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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Sesión III: Visualizando datos</title>
<meta charset="utf-8" />
<meta name="author" content="Guillermo de Anda-Jáuregui y Laura Gómez-Romero" />
<meta name="date" content="2022-01-01" />
<script src="libs/header-attrs/header-attrs.js"></script>
<link href="libs/remark-css/default.css" rel="stylesheet" />
<link href="libs/remark-css/default-fonts.css" rel="stylesheet" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# Sesión III: Visualizando datos
### Guillermo de Anda-Jáuregui y Laura Gómez-Romero
### Instituto Nacional de Medicina Genómica
### 2022
---
class: inverse, center, middle
# ¿Por qué **visualizamos los datos** ?
---
class: inverse, center, middle
# ¿Por qué **visualizamos los datos** ?
.left[
# -> Explorar los datos
# -> Reconocer patrones
# -> Transmitir información
# -> Presentar resultados
]
---
class: inverse, center, middle
.left[
<img src="https://www.r-project.org/logo/Rlogo.svg" width="100" height="100" />
# -> Diferentes primitivas a alto nivel (plot, barplot, hist, boxplot, etc.)
# -> Primitivas de bajo nivel (points, lines)
# -> Agregación por orden de la llamada a la función (abline, legend, etc.)
]
---
class: inverse, center, middle
<img src="figures/hex_ggplot.png">
.left[
# -> Gramática de gráficos (Pre-tidyverse)
# -> Objetos + Transform + Persistencia
]
---
class: inverse, center, middle
# Ejemplo de dispersión
## Hagamos el gráfico de dispersión usando **iris** para Petal.Length vs Petal.Width en versión R-base y con ggplot2:
```r
iris %>% head
```
```
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
```
##Veamos la versión de *R-base* y la de *ggplot2*
---
class: inverse, center, middle
#R version
```r
plot(
x = iris$Petal.Length,
y = iris$Petal.Width,
col = iris$Species,
type = "p", #for points
xlab = "Petal.Length",
ylab = "Petal.Width"
)
legend(
legend = levels(iris$Species),
col = seq(levels(iris$Species)),
x = 1,
y = 2.5,
lty = 1
)
```
---
class: inverse, center, middle
<!-- -->
---
class: inverse, center, middle
# ggplot version
```r
iris %>%
ggplot(mapping = aes(x = Petal.Length,
y = Petal.Width,
colour = Species
)
) +
geom_point()
```
---
class: inverse, center, middle
<!-- -->
---
class: inverse, center, middle
#Hacemos un paréntesis y volvemos sobre este ejemplo ...
---
class: inverse, center, middle
#ggplot2: Filosofía
https://ggplot2.tidyverse.org/reference/
<img src="figures/ggplo2book.png">
---
class: inverse, center, middle
#ggplot2: Filosofía
```r
ggplot(data = <DATA>) +
<GEOM_FUNCTION>(
mapping = aes(<MAPPINGS>),
stat = <STAT>,
position = <POSITION>
) +
<COORDINATE_FUNCTION> +
<FACET_FUNCTION> +
<THEME>
```
# OJO con la maldición de %>% vs "+"
---
class: inverse, center, middle
<img src="figures/ggplot2_masterpiece.png">"
---
class: inverse, center, middle
#ggplot2: Gráfica
##Debemos explicitar qué datos se utilizan
## -> Desde una consecución de comandos
```r
Nuestros_Datos %>%
ggplot()
```
## -> De forma explícita
```r
ggplot(data = Nuestros_Datos)
```
---
class: inverse, center, middle
#ggplot2: Mapping
##aes
### Definen como las variables son **mapeadas** a las propiedades visuales (estéticas o aesthetic en inglés) de los geom.
---
class: inverse, center, middle
#ggplot2: PRIMITIVAS
##geom
### Objetos **geométricos** que son la representación visual de las observaciones.
---
class: inverse, center, middle
<img src="figures/ggplot2_exploratory.png">
---
class: inverse, center, middle
#ggplot2: PRIMITIVAS
<img src="figures/geom1d.png">
---
class: inverse, center, middle
<img src="figures/geom2d.png">
---
class: inverse, center, middle
#ggplot2: PRIMITIVAS
<img src="figures/geom3d.png">
# Tenemos ** GEOMs ** para 1, 2 y 3D
---
class: inverse, center, middle
#ggplot2: PRIMITIVAS - geom_point - shape
<img src="figures/points.png">
---
class: inverse, center, middle
#Terminado el paréntesis y volvemos sobre el ejemplo ...
