Discriminacion de las mujeres no heterosexuales
Maria Goessmann
2025-01-31
1. Tema
Discriminación hacia las mujeres lesbianas en el mercado laboral en México.
2. Planeacion del tema
En el siguiente trabajo, utilicé los datos de la Encuesta Nacional sobre Diversidad Sexual y de Género (ENDISEG) 2021, realizada por el Instituto Nacional de Estadística y Geografía (INEGI), con el fin de conocer aspectos relacionados con las características sexuales, identidad de género y orientación sexual de la población de 15 años y más. A partir de esta encuesta también se abordó la discriminación en los ámbitos laboral y privado, temas que analizaremos a continuación:
3. Explicación de los variables
| Variable | Pregunta | Respuesta |
|---|---|---|
| P9_1 | Usted se considera | 1- hombre, 2- mujer, 3-tanto hombre como mujer, 3-ni hombre, ni mujer, 5-de otro género |
| P8_1A | Usted considera que su orientación es: | 1 lesbiana, 2 gay homosexual, 3 bisexual, 4 otra, por ejemplo: pansexual, asexual, b-blanco |
Discriminación laboral
| Variable | Pregunta | Respuesta |
|---|---|---|
| P11_4_1 | Durante los últimos 12 meses, de agosto de 2020 a la fecha, ¿en el trabajo recibió comentarios ofensivos o burlas? | 1-Si, 2-No, 3-No aplica, b-blanco |
| P11_4_2 | Durante los últimos 12 meses, de agosto de 2020 a la fecha, ¿en el trabajo le excluyeron de eventos o actividades sociales? | 1-Si, 2-No, 3-No aplica, b-blanco |
| P11_4_3 | Durante los últimos 12 meses, de agosto de 2020 a la fecha, ¿en el trabajo le molestaron o acosaron? | 1-Si, 2-No, 3-No aplica, b-blanco |
| P11_4_4 | Durante los últimos 12 meses, de agosto de 2020 a la fecha, ¿en el trabajo recibió un trato desigual respecto a los beneficios, prestaciones laborales o ascensos? | 1-Si, 2-No, 3-No aplica, b-blanco |
| P11_4_5 | Durante los últimos 12 meses, de agosto de 2020 a la fecha, ¿en el trabajo le pegaron, agredieron o amenazaron? | 1-Si, 2-No, 3-No aplica, b-blanco |
| P11_5_7 | En los últimos cinco años, de agosto de 2016 a la fecha, ¿le han negado injustificadamente el empleo o la oportunidad de trabajar? | 1-Si, 2-No, 3-No aplica, b-blanco |
Discriminación general
| Variable | Pregunta | Respuesta |
|---|---|---|
| P11_6_11 | En los últimos 12 meses, de agosto de 2020 a la fecha, ¿ha sido discriminada(o), o menospreciada(o), por su preferencia sexual? | 1-Si, 2-no, 9-no especificado |
| P11_8_1 | ¿Usted no toma de la mano a su pareja o le muestra su afecto en público por miedo a sufrir agresión o violencia? | 1-Si, 2-No se declaró como opción afirmativa |
| P11_8_2 | ¿Usted no toma de la mano a su pareja o le muestra su afecto en público por ser mal visto socialmente? | 1-Si, 2-No se declaró como opción afirmativa |
| P11_8_3 | ¿Usted no toma de la mano a su pareja o le muestra su afecto en público por pena o vergüenza? | 1-Si, 2-No se declaró como opción afirmativa |
| P11_8_4 | ¿Usted no toma de la mano a su pareja o le muestra su afecto en público por temor a ser discriminada(o)? | 1-Si, 2-No se declaró como opción afirmativa |
| P11_8_5 | ¿Usted no toma de la mano a su pareja o le muestra su afecto en público por falta de costumbre? | 1-Si, 2-No se declaró como opción afirmativa |
Aquí vemos la distribución de la identidad de género.
##
## 1 2 3 4 5
## 20005 23932 143 47 62##
## 1 2 3 4 5 6
## 242 688 1130 22849 19170 110library(ggplot2)
library(dplyr)
# NA-Werte filtern
disc_lab_filtered <- disc_lab %>% filter(!is.na(P8_1A))
# Diskriminierungslabels erstellen und untereinander anzeigen
labels <- c(
"1" = "una mujer a la que \n le gustan solamente las mujeres",
"2" = "un hombre al que \n le gustan solamente los hombres",
"3" = "una persona que \n le gustan tanto hombres como mujeres",
"4" = "una mujer que \n le gustan solamente los hombres",
"5" = "un hombre que \n le gustan solamente las mujeres",
"6" = "con otra orientación"
)
# Histogramm mit den genauen Zahlenwerten oberhalb der Balken plotten
orientacion_plot <- ggplot(disc_lab_filtered, aes(x = factor(P8_1A, levels = 1:6, labels = labels))) +
geom_bar(fill = "#760492", color = "white") +
geom_text(stat = 'count', aes(label = ..count..), vjust = -0.5, color = "black", size = 2.5) +
labs(
x = "Orientación Sexual",
y = "Frecuencia",
title = "Distribución de la Orientación Sexual",
title.position = "plot" # Titelposition außerhalb der Grafik
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.margin = unit(c(2, 1, 1, 1), "lines") # Randvergrößerung oben
)
# Plot anzeigen
print(orientacion_plot)## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
ggplot(disc_lab) +
aes(x = P8_1A) +
geom_histogram(bins = 30L, fill = "#760492") +
labs(
x = "Orientación Sexual",
y = "Frecuencia",
title = "Orientación Sexual"
) +
theme_minimal()## Warning: Removed 42019 rows containing non-finite outside the scale range
## (`stat_bin()`).
##
## 1 2 3 4
## 241 599 1107 223ggplot(disc_lab) +
aes(x = P9_1) +
geom_histogram(bins = 30L, fill = "#4682B4") +
labs(
x = "Identidad de Género",
y = "Frecuencia",
title = "Identidad de Género"
) +
theme_minimal()
##
## 1 2 3 4 5
## 20005 23932 143 47 62y de la orientación sexual
##
## 1 2 3 4 5 6
## 242 688 1130 22849 19170 110Para considerar más identidades de género y orientación sexual que no sean heterosexuales, la filtración incluye a personas que se consideran mujeres y lesbianas, gays/homosexuales, bisexuales, u otras identidades como pansexuales o asexuales. La filtración es para eliminar a todas las personas que se consideran cis-hombres y heterosexuales.
Depende del grupo, pero en el caso del lesbianismo se entiende que la mayoría son personas que se consideran mujeres, aunque la identificación puede variar entre lesbiana, gay/homosexual, bisexual o pansexual/asexual, es decir, cualquier orientación que no sea heterosexual.
