Cómo reducir el espacio entre barras de un diagramas de barra con ggplot2

Problema

Queremos reducir el espacio entre las barras de un diagrama de barras con ggplot2.

Diagrama de barras vertical

Diagrama de barras horizontal

library(ggthemes)
library(ggplot2)
df <- data.frame(x = c("firm", "vendor"), y = c(50, 20))

# Vertical
ggplot(df, aes(x = x, y = y)) + 
  geom_bar(stat = "identity", width = 0.4) + 
  theme_tufte() + 
  labs(x = "", y = "")

# Horizontal
ggplot(df, aes(x = x, y = y)) + 
  geom_bar(stat = "identity", width = 0.4) + 
  theme_tufte() +  coord_flip() +
  labs(x = "", y = "")

Solución

Tan sólo necesitamos añadir theme(aspect.ratio = xx), y luego necesitamos ajustar el ese ratio para conseguir las distancia deseada. Con coord_flip() podemos voltear el gráfico

# Vertical
ggplot(df, aes(x = x, y = y)) + 
  geom_bar(stat = "identity") + 
  theme_tufte() + theme(aspect.ratio = 2) +
  labs(x = "", y = "")

# Horizontal
ggplot(df, aes(x = x, y = y)) + 
  geom_bar(stat = "identity") + 
  theme_tufte() + theme(aspect.ratio = .2) +
  coord_flip() +
  labs(x = "", y = "")

Resultados

Diagrama de barras vertical

Diagrama de barras horizontal

How to reduce space between bars in ggplot2

Problem

We want to reduce the space between bars using geom_bar with ggplot2.

Vertical bar plot

Horizontal bar plot

library(ggthemes)
library(ggplot2)
df <- data.frame(x = c("firm", "vendor"), y = c(50, 20))

# Vertical
ggplot(df, aes(x = x, y = y)) + 
  geom_bar(stat = "identity", width = 0.4) + 
  theme_tufte() + 
  labs(x = "", y = "")

# Horizontal
ggplot(df, aes(x = x, y = y)) + 
  geom_bar(stat = "identity", width = 0.4) + 
  theme_tufte() +  coord_flip() +
  labs(x = "", y = "")

Solution

We just need to to introduce theme(aspect.ratio = xx), then we need to play with the ratio to find the desired width. To flip cartesian coordinates we use coord_flip()

# Vertical
ggplot(df, aes(x = x, y = y)) + 
  geom_bar(stat = "identity") + 
  theme_tufte() + theme(aspect.ratio = 2) +
  labs(x = "", y = "")

# Horizontal
ggplot(df, aes(x = x, y = y)) + 
  geom_bar(stat = "identity") + 
  theme_tufte() + theme(aspect.ratio = .2) +
  coord_flip() +
  labs(x = "", y = "")

Results

Vertical bar plot

Horizontal bar plot

Related posts

• How to remove whiskers in a boxplot with the R Base Package

Calendar heatmap by hour and weekday in ggplot2

Problem

We want to create a calendar heatmap using ggplot2.

Solution

In our example we generate random dates, and then group the results by hour of the day and day of the week.

library(tidyverse)
library(lubridate)
# Data
set.seed(2020)
df <-
  data.frame(dates = sample(seq(
    as.POSIXct('2019/01/01', tz = "CET"),
    as.POSIXct('2019/12/31', tz = "CET"),
    by = "sec"
  ), 1000))

# Data manipulation
df %>%
  mutate(days = factor(weekdays(dates, abbreviate = TRUE),
                       levels = rev(
                         c('Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat')
                       ))) %>%
  group_by(hours= hour(dates), days) %>%
  summarise(sessions = n()) %>%

# Plot
ggplot(aes(hours, days)) +
  geom_tile(aes(fill = sessions), colour = "white") +
  scale_fill_distiller(palette = "YlGnBu", direction = 1) +
  scale_x_continuous(breaks = 0:23) +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    legend.key.width = unit(2, "cm"),
    panel.grid = element_blank()
  ) +
  coord_equal()

Results

Related posts

• How to create a calendar heatmap in R

References

• stackoverflow

Calcular y representar la duración del día con R

Problema

Queremos calcular y representar la duración del día con R en función de unas coordinadas geográficas.

