class: center, middle, inverse, title-slide .title[ # Visualización de datos con <code>{ggplot2}</code> ] .subtitle[ ## <a style="float:left"><br /> </div><br /> <hr color="#EB811B" size="1px" width="1125px"> ] .author[ ### <a href="https://linktr.ee/estacion_r"><img 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alt="estacion-R logo" style="width:320px;height:270px;"> </a> ] --- <style>:root{--panel-tab-active-foreground:#0051BA;--panel-tab-hover-foreground:#d22;--panel-tab-font-family:Roboto;}</style> ## ¿Qué vimos? <br> ✅ Conceptos básicos de R <br> ✅ Pensar un proyecto de datos con R <br> ✅ Procesamiento de datos con `{tidyverse}` --- ## Hoja de Ruta <br> 📌 ¿Por qué visualizar datos? 📌 Gramática de los gráficos y `{ggplot2}` - Capas y el operador `+` (más) 📌 Armando un gráfico de barras (columnas) - Función `geom_col()` 📌 Chapa y pintura de un gráfico (atributos) --- ## Configuración para esta clase <br> - Armar un proyeto de trabajo nuevo o abrir aquel con el que veníamos trabajando - Cargar la base que surge del procesamiento del índice [**(click acá para descarga)**](https://estacion-r.github.io/intro_r_aaip/datos/output_visualizaciones_2024-07-23.xlsx) y chequear que esté en la carpeta `datos` - Crear un **script** de trabajo - Carga la librería `{tidyverse}` --- class: middle, center, logo-hide # ¿Por qué visualizar datos? <html> <hr color='#EB811B' size=1px width=1600px> </html> --- ## ¿Por qué visualizar? <br> - _"La visualización es el proceso de hacer visibles los contrastes, ritmos y eventos que los datos expresan, que no podemos percibir cuando vienen en forma de áridas listas de números y categorías."_ [^1] <br> - Interpretar / decodificar la información de forma visual <br> - Guiar hacia el hallazago <br> [^1]: https://bitsandbricks.github.io/ciencia_de_datos_gente_sociable/visualizacion.html --- class: center, middle, logo-hide # ggplot2 <html> <p style="color:grey;" align:"left">Una forma de visualizar</p> </html> --- ## __¿Qué es `{ggplot2}`?__ <br> - Una implementación del sistema **Grammar of graphics** (Wilkinson, 2005). <br> - Un esquema pensado en capas (datos --> plano (ejes **x** e **y**) --> geometrías) <br> - Un paquete de funciones de aplicación intuitiva. --- ## __¿Por qué `{ggplot2}`?__ - Tiene un marco de referencia (El grammar of graphics) -- - Flexible, con especificaciones a nivel de capas. -- - Sistema de `themes`, que permiten _pulir_ la apariencia del gráfico -- - Decenas de extensiones para ampliar la potencia del paquete -- - Comunidad activa y con mucha predisposición a ayudar. --- # ¿A dónde vamos? ``` r library(tidyverse) library(rio) # Importo base base_viz <- import(here::here("datos/output_visualizaciones_2024-07-23.xlsx")) ``` ``` r head(base_viz) Período Tipo de SO 1 2° Trimestre 2024 (Abril - Junio) Organismos Descentralizados 2 2° Trimestre 2024 (Abril - Junio) Organismos Descentralizados 3 2° Trimestre 2024 (Abril - Junio) Organismos Descentralizados 4 2° Trimestre 2024 (Abril - Junio) Organismos Descentralizados 5 2° Trimestre 2024 (Abril - Junio) Entes del Sector Público Nacional 6 2° Trimestre 2024 (Abril - Junio) Entes del Sector Público Nacional Sujeto obligado TA TP IT 1 Instituto Nacional del Teatro 4.5 5.6 4.6 2 Agencia de Acceso a la Información Pública 100.0 100.0 100.0 3 Instituto Nacional del Cáncer 5.8 2.8 5.5 4 Instituto Nacional de Tecnología Industrial 82.6 66.7 81.0 5 Autoridad de Cuenca Matanza Riachuelo (ACUMAR) 59.7 58.3 59.6 6 Agencia de Planificación (APLA) 30.0 40.3 31.0 ``` --- ## Preparo los datos - Calculo el promedio total de los índices para todos los organismos ``` r base_grafico <- base_viz |> summarise(prom_ta = mean(TA), prom_tp = mean(TP), prom_ti = mean(IT)) ``` ``` r base_grafico prom_ta prom_tp prom_ti 1 43.68816 24.92237 41.81184 ``` --- ## Preparo los datos ``` r base_grafico <- base_grafico %>% pivot_longer(cols = starts_with("prom"), names_to = "tipo_indice", values_to = "promedio") ``` ``` r base_grafico # A tibble: 3 × 2 tipo_indice promedio <chr> <dbl> 1 prom_ta 43.7 2 prom_tp 24.9 3 prom_ti 41.8 ``` --- ## Armo gráfico <img src="data:image/png;base64,#03-visualizacion_de_datos_files/figure-html/unnamed-chunk-9-1.png" width="60%" style="display: block; margin: auto;" /> --- ## El código ``` r ggplot(data = base_grafico, mapping = aes(x = tipo_indice, y = promedio)) + geom_col(aes(fill = tipo_indice)) + geom_hline(yintercept = 0) + ## Etiquetas geom_text(aes(label = round(promedio, 1), vjust = -0.5)) + ### Titulos labs(title = "Promedio total de los índices", subtitle = "Argentina, año 2024", x = "Tipo de índice", y = "Valor del índice", caption = "Fuente: AAIP") + ## Estetica theme_minimal() + theme(legend.position = "none") ``` --- # ¿Por dónde