1. Input Variables

Time series clustering starts with the selection and preparation of the variables of interest. While the selection will necessarily be field- and concept-specific, there are a few conceptual and methodological issues that should be considered. Conceptually, we will need to make a choice of key variables that comprehensively capture the concept you would like to assess over time. Once the key variables are selected, we will need make sure that the variables are ready to be summarized using the time series features and that the format fits the chosen clustering procedure.

Variable Selection

The included variables should adequately capture the concept of interest and should be meaningful to the understanding of the time series. One of the advantages of feature-based clustering is that it is inherently adept at accommodating multi-variate concepts — a common aim in ESM research. In our illustration, we apply the clustering process to a recent set of studies that collected data on migration experiences. The data seek to capture the process of cultural adaptation and aims to identify diverging trajectories. Importantly for our illustration, the migration ESM research, also, exemplifies the real-world data issues that ESM data commonly face, including a multivariate conceptualization with event-specific missingness patterns.

For our illustration, we include 12 variables that were measured as part of the ESM surveys in all three studies and captured information about the participant’s interactions, as well as cognitive-, emotional-, and motivational self in relationship with the majority group (Kreienkamp et al., 2023).

Import the raw data

We begin by importing the raw data as well as the variable meta data. Both of these data files are available as part of our OSF repository. To ensure the privacy and confidentiality of the study participants, we took a number of steps to pre-process the data:

  • consistent naming
  • key variables only (participant privacy)
  • post-test variable pre-calculated
  • long format ESM data of all three studies combined (indicator variable: study)

This means that we import the minimal reproducible dataset of the key variables (dt_raw using the data/osf_mini.Rda file) as well as a Microsoft Excel sheet providing a range of supplementary information, including the survey questions and variable labels (var_meta using the data/osf_var_meta.xlsx file).

dt_raw <- readRDS(file="data/osf_mini.Rda")
var_meta <- readxl::read_excel("data/osf_var_meta.xlsx")

Variable Overview

To get an overview of the variables we display the variable names, labels, item descriptions, as well as the survey and analysis context (see Table 1).

Code
var_meta %>%
  transmute(
    "Variable Name" = name,
    "Label" = label,
    "Description" = description,
    "Type" = type,
    "Contact Specific" = recode(contact, `0`="", ` 1`="✔"),
    "ESM" = recode(esm, `0`="", ` 1`="✔"),
    "Post" = recode(post, `0`="", ` 1`="✔"),
    "Cluster" = recode(cluster, `0`="", ` 1`="✔"),
    "Validation" = recode(interpretation, `0`="", ` 1`="✔")
  ) %>%
  kbl(.,
      escape = FALSE,
      booktabs = TRUE,
      label = "var_overview",
      format = "html",
      align = c(rep("l", 4), rep("c", ncol(.)-4)),
      digits = 2,
      linesep = "",
      caption = "Variable Overview") %>%
  add_header_above(c(" " = 5, "Survey" = 2, "Analysis" = 2)) %>%
  kable_classic(
    full_width = FALSE,
    lightable_options = "hover",
    html_font = "Cambria"
  ) %>%
  scroll_box(width = "100%", height = "500px") %>%
  footnote(
    general = c('All ESM items used a continuous slider and were rescaled to a range of 0—100.')
  )
Table 1: Variable Overview
Variable Overview
Survey
Analysis
Variable Name Label Description Type Contact Specific ESM Post Cluster Validation
PID Person ID participant ID id
TIDnum Measurement ID time ID id
TID Datetime date – daytime id
KeyNeedFulfillment Int: Need Fulfillment During your interaction with -NAME- your goal (-GOAL-) was fulfilled. needs
KeyNeedDueToPartner Int: Need Fulfillment Partner -NAME- helped fulfill your goal (-GOAL-) needs
InteractionContextAccidental Int: Accidental The interaction with -NAME- was accidental. cognition
InteractionContextvoluntary Int: Voluntary The interaction with -NAME- was voluntary. cognition
InteractionContextCooperative Int: Cooperative The interaction with -NAME- was cooperative. cognition
InteractionContextRepresentativeNL Int: Representative The interaction with -NAME- was representative of the Dutch. cognition
qualityOverall Int: Quality Overall, the interaction with -NAME- was: Unplesant --- Plesant cognition
qualityMeaning Int: Meaningful Overall, the interaction with -NAME- was: Superficial --- Meaningful cognition
DaytimeNeedFulfillment Need Fulfillment During this -morning/afternoon- your goal (-GOAL-) was fulfilled. needs
AttitudesDutch Outgroup Attitude At the moment, how favorably do you feel towards the Dutch. cognition
AttitudesPartner Int: Partner Attitude At the moment, how favorably do you feel towards -NAME- cognition
exWB Well-Being How do you feel right now? very sad --- very happy emotion
EvDayDiscr.post OOC: Discrimination Mean of the Everyday Discrimination scale (past month) emotion
Note:
All ESM items used a continuous slider and were rescaled to a range of 0—100.

Variable Preparation

the important variables have been selected, the data needs to be prepared for the analysis steps. Importantly, this not only means validating and cleaning the data (e.g., re-coding, removing duplicate or unwanted measurements) but also making the time-series comparable. Two important steps are making the time-frames and response scales comparable across participants — for example, by choosing a time frame that is common to most participants and standardizing the participants’ responses (Liao, 2005).

Data Availability

In our illustration data set, the studies differed substantially in the maximum length of participation (\(max(t_{S1})\) = 63, \(max(t_{S2})\) = 69, \(max(t_{S3})\) = 155). To make the three studies comparable in participation and time frames, we iteratively removed all measurement occasions and participants that had more than 45% missingness. This procedure is in line with the general recommendation for data that might still need to rely on imputations for later model testing (Madley-Dowd et al., 2019).

