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server.R
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##########################################
# MetaZipf | server file
# Clementine Cottineau, 2016
# UCL - CASA - UDL
##########################################
library(ggplot2)
library(RColorBrewer)
library(plyr)
library(shiny)
library(rgdal)
library(rgeos)
library(leaflet)
library(data.table)
library(plm)
library(lme4)
#############################
###### Import and pre-process Data
##############################
meta = read.csv("data/zipf_meta.csv", sep = ",", dec = ".")
meta$REFERENCE = as.character(meta$REFERENCE)
meta$DECADE = as.factor(paste0(substr(meta$DATE, 1, 3), "0s"))
lkp = meta[, c("TERRITORY", "CNTR_ID")]
lookupCNTR = lkp[lkp$CNTR_ID != "",]
lookupCNTR_ID = lookupCNTR[!duplicated(lookupCNTR), ]
refs = read.csv("data/zipf_refs.csv", sep = ",", dec = ".")
metaToAdd = data.frame()
refsToAdd = data.frame()
delga2 = refs[refs$REFID == "Del04Con",]
delga2[,"REFID"] = "Del04Con2"
refs2 = rbind(refs, delga2)
meta = data.frame(meta, refs2[match(meta$REFID, refs2$REFID),])
rownames(meta) = 1:dim(meta)[1]
meta$REFID.1 = NULL
meta$AUTHOR = NULL
meta$YEARPUB = meta$YEAR
meta$YEAR = NULL
meta$PAGE = NULL
meta$SOURCE = NULL
min_year_by_study = ddply(
meta,
~ REFERENCE,
summarise,
min = min(DATE),
max = max(DATE),
N_COUNTRIES = length(unique(TERRITORY))
)
min_year_by_study$StudyPeriod = min_year_by_study$max - min_year_by_study$min
meta = data.frame(meta, min_year_by_study[match(meta$REFERENCE, min_year_by_study$REFERENCE),])
pops = read.csv("data/UN_Population_1950_2015.csv",
sep = ",",
dec = ".")
colnames(pops) = c("CNTR_ID", "Name", "CC", paste0("POP", 1950:2015))
pops[pops == 0] <- NA
pops = pops[pops$CNTR_ID != "",]
pops$Pct_POP_1950s = AAGR_pct(pops$POP1950, pops$POP1960, 10)
pops$Pct_POP_1960s = AAGR_pct(pops$POP1960, pops$POP1970, 10)
pops$Pct_POP_1970s = AAGR_pct(pops$POP1970, pops$POP1980, 10)
pops$Pct_POP_1980s = AAGR_pct(pops$POP1980, pops$POP1990, 10)
pops$Pct_POP_1990s = AAGR_pct(pops$POP1990, pops$POP2000, 10)
pops$Pct_POP_2000s = AAGR_pct(pops$POP2000, pops$POP2010, 10)
pops$Pct_POP_2010s = AAGR_pct(pops$POP2010, pops$POP2015, 5)
urbs = read.csv("data/UN_Urbanization_1950_2050.csv",
sep = ",",
dec = ".")
colnames(urbs) = c("CNTR_ID", "Name", "CC", paste0("URB", 1950:2050))
urbs[urbs == 0] <- NA
urbs = urbs[urbs$CNTR_ID != "",]
urbs$Pct_URB_1950s = AAGR_pct(urbs$URB1950, urbs$URB1960, 10)
urbs$Pct_URB_1960s = AAGR_pct(urbs$URB1960, urbs$URB1970, 10)
urbs$Pct_URB_1970s = AAGR_pct(urbs$URB1970, urbs$URB1980, 10)
urbs$Pct_URB_1980s = AAGR_pct(urbs$URB1980, urbs$URB1990, 10)
urbs$Pct_URB_1990s = AAGR_pct(urbs$URB1990, urbs$URB2000, 10)
urbs$Pct_URB_2000s = AAGR_pct(urbs$URB2000, urbs$URB2010, 10)
urbs$Pct_URB_2010s = AAGR_pct(urbs$URB2010, urbs$URB2015, 5)
summary(meta$Pct_GDP_1960s)
summary(meta$Pct_URB_2010s)
gdps = read.csv("data/WB_GDP_1960_2015.csv", sep = ",", dec = ".")
colnames(gdps) = c("CNTR_ID", "Name", "CC", paste0("GDP", 1960:2015))
gdps[gdps == 0] <- NA
gdps = gdps[gdps$CNTR_ID != "",]
gdps$Pct_GDP_1960s = AAGR_pct(gdps$GDP1960, gdps$GDP1970, 10)
gdps$Pct_GDP_1970s = AAGR_pct(gdps$GDP1970, gdps$GDP1980, 10)
gdps$Pct_GDP_1980s = AAGR_pct(gdps$GDP1980, gdps$GDP1990, 10)
gdps$Pct_GDP_1990s = AAGR_pct(gdps$GDP1990, gdps$GDP2000, 10)
gdps$Pct_GDP_2000s = AAGR_pct(gdps$GDP2000, gdps$GDP2010, 10)
gdps$Pct_GDP_2010s = AAGR_pct(gdps$GDP2010, gdps$GDP2015, 5)
meta = data.frame(meta, pops[match(meta$CNTR_ID, pops$CNTR_ID),])
meta = data.frame(meta, gdps[match(meta$CNTR_ID, gdps$CNTR_ID),])
meta = data.frame(meta, urbs[match(meta$CNTR_ID, urbs$CNTR_ID),])
meta$TOTAL_POP = NA
meta$URBP = NA
meta$GDPPC = NA
for (i in 1:dim(meta)[1]) {
if (meta[i, "DATE"] %in% 1950:2015) {
meta[i, "TOTAL_POP"] = round(as.numeric(meta[i, paste0("POP", meta[i, "DATE"])]), 0)
meta[i, "URBP"] = round(as.numeric(meta[i, paste0("URB", meta[i, "DATE"])]), 2)
if (meta[i, "DATE"] %in% 1960:2015)
meta[i, "GDPPC"] = round(as.numeric(meta[i, paste0("GDP", meta[i, "DATE"])]), 0)
}
}
rownames(meta) = 1:dim(meta)[1]
meta$REFID.1 = NULL
meta$AUTHOR = NULL
meta$YEAR = NULL
meta$PAGE = NULL
meta$SOURCE = NULL
DARIUS_A <- read.csv("data/DARIUS_A.csv", sep = ",", dec = ".")
