A13_MAE118_HP18033

library(dplyr)

## 
## 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(tibble)
library(stringr)
library(tidyr)
library(pwt10)

Ejercicio 1

Literal A

autos<-mtcars %>% rownames_to_column(var = "Modelos")
head(autos)

peso_autos<- autos %>% filter(mpg>25 & wt<2.5)
Dato_especifico<-peso_autos %>% select(Modelos, mpg, cyl, wt)
head(Dato_especifico)

literal B

orden_hp<-mtcars %>% arrange(hp) %>% select(mpg, hp, wt)
head(orden_hp)

literal C

Nueva_kpl<-autos %>% mutate(kpl = mpg*0.4251)
Nueva_kpl %>% select(Modelos, mpg, kpl, wt) %>%
head()

literal D

M_M_D<-autos %>% summarise(
  media=mean(mpg),
  mediana=median(mpg),
  desviacionSTD= sd(mpg)
)

head(M_M_D)

literal E

grup_med_std<-autos %>% group_by(cyl) %>%
  summarise(media_mpg=mean(mpg),
           desviacion_mpg= sd(mpg) )
head(grup_med_std)

Ejercicio 2

Literal A

pwt<-pwt10::pwt10.01
codigos_L<-c("ARG", "BOL", "BRA", "CHL", "COL", "CRI", "DOM", 
                   "ECU", "GTM", "HND", "MEX", "NIC", "PAN", "PER", 
                   "PRY", "SLV", "URY", "VEN")
pwt_latam <- pwt %>% 
  filter(isocode %in% codigos_L)
head(pwt_latam)

literal B

VarN_pk <- pwt_latam %>% 
  filter(year == 2019) %>% 
  mutate(ingreso_pk = rgdpe / pop) %>% 
  summarise(
    ing_pk_prome = mean(ingreso_pk),
    hc_prome = mean(hc)
  )
print(VarN_pk)

##   ing_pk_prome hc_prome
## 1     14035.52 2.718799

###. literal C

pwt_latam_nivel_ing <- pwt_latam %>% 
  filter(year == 2019) %>% 
  mutate(ingreso_pk = rgdpe / pop) %>% 
  mutate(nivel_ingreso = case_when(
    ingreso_pk < 1026 ~ "bajo",
    ingreso_pk >= 1026 & ingreso_pk <= 3995 ~ "bajo-medio",
    ingreso_pk > 3995 & ingreso_pk <= 12375 ~ "medio-alto",
    ingreso_pk > 12375 ~ "alto"
  ))

head(pwt_latam_nivel_ing)

literal D

creci_prom_PIB <- pwt_latam %>% 
  filter(year %in% c(2015, 2019)) %>% 
  select(isocode, year, rgdpe) %>% 
  tidyr::pivot_wider(names_from = year, values_from = rgdpe, names_prefix = "pib_") %>% 
  mutate(crecimiento = (pib_2019 - pib_2015) / pib_2015) %>% 
  left_join(select(pwt_latam_nivel_ing, isocode, nivel_ingreso), by = "isocode") %>% 
  group_by(nivel_ingreso) %>% 
  summarise(crecimiento_promedio = mean(crecimiento))