Kamila Kolpashnikova[1]
Traditionally, a tool for analysing life events in social sciences, sequence analysis is becoming more and more popular for dealing with time-use data. In this tutorial, I’ll walk you through the steps how to do sequence analysis with time-use data (American Time Use Survey, ATUS).
To follow this tutotial:
Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 892101 (awardee: Kamila Kolpashnikova).
Here is an example how a published paper using sequence analysis looks like (my recent research in the Journal of Population Ageing):
Kolpashnikova, K., & Kan, M. Y. (2020). Eldercare in Japan: Cluster Analysis of daily time-use patterns of elder caregivers. Journal of Population Ageing, 1-23.
If you follow this tutorial, you will be able to recreate the analyses in the paper.
#### Loading Necessary Packages ####
if (!require("pacman")) install.packages("pacman")## Loading required package: pacmanlibrary(pacman)
# load and install packages
pacman::p_load(TraMineR, TraMineRextras, downloader, cluster, RColorBrewer, devtools, haven,
tidyverse, reshape2, WeightedCluster, nnet, plyr)Remember that in r, it’s forward slashes. Here I created a function that can unzip and create dataframes from the zipped BLS ATUS files.
#' Download, unzip, and create a dataframe from ATUS files. Note: the zipped file will be downloaded as "data" in your working directory.
#'
#' @param url A string containing a link to a zipped file from the Bureau of Labor Statistics website.
#' @return The dataframe for ATUS.
#' @examples
#' zip_to_df("https://www.bls.gov/tus/special.requests/atusact-0320.zip")
#' zip_to_df("https://www.bls.gov/tus/special.requests/atusrostec-1120.zip")
zip_to_df<-function(url) {
d <- "data"
download(url, destfile=d)
data <- read.table(unzip(d, files=paste(str_replace(str_split(tail(str_split(url, "/")[[1]], 1), "\\.")[[1]][1], "-", "_"), ".dat", sep="")), header = T, sep = ",", colClasses = "character")
return(data)
}
## download the part of ATUS that contains diaries
df <- zip_to_df("https://www.bls.gov/tus/special.requests/atusact-0320.zip")
head(df)## TUCASEID TUACTIVITY_N TUACTDUR24 TUCC5 TUCC5B TRTCCTOT_LN TRTCC_LN
## 1 20030100013280 1 60 -1 -1 -1 -1
## 2 20030100013280 2 30 -1 -1 -1 -1
## 3 20030100013280 3 600 -1 -1 -1 -1
## 4 20030100013280 4 150 -1 -1 -1 -1
## 5 20030100013280 5 5 -1 -1 -1 -1
## 6 20030100013280 6 175 -1 -1 -1 -1
## TRTCOC_LN TUSTARTTIM TUSTOPTIME TRCODEP TRTIER1P TRTIER2P TUCC8 TUCUMDUR
## 1 -1 04:00:00 05:00:00 130124 13 1301 97 60
## 2 -1 05:00:00 05:30:00 010201 01 0102 0 90
## 3 -1 05:30:00 15:30:00 010101 01 0101 0 690
## 4 -1 15:30:00 18:00:00 120303 12 1203 0 840
## 5 -1 18:00:00 18:05:00 110101 11 1101 0 845
## 6 -1 18:05:00 21:00:00 120303 12 1203 0 1020
## TUCUMDUR24 TUACTDUR TR_03CC57 TRTO_LN TRTONHH_LN TRTOHH_LN TRTHH_LN
## 1 60 60 -1 -1 -1 -1 -1
## 2 90 30 -1 -1 -1 -1 -1
## 3 690 600 -1 -1 -1 -1 -1
## 4 840 150 -1 -1 -1 -1 -1
## 5 845 5 -1 -1 -1 -1 -1
## 6 1020 175 -1 -1 -1 -1 -1
## TRTNOHH_LN TEWHERE TUCC7 TRWBELIG TRTEC_LN TUEC24 TUDURSTOP
## 1 -1 9 -1 -1 -1 -1 -1
## 2 -1 -1 -1 -1 -1 -1 -1
## 3 -1 -1 -1 -1 -1 -1 -1
## 4 -1 1 -1 -1 -1 -1 -1
## 5 -1 1 -1 -1 -1 -1 -1
## 6 -1 1 -1 -1 -1 -1 -1## download the part of ATUS with the information about self-reported elder caregivers
df_ec <- zip_to_df("https://www.bls.gov/tus/special.requests/atusrostec-1120.zip")
head(df_ec)## TUCASEID TEAGE_EC TEELDUR TEELWHO TEELYRS TRELHH TUECLNO TULINENO
## 1 20110101110074 70 4 44 2 0 5 -1
## 2 20110101110156 85 4 46 2 0 5 -1
## 3 20110101110507 80 1 55 -1 0 2 -1
## 4 20110101110521 85 3 43 -1 0 3 -1
## 5 20110101110522 80 4 44 6 0 2 -1
## 6 20110101110639 64 1 38 -1 0 2 -1Before we create our sequence dataframes, we need to wrangle the original data to make it usable.
