Tarea 3,

La base de datos gapminder contiene los paises de los diferentes continentes, la expectativa de vida, la poblacion y el PIB per capita para los quinquenios entre los años 1952 y 2007. (No olvide practicar los comandos head, tail, str/glimpse). Se busca que el codigo me de de respuesta puntualmente lo que se pide

  1. ¿Cuales son las poblaciones de Japon en los diferentes años?
library(dplyr)
## 
## Adjuntando el paquete: 'dplyr'
## The following objects are masked from 'package:stats':
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##     filter, lag
## The following objects are masked from 'package:base':
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library(readxl)
gapminder <- read_excel("C:/Users/alani/OneDrive/Escritorio/Curso R/Clase 3/gapminder.xlsx")
View(gapminder)

library(gapminder)
## Warning: package 'gapminder' was built under R version 4.5.1
## 
## Adjuntando el paquete: 'gapminder'
## The following object is masked _by_ '.GlobalEnv':
## 
##     gapminder
gapminder_Japan<- gapminder%>%
filter(country=="Japan")%>%
  select(country,year,pop)
gapminder_Japan
## # A tibble: 12 × 3
##    country  year       pop
##    <chr>   <dbl>     <dbl>
##  1 Japan    1952  86459025
##  2 Japan    1957  91563009
##  3 Japan    1962  95831757
##  4 Japan    1967 100825279
##  5 Japan    1972 107188273
##  6 Japan    1977 113872473
##  7 Japan    1982 118454974
##  8 Japan    1987 122091325
##  9 Japan    1992 124329269
## 10 Japan    1997 125956499
## 11 Japan    2002 127065841
## 12 Japan    2007 127467972

2.¿Cuales son los PIB per capita de Mexico?

library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.5.1
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
gapminder_Mexico<-gapminder%>%
  filter(country=="Mexico")%>%
    select(country,gdpPercap)
gapminder_Mexico
## # A tibble: 12 × 2
##    country gdpPercap
##    <chr>       <dbl>
##  1 Mexico      3478.
##  2 Mexico      4132.
##  3 Mexico      4582.
##  4 Mexico      5755.
##  5 Mexico      6809.
##  6 Mexico      7675.
##  7 Mexico      9611.
##  8 Mexico      8688.
##  9 Mexico      9472.
## 10 Mexico      9767.
## 11 Mexico     10742.
## 12 Mexico     11978.

3 ¿Cual es el pais de mayor expectativa de vida en el 2007?

gapminder_2007<-gapminder%>%
  filter(year == 2007)%>%
    filter(lifeExp == max(lifeExp))%>% 
      select(country,year,lifeExp)
gapminder_2007
## # A tibble: 1 × 3
##   country  year lifeExp
##   <chr>   <dbl>   <dbl>
## 1 Japan    2007    82.6

4.¿Cual es el pais de menor PIB per capita?

gapminder_perCap<-gapminder%>%
    filter(gdpPercap == min(gdpPercap))%>% 
      select(country,gdpPercap)
gapminder_perCap
## # A tibble: 1 × 2
##   country gdpPercap
##   <chr>       <dbl>
## 1 Congo        241.

5.¿Cual era la poblacion de Argentina en 1992?

gapminder_Argentina<-gapminder%>%
  filter(country == "Argentina", year == 1992)%>%
    select(country,year,pop)
      gapminder_Argentina
## # A tibble: 1 × 3
##   country    year      pop
##   <chr>     <dbl>    <dbl>
## 1 Argentina  1992 33958947

6.Agrupe por continente y obtenga la media de la poblacion, expectativa de vida y PIB per capita.

gapminder_agrupacion<-gapminder%>%
  group_by(continent)%>%
      summarise(media_pop=mean(gapminder$pop),
                media_lifeExp=mean(gapminder$lifeExp),
                media_gdpPercap=mean(gapminder$gdpPercap))
gapminder_agrupacion
## # A tibble: 5 × 4
##   continent media_pop media_lifeExp media_gdpPercap
##   <chr>         <dbl>         <dbl>           <dbl>
## 1 Africa    29601212.          59.5           7215.
## 2 Americas  29601212.          59.5           7215.
## 3 Asia      29601212.          59.5           7215.
## 4 Europe    29601212.          59.5           7215.
## 5 Oceania   29601212.          59.5           7215.

7.Lo mismo del punto anterior, pero filtre para el año 2007.

gapminder_agrupacion_2007 <- gapminder %>%
  filter(year == 2007) %>%
    group_by(continent) %>%
      summarise(media_pop = mean(pop),
        media_lifeExp = mean(lifeExp),
         media_gdpPercap = mean(gdpPercap))

gapminder_agrupacion_2007
## # A tibble: 5 × 4
##   continent  media_pop media_lifeExp media_gdpPercap
##   <chr>          <dbl>         <dbl>           <dbl>
## 1 Africa     17875763.          54.8           3089.
## 2 Americas   35954847.          73.6          11003.
## 3 Asia      115513752.          70.7          12473.
## 4 Europe     19536618.          77.6          25054.
## 5 Oceania    12274974.          80.7          29810.