rear
2024-05-22
Curso de gráficas
1.1 Gráficas con una variable
1.1.1 Gráficas con 1 variable con formato
Este va a ser el espacio de trabajo para mi cuarta gráfica.
Los datos fueron tomados de la ENOE
El objetivo es describir el comportamiento de las personas ocupadas por sexo y estado conyugal para tener una idea más clara de las características socioeconómicas de las personas ocupadas según la ENOE.
#1. Cargar librerias
paquetes=c("scales","survey","ggsurvey","forcats","haven","readxl","fastDummies","readr", "dplyr", "foreign", "tidyr", "ggplot2","gridExtra")
for (i in paquetes) {if (!require(i, character.only = TRUE)) {install.packages(i);library(i, character.only = TRUE)} else {library(i, character.only = TRUE)}}
#2. Directorio de trabajo
setwd("~/Desktop/Cosas en R/Clase1-Visualización")
enoe=read_dta("ENOE_SDEMT423.dta")
# Variable filtro
enoe <- enoe %>%
mutate(f = r_def == 0 & (c_res == 1 | c_res == 3) & eda >= 15 & eda <= 98)
enoe=enoe %>% filter(f==1)
#3. Preparar datos
#3.1 Subconjuntos de datos
# CLASE2=Condicion de ocupacion
# Codigos: 1=Ocupado, 2=Desocupado, 3=Disponible, 4=No disponible
# Convertir la variable a dato numerico
enoe$clase2=as.numeric(enoe$clase2)
# Filtrar la base, necesitamos a los ocupados CLASE2=1
ocupados=enoe%>%filter(clase2==1)
#3.2 Etiquetado de variables
# Codigos: 1=Hombre, 2=Mujer
ocupados <- ocupados %>%
mutate(sex = fct_recode(factor(sex),#miutei
Hombre = "1",
Mujer = "2"))
# pos_ocu=Posicion en la ocupacion
# Codigos: 1=Suborninado, 2=Independiente, 3=Disponible, 0=No aplica
ocupados <- ocupados %>%
mutate(pos_ocu = fct_recode(factor(pos_ocu),
"No Aplica" = "0",
"Suborninado" = "1",
"Empleadores" = "2",
"Cuenta propia" = "3",
"Sin pago" = "4"))
#---------------------------------------
# NOTA:
# Estamos recodificando en la misma
# variable, entonces se pierden los
# valores originales.
#---------------------------------------
ocupados <- ocupados %>%
mutate(e_con = fct_recode(factor(e_con),#miutei
"Unión libre" = "1",
"Separado (a)"= "2",
"Divorciado (a)"="3",
"Viudo (a)"="4",
"Casado (a)"="5",
"Soltero (a)"="6",
"No sabe"="9"))
#4. Graficas (ggplot)
#4.1 Con 1 variable discreta
class(ocupados$sex) ## [1] "factor"names(enoe)## [1] "r_def" "loc" "mun" "est" "est_d_tri"
## [6] "est_d_men" "ageb" "t_loc_tri" "t_loc_men" "cd_a"
## [11] "ent" "con" "upm" "d_sem" "n_pro_viv"
## [16] "v_sel" "n_hog" "h_mud" "n_ent" "per"
## [21] "n_ren" "c_res" "par_c" "sex" "eda"
## [26] "nac_dia" "nac_mes" "nac_anio" "l_nac_c" "cs_p12"
## [31] "cs_p13_1" "cs_p13_2" "cs_p14_c" "cs_p15" "cs_p16"
## [36] "cs_p17" "n_hij" "e_con" "cs_p20a_1" "cs_p20a_c"
## [41] "cs_p20b_1" "cs_p20b_c" "cs_p20c_1" "cs_ad_mot" "cs_p21_des"
## [46] "cs_ad_des" "cs_nr_mot" "cs_p23_des" "cs_nr_ori" "ur"
## [51] "zona" "salario" "fac_tri" "fac_men" "clase1"
## [56] "clase2" "clase3" "pos_ocu" "seg_soc" "rama"
## [61] "c_ocu11c" "ing7c" "dur9c" "emple7c" "medica5c"
## [66] "buscar5c" "rama_est1" "rama_est2" "dur_est" "ambito1"
## [71] "ambito2" "tue1" "tue2" "tue3" "busqueda"
## [76] "d_ant_lab" "d_cexp_est" "dur_des" "sub_o" "s_clasifi"
## [81] "remune2c" "pre_asa" "tip_con" "dispo" "nodispo"
## [86] "c_inac5c" "pnea_est" "niv_ins" "eda5c" "eda7c"
## [91] "eda12c" "eda19c" "hij5c" "domestico" "anios_esc"
## [96] "hrsocup" "ingocup" "ing_x_hrs" "tpg_p8a" "tcco"
## [101] "cp_anoc" "imssissste" "ma48me1sm" "p14apoyos" "scian"
## [106] "t_tra" "emp_ppal" "tue_ppal" "trans_ppal" "mh_fil2"
## [111] "mh_col" "sec_ins" "tipo" "mes_cal" "f"glimpse(enoe)## Rows: 323,118
## Columns: 115
## $ r_def <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ loc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ mun <dbl> 10, 13, 5, 7, 5, 14, 13, 7, 7, 7, 2, 14, 14, 11, 11, 13, 12…
## $ est <dbl> 30, 30, 20, 30, 20, 30, 30, 20, 20, 