Sequence Entropy Plot — ggseqeplot • ggseqplot

Function for plotting the development of cross-sectional entropies across sequence positions with ggplot2 (Wickham 2016) instead of base R's plot function that is used by TraMineR::seqplot (Gabadinho et al. 2011) . Other than in TraMineR::seqHtplot group-specific entropy lines are displayed in a common plot.

Usage

ggseqeplot(
  seqdata,
  group = NULL,
  weighted = TRUE,
  with.missing = FALSE,
  linewidth = 1,
  linecolor = "Okabe-Ito",
  gr.linetype = FALSE
)

Arguments

seqdata: State sequence object (class stslist) created with the TraMineR::seqdef function.
group: If grouping variable is specified plot shows one line for each group
weighted: Controls if weights (specified in TraMineR::seqdef) should be used. Default is TRUE, i.e. if available weights are used
with.missing: Specifies if missing states should be considered when computing the entropy index (default is FALSE).
linewidth: Specifies the with of the entropy line; default is 1
linecolor: Specifies color palette for line(s); default is "Okabe-Ito" which contains up to 9 colors (first is black). if more than 9 lines should be rendered, user has to specify an alternative color palette
gr.linetype: Specifies if line type should vary by group; hence only relevant if group argument is specified; default is FALSE

Value

A line plot of entropy values at each sequence position. If stored as object the resulting list object also contains the data (long format) used for rendering the plot.

Details

The function uses TraMineR::seqstatd to compute entropies. This requires that the input data (seqdata) are stored as state sequence object (class stslist) created with the TraMineR::seqdef function.

The entropy values are plotted with geom_line. The data and specifications used for rendering the plot can be obtained by storing the plot as an object. The appearance of the plot can be adjusted just like with every other ggplot (e.g., by changing the theme or the scale using + and the respective functions).

References

Gabadinho A, Ritschard G, Müller NS, Studer M (2011). “Analyzing and Visualizing State Sequences in R with TraMineR.” Journal of Statistical Software, 40(4), 1--37. doi:10.18637/jss.v040.i04 .

Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis, Use R!, 2nd ed. edition. Springer, Cham. doi:10.1007/978-3-319-24277-4 .

Author

Marcel Raab

Examples

# Use example data from TraMineR: actcal data set
data(actcal)

# We use only a sample of 300 cases
set.seed(1)
actcal <- actcal[sample(nrow(actcal), 300), ]
actcal.lab <- c("> 37 hours", "19-36 hours", "1-18 hours", "no work")
actcal.seq <- seqdef(actcal, 13:24, labels = actcal.lab)
#>  [>] 4 distinct states appear in the data: 
#>      1 = A
#>      2 = B
#>      3 = C
#>      4 = D
#>  [>] state coding:
#>        [alphabet]  [label]  [long label] 
#>      1  A           A        > 37 hours
#>      2  B           B        19-36 hours
#>      3  C           C        1-18 hours
#>      4  D           D        no work
#>  [>] 300 sequences in the data set
#>  [>] min/max sequence length: 12/12

# sequences sorted by age in 2000 and grouped by sex
# with TraMineR::seqplot (entropies shown in two separate plots)
seqHtplot(actcal.seq, group = actcal$sex)

# with ggseqplot (entropies shown in one plot)
ggseqeplot(actcal.seq, group = actcal$sex)

ggseqeplot(actcal.seq, group = actcal$sex, gr.linetype = TRUE)


# manual color specification
ggseqeplot(actcal.seq, linecolor = "darkgreen")

ggseqeplot(actcal.seq, group = actcal$sex,
           linecolor = c("#3D98D3FF", "#FF363CFF"))