl (3): %, date_of_information, ranking
##
## 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(Youth_unemployment_rate_ages_15_24_)#Limpieza
Broadband_fixed_subscriptions <- Broadband_fixed_subscriptions[, !(names(Broadband_fixed_subscriptions) %in% "slug")]Carbon_dioxide_emissions <- Carbon_dioxide_emissions[, !(names(Carbon_dioxide_emissions) %in% "slug")]
Debt_external <- Debt_external[, !(names(Debt_external) %in% "slug")]
Electricity_installed_generating_capacity <- Electricity_installed_generating_capacity[, !(names(Electricity_installed_generating_capacity) %in% "slug")]
Energy_consumption_per_capita <- Energy_consumption_per_capita[, !(names(Energy_consumption_per_capita) %in% "slug")]
Inflation_rate_consumer_prices_ <- Inflation_rate_consumer_prices_[, !(names(Inflation_rate_consumer_prices_) %in% "slug")]
Public_debt <- Public_debt[, !(names(Public_debt) %in% "slug")]
Refined_petroleum_products_production <- Refined_petroleum_products_production[, !(names(Refined_petroleum_products_production) %in% "slug")]
Telephones_fixed_lines <- Telephones_fixed_lines[, !(names(Telephones_fixed_lines) %in% "slug")]
Telephones_mobile_cellular <- Telephones_mobile_cellular[, !(names(Telephones_mobile_cellular) %in% "slug")]
Youth_unemployment_rate_ages_15_24_ <- Youth_unemployment_rate_ages_15_24_[, !(names(Youth_unemployment_rate_ages_15_24_) %in% "slug")]Broadband_fixed_subscriptions <- Broadband_fixed_subscriptions[, !(names(Broadband_fixed_subscriptions) %in% "region")]
Carbon_dioxide_emissions <- Carbon_dioxide_emissions[, !(names(Carbon_dioxide_emissions) %in% "region")]
Debt_external <- Debt_external[, !(names(Debt_external) %in% "region")]
Electricity_installed_generating_capacity <- Electricity_installed_generating_capacity[, !(names(Electricity_installed_generating_capacity) %in% "region")]
Energy_consumption_per_capita <- Energy_consumption_per_capita[, !(names(Energy_consumption_per_capita) %in% "region")]
Inflation_rate_consumer_prices_ <- Inflation_rate_consumer_prices_[, !(names(Inflation_rate_consumer_prices_) %in% "region")]
Public_debt <- Public_debt[, !(names(Public_debt) %in% "region")]
Refined_petroleum_products_production <- Refined_petroleum_products_production[, !(names(Refined_petroleum_products_production) %in% "region")]
Telephones_fixed_lines <- Telephones_fixed_lines[, !(names(Telephones_fixed_lines) %in% "region")]
Telephones_mobile_cellular <- Telephones_mobile_cellular[, !(names(Telephones_mobile_cellular) %in% "region")]
Youth_unemployment_rate_ages_15_24_ <- Youth_unemployment_rate_ages_15_24_[, !(names(Youth_unemployment_rate_ages_15_24_) %in% "region")]Broadband_fixed_subscriptions <- Broadband_fixed_subscriptions[, !(names(Broadband_fixed_subscriptions) %in% "ranking")]
Carbon_dioxide_emissions <- Carbon_dioxide_emissions[, !(names(Carbon_dioxide_emissions) %in% "ranking")]
Debt_external <- Debt_external[, !(names(Debt_external) %in% "ranking")]
Electricity_installed_generating_capacity <- Electricity_installed_generating_capacity[, !(names(Electricity_installed_generating_capacity) %in% "ranking")]
Energy_consumption_per_capita <- Energy_consumption_per_capita[, !(names(Energy_consumption_per_capita) %in% "ranking")]
Inflation_rate_consumer_prices_ <- Inflation_rate_consumer_prices_[, !(names(Inflation_rate_consumer_prices_) %in% "ranking")]
Public_debt <- Public_debt[, !(names(Public_debt) %in% "ranking")]