20, 20, 40, 30, 30, 20,…
## $ est_d_tri <dbl> 124, 124, 123, 124, 123, 124, 124, 123, 123, 123, 123, 125,…
## $ est_d_men <dbl> NA, NA, NA, NA, 115, 116, 116, 115, 115, 115, 115, 117, 116…
## $ ageb <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ t_loc_tri <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1…
## $ t_loc_men <dbl+lbl> NA, NA, NA, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ cd_a <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ ent <dbl+lbl> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9…
## $ con <dbl> 40002, 40013, 40032, 40035, 40071, 40081, 40085, 40087, 400…
## $ upm <dbl> 912033, 915488, 902860, 909615, 905080, 916744, 915917, 908…
## $ d_sem <dbl+lbl> 101, 201, 401, 401, 302, 402, 405, 402, 402, 402, 203, …
## $ n_pro_viv <dbl> 76, 54, 85, 36, 100, 114, 15, 61, 61, 61, 121, 20, 5, 129, …
## $ v_sel <dbl+lbl> 4, 3, 5, 4, 3, 2, 5, 5, 5, 5, 4, 4, 1, 5, 2, 3, 4, 4, 1…
## $ n_hog <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ h_mud <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ n_ent <dbl+lbl> 5, 4, 2, 2, 3, 2, 2, 2, 2, 2, 4, 3, 3, 1, 4, 4, 3, 1, 2…
## $ per <dbl+lbl> 423, 423, 423, 423, 423, 423, 423, 423, 423, 423, 423, …
## $ n_ren <dbl+lbl> 3, 1, 1, 4, 1, 1, 2, 1, 2, 6, 5, 1, 2, 1, 4, 1, 2, 1, 1…
## $ c_res <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ par_c <dbl> 401, 101, 101, 408, 101, 101, 301, 101, 201, 301, 408, 101,…
## $ sex <dbl+lbl> 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 1, 2…
## $ eda <dbl> 93, 67, 43, 27, 53, 70, 33, 34, 35, 15, 17, 45, 65, 61, 34,…
## $ nac_dia <dbl+lbl> 10, 1, 15, 2, 6, 29, 15, 28, 25, 21, 23, 21, 26, 10,…
## $ nac_mes <dbl+lbl> 2, 9, 6, 7, 10, 11, 11, 3, 2, 1, 8, 9, 1, 3,…
## $ nac_anio <dbl> 1930, 1956, 1980, 1996, 1970, 1952, 1989, 1989, 1988, 2008,…
## $ l_nac_c <dbl+lbl> 21, 13, 9, 9, 9, 9, 9, 9, 21, 21, 9, 9, 15, 12,…
## $ cs_p12 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ cs_p13_1 <dbl+lbl> 6, 2, 3, 4, 3, 8, 4, 3, 4, 4, 4, 7, 7, 6, 3, 2, 5, 7, 7…
## $ cs_p13_2 <dbl+lbl> 3, 6, 3, 3, 3, 2, 3, 3, 3, 1, 1, 4, 4, 3, 3, 6, 4, 4, 5…
## $ cs_p14_c <chr> " 21600", "", "", "", "", " 31200", "", "", "", "", "", " 3…
## $ cs_p15 <dbl+lbl> 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3, 3, 2,…
## $ cs_p16 <dbl+lbl> 1, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, 1, 1, 1,…
## $ cs_p17 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2…
## $ n_hij <dbl+lbl> 7, 4, NA, 0, NA, NA, NA, NA, 5, NA, 0, NA, 0, 1,…
## $ e_con <dbl+lbl> 4, 2, 5, 6, 5, 5, 6, 5, 5, 6, 6, 6, 5, 6, 2, 5, 5, 5, 6…
## $ cs_p20a_1 <dbl> 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ cs_p20a_c <dbl> 9, 9, 9, 9, 9, 9, 9, 21, 21, 21, 9, 9, 9, 9, 9, 9, 9, 9, 9,…
## $ cs_p20b_1 <dbl> 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ cs_p20b_c <dbl> 10, 13, 5, 7, 5, 14, 13, 71, 71, 71, 2, 14, 14, 11, 11, 13,…
## $ cs_p20c_1 <dbl> NA, NA, NA, NA, NA, NA, NA, 1, 3, 3, NA, NA, NA, NA, NA, NA…
## $ cs_ad_mot <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ cs_p21_des <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",…
## $ cs_ad_des <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ cs_nr_mot <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ cs_p23_des <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",…
## $ cs_nr_ori <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ur <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ zona <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
## $ salario <dbl> 6223, 6223, 6223, 6223, 6223, 6223, 6223, 6223, 6223, 6223,…
## $ fac_tri <dbl> 1058, 1109, 907, 896, 419, 578, 687, 853, 853, 853, 1025, 2…
## $ fac_men <dbl> 0, 0, 0, 0, 1154, 1945, 2312, 2568, 2568, 2568, 3080, 6477,…
## $ clase1 <dbl+lbl> 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1…
