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)
Presentación
En el marco del cálculo del costo de menstruar en Argentina, llevado a cabo desde la campaña #MenstruAcción y EcoFeminitas, en el presente documento de trabajo se presenta el cálculo del costo de menstruar en Argentina. Para ello se utilizan los datos obtenidos mediante el #MenstruScrapper, desarrollado por Nayla Portas de [LAS] de sistemas. El #MenstruScrapper fue ejecutado el día 17 de Septiembre de 2021 sobre la página Precios Claros, sumándose esta información a la obtenida anteriormente en Septiembre y Marzo de 2019, Marzo y Septiembre de 2020, y Marzo de 2021. En esta instancia, se obtuvieron 83.317 observaciones en total.
- Respecto de los tampones: Se obtuvieron 17.799 precios.
- Respecto de las toallitas: Se obtuvieron 65.518 precios.
Estos datos fueron explorados, organizados y limpiados en otra instancia también publicada. En nuestro repositorio se encuentran aclaradas todas las fuentes adicionales a las que se recurrió para realizar el cálculo y los archivos correspondientes.
Preparando el entorno de trabajo
Comenzamos levantando las librerías necesarias, y cargando los datos que se encuentran en formato .RDS. Aprovechamos para tener una vista previa de los datos.
library(tidyverse)
library(readxl)
library(DT)
datos <- readRDS("Fuentes/precios-gestion-menstrual-2021-09-17-limpio.RDS")
head(datos)
A su vez, se carga la estimación, por cada provincia, de personas que menstrúan. Como está aclarado en la carpeta de fuentes, esto sale de las proyecciones poblacionales del INDEC para 2021, teniendo en cuenta que la menarca se estima en los 13 años y la menopausia en 49 años. Estos datos se van a utilizar para ponderar el precio promedio de cada provincia a la hora de agregar en un indicador nacional. Como se encuentran en un archivo .xls
, usamos la función read_excel()
. Obtenemos también una vista previa de la estructura de estos datos.
menstruan <- read_excel("Fuentes/poblaciones.xls") %>%
select(Provincia, Menstruan) # Para sacar las columnas con comentarios
head(menstruan)
A continuación, calculamos el total a nivel nacional de personas que menstrúan, sumando los datos de todas las provincias. Y, dividiendo los datos de cada provincia por dicho total, obtenemos las proporciones que nos van a servir para ponderar los promedios de precio según el peso de cada una de las provincias. Como guardo estas modificaciones bajo el mismo nombre, la información será pisada en la misma tabla menstruan
, de la que se vuelve a presentar una vista previa.
menstruan <- menstruan %>%
mutate(total = sum(Menstruan),
pond = Menstruan/total) %>%
select(Provincia, pond)
head(menstruan)
Además, considerando que hay outliers (valores atípicos) en los precios unitarios, a la hora de calcular los promedios de precios provinciales se remueven “las colas” de la distribución, utilizando una media alfa podada:
Cálculo y Resultados
A continuación ya procedemos a calcular los precios promedio de toallitas y tampones. Para el caso del total nacional, primero habrá que calcular los precios promedio (por unidad) de cada provincia, para luego agregarlos en un promedio general. Una vez que tenemos precios representativos de toallitas y tampones, podremos estimar un costo anual por persona de acuerdo a las estimaciones de cuántos ciclos por año presentan las personas que menstrúan, y cuántos productos utilizan por ciclo.
Total Nacional
Toallitas
Promedio del precio por unidad
Para calcular el precio promedio de cada provincia tomamos los datos, los filtramos para quedarnos únicamente con las toallitas, agrupando las observaciones según la variable Provincia
. Con summarise()
