Tarea_2

Tarea Clase 2

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(readr)

Costos mundiales para tener una dieta sana

foodprice<-read.csv("/cloud/home/r1532140/R/x86_64-pc-linux-gnu-library/4.2/002065d4-ac26-4c39-a7d8-b43ef58c161e_Data.csv")
foodprice %>% 
  select(Country.Name, Classification.Name, Time, Cost.of.a.healthy.diet..CoHD., Cost.of.a.nutrient.adequate.diet..CoNA.) %>% 
  filter(Cost.of.a.nutrient.adequate.diet..CoNA. >= 1) %>%
  filter(Cost.of.a.healthy.diet..CoHD. >= 1) %>%
  arrange(Cost.of.a.healthy.diet..CoHD.) %>% 
  mutate(Precio.en.pesos.arg. = 127.99 *as.numeric(Cost.of.a.nutrient.adequate.diet..CoNA.)) %>% 
  group_by(Country.Name, Cost.of.a.healthy.diet..CoHD., Precio.en.pesos.arg., Cost.of.a.nutrient.adequate.diet..CoNA.) %>%
  summarise(promedios.totales = mean(as.numeric(Cost.of.a.healthy.diet..CoHD., Precio.en.pesos.arg., Cost.of.a.nutrient.adequate.diet..CoNA.)))

## `summarise()` has grouped output by 'Country.Name',
## 'Cost.of.a.healthy.diet..CoHD.', 'Precio.en.pesos.arg.'. You can override using
## the `.groups` argument.

## # A tibble: 23 × 5
## # Groups:   Country.Name, Cost.of.a.healthy.diet..CoHD., Precio.en.pesos.arg.
## #   [23]
##    Country.Name        Cost.of.a.healthy.diet… Precio.en.pesos… Cost.of.a.nutri…
##    <chr>               <chr>                              <dbl> <chr>           
##  1 Albania             3.952                               316. 2.471           
##  2 Algeria             3.763                               297. 2.323           
##  3 Angola              4.327                               414. 3.231           
##  4 Anguilla            3.717                               311. 2.433           
##  5 Antigua and Barbuda 4.112                               386. 3.017           
##  6 Argentina           3.341                               336. 2.625           
##  7 Armenia             3.096                               283. 2.208           
##  8 Aruba               3.418                               363. 2.835           
##  9 Australia           2.259                               204. 1.595           
## 10 Austria             2.772                               289. 2.256           
## # … with 13 more rows, and 1 more variable: promedios.totales <dbl>