Study 1

For the Study 1 we first assess the data availability pattern. We do this visually by representing participants as rows and measurement occasions as columns:

Study 1: Data Availability
t_1 t_2 t_3 t_4 t_5 t_6 t_7 t_8 t_9 t_10 t_11 t_12 t_13 t_14 t_15 t_16 t_17 t_18 t_19 t_20 t_21 t_22 t_23 t_24 t_25 t_26 t_27 t_28 t_29 t_30 t_31 t_32 t_33 t_34 t_35 t_36 t_37 t_38 t_39 t_40 t_41 t_42 t_43 t_44 t_45 t_46 t_47 t_48 t_49 t_50 t_51 t_52 t_53 t_54 t_55 t_56 t_57 t_58 t_59 t_60 t_61 t_62 t_63
PP_1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
PP_2 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_3 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0
PP_4 0 0 0 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0
PP_5 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
PP_6 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_7 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_8 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_9 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_10 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_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 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_12 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_13 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1
PP_14 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_15 0 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1
PP_17 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_18 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_19 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_21 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_22 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_23 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1

We then run the cleanM function, where sequentially remove the row or column with the most missing data until only rows and columns remain that have less than 45% missingness.

source("scripts/cleanM.R")
dtS1RedInfo <- cleanM(M = as.data.frame(dtS1Availability), c = 55)
1: Row PP_20 had a completion rate of 3.17% and was removed.
2: Row PP_22 had a completion rate of 4.76% and was removed.
3: Column t_1 had a completion rate of 23.81% and was removed.
4: Column t_2 had a completion rate of 52.38% and was removed.
5: All row- and column means are over 55%. The final matrix has 21 rows and 61 columns.

With the adjusted dataset we again visualize the data availability.

PIDout <- gsub("PP_", "", dtS1RedInfo$rowNamesOut) %>% as.numeric
TIDout <- gsub("t_", "", dtS1RedInfo$colNamesOut) %>% as.numeric
TIDInRed <- gsub("t_", "", dtS1RedInfo$colNamesIn) %>% as.numeric
TIDIn <- seq(min(TIDInRed), max(TIDInRed), 1)

dtS1Red <- dt_raw %>%
  filter(study == "S1") %>%
  filter(
    !PID %in% PIDout,
    TIDnum %in% TIDIn
  ) %>% 
  mutate(TIDnum = TIDnum - min(TIDnum))
rm(PIDout, TIDout, TIDInRed, TIDIn)

# change glitch where survey was sent too early
dtS1Red$TIDnum[dtS1Red$PID == 7 & as.character(dtS1Red$created) == "2018-05-18 11:46:40"] <- 10
dtS1Red$TID[dtS1Red$PID == 7 & as.character(dtS1Red$created) == "2018-05-18 11:46:40"] <- "2018-05-18 Morning"

dtS1AvailabilityRedKbl <- dtS1RedInfo$reducedMatrix
dtS1AvailabilityRedKbl[1:ncol(dtS1AvailabilityRedKbl)] <- lapply(dtS1AvailabilityRedKbl[1:ncol(dtS1AvailabilityRedKbl)], function(x) {
    cell_spec(x,
              bold = FALSE,
              color = "white",
              background = ifelse(x == 1, "green", "red")
              )
})
kbl(
  dtS1AvailabilityRedKbl,
  format = "html",
  escape = FALSE,
  align = "c",
  booktabs = TRUE,
  caption = "Study 1: Final Data Availability" # complete caption for main document
) %>%
  kable_classic(
    full_width = FALSE,
    lightable_options = "hover",
    html_font = "Cambria"
  ) %>%
  scroll_box(width = "100%", height = "500px")
Study 1: Final Data Availability
t_3 t_4 t_5 t_6 t_7 t_8 t_9 t_10 t_11 t_12 t_13 t_14 t_15 t_16 t_17 t_18 t_19 t_20 t_21 t_22 t_23 t_24 t_25 t_26 t_27 t_28 t_29 t_30 t_31 t_32 t_33 t_34 t_35 t_36 t_37 t_38 t_39 t_40 t_41 t_42 t_43 t_44 t_45 t_46 t_47 t_48 t_49 t_50 t_51 t_52 t_53 t_54 t_55 t_56 t_57 t_58 t_59 t_60 t_61 t_62 t_63
PP_1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
PP_2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_3 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0
PP_4 0 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0
PP_5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
PP_6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_7 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_8 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_9 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_10 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_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 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_12 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_13 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1
PP_14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_15 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1
PP_17 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_18 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_19 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_21 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_23 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1

Study 2

For Study 2 we follow the same procedure, where we first visualize the original data availability:

