plotAnimatedFlowmap {HVT}

Generating flow maps and animations based on transition probabilities

Description

This is the main function for generating flow maps and animations based on transition probabilities including self states and excluding self states. Flow maps are a type of data visualization used to represent the transition probability of different states. Animations are the gifs used to represent the movement of data through the cells.

Usage

plotAnimatedFlowmap(
  hvt_model_output,
  transition_probability_df,
  df,
  animation = NULL,
  flow_map = NULL,
  fps_time = 1,
  fps_state = 1,
  time_duration = 2,
  state_duration = 2,
  cellid_column,
  time_column
)

Arguments

hvt_model_output

List. Output from a trainHVT function.

transition_probability_df

List. Output from getTransitionProbability function

df

Data frame. The input dataframe should contain two columns, cell ID from scoreHVT function and time stamp of that dataset.

animation

Character. Type of animation ('state_based', 'time_based', 'All' or NULL)

flow_map

Character. Type of flow map ('self_state', 'without_self_state', 'All' or NULL)

fps_time

Numeric. A numeric value for the frames per second of the time transition gif. (Must be a numeric value and a factor of 100). Default value is 1.

fps_state

Numeric. A numeric value for the frames per second of the state transition gif. (Must be a numeric value and a factor of 100). Default value is 1.

time_duration

Numeric. A numeric value for the duration of the time transition gif. Default value is 2.

state_duration

Numeric. A numeric value for the duration of the state transition gif. Default value is 2.

cellid_column

Character. Name of the column containing cell IDs.

time_column

Character. Name of the column containing time stamps

Value

A list of flow maps and animation gifs.

Author(s)

PonAnuReka Seenivasan <[e-mail]>, Vishwavani <[e-mail]>

See Also

trainHVT
scoreHVT
getTransitionProbability

Examples

dataset <- data.frame(date = as.numeric(time(EuStockMarkets)),
                      DAX = EuStockMarkets[, "DAX"],
                      SMI = EuStockMarkets[, "SMI"],
                      CAC = EuStockMarkets[, "CAC"],
                      FTSE = EuStockMarkets[, "FTSE"])
                      
hvt.results<- trainHVT(dataset,n_cells = 60, depth = 1, quant.err = 0.1,
                       distance_metric = "L1_Norm", error_metric = "max",
                       normalize = TRUE,quant_method = "kmeans")
                       
scoring <- scoreHVT(dataset, hvt.results)
cell_id <- scoring$scoredPredictedData$Cell.ID
time_stamp <- dataset$date
dataset <- data.frame(cell_id, time_stamp)

table <- getTransitionProbability(dataset, cellid_column = "cell_id",time_column = "time_stamp")
plots <- plotAnimatedFlowmap(hvt_model_output = hvt.results, transition_probability_df = table,
df = dataset, animation = 'All', flow_map = 'All',fps_time = 1,fps_state =  1,time_duration = 2,
state_duration = 2,cellid_column = "cell_id", time_column = "time_stamp")