## $ clase2 <dbl> 4, 1, 1, 1, 1, 4, 1, 1, 4, 4, 4, 1, 4, 1, 1, 1, 4, 1, 1, 4,…
## $ clase3 <dbl+lbl> 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1…
## $ pos_ocu <dbl+lbl> 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 3, 0, 1, 3, 3, 0, 3, 2…
## $ seg_soc <dbl+lbl> 0, 1, 2, 1, 1, 0, 1, 2, 0, 0, 0, 2, 0, 1, 2, 2, 0, 2, 2…
## $ rama <dbl+lbl> 0, 4, 3, 4, 4, 0, 4, 3, 0, 0, 0, 3, 0, 4, 4, 1, 0, 6, 4…
## $ c_ocu11c <dbl+lbl> 0, 8, 6, 4, 5, 0, 5, 10, 0, 0, 0, 6, 0, 1,…
## $ ing7c <dbl+lbl> 0, 2, 4, 2, 1, 0, 2, 2, 0, 0, 0, 2, 0, 2, 2, 2, 0, 7, 2…
## $ dur9c <dbl+lbl> 0, 6, 6, 6, 6, 0, 7, 7, 0, 0, 0, 5, 0, 5, 8, 6, 0, 2, 7…
## $ emple7c <dbl+lbl> 0, 5, 2, 7, 6, 0, 4, 2, 0, 0, 0, 1, 0, 6, 1, 1, 0, 1, 2…
## $ medica5c <dbl+lbl> 0, 3, 4, 3, 3, 0, 3, 4, 0, 0, 0, 1, 0, 3, 1, 1, 0, 1, 1…
## $ buscar5c <dbl+lbl> 0, 4, 4, 4, 4, 0, 4, 4, 0, 0, 0, 4, 0, 4, 4, 4, 0, 2, 4…
## $ rama_est1 <dbl+lbl> 0, 3, 3, 3, 3, 0, 3, 3, 0, 0, 0, 3, 0, 3, 3, 2, 0, 1, 3…
## $ rama_est2 <dbl+lbl> 0, 10, 5, 8, 11, 0, 8, 5, 0, 0, 0, 5, 0, 11,…
## $ dur_est <dbl+lbl> 0, 4, 4, 4, 4, 0, 5, 5, 0, 0, 0, 4, 0, 4, 5, 4, 0, 2, 5…
## $ ambito1 <dbl+lbl> 0, 2, 2, 3, 2, 0, 2, 2, 0, 0, 0, 2, 0, 2, 2, 2, 0, 1, 2…
## $ ambito2 <dbl+lbl> 0, 4, 3, 0, 7, 0, 4, 3, 0, 0, 0, 2, 0, 7, 2, 2, 0, 0, 3…
## $ tue1 <dbl+lbl> 0, 2, 1, 1, 2, 0, 1, 3, 0, 0, 0, 3, 0, 2, 1, 3, 0, 1, 1…
## $ tue2 <dbl+lbl> 0, 3, 2, 1, 4, 0, 1, 5, 0, 0, 0, 5, 0, 4, 2, 5, 0, 2, 1…
## $ tue3 <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
## $ busqueda <dbl+lbl> 0, 2, 2, 2, 2, 0, 2, 2, 0, 0, 0, 2, 0, 2, 2, 2, 0, 2, 2…
## $ d_ant_lab <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ d_cexp_est <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ dur_des <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ sub_o <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…
## $ s_clasifi <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0…
## $ remune2c <dbl+lbl> 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
## $ pre_asa <dbl+lbl> 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
## $ tip_con <dbl+lbl> 0, 3, 5, 3, 3, 0, 2, 5, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0…
## $ dispo <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ nodispo <dbl+lbl> 3, 0, 0, 0, 0, 3, 0, 0, 3, 3, 3, 0, 3, 0, 0, 0, 3, 0, 0…
## $ c_inac5c <dbl+lbl> 5, 0, 0, 0, 0, 3, 0, 0, 2, 1, 1, 0, 3, 0, 0, 0, 3, 0, 0…
## $ pnea_est <dbl+lbl> 6, 0, 0, 0, 0, 4, 0, 0, 4, 4, 4, 0, 4, 0, 0, 0, 4, 0, 0…
## $ niv_ins <dbl+lbl> 2, 2, 3, 4, 3, 4, 4, 3, 4, 3, 3, 4, 4, 3, 3, 2, 3, 4, 4…
## $ eda5c <dbl+lbl> 4, 4, 2, 2, 3, 4, 2, 2, 2, 1, 1, 3, 4, 3, 2, 3, 4, 3, 2…
## $ eda7c <dbl+lbl> 6, 6, 4, 2, 5, 6, 3, 3, 3, 1, 1, 4, 6, 6, 3, 4, 6, 4, 3…
## $ eda12c <dbl+lbl> 11, 11, 6, 3, 8, 11, 4, 4, 5, 1, 1, 7, 11, 10,…
## $ eda19c <dbl+lbl> 18, 16, 11, 8, 13, 17, 9, 9, 10, 6, 6, 12, 16, 15,…
## $ hij5c <dbl+lbl> 4, 3, 0, 1, 0, 0, 0, 0, 3, 0, 1, 0, 1, 2, 0, 0, 3, 0, 1…
## $ domestico <dbl+lbl> 6, 3, 4, 3, 3, 8, 4, 4, 8, 7, 7, 3, 8, 3, 3, 3, 9, 3, 3…
## $ anios_esc <dbl> 9, 6, 9, 12, 9, 18, 12, 9, 12, 10, 10, 16, 16, 12, 9, 6, 13…
## $ hrsocup <dbl> 0, 45, 48, 44, 40, 0, 54, 56, 0, 0, 0, 36, 0, 35, 70, 40, 0…
## $ ingocup <dbl> 0, 0, 20000, 0, 6000, 0, 12000, 12040, 0, 0, 0, 8600, 0, 10…
## $ ing_x_hrs <dbl> 0.00000, 0.00000, 96.89922, 0.00000, 34.88372, 0.00000, 51.