Study 2: Data Availability
t_1 t_2 t_3 t_4 t_5 t_6 t_7 t_8 t_9 t_10 t_11 t_12 t_13 t_14 t_15 t_16 t_17 t_18 t_19 t_20 t_21 t_22 t_23 t_24 t_25 t_26 t_27 t_28 t_29 t_30 t_31 t_32 t_33 t_34 t_35 t_36 t_37 t_38 t_39 t_40 t_41 t_42 t_43 t_44 t_45 t_46 t_47 t_48 t_49 t_50 t_51 t_52 t_53 t_54 t_55 t_56 t_57 t_58 t_59 t_60 t_61 t_62 t_63 t_64 t_65 t_66 t_67 t_68 t_69
PP_1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 0 0
PP_2 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 0 0 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
PP_3 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
PP_4 0 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 0 0 1 1 0 1 1 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0
PP_5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0
PP_6 0 0 0 0 0 1 1 0 0 1 0 0 0 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 1 0 0 1 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0
PP_7 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 0
PP_8 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 0 0 0 0 0
PP_9 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0
PP_10 0 0 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0
PP_11 0 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 1 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_12 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 1 1 0 1 0 1 0 0 0 1 0 0 1 1 0 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0
PP_13 1 0 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 1
PP_15 0 0 0 0 0 0 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 0 0 0 1 1 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0
PP_16 0 0 0 0 1 0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0
PP_17 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0
PP_18 0 0 1 0 1 1 1 1 1 0 1 0 1 1 1 0 1 0 0 0 1 0 1 1 0 1 1 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 1 0 1 0 1 1 0 0 0 0 0 0 0
PP_19 0 0 1 1 1 1 1 1 0 0 1 0 1 1 1 0 0 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 0 1 0 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
PP_20 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 0 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0
PP_21 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 0 0 0
PP_22 0 0 0 0 1 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 0
PP_23 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0
PP_24 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 0 0 1 0 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0
PP_25 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 1 1 0 0 1 1 0 1 1 1 1 1 1 0 0 0 0 0
PP_26 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_27 0 0 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0
PP_28 0 0 0 0 1 0 1 1 1 1 1 0 1 0 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0
PP_29 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0 0 1 1 1 1 0 1 1 1 0 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0
PP_30 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0
PP_31 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 0 1 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_32 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0
PP_33 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0
PP_34 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0
PP_35 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0
PP_36 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0
PP_37 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0
PP_38 1 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0
PP_39 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0
PP_40 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 0 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_41 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0
PP_42 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 0 0 0
PP_43 1 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 1 0 1 1 1 0 1 0 1 0 1 0 0 0 1 0 0 1 1 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 0 0 0
PP_44 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0
PP_45 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 1 0 1 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0
PP_46 0 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 2 1 1 0 0 0 0 0
PP_47 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0
PP_48 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0
PP_49 0 0 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_50 0 0 0 0 1 0 1 1 1 0 1 1 1 0 1 0 1 1 0 0 1 0 0 0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 0 0 1 1 0 1 1 0 0 0
PP_51 0 0 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 1 0 0 0
PP_52 0 0 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0
PP_53 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 0
PP_54 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 1 0 1 1 0 1 1 1 1 1 0 1 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0
PP_55 1 0 1 1 1 1 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0
PP_56 0 0 0 0 1 1 1 1 0 0 1 1 1 1 1 0 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0
PP_57 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 0 0
PP_58 0 0 0 0 1 0 1 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 0
PP_59 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_60 0 0 1 1 0 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 1 1 1 0 1 0 0 0 1 1 0 1 1 1 1 1 1 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
PP_61 0 0 1 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 0 0 0
PP_62 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0
PP_63 0 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 0 0 0
PP_64 0 0 0 0 1 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 1 0 1 1 0 1 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 0 0
PP_65 0 0 0 0 0 0 1 1 1 1 0 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0
PP_66 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_67 0 0 0 0 0 0 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 0 0 0
PP_68 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 0 1 0 0 0 0 1 1 0 1 1 0 1 1 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0
PP_69 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 0
PP_70 1 1 1 1 1 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 1 0 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0
PP_71 0 0 1 0 1 0 1 1 1 0 0 0 0 0 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_72 0 0 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 1 1 0 1 1 1 0 1 1 0 1 1 1 0 0 0 0 0
PP_73 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 0
PP_74 0 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1 1 0 1 0 0 1 0 0 1 0 0 0 0 0
PP_75 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 0 1 1 0
PP_76 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0
PP_77 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 0 0 1 1 0 1 0 1 0 0 1 1 0 1 1 0 0 0 1 1 1 0 1 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0
PP_78 0 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 0 0
PP_79 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 0 0 1 0 0 0 0 0 0
PP_80 0 0 0 0 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0
PP_81 0 0 0 0 1 0 1 0 0 0 1 1 1 1 1 0 1 1 0 1 0 0 1 1 1 0 0 0 0 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_82 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 0
PP_83 0 0 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0
PP_84 0 0 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 0 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0
PP_85 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 1 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0
PP_86 0 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 0 0 0 0
PP_87 0 0 1 1 0 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_88 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 1 0 0 0 0 0 0
PP_89 0 0 0 0 0 0 0 1 1 1 0 1 1 0 1 1 1 1 0 1 1 0 0 0 1 0 1 1 0 1 1 0 1 0 1 1 1 0 0 1 0 1 1 0 1 1 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 0 0
PP_90 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0
PP_91 0 0 0 0 0 0 1 1 1 0 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 0 0 1 1 1 0 1 0
PP_92 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0
PP_93 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 0 0 1 1 1 1 0 0 0 0 1 0 1 1 1 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0
PP_94 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
PP_95 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0
PP_96 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
PP_97 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0
PP_98 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 0 0 0
PP_99 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
PP_100 1 0 1 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_101 0 0 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 0 0 1 1 1 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
PP_102 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0
PP_103 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_104 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 0 1 0 0 1 0 0 0 0 0
PP_105 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0
PP_106 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 1 0 0 1 0 1 0 1 0
PP_107 0 0 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_108 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
PP_109 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 0 0
PP_110 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_111 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_112 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 0 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0
PP_113 0 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 0 1 1 1 1 1 0 0 0 1 1 1 0 0 1 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 0 0 1 1 1 0 1 0 0 0

Again run the cleanM function to harmonize the missingness:

dtS2RedInfo <- cleanM(M = as.data.frame(dtS2Availability), c = 55)
1: Column t_69 had a completion rate of 0.88% and was removed.
2: Column t_67 had a completion rate of 7.08% and was removed.
3: Column t_68 had a completion rate of 7.08% and was removed.
4: Row PP_107 had a completion rate of 7.58% and was removed.
5: Row PP_26 had a completion rate of 9.09% and was removed.
6: Row PP_100 had a completion rate of 9.09% and was removed.
7: Row PP_110 had a completion rate of 9.09% and was removed.
8: Row PP_111 had a completion rate of 10.61% and was removed.
9: Column t_2 had a completion rate of 15.74% and was removed.
10: Column t_1 had a completion rate of 18.52% and was removed.
11: Column t_65 had a completion rate of 18.52% and was removed.
12: Row PP_31 had a completion rate of 20.63% and was removed.
13: Column t_66 had a completion rate of 23.36% and was removed.
14: Row PP_87 had a completion rate of 25.81% and was removed.
15: Row PP_40 had a completion rate of 27.42% and was removed.
16: Row PP_70 had a completion rate of 29.03% and was removed.
17: Row PP_81 had a completion rate of 29.03% and was removed.
18: Row PP_12 had a completion rate of 30.65% and was removed.
19: Row PP_66 had a completion rate of 30.65% and was removed.
20: Row PP_103 had a completion rate of 32.26% and was removed.
21: Row PP_85 had a completion rate of 37.10% and was removed.
22: Row PP_71 had a completion rate of 38.71% and was removed.
23: Row PP_11 had a completion rate of 40.32% and was removed.
24: Row PP_15 had a completion rate of 41.94% and was removed.
25: Row PP_43 had a completion rate of 41.94% and was removed.
26: Column t_64 had a completion rate of 43.16% and was removed.
27: Row PP_6 had a completion rate of 45.90% and was removed.
28: Row PP_7 had a completion rate of 45.90% and was removed.
29: Row PP_18 had a completion rate of 45.90% and was removed.
30: Row PP_13 had a completion rate of 47.54% and was removed.
31: Column t_63 had a completion rate of 48.35% and was removed.
32: Row PP_29 had a completion rate of 50.00% and was removed.
33: Column t_4 had a completion rate of 51.11% and was removed.
34: Row PP_101 had a completion rate of 50.85% and was removed.
35: Row PP_28 had a completion rate of 52.54% and was removed.
36: Row PP_77 had a completion rate of 52.54% and was removed.
37: Row PP_49 had a completion rate of 54.24% and was removed.
38: All row- and column means are over 55%. The final matrix has 86 rows and 59 columns.