library(dplyr)
# Dummy-Variable 'les' erstellen
disc_lab <- disc_lab %>%
mutate(les = ifelse(P8_1 %in% c(1, 3, 6), 1, 0))
# Verteilung der Dummy-Variable anzeigen
table(disc_lab$les)##
## 0 1
## 42707 1482library(dplyr)
# Werte 9 aus den Variablen P11_5_7, P11_6_11 und P11_8_1 entfernen
disc_lab_cleaned <- disc_lab %>%
mutate(
P11_5_7 = ifelse(P11_5_7 == 9, NA, P11_5_7),
P11_6_11 = ifelse(P11_6_11 == 9, NA, P11_6_11),
P11_8_1 = ifelse(P11_8_1 == 9, NA, P11_8_1)
)
# Überprüfen der Änderungen
summary(disc_lab_cleaned)## FOLIO VIV_SEL HOGAR N_REN
## Min. : 100019 Min. : 1.000 Min. :1.000 Min. : 1.000
## 1st Qu.: 901874 1st Qu.: 3.000 1st Qu.:1.000 1st Qu.: 1.000
## Median :1661245 Median : 4.000 Median :1.000 Median : 2.000
## Mean :1668120 Mean : 6.735 Mean :1.012 Mean : 1.812
## 3rd Qu.:2460963 3rd Qu.:10.000 3rd Qu.:1.000 3rd Qu.: 2.000
## Max. :3260794 Max. :37.000 Max. :5.000 Max. :14.000
##
## P4_1 P4_2 P4_3 P4_3A
## Min. :15.0 Min. :1.000 Min. :1.000 Min. : 1.000
## 1st Qu.:28.0 1st Qu.:2.000 1st Qu.:1.000 1st Qu.: 1.000
## Median :41.0 Median :2.000 Median :1.000 Median : 2.000
## Mean :43.2 Mean :3.212 Mean :1.089 Mean : 1.663
## 3rd Qu.:57.0 3rd Qu.:6.000 3rd Qu.:1.000 3rd Qu.: 2.000
## Max. :98.0 Max. :6.000 Max. :2.000 Max. :17.000
## NA's :19686 NA's :21858
## P4_4 P4_5 P4_6 P4_7 P4_8
## Min. :1.000 Min. :1.00 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.00 Median :2.00 Median :2.000 Median :4.000
## Mean :1.973 Mean :2.49 Mean :1.93 Mean :1.797 Mean :3.172
## 3rd Qu.:2.000 3rd Qu.:3.00 3rd Qu.:2.00 3rd Qu.:2.000 3rd Qu.:4.000
## Max. :2.000 Max. :5.00 Max. :2.00 Max. :9.000 Max. :5.000
## NA's :42999 NA's :29494
## P4_9C PD4_10_1 PD4_10_2 PD4_10_3
## Min. :110101 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:110103 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :110103 Median :1.000 Median :1.000 Median :1.000
## Mean :140491 Mean :1.272 Mean :1.352 Mean :1.119
## 3rd Qu.:130600 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:1.000
## Max. :999999 Max. :4.000 Max. :4.000 Max. :4.000
##
## PD4_10_4 PD4_10_5 PD4_10_6 PD4_10_7
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :1.231 Mean :1.125 Mean :1.058 Mean :1.045
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
##
## PD4_10_8 P4_10 NIV GRA
## Min. :1.000 Min. :1.000 Min. : 0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.: 2.000 1st Qu.:3.000
## Median :1.000 Median :2.000 Median : 3.000 Median :3.000
## Mean :1.073 Mean :2.515 Mean : 4.484 Mean :3.199
## 3rd Qu.:1.000 3rd Qu.:3.000 3rd Qu.: 6.000 3rd Qu.:4.000
## Max. :4.000 Max. :6.000 Max. :10.000 Max. :6.000
##
## FILTRO_4_12 P4_12 P4_13 P4_14 P4_15
## Min. :1.00 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:6.000 1st Qu.:1.000
## Median :2.00 Median :2.00 Median :1.000 Median :6.000 Median :1.000
## Mean :1.73 Mean :1.69 Mean :2.849 Mean :5.504 Mean :2.434
## 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:5.000
## Max. :2.00 Max. :2.00 Max. :8.000 Max. :6.000 Max. :6.000
## NA's :32276 NA's :27300 NA's :15401
## P4_17C P4_18_1 P4_18_2 P4_18_3
## Min. : 980 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3121 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :5242 Median :2.000 Median :2.000 Median :2.000
## Mean :5635 Mean :1.941 Mean :1.918 Mean :1.866
## 3rd Qu.:8199 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :9999 Max. :2.000 Max. :2.000 Max. :2.000
## NA's :15401
## P4_18_4 P4_18_5 P4_19 P5_1
## Min. :1.000 Min. :1.000 Min. : 1.000 Min. : 1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.: 6.000 1st Qu.: 5.000
## Median :2.000 Median :2.000 Median : 7.000 Median : 6.000
## Mean :1.926 Mean :1.978 Mean : 7.239 Mean : 6.829
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.: 8.000 3rd Qu.: 8.000
## Max. :2.000 Max. :2.000 Max. :99.000 Max. :30.000
##
## P5_2_1 P5_2_2 P5_2_3 P5_2_4 P5_2_5
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000
## Median :1.000 Median :1.00 Median :1.000 Median :2.000 Median :2.000
## Mean :1.059 Mean :1.17 Mean :1.075 Mean :1.875 Mean :1.933
## 3rd Qu.:1.000 3rd Qu.:1.00 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :9.000 Max. :9.00 Max. :9.000 Max. :9.000 Max. :9.000
##
## P5_2_6 P5_2_7 P5_2_8 P5_3_1 P5_3_2
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.00 Median :2.000 Median :2.000 Median :2.000
## Mean :1.969 Mean :1.98 Mean :1.988 Mean :1.834 Mean :1.879
## 3rd Qu.:2.000 3rd Qu.:2.00 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :9.000 Max. :9.00 Max. :9.000 Max. :2.000 Max. :2.000
##
## P5_3_3 P5_3_4 P5_4_1 P5_4_2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :1.883 Mean :1.873 Mean :1.877 Mean :1.764
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
##
## P5_4_3 P5_4_4 P5_4_5 P7_1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :1.852 Mean :1.925 Mean :1.828 Mean :1.545
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
##
## P7_1A P7_2 P7_3 P7_4_1
## Min. :1.000 Min. : 0.00 Min. : 0.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.:12.00 1st Qu.:14.00 1st Qu.:1.000
## Median :2.000 Median :14.00 Median :16.00 Median :1.000
## Mean :2.167 Mean :13.56 Mean :15.49 Mean :1.457
## 3rd Qu.:2.000 3rd Qu.:15.00 3rd Qu.:18.00 3rd Qu.:2.000
## Max. :9.000 Max. :99.00 Max. :99.00 Max. :2.000
## NA's :2093
## P7_4_2 P7_4_3 P7_5 P7_6_1 P7_6_2
## Min. :1.000 Min. :1 Min. : 0.00 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2 1st Qu.:15.00 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2 Median :18.00 Median :1.000 Median :2.000
## Mean :1.542 Mean :2 Mean :17.03 Mean :1.453 Mean :1.547
## 3rd Qu.:2.000 3rd Qu.:2 3rd Qu.:20.00 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2 Max. :99.00 Max. :2.000 Max. :2.000
## NA's :2093 NA's :2093 NA's :3767 NA's :3767
## P7_6_3 FILTRO_7_7 P7_7 FILTRO_7_8 P7_8
## Min. :1 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2 1st Qu.:2.000 1st Qu.:4.000 1st Qu.:2.000 1st Qu.:4.000
## Median :2 Median :2.000 Median :4.000 Median :2.000 Median :4.000
## Mean :2 Mean :1.961 Mean :4.288 Mean :1.915 Mean :4.375
## 3rd Qu.:2 3rd Qu.:2.000 3rd Qu.:5.000 3rd Qu.:2.000 3rd Qu.:5.000
## Max. :2 Max. :2.000 Max. :5.000 Max. :2.000 Max. :5.000
## NA's :3767 NA's :1706 NA's :3767
## P7_9 P7_10 P7_11 P8_1
## Min. :1.000 Min. : 1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.: 1.000 1st Qu.:2.000 1st Qu.:4.000
## Median :5.000 Median : 1.000 Median :4.000 Median :4.000
## Mean :4.799 Mean : 1.768 Mean :2.984 Mean :4.366
## 3rd Qu.:5.000 3rd Qu.: 1.