---
class: inverse, center, middle
<!-- -->
---
class: inverse, center, middle
##EJERCICIOS:
## - Cambie el punto de la gráfica de dispersión para que utilice un rombo relleno de color
---
class: inverse, center, middle
# ¿Qué más le podemos hacer a la gráfica?
<!-- -->
---
class: inverse, center, middle
.left[
# Pensemos en un...
# -> Título y subtítulo...
# -> Leyenda abajo...
# -> Quitar el fondo gris...
# -> Cambiar la paleta de colores...
# -> Separar en paneles por Specie...
]
---
class: inverse, center, middle
#ggplot2: Títulos y subtítulos
```r
labs(
title = "Mi título",
subtitle = "Mi subtítulo",
caption = "Un pie de figura"
)
```
---
class: inverse, center, middle
```r
iris %>%
ggplot(mapping = aes(x = Petal.Length,
y = Petal.Width,
fill = Species
)
) +
geom_point(shape=23) +
labs(
title = "IRIS",
subtitle = "Dispersión de dimensión de pétalos",
caption = "Acá va el pie de figura ..."
)
```
---
class: inverse, center, middle
<!-- -->
---
class: inverse, center, middle
# ¿Y si tenemos que poner símbolos matemáticos en los ejes?
```r
labs(
x = quote(Petal.Length=sum(x[i] ^ 2, i == 1, n)),
y = quote(Petal.Widht=alpha + beta + frac(delta, theta))
)
```
---
class: inverse, center, middle
```r
iris %>%
ggplot(mapping = aes(x = Petal.Length,
y = Petal.Width,
fill = Species
)
) +
geom_point(shape=23) +
labs(
title = "IRIS",
subtitle = "Dispersión de dimensión de pétalos",
caption = "Acá va el pie de figura ...",
x = quote(sum(x[i] ^ 2, i == 1, n)),
y = quote(alpha + beta + frac(delta, theta))
)
```
---
class: inverse, center, middle
<!-- -->
---
class: inverse, center, middle
.left[
# Pensemos en un...
# -> ~~Título y subtítulo...~~
# -> Leyenda abajo...
# -> Quitar el fondo gris...
# -> Cambiar la paleta de colores...
# -> Separar en paneles por Specie...
]
---
class: inverse, center, middle
#ggplot2: THEMES
<img src="figures/themes.png">
---
class: inverse, center, middle
#ggplot2: THEMES - Legend
```r
+ theme(legend.position = NULL) # remove the legend
+ theme(legend.position = "left")
+ theme(legend.position = "top")
+ theme(legend.position = "bottom")
+ theme(legend.position = "right") # the default
```
---
class: inverse, center, middle
```r
iris %>%
ggplot(mapping = aes(x = Petal.Length,
y = Petal.Width,
fill = Species
)
) +
geom_point(shape=23) +
labs(
title = "IRIS",
subtitle = "Dispersión de dimensión de pétalos",
caption = "Acá va el pie de figura ...",
x = quote(sum(x[i] ^ 2, i == 1, n)),
y = quote(alpha + beta + frac(delta, theta))
) +
theme_bw() +
theme(legend.position = "bottom")
```
---
class: inverse, center, middle
<!-- -->
---
class: inverse, center, middle
.left[
# Pensemos en un...
# -> ~~Título y subtítulo...~~
# -> ~~Leyenda abajo...~~
# -> ~~Quitar el fondo gris...~~
# -> Cambiar la paleta de colores...
# -> Separar en paneles por Specie...
]
---
class: inverse, center, middle
#ggplot2: SCALES
# Tenemos de todos los sabores!!!
```r
scale_x/y/colour/fill_manual/continuos/discrete/log10
scale_x_continuous()
scale_fill_manual()
scale_x_log10()
scale_x_continuous(limits = range(iris$Petal.Length))
scale_y_continuous(limits = c(min=0, max=10))
scale_colour_brewer(palette = "Set1")
scale_fill_distiller("spectral")
```
---
class: inverse, center, middle
#ggplot2: SCALES colors
<img src="figures/paletas.png">
---
class: inverse, center, middle
```r
iris %>%
ggplot(mapping = aes(x = Petal.Length,
y = Petal.Width,
fill = as.factor(Species)
)
) +
geom_point(shape=23) +
labs(
title = "IRIS",
subtitle = "Dispersión de dimensión de pétalos",
caption = "Acá va el pie de figura ...",
x = quote(sum(x[i] ^ 2, i == 1, n)),
y = quote(alpha + beta + frac(delta, theta))
) +
theme_bw() +
theme(legend.position = "bottom") +
scale_fill_manual(
values = c("yellow", "blue", "green"),
name = "Species"
)