And visualize the adjusted data availability.

PIDout <- gsub("PP_", "", dtS2RedInfo$rowNamesOut) %>% as.numeric
TIDout <- gsub("t_", "", dtS2RedInfo$colNamesOut) %>% as.numeric
TIDInRed <- gsub("t_", "", dtS2RedInfo$colNamesIn) %>% as.numeric
TIDIn <- seq(min(TIDInRed), max(TIDInRed), 1)

dtS2Red <- dt_raw %>%
  filter(study == "S2") %>%
  filter(
    !PID %in% PIDout,
    TIDnum %in% TIDIn
  ) %>% 
  mutate(TIDnum = TIDnum - min(TIDnum))
rm(PIDout, TIDout, TIDInRed, TIDIn)

# remove glitch entries 
dtS2Red <- dtS2Red[!(dtS2Red$PID == 46 & dtS2Red$TIDnum == 57 & dtS2Red$ended == "2018-12-20 21:58:31"),]
dtS2Red <- dtS2Red[!(dtS2Red$PID == 46 & dtS2Red$TIDnum == 59 & dtS2Red$ended == "2018-12-20 21:58:31"),]

dtS2AvailabilityRedKbl <- dtS2RedInfo$reducedMatrix
dtS2AvailabilityRedKbl[1:ncol(dtS2AvailabilityRedKbl)] <- lapply(dtS2AvailabilityRedKbl[1:ncol(dtS2AvailabilityRedKbl)], function(x) {
    cell_spec(x,
              bold = FALSE,
              color = "white",
              background = ifelse(x == 1, "green", "red")
              )
})
kbl(
  dtS2AvailabilityRedKbl,
  format = "html",
  escape = FALSE,
  align = "c",
  booktabs = TRUE,
  caption = "Study 2: Final Data Availability" # complete caption for main document
) %>%
  kable_classic(
    full_width = FALSE,
    lightable_options = "hover",
    html_font = "Cambria"
  ) %>%
  scroll_box(width = "100%", height = "500px")
Study 2: Final Data Availability
t_3 t_5 t_6 t_7 t_8 t_9 t_10 t_11 t_12 t_13 t_14 t_15 t_16 t_17 t_18 t_19 t_20 t_21 t_22 t_23 t_24 t_25 t_26 t_27 t_28 t_29 t_30 t_31 t_32 t_33 t_34 t_35 t_36 t_37 t_38 t_39 t_40 t_41 t_42 t_43 t_44 t_45 t_46 t_47 t_48 t_49 t_50 t_51 t_52 t_53 t_54 t_55 t_56 t_57 t_58 t_59 t_60 t_61 t_62
PP_1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1
PP_2 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 0 0 1 1 1 0 1 1 0 0 0 0 0
PP_3 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_4 0 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 0 0 1 1 0 1 1 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 0 0
PP_5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 0
PP_8 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 0
PP_9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_10 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1
PP_14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1
PP_16 0 1 0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1
PP_17 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 0 0 0 0 0
PP_19 1 1 1 1 1 0 0 1 0 1 1 1 0 0 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 0 1 0 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 0 1 0 0 0 0 0 0 0 1
PP_20 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 0 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 0 1 0
PP_21 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1
PP_22 0 1 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1
PP_23 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_24 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 0 0 1 0 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0
PP_25 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 1 1 0 0 1 1 0 1 1 1 1
PP_27 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 0
PP_30 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 1 1 1 0 1 1 0
PP_32 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 0 0 0 1 1 0
PP_33 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_34 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_35 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 0 1 1 1 0
PP_36 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 0
PP_37 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_38 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 0 1 1 0 1 0 0
PP_39 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1
PP_41 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 0 0 1 1
PP_42 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1
PP_44 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
PP_45 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 1 0 1 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 1 0 1 0
PP_46 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 2
PP_47 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1
PP_48 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_50 0 1 0 1 1 1 0 1 1 1 0 1 0 1 1 0 0 1 0 0 0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 0 0 1
PP_51 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1
PP_52 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 0 0 1 1 1 1 0 1 1
PP_53 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1
PP_54 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 1 0 1 1 0 1 1 1 1 1 0 1 0 1 1 0 0 1 1 0 0 1
PP_55 1 1 1 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0
PP_56 0 1 1 1 1 0 0 1 1 1 1 1 0 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1 1 0 0 0 1 1 0 0 0
PP_57 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1
PP_58 0 1 0 1 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1
PP_59 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 0 0
PP_60 1 0 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 1 1 1 0 1 0 0 0 1 1 0 1 1 1 1 1 1 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0
PP_61 1 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1
PP_62 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0
PP_63 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1
PP_64 0 1 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 1 0 1 1 0 1 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 1 0 1
PP_65 0 0 0 1 1 1 1 0 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1
PP_67 0 0 0 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1
PP_68 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 0 1 0 0 0 0 1 1 0 1 1 0 1 1 1 1 1 1 0 0 0 0 1 1
PP_69 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1
PP_72 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 1 1 0 1 1 1 0 1 1 0 1
PP_73 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1
PP_74 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1 1 0 1 0 0 1 0
PP_75 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1
PP_76 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_78 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1
PP_79 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 0 0
PP_80 0 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1
PP_82 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1
PP_83 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 1 0 1
PP_84 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 0 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1 1 0 0 1
PP_86 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1
PP_88 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0
PP_89 0 0 0 0 1 1 1 0 1 1 0 1 1 1 1 0 1 1 0 0 0 1 0 1 1 0 1 1 0 1 0 1 1 1 0 0 1 0 1 1 0 1 1 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
PP_90 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
PP_91 0 0 0 1 1 1 0 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 0
PP_92 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_93 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 0 0 1 1 1 1 0 0 0 0 1 0 1 1 1 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0
PP_94 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1
PP_95 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 0 0 1 1 0 1 0 1
PP_96 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1
PP_97 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 0
PP_98 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1
PP_99 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_102 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_104 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 0 1 0
PP_105 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1
PP_106 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 1 0
PP_108 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_109 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0
PP_112 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 0 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0
PP_113 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 0 1 1 1 1 1 0 0 0 1 1 1 0 0 1 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 0 0 1