## select ids in diaries dataframe that are in eldercare dataframe
data <- df[df$TUCASEID %in% df_ec$TUCASEID,]
head(data)## TUCASEID TUACTIVITY_N TUACTDUR24 TUCC5 TUCC5B TRTCCTOT_LN
## 2228592 20110101110074 1 10 0 0 0
## 2228593 20110101110074 2 300 0 0 0
## 2228594 20110101110074 3 5 0 0 0
## 2228595 20110101110074 4 525 0 0 0
## 2228596 20110101110074 5 5 0 0 0
## 2228597 20110101110074 6 30 0 0 0
## TRTCC_LN TRTCOC_LN TUSTARTTIM TUSTOPTIME TRCODEP TRTIER1P TRTIER2P
## 2228592 -1 0 04:00:00 04:10:00 110101 11 1101
## 2228593 -1 0 04:10:00 09:10:00 020402 02 0204
## 2228594 -1 0 09:10:00 09:15:00 180482 18 1804
## 2228595 -1 0 09:15:00 18:00:00 500101 50 5001
## 2228596 -1 0 18:00:00 18:05:00 180482 18 1804
## 2228597 -1 0 18:05:00 18:35:00 010201 01 0102
## TUCC8 TUCUMDUR TUCUMDUR24 TUACTDUR TR_03CC57 TRTO_LN TRTONHH_LN
## 2228592 97 10 10 10 -1 -1 -1
## 2228593 0 310 310 300 -1 -1 -1
## 2228594 0 315 315 5 -1 -1 -1
## 2228595 0 840 840 525 -1 -1 -1
## 2228596 0 845 845 5 -1 -1 -1
## 2228597 0 875 875 30 -1 -1 -1
## TRTOHH_LN TRTHH_LN TRTNOHH_LN TEWHERE TUCC7 TRWBELIG TRTEC_LN TUEC24
## 2228592 -1 -1 -1 1 0 -1 -1 -1
## 2228593 -1 -1 -1 1 0 -1 -1 -1
## 2228594 -1 -1 -1 13 0 -1 -1 -1
## 2228595 -1 -1 -1 3 0 -1 -1 -1
## 2228596 -1 -1 -1 13 0 -1 -1 -1
## 2228597 -1 -1 -1 -1 0 -1 -1 -1
## TUDURSTOP
## 2228592 -1
## 2228593 -1
## 2228594 -1
## 2228595 -1
## 2228596 -1
## 2228597 -1## subset the data to the variables needed
data <- data[c('TUCASEID', 'TUACTIVITY_N', 'TRCODEP', 'TUACTDUR24', 'TUSTARTTIM', 'TUSTOPTIME')]
## drop na
diary = data[complete.cases(data), ]
## rename columns to readable names
colnames(diary) <- c('caseid', 'actline', 'activity', 'duration', 'start', 'stop')
head(diary)## caseid actline activity duration start stop
## 2228592 20110101110074 1 110101 10 04:00:00 04:10:00
## 2228593 20110101110074 2 020402 300 04:10:00 09:10:00
## 2228594 20110101110074 3 180482 5 09:10:00 09:15:00
## 2228595 20110101110074 4 500101 525 09:15:00 18:00:00
## 2228596 20110101110074 5 180482 5 18:00:00 18:05:00
## 2228597 20110101110074 6 010201 30 18:05:00 18:35:00## create activity dictionary
## this list contains information to reducing activity coding to 11 main categories (listed below this)
## you can rename the original categories yourself, but you have to figure it out on your own
act_dict <- c("010101" = 1, "010102" = 1, "010199" = 1, "010201" = 2, "010299" = 2, "010301" = 2, "010399" = 2, "010401" = 2, "010499" = 2, "010501" = 2, "010599" = 2, "019999" = 2, "020101" = 3, "020102" = 3, "020103" = 3, "020104" = 3, "020199" = 3, "020201" = 3, "020202" = 3, "020203" = 3, "020299" = 3, "020301" = 3, "020302" = 3, "020303" = 3, "020399" = 3, "020400" = 3, "020401" = 3, "020402" = 3, "020499" = 3, "020500" = 3, "020501" = 3, "020502" = 3, "020599" = 3, "020600" = 3, "020601" = 3, "020602" = 3, "020603" = 3, "020681" = 10, "020699" = 3, "020700" = 3, "020701" = 3, "020799" = 3, "020800" = 3, "020801" = 3, "020899" = 3, "020900" = 3, "020901" = 3, "020902" = 3, "020903" = 3, "020904" = 3, "020905" = 3, "020999" = 3, "029900" = 3, "029999" = 3, "030100" = 4, "030101" = 4, "030102" = 4, "030103" = 4, "030104" = 4, "030105" = 4, "030106" = 4, "030107" = 4, "030108" = 4, "030109" = 4, "030110" = 4, "030111" = 4, "030112" = 4, "030199" = 4, "030200" = 4, "030201" = 4, "030202" = 4, "030203" = 4, "030204" = 4, "030299" = 4, "030300" = 4, "030301" = 4, "030302" = 4, "030303" = 4, "030399" = 4, "040100" = 4, "040101" = 4, "040102" = 4, "040103" = 4, "040104" = 4, "040105" = 4, "040106" = 4, "040107" = 4, "040108" = 4, "040109" = 4, "040110" = 4, "040111" = 4, "040112" = 4, "040199" = 4, "040200" = 