I've been trying to produce a similar plot to this using ggplot2:

And this is my R code:

df <- data.frame(
  tecnica = factor(rep(1:4, each = 4)),  # Repite cada tecnica 4 veces
  bloque = factor(rep(1:4, times = 4)),  # Iitera por cada bloque
  resistencia = c(3129, 3000, 2865, 2890,
                  3200, 3300, 2975, 3150,
                  2800, 2900, 2985, 3050,
                  2600, 2700, 2600, 2765)   # Observaciones en orden fila por fila
)


# Calcular la media y desviación estándar para la muestra.
media_resistencia <- mean(df$resistencia)
sd_resistencia <- sd(df$resistencia)

# Calcular la media por técnica y convertir los resultados en un dataframe
medias_df <- aggregate(resistencia ~ tecnica, data = df, mean)



ticks_values <- unlist(lapply(c(-3,-2,-1,0,1,2,3), function(x) round((x * sd_resistencia)+media_resistencia,2)))
ticks_values[[1]]<-ticks_values[[1]]-10
ticks_values[[length(ticks_values)]]<-ticks_values[[length(ticks_values)]]+10



library(ggplot2)

# Crear un data frame para la función de densidad
x_vals <- seq(min(ticks_values), max(ticks_values), length.out = 100)
density_vals <- data.frame(
  x = x_vals,
  y = dnorm(x_vals, mean = media_resistencia, sd = sd_resistencia)
)

# Crear la gráfica con ggplot2
ggplot(density_vals, aes(x = x, y = y)) +
  geom_line(size = 0.5) +  # Línea de la distribución normal
  geom_point(data = medias_df, aes(x = resistencia, y = 0, color = tecnica), size = 4) +
  geom_vline(xintercept = ticks_values[[4]], colour = "black", linetype=3, size = .5) +
  scale_x_continuous(breaks = ticks_values, labels = ticks_values) +  
  scale_y_continuous(limits = c(0, max(density_vals$y))) +
  theme_minimal() +  
  theme(axis.title.y = element_blank(),   
        axis.text.y = element_blank(),     
        axis.ticks.y = element_blank(),    
        panel.grid.major.y = element_blank(),   
        panel.grid.minor.y = element_blank(),
        axis.ticks.x = element_line(size = 0.7),   
        axis.ticks.length = unit(7, "pt"), 
        axis.line.x = element_line(size = 0.5, linetype=1),
        axis.text.x = element_text(size = 10, angle = 0, hjust = 0.5, vjust = 0.5)) +  # Rotar etiquetas X
  labs(x = "Resistencia", y = NULL)  # Etiqueta del eje X

And this is my result:

How can I add the values from the media_df data frame to the plot as labels for the points? How can I adjust the position of the points in the media_df data frame so that the x-axis line crosses them?

I've been trying to produce a similar plot to this using ggplot2:

And this is my R code:

df <- data.frame(
  tecnica = factor(rep(1:4, each = 4)),  # Repite cada tecnica 4 veces
  bloque = factor(rep(1:4, times = 4)),  # Iitera por cada bloque
  resistencia = c(3129, 3000, 2865, 2890,
                  3200, 3300, 2975, 3150,
                  2800, 2900, 2985, 3050,
                  2600, 2700, 2600, 2765)   # Observaciones en orden fila por fila
)


# Calcular la media y desviación estándar para la muestra.
media_resistencia <- mean(df$resistencia)
sd_resistencia <- sd(df$resistencia)

# Calcular la media por técnica y convertir los resultados en un dataframe
medias_df <- aggregate(resistencia ~ tecnica, data = df, mean)



ticks_values <- unlist(lapply(c(-3,-2,-1,0,1,2,3), function(x) round((x * sd_resistencia)+media_resistencia,2)))
ticks_values[[1]]<-ticks_values[[1]]-10
ticks_values[[length(ticks_values)]]<-ticks_values[[length(ticks_values)]]+10



library(ggplot2)

