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QCA.R
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#' ---
#' title: "Appendix C: Results from Qualitative Comparative Analyses"
#' author: "Riley M. Smith"
#' date: "`r format(Sys.Date(), '%d %B %Y')`"
#' ---
#'
#+ setup, echo=FALSE, results='hide', message=FALSE, warning=FALSE, cache=FALSE
# SETUP ----------------
source("../SETUP.R") ## SEE EccRiley.github.io/Rrscs/SETUP.R ##
options(width = 60)
knitr::opts_chunk$set(
tidy = TRUE,
echo = TRUE,
fig.keep = 'high',
fig.show = 'asis',
results = 'asis',
tidy.opts = list(comment = FALSE, blank = FALSE),
# echoRule = NULL,
# echoRuleb = NULL,
# fig.height = 5,
fig.path = "graphics/bibs/rplot-",
fignos = TRUE,
dev = c('png', 'svg'),
fig.retina = 6,
Rplot = TRUE,
Rplot_whbg = NULL,
hideCap = FALSE,
figPath = FALSE,
fpath = NULL)
op <- par(no.readonly = TRUE) ## current values for settable plotting params (see "?par") ##
pmar <- par(mar = c(5, 4, 4, 2))
sci <- colorRampPalette(pal_sci[c(1, 2, 4, 5, 8)])
# panderOptions("table.emphasize.rownames", "FALSE")
# rpm()
knitr::opts_template$set(invisible = list(echo = FALSE, results = 'hide', message = FALSE, warning = FALSE, cache = FALSE, fig.keep = 'none', fig.show = 'none'))
hideLabs <- function(x, labs) {
if (knitr::opts_chunk$get("hideCap") == FALSE) {
x + labs
} else {
x
}
}
## FOR DOCX/HTML OUTPUT ##
knit_hooks$set(plot = function(x, options) {
if (!is.null(options$fig.lab)) {
paste(', paste(x, collapse = '.'),
'){#fig:', options$fig.lab, "}",
sep = '')
}
})
#'
#' -----
#'
#' # Systematic Database Search
#'
#' Six separate literature searches were conducted using the [_PsycINFO_](http://www.apa.org/pubs/databases/psycinfo/) and [_Web of Science_](http://wokinfo.com) online citation indexing databases via the [Portland State University library website](library.pdx.edu)^[Note that (1) "intimate partner violence" included "domestic violence" and "partner abuse", (2) "same-sex" included "same-gender", and (3) the results ranges provided after each search description listed reflect the minimum and maximum number of results returned across the two databases searched.]:
#'
#'
#' 1. _**IPV - General**_
#' 2. --- _IPV Interventions_
#' 3. --- _IPV Intervention Evaluations_
#' 4. _**Female Same-Sex/Same-Gender IPV (FSSIPV) - General**_
#' 5. --- _FSSIPV Interventions_
#' 6. --- _FSSIPV Intervention Evaluations_
#'
#+ src_dbsrch, opts.label='invisible'
# dbsrch ----------------
source("dbsrch.R")
#'
#+ dbsrch, results='asis',echo=FALSE
# pander(dbsrch, justify = c("right", "left", "centre"), caption = "Descriptions of database searches conducted with corresponding ranges of the number of results returned")
library(kableExtra)
kable(dbsrch, caption = "Descriptions of database searches conducted with corresponding ranges of the number of results returned", justify = c("r", "l", "l")) %>%
add_footnote(c("Note: For each database search, multiple search terms were included for the subject/keywords parameters to represent intimate partner violence", "'PI' = PsycINFO; 'WoS' = Web of Science", threeparttable = TRUE))
#'
#' # Community Psychology Publications & Closely-Related Violence-Specific Publications
#'
#+ src_journals, opts.label='invisible'
# JOURNALS ----------------
source("journals.R")
#'
#' **Community-psychology journals.** included in database searches:
#'
#+ journals, results='asis', echo=FALSE
j.cpp %>% as.list() %>% pander()
#'
#' **Violence-specific journals.** selected for inclusion in database searches.
#'
#+ journals2, results='asis', echo=FALSE
j.vp %>% as.list() %>% pander()
#'
#' # Results of Systematic Database Searches:
#'
#+ bibdf
### FUN - 'Rbibkeys()' ####
Rbibkeys <- function(bib) {
keys <- bib[grep("@.*?\\{.*?,", bib, perl = TRUE)]
keys <- gsub("@\\w+\\{(.*?)", "\\1", keys, perl = TRUE)
keys <- keys[!grepl("%.*?,", keys, perl = TRUE)]
keys <- gsub(" ", NA_character_, keys)
keys <- gsub(",", "", keys)
return(keys)
}
# BIB ----------------
bib <- readLines("MAP.bib")
BIBKEY <- Rbibkeys(bib)
library(bib2df)
bibdf <- bib2df("MAP.bib")
### n.init ####
n.init <- nrow(bibdf)
ID <- seq(1:nrow(bibdf))
MAP.au <- cbind(BIBKEY, bibdf[, "AUTHOR"]) ## bibdf[,2] ##
#'
#+ MAP1
# MAP1 ----------------
MAP1 <- cbind(ID,
BIBKEY,
bibdf[, c("YEAR", "TITLE", "JOURNAL", "ABSTRACT")]) %>%
as.data.frame() ## bibdf[c(3:5, 8)] ##
names(MAP1)[-1] <- tolower(names(MAP1)[-1])
# KEYSv1 ----------------
KEYSv0 <- as.character(MAP1$bibkey)
#'
#+ src_MAPrqda, opts.label='invisible'
# MAP-RQDA ----------------
source("MAPrqda.R", echo = FALSE)
#'
#+ csid, results='asis'
csid <- caseids[, c("caseid", "case", "RM", "scat")] ## caseids[, -3] ##
csid$case <- factor(csid$case)
# MAP2 ----------------
MAP2 <- merge(MAP1, csid, by.x = "bibkey", by.y = "case")
n.inits3 <- MAP2[MAP2$scat == "S3", ] %>% nrow()
n.inits4 <- MAP2[MAP2$scat == "S4", ] %>% nrow()
#'
#+ ctblz1
# ctbl.z1 ----------------
ctbl.z1 <- merge(ctbl.z1, cbk, all.x = TRUE, all.y = FALSE) ## from MAPrqda.R ##
ctbl.z1$clab <- factor(ctbl.z1$clab) %>% droplevels()
t.excl <- Rtdf(ctbl.z1$clab, names = c("Reason for Exclusion", "$N_{excluded~articles}$"))
v1v2 <- paste0("@", as.character(ctbl.z1[, "case"]))
#'
#' `r tufte::newthought(paste0("$N = ", length(v1v2), "$ items excluded "))` after restricting results to only _U.S.-based empirical_ studies.