```
---
class: inverse, center, middle
<!-- -->
---
class: inverse, center, middle
.left[
# Pensemos en un...
# -> ~~Título y subtítulo...~~
# -> ~~Leyenda abajo...~~
# -> ~~Quitar el fondo gris...~~
# -> ~~Cambiar la paleta de colores...~~
# -> Separar en paneles por Specie...
]
---
class: inverse, center, middle
#ggplot2: Facets
## Facets
### Define como los datos pueden ser agrupados en grillas (filas y/o columnas)
---
class: inverse, center, middle
#ggplot2: Facets: wrap vs grid
## Tenemos de dos sabores
### -> facet_wrap: Para una variable.
### -> facet_grid: Una matriz de 2 variables.
---
class: inverse, center, middle
```r
iris %>%
ggplot(mapping = aes(x = Petal.Length,
y = Petal.Width,
fill = as.factor(Species)
)
) +
geom_point(shape=23) +
labs(
title = "IRIS",
subtitle = "Dispersión de dimensión de pétalos",
caption = "Acá va el pie de figura ...",
x = quote(sum(x[i] ^ 2, i == 1, n)),
y = quote(alpha + beta + frac(delta, theta))
) +
theme_bw() +
theme(legend.position = "bottom") +
scale_fill_manual(
values = c("yellow", "blue", "green"),
name = "Species"
) +
facet_wrap(~Species)
```
---
class: inverse, center, middle
<!-- -->
---
class: inverse, center, middle
<!-- -->
---
class: inverse, center, middle
```r
iris %>%
ggplot(mapping = aes(x = Petal.Length,
y = Petal.Width,
fill = as.factor(Species)
)
) +
geom_point(shape=23) +
labs(
title = "IRIS",
subtitle = "Dispersión de dimensión de pétalos",
caption = "Acá va el pie de figura ...",
x = quote(sum(x[i] ^ 2, i == 1, n)),
y = quote(alpha + beta + frac(delta, theta))
) +
theme_bw() +
theme(legend.position = "bottom") +
scale_fill_manual(
values = c("yellow", "blue", "green"),
name = "Species"
) +
facet_wrap(~Species, scales = "free")
```
---
class: inverse, center, middle
<!-- -->
---
class: inverse, center, middle
.left[
# Pensemos en un...
# -> ~~Título y subtítulo...~~
# -> ~~Leyenda abajo...~~
# -> ~~Quitar el fondo gris...~~
# -> ~~Cambiar la paleta de colores...~~
# -> ~~Separar en paneles por Specie...~~
]
---
class: inverse, center, middle
.left[
##Ejercicios:
## Utilizando MTCARS retome la gráfica de dispersión de la clase pasada donde logró agrupar por cantidad de cilindros.
### - Agregue título a la gráfica.
### - Utilice el theme de blanco y negro
### - Genere los paneles correspondinete a la cantidad de cilindros.
]
---
class: inverse, center, middle
#BONUS: geom_smooth
```r
iris %>%
ggplot(mapping = aes(x = Petal.Length,
y = Petal.Width,
fill = as.factor(Species)
)
) +
geom_point(shape=23) +
theme_bw() +
theme(legend.position = "bottom") +
geom_smooth()
```
---
class: inverse, center, middle
#BONUS: geom_smooth
```
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
```
<!-- -->
---
class: inverse, center, middle
# Trabajemos con boxplots de ggplot2
# geom_boxplot
## -> Ojo con el formato de los datos
## -> geom_boxplot los requiere en formato largo
---
class: inverse, center, middle
```r
iris %>% head
```
```
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
```
```r
iris %>%
tidyr::pivot_longer(cols = -Species, names_to = "variables", values_to = "values") %>%
head
```
```
## # A tibble: 6 x 3
## Species variables values
## <fct> <chr> <dbl>
## 1 setosa Sepal.Length 5.1
## 2 setosa Sepal.Width 3.5
## 3 setosa Petal.Length 1.4
## 4 setosa Petal.Width 0.2
## 5 setosa Sepal.Length 4.9
## 6 setosa Sepal.Width 3
```
---
class: inverse, center, middle
# geom_boxplot
# Ahora podemos hacer el boxplot
```r
iris %>%
tidyr::pivot_longer(
cols = -Species,
names_to = "variables",
values_to = "values") %>%
ggplot(mapping = aes(x = variables,
y = values,
fill = as_factor(Species))) +
geom_boxplot()
```
---
class: inverse, center, middle
# geom_boxplot
<!-- -->
---
class: inverse, center, middle
# Ejercicio
.left[
## - Hacer un ggplot's boxplot con **MTCARS** para las variables "disp" y "hp"
Sugerencia:
- Cambie la representación de los datos
- Utilice verbos para seleccionar o filtrar, dependiendo de su razonamiento.