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :6.000 Max. :99.000 Max. :9.000 Max. :6.000
## NA's :3767 NA's :12917 NA's :12917
## P8_1A P8_1B P8_2 P8_3_1
## Min. :1.0 Length:44189 Min. : 1.0 Min. :1.00
## 1st Qu.:2.0 Class :character 1st Qu.: 1.0 1st Qu.:1.00
## Median :3.0 Mode :character Median :11.0 Median :1.00
## Mean :2.6 Mean : 9.6 Mean :1.43
## 3rd Qu.:3.0 3rd Qu.:16.0 3rd Qu.:2.00
## Max. :4.0 Max. :98.0 Max. :2.00
## NA's :42019 NA's :42019 NA's :42019
## P8_3_2 P8_3_3 P8_3_4 P8_3_5
## Min. :1.00 Min. :1.00 Min. :1.0 Min. :1.00
## 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:2.0 1st Qu.:2.00
## Median :2.00 Median :2.00 Median :2.0 Median :2.00
## Mean :1.66 Mean :1.56 Mean :1.8 Mean :1.75
## 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:2.0 3rd Qu.:2.00
## Max. :2.00 Max. :2.00 Max. :2.0 Max. :2.00
## NA's :42019 NA's :42019 NA's :42019 NA's :42019
## P8_3_6 P8_3_7 P8_3_8 P8_3_9
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00
## 1st Qu.:1.00 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:2.00
## Median :2.00 Median :2.00 Median :2.00 Median :2.00
## Mean :1.54 Mean :1.87 Mean :1.87 Mean :1.86
## 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :2.00 Max. :2.00 Max. :2.00 Max. :2.00
## NA's :42019 NA's :42019 NA's :42019 NA's :42019
## P8_4_1 P8_4_2 P8_4_3 P8_5
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00
## 1st Qu.:1.00 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:2.00
## Median :1.00 Median :2.00 Median :2.00 Median :2.00
## Mean :1.12 Mean :1.89 Mean :1.84 Mean :2.46
## 3rd Qu.:1.00 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :2.00 Max. :2.00 Max. :2.00 Max. :9.00
## NA's :42902 NA's :42902 NA's :42902 NA's :42019
## P9_1 FILTRO_9_2 P9_2 P9_3 P9_4_1
## Min. :1.000 Min. :1.00 Min. : 1.00 Min. :1.00 Min. :1.00
## 1st Qu.:1.000 1st Qu.:2.00 1st Qu.: 1.00 1st Qu.:1.00 1st Qu.:1.00
## Median :2.000 Median :2.00 Median : 1.00 Median :2.00 Median :2.00
## Mean :1.557 Mean :1.99 Mean : 9.93 Mean :1.81 Mean :1.87
## 3rd Qu.:2.000 3rd Qu.:2.00 3rd Qu.:14.00 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :5.000 Max. :2.00 Max. :99.00 Max. :9.00 Max. :9.00
## NA's :43748 NA's :43748 NA's :43748
## P9_4_2 P9_4_3 P9_4_4 P9_4_5
## Min. :1.00 Min. :1.00 Min. :1.0 Min. :1.00
## 1st Qu.:2.00 1st Qu.:1.00 1st Qu.:2.0 1st Qu.:2.00
## Median :2.00 Median :2.00 Median :2.0 Median :2.00
## Mean :2.04 Mean :1.99 Mean :2.1 Mean :2.08
## 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:2.0 3rd Qu.:2.00
## Max. :9.00 Max. :9.00 Max. :9.0 Max. :9.00
## NA's :43748 NA's :43748 NA's :43748 NA's :43748
## P9_4_6 P9_4_7 P9_4_8 P9_4_9
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00
## 1st Qu.:1.00 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:1.00
## Median :2.00 Median :2.00 Median :2.00 Median :2.00
## Mean :1.95 Mean :2.18 Mean :2.13 Mean :1.95
## 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :9.00 Max. :9.00 Max. :9.00 Max. :9.00
## NA's :43748 NA's :43748 NA's :43748 NA's :43748
## P9_5_1 P9_5_2 P9_5_3 P9_6
## Min. :1.0 Min. :1.00 Min. :1.0 Min. : 1.00
## 1st Qu.:1.0 1st Qu.:2.00 1st Qu.:2.0 1st Qu.:16.00
## Median :1.0 Median :2.00 Median :2.0 Median :99.00
## Mean :1.2 Mean :1.84 Mean :1.8 Mean :64.32
## 3rd Qu.:1.0 3rd Qu.:2.00 3rd Qu.:2.0 3rd Qu.:99.00
## Max. :2.0 Max. :2.00 Max. :2.0 Max. :99.00
## NA's :44006 NA's :44006 NA's :44006 NA's :43748
## P9_7 P9_8 P9_9 P9_10
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00
## 1st Qu.:2.00 1st Qu.:1.00 1st Qu.:2.00 1st Qu.:1.00
## Median :9.00 Median :1.00 Median :2.00 Median :2.00
## Mean :6.01 Mean :1.66 Mean :3.62 Mean :1.66
## 3rd Qu.:9.00 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :9.00 Max. :9.00 Max. :9.00 Max. :2.00
## NA's :43748 NA's :43748 NA's :43748 NA's :43748
## P9_10A P10_1_1 P10_1_2 P10_1_3
## Length:44189 Min. :1.000 Min. :1.000 Min. :1.000
## Class :character 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Mode :character Median :2.000 Median :1.000 Median :2.000
## Mean :1.577 Mean :1.363 Mean :1.692
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000
##
## P10_1_4 P10_1_5 P10_2 P10_3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :1.625 Mean :1.624 Mean :1.904 Mean :1.949
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
##
## FILTRO_10_4 P10_4_1 P10_4_2 P10_4_3
## Min. :1.000 Min. :1.0 Min. :1.00 Min. :1.00
## 1st Qu.:1.000 1st Qu.:2.0 1st Qu.:1.00 1st Qu.:2.00
## Median :1.000 Median :2.0 Median :1.00 Median :2.00
## Mean :1.108 Mean :1.8 Mean :1.37 Mean :1.82
## 3rd Qu.:1.000 3rd Qu.:2.0 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :2.000 Max. :2.0 Max. :2.00 Max. :2.00
## NA's :39423 NA's :39423 NA's :39423
## P10_4_4 P10_4_5 P10_4_6 P10_4_7
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00
## 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:2.00
## Median :2.00 Median :2.00 Median :2.00 Median :2.00
## Mean :1.92 Mean :1.94 Mean :1.87 Mean :2.08
## 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :2.00 Max. :2.00 Max. :9.00 Max. :9.00
## NA's :39423 NA's :39423 NA's :43570 NA's :44051
## P10_4_8 FILTRO_10_5 P10_5_1 P10_5_2
## Min. :1.00 Min. :1.000 Min. :1.00 Min. :1.00
## 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:2.00
## Median :2.00 Median :2.000 Median :2.00 Median :2.00
## Mean :1.94 Mean :1.947 Mean :1.99 Mean :1.89
## 3rd Qu.:2.00 3rd Qu.:2.000 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :2.00 Max. :2.000 Max. :9.00 Max. :9.00
## NA's :39423 NA's :41827 NA's :41827
## P10_5_3 P10_5_4 P10_5_5 P10_5_6
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1