#'
#+ texcl, echo=FALSE
# v1v2 %>% sort() %>% as.list() %>% pander()
t.excl %>% kable(caption = "Number of Articles Removed per Exclusion Criteria",
align = c('l', 'r'))
#'
#'
#+ MAP2rm
# MAP2$RM ----------------
MAP2$RM <- ifelse(MAP2$RM == 1, NA, 0)
MAP2rm <- MAP2[is.na(MAP2$RM), "bibkey"] %>% droplevels() %>% as.character()
MAP2 <- na.omit(MAP2)
MAP2 <- droplevels(MAP2)
KEYSv1 <- as.character(MAP2$bibkey)
#'
#+ MAP_CPV
# MAP-CPV ----------------
MAP2$journal <- sapply(MAP2$journal, tolower)
MAP2$journal <- gsub(" & ", " and ", MAP2$journal)
cp <- MAP2$journal %in% j.cp
map.cp <- MAP2[cp, ] %>% data.frame()
vlc <- MAP2$journal %in% j.v
map.v <- MAP2[vlc, ] %>% data.frame()
map.v <- map.v[map.v$scat == "S3", , drop = FALSE]
map.v$journal <- sapply(map.v$journal, RtCap)
j.cpv <- c(j.v, j.cp)
cpv <- MAP2$journal %in% j.cpv
MAP3 <- MAP2[cpv, ] %>% data.frame()
## !!! 2017-07-14: DECIDED TO FILTER BASED ON TOPIC CODES RATHER THAN JOURNAL CATEGORY (DUH! SHOULDA DONE THAT TO BEGIN WITH!!) !!! ##
##
# map.cpv$rms4 <- ifelse(map.cpv$journal %in% j.v & map.cpv$scat == "S4", NA, map.cpv$scat)
# map.cpv <- na.omit(map.cpv)
# MAP3 <- MAP2[vlc | cp, ] %>% data.frame()
MAP3$bibkey <- as.character(MAP3$bibkey)
# KEYSv2 ----------------
KEYSv2 <- as.character(MAP3$bibkey)
v1v2 <- KEYSv1[!KEYSv1 %in% KEYSv2]
# v1v2 <- paste0("@", as.character(v1v2))
# v1v2 %>% as.list() %>% pander()
#' `r tufte::newthought(paste0("$N = ", length(v1v2), "$ items excluded after restricting search results"))` to only those published in community-psychology specific journals or the _four selected_ violence-related journals (i.e., `r paste0("_", j.vp[1:(length(j.vp)-1)], "_, ")` and `r paste0("_", j.vp[length(j.vp)], "_")`.
#'
# MAP3$rms4 <- ifelse(MAP3$journal %in% j.v & MAP3$scat == "S4", NA, MAP3$scat)
# MAP <- na.omit(MAP3)
# KEYSv3 - FINAL ----------------
MAP <- MAP3[MAP3$bibkey %in% keys_tpFilter, ] %>% droplevels()
KEYSv3 <- as.character(MAP$bibkey)
v2v3 <- KEYSv2[!KEYSv2 %in% KEYSv3]
#'
#' `r tufte::newthought(paste0("$N = ", length(v2v3), "$ items excluded after restricting "))` After having the substantive focus results to only include items directly applicable or relevant to IPV interventions and intervention research (e.g., program evaluation research, measures, evaluation research methods, etc.).
#'
#+ v2v3_cat
# v2v3 %>% as.list() %>% pander()
#'
#+ tempPanderOpts,echo=FALSE
#, opts.label="invisible"
panderOptions("p.wrap", "")
panderOptions("p.sep", "; ")
panderOptions("p.copula", "; ")
#' `r tufte::newthought(paste0("$N = ", length(KEYSv3), "$ items included in the formal literature review"))`:
#'
#' `r paste0("@", KEYSv3) %>% as.list %>% pander()`
#'
#'
#+ resetPanderOpts,echo=FALSE
#, opts.label="invisible"
panderOptions("p.wrap", "_")
panderOptions("p.sep", ", ")
panderOptions("p.copula", ", & ")
#'
#'
#'
#+ map_jrnl
## map-jrnl ================
MAP <- merge(MAP, jdat, by = "journal", all.x = TRUE)
MAP$journal <- factor(MAP$journal)
levels(MAP$journal) <- sapply(levels(MAP$journal), RtCap)
MAP$jrnl <- sapply(as.character(MAP$journal), Rabbr)
#'
#'
#'
#' # Systematically-Reviewed Literature
#'
#'
#'
#' **The resulting selection of empirical literature.**, representing a community-psychology-focused subset of the U.S.-based IPV-related literature, was reviewed using a `primarily deductive` _qualitative comparative analytic approach_ [_QCA_; @leech2007array; @onwuegbuzie2017framework]. This approach was conducted as part of an initial data reduction and organization process in which the reviewed literature was categorized according to the commonalities in overarching research topics, target populations, sampling frames, sampling and data collection methodologies, and data analytic approaches. In addition, the QCA approach served as a systematic method for examining the similarities, differences, and anomalies within the groups identified in the initial data reduction and organization process [@onwuegbuzie2017framework; @onwuegbuzie2009qualitative]. The qualitative comparative analysis of the reviewed literature was aided by the _`RQDA`_ package created for use with the _`R` Statistical Programming Language and Environment_ [@R-RQDA; @R-base].
#'
#+ cb
# CTBL/CB ----------------
rec.key2scat <- paste0("\"", MAP$bibkey, "\" = \"", MAP$scat, "\"", collapse = "; ")
cb <- merge(MAP, ctbl, by = c("caseid", "scat"))
cb <- within(cb, {
# journal <- droplevels(journal)
jrnl <- sapply(as.character(journal), Rabbr)
code <- gsub("FG-\\w+", "FG", code)
# code <- gsub("EXP-\w+", "EXP", code)
# code <- gsub("LT-\w+", "LT", code)
code <- gsub("SVY-QL-MM", "SVY-QL", code)
code <- gsub("SVY-QT-MM", "SVY-QT", code)
# code <- gsub("XS-\w+", "XS", code)
code <- gsub("IVW-\\w+", "IVW", code)
code <- gsub("SMIN-\\w+", NA, code)
code <- gsub("HET", NA, code)
scat <- factor(scat, labels = c("IPV Interventions", "SMW-Specific Research"))
})
cb <- na.omit(cb) %>% droplevels()
cb <- cb[, c("caseid", "scat", "journal", "bibkey", "year", "RM", "j.loc", "j.year", "SJR", "Hindex", "jrnl", "case", "cid", "code", "catid", "cat")]
# cbk$clab <- ifelse(cbk$code %in% cb$code, cbk$clab, NA)
# cbk <- na.omit(cbk) %>% droplevels()
cb <- merge(cb, cbk, by = "code")
