#Ejercicio 1

setwd("C:/sesion 9")

library("tidyverse")
## Warning: package 'tidyverse' was built under R version 4.1.3
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.8
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1
## Warning: package 'dplyr' was built under R version 4.1.3
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library("data.table")
## 
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
## 
##     between, first, last
## The following object is masked from 'package:purrr':
## 
##     transpose
library("ggrepel")
## Warning: package 'ggrepel' was built under R version 4.1.3
library("ggthemes")
## Warning: package 'ggthemes' was built under R version 4.1.3
library("maps")
## Warning: package 'maps' was built under R version 4.1.3
## 
## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
## 
##     map
library("gganimate")
## Warning: package 'gganimate' was built under R version 4.1.3
library("GGally")
## Warning: package 'GGally' was built under R version 4.1.3
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
library("gifski")
## Warning: package 'gifski' was built under R version 4.1.3
library("transformr")
## Warning: package 'transformr' was built under R version 4.1.3
library("sf")
## Warning: package 'sf' was built under R version 4.1.3
## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE
## 
## Attaching package: 'sf'
## The following object is masked from 'package:transformr':
## 
##     st_normalize
library("rnaturalearth")
## Warning: package 'rnaturalearth' was built under R version 4.1.3
library("rnaturalearthdata")
## Warning: package 'rnaturalearthdata' was built under R version 4.1.3
library("rgeos")
## Warning: package 'rgeos' was built under R version 4.1.3
## Loading required package: sp
## Warning: package 'sp' was built under R version 4.1.3
## rgeos version: 0.5-9, (SVN revision 684)
##  GEOS runtime version: 3.9.1-CAPI-1.14.2 
##  Please note that rgeos will be retired by the end of 2023,
## plan transition to sf functions using GEOS at your earliest convenience.
##  GEOS using OverlayNG
##  Linking to sp version: 1.4-6 
##  Polygon checking: TRUE
elections <- fread("Elections.csv")
cces <- fread("Congreso.csv")
cel <- fread("CEL.csv")
elections %>%
  filter(congress==115) %>% 
  mutate(Gender=ifelse(female==1,"Female","Male"),
         Mayoria = ifelse(majority==1,"Majority","Minority")) %>% 
  ggplot(aes(Mayoria,les))+
  geom_boxplot()+
  labs(x="Majority or Minority",y="Lesgislative Effectiveness",
       title = "LES in the 115th congress")

#Ejercicio 2

library(stats)
# Creando Vectores.

Estudiante <- c("Diana","David","Carol")

Semestre <- c(1,1,1,2,2,2,
              3,3,3,4,4,4,
              5,5,5,6,6,6)

set.seed(1234)
Grado <- runif(18, min=80, max=100) 

#Data.Frame
datos <- data.frame(Estudiante, Semestre, Grado)

library(ggplot2)
ggplot(datos, aes(x=Semestre, y=Grado, group = Estudiante, colour =Estudiante )) + 
  geom_line()  + 
  geom_point( size=2, shape=21, fill="white") + 
  theme_gray()+
  facet_wrap(vars(Estudiante), nrow = 1)

#Ejercicio 3

elections %>%                        
  mutate(Genre =ifelse(female == 1, "Female", "Male")) %>% 
  ggplot(aes(x = year ,fill=Genre))+ 
  geom_bar(position = "fill")+
  scale_y_continuous(labels = scales::percent)+
  ggthemes::theme_fivethirtyeight()+
  labs(title = "Women Participation")

#Ejercicio 4

mapa_mundo = map_data("world")
ggplot(data = mapa_mundo, aes(x = long, y = lat, group=group))+
  geom_polygon(color= "black", fill = "white")

suramerica<- mapa_mundo %>%
  filter(region%in%c("Argentina","Colombia", "Ecuador", "Peru","Bolivia","Brazil","Chile","Ecuador","Guyana",
                     "Paraguay","Suriname","Uruguay","Venezuela")) 
s_america<-ne_countries(scale="medium",continent='south america',returnclass="sf")%>%
  rename(region=sovereignt)

data_unificada <- suramerica %>%
  left_join(s_america, by = "region")
ggplot(data_unificada, aes(x= long, y = lat,group=group, fill = pop_est))+
  geom_polygon(color = "black")+
  theme(axis.text.x = element_text(),
        axis.text.y = element_text(),
        axis.ticks = element_blank())+
  labs(x="long", y="lat")+
  scale_fill_distiller(palette=10)

#Ejercico 5

Category<-c("Alpha","Beta","Zeta")
City<-c("Hong Kong","London","Nairobi")

my_dat<-expand_grid(Category,City)
set.seed(84684)
my_dat$Value<-sample(1:10,9,replace=T)
ani<- ggplot(my_dat, aes(x = Category, y= Value, fill= City) ) +                        
  geom_bar(width = 0.9, stat="identity", position = position_dodge())+                    
  labs(x="Category", y= "Value") +   
  labs(fill = "City")+
  transition_states(City)+
  enter_fade()+
  exit_shrink() + 
  ease_aes("sine-in-out") 
ani