4, "040201" = 4, "040202" = 4, "040203" = 4, "040204" = 4, "040299" = 4, "040300" = 4, "040301" = 4, "040302" = 4, "040303" = 4, "040399" = 4, "030186" = 4, "040186" = 4, "030000" = 5, "030400" = 5, "030401" = 5, "030402" = 5, "030403" = 5, "030404" = 5, "030405" = 5, "030499" = 5, "030500" = 5, "030501" = 5, "030502" = 5, "030503" = 5, "030504" = 5, "030599" = 5, "039900" = 5, "039999" = 5, "040000" = 5, "040400" = 5, "040401" = 5, "040402" = 5, "040403" = 5, "040404" = 5, "040405" = 5, "040499" = 5, "040500" = 5, "040501" = 5, "040502" = 5, "040503" = 5, "040504" = 5, "040505" = 5, "040506" = 5, "040507" = 5, "040508" = 5, "040599" = 5, "049900" = 5, "049999" = 5, "050000" = 6, "050100" = 6, "050101" = 6, "050102" = 6, "050103" = 6, "050104" = 6, "050199" = 6, "050200" = 6, "050201" = 6, "050202" = 6, "050203" = 6, "050204" = 6, "050205" = 6, "050299" = 6, "050300" = 6, "050301" = 6, "050302" = 6, "050303" = 6, "050304" = 6, "050305" = 6, "050399" = 6, "050400" = 6, "050401" = 6, "050403" = 6, "050404" = 6, "050405" = 6, "050499" = 6, "059900" = 6, "059999" = 6, "060000" = 6, "060100" = 6, "060101" = 6, "060102" = 6, "060103" = 6, "060104" = 6, "060199" = 6, "060200" = 6, "060201" = 6, "060202" = 6, "060203" = 6, "060204" = 6, "060299" = 6, "060300" = 6, "060301" = 6, "060302" = 6, "060303" = 6, "060399" = 6, "060400" = 6, "060401" = 6, "060402" = 6, "060403" = 6, "060499" = 6, "069900" = 6, "069999" = 6, "050481" = 6, "050389" = 6, "050189" = 6, "060289" = 6, "050289" = 6, "070000" = 7, "070100" = 7, "070101" = 7, "070102" = 7, "070103" = 7, "070104" = 7, "070105" = 7, "070199" = 7, "070200" = 7, "070201" = 7, "070299" = 7, "070300" = 7, "070301" = 7, "070399" = 7, "079900" = 7, "079999" = 7, "080000" = 7, "080100" = 7, "080101" = 7, "080102" = 7, "080199" = 7, "080200" = 7, "080201" = 7, "080202" = 7, "080203" = 7, "080299" = 7, "080300" = 7, "080301" = 7, "080302" = 7, "080399" = 7, "080400" = 7, "080401" = 7, "080402" = 7, "080403" = 7, "080499" = 7, "080500" = 7, "080501" = 7, "080502" = 7, "080599" = 7, "080600" = 7, "080601" = 7, "080602" = 7, "080699" = 7, "080700" = 7, "080701" = 7, "080702" = 7, "080799" = 7, "080800" = 7, "080801" = 7, "080899" = 7, "089900" = 7, "089999" = 7, "090000" = 7, "090100" = 7, "090101" = 7, "090102" = 7, "090103" = 7, "090104" = 7, "090199" = 7, "090200" = 7, "090201" = 7, "090202" = 7, "090299" = 7, "090300" = 7, "090301" = 7, "090302" = 7, "090399" = 7, "090400" = 7, "090401" = 7, "090402" = 7, "090499" = 7, "090500" = 7, "090501" = 7, "090502" = 7, "090599" = 7, "099900" = 7, "099999" = 7, "100000" = 7, "100100" = 7, "100101" = 7, "100102" = 7, "100103" = 7, "100199" = 7, "100200" = 7, "100201" = 7, "100299" = 7, "100300" = 7, "100303" = 7, "100304" = 7, "100399" = 7, "100400" = 7, "100401" = 7, "100499" = 7, "109900" = 7, "109999" = 7, "120303" = 8, "120304" = 8, "110000" = 9, "110100" = 9, "110101" = 9, "110199" = 9, "110200" = 9, "110201" = 9, "110299" = 9, "119900" = 9, "110289" = 9, "119999" = 9, "120000" = 10, "120100" = 10, "120101" = 10, "120199" = 10, "120200" = 10, "120201" = 10, "120202" = 10, "120299" = 10, "120300" = 10, "120301" = 10, "120302" = 10, "120305" = 10, "120306" = 10, "120307" = 10, "120308" = 10, "120309" = 10, "120310" = 10, "120311" = 10, "120312" = 10, "120313" = 10, "120399" = 10, "120400" = 10, "120401" = 10, "120402" = 10, "120403" = 10, "120404" = 10, "120405" = 10, "120499" = 10, "120500" = 10, "120501" = 10, "120502" = 10, "120503" = 10, "120504" = 10, "120599" = 10, "129900" = 10, "129999" = 10, "130000" = 10, "130100" = 10, "130101" = 10, "130102" = 10, "130103" = 10, "130104" = 10, "130105" = 10, "130106" = 