cb$code <- factor(cb$code)
cb$clab <- factor(cb$clab)
#'
#'
#'
#'
#' # Codebook
#'
#+ cdbk
catt <- Rtdf(cdbk$catlab, names = c("Information Category", "$N_{sub-codes}$"))
cdbk.ft1 <- with(cdbk, {
ftable(clab, catlab) %>% data.frame()
})
cdbk.ft1$Freq <- ifelse(cdbk.ft1$Freq == 0, NA, cdbk.ft1$Freq)
## filter out unused codes ##
cdbk.ft <- na.omit(cdbk.ft1)[, 1:2] ## rm "Freq" column after filtering, and other oclumns not needed right here ##
rownames(cdbk.ft) <- NULL
cdbk.ft$catlab <- as.character(cdbk.ft$catlab)
cdbk.ft$catlab <- ifelse(duplicated(cdbk.ft$catlab), NA, paste0("**", cdbk.ft$catlab, "**"))
cdbk.ft <- cdbk.ft[, c("catlab", "clab"), drop = FALSE]
#'
#'
#'
#+ echo=FALSE
kable(cdbk.ft, col.names = c("**Information Category**", "Codes"), caption = "Codebook Constructed from the Discrete Summative Data Compiled Across the Formally Reviewed Literature", align = c("r", "l"))
#'
#'
#'
#' # General Research Categories
#'
#'
#+ scat
### catpal ####
catpal <- c(pal_my[16], pal_sci[8])
# DESCRIPTIVES ----------------
## search categories ================
### [MAP-abstracts-S3.csv] ####
cpv.s3 <- MAP[MAP$scat == "S3", ]; ## write.csv(cpv.s3[order(cpv.s3$year), c("bibkey", "year", "title", "journal", "abstract")], "data/MAP-abstracts-S3.csv", row.names = FALSE)
### [MAP-abstracts-S4.csv] ####
cpv.s4 <- MAP[MAP$scat == "S4", ]; ## write.csv(cpv.s4[order(cpv.s4$year), c("bibkey", "year", "title", "journal", "abstract")], "data/MAP-abstracts-S4.csv", row.names = FALSE)
ct.scat <- within(MAP, {
scat <- ifelse(scat == "S3", "IPV Interventions", "SMW-Specific Research")
})
t.scat <- Rtdf(ct.scat$scat, names = c("Category", "N"))
## "ct.scat" created in "MAPrqda.R" ##
t.scat %>% kable(caption = "Count of Articles in Each Research Category")
scat.t <- table(ct.scat$scat)
scat.bn <- Rbinom(scat.t)
scat.bn %>% kable(caption = "Binomial Test of the Difference in Search Category Proportions", col.names = c("Alternative", "Null Value ($\\pi_{0}$)", "Parameter", "Estimate", "$\\chi^{2}$", "$p$-value", "CI"), escape = FALSE)
N.MAP <- nrow(MAP)
options(Rperc_n = N.MAP)
t.scat$prop = t.scat$N / N.MAP
t.scat <- t.scat[order(t.scat$prop), ]
t.scat$ymax <- cumsum(t.scat$prop)
t.scat$ymin <- c(0, head(t.scat$ymax, n = -1))
t.scat$perc <- paste0((round(t.scat$prop, digits = 1)*100), "%")
# t.scat$labs <- paste("n = ", t.scat$N, " (", t.scat$perc, ")", sep = "")
#'
#+ donut_scat, fig.cap="Proportions of reviewed articles in each of the two overarching research categories: IPV interventions research, and SMW-Specific IPV research", fig.lab="donut_scat", fig.fullwidth=TRUE
### PLOT - scat - donut ####
library(ggplot2)
scat.p <- ggplot(t.scat, aes(fill = Category,
ymax = ymax,
ymin = ymin,
xmax = 4,
xmin = 3)) +
scale_fill_manual(values = catpal,
breaks = rev(levels(t.scat$Category)),
labels = paste0(t.scat$Category, " (n = ", t.scat$N, ")")
) +
geom_rect() +
coord_polar(theta = "y") +
xlim(c(0, 4)) +
annotate("text", x = 0, y = 0,
label = "Category Proportions",
family = "serif", size = 5,
fontface = "italic") +
geom_text(aes(x = 3.5, y = c(0.1, 0.60),
label = t.scat$perc, #angle = c(-35, -35),
fontface = "bold"),
colour = pal_my[2], size = 5.5,
family = "serif", vjust = 0.3) +
# labs(title = "", fill = "Research Category") +
thm_Rtft(ytitle = FALSE, xtitle = FALSE) +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
legend.text = element_text(size = rel(1)),
legend.key.width = unit(0.5, "cm"),
legend.key.height = unit(0.5, "cm"))
scat.p
#'
#'
#'
#'
#' # Publication Titles
#'
#'
#'
#+ pub_titles
## publication titles ================
t.jrnl <- Rtdf(MAP$journal)
ft.jrnl <- with(MAP, {
ftable(journal, scat) %>%
matrix(nrow = nrow(t.jrnl),
byrow = FALSE)
})
dimnames(ft.jrnl) <- list("Publication Title" = levels(MAP$journal),
Category = levels(MAP$scat))
ft.jrnl <- ifelse(ft.jrnl == 0, NA, ft.jrnl)
ft.jrnl %>% kable(caption = "Number of Publications in Each Research Category per Journal",
align = c("l", "r", "r"))
cpv.s3$jrnl <- sapply(as.character(cpv.s3$journal), Rabbr) %>% factor()
cpv.s4$jrnl <- sapply(as.character(cpv.s4$journal), Rabbr) %>% factor()
j.cp <- sapply(j.cp, Rabbr, USE.NAMES = FALSE)
cb$cpv <- ifelse(cb$jrnl %in% j.cp, "CP", "V")
j.v <- sapply(j.v, Rabbr, USE.NAMES = FALSE)
pr.jv <- length(j.v)/length(j.cp)
pr.jcp <- 1 - pr.jv
pr.j <- c(pr.jv, pr.jcp)
MAP$cpv <- ifelse(MAP$jrnl %in% j.cp, "CP", "V")
# cpv.bn1 <- table(MAP$cpv) %>% Rbinom()
### H1: unequal proportions (H0: pi_0 = 0.5) ##
# cpv.bn2 <- table(MAP$cpv) %>% rev() %>% Rbinom(pi0 = pr.jv)
## H1: V is less than CP (based on N_{journals}
## per V & CP included in db searches;
### H0: pi_0 = 0.129) ##
# cpv.bn1 %>% pander(caption = "Binomial Test of $N_{articles}$ per Journal Category (Violence vs. Community Psychology ($\pi_{0} = 0.5$).")
# cpv.bn2 %>% pander(caption = paste0("Binomial Test of $N_{articles}$ per Journal Category (Violence vs. Community Psychology [$\pi_{0} = ", round(pr.jv, 3), "$ (based on proportion of $N_{journals}$ per journal category included in database searches; $n_{journals_{V}} = ", length(j.v), "$; $n_{journals_{CP}} = ", length(j.cp), "$)]."))