Study 3

The data availability procedure for Study 3 again mirrors that of the previous two studies, where we assess the original availability (now with much more heterogeneity):

Study 2: Data Availability
t_1 t_2 t_3 t_4 t_5 t_6 t_7 t_8 t_9 t_10 t_11 t_12 t_13 t_14 t_15 t_16 t_17 t_18 t_19 t_20 t_21 t_22 t_23 t_24 t_25 t_26 t_27 t_28 t_29 t_30 t_31 t_32 t_33 t_34 t_35 t_36 t_37 t_38 t_39 t_40 t_41 t_42 t_43 t_44 t_45 t_46 t_47 t_48 t_49 t_50 t_51 t_52 t_53 t_54 t_55 t_56 t_57 t_58 t_59 t_60 t_61 t_62 t_63 t_64 t_65 t_66 t_67 t_68 t_69 t_70 t_71 t_72 t_73 t_74 t_75 t_76 t_77 t_78 t_79 t_80 t_81 t_82 t_83 t_84 t_85 t_86 t_87 t_88 t_89 t_90 t_91 t_92 t_93 t_94 t_95 t_96 t_97 t_98 t_99 t_100 t_101 t_102 t_103 t_104 t_105 t_106 t_107 t_108 t_109 t_110 t_111 t_112 t_113 t_114 t_115 t_116 t_117 t_118 t_119 t_120 t_121 t_122 t_123 t_124 t_125 t_126 t_127 t_128 t_129 t_130 t_131 t_132 t_133 t_134 t_135 t_136 t_137 t_138 t_139 t_140 t_141 t_142 t_143 t_144 t_145 t_146 t_147 t_148 t_149 t_150 t_151 t_152 t_153 t_154 t_155
PP_1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_2 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_3 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 0 1 0 0 0 0 1 0 1 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_4 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 1 1 0 0 0 0 0 0 1 1 0 0 1 1 1 0 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_7 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_8 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_9 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 0 1 0 1 0 1 0 1 1 1 1 0 0 1 0 0 1 0 1 0 1 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_11 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 0 1 0 0 1 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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PP_64 0 0 0 0 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_65 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_66 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 0 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_67 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_68 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 1 1 1 1 0 0 1 1 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_69 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 1 0 1 0 0 0 1 1 0 0 0 1 1 0 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0
PP_70 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PP_71 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 0 0 1 1 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Run the sequential missingess reduction function:

