GGPLOT Y plotly

Carga de bibliotecas

library(tidyverse)
library(plotly)
library(DT)
library(palmerpenguins)
library(scales)
library(ggthemes)
library(hrbrthemes)
library(plotly)

Carga de datos

# Carga de los datos de países
paises <-
  read_csv(
    "https://raw.githubusercontent.com/pf0953-programacionr/2024-ii/refs/heads/main/datos/natural-earth/paises-join-esperanzavida.csv"
  )

# Mostrar paises
# ```{r}
# #| label: tabla-paises
# 
# # Tabla de datos de paises
# paises |>
#   filter(SUBREGION =="Eastern Europe" & POP_EST >=10000000) |>
#   select(ADM0_ISO, NAME, POP_EST)|>
#   arrange(desc (POP_EST))|>
#   datatable(
#     options = list(
#       pageLength = 7,
#       language = list(url = '//cdn.datatables.net/plug-ins/1.10.11/i18n/Spanish.json')
#     )
#   )
# ```

paises |>
  arrange(NAME)|>
  datatable(
     options = list(
       pageLength = 7,
      language = list(url = '//cdn.datatables.net/plug-ins/1.10.11/i18n/Spanish.json')
     )
   )

Uso de ggplot2

Grafico de dispersion de PIB per capita VS Esperanza de vida al nacer

# Gráfico de dispersión de PIB per cápita vs esperanza de vida al nacer
paises |>
  ggplot(aes(x = GDP_PC, y = LIFE_EXPECTANCY)) +
  geom_point() +
  scale_x_continuous(labels = comma, limits = c(0, NA))

Warning: Removed 11 rows containing missing values or values outside the scale range
(`geom_point()`).

# Gráfico de dispersión de PIB per cápita vs esperanza de vida al nacer
paises |>
  ggplot(aes(x = GDP_PC, y = LIFE_EXPECTANCY, color= INCOME_GRP)) +
  geom_point() +
  scale_x_continuous(labels = comma, limits = c(0, NA))

Warning: Removed 11 rows containing missing values or values outside the scale range
(`geom_point()`).

paises |>
  select(NAME, GDP_PC,LIFE_EXPECTANCY) |>
  filter(GDP_PC>= 150000 & LIFE_EXPECTANCY>=80)

# A tibble: 1 × 3
  NAME           GDP_PC LIFE_EXPECTANCY
  <chr>           <dbl>           <dbl>
1 Liechtenstein 180857.            84.3

# Gráfico de dispersión de PIB per cápita vs esperanza de vida al nacer
paises |>
  ggplot(aes(x = GDP_PC, y = LIFE_EXPECTANCY, shape= CONTINENT, color=ECONOMY)) +
  geom_point() +
  scale_x_continuous(labels = comma, limits = c(0, NA))

Warning: The shape palette can deal with a maximum of 6 discrete values because more
than 6 becomes difficult to discriminate
ℹ you have requested 8 values. Consider specifying shapes manually if you need
  that many have them.

Warning: Removed 26 rows containing missing values or values outside the scale range
(`geom_point()`).

# Gráfico de dispersión de PIB per cápita vs esperanza de vida al nacer
# con formas y colores correspondientes al continente
paises |>
  ggplot(aes(x = GDP_PC, y = LIFE_EXPECTANCY, shape = CONTINENT, color = CONTINENT)) +
  geom_point() +
  scale_x_continuous(labels = comma, limits = c(0, NA)) +
  scale_shape_manual(values = c(0, 1, 2, 3, 4, 5, 6, 7)) +
  scale_color_manual(values = c("#ff0000", "blue", "green", "purple", "orange", "brown", "pink", "yellow"))

#| laber: ggplo2-6
#| warning: false
#| message: false

# Gráfico de dispersión de PIB per cápita vs esperanza de vida al nacer
# con formas y colores correspondientes al continente
paises |>
  ggplot(aes(x = GDP_PC, y = LIFE_EXPECTANCY, size= POP_EST, color = CONTINENT)) +
  geom_point() +
  scale_x_continuous(labels = comma, limits = c(0, NA)) +
  scale_color_manual(values = c("#ff0000", "blue", "green", "purple", "orange", "brown", "pink", "yellow"))

Warning: Removed 11 rows containing missing values or values outside the scale range
(`geom_point()`).