#'
#'
#'
#' # Research Category by Journal & Journal Category
#'
#+ dot_scatXjournal
### PLOT - scat-x-journal - dotchart ####
ftm.j <- Rna(ft.jrnl)
sum.j <- apply(ftm.j, 1, sum)
ftm.j <- ifelse(ftm.j == 0, NA, ftm.j)
ftm.jp <- cbind(ft.jrnl, "**Total**" = paste0("**", sum.j, "**"))
ftm.jp %>% kable(align = rep("r", ncol(ftm.jp)),
caption = "$N_{articles}$ in Each Research Category per Journal")
Rdotchart(main = expression(paste(italic(N[Articles]), italic(" per Publication"))),
ftm.j,
pch = 19,
gcolor = pal_my[20],
xlab = expression(N[Articles]),
cex = 0.7,
gcex = 0.75,
gfont = 2,
pt.cex = 1.125,
color = c(rep(catpal[1], nrow(ftm.j)), rep(catpal[2], nrow(ftm.j))),
xaxt = 'n'
); axis(1, at = seq(range(ftm.j, na.rm = TRUE)[1],
range(ftm.j, na.rm = TRUE)[2], by = 3))
#+ parset_scatXjournal, fig.fullwidth=TRUE
### PLOT - scat-x-journal - parset ####
MAP.jrnl <- MAP[, c("scat", "journal", "jrnl")]
names(MAP.jrnl) <- c("Category", "J", "Journal")
MAP.jrnl$Category <- ifelse(MAP.jrnl$Category == "S3",
"IPV Interventions",
"SMW-Specific Research")
pj <- mpal(seq_along(unique(MAP$jrnl)), p = ppal)
library(ggparallel)
pscat <- c("#a6afbb", pal_my[17])
pscat.a <- adjustcolor(pscat, alpha.f = 0.5)
al.j <- c(rep(90, length(unique(MAP.jrnl$Journal))),
rep(0, length(unique(MAP.jrnl$Category))))
t.jrnl$log <- log(t.jrnl[, 2]) + 3
lj <- c(t.jrnl[, 3], rep(3.50, length(unique(MAP.jrnl$Category))))
parset.jrnl <- ggparset2(list("Journal", "Category"),
data = MAP.jrnl,
method = "adj.angle", label = TRUE,
label.size = lj, text.angle = al.j, order = c(0, 0)) +
scale_fill_manual(values = c(pscat.a, adjustcolor(pj, alpha.f = 0.55)),
guide = FALSE) +
scale_colour_manual(values = c(pscat, pj), guide = FALSE) +
coord_flip() +
thm_Rtft(ticks = FALSE, ytext = TRUE, xtext = FALSE) #+ scale_y_discrete(labels = c("Category" = "Category", "J" = "Journal"))
hideLabs(x = parset.jrnl, labs = labs(subtitle = "Journals"))
MAP.jrnl <- MAP[, c("scat", "journal", "cpv", "jrnl")]
pcpv <- pp[c(11, 30)]
clr.cpv1 <- levels(factor(MAP.jrnl$journal))
clr.cpv <- ifelse(clr.cpv1 %in% j.cpp, pcpv[1], pcpv[2])
MAP.jrnl$cpv <- ifelse(MAP.jrnl$cpv == "CP", "Community Psychology",
"Violence-Specific")
MAP.jrnl$scat <- ifelse(MAP.jrnl$scat == "S3", "IPV Interventions",
"SMW-Specific Research")
names(MAP.jrnl) <- c("Category", "J", "Discipline", "Journal")
# al.cj <- c(rep(0, length(unique(MAP.jrnl$Category))), rep(90, length(unique(MAP.jrnl$Journal))), rep(0, length(unique(MAP.jrnl$Discipline))))
al.cj <- c(rep(90, length(unique(MAP.jrnl$J))),
rep(0, length(unique(MAP.jrnl$Category))),
rep(0, length(unique(MAP.jrnl$Discipline))))
ll.cj <- c(t.jrnl[, 3],
rep(3.50, length(unique(MAP.jrnl$Category))),
rep(3.50, length(unique(MAP.jrnl$Discipline))))
parset.jrnl2 <- ggparset2(list("Journal", "Category", "Discipline"),
data = MAP.jrnl, method = "adj.angle",
label = TRUE, label.size = ll.cj,
label.face = "bold",
text.angle = al.cj, order = c(0, 0, 0)) +
scale_fill_manual(values = c(pscat.a,
adjustcolor(pcpv, alpha.f = 0.75),
adjustcolor(clr.cpv, alpha.f = 0.55)),
guide = FALSE) +
scale_colour_manual(values = c(pscat.a,
adjustcolor(pcpv, alpha.f = 0.85),
adjustcolor(clr.cpv, alpha.f = 0.85)),
guide = FALSE) +
coord_flip() +
thm_Rtft(ticks = FALSE, ytext = TRUE, xtext = FALSE);
hideLabs(x = parset.jrnl2,
labs = labs(subtitle = "Journals & Research Disciplines by Research Category"))
#'
#'
#' # Publication Years
#'
#'
#+ hist_yrXscat, echo=FALSE, fig.fullwidth=TRUE, fig.cap="Publication Years Grouped by Research Category", fig.height = 4, figPath=TRUE
par(pmar)
### PLOT - year-X-scat - hist ####
s3hist <- hist(MAP$year[MAP$scat == "S3"], plot = FALSE, right = F, breaks = 25)
s4hist <- hist(MAP$year[MAP$scat == "S4"], plot = FALSE, right = F, breaks = s3hist$breaks)
## col = pal_my.a75[12], border = pal_my[19], lwd = .5
plot(s3hist, col = catpal[1], border = pal_my[19], density = 50, lwd = .25,
main = " ", xlab = "Year Published", ylab = expression(N[Articles]),
ylim = c(0, max(s3hist$counts)), yaxt = "n");
plot(s4hist, col = catpal[2], border = pal_my[19], density = 50, angle = -45, lwd = .25, yaxt = "n", add = TRUE);
axis(2, at = 0:max(s3hist$counts));
legend(x = 1990, y = 2.75, legend = c("SMW-Specific Research", "IPV Interventions Research (general)"), fill = c(pal_sci[8], pal_my[16]), density = 50, angle = c(45, -45), border = NA, bty = 'n', cex = 0.8, text.font = 3, trace = F)
#'
#'
#+ tl_map, fig.fullwidth=TRUE, fig.width=7, out.width='\\linewidth', fig.align='center', figPath=TRUE
# PLOT - MAPtl ----------------
MAP <- MAP[, c("bibkey", "year", "journal", "caseid", "scat", "jrnl", "cpv", "j.loc", "j.year", "SJR", "Hindex", "title")]
MAPtl <- within(MAP, {
## - making a copy so i don't mess up anything already
## written below that may depend on the original
## version of "MAP" ##
# bibkey2 <- as.integer(factor(bibkey))
bibkey2 <- ifelse(bibkey == "boal2014barriers", "boala2014barriers", bibkey)
bibkey2 <- ifelse(bibkey2 == "boal2014impact", "boalb2014impact", bibkey2)
bibkey2 <- gsub("(\\w+)(\\d{4})\\w+", "\\1 (\\2)", bibkey2)
bibkey2 <- ifelse(bibkey == "boala (2014)", "boal (2014a)", bibkey2)