dtS3RedInfo <- cleanM(M = as.data.frame(dtS3Availability), c = 55)
1: Column t_1 had a completion rate of 1.41% and was removed.
2: Column t_2 had a completion rate of 1.41% and was removed.
3: Column t_3 had a completion rate of 1.41% and was removed.
4: Column t_4 had a completion rate of 1.41% and was removed.
5: Column t_5 had a completion rate of 1.41% and was removed.
6: Column t_6 had a completion rate of 1.41% and was removed.
7: Column t_136 had a completion rate of 1.41% and was removed.
8: Column t_138 had a completion rate of 1.41% and was removed.
9: Column t_147 had a completion rate of 1.41% and was removed.
10: Column t_148 had a completion rate of 1.41% and was removed.
11: Column t_149 had a completion rate of 1.41% and was removed.
12: Column t_150 had a completion rate of 1.41% and was removed.
13: Column t_152 had a completion rate of 1.41% and was removed.
14: Column t_153 had a completion rate of 1.41% and was removed.
15: Column t_154 had a completion rate of 1.41% and was removed.
16: Column t_155 had a completion rate of 1.41% and was removed.
17: Column t_137 had a completion rate of 2.82% and was removed.
18: Column t_139 had a completion rate of 2.82% and was removed.
19: Column t_140 had a completion rate of 2.82% and was removed.
20: Column t_141 had a completion rate of 2.82% and was removed.
21: Column t_142 had a completion rate of 2.82% and was removed.
22: Column t_143 had a completion rate of 2.82% and was removed.
23: Column t_144 had a completion rate of 2.82% and was removed.
24: Column t_151 had a completion rate of 2.82% and was removed.
25: Column t_135 had a completion rate of 4.23% and was removed.
26: Column t_145 had a completion rate of 4.23% and was removed.
27: Column t_146 had a completion rate of 4.23% and was removed.
28: Column t_106 had a completion rate of 8.45% and was removed.
29: Column t_122 had a completion rate of 8.45% and was removed.
30: Column t_133 had a completion rate of 8.45% and was removed.
31: Column t_110 had a completion rate of 9.86% and was removed.
32: Column t_111 had a completion rate of 9.86% and was removed.
33: Column t_134 had a completion rate of 9.86% and was removed.
34: Row PP_1 had a completion rate of 10.66% and was removed.
35: Column t_104 had a completion rate of 11.43% and was removed.
36: Column t_107 had a completion rate of 11.43% and was removed.
37: Column t_120 had a completion rate of 11.43% and was removed.
38: Column t_124 had a completion rate of 11.43% and was removed.
39: Column t_128 had a completion rate of 11.43% and was removed.
40: Column t_121 had a completion rate of 12.86% and was removed.
41: Column t_125 had a completion rate of 12.86% and was removed.
42: Column t_126 had a completion rate of 12.86% and was removed.
43: Column t_127 had a completion rate of 12.86% and was removed.
44: Column t_132 had a completion rate of 12.86% and was removed.
45: Column t_92 had a completion rate of 14.29% and was removed.
46: Column t_100 had a completion rate of 14.29% and was removed.
47: Column t_102 had a completion rate of 14.29% and was removed.
48: Column t_105 had a completion rate of 14.29% and was removed.
49: Column t_116 had a completion rate of 14.29% and was removed.
50: Column t_123 had a completion rate of 14.29% and was removed.
51: Column t_88 had a completion rate of 15.71% and was removed.
52: Column t_97 had a completion rate of 15.71% and was removed.
53: Column t_101 had a completion rate of 15.71% and was removed.
54: Column t_113 had a completion rate of 15.71% and was removed.
55: Column t_117 had a completion rate of 15.71% and was removed.
56: Column t_129 had a completion rate of 15.71% and was removed.
57: Column t_131 had a completion rate of 15.71% and was removed.
58: Column t_98 had a completion rate of 17.14% and was removed.
59: Column t_99 had a completion rate of 17.14% and was removed.
60: Column t_112 had a completion rate of 17.14% and was removed.
61: Column t_114 had a completion rate of 17.14% and was removed.
62: Column t_119 had a completion rate of 17.14% and was removed.
63: Column t_130 had a completion rate of 17.14% and was removed.
64: Column t_95 had a completion rate of 18.57% and was removed.
65: Column t_103 had a completion rate of 18.57% and was removed.
66: Column t_108 had a completion rate of 18.57% and was removed.
67: Column t_109 had a completion rate of 18.57% and was removed.
68: Column t_115 had a completion rate of 18.57% and was removed.
69: Row PP_36 had a completion rate of 19.32% and was removed.
70: Column t_84 had a completion rate of 20.29% and was removed.
71: Column t_91 had a completion rate of 20.29% and was removed.
72: Column t_93 had a completion rate of 20.29% and was removed.
73: Column t_96 had a completion rate of 20.29% and was removed.
74: Column t_118 had a completion rate of 20.29% and was removed.
75: Row PP_18 had a completion rate of 21.69% and was removed.
76: Row PP_45 had a completion rate of 22.89% and was removed.
77: Row PP_8 had a completion rate of 24.10% and was removed.
78: Row PP_68 had a completion rate of 24.10% and was removed.
79: Column t_90 had a completion rate of 26.15% and was removed.
80: Row PP_5 had a completion rate of 26.83% and was removed.
81: Column t_86 had a completion rate of 26.56% and was removed.
82: Row PP_6 had a completion rate of 27.16% and was removed.
83: Column t_94 had a completion rate of 26.98% and was removed.
84: Column t_87 had a completion rate of 28.57% and was removed.
85: Row PP_35 had a completion rate of 27.85% and was removed.
86: Column t_89 had a completion rate of 29.03% and was removed.
87: Row PP_46 had a completion rate of 28.21% and was removed.
88: Row PP_38 had a completion rate of 29.49% and was removed.
89: Column t_85 had a completion rate of 30.00% and was removed.
90: Row PP_53 had a completion rate of 33.77% and was removed.
91: Column t_80 had a completion rate of 35.59% and was removed.
92: Column t_82 had a completion rate of 37.29% and was removed.
93: Row PP_54 had a completion rate of 38.67% and was removed.
94: Column t_74 had a completion rate of 39.66% and was removed.
95: Column t_83 had a completion rate of 39.66% and was removed.
96: Row PP_13 had a completion rate of 41.10% and was removed.
97: Row PP_41 had a completion rate of 41.10% and was removed.
98: Column t_72 had a completion rate of 42.86% and was removed.
99: Row PP_39 had a completion rate of 44.44% and was removed.
100: Column t_79 had a completion rate of 45.45% and was removed.
101: Column t_70 had a completion rate of 47.27% and was removed.
102: Column t_81 had a completion rate of 47.27% and was removed.
103: Row PP_31 had a completion rate of 47.83% and was removed.
104: Row PP_47 had a completion rate of 47.83% and was removed.
105: Column t_75 had a completion rate of 49.06% and was removed.
106: Row PP_69 had a completion rate of 48.53% and was removed.
107: Column t_77 had a completion rate of 50.00% and was removed.
108: Row PP_12 had a completion rate of 50.75% and was removed.
109: Row PP_63 had a completion rate of 52.24% and was removed.
110: Column t_69 had a completion rate of 52.00% and was removed.
111: Column t_76 had a completion rate of 52.00% and was removed.
112: Column t_78 had a completion rate of 52.00% and was removed.
113: Column t_8 had a completion rate of 54.00% and was removed.
114: All row- and column means are over 55%. The final matrix has 50 rows and 63 columns.

And re-assess the data availability.