…
## $ tpg_p8a <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…
## $ tcco <dbl+lbl> 0, 0, 0, 0, 2, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3…
## $ cp_anoc <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0…
## $ imssissste <dbl+lbl> 0, 1, 4, 1, 2, 0, 1, 4, 0, 0, 0, 4, 0, 2, 4, 4, 0, 4, 4…
## $ ma48me1sm <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ p14apoyos <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ scian <dbl+lbl> 0, 19, 7, 14, 20, 0, 14, 7, 0, 0, 0, 7, 0, 20,…
## $ t_tra <dbl+lbl> 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ emp_ppal <dbl+lbl> 0, 2, 1, 2, 2, 0, 2, 1, 0, 0, 0, 1, 0, 2, 2, 1, 0, 1, 2…
## $ tue_ppal <dbl+lbl> 0, 2, 2, 2, 2, 0, 2, 1, 0, 0, 0, 1, 0, 2, 2, 1, 0, 2, 2…
## $ trans_ppal <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ mh_fil2 <dbl+lbl> 0, 3, 3, 3, 3, 0, 3, 1, 0, 0, 0, 1, 0, 3, 3, 1, 0, 4, 3…
## $ mh_col <dbl+lbl> 0, 2, 1, 2, 2, 0, 2, 1, 0, 0, 0, 7, 0, 2, 8, 7, 0, 7, 6…
## $ sec_ins <dbl+lbl> 0, 6, 4, 2, 5, 0, 2, 8, 0, 0, 0, 8, 0, 5, 4, 8, 0, 3, 2…
## $ tipo <dbl+lbl> 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1…
## $ mes_cal <dbl+lbl> 99, 99, 99, 99, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,…
## $ f <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…summary(enoe)## r_def loc mun est est_d_tri
## Min. :0 Min. : NA Min. : 1.00 Min. :10.00 Min. : 1.0
## 1st Qu.:0 1st Qu.: NA 1st Qu.: 6.00 1st Qu.:20.00 1st Qu.:111.0
## Median :0 Median : NA Median : 22.00 Median :20.00 Median :237.0
## Mean :0 Mean :NaN Mean : 40.01 Mean :23.63 Mean :234.2
## 3rd Qu.:0 3rd Qu.: NA 3rd Qu.: 49.00 3rd Qu.:30.00 3rd Qu.:359.0
## Max. :0 Max. : NA Max. :570.00 Max. :40.00 Max. :476.0
## NA's :323118 NA's :2515
## est_d_men ageb t_loc_tri t_loc_men cd_a
## Min. : 1.0 Min. :0 Min. :1.000 Min. :1.000 Min. : 1.00
## 1st Qu.: 99.0 1st Qu.:0 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:15.00
## Median :210.0 Median :0 Median :1.000 Median :1.000 Median :33.00
## Mean :210.7 Mean :0 Mean :1.818 Mean :1.852 Mean :43.71
## 3rd Qu.:323.0 3rd Qu.:0 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:83.00
## Max. :430.0 Max. :0 Max. :4.000 Max. :4.000 Max. :86.00
## NA's :16327 NA's :16327
## ent con upm d_sem
## Min. : 1.00 Min. : 13 Min. : 100005 Min. :101.0
## 1st Qu.: 8.00 1st Qu.:40216 1st Qu.: 802719 1st Qu.:202.0
## Median :16.00 Median :40576 Median :1605275 Median :304.0
## Mean :16.38 Mean :45195 Mean :1650335 Mean :308.1
## 3rd Qu.:25.00 3rd Qu.:50037 3rd Qu.:2501131 3rd Qu.:409.0
## Max. :32.00 Max. :90024 Max. :3260800 Max. :513.0
##
## n_pro_viv v_sel n_hog h_mud
## Min. : 1.0 Min. : 1.000 Min. :1.000 Min. :0.0000
## 1st Qu.: 38.0 1st Qu.: 2.000 1st Qu.:1.000 1st Qu.:0.0000
## Median : 76.0 Median : 4.000 Median :1.000 Median :0.0000
## Mean : 93.3 Mean : 5.345 Mean :1.005 Mean :0.0273
## 3rd Qu.: 120.0 3rd Qu.: 5.000 3rd Qu.:1.000 3rd Qu.:0.0000
## Max. :1285.0 Max. :40.000 Max. :5.000 Max. :4.0000
##
## n_ent per n_ren c_res par_c
## Min. :1.00 Min. :423 Min. : 1.000 Min. :1.000 Min. :101.0
## 1st Qu.:2.00 1st Qu.:423 1st Qu.: 1.000 1st Qu.:1.000 1st Qu.:101.0
## Median :3.00 Median :423 Median : 2.000 Median :1.000 Median :201.0
## Mean :2.97 Mean :423 Mean : 2.184 Mean :1.022 Mean :213.8
## 3rd Qu.:4.00 3rd Qu.:423 3rd Qu.: 3.000 3rd Qu.:1.000 3rd Qu.:301.0
## Max. :5.00 Max. :423 Max. :20.000 Max. :3.000 Max. :999.0
##
## sex eda nac_dia nac_mes nac_anio
## Min. :1.00 Min. :15.00 Min. : 1.00 Min. : 1.0 Min. :1905
## 1st Qu.:1.00 1st Qu.:26.00 1st Qu.: 9.00 1st Qu.: 4.0 1st Qu.:1967
## Median :2.00 Median :40.00 Median :17.00 Median : 7.0 Median :1983
## Mean :1.53 Mean :42.15 Mean :22.91 Mean :13.5 Mean :1986
## 3rd Qu.:2.00 3rd Qu.:55.00 3rd Qu.:25.00 3rd Qu.:10.0 3rd Qu.:1996
## Max. :2.00 Max. :98.00 Max. :99.00 Max. :99.0 Max. :9999
##
## l_nac_c cs_p12 cs_p13_1 cs_p13_2
## Min. : 1.00 Min. :1.000 Min. : 0.000 Min. :0.000
## 1st Qu.: 9.00 1st Qu.:1.000 1st Qu.: 3.000 1st Qu.:3.000
## Median : 16.00 Median :1.000 Median : 4.000 Median :3.000
## Mean : 18.73 Mean :1.034 Mean : 4.108 Mean :3.313
## 3rd Qu.: 25.00 3rd Qu.:1.000 3rd Qu.: 6.000 3rd Qu.:4.000
## Max. :999.00 Max. :9.000 Max. :99.000 Max. :9.000
##
## cs_p14_c