bibkey2 <- ifelse(bibkey == "boalb (2014)", "boal (2014b)", bibkey2)
bibkey2 <- sapply(bibkey2, RtCap, USE.NAMES = FALSE)
yrv <- ifelse(cpv == "V", year, 0)
yrcp <- ifelse(cpv == "CP", year, 0)
cpv <- factor(cpv, labels = c("Community-Psychology", "Violence"))
})
MAPtl <- MAPtl[order(MAPtl$yrv), , drop = FALSE] %>% within({
posv <- sequence(rle(sort(yrv))$lengths)
posv <- ifelse(yrv == 0, 0, posv)
posv <- log(posv + 0.5) * -0.5
})
MAPtl <- MAPtl[order(MAPtl$yrcp), , drop = FALSE] %>% within({
poscp <- sequence(rle(sort(yrcp))$lengths)
pos <- ifelse(yrcp == 0, posv, log(poscp - 0.5) * -0.5)
# pos <- jitter(pos, amount = 0)
## could've achieved the same in the previous line,
## but wanted to preserve the separate pos* columns just in case ##
})
## !!!! THANKS TO THIS SO ANSWER FOR THE "sequence(rle())" solution:
## http://stackoverflow.com/a/19998876/5944560
## (i spent HOURS trying to figure out how to do this,
## only to find that the geniuses behind R
## [specifically the {utils} pkg] had already developed
## an effecient, vectorized, solution) ##
# grays_nord <- colorRampPalette(pal_nord$polar[c(8, 1)]) ## add to pkg::Riley (in "Rpals.R")##
vawa <- 1994 ## year original VAWA was passed ##
vawaclr <- grays_nord(12)[7]
# yrcnt <- Rtdf(MAPtl$year, names = c("year", "yrcnt"))#[, 1, drop == FALSE]
# as.integer(MAPtl$year) %>% min() -> yrmin
# as.integer(MAPtl$year) %>% max()+1 -> yrmax
# GGPLOT - tl ----------------
gg.tl <- ggplot(MAPtl, aes(x = year, y = 0, colour = cpv)) +
thm_Rtft(yticks = FALSE, ytext = FALSE, ytitle = FALSE, ltitle = TRUE,
ptitle = TRUE, xtext = FALSE, xticks = FALSE) +
theme(legend.text = element_text(size = rel(0.75), face = "italic"),
legend.title = element_text(size = rel(0.95), face = "bold.italic"),
legend.background = element_rect(fill = "transparent", size = 0.1, colour = pal_my[19]),
legend.key.height = unit(0.5, "cm"),
# legend.key = element_rect(size = 1),
legend.justification = c(1, 0.67),
legend.box.spacing = unit(0.1, "cm")) +
labs(colour = "Journal Category", title = "Timeline of Reviewed Research\n") +
scale_colour_manual(values = pcpv) + #, guide = FALSE) +
geom_hline(yintercept = 0, size = 0.25, color = pal_nord$polar[7], alpha = 0.5) +
geom_segment(aes(y = 0, yend = pos, x = year, xend = year),
colour = pal_my[19], alpha = 0.55,
na.rm = TRUE, size = 0.2, linetype = 3) +
geom_text(aes(y = pos, x = year, label = bibkey2), hjust = 0.5, vjust = 0,
angle = 25, size = 1.9, fontface = "bold", check_overlap = FALSE) +
# geom_vline(xintercept = vawa, size = 0.45, color = pal_nord$polar[1], linetype = 3) +
geom_text(aes(y = 0, x = vawa, label = "1994 Violence Against Women Act"),
alpha = 0.5, angle = 90, colour = adjustcolor(pal_my[19], alpha.f = 0.5), size = 2.75,
nudge_x = -0.25,
family = "serif", fontface = "italic") +
geom_text(aes(y = 0, x = year, label = year), check_overlap = TRUE,
vjust = 0.5, hjust = 0.5, angle = 0, colour = pal_my[20],
size = 2, fontface = "bold") +
xlim(min(MAPtl$year) - 0.25, max(MAPtl$year)) +
ylim(min(MAPtl$pos) - 0.15, max(MAPtl$pos) + 0.15)
gg.tl
#'
#'
#'
#' # Publication Dates & Journals
#'
#'
#'
#+ FUN_Rftm
### FUN - 'Rftm()' ####
Rftm <- function(x1, x2, dnn = NULL, zero.action = NA, zero.qt = FALSE) {
if (!is.null(dnn)) {
tx <- Rtdf(x1, names = dnn[[1]])
ftm <- ftable(x1, x2, row.vars = 1) %>%
matrix(nrow = nrow(tx), byrow = FALSE)
dimnames(ftm) <- list(levels(x1), dnn[[2]])
} else {
tx <- Rtdf(x1)
ftm <- ftable(x1, x2, row.vars = 1) %>%
matrix(nrow = nrow(tx), byrow = FALSE)
dimnames(ftm) <- list(levels(x1))
}
if (!is.null(zero.action)) {
if (zero.qt == 0 | zero.qt == 1) {
zero.qt <- as.logical(zero.qt)
}
if (zero.qt == TRUE) {
ftm <- ifelse(ftm == 0, quote(zero.action), ftm)
} else {
ftm <- ifelse(ftm == 0, noquote(zero.action), ftm)
}
}
y <- list(tx, ftm)
names(y) <- c(paste0("Tabulation of ", deparse(substitute(x1))),
paste0("Cross-Tabulation of ",
deparse(substitute(x1)),
" & ",
deparse(substitute(x2))))
return(y)
}
### recode cb$scat & cb$sclab ####
cb$scat2 <- factor(cb$scat, labels = c(1, 2))
cb$clab <- factor(cb$clab)
### s3cb ####
s3cb <- cb[cb$scat2 == 1, ] %>% droplevels
s3cb <- s3cb[!duplicated(s3cb), ]
s3cb.keys <- paste0("@", levels(factor(s3cb$bibkey)))
#'
#'
#+ tl_inv, fig.fullwidth=TRUE, fig.height=2.75, out.width='\\linewidth', figPath=TRUE
inv <- MAPtl[MAPtl$scat == "S3", ]
tl.inv <- inv[order(inv$year), c("bibkey", "year", "cpv", "journal", "title"), drop = FALSE] %>% droplevels()
tl.inv$bibkey <- paste0("@", tl.inv$bibkey)
tl.inv$journal <- paste0("_", tl.inv$journal, "_")
tl.inv <- dplyr::rename(tl.inv, "Study" = bibkey, "Journal" = journal, "Year Published" = year)
rownames(tl.inv) <- NULL
inv <- inv[order(inv$yrv), , drop = FALSE] %>% within({
posv <- sequence(rle(sort(yrv))$lengths)
posv <- ifelse(yrv == 0, 0, posv)
posv <- log(posv + 0.75) * -1
})
inv <- inv[order(inv$yrcp), , drop = FALSE] %>% within({
poscp <- sequence(rle(sort(yrcp))$lengths)
pos <- ifelse(yrcp == 0, posv, log(poscp - 0.5) * -1)
})
# GGPLOT - invtl ----------------
gg.invtl <- ggplot(inv, aes(x = year, y = 0, colour = cpv)) +