10, "130107" = 10, "130108" = 10, "130109" = 10, "130110" = 10, "130111" = 10, "130112" = 10, "130113" = 10, "130114" = 10, "130115" = 10, "130116" = 10, "130117" = 10, "130118" = 10, "130119" = 10, "130120" = 10, "130121" = 10, "130122" = 10, "130123" = 10, "130124" = 10, "130125" = 10, "130126" = 10, "130127" = 10, "130128" = 10, "130129" = 10, "130130" = 10, "130131" = 10, "130132" = 10, "130133" = 10, "130134" = 10, "130135" = 10, "130136" = 10, "130199" = 10, "130200" = 10, "130201" = 10, "130202" = 10, "130203" = 10, "130204" = 10, "130205" = 10, "130206" = 10, "130207" = 10, "130208" = 10, "130209" = 10, "130210" = 10, "130211" = 10, "130212" = 10, "130213" = 10, "130214" = 10, "130215" = 10, "130216" = 10, "130217" = 10, "130218" = 10, "130219" = 10, "130220" = 10, "130221" = 10, "130222" = 10, "130223" = 10, "130224" = 10, "130225" = 10, "130226" = 10, "130227" = 10, "130228" = 10, "130229" = 10, "130230" = 10, "130231" = 10, "130232" = 10, "130299" = 10, "130300" = 10, "130301" = 10, "130302" = 10, "130399" = 10, "130400" = 10, "130401" = 10, "130402" = 10, "130499" = 10, "139900" = 10, "139999" = 10, "140000" = 10, "140100" = 10, "140101" = 10, "140102" = 10, "140103" = 10, "140104" = 10, "140105" = 10, "149900" = 10, "149999" = 10, "150000" = 10, "150100" = 10, "150101" = 10, "150102" = 10, "150103" = 10, "150104" = 10, "150105" = 10, "150106" = 10, "150199" = 10, "150200" = 10, "150201" = 10, "150202" = 10, "150203" = 10, "150204" = 10, "150299" = 10, "150300" = 10, "150301" = 10, "150302" = 10, "150399" = 10, "150400" = 10, "150401" = 10, "150402" = 10, "150499" = 10, "150500" = 10, "150501" = 10, "150599" = 10, "150600" = 10, "150601" = 10, "150602" = 10, "150699" = 10, "150700" = 10, "150701" = 10, "150799" = 10, "150800" = 10, "150801" = 10, "150899" = 10, "159900" = 10, "159999" = 10, "160000" = 10, "160100" = 10, "160101" = 10, "160102" = 10, "160103" = 10, "160104" = 10, "160105" = 10, "160106" = 10, "160107" = 10, "160108" = 10, "160199" = 10, "160200" = 10, "160201" = 10, "160299" = 10, "169900" = 10, "169999" = 10, "159989" = 10, "169989" = 10, "110281" = 10, "100381" = 10, "100383" = 10, "180000" = 11, "180100" = 11, "180101" = 11, "180199" = 11, "180200" = 11, "180201" = 11, "180202" = 11, "180203" = 11, "180204" = 11, "180205" = 11, "180206" = 11, "180207" = 11, "180208" = 11, "180209" = 11, "180280" = 11, "180299" = 11, "180300" = 11, "180301" = 11, "180302" = 11, "180303" = 11, "180304" = 11, "180305" = 11, "180306" = 11, "180307" = 11, "180399" = 11, "180400" = 11, "180401" = 11, "180402" = 11, "180403" = 11, "180404" = 11, "180405" = 11, "180406" = 11, "180407" = 11, "180482" = 11, "180499" = 11, "180500" = 11, "180501" = 11, "180502" = 11, "180503" = 11, "180504" = 11, "180599" = 11, "180600" = 11, "180601" = 11, "180602" = 11, "180603" = 11, "180604" = 11, "180605" = 11, "180699" = 11, "180700" = 11, "180701" = 11, "180702" = 11, "180703" = 11, "180704" = 11, "180705" = 11, "180782" = 11, "180799" = 11, "180800" = 11, "180801" = 11, "180802" = 11, "180803" = 11, "180804" = 11, "180805" = 11, "180806" = 11, "180807" = 11, "180899" = 11, "180900" = 11, "180901" = 11, "180902" = 11, "180903" = 11, "180904" = 11, "180905" = 11, "180999" = 11, "181000" = 11, "181001" = 11, "181002" = 11, "181099" = 11, "181100" = 11, "181101" = 11, "181199" = 11, "181200" = 11, "181201" = 11, "181202" = 11, "181203" = 11, "181204" = 11, "181205" = 11, "181206" = 11, "181283" = 11, "181299" = 11, "181300" = 11, "181301" = 11, "181302" = 11, "181399" = 11, "181400" = 11, "181401" = 11, "181499" = 11, "181500" = 11, "181501" = 11, "181599" = 11, "181600" = 11, "181601" = 11, "181699" = 11, "181800" = 11, "181801" = 11, "181899" = 11, "189900" = 11, "189999" = 11, "180481" = 11, "180381" = 11, "180382" = 11, "181081" = 11, "180589" = 11, "180682" = 11, "500000" = 11, "500100" = 11, "500101" = 11, "500102" = 11, "500103" = 11, "500104" = 11, "500105" = 11, "500106" = 11, "500107" = 11, "509900" = 11, "509989" = 11, "509999" = 11)