podemos definir indicadores que agregan la información para cada grupo, así que definimos el promedio provincial como una media podada. Es en el parámetro trim
que se aclara la proporción de los datos que se podan. Finalmente, anexamos en una nueva columna los ponderadores de cada provincia, así toda la información necesaria para el cálculo del promedio nacional se encuentra en la misma tabla.
toallitas <- datos %>%
filter(Categoría == "toallitas") %>%
group_by(Provincia) %>%
summarise(prom = mean(precio_unidad, trim = 0.025)) %>%
left_join(., menstruan, by = "Provincia")
head(toallitas)
Luego, el valor promedio para el total nacional se calcula como una media ponderada. La función weighted.mean()
nos permite aclarar la variable a promediar, y la variable que funciona como ponderador.
valor_toallitas <- weighted.mean(toallitas$prom, toallitas$pond)
Toallitas: El precio promedio por unidad es de $14.34
Gasto por año por persona
El gasto anual por persona se calcula teniendo en cuenta:
- ciclos por año: 13
- productos utilizados por ciclo: 22
(Ambos datos se encuentran aclarados en las Fuentes
)
ciclos_por_anio <- 13
productos_por_ciclo <- 22
productos_por_anio <- ciclos_por_anio * productos_por_ciclo
gasto_por_anio_toallitas <- valor_toallitas * productos_por_anio
Toallitas: El gasto promedio por año por persona es de $4101.75
Tampones
Se realiza un procedimiento semejante para el caso de los tampones.
Promedio del precio por unidad
tampones <- datos %>%
filter(Categoría == "tampones") %>%
group_by(Provincia) %>%
summarise(prom = mean(precio_unidad, trim = 0.025)) %>%
left_join(., menstruan, by = "Provincia")
valor_tampones <- weighted.mean(tampones$prom, tampones$pond)
Tampones: El precio promedio por unidad es de $16.38
Gasto por año por persona
El gasto anual por persona se calcula teniendo en cuenta:
- ciclos por año: 13
- productos utilizados por ciclo: 22
gasto_por_anio_tampones <- valor_tampones * productos_por_anio
Tampones: El gasto promedio por año por persona es de $4686.05
Por provincia
Para obtener los datos de cada provincia el procedimiento es aún más sencillo. En la variable gasto_por_anio
, cada precio promedio se multiplica por la cantidad de productos que se utilizan anualmente. Por una cuestión de exposición, se quitan de la tabla las columnas con aquellos precios promedios por unidad, y los ponderadores provinciales.
Toallitas
Gasto por año por persona
toallitas <- toallitas %>%
mutate(gasto_por_anio = round(prom * productos_por_anio, 2)) %>%
select(-prom, -pond) %>%
arrange(gasto_por_anio)
toallitas %>%
datatable(
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print')))
Tampones
Gasto por año por persona
tampones <- tampones %>%
mutate(gasto_por_anio = round(prom * productos_por_anio, 2)) %>%
select(-prom, -pond) %>%
arrange(gasto_por_anio)
tampones %>%
datatable(
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print')))
Aumentos
Como esta es la cuarta vez que realizamos este ejercicio de estimación semestral, podemos ver cómo evolucionaron los precios de toallitas y tampones en el último año. También, con los datos del Índice de Precios al Consumidor (IPC - INDEC), se puede realizar una comparación entre la evolución de estos productos de gestión menstrual y el nivel general de precios, el rubro alimentos y bebidas no alcohólicas, y el de Salud. Ambas series se levantan desde archivos de extensión .xlsx en las tablas serie_inflacion
y precios_pgm
.
serie_inflacion <- read_excel("Fuentes/serie_inflacion.xlsx")
precios_pgm <- read_excel("Fuentes/serie_precios_pgm.xlsx")
head(serie_inflacion)
head(precios_pgm)