thm_Rtft(yticks = FALSE, ytext = FALSE, ytitle = FALSE, ltitle = TRUE,
ptitle = TRUE, xtext = FALSE, xticks = FALSE) +
theme(legend.text = element_text(size = rel(0.55)),
legend.title = element_text(size = rel(0.65), face = "bold"),
legend.justification = c(1, 0.8),
legend.box.spacing = unit(0, "cm")) +
# plot.margin = unit(c(1, rep(0.15, 3)), "cm")) +
ylim(min(inv$pos) - 0.5, max(inv$pos) + 0.5) +
labs(colour = "Journal Category", title = "IPV-Interventions Research Timeline") +
scale_colour_manual(values = pcpv) + #, guide = FALSE) +
geom_hline(yintercept = 0, size = 0.25, color = pal_nord$polar[7], alpha = 0.5) +
geom_segment(aes(y = 0, yend = pos, x = year, xend = year),
colour = pal_my[19], alpha = 0.55,
na.rm = TRUE, size = 0.2, linetype = 3) +
geom_text(aes(y = pos, x = year, label = bibkey2), hjust = 0.5, vjust = 1,
angle = 25, size = 2.5, fontface = "bold") + #, nudge_y = -0.05) +
# geom_vline(xintercept = vawa, size = 0.45, color = pal_nord$polar[1], linetype = 3) +
geom_text(aes(y = 0, x = vawa, label = "1994 Violence Against Women Act"),
alpha = 0.5, angle = 90, colour = vawaclr, size = 2.5,
nudge_x = -0.25, family = "serif", fontface = "italic") +
geom_text(aes(y = 0, x = year, label = year), check_overlap = TRUE,
vjust = 0.5, hjust = 0.5, angle = 0, colour = pal_my[20],
size = 2.5, family = "serif", fontface = "bold") +
xlim(min(inv$year) - 0.25, max(inv$year)) +
ylim(min(inv$pos) - 0.15, max(inv$pos) + 0.15)
gg.invtl
tl.inv[, c(1, 4, 2)] %>% kable(caption = "IPV Interventions Research Timeline")
#'
#'
#'
### s4cb ####
s4cb <- cb[cb$scat2 == 2, ] %>% droplevels
s4cb <- s4cb[!duplicated(s4cb), ]
s4cb.keys <- paste0("@", levels(factor(s4cb$bibkey)))
#'
#'
#+ tl_smw, fig.fullwidth=TRUE, fig.height=2.75, out.width='\\linewidth', figPath=TRUE
smw <- MAPtl[MAPtl$scat == "S4", ]
tl.smw <- smw[order(smw$year), c("bibkey", "year", "cpv", "journal", "title"), drop = FALSE] %>% droplevels()
tl.smw$bibkey <- paste0("@", tl.smw$bibkey)
tl.smw$journal <- paste0("_", tl.smw$journal, "_")
tl.smw <- dplyr::rename(tl.smw, "Study" = bibkey, "Journal" = journal, "Year Published" = year)
rownames(tl.smw) <- NULL
smw <- smw[order(smw$yrv), , drop = FALSE] %>% within({
posv <- sequence(rle(sort(yrv))$lengths)
posv <- ifelse(yrv == 0, 0, posv)
posv <- log(posv + 0.75) * -1
})
smw <- smw[order(smw$yrcp), , drop = FALSE] %>% within({
poscp <- sequence(rle(sort(yrcp))$lengths)
pos <- ifelse(yrcp == 0, posv, log(poscp - 0.5) * -1)
})
# GGPLOT - smwtl ----------------
gg.smwtl <- ggplot(smw, aes(x = year, y = 0, colour = cpv)) +
thm_Rtft(yticks = FALSE, ytext = FALSE, ytitle = FALSE, ltitle = TRUE,
ptitle = TRUE, xtext = FALSE, xticks = FALSE) +
theme(legend.text = element_text(size = rel(0.55)),
legend.title = element_text(size = rel(0.65), face = "bold"),
legend.justification = c(1, 0.8),
legend.box.spacing = unit(0, "cm")) +
# plot.margin = unit(c(1, rep(0.15, 3)), "cm")) +
ylim(min(smw$pos) - 0.5, max(smw$pos) + 0.5) +
labs(colour = "Journal Category", title = "IPV-Interventions Research Timeline") +
scale_colour_manual(values = pcpv) + #, guide = FALSE) +
geom_hline(yintercept = 0, size = 0.25, color = pal_nord$polar[7], alpha = 0.5) +
geom_segment(aes(y = 0, yend = pos, x = year, xend = year),
colour = pal_my[19], alpha = 0.55,
na.rm = TRUE, size = 0.2, linetype = 3) +
geom_text(aes(y = pos, x = year, label = bibkey2), hjust = 0.5, vjust = 1,
angle = 25, size = 2.5, fontface = "bold") + #, nudge_y = -0.05) +
# geom_vline(xintercept = vawa, size = 0.45, color = pal_nord$polar[1], linetype = 3) +
geom_text(aes(y = 0, x = vawa, label = "1994 Violence Against Women Act"),
alpha = 0.5, angle = 90, colour = vawaclr, size = 2.5,
nudge_x = -0.25, family = "serif", fontface = "italic") +
geom_text(aes(y = 0, x = year, label = year), check_overlap = TRUE,
vjust = 0.5, hjust = 0.5, angle = 0, colour = pal_my[20],
size = 2.5, family = "serif", fontface = "bold") +
xlim(min(smw$year) - 0.25, max(smw$year)) +
ylim(min(smw$pos) - 0.15, max(smw$pos) + 0.15)
gg.smwtl
tl.smw[, c(1, 4, 2)] %>% kable(caption = "SMW-Specific IPV Research Timeline")
#'
#'
#'
#' # Primary Topics
#'
#+ topics
## topics ================
codes.tp <- cb[cb$cat == "TOPIC", "clab"] %>% droplevels()
ctp.dnn <- c("Topic", "$N_{Articles}$")
scats.tp <- cb[cb$cat == "TOPIC", "scat"] %>% droplevels()
stp.dnn <- c("IPV Interventions", "SMW-Specific Research")
topics <- Rftm(codes.tp, scats.tp, dnn = list(ctp.dnn, stp.dnn))
t.tp <- topics[[1]]
ftm.tp <- topics[[2]]
ftm.tp2 <- Rna(ftm.tp)
sum.tp <- apply(ftm.tp2, 1, sum)
ftm.tpp <- cbind(ftm.tp, "**Total**" = paste0("**", sum.tp, "**"))
ftm.tpp %>% kable(align = rep("r", ncol(ftm.tpp)),
caption = "Research Topics")
#'
#+ dot_topics, fig.fullwidth=TRUE, fig.height = 7.5, figPath=TRUE
### PLOT - topics - dotchart ####
Rdotchart(main = "Research Topics",
ftm.tp,
pch = 19,
gcolor = pal_my[20],
xlab = expression(N[Articles]),
cex = 0.7,
gcex = 0.75,
gfont = 2,
pt.cex = 1.125,
color = c(rep(catpal[1], nrow(ftm.tp)), rep(catpal[2], nrow(ftm.tp))))
## xaxt = 'n'
#; axis(1, at = seq(range(ftm.tp, na.rm = TRUE)[1],