## this list is not used but it contains the codes for the 11 main activities
act = c("1" = "Sleep",
"2" = "Personal Care",
"3" = "Housework",
"4" = "Child Care",
"5" = "Adult Care",
"6" = "Work and Education",
"7" = "Shopping",
"8" = "TV Watching",
"9" = "Eating",
"10" = "Leisure",
"11" = "Travel and Other")
### recode the activities using the activity dictionary
diary$lst_act <- revalue(diary$activity, act_dict)## The following `from` values were not present in `x`: 010199, 010599, 019999, 020400, 020500, 020600, 020601, 020602, 020603, 020700, 020800, 020900, 029900, 030100, 030106, 030107, 030200, 030204, 030300, 040100, 040106, 040107, 040200, 040204, 040299, 040300, 040399, 030000, 030400, 030500, 039900, 040000, 040400, 040500, 049900, 050000, 050100, 050104, 050199, 050200, 050205, 050299, 050300, 050305, 050399, 050400, 050401, 059900, 060000, 060100, 060200, 060204, 060299, 060300, 060303, 060400, 069900, 070000, 070100, 070200, 070299, 070300, 070399, 079900, 079999, 080000, 080100, 080199, 080200, 080300, 080399, 080400, 080500, 080600, 080602, 080699, 080700, 080799, 080800, 080899, 089900, 090000, 090100, 090200, 090300, 090400, 090499, 090500, 099900, 100000, 100100, 100200, 100299, 100300, 100303, 100304, 100400, 100499, 109900, 110000, 110100, 110199, 110200, 110201, 110299, 119900, 110289, 119999, 120000, 120100, 120200, 120300, 120400, 120500, 120599, 129900, 130000, 130100, 130111, 130121, 130123, 130200, 130201, 130205, 130206, 130208, 130209, 130211, 130221, 130228, 130230, 130300, 130399, 130400, 130401, 130402, 130499, 139900, 140000, 140100, 149900, 150000, 150100, 150200, 150300, 150400, 150500, 150600, 150700, 150701, 150799, 150800, 150801, 150899, 159900, 159999, 160000, 160100, 160199, 160200, 160201, 160299, 169900, 169999, 180000, 180100, 180200, 180201, 180202, 180203, 180204, 180205, 180206, 180207, 180208, 180209, 180299, 180300, 180301, 180302, 180303, 180304, 180305, 180306, 180307, 180400, 180401, 180402, 180403, 180404, 180405, 180406, 180407, 180500, 180503, 180504, 180599, 180600, 180602, 180603, 180604, 180605, 180700, 180702, 180703, 180704, 180705, 180799, 180800, 180900, 181000, 181001, 181100, 181199, 181200, 181203, 181205, 181206, 181300, 181400, 181500, 181600, 181699, 181800, 189900, 500000, 500100, 500102, 509900, 509989, 509999head(diary)## caseid actline activity duration start stop lst_act
## 2228592 20110101110074 1 110101 10 04:00:00 04:10:00 9
## 2228593 20110101110074 2 020402 300 04:10:00 09:10:00 3
## 2228594 20110101110074 3 180482 5 09:10:00 09:15:00 11
## 2228595 20110101110074 4 500101 525 09:15:00 18:00:00 11
## 2228596 20110101110074 5 180482 5 18:00:00 18:05:00 11
## 2228597 20110101110074 6 010201 30 18:05:00 18:35:00 2## print out unique values for activities
unique(diary$lst_act)## [1] "9" "3" "11" "2" "8" "1" "10" "5" "7" "4" "6"## rename some of the activities even to fewer categories
## the activity dictionary created above gives 11 categories of activities
## in case some of the activities need to be combined you can follow the same logic in the following lines:
diary$lst_act = revalue(diary$lst_act, c("2" = 1, "8" = 10, "9" = 10))
unique(diary$lst_act)## [1] "10" "3" "11" "1" "5" "7" "4" "6"Then, let’s create sequence dataframe, specify column names, and add ids.