Asegurándonos de que los datos se encuentran ordenados por fecha, podemos transformar ambas series para que se encuentren expresadas con el mes de marzo 2019 = 100. De esta forma podremos comparar la evolución de ambas. Esto lo hacemos modificando las series con las que contamos dividiendo todos los valores por el primero de ellos (y multiplicando por 100). La selección del primer elemento de la serie se realizó con la función first()
.
base100 <- serie_inflacion %>%
mutate(`Nivel General` = `Nivel General`/first(`Nivel General`) * 100,
`Alimentos y bebidas no alcohólicas` = `Alimentos y bebidas no alcohólicas`/
first(`Alimentos y bebidas no alcohólicas`) * 100,
Salud = Salud/first(Salud) * 100)
head(base100)
pgm_100 <- precios_pgm %>%
mutate(Toallitas = Toallitas/first(Toallitas) * 100,
Tampones = Tampones/first(Tampones) * 100)
head(pgm_100)
Podemos anexar la evolución de los precios de los productos de gestión menstrual a la serie de inflación, por más que esta última tenga valores mensuales y nuestros datos se hayan recolectado cada seis meses, utilizando Mes
como variable de link. Luego reordenamos la información para que todas las variables, excepto Mes
, se presenten en una variable Categoría
(con valores “Nivel General”, “Salud”, “Toallitas”, etc.), y los valores de cada serie se agrupen en la variable Indice
. No habrá datos en diversos meses para toallitas y tampones, así que filtramos esas filas vacías, y finalmente creamos la variable Grupo
para identificar como “IPC” a las categorías del índice de precios al consumidor, y como “PGM” a los productos de gestión menstrual.
# Esto se puede correr "de a poco" para ver cómo cambian las cosas en cada paso
base100 <- base100 %>%
left_join(., pgm_100, by = "Mes") %>%
pivot_longer(-Mes, names_to = "Categoria", values_to = "Indice") %>%
filter(!is.na(Indice)) %>%
mutate(Grupo = case_when(Categoria %in% c("Nivel General", "Alimentos y bebidas no alcohólicas", "Salud") ~ "IPC",
Categoria %in% c("Toallitas", "Tampones") ~ "PGM"))
head(base100)
A fines de graficar esta información, por esta vez se prescinde de la serie de “Alimentos y bedidas no alcohólicas” porque no agrega tanta información en la comparabilidad, y sí ocupa espacio visual que puede obstruir la comparación de las demás series.
base100bis <- base100 %>%
filter(Categoria != "Alimentos y bebidas no alcohólicas")
Para el gráfico puedo definir un vector de colores que sirva para distinguir las series de IPC (en la gama de azules) y las de productos de gestión menstrual (en la gama del rojo).
colorcitos <- c('royalblue4', 'royalblue',
'violetred', 'red')
Finalmente, definimos nuestro gráfico. Tomando la tabla base100bis
,
library(ggrepel)
ggplot(base100bis, aes(x = Mes, y = Indice, group = Categoria, color = Categoria)) +
geom_line(data = base100bis %>% filter(Grupo == "PGM"), size = 1, alpha = 1) +
geom_line(data = base100bis %>% filter(Grupo == "IPC"), alpha = 1, size = 1) +
geom_point(data = base100bis %>% filter(Grupo == "PGM"), size = 2) +
theme_minimal() +
theme(legend.position = "top",
text = element_text(size = 12)) +
scale_color_manual(values = colorcitos) +
expand_limits(x = as.POSIXct(c("2019-03-01", "2020-11-01"))) +
labs(title = "Inflación de Toallitas y Tampones vs. Salud y Nivel General del IPC-INDEC",
subtitle = "Marzo 2019 - Marzo 2021",
x = "",
y = "Indice de precios",
caption = "Fuente: #MenstruAcción y #EcoFemiData (EcoFeminita)") +
scale_x_datetime(breaks = seq(as.POSIXct('2019-03-01'), by='6 months', length = 5),
labels = c("03-2019", "09-2019", "03-2020", "09-2020", "03-2021"))
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)
---
title: "Cálculo: ¿Cuánto cuesta menstruar?"
subtitle: "Datos de Septiembre 2021"
author: "Ecofeminita / #MenstruAcción / Ecofemidata"
output:
  html_notebook:
    toc: false
    toc_float: true
    toc_depth: 5
---