## 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:2
## Median :2.00 Median :2.00 Median :2.00 Median :2
## Mean :1.99 Mean :1.97 Mean :1.99 Mean :2
## 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:2
## Max. :9.00 Max. :9.00 Max. :9.00 Max. :9
## NA's :41827 NA's :41827 NA's :41827 NA's :41827
## P10_5_7 P10_6_1 P10_6_2 P10_6_3
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00
## 1st Qu.:1.00 1st Qu.:2.00 1st Qu.:1.00 1st Qu.:1.00
## Median :1.00 Median :2.00 Median :2.00 Median :1.00
## Mean :1.23 Mean :1.78 Mean :1.73 Mean :1.28
## 3rd Qu.:1.00 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :9.00 Max. :2.00 Max. :2.00 Max. :2.00
## NA's :41827 NA's :43715 NA's :43715 NA's :43715
## P10_6_4 P11_1_1 P11_2_1_1 P11_2_1_2
## Min. :1.00 Min. :1.000 Min. : 1.00 Min. : 2.00
## 1st Qu.:2.00 1st Qu.:2.000 1st Qu.: 3.00 1st Qu.: 5.00
## Median :2.00 Median :2.000 Median : 6.00 Median : 6.00
## Mean :1.85 Mean :1.909 Mean : 6.08 Mean : 7.15
## 3rd Qu.:2.00 3rd Qu.:2.000 3rd Qu.:10.00 3rd Qu.:10.00
## Max. :2.00 Max. :2.000 Max. :99.00 Max. :99.00
## NA's :43715 NA's :40162 NA's :43264
## P11_2_1_3 P11_1_2 P11_2_2_1 P11_2_2_2
## Min. : 3.00 Min. :1.000 Min. : 1 Min. : 2.00
## 1st Qu.: 6.00 1st Qu.:2.000 1st Qu.: 5 1st Qu.: 6.00
## Median : 9.00 Median :2.000 Median : 6 Median : 6.00
## Mean : 8.59 Mean :1.906 Mean : 7 Mean : 7.32
## 3rd Qu.:10.00 3rd Qu.:2.000 3rd Qu.:10 3rd Qu.:10.00
## Max. :99.00 Max. :2.000 Max. :10 Max. :10.00
## NA's :43890 NA's :40030 NA's :43318
## P11_2_2_3 P11_1_3 P11_2_3_1 P11_2_3_2
## Min. : 3.00 Min. :1.000 Min. : 1.00 Min. : 2.00
## 1st Qu.: 6.00 1st Qu.:2.000 1st Qu.: 3.00 1st Qu.: 6.00
## Median :10.00 Median :2.000 Median : 6.00 Median : 6.00
## Mean : 8.62 Mean :1.948 Mean : 6.38 Mean : 7.88
## 3rd Qu.:10.00 3rd Qu.:2.000 3rd Qu.:10.00 3rd Qu.:10.00
## Max. :10.00 Max. :2.000 Max. :99.00 Max. :99.00
## NA's :43889 NA's :41885 NA's :43843
## P11_2_3_3 P11_1_4 P11_2_4_1 P11_2_4_2
## Min. : 4.00 Min. :1.000 Min. : 1.00 Min. : 2.00
## 1st Qu.: 6.00 1st Qu.:1.000 1st Qu.: 3.00 1st Qu.: 5.00
## Median :10.00 Median :2.000 Median : 5.00 Median : 6.00
## Mean :10.24 Mean :1.724 Mean : 5.48 Mean : 6.18
## 3rd Qu.:10.00 3rd Qu.:2.000 3rd Qu.: 7.00 3rd Qu.: 8.00
## Max. :99.00 Max. :2.000 Max. :10.00 Max. :10.00
## NA's :44077 NA's :32005 NA's :39304
## P11_2_4_3 P11_1_5 P11_2_5_1 P11_2_5_2
## Min. : 3.00 Min. :1.000 Min. : 1.00 Min. : 2.00
## 1st Qu.: 6.00 1st Qu.:2.000 1st Qu.: 5.00 1st Qu.: 6.00
## Median : 9.00 Median :2.000 Median : 6.00 Median : 6.00
## Mean : 7.79 Mean :1.814 Mean : 6.92 Mean : 6.99
## 3rd Qu.:10.00 3rd Qu.:2.000 3rd Qu.:10.00 3rd Qu.:10.00
## Max. :10.00 Max. :2.000 Max. :10.00 Max. :10.00
## NA's :41654 NA's :35960 NA's :41868
## P11_2_5_3 P11_1_6 P11_2_6_1 P11_2_6_2
## Min. : 3.00 Min. :1.000 Min. : 1.00 Min. : 2.0
## 1st Qu.: 6.00 1st Qu.:2.000 1st Qu.: 4.00 1st Qu.: 6.0
## Median :10.00 Median :2.000 Median : 6.00 Median : 7.0
## Mean : 8.56 Mean :1.857 Mean : 6.94 Mean : 7.7
## 3rd Qu.:10.00 3rd Qu.:2.000 3rd Qu.:10.00 3rd Qu.:10.0
## Max. :10.00 Max. :2.000 Max. :99.00 Max. :99.0
## NA's :43289 NA's :37850 NA's :42898
## P11_2_6_3 P11_3 P11_4_1 P11_4_2
## Min. : 3.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.: 8.00 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000
## Median :10.00 Median :1.000 Median :2.000 Median :2.000
## Mean : 9.13 Mean :1.451 Mean :1.947 Mean :2.056
## 3rd Qu.:10.00 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :99.00 Max. :2.000 Max. :3.000 Max. :3.000
## NA's :43839 NA's :19933 NA's :19933
## P11_4_3 P11_4_4 P11_4_5 P11_5_1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :1.983 Mean :2.027 Mean :2.019 Mean :1.985
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :3.000 Max. :3.000 Max. :3.000 Max. :9.000
## NA's :19933 NA's :19933 NA's :19933
## P11_5_2 P11_5_3 P11_5_4 P11_5_5
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :2.198 Mean :2.029 Mean :2.378 Mean :2.064
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:2.000
## Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.000
##
## P11_5_6 P11_5_7 P11_5_8 P11_6_01 P11_6_02
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.00
## Median :2.000 Median :2.000 Median :2.000 Median :2.000 Median :2.00
## Mean :2.332 Mean :2.128 Mean :2.469 Mean :1.983 Mean :1.97
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.00
## Max. :9.000 Max. :3.000 Max. :9.000 Max. :9.000 Max. :9.00
## NA's :26708 NA's :10908 NA's :10763
## P11_6_03 P11_6_04 P11_6_05 P11_6_06 P11_6_07
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.00
## Median :2.000 Median :2.000 Median :2.000 Median :2.000 Median :2.00
## Mean :1.951 Mean :1.962 Mean :1.978 Mean :1.976 Mean :1.96
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.00
## Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.00
##
## P11_6_08 P11_6_09 P11_6_10 P11_6_11
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :1.971 Mean :1.966 Mean :1.991 Mean :1.999
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :9.000 Max. :9.000 Max. :9.000 Max. :2.000
## NA's :1
## P11_7 P11_8_1 P11_8_2 P11_8_3
## Min. :1.000 Min. :1.00 Min. :1.00 Min. :1.00
## 1st Qu.:1.000 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:2.00
## Median :1.000 Median :2.00 Median :2.00 Median :2.00
## Mean :1.669 Mean :1.99 Mean :1.97 Mean :1.89
## 3rd Qu.:3.000 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :3.000 Max. :2.00 Max. :2.00 Max. :2.00
## NA's :37556 NA's :37556 NA's :37556
## P11_8_4 P11_8_5 P11_8_6 P12_1_1
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.000
## 1st Qu.:2.00 1st Qu.:1.00 1st Qu.:2.00 1st Qu.:1.000
## Median :2.00 Median :1.00 Median :2.00 Median :1.000
## Mean :1.99 Mean :1.15 Mean :1.97 Mean :1.736
## 3rd Qu.:2.00 3rd Qu.:1.00 3rd Qu.:2.00 3rd Qu.:2.000
## Max. :2.00 Max. :2.00 Max. :2.00 Max. :9.000
## NA's :37556 NA's :37556 NA's :37556
## P12_1_2 P12_1_3 P12_2 P12_3_1 P12_3_2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:3.00 1st Qu.:3.000
## Median :1.000 Median :2.000 Median :3.000 Median :3.00 Median :4.000
## Mean :1.698 Mean :1.861 Mean :3.052 Mean :3.13 Mean :3.624
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:5.000
## Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.00 Max. :9.000
##
## P12_3_3 P12_3_4 P12_3_5 P12_4_1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :3.000 Median :2.000 Median :2.000
## Mean :3.305 Mean :2.609 Mean :2.693 Mean :1.987
## 3rd Qu.:5.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.000
##
## P12_4_2 P12_4_3 P12_4_4 P12_4_5 P12_4_6
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000
## Median :2.000 Median :1.000 Median :1.000 Median :1.00 Median :1.000
## Mean :2.783 Mean :1.286 Mean :1.384 Mean :1.26 Mean :1.276
## 3rd Qu.:5.000 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:1.00 3rd Qu.:1.000
## Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.00 Max. :9.000
##
## P12_5_1 P12_5_2 P12_5_3 P12_5_4 P12_5_5
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.00 Median :1.000 Median :1.000 Median :1.000
## Mean :1.179 Mean :1.26 Mean :1.202 Mean :1.184 Mean :1.154
## 3rd Qu.:1.000 3rd Qu.:1.00 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :9.000 Max. :9.00 Max. :9.000 Max. :9.000 Max. :9.000
##
## P12_5_6 P12_5_7 P12_6_1 P12_6_2 P12_6_3
## Min. :1.0 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.0 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :1.0 Median :1.000 Median :2.000 Median :2.000 Median :2.000
## Mean :1.2 Mean :1.361 Mean :1.982 Mean :1.884 Mean :1.917
## 3rd Qu.:1.0 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :9.0 Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.000
##
## P12_6_4 P12_6_5 P12_6_6 ENT
## Min. :1.000 Min. :1.000 Min. :1.000 Min. : 1.00
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.: 9.00
## Median :2.000 Median :2.000 Median :2.000 Median :16.00
## Mean :1.932 Mean :2.001 Mean :2.012 Mean :16.51
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:24.00
## Max. :9.000 Max. :9.000 Max. :9.000 Max. :32.00
##
## EST_DIS UPM_DIS FACTOR les
## Min. : 1.0 Min. : 1 Min. : 77 Min. :0.00000
## 1st Qu.:127.0 1st Qu.:1738 1st Qu.: 771 1st Qu.:0.00000
## Median :250.0 Median :3291 Median : 1453 Median :0.00000
## Mean :249.6 Mean :3314 Mean : 2200 Mean :0.03354
## 3rd Qu.:369.0 3rd Qu.:4863 3rd Qu.: 2622 3rd Qu.:0.00000
## Max. :513.0 Max. :6374 Max. :88053 Max. :1.00000
## library(dplyr)
# NA-Werte filtern und Dummy-Variable 'les' erstellen
disc_lab <- disc_lab %>%
mutate(
les = ifelse(P8_1 %in% c(1, 3, 6), 1, 0),
P11_5_7 = ifelse(P11_5_7 == 9, NA, P11_5_7),
P11_6_11 = ifelse(P11_6_11 == 9, NA, P11_6_11),
P11_8_1 = ifelse(P11_8_1 == 9, NA, P11_8_1)
)
table(disc_lab$les)##
## 0 1
## 42707 1482# Datensatz filtern, um nur 'les' == 1 zu behalten
quer_les <- disc_lab %>% filter(les == 1)
# Discriminierung laboral Codieren: 1 für "Sí", 0 für "Nein" und "No aplica"
quer_les <- quer_les %>%
mutate(
dicriminacion_lab1 = ifelse(P11_4_1 == 1, 1, 0),
dicriminacion_lab2 = ifelse(P11_4_2 == 1, 1, 0),
dicriminacion_lab3 = ifelse(P11_4_3 == 1, 1, 0),
dicriminacion_lab4 = ifelse(P11_4_4 == 1, 1, 0),
dicriminacion_lab5 = ifelse(P11_4_5 == 1, 1, 0),
dicriminacion_lab6 = ifelse(P11_5_7 == 1, 1, 0)
)
# Tabelle für 'dicriminacion_lab6' erstellen
table(quer_les$dicriminacion_lab6)##
## 0 1
## 1050 82# Discriminierung diario Codieren: 1 für "Sí", 0 für "Nein" und "No aplica"
quer_les <- quer_les %>%
mutate(
dicriminacion1 = ifelse(P11_6_11 == 1, 1, 0),
dicriminacion2 = ifelse(P11_8_1 == 1, 1, 0),
dicriminacion3 = ifelse(P11_8_2 == 1, 1, 0),
dicriminacion4 = ifelse(P11_8_3 == 1, 1, 0),
dicriminacion5 = ifelse(P11_8_4 == 1, 1, 0),
dicriminacion6 = ifelse(P11_8_5 == 1, 1, 0)
)
# Tabellen für die Diskriminierungsvariablen erstellen
table(quer_les$dicriminacion_lab1)##
## 0 1
## 697 127##
## 0 1
## 765 59##
## 0 1
## 715 109##
## 0 1
## 721 103##
## 0 1
## 794 30##
## 0 1
## 1050 82##
## 0 1
## 1458 24##
## 0 1
## 232 9##
## 0 1
## 209 32##
## 0 1
## 208 33##
## 0 1
## 220 21##
## 0 1
## 85 1564. Discriminierung laboral
variables <- c("dicriminacion_lab1", "dicriminacion_lab2", "dicriminacion_lab3",
"dicriminacion_lab4", "dicriminacion_lab5", "dicriminacion_lab6")
# Berechne die Häufigkeit der '1'-Werte (sí) für jede Variable
freq_si <- sapply(variables, function(var) sum(quer_les[[var]] == 1, na.rm = TRUE))
# Erstelle ein Balkendiagramm
barplot(freq_si,
names.arg = variables, # Namen der Variablen auf der X-Achse
col = "#FDFD96", # Farbe für die Balken
main = "Confirmación de la experiencia de discriminación laboral de lesbianas",
xlab = "Variable",
ylab = "Respuestas 'Sí' (1)",
ylim = c(0, max(freq_si) + 10)) # Y-Achse dynamisch basierend auf den Häufigkeiten
# Variablen definieren
variables <- c("dicriminacion_lab1", "dicriminacion_lab2", "dicriminacion_lab3",
"dicriminacion_lab4", "dicriminacion_lab5", "dicriminacion_lab6")
# Berechne die Häufigkeit der '1'-Werte (sí) für jede Variable
freq_si <- sapply(variables, function(var) sum(quer_les[[var]] == 1, na.rm = TRUE))
# Erstelle ein Balkendiagramm
barplot_heights <- barplot(freq_si,
names.arg = variables, # Namen der Variablen auf der X-Achse
col = "#FDFD96" , # Farbe für die Balken
main = "Confirmación de la experiencia de discriminación laboral de lesbianas",
xlab = "Variable",
ylab = "Respuestas 'Sí' (1)",
ylim = c(0, max(freq_si) + 10)) # Y-Achse dynamisch basierend auf den Häufigkeiten
# Hinzufügen von genauen Werten auf den Balken
text(x = barplot_heights, y = freq_si, label = freq_si, pos = 3, cex = 0.8, col = "black")
# Variablen und die entsprechenden Fragen
variables <- c("dicriminacion_lab1", "dicriminacion_lab2", "dicriminacion_lab3",
"dicriminacion_lab4", "dicriminacion_lab5", "dicriminacion_lab6")
questions <- c(
"¿en el trabajo recibió comentarios ofensivos o burlas?",
"¿en el trabajo le excluyeron de eventos o actividades sociales?",
"¿en el trabajo le molestaron o acosaron?",
"¿en el trabajo recibió un trato desigual respecto a los beneficios, prestaciones laborales o ascensos?",
"¿en el trabajo le pegaron, agredieron o amenazaron?",
"¿le han negado injustificadamente el empleo o la oportunidad de trabajar?"