PIDout <- gsub("PP_", "", dtS3RedInfo$rowNamesOut) %>% as.numeric
TIDout <- gsub("t_", "", dtS3RedInfo$colNamesOut) %>% as.numeric
TIDInRed <- gsub("t_", "", dtS3RedInfo$colNamesIn) %>% as.numeric
TIDIn <- seq(min(TIDInRed), max(TIDInRed), 1)

dtS3Red <- dt_raw %>%
  filter(study == "S3") %>%
  filter(
    !PID %in% PIDout,
    TIDnum %in% TIDIn
  ) %>% 
  mutate(TIDnum = TIDnum - min(TIDnum))
rm(PIDout, TIDout, TIDInRed, TIDIn)

dtS3AvailabilityRedKbl <- dtS3RedInfo$reducedMatrix
dtS3AvailabilityRedKbl[1:ncol(dtS3AvailabilityRedKbl)] <- lapply(dtS3AvailabilityRedKbl[1:ncol(dtS3AvailabilityRedKbl)], function(x) {
    cell_spec(x,
              bold = FALSE,
              color = "white",
              background = ifelse(x == 1, "green", "red")
              )
})
kbl(
  dtS3AvailabilityRedKbl,
  format = "html",
  escape = FALSE,
  align = "c",
  booktabs = TRUE,
  caption = "Study 3: Final Data Availability" # complete caption for main document
) %>%
  kable_classic(
    full_width = FALSE,
    lightable_options = "hover",
    html_font = "Cambria"
  ) %>%
  scroll_box(width = "100%", height = "500px")
Study 3: Final Data Availability
t_7 t_9 t_10 t_11 t_12 t_13 t_14 t_15 t_16 t_17 t_18 t_19 t_20 t_21 t_22 t_23 t_24 t_25 t_26 t_27 t_28 t_29 t_30 t_31 t_32 t_33 t_34 t_35 t_36 t_37 t_38 t_39 t_40 t_41 t_42 t_43 t_44 t_45 t_46 t_47 t_48 t_49 t_50 t_51 t_52 t_53 t_54 t_55 t_56 t_57 t_58 t_59 t_60 t_61 t_62 t_63 t_64 t_65 t_66 t_67 t_68 t_71 t_73
PP_2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_3 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_4 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
PP_7 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 1 1 0 1 1 1 0 0
PP_9 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 0 1 0 1 0 1 0 1 1 1 1 0 0 1 0 0 1 0 1 0 1 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1
PP_10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1
PP_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 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0
PP_14 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 0 0 1 0 1 0 1 0 1 1 1 0 0 0 1 1 1 0 1 0 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1
PP_15 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1
PP_16 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1
PP_17 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1
PP_19 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 0 0 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0
PP_20 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1
PP_21 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0
PP_22 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0
PP_23 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1
PP_24 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_25 1 1 0 1 1 0 0 1 1 1 0 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 0 0 1 1
PP_26 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 0 0 1 0 1 1 0 1 1 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 1 1
PP_27 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_28 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 0 1 1 1 0 1 1 1 0 0 0 0 1 1 1 1 1 0 1 0 0 0
PP_29 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 0 1 0 1 0 0
PP_30 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1
PP_32 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 0 0 0 0
PP_33 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0
PP_34 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_37 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 0 1 0 1 0 1 1 1 0 1 1 1 0 0 1 1
PP_40 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_42 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 1 1 1 0 0 1 1 0 1 1 1 1 1 0 0 0 1
PP_43 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1
PP_44 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1
PP_48 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_49 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
PP_50 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
PP_51 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0
PP_52 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 1 0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 0 1 1
PP_55 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 1
PP_56 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 0 0 0
PP_57 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0
PP_58 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1
PP_59 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1
PP_60 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 1 0 0 1 1 1 1 0 0 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 0 0 1 1
PP_61 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0
PP_62 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0
PP_64 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PP_65 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0
PP_66 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1
PP_67 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0
PP_70 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 0 1 1 1 1 1 1 0 0
PP_71 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 0 0 1 1 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 0 0 1 1 0 1 1 0 0 0 0

We then assess the missingness within three adjusted datasets by comparing how many measurements, participants, and timepoints were removed for each study. In general, we also find that the number of time points are now much more comparable (\(max(t_{S1})\) = 61, \(max(t_{S2})\) = 60, \(max(t_{S3})\) = 67).

source("scripts/missInfo.R")

missS1 <- missInfo(full = dt_raw %>% filter(study == "S1"), reduced = dtS1Red)
missS2 <- missInfo(full = dt_raw %>% filter(study == "S2"), reduced = dtS2Red)
missS3 <- missInfo(full = dt_raw %>% filter(study == "S3"), reduced = dtS3Red)

missS123 <- rbind(
  missS1,
  missS2,
  missS3
) %>%
  mutate(study = c(1, 2, 3)) %>%
  select(study, everything())

missS123 %>%
  kbl(.,
      escape = FALSE,
      format = "html",
      booktabs = TRUE,
      align = "c", #c("l", rep("c", ncol(.)-1)),
      col.names = c("Study", rep(c("Full","Reduced","&Delta;", "%"), 3)),
      digits = 2,
      caption = "Missingness Info by Study") %>%
  add_header_above(c(" " = 1, "Measurements" = 4, "Participants" = 4, "Timepoints" = 4)) %>%
  kable_classic(
    full_width = F,
    lightable_options = "hover",
    html_font = "Cambria"
  )
Missingness Info by Study
Measurements
Participants
Timepoints
Study Full Reduced Δ % Full Reduced Δ % Full Reduced Δ %
1 1225 1204 21 1.71 23 21 2 8.70 63 61 2 3.17
2 4965 4218 747 15.05 113 86 27 23.89 69 60 9 13.04
3 4107 2710 1397 34.02 71 50 21 29.58 155 67 88 56.77

Data Descriptives

To get a better understanding of the resulting dataset, we as look at the variable descriptives and inter-variable relationships. We combine the harmonized datasets into a single data frame dtAll and use our custom MlCorMat function to create a multilevel correlation and descriptives object: allMlCor.

dtAll <- rbind(
  dtS1Red %>% select(-c("created", "ended")) %>% mutate(study = "S1") %>%
    mutate(across(!TID & !study, as.numeric)),
  dtS2Red %>% select(-c("created", "ended")) %>% mutate(study = "S2"),
  dtS3Red %>% select(-c("created", "ended")) %>% mutate(study = "S3")
) %>%
  group_by(study, PID) %>%
  mutate(ID = cur_group_id()) %>%
  ungroup %>%
  mutate(date = as.Date(gsub(" .*", "", TID)),
         week = strftime(date, format = "%Y-W%V")) %>%
  select(ID,
         PID,
         study,
         date,
         week,
         TIDnum,
         everything()) %>%
  arrange(ID, TIDnum) %>%
  select_if(~ sum(!is.na(.)) > 1) %>% # only include variables that have any data (i.e., not all NA)
  as.data.frame

source("scripts/MlCorMat.R")
allMlCor <-
  MlCorMat(
    data = dtAll,
    id = "ID",
    selection = var_meta$name[var_meta$cluster == 1],
    labels = var_meta$label[var_meta$cluster == 1]
  )

