ggplot2 Colored Lines According to Group: Avoiding Missing Values

When working with time series data in R using the popular package ggplot2, it’s not uncommon to encounter missing values. In this article, we’ll explore how to create a colored line plot where missing values are treated as separate groups, avoiding any connections between consecutive seasons.

Introduction to ggplot2 and Missing Values

ggplot2 is an excellent data visualization library in R that provides a powerful way to create beautiful and informative plots. One of its key features is the ability to easily color lines according to different variables. However, when dealing with missing values, it’s essential to understand how they’re handled.

In ggplot2, missing values are represented by NA. When creating a line plot, if two consecutive data points have missing values for the same variable (e.g., season), the plotting process will attempt to connect these points. This can result in unwanted lines connecting missing values across seasons.

Understanding Season Categories

The question mentions that there are multiple missing data points with one gap being a couple of months and another one resulting from the transition between winter and spring. To solve this issue, we need to create a column that specifies the season for each date.

# Add a column that specifies the season
library(lubridate)
mydata$season <- time2season(mydata$date, out.fmt = "seasons", type = "default")

Capitalizing Season Categories

The next step is to capitalize the season categories. This can be done using the capitalize() function from the stringr package.

# Capitalize season categories
library(stringr)
mydata$season <- capitalize(mydata$season)

Creating a Group Variable

To avoid connecting lines between missing values, we need to create a group variable that distinguishes between these points. This can be done by counting the number of consecutive NA values.

# Create a group variable and plot it
g <- ggplot(mydata, aes(date, wspd_havg10m_kn, color = season)) + 
  geom_line(size = 0.1) +
  geom_smooth(colour = "black", size = 1, method = "gam", formula = y ~ s(x), bs = "cs") +  
  scale_y_continuous(limits = c(0, max(mydata$wspd_havg10m_kn)), oob = c("white")) +
  theme(axis.text.y = element_text(color = "white"))+
  scale_color_manual(values = c("Fall" = "brown", "Winter" = "darkblue"))

Complete Cases

To avoid any NA values in the data frame, we use complete.cases() to select only rows where no value is missing.

# Select complete cases
df <- mydata[complete.cases(mydata),]

Grouping by Season and Group Variable

Finally, we group the data points according to their season and group variable. This will ensure that lines are drawn separately for each group of consecutive seasons with no missing values.

# Define our group variable and plot it
df$grp <- cumsum(is.na(df$wind))
ggplot(data = df[complete.cases(df),], aes(date, wind, color = season)) + 
  geom_line(aes(group = grp)) +
  scale_color_manual(values = c("Fall" = "brown", "Winter" = "darkblue"))

Example Data

Here is the example data used in this article:

# Read the data
library(lubridate)
df <- read.csv("data.csv",
               strip.white=T,
               colClasses=c("character","numeric","numeric","factor"))

df$date <- ymd_hms(df$date, tz = "UCT")

# Add a column that specifies the season
df$season <- time2season(df$date, out.fmt = "seasons", type = "default")

# Capitalize season categories
df$season <- capitalize(df$season)

# Create a group variable and plot it
df$grp <- cumsum(is.na(df$wind))
ggplot(data = df[complete.cases(df),], aes(date, wind, color = season)) + 
  geom_line(aes(group = grp)) +
  scale_color_manual(values = c("Fall" = "brown", "Winter" = "darkblue"))

Conclusion

In this article, we demonstrated how to create a colored line plot with missing values treated as separate groups. By using the cumsum() function to count consecutive NA values and selecting only complete cases in the data frame, we can avoid unwanted lines connecting missing values across seasons.

Last modified on 2025-05-02