)
# Berechne die Häufigkeit der '1'-Werte (sí) und der '0'-Werte (no) für jede Variable
freq_si <- sapply(variables, function(var) sum(quer_les[[var]] == 1, na.rm = TRUE))
freq_no <- sapply(variables, function(var) sum(quer_les[[var]] == 0, na.rm = TRUE))
# Kombiniere die Häufigkeiten in einer Matrix
freq_matrix <- rbind(freq_si, freq_no)
# Erstelle Tortendiagramme für jede Variable
par(mfrow = c(2, 3), mar = c(5, 5, 2, 2)) # Setze Ränder für bessere Sichtbarkeit
for (i in 1:length(variables)) {
# Erstelle das Tortendiagramm
pie(freq_matrix[, i],
labels = paste(c("Sí", "No"), ": ", freq_matrix[, i]),
col = c("#FFFACD","#CBAACB" ),
main = strwrap(questions[i], width = 40), # Umbrüche einfügen
cex.main = 0.8) # Verkleinerung der Schriftgröße des Titels
}
# Variable und entsprechende Fragen
variables <- c("dicriminacion_lab1", "dicriminacion_lab2", "dicriminacion_lab3",
"dicriminacion_lab4", "dicriminacion_lab5", "dicriminacion_lab6")
questions <- c(
"¿en el trabajo recibió comentarios ofensivos o burlas?",
"¿en el trabajo le excluyeron de eventos o actividades sociales?",
"¿en el trabajo le molestaron o acosaron?",
"¿en el trabajo recibió un trato desigual respecto a los beneficios, prestaciones laborales o ascensos?",
"¿en el trabajo le pegaron, agredieron o amenazaron?",
"¿le han negado injustificadamente el empleo o la oportunidad de trabajar?"
)
# Berechne die Häufigkeit der '1'-Werte (sí) und der '0'-Werte (no) für jede Variable
freq_si <- sapply(variables, function(var) sum(quer_les[[var]] == 1, na.rm = TRUE))
freq_no <- sapply(variables, function(var) sum(quer_les[[var]] == 0, na.rm = TRUE))
# Berechne die Gesamtanzahl der Beobachtungen für jede Variable
total <- freq_si + freq_no
# Berechne die prozentuale Häufigkeit
percent_si <- (freq_si / total) * 100
percent_no <- (freq_no / total) * 100
# Kombiniere die Prozentangaben in einer Matrix
percent_matrix <- rbind(percent_si, percent_no)
# Erstelle Tortendiagramme für jede Variable
par(mfrow = c(2, 3), mar = c(5, 5, 2, 2)) # Setze Ränder für bessere Sichtbarkeit
for (i in 1:length(variables)) {
# Erstelle das Tortendiagramm
pie(percent_matrix[, i],
labels = paste(c("Sí", "No"), ": ", round(percent_matrix[, i], 1), "%"),
col = c("#FFFACD","#CBAACB" ),
main = strwrap(questions[i], width = 40), # Umbrüche aAdding interpretable line breaks Haupttitel
cex.main = 0.8) # Verkleinerung der Schriftgröße des Titels
}
# Variablen, die du vergleichen möchtest
variables <- c("dicriminacion_lab1", "dicriminacion_lab2", "dicriminacion_lab3",
"dicriminacion_lab4", "dicriminacion_lab5", "dicriminacion_lab6")
# Berechne die Häufigkeit der '1'-Werte (Sí) und '0'-Werte (No) für jede Variable
freq_si <- sapply(variables, function(var) sum(quer_les[[var]] == 1, na.rm = TRUE))
freq_no <- sapply(variables, function(var) sum(quer_les[[var]] == 0, na.rm = TRUE))
# Berechne die Verhältnisse (prozentuale Anteile) für Sí und No
total_responses <- freq_si + freq_no
percent_si <- (freq_si / total_responses) * 100
percent_no <- (freq_no / total_responses) * 100
# Erstelle eine Matrix mit den Häufigkeiten
freq_matrix <- rbind(freq_no, freq_si)
# Farben definieren
farben <- c("#CBAACB", "#FFFACD") # Farben für No und Sí
# Erstelle das Balkendiagramm der Häufigkeiten
barplot_heights <- barplot(freq_matrix,
beside = TRUE, # Nebeneinander darstellen
col = farben, # Farben für No und Sí
names.arg = variables, # Variablennamen auf der x-Achse
main = "Comparación de los porcentajes de 'Sí' (1) y 'No' (0)", # Titel
xlab = "Variable",
ylab = "Frecuencia",
legend.text = c("No", "Sí"), # Legende
args.legend = list(x = "topright", bty = "n"), # Legendenposition
ylim = c(0, max(freq_matrix) + 50)) # Y-Achse dynamisch basierend auf den Häufigkeiten
# Hinzufügen von genauen Werten und Prozenten direkt an den Balken
text(x = barplot_heights[1,], y = freq_matrix[1,] + 5, labels = paste0(freq_matrix[1,], " (", round(percent_no, 1), "%)"), pos = 3, cex = 0.8, col = "black")
text(x = barplot_heights[2,], y = freq_matrix[2,] + 5, labels = paste0(freq_matrix[2,], " (", round(percent_si, 1), "%)"), pos = 3, cex = 0.8, col = "black")
##
## 0 1
## 697 127variables <- c("dicriminacion_lab1", "dicriminacion_lab2", "dicriminacion_lab3",
"dicriminacion_lab4", "dicriminacion_lab5", "dicriminacion_lab6")
freq_si <- sapply(variables, function(var) sum(quer_les[[var]] == 1, na.rm = TRUE))
freq_no <- sapply(variables, function(var) sum(quer_les[[var]] == 0, na.rm = TRUE))
freq_matrix <- rbind(freq_no, freq_si)
barplot(freq_matrix,
beside = TRUE,
col = c("#CBAACB", "#FFFACD"),
names.arg = variables,
main = "Comparación de los porcentajes de 'Sí' (1) y 'No' (0)",
xlab = "Variable",
ylab = "Frecuencia",
legend.text = c("No", "Sí"),
ylim = c(0, max(freq_matrix) + 50)) 
5. Discriminación general
Aunque la investigación se centra en la discriminación hacia las mujeres lesbianas, también se considera importante mostrar cómo es el mundo, donde existe discriminación contra las mujeres no heterosexuales en general.