## create sequences of activities per activity (result: long list of combined sequences)
sequences = rep(as.numeric(diary$lst_act), as.numeric(diary$duration))
## separate the long list into sequences (1440 min in each sequence)
seq = matrix(sequences, nrow=length(sequences)/1440, ncol=1440, byrow=T)
seq[1:5, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 10 10 10 10 10 10 10 10 10 10
## [2,] 1 1 1 1 1 1 1 1 1 1
## [3,] 1 1 1 1 1 1 1 1 1 1
## [4,] 10 10 10 10 10 10 10 10 10 10
## [5,] 1 1 1 1 1 1 1 1 1 1## specify the names for the activity variables
activities<-c()
for(i in 0:1439) {
activities<-c(activities, paste("var", i, sep = ""))
}
## transform matrix to dataframe
data <- as.data.frame(seq, row.names = unique(diary$caseid))
## used created vars names to name the columns in the dataframe
colnames(data) <-activities
## create id
data$id <- as.numeric(row.names(data))I create an object with intervals’ labels. Sequences start at 04:00 AM: Depending on your own sequence intervals (if you are not using the ATUS) these labels need to be adjusted
t_intervals_labels <- format( seq.POSIXt(as.POSIXct("2021-11-08 04:00:00 GMT"), as.POSIXct("2021-11-09 03:59:00 GMT"), by = "1 min"),
"%H:%M", tz="GMT")In my experience, brewing colours in R is one of the most painful experiences, especially if you have many categories of activities. Things can get even worse if you have to go grayscale if publishing articles with colour images is too expensive.
Let’s brew some colours first.The number of colours is dictated by the number of states (in the alphabet). We reduced it to 8, so it won’t create difficulties (difficulties arise when there are more than 12 categories).
Interesting resource on colors (cheatsheet):
## define labels first and count:
labels = c("sleep", "housework",
"childcare", "adult care",
"paid work", "shopping", "leisure",
"travel")
colourCount = length(labels)
getPalette = colorRampPalette(brewer.pal(8, "Set3"))
## let's see how our colours look like
axisLimit <- sqrt(colourCount)+1
colours=data.frame(x1=rep(seq(1, axisLimit, 1), length.out=colourCount),
x2=rep(seq(2, axisLimit+1, 1), length.out=colourCount),
y1=rep(1:axisLimit, each=axisLimit,length.out=colourCount),
y2=rep(2:(axisLimit+1), each=axisLimit,length.out=colourCount),
t=letters[1:colourCount], r=labels)
## check the created pallette:
ggplot() +
scale_x_continuous(name="x") +
scale_y_continuous(name="y") +
geom_rect(data=colours, mapping=aes(xmin=x1, xmax=x2, ymin=y1, ymax=y2, fill=t), color="black", alpha=0.5) +
geom_text(data=colours, aes(x=x1+(x2-x1)/2, y=y1+(y2-y1)/2, label=r), size=4) +
scale_fill_manual(values = getPalette(colourCount)) + theme(legend.position = "none")
Before defining a sequence object, I reduced the dataframe to 2000 sequences. This is because I wanted for the tutorial demostration to run faster. You can use all sequences as long as they are less than about 46000 in total. On my laptop 46000’s dissimilarity matrix calculation ran for 4 hours but the cluster analysis crushed. So, I would recommend to use a random smaller sample if you have huge ones. Or, run the analyses on chunks and then combine the similar-looking clusters.
## subsetting is not necessary, but for the sake of efficiency in here I'll subset it to 2000 observations
MyData <- as_tibble(data[1:2000,])
## you want to use the full categories of states:
## (you need to change if you only focus on specific activities)
seq <- seqdef(MyData,
var = activities,
cnames = t_intervals_labels,
alphabet = c("1", "3", "4", "5",
"6", "7", "10",
"11"),
labels = labels,
cpal = getPalette(colourCount),
xtstep = 18, ##step between displayed tick-marks and labels on the time x-axis
id = MyData$id)
## if you have weights then add ===> weights = MyData$Weight)
## check how the sequence looks like
print(seq[1:5, ], format = "STS")## Sequence
## 20110101110074 10-10-10-10-10-10-10-10-10-10-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1
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## 20110101110521 10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1
## 20110101110522 1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-3-3-3-3-3-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-3-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-4-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-4-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-4-4-4-4-4-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1## "STS" format shows each stepWith a small i, the default for idxs is 1:10, plotting the first 10 sequences. If you set idxs to 0, it plots all sequences (might take a long time).
seqiplot(seq, border = NA, with.legend = "right", legend.prop=0.4)
You can also use the same command with a capital I. It wiil plot all unless you specify idxs option.
seqIplot(seq, border = NA, with.legend = "right", legend.prop=0.4, idxs = 1:4)
Unfortunately, for time-use data, it is usually useless to plot the most frequent sequences. If you tabulate 4 frequent sequences you will see what I mean. It is because there are 1440 steps and there are barely any same sequences. This command is more useful for shorter sequences with many commonalities (as in life-course research).