Solución

1. Calculamos la salida y puesta de sol

Primero calculamos la salida y la puesta de sol con la función getSunlightTimes del paquete suncalc. Indicamos el intervalo deseado, las coordinadas (latitud y longitud), y el huso horario (tz, time zone) correspondiente.

library(suncalc) 
library(tidyverse)
library(scales)
df <-
  getSunlightTimes(
    date = seq.Date(as.Date("2017-12-01"), as.Date("2018-12-31"), by = 1),
    keep = c("sunrise", "sunriseEnd", "sunset", "sunsetStart"),
    lat = 39.8628,
    lon = 4.0273,
    tz = "CET"
  )

2. Gráfico de la salida y puesta de sol

Necesitamos manipular los datos originales para calcular la diferencia entre el inicio del día, y la salida y puesta de sol. Después representamos las dos nuevas variables usando geom_ribbon de ggplot2. Luego personalizamos los ejes y el título.

# Amanecer/ocaso
df %>%
  mutate(
    date = as.POSIXct(date) - 12 * 60 * 60 ,
    sunrise = sunrise - date,
    sunset =  sunset - date,
  ) %>%
  ggplot() +
  geom_ribbon(aes(x = date, ymin = sunrise, ymax = sunset),
              fill = "#FDE725FF",
              alpha = .8) + # "#ffeda0"
  scale_x_datetime(
    breaks = seq(as.POSIXct(min(df$date)), as.POSIXct(max(df$date)), "month"),
    expand = c(0, 0),
    labels = date_format("%b %y"),
    minor_breaks = NULL
  ) +
  scale_y_continuous(
    limits = c(0, 24),
    breaks = seq(0, 24, 2),
    expand = c(0, 0),
    minor_breaks = NULL
  ) +
  labs(
    x = "Date",
    y = "Hours",
    title = sprintf(
      "Sunrise and Sunset for %s\n%s ",
      "Toledo (Spain)",
      paste0(as.Date(range(df$date)), sep = " ", collapse = "to ")
    )
  ) +
  theme(
    panel.background = element_rect(fill = "#180F3EFF"),
    panel.grid = element_line(colour = "grey", linetype = "dashed")
  )

3. Duración del día

Muy similar al gráfico anterior. Ahora solamente necesitamos calcular la duración del día day_length y representar los resultados con geom_area and geom_line.

df %>%
  mutate(
    date = as.POSIXct(date),
    day_length = as.numeric(sunset - sunrise)
  ) %>%
  ggplot(aes(x = date, y = day_length)) +
  geom_area(fill = "#FDE725FF", alpha = .4) +
  geom_line(color = "#525252") +
  scale_x_datetime(
    expand = c(0, 0),
    labels = date_format("%b '%y"),
    breaks =  seq(as.POSIXct(min(df$date)), as.POSIXct(max(df$date)), "month"),
    minor_breaks = NULL
  ) +
  scale_y_continuous(
    limits = c(0, 24),
    breaks = seq(0, 24, 2),
    expand = c(0, 0),
    minor_breaks = NULL
  ) +
  labs(x = "Date", y = "Hours", title = "Toledo (Spain) - Daytime duration") +
  theme_bw()

Entradas relacionadas

• Versión en inglés: Calculate and plot sunrise and sunset times with R
• English posts

Referencias

• suncalc
• ggplot2 formats

Calculate and plot sunrise and sunset times with R

Problem

We would like to calculate and plot the sunrise and sunset times based on any location's latitude and longitude coordinates with R.

Solution

1. Compute sunrise and sunset times

First we calculate the sunrise and sunset times using the function getSunlightTimes from the package suncalc. We pass the desired date interval, the appropiate latitude and longitude coordinates, and time zone (tz).

library(suncalc) 
library(tidyverse)
library(scales)
df <-
  getSunlightTimes(
    date = seq.Date(as.Date("2017-12-01"), as.Date("2018-12-31"), by = 1),
    keep = c("sunrise", "sunriseEnd", "sunset", "sunsetStart"),
    lat = 39.8628,
    lon = 4.0273,
    tz = "CET"
  )

2. Sunrise and sunset times plot

We need to manipulate the original data frame to calculate the difference between midnight start of day, and the sunrise and sunset times. Then we plot those two new variables using geom_ribbon from ggplot2. We further customize the axes, and title.