# Variablen, die du vergleichen möchtest
variables <- c("dicriminacion1", "dicriminacion2", "dicriminacion3",
"dicriminacion4", "dicriminacion5", "dicriminacion6")
# Berechne die Häufigkeit der '1'-Werte (Sí) und '0'-Werte (No) für jede Variable
freq_si <- sapply(variables, function(var) sum(quer_les[[var]] == 1, na.rm = TRUE))
freq_no <- sapply(variables, function(var) sum(quer_les[[var]] == 0, na.rm = TRUE))
# Berechne die Verhältnisse (prozentuale Anteile) für Sí und No
total_responses <- freq_si + freq_no
ratio_si <- freq_si / total_responses
ratio_no <- freq_no / total_responses
# Erstelle eine Matrix mit den Verhältnissen
ratio_matrix <- rbind(ratio_no, ratio_si)
# Definiere Farben
farben <- c("Sí" = "#FFC0CB", "No" = "#AEC6CF")
# Erstelle das Balkendiagramm der Verhältnisse
barplot_heights <- barplot(ratio_matrix,
beside = TRUE, # Nebeneinander darstellen
col = farben, # Farben für No und Sí
names.arg = variables, # Variablennamen auf der x-Achse
main = "Comparación de los porcentajes de 'Sí' (1) y 'No' (0)", # Titel
xlab = "Variable",
ylab = "Relación",
legend.text = c("No", "Sí"), # Legende
args.legend = list(x = "topright", bty = "n"), # Legendenposition
ylim = c(0, 1)) # Y-Achse für Verhältnisse zwischen 0 und 1
# Hinzufügen von genauen Werten auf den Balken
text(x = barplot_heights[1,], y = ratio_matrix[1,] - 0.05, labels = round(ratio_matrix[1,], 2), pos = 1, cex = 0.8, col = "black")
text(x = barplot_heights[2,], y = ratio_matrix[2,] - 0.05, labels = round(ratio_matrix[2,], 2), pos = 1, cex = 0.8, col = "black")
# Variablen, die du vergleichen möchtest
variables <- c("dicriminacion1", "dicriminacion2", "dicriminacion3",
"dicriminacion4", "dicriminacion5", "dicriminacion6")
# Berechne die Häufigkeit der '1'-Werte (Sí) für jede Variable
freq_si <- sapply(variables, function(var) sum(quer_les[[var]] == 1, na.rm = TRUE))
# Erstelle das Balkendiagramm nur für die Sí-Antworten
barplot_heights <- barplot(freq_si,
col = "#AEC6CF", # Farbe für Sí-Antworten
names.arg = variables, # Variablennamen auf der x-Achse
main = "Confirmación de la experiencia de discriminación laboral de lesbianas", # Titel
xlab = "Variable",
ylab = "Respuestas Sí (1)",
ylim = c(0, max(freq_si) + 50)) # Y-Achse dynamisch basierend auf den Häufigkeiten
# Hinzufügen von genauen Werten auf den Balken
text(x = barplot_heights, y = freq_si, label = freq_si, pos = 3, cex = 0.8, col = "black")
# Variablen und die zugehörigen Fragen
questions <- c(
"¿ha sido discriminada(o), o menospreciada(o), por su preferencia sexual?",
"¿Usted no toma de la mano a su pareja o le muestra su afecto en público por miedo a sufrir agresión o violencia?",
"¿Usted no toma de la mano a su pareja o le muestra su afecto en público por ser mal visto socialmente?",
"¿Usted no toma de la mano a su pareja o le muestra su afecto en público por pena o vergüenza?",
"¿Usted no toma de la mano a su pareja o le muestra su afecto en público por temor a ser discriminada(o)?",
"¿Usted no toma de la mano a su pareja o le muestra su afecto en público por falta de costumbre?"
)
# Variablen, die du vergleichen möchtest
variables <- c("dicriminacion1", "dicriminacion2", "dicriminacion3",
"dicriminacion4", "dicriminacion5", "dicriminacion6")
# Erstelle eine Funktion, um für jede Variable ein Tortendiagramm zu erstellen
par(mfrow = c(2, 3)) # Zwei Reihen und drei Spalten für die Diagramme
for (i in 1:length(variables)) {
var <- variables[i]
question <- questions[i]
# Breche die Frage in mehrere Zeilen
wrapped_question <- strwrap(question, width = 40) # 40 Zeichen pro Zeile
# Berechne die Häufigkeit der '1' (Sí) und '0' (No) für jede Variable
freq <- table(quer_les[[var]])
# Erstelle das Tortendiagramm für jede Variable
pie(freq,
main = paste(wrapped_question, collapse = "\n"), # Frage in mehreren Zeilen als Titel
col = c("#FFC0CB", "#AEC6CF"), # Farben für No (0) und Sí (1)
labels = paste(c("No", "Sí"), "\n", round(100 * freq / sum(freq), 1), "%"), # Prozentsätze anzeigen
cex.main = 0.8) # Verkleinere die Schriftgröße des Titels (Fragen)
}
Matrimonio igualitario
## Warning: Paket 'sf' wurde unter R Version 4.4.2 erstellt## Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUElibrary(ggplot2)
library(dplyr)
# Shapefile einlesen
mexmex <- st_read("C:/Master/3. Semester/R/Dataset/gadm41_MEX.gpkg", layer = "ADM_ADM_1")## Reading layer `ADM_ADM_1' from data source
## `C:\Master\3. Semester\R\Dataset\gadm41_MEX.gpkg' using driver `GPKG'
## Simple feature collection with 32 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -118.3665 ymin: 14.53507 xmax: -86.71074 ymax: 32.71863
## Geodetic CRS: WGS 84# Daten zu den Jahren der Einführung der Ehe für alle in den Bundesstaaten
matrimonio_data <- data.frame(
estado = c( "Quintana Roo", "Coahuila", "Chihuahua", "Nayarit",
"Campeche", "Michoacán", "Morelos", "Colima", "Chiapas",
"Nuevo León", "San Luis Potosí", "Hidalgo", "Baja California Sur",
"Aguascalientes", "Oaxaca", "Puebla", "Tlaxcala", "Sinaloa",
"Baja California", "Yucatán", "Querétaro", "Sonora", "Zacatecas",
"Guanajuato", "Jalisco", "Veracruz", "Durango", "México",
"Tabasco", "Guerrero", "Tamaulipas", "Distrito Federal"),
jahr = c(2012, 2014, 2015, 2015, 2016, 2016, 2016, 2016, 2017,
2019, 2019, 2019, 2019, 2019, 2019, 2020, 2020, 2021,
2021, 2021, 2021, 2021, 2021, 2021, 2022, 2022, 2022,
2022, 2022, 2022, 2022, 2010)
)
# Daten mit Shapefile verbinden
mexmex <- mexmex %>%
left_join(matrimonio_data, by = c("NAME_1" = "estado"))
# Karte erstellen
ggplot(data = mexmex) +
geom_sf(aes(fill = jahr)) +
scale_fill_continuous(name = "Jahr der Einführung", low = "lightblue", high = "darkblue") +
labs(title = "Einführung der Ehe für alle in Mexiko",
caption = "Quelle: Maguey 2022") +
theme_minimal()
## [1] "Aguascalientes" "Baja California" "Baja California Sur"
## [4] "Campeche" "Chiapas" "Chihuahua"
## [7] "Coahuila" "Colima" "Distrito Federal"
## [10] "Durango" "Guanajuato" "Guerrero"
## [13] "Hidalgo" "Jalisco" "México"
## [16] "Michoacán" "Morelos" "Nayarit"
## [19] "Nuevo León" "Oaxaca" "Puebla"
## [22] "Querétaro" "Quintana Roo" "San Luis Potosí"
## [25] "Sinaloa" "Sonora" "Tabasco"
## [28] "Tamaulipas" "Tlaxcala" "Veracruz"
## [31] "Yucatán" "Zacatecas"# Benötigte Bibliotheken laden
library(sf)
