Se replican cinco grĆ”ficas y se pone el cĆ³digo adjunto de cada una de ellas.

1. Directorio
setwd("C:/Programacion en R/1. INF. PARA ECONOMISTAS/SISECO-EJ9")
2. Librerias necesarias
library(tidyverse)
library(data.table)
library(ggrepel)
library(ggthemes)
library(maps)
library(gganimate)
library(GGally)
library(gifski)
library(transformr)
3. Carga de las bases de datos
elections <- fread("Elections.csv")
View(elections)
cces <- fread("Congreso.csv")
View(cces)
cel <- fread("CEL.csv")
View(cel)
4. CĆ³digos y GrĆ”ficos
CĆ³digo del GrĆ”fico 1.
elections %>% 
  filter(congress == 115) %>% 
  mutate(Conjunto = ifelse(majority == 1, "Majority", "Minority")) %>% 
  ggplot(aes(Conjunto, les)) + 
  geom_boxplot() +                                                                 
  labs(x="Majority or Minority",                                                          
       y="Legislative Effectiveness",                                                   
       title = "LES in the 115th Congress")
VisualizaciĆ³n del GrĆ”fico 1.

1000

CĆ³digo del GrĆ”fico 2.
Semestre <- c(1,2,3,4,5,6,
              1,2,3,4,5,6,
              1,2,3,4,5,6)
Estudiante <- c("Carol", "Carol","Carol",
                "Carol", "Carol","Carol",
                "David", "David","David",
                "David", "David","David",
                "Diana", "Diana","Diana",
                "Diana", "Diana","Diana")
runif(18, min=80, max=100)
Grado <- c(82.34141, 83.73772, 81.65542, 
           94.02424, 81.62462, 86.47438,
           85.93032, 99.18622, 93.60905, 
           87.45710, 94.56546, 82.37123, 
           86.70199, 87.07580, 97.28708, 
           93.32994, 80.36158, 90.79674)
DataFrame <- data.frame(Semestre, Estudiante, Grado)
View(DataFrame)
DataFrame %>% 
  ggplot(aes(Semestre, Grado, color=Estudiante)) + 
  geom_line() +                                                               
  facet_grid(. ~ Estudiante) +   
  theme(legend.position = "none") +                                                                 
  labs(x="Semestre",                                                      
       y="Grado",                                                     
       title = "Student Grades by Semester")
VisualizaciĆ³n del GrĆ”fico 2.

1000

CĆ³digo del GrĆ”fico 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)+
  scale_fill_manual(values=c("#FF6A6A", "#009ACD"))+
  ggthemes::theme_fivethirtyeight()+
  labs(title = "Women Participation")
VisualizaciĆ³n del GrĆ”fico 3.

1000

CĆ³digo del GrĆ”fico 4.
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(rgeos)
s_america <- ne_countries(scale="medium",continent='south america',returnclass="sf") 
mapa_mundo = map_data("world")
Mapa_LTA = mapa_mundo %>% 
  filter(region %in% c("Argentina", "Bolivia", "Brazil", "Uruguay","Chile",
                       "Colombia" , "Ecuador", "Guyana", "Peru"   ,"Paraguay",
                        "Suriname",  "Venezuela","Falkland Islands")) 
View(Mapa_LTA)
data_inventada <- data.frame(
  region = unique(Mapa_LTA$region),
  pop_est =unique(s_america$pop_est))  
data_unificada <- Mapa_LTA %>%
  left_join(data_inventada, by="region") 
ggplot(data_unificada, aes(x = long, y = lat, group=group, fill=pop_est))+
  geom_polygon(color="black")+
  scale_fill_distiller(palette=10)
VisualizaciĆ³n del GrĆ”fico 4.

1000

CĆ³digo del GrĆ”fico 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)
View(my_dat)
ggplot(my_dat, aes(x=Category, y=Value, fill=City)) +
  geom_bar(stat="identity", position="stack")+
  transition_states(City)+
  enter_fade()+
  exit_shrink()+ 
  ease_aes('sine-in-out')
VisualizaciĆ³n del GrĆ”fico 5.

1000