We can then use the kableExtra package (Zhu, 2024) to display the descriptives table, which includes between-participant and within-participant bi-variate relationship information (correlations) as well as general descriptive statistics (mean, sd, ICC).

allMlCor %>%
  as.data.frame %>%
  kbl(.,
      format = "html",
      caption = "Correlation Table and Descriptive Statistics",
      booktabs = TRUE,
      align = "c", #c("l", rep("c", ncol(.) - 1)),
      escape = FALSE,
      label = "descrLong",
      col.names = c(
        #"",
        "Int: Accidental",
        "Int: Voluntary", 
        "Int: Cooperative",
        "Int: Representative",
        "Int: Meaningful",
        "Int: Quality", 
        "Int: Need Fulfil.", 
        "Int: Need Fulfil. Partner", 
        "Int: Attitude Partner",
        "Daytime Core Need",
        "Outgroup Attitude",
        "Well-being"
      )) %>%
  kable_classic(
    full_width = FALSE,
    lightable_options = "hover",
    html_font = "Cambria"
  ) %>%
  pack_rows("Correlations", 1, ncol(allMlCor)) %>%
  pack_rows("Descriptives", ncol(allMlCor)+1, nrow(allMlCor)) %>%
  footnote(
    general = c(
      '"Int." = interaction.',
      'Upper triangle: Between-person correlations;',
      'Lower triangle: Within-person correlations;',
      '*** p < .001, ** p < .01,  * p < .05'
    )
  )
Correlation Table and Descriptive Statistics
Int: Accidental Int: Voluntary Int: Cooperative Int: Representative Int: Meaningful Int: Quality Int: Need Fulfil. Int: Need Fulfil. Partner Int: Attitude Partner Daytime Core Need Outgroup Attitude Well-being
Correlations
Int: Need Fulfillment 0.65*** -0.35*** 0.17 0.42*** -0.08 0.41*** 0.08 0.64*** 0.15 0.09 0.31***
Int: Need Fulfillment Partner 0.52*** -0.23* 0.14 0.52*** -0.06 0.33*** 0.17 0.52*** 0.12 0.13 0.08
Int: Accidental -0.08*** 0.08*** -0.18 -0.20 0.05 -0.09 -0.09 -0.25* -0.01 -0.04 0.18
Int: Voluntary -0.11*** 0.00 0.17*** 0.61*** -0.06 0.40*** 0.09 0.08 0.22* 0.40*** -0.05
Int: Cooperative 0.18*** 0.07** 0.20*** -0.06** 0.17 0.66*** 0.36*** 0.25* 0.25** 0.45*** -0.07
Int: Representative 0.20*** 0.12*** -0.19*** 0.11*** -0.03 0.13 0.09 -0.02 0.41*** 0.13 -0.03
Int: Quality -0.15*** 0.32*** 0.21*** 0.17*** 0.14*** 0.23*** 0.65*** 0.23* 0.23* 0.63*** 0.10
Int: Meaningful 0.27*** 0.32*** 0.28*** 0.02 0.16*** 0.26*** 0.58*** 0.08 -0.03 0.43*** -0.05
Need Fulfillment 0.33*** -0.09*** 0.00 0.16*** 0.14*** -0.05 0.41*** 0.13*** 0.07 -0.11 0.15
Outgroup Attitude -0.14*** 0.32*** 0.44*** 0.15*** 0.31*** 0.30*** 0.09*** -0.05* -0.03 0.56*** 0.25**
Int: Partner Attitude 0.28*** 0.39*** 0.15*** 0.19*** 0.20*** 0.30*** 0.37*** 0.16*** 0.32*** 0.19*** 0.11
Well-Being 0.10*** 0.06** 0.14*** 0.21*** 0.09*** 0.03 0.15*** 0.16*** 0.22*** 0.24*** 0.26***
Descriptives
Grand Mean 85.42 78.52 39.10 80.08 79.55 64.65 79.85 61.16 76.48 66.84 80.59 74.82
Between SD 16.01 21.53 31.14 20.61 18.41 21.12 17.05 24.62 21.63 18.54 16.33 15.97
Within SD 18.63 20.02 28.72 19.27 17.43 19.92 16.37 22.32 22.26 9.45 15.81 12.86
ICC(1) 0.18 0.26 0.21 0.29 0.27 0.35 0.25 0.31 0.20 0.77 0.25 0.52
ICC(2) 0.91 0.92 0.90 0.93 0.93 0.89 0.92 0.94 0.92 0.99 0.91 0.98
Note:
"Int." = interaction.
Upper triangle: Between-person correlations;
Lower triangle: Within-person correlations;
*** p < .001, ** p < .01, * p < .05

Prepared with a cleaned and harmonized set of data we can move on to the feature extraction step.

References

Kreienkamp, J., Bringmann, L. F., Engler, R. F., Jonge, P. de, & Epstude, K. (2023). The Migration Experience: A Conceptual Framework and Systematic Scoping Review of Psychological Acculturation. Personality and Social Psychology Review. https://doi.org/10.1177/10888683231183479
Liao, T. W. (2005). Clustering of time series data—a survey. Pattern Recognition, 38(11), 1857–1874. https://doi.org/10.1016/j.patcog.2005.01.025
Madley-Dowd, P., Hughes, R., Tilling, K., & Heron, J. (2019). The proportion of missing data should not be used to guide decisions on multiple imputation. Journal of Clinical Epidemiology, 110, 63–73. https://doi.org/10.1016/j.jclinepi.2019.02.016
Zhu, H. (2024). kableExtra: Construct complex table with kable and pipe syntax. https://CRAN.R-project.org/package=kableExtra