## range(ftm.tp, na.rm = TRUE)[2], by = 3))
#'
#'
#'
topcb <- cb[cb$cat == "TOPIC", ] %>% droplevels()
topcb <- topcb[!duplicated(topcb), ]
# x <- s4qt[, c("bibkey", "clab")] %>% ftable() %>% data.frame()
# x[, 3] %>% unique()
topcb.tly <- tally(group_by(topcb, clab))
lvl.top <- paste0(seq(1:nrow(topcb.tly)), " = ", levels(topcb$clab))
topcb$clab2 <- factor(topcb$clab, labels = seq(1:nrow(topcb.tly)))
# levels(topcb$clab) <- seq(1:length(unique(topcb$clab)))
ktop <- ftable(topcb$bibkey, topcb$clab2) %>% as.matrix ## "ks3" == "bibkeys - s3" ##
ktop <- ifelse(ktop >= 1, "✓", "·")
ktopscat <- car::recode(rownames(ktop), rec.key2scat)
ktopscat <- ifelse(ktopscat == "S4", paste0("@", rownames(ktop), "$^*$"), paste0("@", rownames(ktop)))
rownames(ktop) <- ktopscat
# rownames(ktop) <- paste0("@", rownames(ktop))
#'
#'
#'
#+ echo=FALSE
# panderOptions("table.split.table", 120)
kable(ktop[, 1:11], caption = "Primary Topics by Study (1/2)", col.names = c()) %>%
add_footnote("SMW-Specific", notation = "symbol")
pander(lvl.top[1:11])
#'
#'
#'
#+ echo=FALSE
kable(ktop[, 12:ncol(ktop)],
caption = "Primary Topics by Study (2/2)") %>%
add_footnote("SMW-Specific", notation = "symbol")
pander(lvl.top[12:length(lvl.top)])
#'
#'
#'
#' # Research Designs
#'
#+ designs
## designs ================
ct.d <- cb[cb$cat == "DESIGN", ] %>% droplevels()
ct.d <- ct.d[!duplicated(ct.d), ]
# x <- ct.d[, c("bibkey", "clab")] %>% ftable() %>% data.frame()
# x[, 3] %>% unique()
t.d <- Rtdf(ct.d$clab)
ct.d$clab <- gsub(" Design", "", ct.d$clab) %>% factor()
ft.d <- ftable(ct.d[, c("clab", "scat")], row.vars = 1)
ftm.d <- matrix(ft.d, nrow = nrow(t.d), byrow = FALSE)
dimnames(ftm.d) <- list(Design = levels(ct.d$clab),
Category = levels(ct.d$scat))
sum.d <- apply(ftm.d, 1, sum)
ftm.d <- ifelse(ftm.d == 0, NA, ftm.d)
ftm.d <- cbind(ftm.d, "**Total**" = paste0("**", sum.d, "**"))
ftm.d %>% kable(align = rep("r", ncol(ftm.d)),
caption = "Research Designs")
nlabs <- length(unique(ct.d$clab))
pd <- mpal(seq_along(unique(ct.d$clab)), p = ppal)
ct.d <- dplyr::rename(ct.d, "Category" = scat, "Design" = clab)
#+ parset_designs, fig.fullwidth=TRUE
### PLOT - designs - parset ####
library(ggparallel)
pscat <- c("#a6afbb", pal_my[17])
t.d$log <- log(t.d[, 2]) + 2
al.d0 <- ifelse(t.d[, 2] < 3, 90, 0)
al.d <- c(al.d0, rep(0, length(unique(ct.d$Category))))
ld <- c(t.d[, 3], rep(3.50, length(unique(ct.d$Category))))
parset.dsgn <- ggparset2(list("Design", "Category"),
data = ct.d,
method = "adj.angle", label = TRUE,
label.size = ld, text.angle = al.d, order = c(0, 0)) +
scale_fill_manual(values = c(pscat.a, adjustcolor(pd, alpha.f = 0.45)),
guide = FALSE) +
scale_colour_manual(values = c(pscat, pd), guide = FALSE) + coord_flip() +
thm_Rtft(ticks = FALSE, ytext = TRUE, xtext = FALSE)
hideLabs(x = parset.dsgn, labs = labs(subtitle = "Research Designs"))
#'
#'
#'
descb <- cb[cb$cat == "DESIGN", ] %>% droplevels()
descb <- descb[!duplicated(descb), ]
# x <- s4qt[, c("bibkey", "clab")] %>% ftable() %>% data.frame()
# x[, 3] %>% unique()
descb.tly <- tally(group_by(descb, clab))
lvl.des <- paste0(seq(1:nrow(descb.tly)), " = ", levels(descb$clab))
descb$clab2 <- factor(descb$clab, labels = seq(1:nrow(descb.tly)))
# levels(descb$clab) <- seq(1:length(unique(descb$clab)))
kdes <- ftable(descb$bibkey, descb$clab2) %>% as.matrix ## "ks3" == "bibkeys - s3" ##
kdes <- ifelse(kdes >= 1, "✓", "·")
kdesscat <- car::recode(rownames(kdes), rec.key2scat)
kdesscat <- ifelse(kdesscat == "S4", paste0("@", rownames(kdes), "$^*$"), paste0("@", rownames(kdes)))
rownames(kdes) <- kdesscat
# rownames(kdes) <- paste0("@", rownames(kdes))
#'
#'
#'
#+ echo=FALSE
# panderOptions("table.split.table", 120)
kable(kdes, caption = "Primary Research Designs by Study") %>%
add_footnote("SMW-Specific", notation = "symbol")
pander(lvl.des)
#'
#'
#'
#' **Experimental Research Designs.**
#'
#+ exp
## experimental designs ================
ct.exp <- cb[cb$cat == "DESIGN-EXP", ] %>% droplevels()
ct.exp <- ct.exp[!duplicated(ct.exp), ]
# x <- ct.exp[, c("bibkey", "clab")] %>% ftable() %>% data.frame()
# x[, 3] %>% unique()
t.exp <- Rtdf(ct.exp$clab)
ct.exp$clab <- gsub(" Design", "", ct.exp$clab) %>% factor()
ct.exp$clab <- gsub(" \\(", " \n\\(", ct.exp$clab) %>% factor()
ft.exp <- ftable(ct.exp[, c("clab", "scat")], row.vars = 1)
ftm.exp <- matrix(ft.exp, nrow = nrow(t.exp), byrow = FALSE)
dimnames(ftm.exp) <- list("Experimental Design" = levels(ct.exp$clab),
Category = levels(ct.exp$scat))
sum.exp <- apply(ftm.exp, 1, sum)
ftm.exp <- ifelse(ftm.exp == 0, NA, ftm.exp)
ftm.expp <- cbind(ftm.exp, "**Total**" = paste0("**", sum.exp, "**"))
# rownames(ftm.expp) <- gsub("\n", "", rownames(ftm.expp))
ftm.expp %>% kable(align = rep("r", ncol(ftm.expp)),
caption = "Experimental Research Designs")
nlabs <- length(unique(ct.exp$clab))
pexp <- mpal(seq_along(unique(ct.exp$clab)), p = ppal)
ct.exp <- dplyr::rename(ct.exp, "Category" = scat, "Experimental Design" = clab)
#+ parset_exp, fig.fullwidth = TRUE
### PLOT - experimental designs - parset ####
library(ggparallel)
pscat <- c("#a6afbb", pal_my[17])
t.exp$log <- log(t.exp[, 2]) + 2
