Sequence Index PlotSource:
Function for rendering sequence index plots with
ggplot2 (Wickham 2016)
of base R's
plot function that is used by
(Gabadinho et al. 2011)
ggseqiplot( seqdata, no.n = FALSE, group = NULL, sortv = NULL, weighted = TRUE, border = FALSE, facet_scale = "free_y", facet_ncol = NULL, facet_nrow = NULL, ... )
State sequence object (class
stslist) created with the
specifies if number of (weighted) sequences is shown as part of the y-axis title or group/facet title (default is
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.
Vector of numerical values sorting the sequences or a sorting method (either
"from.end"). See details.
Controls if weights (specified in
TraMineR::seqdef) should be used. Default is
TRUE, i.e. if available weights are used
TRUEbars are plotted with black outline; default is
Specifies if y-scale in faceted plot should be free (
"free_y"is default) or
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
"strip.position"can be used to change the appearance of the plot
A sequence index plot. If stored as object the resulting list object also contains the data (spell format) used for rendering the plot.
Sequence index plots have been introduced by Scherer (2001) and display each sequence as horizontally stacked bar or line. 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
seqdata stored in wide format into a spell/episode format.
Then the data are further reshaped into the long format, i.e. for
every sequence each row in the data represents one specific sequence
position. For example, if we have 5 sequences of length 10, the long file
will have 50 rows. In the case of sequences of unequal length not every
sequence will contribute the same number of rows to the long data.
The reshaped data are used as input for rendering the index plot using
geom_rect instead of
because this allows for a straight forward implementation of weights.
If weights are specified for
the sequence height corresponds to its weight.
If weights and a grouping variable are used, and
the values of the y-axis are not labeled, because
ggplot2 reasonably does not allow for varying scales
when the facet scale is fixed.
sortv is specified, the sequences are arranged in the order of
its values. With
sortv="from.start" sequence data are sorted
according to the states of the alphabet in ascending order starting with the
first sequence position, drawing on succeeding positions in the case of
sortv="from.end" sorts a reversed version of the
sequence data, starting with the final sequence position turning to
preceding positions in case of ties.
Note that the default aspect ratio of
ggseqiplot is different from
TraMineR::seqIplot. This is most obvious
border=TRUE. You can change the ratio either by adding code to
ggseqiplot or by specifying the ratio when saving the code with
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.
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. https://sa-book.github.io/.
Scherer S (2001). “Early Career Patterns: A Comparison of Great Britain and West Germany.” European Sociological Review, 17(2), 119--144. doi:10.1093/esr/17.2.119 .
Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis, Use R!, 2nd ed. edition. Springer, Cham. doi:10.1007/978-3-319-24277-4 .
# Use examples from TraMineR library(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 # ex1 using weights data(ex1) ex1.seq <- seqdef(ex1, 1:13, weights = ex1$weights) #> [>] found missing values ('NA') in sequence data #> [>] preparing 7 sequences #> [>] coding void elements with '%' and missing values with '*' #> [!!] 1 empty sequence(s) with index: 7 #> may produce inconsistent results. #> [>] 4 distinct states appear in the data: #> 1 = A #> 2 = B #> 3 = C #> 4 = D #> [>] state coding: #> [alphabet] [label] [long label] #> 1 A A A #> 2 B B B #> 3 C C C #> 4 D D D #> [>] sum of weights: 60 - min/max: 0/29.3 #> [>] 7 sequences in the data set #> [>] min/max sequence length: 0/13 # sequences sorted by age in 2000 and grouped by sex # with TraMineR::seqplot seqIplot(actcal.seq, group = actcal$sex, sortv = actcal$age00) # with ggseqplot ggseqiplot(actcal.seq, group = actcal$sex, sortv = actcal$age00) # sequences of unequal length with missing state, and weights seqIplot(ex1.seq) ggseqiplot(ex1.seq) # ... turn weights off and add border seqIplot(ex1.seq, weighted = FALSE, border = TRUE) ggseqiplot(ex1.seq, weighted = FALSE, border = TRUE)