ggseqmsplot {ggseqplot}

Modal State Sequence Plot

Description

Function for rendering modal state sequence plot with ggplot2 (Wickham 2016) instead of base R's plot function that is used by TraMineR::seqplot (Gabadinho et al. 2011).

Usage

ggseqmsplot(
  seqdata,
  no.n = FALSE,
  barwidth = NULL,
  group = NULL,
  weighted = TRUE,
  with.missing = FALSE,
  border = FALSE,
  facet_ncol = NULL,
  facet_nrow = NULL
)

Arguments

seqdata

State sequence object (class stslist) created with the TraMineR::seqdef function.

no.n

specifies if number of (weighted) sequences is shown (default is TRUE)

barwidth

specifies width of bars (default is NULL); valid range: (0, 1]

group

A vector of the same length as the sequence data indicating group membership. When not NULL, a distinct plot is generated for each level of 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 state distributions (default is FALSE).

border

if TRUE bars are plotted with black outline; default is FALSE (also accepts NULL)

facet_ncol

Number of columns in faceted (i.e. grouped) plot

facet_nrow

Number of rows in faceted (i.e. grouped) plot

Details

The function uses TraMineR::seqmodst to obtain the modal states and their prevalence. This requires that the input data (seqdata) are stored as state sequence object (class stslist) created with the TraMineR::seqdef function.

The data on the modal states and their prevalences are reshaped to be plotted with ggplot2::geom_bar. 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).

Value

A modal state sequence plot. If stored as object the resulting list object also contains the data (long format) used for rendering the plot

Author(s)

Marcel Raab

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.

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)

# modal state sequence plot; grouped by sex
# with TraMineR::seqplot
seqmsplot(actcal.seq, group = actcal$sex)
# with ggseqplot
ggseqmsplot(actcal.seq, group = actcal$sex)
# with ggseqplot and some layout changes
ggseqmsplot(actcal.seq, group = actcal$sex, no.n = TRUE, border = FALSE, facet_nrow = 2)