knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
Sequence analysis tool for applied researchers. Designed for faster analysis iterations or for whom just prefer an interactive interface. Supplements the powerful TraMineR package.
You can install the latest version of sequenchr via:
# install.packages("devtools") devtools::install_github("joemarlo/sequenchr")
library(TraMineR) library(sequenchr) # load data and convert to a sequence object data(mvad) seqstatl(mvad[, 17:86]) mvad.alphabet <- c("employment", "FE", "HE", "joblessness", "school", "training") mvad.labels <- c("employment", "further education", "higher education", "joblessness", "school", "training") mvad.scodes <- c("EM", "FE", "HE", "JL", "SC", "TR") mvad.seq <- seqdef(mvad, 17:86, alphabet = mvad.alphabet, states = mvad.scodes, labels = mvad.labels, xtstep = 6) # launch the sequenchr app launch_sequenchr(mvad.seq) ### Or use the plotting functions directly .... # tidy the data seq_def_tidy <- tidy_sequence_data(mvad.seq) # plot the sequence index plot_sequence_index(seq_def_tidy) # cluster the data dist_matrix <- TraMineR::seqdist(seqdata = mvad.seq, method = "DHD") cluster_model <- hclust(d = as.dist(dist_matrix), method = 'ward.D2') cluster_labels <- label_clusters(cluster_model, k = 5) # plot the sequence index by cluster plot_sequence_index(seq_def_tidy, cluster_labels = cluster_labels) # customize your plots via standard ggplot functions library(ggplot2) theme_set(theme_minimal()) plot_sequence_index(seq_def_tidy, cluster_labels = cluster_labels) + scale_x_continuous(breaks = seq(0, 70, by = 5)) + labs(title = 'My seqI plot', subtitle = 'A helpful subtitle', x = 'Month', fill = 'States', caption = 'Data from McVicar and Anyadike-Danes') + theme(legend.position = 'bottom')
See the vignette for more information:
devtools::install_github("joemarlo/sequenchr", build_vignettes = TRUE) vignette('sequenchr')
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