if(!require("pacman")) install.packages("pacman")Cargando paquete requerido: pacmanpacman::p_load(tidyverse,
haven,
sjlabelled,
ggthemes,
broom,
janitor,
jtools,
sjPlot,
car,
performance,
GGally)#concentradohogar <- haven::read_dta("datos/concentradohogar.dta", encoding = "latin1") # si hay problemas de encoding
concentradohogar <- haven::read_dta("datos/concentradohogar.dta")head(concentradohogar)# A tibble: 6 × 126
folioviv foliohog ubica_geo tam_loc est_socio est_dis upm factor clase_hog
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
1 0100005002 1 01001 1 4 003 0000… 206 3
2 0100005003 1 01001 1 4 003 0000… 206 2
3 0100005004 1 01001 1 4 003 0000… 206 2
4 0100012002 1 01001 1 3 002 0000… 167 3
5 0100012002 2 01001 1 3 002 0000… 167 1
6 0100012004 1 01001 1 3 002 0000… 167 2
# ℹ 117 more variables: sexo_jefe <chr>, edad_jefe <dbl>, educa_jefe <chr>,
# tot_integ <dbl>, hombres <dbl>, mujeres <dbl>, mayores <dbl>,
# menores <dbl>, p12_64 <dbl>, p65mas <dbl>, ocupados <dbl>,
# percep_ing <dbl>, perc_ocupa <dbl>, ing_cor <dbl>, ingtrab <dbl>,
# trabajo <dbl>, sueldos <dbl>, horas_extr <dbl>, comisiones <dbl>,
# aguinaldo <dbl>, indemtrab <dbl>, otra_rem <dbl>, remu_espec <dbl>,
# negocio <dbl>, noagrop <dbl>, industria <dbl>, comercio <dbl>, …names(concentradohogar) ## revisar contra la documentación de la base [1] "folioviv" "foliohog" "ubica_geo" "tam_loc" "est_socio"
[6] "est_dis" "upm" "factor" "clase_hog" "sexo_jefe"
[11] "edad_jefe" "educa_jefe" "tot_integ" "hombres" "mujeres"
[16] "mayores" "menores" "p12_64" "p65mas" "ocupados"
[21] "percep_ing" "perc_ocupa" "ing_cor" "ingtrab" "trabajo"
[26] "sueldos" "horas_extr" "comisiones" "aguinaldo" "indemtrab"
[31] "otra_rem" "remu_espec" "negocio" "noagrop" "industria"
[36] "comercio" "servicios" "agrope" "agricolas" "pecuarios"
[41] "reproducc" "pesca" "otros_trab" "rentas" "utilidad"
[46] "arrenda" "transfer" "jubilacion" "becas" "donativos"
[51] "remesas" "bene_gob" "transf_hog" "trans_inst" "estim_alqu"
[56] "otros_ing" "gasto_mon" "alimentos" "ali_dentro" "cereales"
[61] "carnes" "pescado" "leche" "huevo" "aceites"
[66] "tuberculo" "verduras" "frutas" "azucar" "cafe"
[71] "especias" "otros_alim" "bebidas" "ali_fuera" "tabaco"
[76] "vesti_calz" "vestido" "calzado" "vivienda" "alquiler"
[81] "pred_cons" "agua" "energia" "limpieza" "cuidados"
[86] "utensilios" "enseres" "salud" "atenc_ambu" "hospital"
[91] "medicinas" "transporte" "publico" "foraneo" "adqui_vehi"
[96] "mantenim" "refaccion" "combus" "comunica" "educa_espa"
[101] "educacion" "esparci" "paq_turist" "personales" "cuida_pers"
[106] "acces_pers" "otros_gas" "transf_gas" "percep_tot" "retiro_inv"
[111] "prestamos" "otras_perc" "ero_nm_viv" "ero_nm_hog" "erogac_tot"
[116] "cuota_viv" "mater_serv" "material" "servicio" "deposito"
[121] "prest_terc" "pago_tarje" "deudas" "balance" "otras_erog"
[126] "smg" concentradohogar %>%
dplyr::glimpse()Rows: 90,102
Columns: 126
$ folioviv <chr> "0100005002", "0100005003", "0100005004", "0100012002", "01…
$ foliohog <chr> "1", "1", "1", "1", "2", "1", "1", "1", "1", "1", "1", "1",…
$ ubica_geo <chr> "01001", "01001", "01001", "01001", "01001", "01001", "0100…
$ tam_loc <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",…
$ est_socio <chr> "4", "4", "4", "3", "3", "3", "3", "4", "4", "4", "4", "3",…
$ est_dis <chr> "003", "003", "003", "002", "002", "002", "002", "003", "00…
