dygraphs for interactive time series oceanographical data

Interactive plots can help users overcome the dilemma of complex or detailed plot like the hour temperature records plotted over a year. They also offers readers an option or tools to explore the plot in detail. R has several package that turn static plot into interactive ones. In this post, I will show you how to create an interactive time series using the dygraphs package developed and maintained by Vanderkam et al. (2018). We first load the packages into the workspace in R (R Core Team 2018). Other packages I need for this task include the lubridate(Grolemund and Wickham 2011) for manipulating date and the tidyverse (Wickham 2017) for data manipulation

require(tidyverse)
require(lubridate)
require(dygraphs)

Once the packages are loaded, its time to load the dataset from the working directory into R. The function read.table() was used to import the dataset.

temperature = read.table("./Temperature data/Chumbe_19Jan12_in.txt", sep = "\t", header = T) 

Table 1 show the sample of the first five and last five records of the dataset. You notice that the first record of the temperature was measured on 2012-01-19 00:01:00 and the last records was captured on 2012-01-19 00:01:00. You notice also that the temperature was recorded at an interval of 15 mins.

A keen observation of the Time variable in table 1 you notice that the minutes have been placed in the position of hours—which is totally missing in the dataset. Since we know the begin time and the time interval, we can create a new variable muda using the seq() as shown in the chunk below.

temperature.15min = temperature %>% 
  mutate(date = mdy(Date)) %>% 
  filter(date <= dmy(310812)) %>% 
  mutate(muda = seq(dmy_hms("190112 00:00:00"),
                    dmy_hms("310812 23:59:00"),
                    length.out = n()))

Although the dataset contain time in the standard R format, we can not plug this dataset direct into the dygraphs. This is because the dataset must be in the xts format to work with the package. The chunk below show how to convert this dataset into the xts format.

muda.wakati = xts::xts(x = temperature.15min$Temperature, 
                       order.by = temperature.15min$muda)

Now we have the dataset that we used to make an interactive chart shown in figure 1 using the dygraphs package. This chart was created by adding two options. First, add a date range selector with the dyRoller() function. The function allows the person interacting with the plot to average the time by entering the number at the bottom left of the plot, which smooth the Y-value over the specified number of time scale units. Second, add a range selector to the bottom of the chart with dyRangeSelector(). This allow the users to pan and zoom to various date ranges.

dygraph(data = muda.wakati, main = "Sea surface temperature", ylab = "Degree Celcius") %>%
  dyRoller(rollPeriod = 96) %>%
  dyRangeSelector()

Pretty simple, Play around with the changing the rollperiod number on the chart to smooth. Since the records are 15 minutes interval, to obtain a hourly average, you can punch 4 in the box, and if you want a daily smooth, you have to get a product of 24 hours by 4 (four records in 1 hour) to obtain 96, which you I have used in the and smooth the line to daily average.

We can further specify the region with highest and lowest value of temperature with the dyShading() as shown in figure 2

dygraph(data = muda.wakati, main = "Sea surface temperature", ylab = "Degree Celcius") %>%
  dyRoller(rollPeriod = 96) %>%
  dyRangeSelector() %>% 
  dyShading(from = "2012-3-15", to = "2012-4-15", color = "lightpink")%>% 
  dyShading(from = "2012-7-20", to = "2012-8-5", color = "palegreen")

alternative todyShading() function that add region, we use the dyEvent()function that add a vertical line mark the occurrence of a particular event as shown in figure as shown in figure 3

dygraph(data = muda.wakati, main = "Sea surface temperature", ylab = "Degree Celcius") %>%
  dyRoller(rollPeriod = 96) %>%
  dyRangeSelector() %>% 
  dyEvent(x = c("2012-2-21","2012-4-2", "2012-7-24"), 
          label = c("Kaskazi","Matarahi", "Kusi"), 
          labelLoc = "bottom", color = "red")

Sometime we may wish to zoom back to original state of the chart, rather than double click, you add the dyUnzoom() function that add a button to zoom back when the chart is zoomed.

dygraph(data = muda.wakati, main = "Sea surface temperature", ylab = "Degree Celcius") %>%
  dyRoller(rollPeriod = 96) %>%
  dyRangeSelector() %>% 
  dyEvent(x = c("2012-2-21","2012-4-2", "2012-7-24"), 
          label = c("Kaskazi","Matarahi", "Kusi"), 
          labelLoc = "bottom", color = "red") %>%
  dyUnzoom()

pp.sites = cbind(EEZ = ts(data = pp.wide$EEZ, start = c(2003,1), frequency = 12),
                 Mafia = ts(data = pp.wide$Mafia, start = c(2003,1), frequency = 12),
                 Pemba = ts(data = pp.wide$Pemba, start = c(2003,1), frequency = 12),
                 Zanzibar = ts(data = pp.wide$Zanzibar, start = c(2003,1), frequency = 12))

dygraph(data = pp.sites, 
        main = "Primary Production") %>%
  dySeries(name = "EEZ", stepPlot = FALSE, color = "red") %>%
  dyGroup(c("Mafia", "Pemba", "Zanzibar"), drawPoints = FALSE, 
          color = c("blue", "green", "black")) %>%
  dyRoller(rollPeriod = 12)%>%
  dyRangeSelector()

## compute anomlay
pp.anomaly = pp.wide %>% 
  mutate(eez_an = EEZ-mean(EEZ, na.rm = T),
         mafia_an = Mafia-mean(Mafia, na.rm = T),
         pemba_an = Pemba-mean(Pemba, na.rm = T),
         zenji_an = Zanzibar-mean(Zanzibar, na.rm = T) ) %>% 
  select(date, EEZ = eez_an, Mafia = mafia_an, Pemba = pemba_an, Zanzibar = zenji_an)

## make a group time series
pp.anomaly.sites = cbind(EEZ = ts(data = pp.anomaly$EEZ, start = c(2003,1), frequency = 12),
                         Mafia = ts(data = pp.anomaly$Mafia, start = c(2003,1), frequency = 12),
                         Pemba = ts(data = pp.anomaly$Pemba, start = c(2003,1), frequency = 12),
                         Zanzibar = ts(data = pp.anomaly$Zanzibar, start = c(2003,1), frequency = 12))

## plot anomaly chart
dygraph(data = pp.anomaly.sites, 
        main = "Anomaly of Primary Production") %>%
  dySeries(name = "EEZ", stepPlot = FALSE, color = "red") %>%
  dyGroup(c("Mafia", "Pemba", "Zanzibar"), drawPoints = FALSE, 
          color = c("blue", "green", "black")) %>%
  dyRoller(rollPeriod = 12)%>%
  dyRangeSelector()%>%
  dyCrosshair(direction = "vertical")