cs_p15 cs_p16 cs_p17
## Length:323118 Min. :1.00 Min. :1.00 Min. :1.00
## Class :character 1st Qu.:3.00 1st Qu.:1.00 1st Qu.:2.00
## Mode :character Median :3.00 Median :1.00 Median :2.00
## Mean :2.86 Mean :1.25 Mean :1.87
## 3rd Qu.:3.00 3rd Qu.:1.00 3rd Qu.:2.00
## Max. :9.00 Max. :9.00 Max. :9.00
## NA's :240525 NA's :234353
## n_hij e_con cs_p20a_1 cs_p20a_c
## Min. : 0.00 Min. :1.00 Min. :1.000 Min. : 1.00
## 1st Qu.: 0.00 1st Qu.:4.00 1st Qu.:1.000 1st Qu.: 8.00
## Median : 2.00 Median :5.00 Median :1.000 Median : 16.00
## Mean : 2.13 Mean :4.41 Mean :1.009 Mean : 16.65
## 3rd Qu.: 3.00 3rd Qu.:6.00 3rd Qu.:1.000 3rd Qu.: 25.00
## Max. :99.00 Max. :9.00 Max. :4.000 Max. :999.00
## NA's :151713
## cs_p20b_1 cs_p20b_c cs_p20c_1 cs_ad_mot
## Min. :1.00 Min. : 1.00 Min. : 1.0 Min. : NA
## 1st Qu.:1.00 1st Qu.: 6.00 1st Qu.: 1.0 1st Qu.: NA
## Median :1.00 Median : 22.00 Median : 3.0 Median : NA
## Mean :1.01 Mean : 42.56 Mean : 3.7 Mean :NaN
## 3rd Qu.:1.00 3rd Qu.: 50.00 3rd Qu.: 4.2 3rd Qu.: NA
## Max. :2.00 Max. :999.00 Max. :11.0 Max. : NA
## NA's :351 NA's :319702 NA's :323118
## cs_p21_des cs_ad_des cs_nr_mot cs_p23_des
## Length:323118 Min. : NA Min. : 1.0 Length:323118
## Class :character 1st Qu.: NA 1st Qu.: 4.0 Class :character
## Mode :character Median : NA Median : 6.0 Mode :character
## Mean :NaN Mean : 6.1
## 3rd Qu.: NA 3rd Qu.: 9.0
## Max. : NA Max. :99.0
## NA's :323118 NA's :319629
## cs_nr_ori ur zona salario
## Min. :1.0 Min. :1.000 Min. :1.000 Min. :6223
## 1st Qu.:1.0 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:6223
## Median :1.0 Median :1.000 Median :2.000 Median :6223
## Mean :1.3 Mean :1.362 Mean :1.918 Mean :6480
## 3rd Qu.:1.0 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:6223
## Max. :9.0 Max. :2.000 Max. :2.000 Max. :9372
## NA's :320552
## fac_tri fac_men clase1 clase2
## Min. : 9.0 Min. : 0.0 Min. :1.000 Min. :1.000
## 1st Qu.: 103.0 1st Qu.: 292.0 1st Qu.:1.000 1st Qu.:1.000
## Median : 171.0 Median : 525.0 Median :1.000 Median :1.000
## Mean : 312.1 Mean : 930.2 Mean :1.388 Mean :2.129
## 3rd Qu.: 377.0 3rd Qu.: 1117.0 3rd Qu.:2.000 3rd Qu.:4.000
## Max. :13108.0 Max. :43369.0 Max. :2.000 Max. :4.000
##
## clase3 pos_ocu seg_soc rama
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
## Median :1.0000 Median :1.0000 Median :1.0000 Median :2.000
## Mean :0.7389 Mean :0.9322 Mean :0.9404 Mean :2.035
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:2.0000 3rd Qu.:4.000
## Max. :7.0000 Max. :4.0000 Max. :3.0000 Max. :7.000
##
## c_ocu11c ing7c dur9c emple7c
## Min. : 0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median : 2.000 Median :1.000 Median :3.000 Median :1.000
## Mean : 3.225 Mean :1.681 Mean :3.317 Mean :2.121
## 3rd Qu.: 6.000 3rd Qu.:2.000 3rd Qu.:6.000 3rd Qu.:4.000
## Max. :11.000 Max. :7.000 Max. :9.000 Max. :7.000
##
## medica5c buscar5c rama_est1 rama_est2
## Min. :0.000 Min. :0.000 Min. :0.000 Min. : 0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 0.000
## Median :1.000 Median :4.000 Median :2.000 Median : 3.000
## Mean :1.271 Mean :2.342 Mean :1.544 Mean : 3.462
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.: 6.000
## Max. :5.000 Max. :5.000 Max. :4.000 Max. :11.000
##
## dur_est ambito1 ambito2 tue1
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :3.000 Median :2.000 Median :2.000 Median :1.000