$ upm <chr> "0000001", "0000001", "0000001", "0000002", "0000002", "000…
$ factor <dbl> 206, 206, 206, 167, 167, 167, 167, 212, 212, 212, 212, 184,…
$ clase_hog <chr> "3", "2", "2", "3", "1", "2", "2", "1", "2", "2", "2", "1",…
$ sexo_jefe <chr> "2", "1", "1", "1", "1", "1", "2", "2", "2", "1", "1", "1",…
$ edad_jefe <dbl> 91, 68, 56, 87, 27, 57, 47, 75, 70, 69, 48, 73, 64, 55, 58,…
$ educa_jefe <chr> "03", "08", "10", "11", "08", "08", "10", "06", "10", "04",…
$ tot_integ <dbl> 3, 2, 3, 4, 1, 4, 4, 1, 3, 2, 5, 1, 4, 3, 1, 6, 4, 2, 3, 1,…
$ hombres <dbl> 0, 1, 2, 2, 1, 2, 2, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 3, 0,…
$ mujeres <dbl> 3, 1, 1, 2, 0, 2, 2, 1, 2, 1, 4, 0, 3, 2, 1, 5, 3, 1, 0, 1,…
$ mayores <dbl> 3, 2, 3, 4, 1, 3, 4, 1, 3, 2, 5, 1, 4, 2, 1, 4, 4, 2, 1, 1,…
$ menores <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 2, 0,…
$ p12_64 <dbl> 2, 1, 3, 2, 1, 3, 4, 0, 2, 0, 5, 0, 4, 2, 1, 4, 3, 0, 1, 1,…
$ p65mas <dbl> 1, 1, 0, 2, 0, 0, 0, 1, 1, 2, 0, 1, 0, 0, 0, 0, 1, 2, 0, 0,…
$ ocupados <dbl> 1, 2, 2, 0, 1, 3, 1, 0, 3, 1, 1, 0, 1, 1, 0, 3, 1, 1, 1, 1,…
$ percep_ing <dbl> 3, 2, 2, 2, 1, 4, 2, 1, 3, 2, 1, 1, 2, 2, 1, 3, 2, 2, 1, 1,…
$ perc_ocupa <dbl> 1, 2, 2, 0, 1, 3, 1, 0, 3, 1, 1, 0, 1, 1, 0, 3, 1, 1, 1, 1,…
$ ing_cor <dbl> 56123.75, 108048.87, 133852.88, 105054.15, 24211.95, 121649…
$ ingtrab <dbl> 35706.51, 66766.28, 93081.50, 0.00, 22255.43, 40255.41, 333…
$ trabajo <dbl> 35706.51, 66766.28, 51603.24, 0.00, 17364.13, 40255.41, 327…
$ sueldos <dbl> 33749.99, 61630.42, 41086.95, 0.00, 17364.13, 36586.94, 246…
$ horas_extr <dbl> 0.00, 0.00, 978.26, 0.00, 0.00, 0.00, 7092.39, 0.00, 0.00, …
$ comisiones <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ aguinaldo <dbl> 1956.52, 4646.73, 5135.86, 0.00, 0.00, 3668.47, 1027.17, 0.…
$ indemtrab <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ otra_rem <dbl> 0.00, 489.13, 4402.17, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, …
$ remu_espec <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ negocio <dbl> 0.00, 0.00, 41478.26, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0…
$ noagrop <dbl> 0.00, 0.00, 41478.26, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0…
$ industria <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ comercio <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ servicios <dbl> 0.00, 0.00, 41478.26, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0…
$ agrope <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ agricolas <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ pecuarios <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ reproducc <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ pesca <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ otros_trab <dbl> 0.00, 0.00, 0.00, 0.00, 4891.30, 0.00, 586.95, 0.00, 0.00, …
$ rentas <dbl> 0.00, 32282.60, 11739.13, 0.00, 0.00, 72684.78, 0.00, 0.00,…
$ utilidad <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 72684.78, 0.00, 0.00, 16007.2…
$ arrenda <dbl> 0.00, 32282.60, 11739.13, 0.00, 0.00, 0.00, 0.00, 0.00, 0.0…
$ transfer <dbl> 8804.34, 8999.99, 0.00, 90538.03, 1956.52, 0.00, 26902.17, …
$ jubilacion <dbl> 0.00, 0.00, 0.00, 79239.13, 0.00, 0.00, 0.00, 73369.56, 440…
$ becas <dbl> 391.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.…
$ donativos <dbl> 0.00, 0.00, 0.00, 0.00, 1956.52, 0.00, 26902.17, 0.00, 0.00…
