How to Plot Timeseries using highcharter library in R

Time series data is a sequence of data points that are measured at regular intervals over time. Plotting time series data is a useful way to visualize trends and patterns over time. Highcharter is an R package that provides an interface to the Highcharts JavaScript charting library. Highcharts is a popular web application library for creating interactive charts and graphs. In this article, we will explore how to use Highcharter to plot time series data in R.

Before we can use Highcharter, we need to install it. We can do this using the install.packages() function in R Programming Language:

# Installing the required R packages
install.packages("highcharter")

We will use the AirPassengers dataset, which contains monthly totals of international airline passengers from January 1949 to December 1960. This dataset is included with the base R installation and can be loaded using the data() function:

data(AirPassengers)
force(AirPassengers)

Output:

     Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1949 112 118 132 129 121 135 148 148 136 119 104 118
1950 115 126 141 135 125 149 170 170 158 133 114 140
1951 145 150 178 163 172 178 199 199 184 162 146 166
1952 171 180 193 181 183 218 230 242 209 191 172 194
1953 196 196 236 235 229 243 264 272 237 211 180 201
1954 204 188 235 227 234 264 302 293 259 229 203 229
1955 242 233 267 269 270 315 364 347 312 274 237 278
1956 284 277 317 313 318 374 413 405 355 306 271 306
1957 315 301 356 348 355 422 465 467 404 347 305 336
1958 340 318 362 348 363 435 491 505 404 359 310 337
1959 360 342 406 396 420 472 548 559 463 407 362 405
1960 417 391 419 461 472 535 622 606 508 461 390 432

Creating a basic time series plot:

To create a basic time series plot using Highcharter, we need to first convert our data into a format that Highcharter can understand. We will use the xts package to convert our data into a time series object, and then use the hchart() function in Highcharter to create the plot:

library(highcharter)
library(datasets)

# Convert the nottem data to a time series object
ts_data <- ts(nottem, start = c(1920, 1), frequency = 12)

# Convert the time series object to a data frame
df <- data.frame(date = time(ts_data),
                 value = as.numeric(ts_data))

# Create a highcharter time series plot
hchart(df, "line", hcaes(x = date,
                         y = value)) %>% 
  hc_title(text = "Monthly Average Temperatures at Nottingham Castle") %>% 
  hc_yAxis(title = list(text = "Temperature (Fahrenheit)")) %>% 
  hc_xAxis(type = "datetime",
           labels = list(format = "%b %Y"))

Output:

Adding plot options

Highcharter provides many options for customizing the appearance of the plot. Here are a few examples:

library(highcharter)

# Load nottem dataset
data(nottem)

# Create a time series object
ts_nottem <- ts(coredata(nottem), start = c(1920, 1),
                frequency = 12)

# Create a highcharter plot
hchart(ts_nottem, type = "line", name = "Monthly Temperatures") %>% 
  hc_title(text = "Monthly Average Temperatures in Nottingham") %>% 
  hc_yAxis(title = list(text = "Temperature (°F)")) %>% 
  hc_tooltip(pointFormat = "Temperature: <b>{point.y:.1f} °F</b>")

Output:

Customizing Time Series Plot using highcharter

highcharter provides many options for customizing time series plots. Here, we will demonstrate some of the commonly used customizations.

Adding Title and Subtitle

We can add a title and subtitle to our plot using the hc_titel( ) and hc_subtitle( ) functions. These functions take similar arguments and have similar functionality. Here are the main arguments that both functions take:

library(highcharter)
library(xts)

# load the nottem dataset
data("nottem")

# remove any rows with missing dates
nottem <- na.omit(nottem)

# create a basic time series plot
chart <- hchart(ts_data, type = "line") %>% 
  
  # Adding Title and Subtitle
  hc_title(text = "Average Monthly Temperatures in Nottingham, England", 
           align = "center",
           style = list(fontSize = "20px")) %>% 
  
  hc_subtitle(text = "1920-1939", 
              align = "center",
              style = list(fontSize = "16px")) %>% 
print(chart)

Output:

Observe in the above graph, Title "Average Monthly Temperatures in Nottingham, England" and the subtitle "1920-1939" have been added to the basic timeseries plot.