empezamos? ## Cargamos el paquete ``` r library(ggplot2) ``` <br> o... <br> ``` r library(tidyverse) ``` --- ## Gráfico en clave de capas <br> 3 Capas son las indispensables al pensar nuestro gráfico: --- ## Gráfico en clave de capas - Los **datos** (argumento: `data =`): - El dataframe que sirve de insumo <br> --- ## Gráfico en clave de capas - Los **datos** (argumento: `data =`): - El dataframe que sirve de insumo <br> - Las **aesthetics** (función `aes()`: - Defino el vínculo entre los datos y las propiedades visuales (ejes x e y, por ej.) --- ## Gráfico en clave de capas - Los **datos** (argumento: `data =`): - El dataframe que sirve de insumo <br> - Las **aesthetics** (función `aes()`: - Defino el vínculo entre los datos y las propiedades visuales (ejes x e y, por ej.) <br> - Las **geometrías** (función `geom_*()`: - La geometría con la que se representan los datos --- ## Gráfico en clave de capas <br> - **Pregunta-problema: Quiero representar los valores totales de los índices:** - Transparencia Activa - Transparencia Proactiva - Índice de Transparencia --- ## Gráfico en clave de capas - Preparo los datos: ``` r library(tidyverse) library(rio) base_viz <- import("datos/output_visualizaciones_2024-07-23.xlsx") base_grafico <- base_viz |> summarise(prom_ta = mean(TA), prom_tp = mean(TP), prom_ti = mean(IT)) %>% pivot_longer(cols = starts_with("prom"), names_to = "tipo_indice", values_to = "promedio") ``` --- ## Gráfico en clave de capas <br> - Así queda nuestra tabla: ``` r base_grafico # A tibble: 3 × 2 tipo_indice promedio <chr> <dbl> 1 prom_ta 43.7 2 prom_tp 24.9 3 prom_ti 41.8 ``` --- ## Gráfico en clave de capas ``` r ggplot(data = base_grafico, aes(x = tipo_indice, y = promedio)) ``` <img src="data:image/png;base64,#03-visualizacion_de_datos_files/figure-html/ggplot-aesthetics-outside-1.png" width="300" /> --- ## Gráfico en clave de capas ``` r ggplot(data = base_grafico, aes(x = tipo_indice, y = promedio)) + geom_col() ``` <img src="data:image/png;base64,#03-visualizacion_de_datos_files/figure-html/ggplot-aesthetics-outside-geom-1.png" width="300" /> --- class: center, middle, logo-hide # Chapa y pintura --- ## Chapa y pintura - Relleno ``` r ggplot(data = base_grafico, aes(x = tipo_indice, y = promedio)) + geom_col(fill = "red") ``` <img src="data:image/png;base64,#03-visualizacion_de_datos_files/figure-html/ggplot-aesthetics-outside-fill-1.png" width="300" /> --- ## Chapa y pintura - Relleno ``` r ggplot(data = base_grafico, aes(x = tipo_indice, y = promedio)) + geom_col(aes(fill = tipo_indice)) ``` <img src="data:image/png;base64,#03-visualizacion_de_datos_files/figure-html/ggplot-aesthetics-outside-fill-aes-1.png" width="300" /> --- ## Chapa y pintura - Contorno ``` r ggplot(data = base_grafico, aes(x = tipo_indice, y = promedio)) + geom_col(aes(fill = tipo_indice), color = "black") ``` <img src="data:image/png;base64,#03-visualizacion_de_datos_files/figure-html/ggplot-aesthetics-outside-color-1.png" width="300" /> --- ## Chapa y pintura - Referencias ``` r ggplot(data = base_grafico, aes(x = tipo_indice, y = promedio)) + geom_col(aes(fill = tipo_indice), color = "black") + labs(title = "Promedio Total por tipo de índice") ``` <img src="data:image/png;base64,#03-visualizacion_de_datos_files/figure-html/ggplot-aesthetics-outside-labs1-1.png" width="300" /> --- ## Chapa y pintura - Referencias ``` r ggplot(data = base_grafico, aes(x = tipo_indice, y = promedio)) + geom_col(aes(fill = tipo_indice), color = "black") + labs(title = "Promedio Total por tipo de índice", subtitle = "Argentina, año 2024", x = "Tipo de índice", y = "Valor del índice", caption = "Fuente: AAIP") ``` <img src="data:image/png;base64,#03-visualizacion_de_datos_files/figure-html/ggplot-aesthetics-outside-labs2-1.png" width="300" /> --- ## Chapa y pintura - theme ``` r ggplot(data = base_grafico, aes(x = tipo_indice, y = promedio)) + geom_col(aes(fill = tipo_indice), color = "black") + labs(title = "Promedio Total por tipo de índice", subtitle = "Argentina, año 2024", x = "Tipo de índice", y = "Valor del índice", caption = "Fuente: AAIP") + theme_minimal() ``` <img src="data:image/png;base64,#03-visualizacion_de_datos_files/figure-html/ggplot-aesthetics-outside-theme-1.png" width="300" /> --- ## Chapa y pintura - theme ``` r ggplot(data = base_grafico, aes(x = tipo_indice, y = promedio)) + geom_col(aes(fill = tipo_indice), color = "black") + labs(title = "Promedio Total por tipo de índice", subtitle = "Argentina, año 2024", x = "Tipo de índice", y = "Valor del índice", caption = "Fuente: AAIP") + theme_minimal() + theme(legend.position = "none") ``` <img src="data:image/png;base64,#03-visualizacion_de_datos_files/figure-html/ggplot-aesthetics-outside-theme2-1.png" width="300" />