$ remesas <dbl> 978.26, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.0…
$ bene_gob <dbl> 7434.78, 0.00, 0.00, 11298.90, 0.00, 0.00, 0.00, 5649.45, 0…
$ transf_hog <dbl> 0.00, 8999.99, 0.00, 0.00, 0.00, 0.00, 0.00, 2442.84, 0.00,…
$ trans_inst <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ estim_alqu <dbl> 11612.90, 0.00, 29032.25, 14516.12, 0.00, 8709.67, 0.00, 14…
$ otros_ing <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ gasto_mon <dbl> 35091.17, 78670.73, 101647.27, 46702.31, 26927.85, 51176.07…
$ alimentos <dbl> 9514.19, 17524.25, 18321.36, 14759.90, 12458.47, 6351.40, 1…
$ ali_dentro <dbl> 6814.20, 5181.41, 16907.08, 6274.20, 7315.63, 951.42, 11828…
$ cereales <dbl> 1465.70, 231.42, 1362.84, 1928.53, 308.56, 617.14, 1915.67,…
$ carnes <dbl> 617.14, 4114.28, 5142.85, 1928.57, 2442.84, 0.00, 6685.69, …
$ pescado <dbl> 0.00, 0.00, 0.00, 0.00, 1799.99, 0.00, 1414.28, 0.00, 0.00,…
$ leche <dbl> 269.99, 578.57, 0.00, 1414.26, 0.00, 334.28, 0.00, 565.70, …
$ huevo <dbl> 0.00, 257.14, 1028.57, 0.00, 321.42, 0.00, 0.00, 0.00, 1002…
$ aceites <dbl> 0.00, 0.00, 565.71, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.0…
$ tuberculo <dbl> 0.00, 0.00, 0.00, 0.00, 1028.56, 0.00, 321.42, 621.38, 0.00…
$ verduras <dbl> 2288.53, 0.00, 1735.69, 1002.84, 642.85, 0.00, 951.41, 2069…
$ frutas <dbl> 1954.27, 0.00, 0.00, 0.00, 0.00, 0.00, 539.99, 1234.27, 195…
$ azucar <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ cafe <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ especias <dbl> 218.57, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.0…
$ otros_alim <dbl> 0.00, 0.00, 5142.85, 0.00, 0.00, 0.00, 0.00, 3857.14, 1928.…
$ bebidas <dbl> 0.00, 0.00, 1928.57, 0.00, 771.41, 0.00, 0.00, 462.84, 2378…
$ ali_fuera <dbl> 2699.99, 12342.84, 1414.28, 8485.70, 5142.84, 5399.98, 5528…
$ tabaco <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ vesti_calz <dbl> 2445.64, 684.78, 0.00, 1369.56, 0.00, 1751.06, 9782.60, 489…
$ vestido <dbl> 2445.64, 684.78, 0.00, 1369.56, 0.00, 1751.06, 5380.43, 489…
$ calzado <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 4402.17, 0.00, 0.00, 0.…
$ vivienda <dbl> 1736.75, 29649.66, 3232.25, 2850.00, 2700.00, 3660.00, 1822…
$ alquiler <dbl> 0.00, 24677.41, 0.00, 0.00, 0.00, 0.00, 13935.48, 0.00, 0.0…
$ pred_cons <dbl> 116.75, 2032.25, 2032.25, 150.00, 0.00, 150.00, 0.00, 750.0…
$ agua <dbl> 780, 540, 750, 450, 450, 1200, 1410, 420, 840, 420, 900, 87…
$ energia <dbl> 840.00, 2400.00, 450.00, 2250.00, 2250.00, 2310.00, 2876.61…
$ limpieza <dbl> 2075.80, 2816.11, 1422.55, 1228.04, 890.36, 3518.67, 2386.3…
$ cuidados <dbl> 2075.80, 2816.11, 1422.55, 1228.04, 792.54, 3518.67, 2386.3…
$ utensilios <dbl> 0.00, 0.00, 0.00, 0.00, 97.82, 0.00, 0.00, 0.00, 489.13, 23…
$ enseres <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ salud <dbl> 2641.29, 0.00, 0.00, 0.00, 0.00, 1007.60, 8902.16, 3277.16,…
$ atenc_ambu <dbl> 2641.29, 0.00, 0.00, 0.00, 0.00, 1007.60, 7923.90, 3277.16,…
$ hospital <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ medicinas <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 978.26, 0.00, 978.26, 0…
$ transporte <dbl> 6773.62, 6706.44, 23312.90, 23574.19, 5080.63, 20601.28, 84…
$ publico <dbl> 2314.28, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 771.42, 1157.1…