Refined_petroleum_products_production <- Refined_petroleum_products_production[, !(names(Refined_petroleum_products_production) %in% "ranking")]
Telephones_fixed_lines <- Telephones_fixed_lines[, !(names(Telephones_fixed_lines) %in% "ranking")]
Telephones_mobile_cellular <- Telephones_mobile_cellular[, !(names(Telephones_mobile_cellular) %in% "ranking")]
Youth_unemployment_rate_ages_15_24_ <- Youth_unemployment_rate_ages_15_24_[, !(names(Youth_unemployment_rate_ages_15_24_) %in% "ranking")]Broadband_fixed_subscriptions <- Broadband_fixed_subscriptions[, !(names(Broadband_fixed_subscriptions) %in% "date_of_information")]
Carbon_dioxide_emissions <- Carbon_dioxide_emissions[, !(names(Carbon_dioxide_emissions) %in% "date_of_information")]
Debt_external <- Debt_external[, !(names(Debt_external) %in% "date_of_information")]
Electricity_installed_generating_capacity <- Electricity_installed_generating_capacity[, !(names(Electricity_installed_generating_capacity) %in% "date_of_information")]
Energy_consumption_per_capita <- Energy_consumption_per_capita[, !(names(Energy_consumption_per_capita) %in% "date_of_information")]
Inflation_rate_consumer_prices_ <- Inflation_rate_consumer_prices_[, !(names(Inflation_rate_consumer_prices_) %in% "date_of_information")]
Public_debt <- Public_debt[, !(names(Public_debt) %in% "date_of_information")]
Refined_petroleum_products_production <- Refined_petroleum_products_production[, !(names(Refined_petroleum_products_production) %in% "date_of_information")]
Telephones_fixed_lines <- Telephones_fixed_lines[, !(names(Telephones_fixed_lines) %in% "date_of_information")]
Telephones_mobile_cellular <- Telephones_mobile_cellular[, !(names(Telephones_mobile_cellular) %in% "date_of_information")]
Youth_unemployment_rate_ages_15_24_ <- Youth_unemployment_rate_ages_15_24_[, !(names(Youth_unemployment_rate_ages_15_24_) %in% "date_of_information")]#Uniendo las datas
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlista_dataframes <- list(Broadband_fixed_subscriptions, Carbon_dioxide_emissions, Debt_external, Electricity_installed_generating_capacity, Energy_consumption_per_capita, Inflation_rate_consumer_prices_, Public_debt, Refined_petroleum_products_production, Telephones_fixed_lines, Telephones_mobile_cellular, Youth_unemployment_rate_ages_15_24_)
dataunida <- Reduce(function(x, y) full_join(x, y, by = "name"), lista_dataframes)
print(dataunida)## # A tibble: 231 × 12
## name value.x `metric tonnes of CO2` value.y kW `Btu/person` `%.x`
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 China 4.84e8 10773248000 $2,027… 2.22e9 105687000 1.97
## 2 United Stat… 1.21e8 5144361000 $20,27… 1.14e9 304414000 8
## 3 Japan 4.40e7 1103234000 $4,254… 3.49e8 147107000 2.5
## 4 Brazil 3.63e7 456670000 $681,3… 1.95e8 59444000 9.28
## 5 Germany 3.62e7 726881000 $5,671… 2.48e8 161174000 6.87
## 6 Russia 3.39e7 1848070000 $479,8… 2.76e8 227898000 6.69
## 7 France 3.06e7 338425000 $6,356… 1.39e8 151053000 5.22
## 8 United King… 2.73e7 398084000 $8,722… 1.13e8 119894000 7.92
## 9 India 2.29e7 2314738000 $555,3… 4.33e8 23231000 6.7
## 10 Korea, South 2.23e7 686954000 $457,7… 1.36e8 242346000 5.09
## # ℹ 221 more rows
## # ℹ 5 more variables: `% of GDP` <dbl>, `bbl/day` <dbl>, value.x.x <dbl>,
## # value.y.y <dbl>, `%.y` <dbl>dataunida <- Reduce(function(x, y) full_join(x, y, by = "name"), lista_dataframes)#PREGUNTA 1 (AF) #####—-
#PREGUNTA 2