library(ggplot2)
library(dplyr)
# Shapefile einlesen
mexmex <- st_read("C:/Master/3. Semester/R/Dataset/gadm41_MEX.gpkg", layer = "ADM_ADM_1")## Reading layer `ADM_ADM_1' from data source
## `C:\Master\3. Semester\R\Dataset\gadm41_MEX.gpkg' using driver `GPKG'
## Simple feature collection with 32 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -118.3665 ymin: 14.53507 xmax: -86.71074 ymax: 32.71863
## Geodetic CRS: WGS 84# Daten zu den Jahren der Einführung der Ehe für alle in den Bundesstaaten
matrimonio_data <- data.frame(
estado = c("Distrito Federal", "Quintana Roo", "Coahuila", "Chihuahua", "Nayarit",
"Campeche", "Michoacán", "Morelos", "Colima", "Chiapas",
"Nuevo León", "San Luis Potosí", "Hidalgo", "Baja California Sur",
"Aguascalientes", "Oaxaca", "Puebla", "Tlaxcala", "Sinaloa",
"Baja California", "Yucatán", "Querétaro", "Sonora", "Zacatecas",
"Guanajuato", "Jalisco", "Veracruz", "Durango", "México",
"Tabasco", "Guerrero", "Tamaulipas"),
jahr = c(2010, 2012, 2014, 2015, 2015, 2016, 2016, 2016, 2016, 2017,
2019, 2019, 2019, 2019, 2019, 2019, 2020, 2020, 2021,
2021, 2021, 2021, 2021, 2021, 2021, 2022, 2022, 2022,
2022, 2022, 2022, 2022)
)
# Anpassung der Namen in den Daten, um sie den Namen im Shapefile anzupassen
matrimonio_data$estado <- gsub("Ciudad de México", "Mexico City", matrimonio_data$estado)
matrimonio_data$estado <- gsub("Estado de México", "Mexico State", matrimonio_data$estado)
# Daten mit Shapefile verbinden
mexmex <- mexmex %>%
left_join(matrimonio_data, by = c("NAME_1" = "estado"))
# Farben definieren
farben <- c("2010" = "#FFD700", "2012" = "#FFA500", "2014" = "#800080", "2015" = "#FF0000",
"2016" = "#FF69B4", "2017" = "#0000FF", "2019" = "#008000", "2020" = "#2171b5",
"2021" = "#FF6347", "2022" = "#6A5ACD")
# Karte erstellen
ggplot(data = mexmex) +
geom_sf(aes(fill = as.factor(jahr))) +
scale_fill_manual(values = farben, name = "Jahr der Einführung") +
labs(title = "Einführung der Ehe für alle in Mexiko",
caption = "Quelle: Maguey 2022") +
theme_minimal()
# Benötigte Bibliotheken laden
library(sf)
library(ggplot2)
library(dplyr)
# Shapefile einlesen
mexmex <- st_read("C:/Master/3. Semester/R/Dataset/gadm41_MEX.gpkg", layer = "ADM_ADM_1")## Reading layer `ADM_ADM_1' from data source
## `C:\Master\3. Semester\R\Dataset\gadm41_MEX.gpkg' using driver `GPKG'
## Simple feature collection with 32 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -118.3665 ymin: 14.53507 xmax: -86.71074 ymax: 32.71863
## Geodetic CRS: WGS 84# Daten zu den Jahren der Einführung der Ehe für alle in den Bundesstaaten
matrimonio_data <- data.frame(
estado = c("Distrito Federal", "Quintana Roo", "Coahuila", "Chihuahua", "Nayarit",
"Campeche", "Michoacán", "Morelos", "Colima", "Chiapas",
"Nuevo León", "San Luis Potosí", "Hidalgo", "Baja California Sur",
"Aguascalientes", "Oaxaca", "Puebla", "Tlaxcala", "Sinaloa",
"Baja California", "Yucatán", "Querétaro", "Sonora", "Zacatecas",
"Guanajuato", "Jalisco", "Veracruz", "Durango", "México",
"Tabasco", "Guerrero", "Tamaulipas"),
jahr = c(2010, 2012, 2014, 2015, 2015, 2016, 2016, 2016, 2016, 2017,
2019, 2019, 2019, 2019, 2019, 2019, 2020, 2020, 2021,
2021, 2021, 2021, 2021, 2021, 2021, 2022, 2022, 2022,
2022, 2022, 2022, 2022)
)
# Anpassung der Namen in den Daten, um sie den Namen im Shapefile anzupassen
matrimonio_data$estado <- gsub("Ciudad de México", "Mexico City", matrimonio_data$estado)
matrimonio_data$estado <- gsub("Estado de México", "Mexico State", matrimonio_data$estado)
# Daten mit Shapefile verbinden
mexmex <- mexmex %>%
left_join(matrimonio_data, by = c("NAME_1" = "estado"))
# Farben definieren
farben <- c("2010" = "#1f78b4", "2012" = "#33a02c", "2014" = "#e31a1c", "2015" = "#ff7f00",
"2016" = "#6a3d9a", "2017" = "#b15928", "2019" = "#a6cee3", "2020" = "#b2df8a",
"2021" = "#fb9a99", "2022" = "#fdbf6f")
# Karte erstellen
ggplot(data = mexmex) +
geom_sf(aes(fill = as.factor(jahr))) +
scale_fill_manual(values = farben, name = "Jahr der Einführung") +
labs(title = "Einführung der Ehe für alle in Mexiko",
caption = "Quelle: Maguey 2022") +
theme_minimal()
# Bibliotecas necesarias
library(sf)
library(ggplot2)
library(dplyr)
# Leer el archivo Shapefile
mexmex <- st_read("C:/Master/3. Semester/R/Dataset/gadm41_MEX.gpkg", layer = "ADM_ADM_1")## Reading layer `ADM_ADM_1' from data source
## `C:\Master\3. Semester\R\Dataset\gadm41_MEX.gpkg' using driver `GPKG'
## Simple feature collection with 32 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -118.3665 ymin: 14.53507 xmax: -86.71074 ymax: 32.71863
## Geodetic CRS: WGS 84# Datos sobre los años de introducción del matrimonio igualitario en los estados
datos_matrimonio <- data.frame(
estado = c("Distrito Federal", "Quintana Roo", "Coahuila", "Chihuahua", "Nayarit",
"Campeche", "Michoacán", "Morelos", "Colima", "Chiapas",
"Nuevo León", "San Luis Potosí", "Hidalgo", "Baja California Sur",
"Aguascalientes", "Oaxaca", "Puebla", "Tlaxcala", "Sinaloa",
"Baja California", "Yucatán", "Querétaro", "Sonora", "Zacatecas",
"Guanajuato", "Jalisco", "Veracruz", "Durango", "México",
"Tabasco", "Guerrero", "Tamaulipas"),
año = c(2010, 2012, 2014, 2015, 2015, 2016, 2016, 2016, 2016, 2017,
2019, 2019, 2019, 2019, 2019, 2019, 2020, 2020, 2021,
2021, 2021, 2021, 2021, 2021, 2021, 2022, 2022, 2022,
2022, 2022, 2022, 2022)
)
# Ajustar los nombres en los datos para que coincidan con los nombres en el Shapefile
datos_matrimonio$estado <- gsub("Ciudad de México", "Mexico City", datos_matrimonio$estado)
datos_matrimonio$estado <- gsub("Estado de México", "Mexico State", datos_matrimonio$estado)
# Unir los datos con el Shapefile
mexmex <- mexmex %>%
left_join(datos_matrimonio, by = c("NAME_1" = "estado"))
# Definir colores
colores <- c("2010" = "#1f78b4", "2012" = "#33a02c", "2014" = "#e31a1c", "2015" = "#ff7f00",
"2016" = "#6a3d9a", "2017" = "#FFD700", "2019" = "#a6cee3", "2020" = "#b2df8a",
"2021" = "#fb9a99", "2022" = "#fdbf6f")
# Crear el mapa
ggplot(data = mexmex) +
geom_sf(aes(fill = as.factor(año))) +
scale_fill_manual(values = colores, name = "Año de Introducción") +
labs(title = "Introducción del Matrimonio Igualitario en México",
caption = "Fuente: Maguey 2022") +
theme_minimal()