$ foraneo <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ adqui_vehi <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ mantenim <dbl> 2903.22, 4354.83, 11612.90, 20322.58, 4064.51, 17709.67, 53…
$ refaccion <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ combus <dbl> 2903.22, 4354.83, 11612.90, 20322.58, 4064.51, 17709.67, 53…
$ comunica <dbl> 1556.12, 2351.61, 11700.00, 3251.61, 1016.12, 2891.61, 3033…
$ educa_espa <dbl> 2903.22, 0.00, 34728.25, 0.00, 4209.66, 6967.74, 9058.05, 0…
$ educacion <dbl> 2903.22, 0.00, 0.00, 0.00, 0.00, 6967.74, 6735.47, 0.00, 0.…
$ esparci <dbl> 0.00, 0.00, 5380.43, 0.00, 4209.66, 0.00, 2322.58, 0.00, 0.…
$ paq_turist <dbl> 0.00, 0.00, 29347.82, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0…
$ personales <dbl> 4097.44, 3870.14, 13416.08, 2920.62, 1344.17, 812.90, 4918.…
$ cuida_pers <dbl> 673.53, 3745.14, 1916.09, 2920.62, 1344.17, 812.90, 4708.95…
$ acces_pers <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ otros_gas <dbl> 3423.91, 125.00, 11499.99, 0.00, 0.00, 0.00, 210.00, 0.00, …
$ transf_gas <dbl> 2903.22, 17419.35, 7213.88, 0.00, 244.56, 6505.42, 73.36, 4…
$ percep_tot <dbl> 0.00, 0.00, 0.00, 0.00, 3214.27, 0.00, 0.00, 0.00, 0.00, 0.…
$ retiro_inv <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ prestamos <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ otras_perc <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ ero_nm_viv <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ ero_nm_hog <dbl> 0.00, 0.00, 0.00, 0.00, 3214.27, 0.00, 0.00, 0.00, 0.00, 0.…
$ erogac_tot <dbl> 0.00, 19565.21, 0.00, 28124.99, 0.00, 5771.73, 360.97, 2445…
$ cuota_viv <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ mater_serv <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ material <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ servicio <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ deposito <dbl> 0.00, 19565.21, 0.00, 28124.99, 0.00, 5771.73, 0.00, 2445.6…
$ prest_terc <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ pago_tarje <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ deudas <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ balance <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ otras_erog <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 360.97, 0.00, 0.00, 0.0…
$ smg <dbl> 15558.3, 15558.3, 15558.3, 15558.3, 15558.3, 15558.3, 15558…# Claridad del tipo de variables
# Naturaleza de la variable vs el almacenamientoconcentradohogar %>%
dplyr::select(ingtrab) %>%
base::summary() #con summary ingtrab
Min. : 0
1st Qu.: 12171
Median : 29899
Mean : 40360
3rd Qu.: 54328
Max. :5891914 concentradohogar %>%
dplyr::summarise(media=mean(ingtrab))# A tibble: 1 × 1
media
<dbl>
1 40360.histogramas, densidad, boxplot
concentradohogar %>%
ggplot2::ggplot() +
aes(x=ingtrab) +
geom_density()
concentradohogar %>%
ggplot2::ggplot() +
aes(x=log(ingtrab)) +
geom_density()Warning: Removed 10749 rows containing non-finite outside the scale range
(`stat_density()`).
concentradohogar %>%
ggplot2::ggplot() +
aes(x=log(ingtrab)) +
geom_histogram()`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 10749 rows containing non-finite outside the scale range
(`stat_bin()`).
concentradohogar %>%
ggplot2::ggplot() +
aes(x=log(ingtrab)) +
geom_boxplot() +
theme_excel_new()Warning: Removed 10749 rows containing non-finite outside the scale range
(`stat_boxplot()`).
