Sequence Index Plot — ggseqiplot • ggseqplot

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

Usage

ggseqiplot(
  seqdata,
  no.n = FALSE,
  group = NULL,
  sortv = NULL,
  weighted = TRUE,
  border = FALSE,
  facet_scale = "free_y",
  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 as part of the y-axis title or group/facet title (default is TRUE)
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.
sortv: Vector of numerical values sorting the sequences or a sorting method (either "from.start" or "from.end"). See details.
weighted: Controls if weights (specified in TraMineR::seqdef) should be used. Default is TRUE, i.e. if available weights are used
border: if TRUE bars are plotted with black outline; default is FALSE (also accepts NULL)
facet_scale: Specifies if y-scale in faceted plot should be free ("free_y" is default) or "fixed"
facet_ncol: Number of columns in faceted (i.e. grouped) plot
facet_nrow: 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

Value

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

Details

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 TraMineR::seqformat to reshape 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 ggplot2's geom_rect. ggseqiplot uses geom_rect instead of geom_tile because this allows for a straight forward implementation of weights. If weights are specified for seqdata and weighted=TRUE the sequence height corresponds to its weight.

If weights and a grouping variable are used, and facet_scale="fixed" the values of the y-axis are not labeled, because ggplot2 reasonably does not allow for varying scales when the facet scale is fixed.

When a 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 ties. Likewise, 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 when border=TRUE. You can change the ratio either by adding code to ggseqiplot or by specifying the ratio when saving the code with ggsave.

References

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. 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 .

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

# 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)