<style type="text/css">
  body{
  font-size: 14pt;
}
</style>

![](img/Logos EcoFeminita-03.png){width=130px} ![](img/MENSTRUACCION.png){width=110px} ![](img/Ecofemidata.png){width=100px}


## Presentación

En el marco del cálculo del costo de menstruar en Argentina, llevado a cabo desde la campaña #MenstruAcción y EcoFeminitas, en el presente documento de trabajo se presenta el cálculo del costo de menstruar en Argentina. Para ello se utilizan los datos obtenidos mediante el [#MenstruScrapper](https://github.com/nportas/menstruscrapper), desarrollado por Nayla Portas de [LAS] de sistemas. El #MenstruScrapper fue ejecutado el día 17 de Septiembre de 2021 sobre la página Precios Claros, sumándose esta información a la obtenida anteriormente en Septiembre y Marzo de 2019, Marzo y Septiembre de 2020, y Marzo de 2021. En esta instancia, se obtuvieron 83.317 observaciones en total.

+ Respecto de los tampones: Se obtuvieron 17.799 precios.
+ Respecto de las toallitas: Se obtuvieron 65.518 precios.

Estos datos fueron explorados, organizados y limpiados en otra instancia también publicada. En [nuestro repositorio](https://github.com/Ecofeminita/EcoFemiData/tree/master/MenstruAccion/cuanto_cuesta_menstruar) se encuentran aclaradas todas las fuentes adicionales a las que se recurrió para realizar el cálculo y los archivos correspondientes.

## Preparando el entorno de trabajo

Comenzamos levantando las librerías necesarias, y cargando los datos que se encuentran en formato .RDS. Aprovechamos para tener una vista previa de los datos.

```{r echo=TRUE, message=FALSE, warning=FALSE}
library(tidyverse)
library(readxl)
library(DT)
```

```{r echo=TRUE, message=FALSE, warning=FALSE}
datos <- readRDS("Fuentes/precios-gestion-menstrual-2021-09-17-limpio.RDS")

head(datos)
```

A su vez, se carga la estimación, por cada provincia, de personas que menstrúan. Como está aclarado en la carpeta de fuentes, esto sale de las proyecciones poblacionales del INDEC para 2021, teniendo en cuenta que la menarca se estima en los 13 años y la menopausia en 49 años. Estos datos se van a utilizar para ponderar el precio promedio de cada provincia a la hora de agregar en un indicador nacional. Como se encuentran en un archivo `.xls`, usamos la función `read_excel()`. Obtenemos también una vista previa de la estructura de estos datos.

```{r echo=TRUE, message=FALSE, warning=FALSE}
menstruan <- read_excel("Fuentes/poblaciones.xls") %>% 
  select(Provincia, Menstruan) # Para sacar las columnas con comentarios

head(menstruan)
```

A continuación, calculamos el total a nivel nacional de personas que menstrúan, sumando los datos de todas las provincias. Y, dividiendo los datos de cada provincia por dicho total, obtenemos las proporciones que nos van a servir para ponderar los promedios de precio según el peso de cada una de las provincias. Como guardo estas modificaciones bajo el mismo nombre, la información será pisada en la misma tabla `menstruan`, de la que se vuelve a presentar una vista previa.

```{r echo=TRUE, message=FALSE, warning=FALSE}
menstruan <- menstruan %>% 
  mutate(total = sum(Menstruan),
         pond = Menstruan/total) %>% 
  select(Provincia, pond)

head(menstruan)
```

Además, considerando que hay outliers (valores atípicos) en los precios unitarios, a la hora de calcular los promedios de precios provinciales se remueven "las colas" de la distribución, utilizando una media alfa podada:     

- alfa = 2.5%

## Cálculo y Resultados

A continuación ya procedemos a calcular los precios promedio de toallitas y tampones. Para el caso del total nacional, primero habrá que calcular los precios promedio (por unidad) de cada provincia, para luego agregarlos en un promedio general. Una vez que tenemos precios representativos de toallitas y tampones, podremos estimar un costo anual por persona de acuerdo a las estimaciones de cuántos ciclos por año presentan las personas que menstrúan, y cuántos productos utilizan por ciclo.

## Total Nacional

### Toallitas

### Promedio del precio por unidad

Para calcular el precio promedio de cada provincia tomamos los datos, los filtramos para quedarnos únicamente con las toallitas, agrupando las observaciones según la variable `Provincia`. Con `summarise()` podemos definir indicadores que agregan la información para cada grupo, así que definimos el promedio provincial como una media podada. Es en el parámetro `trim` que se aclara la proporción de los datos que se podan. Finalmente, anexamos en una nueva columna los ponderadores de cada provincia, así toda la información necesaria para el cálculo del promedio nacional se encuentra en la misma tabla.

```{r echo=TRUE, message=FALSE, warning=FALSE}
toallitas <- datos %>% 
  filter(Categoría == "toallitas") %>%
  group_by(Provincia) %>% 
  summarise(prom = mean(precio_unidad, trim = 0.025)) %>% 
  left_join(., menstruan, by = "Provincia")

head(toallitas)
```

Luego, el valor promedio para el total nacional se calcula como una media ponderada. La función `weighted.mean()` nos permite aclarar la variable a promediar, y la variable que funciona como ponderador.

```{r echo=TRUE, message=FALSE, warning=FALSE}
valor_toallitas <- weighted.mean(toallitas$prom, toallitas$pond)
```

<font size="6"> Toallitas: El precio promedio por unidad es de $`r round(valor_toallitas, 2)` </font> 