# en ocasiones hay que etiquetar
concentradohogar<-concentradohogar %>%
dplyr::mutate(sexo_jefe=as.numeric(sexo_jefe)) %>%
sjlabelled::set_labels(sexo_jefe, labels=c("Hombre", "Mujer"))
concentradohogar %>%
mutate(sexo_jefe=as_label(sexo_jefe)) %>%
janitor::tabyl(sexo_jefe) %>%
janitor::adorn_totals() sexo_jefe n percent
Hombre 61905 0.6870547
Mujer 28197 0.3129453
Total 90102 1.0000000OJO SI LA VARIABLE ESTA COMO CARACTER NO HAY QUE HACER ESTO, SE PUEDE HACER EL TABULADO DIRECTO
concentradohogar %>%
ggplot() +
aes(x=as_label(sexo_jefe)) +
geom_bar(stat="count", fill = "#CC6CE7") +
coord_flip() +
theme_nice()
Revisa los colores acá https://r-charts.com/es/colores/
concentradohogar %>%
ggplot() +
aes(x=as_label(sexo_jefe)) +
geom_bar(stat="count", fill = "#CC6CE7", color = "blue4") +
coord_flip() + #ojo con esto para las etiquetas
theme_nice() +
labs(title = "Frecuencias de hogares según sexo del jefe",
x = "Sexo",
y = "Número de hogares")
concentradohogar %>%
group_by(as_label(sexo_jefe)) %>% # renglones en mi tabla
summarise(media = mean(ingtrab, na.rm = T), # quitar los missings no duele
sd = sd(ingtrab, na.rm = T))# A tibble: 2 × 3
`as_label(sexo_jefe)` media sd
<fct> <dbl> <dbl>
1 Hombre 43742. 57010.
2 Mujer 32934. 39164.concentradohogar %>%
ggplot()+
aes(x = log(ingtrab)) +
geom_density() +
facet_wrap(~as_label(sexo_jefe))Warning: Removed 10749 rows containing non-finite outside the scale range
(`stat_density()`).
concentradohogar %>%
ggplot()+
aes(x = log(ingtrab), color = sjlabelled::as_label(sexo_jefe)) +
geom_density() +
theme_light() +
labs(title = "Distribución ingresos por trabajo",
subtitle = "Según sexo del jefe(a) del hogar",
x = "Logaritmos de los ingresos por trabajo (trimestrales)",
y = "Densidad",
caption = "Fuente: ENIGH 2022 datos no ponderados",
color = "Sexo")Warning: Removed 10749 rows containing non-finite outside the scale range
(`stat_density()`).
Ojo en color o fill la variable debe ser factor o caracter, por eso hay que usar el as_label. Si la variable está como caracter se puede poner directo
## Uso de paletas de colores
RColorBrewer::display.brewer.all()
concentradohogar %>%
ggplot()+
aes(x = log(ingtrab), color = sjlabelled::as_label(sexo_jefe)) +
geom_density() +
scale_color_brewer(palette = "Accent", direction = 1 )+
theme_light() +
labs(title = "Distribución ingresos por trabajo",
subtitle = "Según sexo del jefe(a) del hogar",
x = "Logaritmos de los ingresos por trabajo (trimestrales)",
y = "Densidad",
caption = "Fuente: ENIGH 2022 datos no ponderados",
color = "Sexo")Warning: Removed 10749 rows containing non-finite outside the scale range
(`stat_density()`).
concentradohogar %>%
ggplot()+
aes(x = log(ingtrab), color = sjlabelled::as_label(sexo_jefe)) +
geom_density() +
scale_color_manual(values = c("blue", "green"))+
theme_light() +
labs(title = "Distribución ingresos por trabajo",
subtitle = "Según sexo del jefe(a) del hogar",
x = "Logaritmos de los ingresos por trabajo (trimestrales)",
y = "Densidad",
caption = "Fuente: ENIGH 2022 datos no ponderados",
color = "Sexo")Warning: Removed 10749 rows containing non-finite outside the scale range
(`stat_density()`).
ggsave(plot = last_plot(),
filename = "grafico_final.png",
dpi = 500,
width = 9,
height = 7)Warning: Removed 10749 rows containing non-finite outside the scale range
(`stat_density()`).scale_fill_brewer() y scale_fill_manual() sería para el relleno fill, pero funcionan igual.
Cuando tenemos muchas variables cuanti y queremos ver su relación, aquí hay un atajo
## gg gally
concentradohogar %>%
dplyr::select(ing_cor, edad_jefe, gasto_mon, ingtrab) %>%
GGally::ggpairs() # hoy sí funcionó