Understanding geom_vlines and Customizing Colors

In this article, we’ll explore the geom_vline() function in ggplot2, a popular data visualization library in R. We’ll delve into the world of customized colors and how to create visually appealing plots.

Introduction to geom_vline()

geom_vline() is used to add vertical lines to a plot. These lines can represent significant points or changes in your dataset. In the context of this article, we’re interested in using geom_vline() to highlight specific dates when the “cas” variable changes value.

Understanding the Problem and Error

The original code attempts to create geom_vline() with different colors relative to the modalities of a variable (“cas”). However, the error message indicates that there’s an issue with aesthetics being inconsistent. This is because xintercept and colour are not aligned properly.

{< highlight r >}
geom_vline( aes ( xintercept = lubridate::date(df$date[which(df$date %in% date_cas), colour = df$cas[which((df$date) %in% date_cas)] ])  , size=1, linetype = "dashed")+
</ highlight >}

Solution and Explanation

To solve this issue, we need to simplify the code by moving parentheses/brackets to correct positions. Additionally, we should subset df in geom_vline() with data = df[df$date %in% date_cas, ].

{< highlight r >}
ggplot(df, aes(x=date, y=valeur, colour=meteo, group=meteo)) +
  geom_line(size=0.8)+
  geom_vline(data = df[df$date %in% date_cas, ],
             aes(xintercept=date, colour=cas),
             size=1, 
             linetype = "dashed")+
  labs(y="", x = "Date")+
  theme_minimal()
</ highlight >}

Subsetting and Custom Colors

By using data = df[df$date %in% date_cas, ], we ensure that only the data points within date_cas are used for creating the vertical lines. This helps simplify the code and improves readability.

To customize colors, we can use a mapping function like aes_string(). In this case, we want to map “cas” values to specific colors. We’ll create a color palette with c("red", "blue", "green"), which represents the three modalities of “cas”.

{< highlight r >}
library(ggplot2)

ggplot(df, aes(x=date, y=valeur, colour=meteo, group=meteo)) +
  geom_line(size=0.8)+
  geom_vline(data = df[df$date %in% date_cas, ],
             aes(xintercept=date, colour=cas),
             size=1, 
             linetype = "dashed")+
  labs(y="", x = "Date")+
  theme_minimal() +
  scale_color_manual(values = c("cas 0" = "red", "cas 1" = "blue", "cas 2" = "green"))
</ highlight >}

Advanced Customization

To further customize our plot, we can use scale_color_discrete() to map color names to specific colors.

{< highlight r >}
library(ggplot2)

ggplot(df, aes(x=date, y=valeur, colour=meteo, group=meteo)) +
  geom_line(size=0.8)+
  geom_vline(data = df[df$date %in% date_cas, ],
             aes(xintercept=date, colour=cas),
             size=1, 
             linetype = "dashed")+
  labs(y="", x = "Date")+
  theme_minimal() +
  scale_color_discrete(name = "Cas", values = c("cas 0" = "red", "cas 1" = "blue", "cas 2" = "green"))+
  scale_x_date(date_breaks = "1 year", date_labels = function(x) format(x, "%Y"))
</ highlight >}

Additional Tips and Best Practices

• Always check the ggplot documentation for updates on new features and best practices.
• Use theme_minimal() or a similar theme to keep your plots clean and simple.
• Customize colors using scale_color_manual() or scale_color_discrete().
• Experiment with different date formats using lubridate and ggplot2.

By following these tips and understanding how to customize colors in ggplot2, you’ll be able to create visually appealing plots that effectively communicate your data insights.

Last modified on 2024-01-16