DF 4 con dplyr : Tablas dinamicas

### Installar gapminder dataset :install.packages(gapminder)

## Cargar paquetes
library(dplyr) ### para manupular dataframes

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(gapminder) ##  para acceder al dataframe gapmider
library(ggplot2)

## explorar el dataframe gapminder
gapminder

## # A tibble: 1,704 × 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333      779.
##  2 Afghanistan Asia       1957    30.3  9240934      821.
##  3 Afghanistan Asia       1962    32.0 10267083      853.
##  4 Afghanistan Asia       1967    34.0 11537966      836.
##  5 Afghanistan Asia       1972    36.1 13079460      740.
##  6 Afghanistan Asia       1977    38.4 14880372      786.
##  7 Afghanistan Asia       1982    39.9 12881816      978.
##  8 Afghanistan Asia       1987    40.8 13867957      852.
##  9 Afghanistan Asia       1992    41.7 16317921      649.
## 10 Afghanistan Asia       1997    41.8 22227415      635.
## # … with 1,694 more rows

glimpse(gapminder)

## Rows: 1,704
## Columns: 6
## $ country   <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, …
## $ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
## $ lifeExp   <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…
## $ pop       <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …

###Tabla dinamica por continente
#tabla
gapminderContinente<- gapminder%>%
  group_by(continent)%>%
  summarise(media_lifeExp=mean(lifeExp), media_pop=mean(pop), 
            media_gdpPercap=mean(gdpPercap))%>%
  print()

## # A tibble: 5 × 4
##   continent media_lifeExp media_pop media_gdpPercap
##   <fct>             <dbl>     <dbl>           <dbl>
## 1 Africa             48.9  9916003.           2194.
## 2 Americas           64.7 24504795.           7136.
## 3 Asia               60.1 77038722.           7902.
## 4 Europe             71.9 17169765.          14469.
## 5 Oceania            74.3  8874672.          18622.

#barplot
gapminderContinente%>%
  ggplot(aes(x=continent, y=media_gdpPercap))+
  geom_col()

##Tabla con estadistica descriptiva para 1997
##tabla
gapminder_gdp_stat_1997<-gapminder%>%
  filter(year==1997)%>%
  group_by(continent)%>%
  summarise(mediaGdpPercap=mean(gdpPercap), sdGDPPercap=sd(gdpPercap), 
      medianaGdpPercap=median(gdpPercap), IQRGdpPercap=IQR(gdpPercap), 
      VarCoefGdpPercap=sdGDPPercap*100/mediaGdpPercap)%>%
  print()

## # A tibble: 5 × 6
##   continent mediaGdpPercap sdGDPPercap medianaGdpPercap IQRGdpPercap VarCoefGd…¹
##   <fct>              <dbl>       <dbl>            <dbl>        <dbl>       <dbl>
## 1 Africa             2379.       2821.            1180.        2064.       119. 
## 2 Americas           8889.       7874.            7114.        5083.        88.6
## 3 Asia               9834.      11094.            3645.       17800.       113. 
## 4 Europe            19077.      10065.           19596.       17243.        52.8
## 5 Oceania           24024.       4206.           24024.        2974.        17.5
## # … with abbreviated variable name ¹​VarCoefGdpPercap

##boxplot
gapminder%>%
  filter(year==1997)%>%
  group_by(continent)%>%
  ggplot(aes(x=continent, y=gdpPercap))+
  geom_boxplot()+
  scale_y_log10()