# Gráfico de dispersión de PIB per cápita vs esperanza de vida al nacer
# en África y Europa coloreado por continente
# + curva de tendencia
paises |>
  filter(CONTINENT == 'Africa' | CONTINENT == 'Europe') |>
  ggplot(aes(x = GDP_PC, y = LIFE_EXPECTANCY, color = CONTINENT)) +
  geom_point() +
  geom_smooth() +
  scale_x_continuous(labels = comma, limits = c(0, NA)) +
  scale_y_continuous(labels = comma, limits = c(50, 90))

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Warning: Removed 7 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning: Removed 7 rows containing missing values or values outside the scale range
(`geom_point()`).

penguins |>
  ggplot(aes(x= body_mass_g, y= bill_length_mm, color= island, size = flipper_length_mm)) +
  geom_point()

Facets

# Gráfico de dispersión de PIB per cápita vs esperanza de vida al nacer
# + paneles por continente
paises |>
  ggplot(aes(x = GDP_PC, y = LIFE_EXPECTANCY)) +
  geom_point() +
  facet_wrap(~ INCOME_GRP, ncol =  2) +
  scale_x_continuous(labels = comma, limits = c(0, NA))

Warning: Removed 11 rows containing missing values or values outside the scale range
(`geom_point()`).

Títulos, etiquetas, estilos y colores

# Gráfico de dispersión de PIB per cápita vs esperanza de vida al nacer
# + paneles por región y subregión de la ONU
paises |>
  ggplot(aes(x = GDP_PC, y = LIFE_EXPECTANCY)) +
  geom_point() +
  facet_grid(REGION_UN ~ INCOME_GRP) +
  scale_x_continuous(labels = comma, limits = c(0, NA))

# Gráfico de dispersión de PIB per cápita vs esperanza de vida al nacer
# en África y Europa coloreado por continente
# + curva de tendencia
paises |>
  filter(CONTINENT == 'Africa' | CONTINENT == 'Europe') |>
  ggplot(aes(x = GDP_PC, y = LIFE_EXPECTANCY, color = CONTINENT)) +
  geom_point() +
  geom_smooth() +
  scale_x_continuous(labels = comma, limits = c(0, NA)) +
  scale_y_continuous(labels = comma, limits = c(50, 90)) +
  ggtitle("PIB per cápita vs esperanza de vida al nacer por continente") +
  xlab("PIB per cápita  (USD)") +
  ylab("Esperanza de vida (años)") +
  labs(subtitle = "Datos de África y Europa", 
       caption = "Fuentes: Natural Earth y Banco Mundial",
       color = "Continente")+
  theme_ft_rc()

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Warning: Removed 7 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning: Removed 7 rows containing missing values or values outside the scale range
(`geom_point()`).

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not found in Windows font database
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# Gráfico de dispersión de PIB per cápita vs esperanza de vida al nacer
#  coloreado por continente
paises |>
  ggplot(aes(x = GDP_PC, y = LIFE_EXPECTANCY, color = CONTINENT)) +
  geom_point() +
  geom_smooth() +
  scale_x_continuous(labels = comma, limits = c(0, NA)) +
  scale_y_continuous(labels = comma, limits = c(50, 90)) +
  ggtitle("PIB per cápita vs esperanza de vida al nacer por continente") +
  xlab("PIB per cápita  (USD)") +
  ylab("Esperanza de vida (años)") +
  labs(caption = "Fuentes: Natural Earth y Banco Mundial",
       color = "Continente") +
  scale_colour_brewer(palette = "Set2", direction = -1) +
  theme_ipsum() # estilo de hrbrthemes

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Warning: Removed 11 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
: span too small.  fewer data values than degrees of freedom.