Changing axis labels

We can change the x-axis and y-axis labels using the hc_xAxis( ) and hc_yAxis( ) functions respectively. hc_xAxis() is used to modify the properties of the X-axis. It takes several arguments, including:

hc_yAxis() is used to modify the properties of the Y axis. It takes similar arguments to hc_xAxis(), including:

library(highcharter)
library(xts)

# load the nottem dataset
data("nottem")

# remove any rows with missing dates
nottem <- na.omit(nottem)

# create a basic time series plot
chart <- hchart(ts_data, type = "line") %>% 
  
  # Adding Title and Subtitle
  hc_title(text = "Average Monthly Temperatures in Nottingham, England", 
           align = "center",
           style = list(fontSize = "20px")) %>% 
  
  hc_subtitle(text = "1920-1939", 
              align = "center",
              style = list(fontSize = "16px")) %>% 
  
  # Changing Axis Labels
  hc_yAxis(title = list(text = "Temperature (°F)"),
           labels = list(style = list(fontSize = "14px"))) %>% 
  
print(chart)

Output:

Observe we have changed the Y-axis label ("Temperature (F)) as compared to our previous time-series plot. 

Adding Data Labels on the ToolTip

The hc_tooltip() function is used to customize the tooltip of the Highcharts object. The tooltip is a floating box that appears when the user hovers over a point on the chart. The hc_tooltip() function takes several arguments that can be used to customize the tooltip, including:

The other arguments taken by this function are "valuePrefix", "valueSuffix", "backgroundColor", "borderColor", "borderRadius", "borderWidth" and "style".

library(highcharter)
library(xts)

# load the nottem dataset
data("nottem")

# remove any rows with missing dates
nottem <- na.omit(nottem)

# create a basic time series plot
chart <- hchart(nottem, type = "line") %>% 
  
  # Adding Title and Subtitle
  hc_title(text = "Average Monthly Temperatures in Nottingham, England", 
           align = "center",
           style = list(fontSize = "20px")) %>% 
  
  hc_subtitle(text = "1920-1939", 
              align = "center",
              style = list(fontSize = "16px")) %>% 
  
  # Changing Axis Labels
  hc_yAxis(title = list(text = "Temperature (°F)"),
           labels = list(style = list(fontSize = "14px"))) %>% 
  
  # Changing Tooltip Format
  hc_tooltip(
    formatter = JS("function() {
      return '<b>' + Highcharts.dateFormat('%b %Y', new Date(this.x)) + '</b><br/>' +
        '<span style=\"color:' + this.series.color + ';font-weight:bold;\">' +
        this.series.name + ': </span>' + Highcharts.numberFormat(this.y, 2) + ' °F';
    }")
  )

print(chart)

Output:

Observe the data label displayed as we hover the cursor on the plot. We can customize the data label elements as per the requirement.

Adding Annotations:

We can add annotations to our plot using the hc_annotations( ) function. Annotations are graphical elements that can be added to a chart to provide additional information or highlight specific data points. The main arguments of the hc_annotations() function are:

library(highcharter)
library(xts)

# load the nottem dataset
data("nottem")

# remove any rows with missing dates
nottem <- na.omit(nottem)

# create a basic time series plot
chart <- hchart(nottem, type = "line") %>% 
  
  # Adding Title and Subtitle
  hc_title(text = "Average Monthly Temperatures in Nottingham, England", 
           align = "center",
           style = list(fontSize = "20px")) %>% 
  
  hc_subtitle(text = "1920-1939", 
              align = "center",
              style = list(fontSize = "16px")) %>% 
  
  # Changing Axis Labels
  hc_yAxis(title = list(text = "Temperature (°F)"),
           labels = list(style = list(fontSize = "14px"))) %>% 
  
  # Changing Tooltip Format
  hc_tooltip(
    formatter = JS("function() {
      return '<b>' + Highcharts.dateFormat('%b %Y', new Date(this.x)) + '</b><br/>' +
        '<span style=\"color:' + this.series.color + ';font-weight:bold;\">' +
        this.series.name + ': </span>' + Highcharts.numberFormat(this.y, 2) + ' °F';
    }")
  ) %>% 
  
  # Adding Annotations
  hc_annotations(
    list(
      labels = list(
        list(point = list(x = as.numeric(as.Date("1934-07-01")),
                          y = 50),
             text = "Summer month with highest temp")
      )
    )
  ) %>%
  
print(chart)

Output:

Observe the annotation added on the y-axis for the temperature of the month of July 1934 which is the month with the highest temperature.

Conclusion:

In conclusion, Highcharter is a powerful R library for creating interactive and dynamic time series visualizations. With its intuitive syntax and easy-to-use functions, Highcharter makes it easy to create professional-looking charts and graphs with minimal effort. In this article, we covered the basics of creating time series plots using Highcharter, including how to set up data, create charts, and customize the appearance of the plot. We also showed some advanced features such as adding annotations, changing the plot type, and adding multiple series. Overall, Highcharter is a great tool for anyone looking to create high-quality time series visualizations quickly and easily.

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Article Tags :

• R Language
• R-Data_Science