## Mean :2.331 Mean :1.187 Mean :2.036 Mean :1.066
## 3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:4.000 3rd Qu.:2.000
## Max. :6.000 Max. :3.000 Max. :8.000 Max. :4.000
##
## tue2 tue3 busqueda d_ant_lab
## Min. :0.000 Min. :0.00000 Min. :0.000 Min. :0.00000
## 1st Qu.:0.000 1st Qu.:0.00000 1st Qu.:0.000 1st Qu.:0.00000
## Median :1.000 Median :0.00000 Median :2.000 Median :0.00000
## Mean :1.768 Mean :0.07513 Mean :1.187 Mean :0.01836
## 3rd Qu.:4.000 3rd Qu.:0.00000 3rd Qu.:2.000 3rd Qu.:0.00000
## Max. :7.000 Max. :2.00000 Max. :2.000 Max. :2.00000
##
## d_cexp_est dur_des sub_o s_clasifi
## Min. :0.00000 Min. :0.00000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.00000 Median :0.00000 Median :0.0000 Median :0.000
## Mean :0.02477 Mean :0.03342 Mean :0.0447 Mean :0.141
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :4.00000 Max. :6.00000 Max. :1.0000 Max. :6.000
##
## remune2c pre_asa tip_con dispo
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.000 Median :0.0000
## Mean :0.4392 Mean :0.5518 Mean :1.578 Mean :0.2052
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:3.000 3rd Qu.:0.0000
## Max. :2.0000 Max. :3.0000 Max. :6.000 Max. :7.0000
##
## nodispo c_inac5c pnea_est niv_ins eda5c
## Min. :0.000 Min. :0.0000 Min. :0.00 Min. :1.00 Min. :1.000
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.00 1st Qu.:2.00 1st Qu.:2.000
## Median :0.000 Median :0.0000 Median :0.00 Median :3.00 Median :2.000
## Mean :1.192 Mean :0.8664 Mean :1.49 Mean :3.06 Mean :2.351
## 3rd Qu.:3.000 3rd Qu.:2.0000 3rd Qu.:4.00 3rd Qu.:4.00 3rd Qu.:3.000
## Max. :9.000 Max. :5.0000 Max. :6.00 Max. :5.00 Max. :5.000
##
## eda7c eda12c eda19c hij5c
## Min. :1.000 Min. : 1.000 Min. : 6.00 Min. :0.000
## 1st Qu.:2.000 1st Qu.: 3.000 1st Qu.: 8.00 1st Qu.:0.000
## Median :4.000 Median : 6.000 Median :11.00 Median :1.000
## Mean :3.643 Mean : 5.859 Mean :10.99 Mean :1.136
## 3rd Qu.:5.000 3rd Qu.: 9.000 3rd Qu.:14.00 3rd Qu.:2.000
## Max. :7.000 Max. :12.000 Max. :19.00 Max. :5.000
##
## domestico anios_esc hrsocup ingocup
## Min. : 1.000 Min. : 0.00 Min. : 0.00 Min. : 0
## 1st Qu.: 3.000 1st Qu.: 8.00 1st Qu.: 0.00 1st Qu.: 0
## Median : 3.000 Median :10.00 Median : 24.00 Median : 0
## Mean : 4.706 Mean :10.44 Mean : 24.47 Mean : 3760
## 3rd Qu.: 8.000 3rd Qu.:12.00 3rd Qu.: 48.00 3rd Qu.: 6450
## Max. :10.000 Max. :99.00 Max. :168.00 Max. :208000
##
## ing_x_hrs tpg_p8a tcco cp_anoc
## Min. : 0.00 Min. :0.00000 Min. :0.0000 Min. :0.000
## 1st Qu.: 0.00 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.000
## Median : 0.00 Median :0.00000 Median :0.0000 Median :0.000
## Mean : 22.97 Mean :0.02001 Mean :0.3915 Mean :0.107
## 3rd Qu.: 35.56 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :6976.74 Max. :1.00000 Max. :3.0000 Max. :1.000
##
## imssissste ma48me1sm p14apoyos scian t_tra
## Min. :0.000 Min. :0.00000 Min. :0 Min. : 0.000 Min. :1.000
## 1st Qu.:0.000 1st Qu.:0.00000 1st Qu.:0 1st Qu.: 0.000 1st Qu.:1.000
## Median :1.000 Median :0.00000 Median :0 Median : 4.000 Median :1.000
## Mean :1.666 Mean :0.02761 Mean :0 Mean : 5.988 Mean :1.037
## 3rd Qu.:4.000 3rd Qu.:0.00000 3rd Qu.:0 3rd Qu.: 9.000 3rd Qu.:1.000
## Max. :5.000 Max. :1.00000 Max. :0 Max. :21.000 Max. :2.000
##
## emp_ppal tue_ppal trans_ppal mh_fil2
## Min. :0.0000 Min. :0.000 Min. :0.000000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.000000 1st Qu.:0.000
## Median :1.0000 Median :1.000 Median :0.000000 Median :1.000
## Mean :0.8892 Mean :1.035 Mean :0.002182 Mean :1.497
## 3rd Qu.:2.0000 3rd Qu.:2.000 3rd Qu.:0.000000 3rd Qu.:3.000
## Max. :2.0000 Max. :2.000 Max. :1.000000 Max. :4.000
##
## mh_col sec_ins tipo mes_cal f
## Min. :0.000 Min. : 0.000 Min. :1.00 Min. :10.00 Mode:logical
## 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.:1.00 1st Qu.:10.00 TRUE:323118
## Median :1.000 Median : 2.000 Median :1.00 Median :11.00
## Mean :1.946 Mean : 2.989 Mean :1.09 Mean :15.45
## 3rd Qu.:2.000 3rd Qu.: 5.000 3rd Qu.:1.00 3rd Qu.:12.00
## Max. :9.000 Max. :11.000 Max. :2.00 Max. :99.00
## # Gráfico = Datos + Estética + Geometría.
ggplot(ocupados) + aes(sex) + geom_bar(fill="yellowgreen")
ggplot(ocupados) + aes(e_con) + geom_bar(fill="orange")
