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


  no.n = FALSE,
  group = NULL,
  dissect = NULL,
  weighted = TRUE,
  with.missing = FALSE,
  border = FALSE,
  with.entropy = FALSE,
  linetype = "dashed",
  linecolor = "black",
  linewidth = 1,
  facet_ncol = NULL,
  facet_nrow = NULL,



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


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


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.


if "row" or "col" are specified separate distribution plots instead of a stacked plot are displayed; "row" and "col" display the distributions in one row or one column respectively; default is NULL


Controls if weights (specified in TraMineR::seqdef) should be used. Default is TRUE, i.e. if available weights are used


Specifies if missing states should be considered when computing the state distributions (default is FALSE).


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


add line plot of cross-sectional entropies at each sequence position


The linetype for the entropy subplot (with.entropy==TRUE) can be specified with an integer (0-6) or name (0 = blank, 1 = solid, 2 = dashed, 3 = dotted, 4 = dotdash, 5 = longdash, 6 = twodash); ; default is "dashed"


Specifies the color of the entropy line if with.entropy==TRUE; default is "black"


Specifies the width of the entropy line if with.entropy==TRUE; default is 1


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


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


if group is specified additional arguments of ggplot2::facet_wrap such as "labeller" or "strip.position" can be used to change the appearance of the plot. Does not work if dissect is used


A sequence distribution plot created by using ggplot2. If stored as object the resulting list object (of class gg and ggplot) also contains the data used for rendering the plot.


Sequence distribution plots visualize the distribution of all states by rendering a series of stacked bar charts at each position of the sequence. Although this type of plot has been used in the life course studies for several decades (see Blossfeld (1987) for an early application), it should be noted that the size of the different bars in stacked bar charts might be difficult to compare - particularly if the alphabet comprises many states (Wilke 2019) . This issue can be addressed by breaking down the aggregated distribution specifying the dissect argument. Moreover, it is important to keep in mind that this plot type does not visualize individual trajectories; instead it displays aggregated distributional information (repeated cross-sections). For a more detailed discussion of this type of sequence visualization see, for example, Brzinsky-Fay (2014) , Fasang and Liao (2014) , and Raab and Struffolino (2022) .

The function uses TraMineR::seqstatd to obtain state distributions (and entropy values). This requires that the input data (seqdata) are stored as state sequence object (class stslist) created with the TraMineR::seqdef function. The state distributions are reshaped into a a long data format to enable plotting with ggplot2. The stacked bars are rendered by calling geom_bar; if entropy = TRUE entropy values are plotted with geom_line. If the group or the dissect argument are specified the sub-plots are produced by using facet_wrap. If both are specified the plots are rendered with facet_grid.

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).


Blossfeld H (1987). “Labor-Market Entry and the Sexual Segregation of Careers in the Federal Republic of Germany.” American Journal of Sociology, 93(1), 89--118. doi:10.1086/228707 .

Brzinsky-Fay C (2014). “Graphical Representation of Transitions and Sequences.” In Blanchard P, Bühlmann F, Gauthier J (eds.), Advances in Sequence Analysis: Theory, Method, Applications, Life Course Research and Social Policies, 265--284. Springer, Cham. doi:10.1007/978-3-319-04969-4_14 .

Fasang AE, Liao TF (2014). “Visualizing Sequences in the Social Sciences: Relative Frequency Sequence Plots.” Sociological Methods & Research, 43(4), 643--676. doi:10.1177/0049124113506563 .

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 .

Raab M, Struffolino E (2022). Sequence Analysis, volume 190 of Quantitative Applications in the Social Sciences. SAGE, Thousand Oaks, CA.

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

Wilke C (2019). Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O'Reilly Media, Sebastopol, CA. ISBN 978-1-4920-3108-6.


Marcel Raab


# Use example data from TraMineR: actcal data set

# We use only a sample of 300 cases
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

# state distribution plots; grouped by sex
# with TraMineR::seqplot
seqdplot(actcal.seq, group = actcal$sex)

# with ggseqplot
ggseqdplot(actcal.seq, group = actcal$sex)

# with ggseqplot applying a few additional arguments, e.g. entropy line
ggseqdplot(actcal.seq, group = actcal$sex,
           no.n = TRUE, with.entropy = TRUE, border = TRUE)

# break down the stacked plot to ease comparisons of distributions
ggseqdplot(actcal.seq, group = actcal$sex, dissect = "row")

# make use of ggplot functions for modifying the plot
ggseqdplot(actcal.seq) +
  scale_x_discrete(labels = +
  labs(title = "State distribution plot", x = "Month") +
  guides(fill = guide_legend(title = "Alphabet")) +
  theme_classic() +
  theme(plot.title = element_text(size = 30,
                                  margin = margin(0, 0, 20, 0)),
    plot.title.position = "plot")
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.