# Crear un data frame para la función de densidad
x_vals <- seq(min(ticks_values), max(ticks_values), length.out = 100)
density_vals <- data.frame(
  x = x_vals,
  y = dnorm(x_vals, mean = media_resistencia, sd = sd_resistencia)
)

# Crear la gráfica con ggplot2
ggplot(density_vals, aes(x = x, y = y)) +
  geom_line(size = 0.5) +  # Línea de la distribución normal
  geom_point(data = medias_df, aes(x = resistencia, y = 0, color = tecnica), size = 4) +
  geom_vline(xintercept = ticks_values[[4]], colour = "black", linetype=3, size = .5) +
  scale_x_continuous(breaks = ticks_values, labels = ticks_values) +  
  scale_y_continuous(limits = c(0, max(density_vals$y))) +
  theme_minimal() +  
  theme(axis.title.y = element_blank(),   
        axis.text.y = element_blank(),     
        axis.ticks.y = element_blank(),    
        panel.grid.major.y = element_blank(),   
        panel.grid.minor.y = element_blank(),
        axis.ticks.x = element_line(size = 0.7),   
        axis.ticks.length = unit(7, "pt"), 
        axis.line.x = element_line(size = 0.5, linetype=1),
        axis.text.x = element_text(size = 10, angle = 0, hjust = 0.5, vjust = 0.5)) +  # Rotar etiquetas X
  labs(x = "Resistencia", y = NULL)  # Etiqueta del eje X

And this is my result:

How can I add the values from the media_df data frame to the plot as labels for the points? How can I adjust the position of the points in the media_df data frame so that the x-axis line crosses them?

• r
• ggplot2
• statistics

2 Answers 2

To place your points on the x axis you can set y = -Inf (which will place the points on the panel border) and set coord_cartesian(clip="off") to prevent that the points get clipped off. To add the labels you can use geom_text or ggrepel::geom_text_repel. I opted for the latter to avoid overlapping labels:

library(ggplot2)
library(ggrepel)

ggplot(density_vals, aes(x = x, y = y)) +
  geom_line(size = 0.5) + # Línea de la distribución normal
  geom_point(data = medias_df, aes(x = resistencia, y = -Inf, color = tecnica), size = 4) +
  geom_text_repel(
    data = medias_df, aes(x = resistencia, y = 0, label = resistencia),
    direction = "y", nudge_y = .00001
  ) +
  geom_vline(xintercept = ticks_values[[4]], colour = "black", linetype = 3, size = .5) +
  scale_x_continuous(breaks = ticks_values, labels = ticks_values) +
  scale_y_continuous(limits = c(0, max(density_vals$y))) +
  theme_minimal() +
  theme(
    axis.title.y = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank(),
    axis.ticks.x = element_line(size = 0.7),
    axis.ticks.length = unit(7, "pt"),
    axis.line.x = element_line(size = 0.5, linetype = 1),
    axis.text.x = element_text(size = 10, angle = 0, hjust = 0.5, vjust = 0.5)
  ) + # Rotar etiquetas X
  labs(x = "Resistencia", y = NULL) +
  coord_cartesian(clip = "off")
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
#> ℹ Please use the `linewidth` argument instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.

^{Created on 2025-04-01 with reprex v2.1.1}

For a few values you could try to add jitter() with a random seed to text(). It may need a few runs.

set.seed(3)
plot(y ~ x, density_vals, type='l', 
     axes=FALSE, ylab='', xlab='Resistencia')
axis(1, at=ticks_values)
with(transform(medias_df, y=0), {
  points(x=resistencia, y=y, col=resistencia, pch=20)
  abline(v=mean(resistencia), lty='dotted')
  legend('topright', title='Tecnia', legend=tecnica, 
         col=resistencia, pch=20, bty='n')
  text(x=resistencia, y=abs(jitter(y, .008)), 
       labels=resistencia, pos=3, col=resistencia)

For several values we should move to a non-random label-placing technique.