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
: pseudoinverse used at 10592

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
: neighborhood radius 506.81

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
: reciprocal condition number 0

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
: There are other near singularities as well. 4.0698e+07

Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : span too small.  fewer
data values than degrees of freedom.

Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
10592

Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
506.81

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else if (is.data.frame(newdata))
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number 0

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else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near
singularities as well. 4.0698e+07

Warning: Removed 11 rows containing missing values or values outside the scale range
(`geom_point()`).

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-Inf

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plotly

# Gráfico de dispersión de PIB per cápita vs esperanza de vida al nacer
grafico_ggplot2 <-
  paises |>
  ggplot(aes(x = GDP_PC, y = LIFE_EXPECTANCY, color = CONTINENT)) +
  geom_point(aes(
    # datos que se muestran al colocar el ratón sobre un punto
    text = paste0(    #pasteO aclara que no va por defecto ningun espacio, solo se pega lo #estrictamente escrito en el codigo
      "PIB per cápita: ", GDP_PC, "\n",
      "Esperanza de vida: ", LIFE_EXPECTANCY,"\n",
      "Pais: ", NAME
    )
  )) +
  scale_x_continuous(labels = comma, limits = c(0, NA)) +
  scale_y_continuous(labels = comma, limits = c(50, 90)) +
  ggtitle("PIB per cápita vs esperanza de vida al nacer por continente") +
  xlab("PIB per cápita  (USD)") +
  ylab("Esperanza de vida (años)") +
  labs(caption = "Fuentes: Natural Earth y Banco Mundial",
       color = "Continente") +
  labs(color = "Población estimada") +
  theme_ipsum() # estilo de hrbrthemes

# Gráfico plotly
ggplotly(grafico_ggplot2, tooltip = "text") |> 
  config(locale = 'es') # para mostrar los controles en español

tipos de gráficos

# Gráfico de dispersión PIB per cápita vs esperanza de vida
# + línea de tendencia
grafico_dispersion_ggplot2 <-
  paises |>
  ggplot(aes(x = GDP_PC, y = LIFE_EXPECTANCY)) +
  geom_point(aes(
    text = paste0(
      "País: ",
      NAME,
      "\n",
      "PIB per cápita: ",
      scales::comma(round(GDP_PC, 2)),
      "\n",
      "Esperanza de vida: ",
      round(LIFE_EXPECTANCY, 2)
    )
  )) +
  geom_smooth(method = "lm") +
  scale_x_continuous(labels = comma, limits = c(0, NA)) +
  scale_y_continuous(labels = comma, limits = c(50, 90)) +
  ggtitle("PIB per cápita vs esperanza de vida al nacer") +
  xlab("PIB per cápita (USD)") +
  ylab("Esperanza de vida (años)") +
  labs(caption = "Fuentes: Natural Earth y Banco Mundial") +
  theme_economist()  

Warning in geom_point(aes(text = paste0("País: ", NAME, "\n", "PIB per cápita:
", : Ignoring unknown aesthetics: text

# Gráfico de dispersión plotly
ggplotly(grafico_dispersion_ggplot2, tooltip = "text") |>
  config(locale = 'es')

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 11 rows containing non-finite outside the scale range
(`stat_smooth()`).

Histogramas

# Histograma ggplot2 de distribución del PIB per cápita
histograma_ggplot2 <- 
  paises |>
  ggplot(aes(x = LIFE_EXPECTANCY)) +
  geom_histogram(
    aes(
      text = paste0(
        "PIB per cápita (valor medio del rango): $", round(after_stat(x), 2), "\n",
        "Frecuencia: ", after_stat(count)
      )
    ), 
    bins = 10
  )  +
  #scale_x_continuous(labels = comma, limits = c(0, NA)) +
  #coord_cartesian(ylim = c(0, 40)) +
  ggtitle("Distribución del PIB per cápita") +
  xlab("Esperanza de vida al nacer (años)") +
  ylab("Frecuencia") +
  labs(subtitle = "Datos de 201 países", caption = "Fuentes: Natural Earth y Banco Mundial") +
  theme_economist()