]
---
class: inverse, center, middle
#geom_violin
<!-- -->
---
class: inverse, center, middle
#geom_density
#Pensemos en datos simulados
```r
datos <- tibble(
valores = c(rnorm(100), rnorm(n = 100, mean = 10)))
datos %>%
ggplot(aes(x = valores))+
geom_density()
```
---
class: inverse, center, middle
#geom_density
#Pensemos en datos simulados
<!-- -->
---
class: inverse, center, middle
# EJERCICIO
## Genere el boxplot de los datos anteriores
## Genere el violín de los datos anteriores
## ¿Qué observa en ambos casos? ¿Es correcto?
---
class: inverse, center, middle
#ggplot2: Statistical transformations
<img src="figures/stat1.png" >
---
class: inverse, center, middle
#ggplot2: Statistical transformations
<img src="figures/stat2.png" >
---
class: inverse, center, middle
#ggplot2: geom_bar
# Trabajemos con diamods
```r
diamonds %>% head
```
```
## # A tibble: 6 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
```
---
class: inverse, center, middle
```r
diamonds %>%
ggplot() +
geom_bar(
mapping = aes(x = cut)
)
```
<!-- -->
---
class: inverse, center, middle
```r
ggplot(data = diamonds) +
stat_count(mapping = aes(x = cut))
```
<!-- -->
---
class: inverse, center, middle
```r
ggplot(data = diamonds) +
stat_summary(mapping = aes(x = cut, y = depth),
fun.ymin = min,fun.ymax = max,fun.y = median)
```
```
## Warning: `fun.y` is deprecated. Use `fun` instead.
```
```
## Warning: `fun.ymin` is deprecated. Use `fun.min` instead.
```
```
## Warning: `fun.ymax` is deprecated. Use `fun.max` instead.
```
<!-- -->
---
class: inverse, center, middle
#ggplot2: position
```r
ggplot(data = diamonds) +
geom_bar(
mapping = aes(x = cut, fill = clarity),
position = "dodge"
)
```
---
class: inverse, center, middle
#ggplot2: position
<!-- -->
---
class: inverse, center, middle
#coor_flip vs coord_polar
```r
bar <- ggplot(data = diamonds) +
geom_bar(
mapping = aes(x = cut, fill = cut),
show.legend = FALSE,
width = 1
) +
theme(aspect.ratio = 1) +
labs(x = NULL, y = NULL)
bar + coord_flip()
bar + coord_polar()
```
---
class: inverse, center, middle
<!-- -->
---
class: inverse, center, middle
<!-- -->
---
class: inverse, center, middle
#geom_tile
```r
USArrests %>%
rownames_to_column(var = "estado") %>%
pivot_longer(cols = -estado, names_to = "type", values_to = "value", values_drop_na = FALSE) %>%
ggplot(mapping = aes(x = type, y = estado, fill = value)) +
geom_tile() +
scale_fill_distiller("spectral")
```
---
class: inverse, center, middle
<!-- -->
---
class: inverse, center, middle
#Ejercicio:
## -> Cambie la tonalidad para que la barra de color quede en verde, amarillo y rojo.
---
class: inverse, center, middle
#ggplot2:Saving
## plots tradicional
### -> ~~**NO se pueden GUARDAR**~~
### -> ~~**NO se pueden Cargar**~~
---
class: inverse, center, middle
#ggplot2:Saving
## plots de ggplot son **Objetos**
### -> Se pueden almacenar .RData, RDS, etc.
### -> Se pueden cargar
### -> Siempre se pueden recuperar los datos
### -> Siempre se puede modificar la gráfica
### -> Se puede exportar a pdf, png, etc
---
class: inverse, center, middle
#ggplot2:Saving
```r
bar <- ggplot(data = diamonds) +
geom_bar(
mapping = aes(x = cut, fill = cut),
show.legend = FALSE,
width = 1
) +
theme(aspect.ratio = 1) +
labs(x = NULL, y = NULL)
ggsave(plot = bar, filename = "results/bar.pdf")
```
```
## Saving 7 x 7 in image
```
```r
save(bar, file = "results/bar.RData")
```
---
class: inverse, center, middle
```r
rm(list=ls())
load("results/bar.RData")
bar
```
<!-- -->
---
class: inverse, center, middle
#EJERCICIO de TAREA!!!
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