# Gráfico = Datos + Estética + Geometría + Etiquetas
ggplot(ocupados) + aes(sex) + geom_bar(fill="yellowgreen") +
xlab("Sexo") + ylab("Personas") + ggtitle("Total de mujeres y hombres ocupados")
ggplot(ocupados) + aes(e_con) + geom_bar(fill="orange") +
xlab("Estado conyugal") + ylab("Personas") + ggtitle("Estado conyugal de las personas ocupadas")
# Gráfico = Datos + Estética + Geometría + Etiquetas + Ajustes
ggplot(ocupados) + aes(sex) + geom_bar(fill="yellowgreen") +
xlab("Sexo") + ylab("Personas") + ggtitle("Total de mujeres y hombres")+
geom_text(aes(label = scales::comma(..count..)), stat = 'count', # Añade el número con comas
position = position_dodge(0.5), vjust = -0.5, size = 3.5) + # Ajusta la posición del texto moviéndolo ligeramente y ajustando su posición vertical
theme_minimal(base_size = 14) + # Ajusta la configuración del gráfico
theme(plot.title = element_text(hjust = 0.5)) # Centrar el título horizontalmente
tabla1 <- ocupados %>%count(e_con)
ggplot(tabla1,
aes(x = reorder(e_con, -n), y = n)) +
geom_bar(fill="orange",stat="identity") +
geom_text(aes(label =n, vjust=-0.5))+
labs(x = "Estado conyugal",
y = "Personas",
title = "Total de personas ocupadas por estado conyugal")+
theme_minimal()+theme(plot.title = element_text(hjust = 0.5))
ggplot(ocupados, aes(x = sex, y = ..prop.. * 100, group = 1)) +
geom_bar(aes(fill = sex), position = "dodge", stat = "count") + # Crea barras con porcentajes
geom_text(aes(label = scales::percent(..prop..)), stat = 'count', # Ajusta el número mostrado arriba de las barras
vjust = -0.5, position = position_dodge(0.5), size = 3.5) +
labs(x = "Sexo", y = "Porcentaje", title = "Porcentaje de mujeres y hombres",
caption = "Nota: población de 15 años y más\nFuente: ENOE cuarto trimestre 2023") + # Añade la fuente y nota como pie de página
theme_minimal(base_size = 14) +
theme(plot.title = element_text(hjust = 0.5), # Centrar el título horizontalmente
plot.caption = element_text(hjust = 0, size = 9)) + # Ajustes de estilo para la nota de fuente y la nota explicativa
scale_y_continuous(labels = scales::percent_format(scale = 1)) # Formatear el eje y como porcentaje
#4.2 Con 2 variables discretas
# Ahora vamos a guardar la definición de la base de datos y la variable en un elemento llamado g1
g1<-ggplot(ocupados) + aes(pos_ocu) # Base de datos y variable
# Ya no bamos a repetir la definición, solo vamos a usar g1
# Opción 1: Pantalla dividida
g1+geom_bar(fill="yellowgreen")+
facet_wrap(~ sex)+ # facet_wrap divide a la pantalla
ggtitle("Ocupacion por sexo")+
xlab("Posicion de la ocupacion")+
ylab("Personas")
# agregar más detalles
g1+geom_bar(fill="yellowgreen")+
facet_wrap(~ sex)+ # facet_wrap divide a la pantalla
ggtitle("Ocupacion por sexo")+
xlab("Posicion de la ocupacion")+
ylab("Personas")+
geom_text(aes(label=..count..), stat='count',
position=position_dodge(0.5),
vjust=-0.5,
size=3.5) 
# Opción 2: Separa las barras
g2<-ggplot(ocupados,aes(x=pos_ocu,fill=sex)) # Base de datos y variable
g2+geom_bar(position = "dodge")+
ggtitle("Ocupacion por sexo")+ # position ajusta la posicion de las barras
xlab("Posicion de la ocupacion")+
ylab("Personas")+
scale_fill_brewer(palette = "Blues")+
scale_y_continuous(labels = scales::comma)
# agregar más detalles
g2+geom_bar(position = "dodge", stat = "count") + # Barras con posición dodge y contando las ocurrencias
geom_text(aes(label = scales::comma(..count..)), stat = "count", position = position_dodge(width = 0.9), vjust = -0.5, size = 2.5) + # Añade etiquetas numeradas con comas
labs(x="Posición de la Ocupación",y="Personas",title="Ocupación por Sexo",caption = "Nota: población de 15 años y más\nFuente: ENOE cuarto trimestre 2023")+theme_minimal(base_size = 12) +
theme(plot.title = element_text(hjust = 0.5), # Centrar el título horizontalmente