Warning in geom_histogram(aes(text = paste0("PIB per cápita (valor medio del
rango): $", : Ignoring unknown aesthetics: text

# Histograma plotly
ggplotly(histograma_ggplot2, tooltip = "text") |> 
  config(locale = 'es')

Warning: Removed 11 rows containing non-finite outside the scale range
(`stat_bin()`).

grafico de torta

# Agrupar y resumir los datos
suma_poblacion_por_region <- paises |>
  group_by(REGION_UN) |>
  summarise(POP_TOTAL = sum(POP_EST))

# Calcular porcentajes
porcentaje_poblacion_por_region <- suma_poblacion_por_region |>
  mutate(POP_PCT = round(POP_TOTAL / sum(POP_TOTAL) * 100, 1))

# Gráfico de pastel
grafico_pastel_ggplot2 <- 
  porcentaje_poblacion_por_region |>
  ggplot(aes(x = "", y = POP_TOTAL, fill = REGION_UN)) +
  geom_bar(width = 1, stat = "identity") +
  coord_polar(theta = "y") +
  geom_text(
    aes(label = paste0(POP_PCT, "%")), 
    color = "black",
    position = position_stack(vjust = 0.6) # para ajustar la posición del texto en cada porción
  ) +
  labs(title = "Distribución de la población por región de la ONU",
       x = NULL,
       y = NULL,
       fill = "Región de la ONU") +
  theme_void()

# Despliegue del gráfico
grafico_pastel_ggplot2

graficos de barras

con transformaciones estadísticas

# Gráfico de barras con conteo de países por región de la ONU
grafico_barras_ggplot2 <-
paises |>
  ggplot(aes(x = fct_rev(fct_infreq(REGION_UN)))) +
  geom_bar(
    aes(
      text = paste0(
        "Cantidad de países: ", after_stat(count)
      )
    )    
  ) +
  ggtitle("Cantidad de países por región de la ONU") +
  xlab("Región de la ONU") +
  ylab("Cantidad de países") +
  labs(caption = "Fuente: Natural Earth") +
  theme_economist()

# Gráfico de barras plotly
ggplotly(grafico_barras_ggplot2, tooltip = "text") |> 
  config(locale = 'es')

# Gráfico de barras con promedio de esperanza de vida
# para cada región de la ONU
grafico_barras_ggplot2 <-
  paises |>
  ggplot(aes(x = fct_infreq(REGION_UN), y = LIFE_EXPECTANCY)) +
  geom_bar(
    stat = "summary", 
    fun.y = "mean",
    aes(
      text = paste0(
        "Promedio de esperanza de vida: ", round(after_stat(y), 2)
      )
    )
  ) +
  ggtitle("Promedio de esperanza de vida por región de la ONU") +
  xlab("Región de la ONU") +
  ylab("Promedio de esperanza de vida") +
  labs(caption = "Fuente: ") +
  theme_economist()

Warning in geom_bar(stat = "summary", fun.y = "mean", aes(text =
paste0("Promedio de esperanza de vida: ", : Ignoring unknown parameters:
`fun.y`

Warning in geom_bar(stat = "summary", fun.y = "mean", aes(text =
paste0("Promedio de esperanza de vida: ", : Ignoring unknown aesthetics: text

# Gráfico de barras plotly
ggplotly(grafico_barras_ggplot2, tooltip = "text") |>
  config(locale = 'es')

Warning: Removed 11 rows containing non-finite outside the scale range
(`stat_summary()`).

No summary function supplied, defaulting to `mean_se()`

# Cálculo del promedio de esperanza de vida por región
promedio_esperanza_vida_por_region <-
  paises |>
  group_by(REGION_UN) |>
  summarize(LIFE_EXPECTANCY_MEAN = mean(LIFE_EXPECTANCY, na.rm = TRUE))

# Despliegue por orden descendente del promedio de esperanza de vida
promedio_esperanza_vida_por_region |>
  arrange(desc(LIFE_EXPECTANCY_MEAN))