lexp <- c(t.exp[, 3], rep(3.50, length(unique(ct.exp$Category))))
parset.exp <- ggparset2(list("Experimental Design",
"Category"),
data = ct.exp,
method = "adj.angle", label = TRUE,
label.size = lexp, text.angle = 0, order = c(0, 0)) +
scale_fill_manual(values = c(pscat.a, adjustcolor(pexp, alpha.f = 0.55)),
guide = FALSE) +
scale_colour_manual(values = c(pscat, pexp), guide = FALSE) +
thm_Rtft(ticks = FALSE, ytext = FALSE)
hideLabs(x = parset.exp, labs = labs(subtitle = "Experimental Designs"))
#'
#'
#'
expcb <- cb[cb$cat == "DESIGN-EXP", ] %>% droplevels()
expcb <- expcb[!duplicated(expcb), ]
# x <- s4qt[, c("bibkey", "clab")] %>% ftable() %>% data.frame()
# x[, 3] %>% unique()
expcb.tly <- tally(group_by(expcb, clab))
lvl.exp <- paste0(seq(1:nrow(expcb.tly)), " = ", levels(expcb$clab))
expcb$clab2 <- factor(expcb$clab, labels = seq(1:nrow(expcb.tly)))
# levels(expcb$clab) <- seq(1:length(unique(expcb$clab)))
kexp <- ftable(expcb$bibkey, expcb$clab2) %>% as.matrix ## "ks3" == "bibkeys - s3" ##
kexp <- ifelse(kexp >= 1, "✓", "·")
kexpscat <- car::recode(rownames(kexp), rec.key2scat)
kexpscat <- ifelse(kexpscat == "S4", paste0("@", rownames(kexp), "$^*$"), paste0("@", rownames(kexp)))
rownames(kexp) <- kexpscat
# rownames(kexp) <- paste0("@", rownames(kexp))
#'
#'
#'
#+ echo=FALSE
# panderOptions("table.split.table", 120)
kable(kexp, caption = "Experimental Designs by Study") %>%
add_footnote("SMW-Specific", notation = "symbol")
pander(lvl.exp)
#'
#'
#'
#' # Data Collection Methodologies
#'
#+ methodologies
## methodologies ================
ct.mo <- cb[cb$cat == "METHODS", ] %>% droplevels()
ct.mo <- ct.mo[!duplicated(ct.mo), ]
# x <- ct.mo[, c("bibkey", "clab")] %>% ftable() %>% data.frame()
# x[, 3] %>% unique()
t.mo <- Rtdf(ct.mo$clab)
ft.mo <- ftable(ct.mo[, c("clab", "scat")], row.vars = 1)
ftm.mo <- matrix(ft.mo, nrow = nrow(t.mo), byrow = FALSE)
dimnames(ftm.mo) <- list(Methodology = levels(ct.mo$clab),
Category = levels(ct.mo$scat))
sum.mo <- apply(ftm.mo, 1, sum)
ftm.mo <- ifelse(ftm.mo == 0, NA, ftm.mo)
ftm.mo <- cbind(ftm.mo, "**Total**" = paste0("**", sum.mo, "**"))
ftm.mo %>% kable(align = rep("r", ncol(ftm.mo)),
caption = "Methodologies")
#+ parset_methodologies, fig.fullwidth=TRUE
### PLOT - methodologies - parset ####
nlabs <- length(unique(ct.mo$clab))
pmo <- mpal(seq_along(unique(ct.mo$clab)), p = ppal)
ct.mo <- dplyr::rename(ct.mo, "Category" = scat, "Methodology" = clab)
t.mo$log <- log(t.mo[, 2]) + 2
lmo <- c(t.mo[, 3], rep(3.50, length(unique(ct.mo$Category))))
pscat <- c("#a6afbb", pal_my[17])
parset.mo <- ggparset2(list("Methodology", "Category"),
data = ct.mo,
method = "adj.angle", label = TRUE,
label.size = lmo, text.angle = 0, order = c(0, 0)) +
scale_fill_manual(values = c(pscat.a, adjustcolor(pmo, alpha.f = 0.55)),
guide = FALSE) +
scale_colour_manual(values = c(pscat, pmo), guide = FALSE) +
thm_Rtft(ticks = FALSE, ytext = TRUE, xtext = FALSE) #+ coord_flip()
hideLabs(x = parset.mo, labs = labs(subtitle = "Methodologies"))
#'
#'
#'
mocb <- cb[cb$cat == "METHODS", ] %>% droplevels()
mocb <- mocb[!duplicated(mocb), ]
# x <- s4qt[, c("bibkey", "clab")] %>% ftable() %>% data.frame()
# x[, 3] %>% unique()
mocb.tly <- tally(group_by(mocb, clab))
lvl.mo <- paste0(seq(1:nrow(mocb.tly)), " = ", levels(mocb$clab))
mocb$clab2 <- factor(mocb$clab, labels = seq(1:nrow(mocb.tly)))
# levels(mocb$clab) <- seq(1:length(unique(mocb$clab)))
kmo <- ftable(mocb$bibkey, mocb$clab2) %>% as.matrix ## "ks3" == "bibkeys - s3" ##
kmo <- ifelse(kmo >= 1, "✓", "·")
kmoscat <- car::recode(rownames(kmo), rec.key2scat)
kmoscat <- ifelse(kmoscat == "S4", paste0("@", rownames(kmo), "$^*$"), paste0("@", rownames(kmo)))
rownames(kmo) <- kmoscat
# rownames(kmo) <- paste0("@", rownames(kmo))
#'
#'
#'
#+ echo=FALSE
# panderOptions("table.split.table", 120)
kable(kmo, caption = "Methodology by Study") %>%
add_footnote("SMW-Specific", notation = "symbol")
pander(lvl.mo)
#'
#'
#'
#'
#' **QuaLitative Research _Designs_.**
#'
#+ qlDesigns
ct.dql <- cb[cb$cat == "D-QL", ] %>% droplevels()
ct.dql <- ct.dql[!duplicated(ct.dql), ]
# x <- ct.dql[, c("bibkey", "clab")] %>% ftable() %>% data.frame()
# x[, 3] %>% unique()
t.dql <- Rtdf(ct.dql$clab)
ft.dql <- ftable(ct.dql[, c("clab", "scat")], row.vars = 1)
ftm.dql <- matrix(ft.dql, nrow = nrow(t.dql), byrow = FALSE)
dimnames(ftm.dql) <- list("Qua**L**itative Design" = levels(ct.dql$clab),
Category = levels(ct.dql$scat))
# t.dql
sum.dql <- apply(ftm.dql, 1, sum)
ftm.dql <- ifelse(ftm.dql == 0, NA, ftm.dql)
ftm.dql <- cbind(ftm.dql, "**Total**" = paste0("**", sum.dql, "**"))
ftm.dql %>% kable(align = rep("r", ncol(ftm.dql)),
caption = "Qua**L**itative Designs")
#+ parset_qlDesigns, fig.fullwidth=TRUE
### PLOT - qual designs - parset ####
nlabs <- length(unique(ct.dql$clab))
pdql <- mpal(seq_along(unique(ct.dql$clab)), p = ppal)
ct.dql <- dplyr::rename(ct.dql, "QuaLitative Design" = clab, "Category" = scat)
t.dql$log <- log(t.dql[, 2]) + 2
ldql <- c(rep(3.50, length(unique(ct.dql$Category))), t.dql[, 3])
parset.dql <- ggparset2(list("QuaLitative Design", "Category"),