# Sunrise/set
df %>%
  mutate(
    date = as.POSIXct(date) - 12 * 60 * 60 ,
    sunrise = sunrise - date,
    sunset =  sunset - date,
  ) %>%
  ggplot() +
  geom_ribbon(aes(x = date, ymin = sunrise, ymax = sunset),
              fill = "#FDE725FF",
              alpha = .8) + # "#ffeda0"
  scale_x_datetime(
    breaks = seq(as.POSIXct(min(df$date)), as.POSIXct(max(df$date)), "month"),
    expand = c(0, 0),
    labels = date_format("%b %y"),
    minor_breaks = NULL
  ) +
  scale_y_continuous(
    limits = c(0, 24),
    breaks = seq(0, 24, 2),
    expand = c(0, 0),
    minor_breaks = NULL
  ) +
  labs(
    x = "Date",
    y = "Hours",
    title = sprintf(
      "Sunrise and Sunset for %s\n%s ",
      "Toledo (Spain)",
      paste0(as.Date(range(df$date)), sep = " ", collapse = "to ")
    )
  ) +
  theme(
    panel.background = element_rect(fill = "#180F3EFF"),
    panel.grid = element_line(colour = "grey", linetype = "dashed")
  )

3. Daytime duration

Very similar to the preceding plot. This time we only need to calculate the day_length and plot the results using geom_area and geom_line.

df %>%
  mutate(
    date = as.POSIXct(date),
    day_length = as.numeric(sunset - sunrise)
  ) %>%
  ggplot(aes(x = date, y = day_length)) +
  geom_area(fill = "#FDE725FF", alpha = .4) +
  geom_line(color = "#525252") +
  scale_x_datetime(
    expand = c(0, 0),
    labels = date_format("%b '%y"),
    breaks =  seq(as.POSIXct(min(df$date)), as.POSIXct(max(df$date)), "month"),
    minor_breaks = NULL
  ) +
  scale_y_continuous(
    limits = c(0, 24),
    breaks = seq(0, 24, 2),
    expand = c(0, 0),
    minor_breaks = NULL
  ) +
  labs(x = "Date", y = "Hours", title = "Toledo (Spain) - Daytime duration") +
  theme_bw()

Related posts

• Spanish version: Cómo controlar la dispersión de puntos dentro de un diagrama de violín con ggplot2
• English posts

References

• suncalc
• ggplot2 formats

Gráficos de An Introduction to Statistical Learning con ggplot2 - Figura 3.1.

Gráfico a replicar

Continuamos con la serie iniciada sobre la creación de gráficos del libro An Introduction to Statistical Learning. En esta ocasión replicaremos el gráfico de la figura 3.1. Utiliza el conjunto de datos Advertising.Los puntos rojos representan los valores observados de ventas (sales) y los presupuestos de televisión (TV). La línea azul representa la recta de regresión. Las líneas grises verticales representan el error asociado con cada observación. Hay errores positivos (si la observación se sitúa por encima de la líneaazul) y negativos (si la observación se sitúa por debajo de la curva).

Solución

Con la función theme personalizamos el formato: rotamos las etiquetas del eje y, eliminamos el color de fondo, y añadimos el borde.

library(tidyverse)
ggplot(ad_fitted, aes(x = budgets, y = sales)) +
  geom_point(colour = "red") +
  geom_smooth(se = FALSE, method = lm) +
  geom_segment(aes(
    x = budgets,
    y = sales,
    xend = budgets,
    yend = fitted
  )) +
  scale_x_continuous(breaks = seq(0, 300, by = 50)) +
  scale_y_continuous(breaks = seq(0, 25, by = 5)) +
  theme(
    axis.text.y = element_text(angle = 90, hjust = 1),
    panel.background = element_blank(),
    panel.border = element_rect(fill = NA)
  )

Si deseamos mantener la cuadrícula, pero eliminar el color de fondo (theme_bw) y modificar el título del eje x (labs):

ggplot(ad_fitted, aes(x = budgets, y = sales)) +
  geom_point(colour = "red") +
  geom_smooth(se = FALSE, method = lm) +
  geom_segment(aes(
    x = budgets,
    y = sales,
    xend = budgets,
    yend = fitted
  )) +
  labs(x = "TV")+
  scale_x_continuous(breaks = seq(0, 300, by = 50)) +
  scale_y_continuous(breaks = seq(0, 25, by = 5)) +
  theme_bw()+
  theme(axis.text.y = element_text(angle = 90, hjust = 1))

Entradas relacionadas

• Gráficos de An Introduction to Statistical Learning con ggplot2 - Figura 1.1.
• Gráficos de An Introduction to Statistical Learning con ggplot2 - Figura 1.2.
• Gráficos de An Introduction to Statistical Learning con ggplot2 - Figura 1.3.
• Gráficos de An Introduction to Statistical Learning con ggplot2 - Figura 1.4.
• Gráficos de An Introduction to Statistical Learning con ggplot2 y plotly - Figura 1.4. interactiva
• Gráficos de An Introduction to Statistical Learning con ggplot2 y plotly - Figura 2.1.
• Entradas de diagramas de dispersión
• Subconjunto de estadísticas descriptivas en R

Referencias

• geom_smooth, method
• Stackoverflow -Connecting points to regression line in plot