# A tibble: 6 × 2
  REGION_UN  LIFE_EXPECTANCY_MEAN
  <chr>                     <dbl>
1 Europe                     78.6
2 Asia                       74.5
3 Americas                   73.5
4 Oceania                    71.0
5 Africa                     63.1
6 Antarctica                NaN  

# Gráfico de barras con promedio de esperanza de vida
# para cada región de la ONU
grafico_barras_ggplot2 <-
  promedio_esperanza_vida_por_region |>
  ggplot(aes(x = reorder(REGION_UN,-LIFE_EXPECTANCY_MEAN), y = LIFE_EXPECTANCY_MEAN)) +
  geom_col(
    aes(
      text = paste0(
        "Promedio de esperanza de vida: ", round(after_stat(y), 2)
      )
    )    
  ) +
  ggtitle("Promedio de esperanza de vida por región de la ONU") +
  xlab("Región de la ONU") +
  ylab("Promedio de esperanza de vida") +
  labs(caption = "Fuente: Natural Earth") +
  theme_economist()

Warning in geom_col(aes(text = paste0("Promedio de esperanza de vida: ", :
Ignoring unknown aesthetics: text

# Gráfico de barras plotly
ggplotly(grafico_barras_ggplot2, tooltip = "text") |>
  config(locale = 'es')

# Gráfico de barras con población de países 
# de América
grafico_barras_ggplot2 <-
paises |>
  filter(REGION_UN == "Americas") |>
  ggplot(aes(x = reorder(ADM0_ISO, POP_EST), y = POP_EST/1000000)) +
  geom_col(
    aes(
      text = paste0(
        "País: ", NAME, "\n",
        "Población (millones de habitantes): ", round(POP_EST/1000000, 2)
      )
    )
  ) +
  scale_y_discrete(expand = expansion(mult = c(0.2, 0.2))) + # agrega un 20% de espacio al inicio y al final del eje y
  coord_flip() + # para mostrar barras horizontales
  ggtitle("Población de países de América") +
  xlab("País") +
  ylab("Población (millones de habitantes)") +
  labs(caption = "Fuente: Natural Earth") +
  theme_economist()

Warning in geom_col(aes(text = paste0("País: ", NAME, "\n", "Población
(millones de habitantes): ", : Ignoring unknown aesthetics: text

# Gráfico de barras plotly
ggplotly(grafico_barras_ggplot2, tooltip = "text") |> 
  config(locale = 'es')

# Gráfico de barras apiladas por región de la ONU y nivel de economía
grafico_barras_ggplot2 <-
paises |>
  ggplot(aes(x = REGION_UN, fill = ECONOMY)) +
  geom_bar() +
  ggtitle("Cantidad de países por región de la ONU y nivel de economía") +
  xlab("Región de la ONU") +
  ylab("Cantidad de países") +
  labs(fill = "Nivel de economía") +
  theme_minimal()

# Gráfico de barras plotly
ggplotly(grafico_barras_ggplot2) |> 
  config(locale = 'es')

# Gráfico de barras apiladas por región de la ONU y nivel de economía
grafico_barras_ggplot2 <-
paises |>
  ggplot(aes(x = REGION_UN, fill = ECONOMY)) +
  geom_bar(position = "fill") +
  ggtitle("Proporción de niveles de economía en regiones de la ONU") +
  xlab("Región de la ONU") +
  ylab("Proporción") +
  labs(fill = "Nivel de economía") +
  theme_minimal()

# Gráfico de barras plotly
ggplotly(grafico_barras_ggplot2) |> 
  config(locale = 'es')

# Gráfico de barras agrupadas por región de la ONU y nivel de economía
grafico_barras_ggplot2 <-
paises |>
  ggplot(aes(x = REGION_UN, fill = ECONOMY)) +
  geom_bar(position = "dodge") +
  ggtitle("Cantidad de países por región de la ONU y nivel de economía") +
  xlab("Región de la ONU") +
  ylab("Cantidad de países") +
  labs(fill = "Nivel de economía") +
  theme_minimal()

# Gráfico de barras plotly
ggplotly(grafico_barras_ggplot2) |> 
  config(locale = 'es')