## tabulate 4 frequent sequences:
## because there are 1440 steps there are barely any
## this is more useful for shorter sequences with many commonalities (as in life-course research)
seqtab(seq, idxs = 1:4)## Freq
## 1/1-10/209-1/45-11/15-10/240-11/6-7/10-11/12-1/180-3/30-4/212-10/180-1/300 1
## 1/1-10/60-3/30-10/110-3/35-10/10-3/150-11/20-7/60-11/20-3/150-11/5-6/249-11/5-10/110-1/425 1
## 1/1-10/60-3/30-10/30-3/60-11/5-10/60-11/5-1/30-11/5-7/5-11/5-7/40-11/5-7/10-11/5-7/60-10/120-11/10-10/60-3/10-10/15-1/90-10/180-3/25-10/255-1/259 1
## 1/10-3/2-10/108-1/30-11/5-5/1-11/3-10/321-11/3-5/1-11/4-6/322-11/2-10/30-11/6-10/97-1/495 1
## Percent
## 1/1-10/209-1/45-11/15-10/240-11/6-7/10-11/12-1/180-3/30-4/212-10/180-1/300 0.05
## 1/1-10/60-3/30-10/110-3/35-10/10-3/150-11/20-7/60-11/20-3/150-11/5-6/249-11/5-10/110-1/425 0.05
## 1/1-10/60-3/30-10/30-3/60-11/5-10/60-11/5-1/30-11/5-7/5-11/5-7/40-11/5-7/10-11/5-7/60-10/120-11/10-10/60-3/10-10/15-1/90-10/180-3/25-10/255-1/259 0.05
## 1/10-3/2-10/108-1/30-11/5-5/1-11/3-10/321-11/3-5/1-11/4-6/322-11/2-10/30-11/6-10/97-1/495 0.05##also can plot frequencies using seqfplot
seqfplot(seq, border = NA, with.legend = "right", legend.prop=0.4)
State distribution plots (aka tempogram aka chronogram) This is an easy way to plot a tempogram (compared to area plots).
## this is an easy way to plot a tempogram
seqdplot(seq, border = NA, with.legend = "right", legend.prop=0.4)
# transitions from state to state (in probabilities)
trate <- seqtrate(seq)## [>] computing transition probabilities for states 1/3/4/5/6/7/10/11 ...round(trate, 2)## [-> 1] [-> 3] [-> 4] [-> 5] [-> 6] [-> 7] [-> 10] [-> 11]
## [1 ->] 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [3 ->] 0 0.98 0.00 0.00 0.00 0.00 0.01 0.00
## [4 ->] 0 0.00 0.97 0.00 0.00 0.00 0.01 0.01
## [5 ->] 0 0.00 0.00 0.98 0.00 0.00 0.00 0.01
## [6 ->] 0 0.00 0.00 0.00 0.99 0.00 0.00 0.00
## [7 ->] 0 0.00 0.00 0.00 0.00 0.97 0.00 0.03
## [10 ->] 0 0.00 0.00 0.00 0.00 0.00 0.99 0.00
## [11 ->] 0 0.01 0.00 0.00 0.00 0.01 0.02 0.95## heatmap of the transitions matrix
heatTrate=melt(trate)
head(heatTrate)## Var1 Var2 value
## 1 [1 ->] [-> 1] 9.962543e-01
## 2 [3 ->] [-> 1] 2.178463e-03
## 3 [4 ->] [-> 1] 3.259718e-03
## 4 [5 ->] [-> 1] 1.190891e-03
## 5 [6 ->] [-> 1] 3.979046e-04
## 6 [7 ->] [-> 1] 6.193964e-05## plot the heatmap
ggplot(heatTrate, aes(Var2, Var1)) +
geom_tile(aes(fill = value)) +
geom_text(aes(label = round(value, 2))) +
scale_fill_continuous(high = "#132B43", low = "#56B1F7", name="Transitions")
TraMineR made it very easy to change the number of steps in a sequence. seqgranularity is the command that will help you do it.
To use the first state to represent all, use method = “first”, the last = “last”, or the most frequent = “mostfreq”.
In the following chunk of code, tspan = 15 means to transform the step to every 15 min (instead of every minute).
time15_seq <- seqgranularity(seq, tspan=15, method="mostfreq")
## plot the tempogram
seqdplot(time15_seq, border = NA, with.legend = "right", legend.prop=0.4)
You can see on the tempograms that the granularity decreased and now each step is a 15-minute slot.
#seqplot(time15_seq, type="ms", with.legend = "right", legend.prop=0.4)
## same as
seqmsplot(time15_seq, with.legend = "right", legend.prop=0.4, main="Modal Sequences")
The higher the value, the more diverse are activities at that time.
#transversal entropy of state distributions
#the number of valid states and the Shannon entropy of the transversal state distribution
# shows the measure of 'chaos' (diversity of activities) in the diaries
seqHtplot(time15_seq, with.legend = "right", legend.prop=0.4)
We need to define the substitution cost for all the transitions (it can be a constant or user-defined).
# seqdist() = for pairwise dissimilarities
# seqsubm() = to compute own substitution matrix
#"TRATE" option, the costs are determined from the estimated transition rates
scost <- seqsubm(time15_seq, method = "TRATE")## [>] creating substitution-cost matrix using transition rates ...