### Gasto por año por persona

El gasto anual por persona se calcula teniendo en cuenta:

- ciclos por año: 13
- productos utilizados por ciclo: 22

(Ambos datos se encuentran aclarados en las `Fuentes`)

```{r echo=TRUE, message=FALSE, warning=FALSE}
ciclos_por_anio <- 13
productos_por_ciclo <- 22
productos_por_anio <- ciclos_por_anio * productos_por_ciclo

gasto_por_anio_toallitas <- valor_toallitas * productos_por_anio
```

<font size="6"> Toallitas: El gasto promedio por año por persona es de $`r round(gasto_por_anio_toallitas, 2)` </font> 

### Tampones

Se realiza un procedimiento semejante para el caso de los tampones.

### Promedio del precio por unidad

```{r echo=TRUE, message=FALSE, warning=FALSE}
tampones <- datos %>% 
  filter(Categoría == "tampones") %>%
  group_by(Provincia) %>% 
  summarise(prom = mean(precio_unidad, trim = 0.025)) %>% 
  left_join(., menstruan, by = "Provincia")

valor_tampones <- weighted.mean(tampones$prom, tampones$pond)
```

<font size="6"> Tampones: El precio promedio por unidad es de $`r round(valor_tampones, 2)` </font> 

### Gasto por año por persona

El gasto anual por persona se calcula teniendo en cuenta:

- ciclos por año: 13
- productos utilizados por ciclo: 22

```{r echo=TRUE, message=FALSE, warning=FALSE}
gasto_por_anio_tampones <- valor_tampones * productos_por_anio
```

<font size="6"> Tampones: El gasto promedio por año por persona es de $`r round(gasto_por_anio_tampones, 2)` </font> 

## Por provincia

Para obtener los datos de cada provincia el procedimiento es aún más sencillo. En la variable `gasto_por_anio`, cada precio promedio se multiplica por la cantidad de productos que se utilizan anualmente. Por una cuestión de exposición, se quitan de la tabla las columnas con aquellos precios promedios por unidad, y los ponderadores provinciales.

### Toallitas

### Gasto por año por persona

```{r echo=TRUE, message=FALSE, warning=FALSE}
toallitas <- toallitas %>% 
  mutate(gasto_por_anio = round(prom * productos_por_anio, 2)) %>% 
  select(-prom, -pond) %>% 
  arrange(gasto_por_anio)

toallitas %>%
    datatable(
    extensions = 'Buttons', 
    options = list(dom = 'Bfrtip', 
                   buttons = c('copy', 'csv', 'excel', 'pdf', 'print')))
```

### Tampones

### Gasto por año por persona

```{r echo=TRUE, message=FALSE, warning=FALSE}
tampones <- tampones %>% 
  mutate(gasto_por_anio = round(prom * productos_por_anio, 2)) %>% 
  select(-prom, -pond) %>% 
  arrange(gasto_por_anio)

tampones %>% 
    datatable(
    extensions = 'Buttons', 
    options = list(dom = 'Bfrtip', 
                   buttons = c('copy', 'csv', 'excel', 'pdf', 'print')))
```

## Aumentos

Como esta es la cuarta vez que realizamos este ejercicio de estimación semestral, podemos ver cómo evolucionaron los precios de toallitas y tampones en el último año. También, con los datos del Índice de Precios al Consumidor (IPC - INDEC), se puede realizar una comparación entre la evolución de estos productos de gestión menstrual y el nivel general de precios, el rubro alimentos y bebidas no alcohólicas, y el de Salud. Ambas series se levantan desde archivos de extensión .xlsx en las tablas `serie_inflacion` y `precios_pgm`.

```{r echo=TRUE, message=FALSE, warning=FALSE}
serie_inflacion <- read_excel("Fuentes/serie_inflacion.xlsx")
precios_pgm <- read_excel("Fuentes/serie_precios_pgm.xlsx")
```

```{r echo=TRUE, message=FALSE, warning=FALSE}
head(serie_inflacion)
```

```{r echo=TRUE, message=FALSE, warning=FALSE}
head(precios_pgm)
```