plot.caption = element_text(hjust = 0, size = 9)) +
scale_fill_brewer(palette = "Reds",name = "Sexo") + # Define la paleta de colores y titula la leyenda como "Sexo"
scale_y_continuous(labels = scales::comma)
ggplot(ocupados, aes(x = sex, y = ..prop.. * 100, group = 1)) +
geom_bar(aes(fill = sex), position = "dodge", stat = "count") + # Crea barras con porcentajes
geom_text(aes(label = scales::percent(..prop..)), stat = 'count', # Ajusta el número mostrado arriba de las barras
vjust = -0.5, position = position_dodge(0.5), size = 3.5) +
labs(x = "Sexo", y = "Porcentaje", title = "Porcentaje de mujeres y hombres",
caption = "Nota: población de 15 años y más\nFuente: ENOE cuarto trimestre 2023") + # Añade la fuente y nota como pie de página
theme_minimal(base_size = 14) +
theme(plot.title = element_text(hjust = 0.5), # Centrar el título horizontalmente
plot.caption = element_text(hjust = 0, size = 9)) + # Ajustes de estilo para la nota de fuente y la nota explicativa
scale_y_continuous(labels = scales::percent_format(scale = 1))
# Opción 3: Apila los elementos uno encima del otro
# Preparación de los datos para calcular los porcentajes correctos
ocupados_r <- ocupados %>%
group_by(pos_ocu, sex) %>%
summarise(count = n(), .groups = 'drop') %>%
group_by(pos_ocu) %>%
mutate(percent = count / sum(count) * 100)
# Creación del gráfico con los datos preparados
g3 <- ggplot(ocupados_r, aes(x = pos_ocu, y = percent, fill = sex))
g3+geom_bar(stat = "identity", position = "dodge") + # Usar barras con posición dodge
geom_text(aes(label = scales::percent(percent / 100)), position = position_dodge(width = 0.9), vjust = -0.5, size = 2.5) +
ggtitle("Ocupación por Sexo") +
xlab("Posición de la Ocupación") +
ylab("Porcentaje") +
scale_fill_brewer(palette = "Blues", name = "Sexo") +
scale_y_continuous(labels = percent_format(scale = 1))
# Creación del gráfico con las cifras divididas entre 100
g3+geom_bar(stat = "identity", position = "fill") + # Usar barras con posición fill para apilar los porcentajes en una barra
geom_text(aes(label = scales::percent(percent / 100, accuracy = 0.1)), position = position_fill(vjust = 0.5), size = 3.5) +
ggtitle("Ocupación por Sexo") +
xlab("Posición de la Ocupación") +
ylab("Porcentaje de Personas") +
scale_fill_brewer(palette = "Reds", name = "Sexo") +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) + # Formatear el eje Y como porcentaje
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
ocupados_r <- ocupados %>%
group_by(e_con, sex) %>%
summarise(count = n(), .groups = 'drop') %>%
group_by(e_con) %>%
mutate(percent = count / sum(count) * 100)
g4 <- ggplot(ocupados_r, aes(x = e_con, y = percent, fill = sex))
g4+geom_bar(stat = "identity", position = "dodge") + # Usar barras con posición dodge
geom_text(aes(label = scales::percent(percent / 100)), position = position_dodge(width = 0.9), vjust = -0.5, size = 2.5) +
ggtitle("Ocupación por Sexo") +
xlab("Posición de la Ocupación") +
ylab("Porcentaje") +
scale_fill_brewer(palette = "Blues", name = "Sexo") +
scale_y_continuous(labels = percent_format(scale = 1))
# Creación del gráfico con las cifras divididas entre 100
g4+geom_bar(stat = "identity", position = "fill") + # Usar barras con posición fill para apilar los porcentajes en una barra
geom_text(aes(label = scales::percent(percent / 100, accuracy = 0.1)), position = position_fill(vjust = 0.5), size = 3.5) +
ggtitle("Estado conyugal por sexo") +
xlab("Estado conyugal") +
ylab("Porcentaje de personas") +
scale_fill_brewer(palette = "Reds", name = "Sexo") +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) + # Formatear el eje Y como porcentaje
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00Including Plots
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