## [>] computing transition probabilities for states 1/3/4/5/6/7/10/11 ...round(scost, 3)## 1-> 3-> 4-> 5-> 6-> 7-> 10-> 11->
## 1-> 0.000 1.962 1.953 1.984 1.992 1.995 1.927 1.956
## 3-> 1.962 0.000 1.929 1.970 1.987 1.963 1.824 1.904
## 4-> 1.953 1.929 0.000 1.995 1.988 1.990 1.906 1.906
## 5-> 1.984 1.970 1.995 0.000 1.995 1.991 1.934 1.898
## 6-> 1.992 1.987 1.988 1.995 0.000 1.993 1.952 1.919
## 7-> 1.995 1.963 1.990 1.991 1.993 0.000 1.949 1.729
## 10-> 1.927 1.824 1.906 1.934 1.952 1.949 0.000 1.769
## 11-> 1.956 1.904 1.906 1.898 1.919 1.729 1.769 0.000## calculated in this way, all are close to 2 anyway (for this dataset) 2 is default
## or we can use the usual default one of constant 2:
ccost <- seqsubm(time15_seq, method="CONSTANT", cval=2)## [>] creating 8x8 substitution-cost matrix using 2 as constant valueround(ccost, 3)## 1-> 3-> 4-> 5-> 6-> 7-> 10-> 11->
## 1-> 0 2 2 2 2 2 2 2
## 3-> 2 0 2 2 2 2 2 2
## 4-> 2 2 0 2 2 2 2 2
## 5-> 2 2 2 0 2 2 2 2
## 6-> 2 2 2 2 0 2 2 2
## 7-> 2 2 2 2 2 0 2 2
## 10-> 2 2 2 2 2 2 0 2
## 11-> 2 2 2 2 2 2 2 0Optimal matching for calculating dissimilarities between sequences need the specification of both substitution and indel costs. The algorithm is developed by Needleman and Wunsch (1970). For the illustration how the algorithm works please link to my explanation of optimal matching
If the sequence file is heavy, calculate only the upper part of the matrix by full.matrix = FALSE The usual default is that substitution cost is twice the indel cost, and default indel cost is 1.
om_time <- seqdist(time15_seq, method = "OM", indel = 1, sm = scost)## [>] 2000 sequences with 8 distinct states
## [>] checking 'sm' (size and triangle inequality)
## [>] 2000 distinct sequences
## [>] min/max sequence lengths: 96/96
## [>] computing distances using the OM metric
## [>] elapsed time: 28.185 secs## this results in a dissimilarity matrix which you can look at using:
round(om_time[1:10, 1:10], 1)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 0.0 72.6 102.9 87.5 86.5 93.1 86.3 95.7 106.2 63.8
## [2,] 72.6 0.0 46.4 67.4 48.6 102.1 46.0 49.1 70.4 53.0
## [3,] 102.9 46.4 0.0 51.2 39.5 93.8 72.9 36.1 72.7 81.3
## [4,] 87.5 67.4 51.2 0.0 64.2 93.7 99.0 63.4 100.3 88.5
## [5,] 86.5 48.6 39.5 64.2 0.0 97.3 73.2 50.3 58.3 66.0
## [6,] 93.1 102.1 93.8 93.7 97.3 0.0 116.3 86.9 111.2 100.6
## [7,] 86.3 46.0 72.9 99.0 73.2 116.3 0.0 66.4 87.1 45.1
## [8,] 95.7 49.1 36.1 63.4 50.3 86.9 66.4 0.0 66.4 68.0
## [9,] 106.2 70.4 72.7 100.3 58.3 111.2 87.1 66.4 0.0 87.9
## [10,] 63.8 53.0 81.3 88.5 66.0 100.6 45.1 68.0 87.9 0.0Let’s run cluster analysis on our dissimilarity matrix
Other common methods are:
The “average” and “single” options do not work well for time-use data (check). The “complete” option is a possibility (check).
## run cluster analysis on the calculated dissimilarity matrix
clusterward <- agnes(om_time, diss = TRUE, method = "ward")
## other common methods are "average", "single", "complete" (instead of "ward")
## "average" and "single" do not work well for time-use data (check if you want)
## "complete" can be an option
## "ward" is the "industry standard"
# Convert hclust into a dendrogram and plot
hcd <- as.dendrogram(clusterward)
# plot the dendrogram
plot(hcd, type = "rectangle", ylab = "Height")
Let’s inspect the splitting tree branches:
#inspect the splitting steps
ward.tree <- as.seqtree(clusterward, seqdata = time15_seq,
diss = om_time,
ncluster = 25)
## plot the tree of tempograms to check how it splits the diaries
seqtreedisplay(ward.tree, type = "d", border = NA, show.depth = TRUE) Test the cluster solution quality:
There are many possible tests:
#test cluster solution quality
wardtest <- as.clustrange(clusterward,
diss = om_time,
ncluster = 25)
#plot the quality criteria
plot(wardtest, stat = c("ASW", "HC", "PBC"), norm = "zscore", lwd = 4)
Let’s say that our solution is pretty good for 8 clusters.
c8 <- cutree(clusterward, k = 8)
## bind with the dataset
MyData<-cbind(MyData, c8)#plot cluster solution
png("plot_clusters.png", 1200, 800)
seqdplot(time15_seq, group = c8, border = NA)
dev.off()## quartz_off_screen
## 2# subset data by cluster
cl1<-(time15_seq[MyData$c8 == "1",])
# plot the selected cluster
par(mfrow=c(1,1))
seqdplot(cl1, main = "",
cex.main = 1.7,
with.legend = FALSE,
yaxis = FALSE,
cpal = getPalette(colourCount),
ylab = "",
border = NA)
## write new data to csv if needed
#write.csv(MyData, "clustered_EC.csv")[1] University of Oxford