Asegurándonos de que los datos se encuentran ordenados por fecha, podemos transformar ambas series para que se encuentren expresadas con el mes de marzo 2019 = 100. De esta forma podremos comparar la evolución de ambas. Esto lo hacemos modificando las series con las que contamos dividiendo todos los valores por el primero de ellos (y multiplicando por 100). La selección del primer elemento de la serie se realizó con la función `first()`.

```{r echo=TRUE, message=FALSE, warning=FALSE}
base100 <- serie_inflacion %>% 
  mutate(`Nivel General` = `Nivel General`/first(`Nivel General`) * 100,
         `Alimentos y bebidas no alcohólicas` = `Alimentos y bebidas no alcohólicas`/
           first(`Alimentos y bebidas no alcohólicas`) * 100,
         Salud = Salud/first(Salud) * 100)

head(base100)
```

```{r echo=TRUE, message=FALSE, warning=FALSE}
pgm_100 <- precios_pgm %>% 
  mutate(Toallitas = Toallitas/first(Toallitas) * 100,
         Tampones = Tampones/first(Tampones) * 100)

head(pgm_100)
```

Podemos anexar la evolución de los precios de los productos de gestión menstrual a la serie de inflación, por más que esta última tenga valores mensuales y nuestros datos se hayan recolectado cada seis meses, utilizando `Mes` como variable de link. Luego reordenamos la información para que todas las variables, excepto `Mes`, se presenten en una variable `Categoría` (con valores "Nivel General", "Salud", "Toallitas", etc.), y los valores de cada serie se agrupen en la variable `Indice`. No habrá datos en diversos meses para toallitas y tampones, así que filtramos esas filas vacías, y finalmente creamos la variable `Grupo` para identificar como "IPC" a las categorías del índice de precios al consumidor, y como "PGM" a los productos de gestión menstrual.

```{r echo=TRUE, message=FALSE, warning=FALSE}
# Esto se puede correr "de a poco" para ver cómo cambian las cosas en cada paso
base100 <- base100 %>% 
  left_join(., pgm_100, by = "Mes") %>% 
  pivot_longer(-Mes, names_to = "Categoria", values_to = "Indice") %>% 
  filter(!is.na(Indice)) %>% 
  mutate(Grupo = case_when(Categoria %in% c("Nivel General", "Alimentos y bebidas no alcohólicas", "Salud") ~ "IPC",
                           Categoria %in% c("Toallitas", "Tampones") ~ "PGM"))

head(base100)
```

A fines de graficar esta información, por esta vez se prescinde de la serie de "Alimentos y bedidas no alcohólicas" porque no agrega tanta información en la comparabilidad, y sí ocupa espacio visual que puede obstruir la comparación de las demás series.

```{r echo=TRUE, message=FALSE, warning=FALSE}
base100bis <- base100 %>% 
  filter(Categoria != "Alimentos y bebidas no alcohólicas")
```

Para el gráfico puedo definir un vector de colores que sirva para distinguir las series de IPC (en la gama de azules) y las de productos de gestión menstrual (en la gama del rojo).

```{r echo=TRUE, message=FALSE, warning=FALSE}
colorcitos <- c('royalblue4', 'royalblue', 
                'violetred', 'red')
```

Finalmente, definimos nuestro gráfico. Tomando la tabla `base100bis`, 

```{r echo=TRUE, message=FALSE, warning=FALSE}
library(ggrepel)

ggplot(base100bis, aes(x = Mes, y = Indice, group = Categoria, color = Categoria)) +
  geom_line(data = base100bis %>% filter(Grupo == "PGM"), size = 1, alpha = 1) +
  geom_line(data = base100bis %>% filter(Grupo == "IPC"), alpha = 1, size = 1) +
  geom_point(data = base100bis %>% filter(Grupo == "PGM"), size = 2) +
  theme_minimal() +
  theme(legend.position = "top",
        text = element_text(size = 12)) +
  scale_color_manual(values = colorcitos) +
  expand_limits(x = as.POSIXct(c("2019-03-01", "2020-11-01"))) + 
  labs(title = "Inflación de Toallitas y Tampones vs. Salud y Nivel General del IPC-INDEC",
       subtitle = "Marzo 2019 - Marzo 2021",
       x = "",
       y = "Indice de precios",
       caption = "Fuente: #MenstruAcción y #EcoFemiData (EcoFeminita)") +
  scale_x_datetime(breaks = seq(as.POSIXct('2019-03-01'), by='6 months', length = 5),
                   labels = c("03-2019", "09-2019", "03-2020", "09-2020", "03-2021"))
```
