DARIUS_L <- read.csv("data/DARIUS_L.csv", sep = ",", dec = ".")
full_countries <-
readOGR(
dsn = 'data/world_SimplifiedGeom.shp',
layer = "world_SimplifiedGeom",
verbose = F,
encoding = "utf8"
)
cw <-
read.csv(
"data/MetaEventsCivilWars.csv",
sep = ",",
dec = ".",
na.strings = "NaN"
)
wi <-
read.csv(
"data/MetaEventsIndependenceWars.csv",
sep = ",",
dec = ".",
na.strings = "NaN"
)
rv <-
read.csv(
"data/MetaEventsRevolutions.csv",
sep = ",",
dec = ".",
na.strings = "NaN"
)
iw <-
read.csv(
"data/MetaEventsWars.csv",
sep = ",",
dec = ".",
na.strings = "NaN"
)
cw$DURATION = as.factor(cw$DURATION)
wi$DURATION = as.factor(wi$DURATION)
rv$DURATION = as.factor(rv$DURATION)
iw$DURATION = as.factor(iw$DURATION)
metaEvents = rbind(iw, cw, wi, rv)
metaEvents$DURATION = as.numeric(
ifelse(
metaEvents$DURATION == "ongoing",
2016 - metaEvents$DATE,
metaEvents$DURATION
)
)
metaEvents$TYPE = substr(metaEvents$ID_EVENT, 1, 2)
metaEvents$END = metaEvents$DATE + metaEvents$DURATION
meta$wi = 0
meta$iw = 0
meta$cw = 0
meta$rv = 0
for (i in 1:dim(meta)[1]) {
date = meta[i, "DATE"]
country = as.character(meta[i, "CNTR_ID"])
wi = metaEvents[metaEvents$COUNTRY_ID == country &
metaEvents$DATE <= date &
metaEvents$END >= date &
metaEvents$TYPE == "wi",]
iw = metaEvents[metaEvents$COUNTRY_ID == country &
metaEvents$DATE <= date &
metaEvents$END >= date &
metaEvents$TYPE == "iw",]
cw = metaEvents[metaEvents$COUNTRY_ID == country &
metaEvents$DATE <= date &
metaEvents$END >= date &
metaEvents$TYPE == "cw",]
rv = metaEvents[metaEvents$COUNTRY_ID == country &
metaEvents$DATE <= date &
metaEvents$END >= date &
metaEvents$TYPE == "rv",]
if (dim(wi)[1] > 0)
meta[i, "wi"] = 1
if (dim(iw)[1] > 0)
meta[i, "iw"] = 1
if (dim(cw)[1] > 0)
meta[i, "cw"] = 1
if (dim(rv)[1] > 0)
meta[i, "rv"] = 1
}
r_colors <- rgb(t(col2rgb(colors()) / 255))
names(r_colors) <- colors()
meta$SAME_SPECIFICATIONS = ifelse(
meta$TRUNCATION == "sample size",
paste(
meta$REFID,
meta$TERRITORY,
meta$URBANSCALE,
meta$ESTIMATION,
meta$TRUNCATION,
meta$N,
sep = "_"
),
paste(
meta$REFID,
meta$TERRITORY,
meta$URBANSCALE,
meta$ESTIMATION,
meta$TRUNCATION_POINT,
sep = "_"
)
)
meta$URBANISATION = as.factor(ifelse(meta$URBANISATION == "", "MissingInfo", as.character(meta$URBANISATION)))
meta[is.na(meta)] <- "MissingInfo"
##############################
###### Outputs functions
##############################
shinyServer(function(input, output, session) {
##############################
###### Reactive functions
##############################
DARIUSSubset <- reactive({
if (input$dariusdef == "Local") {
DARIUS = DARIUS_L
DARIUS$ID = DARIUS$ROKATO
}
if (input$dariusdef == "Morpho") {
DARIUS = DARIUS_A
DARIUS$ID = DARIUS$AROKATO
}
DARIUSsub = DARIUS
year4darius = paste0("Pop", input$dariusyear)
DARIUSsub$Population = DARIUSsub[, year4darius]
dariuscutoff = input$dariuscutoff / 1000
DARIUSsub$Population = ifelse(DARIUSsub$Population >= dariuscutoff,
DARIUSsub$Population,
NA)
return(DARIUSsub)
})
DARIUSzipf <- reactive({
DARIUSdf = DARIUSSubset()
size = DARIUSdf[order(-DARIUSdf$Population) , "Population"]
rank = 1:length(size)
zipf = data.frame(size, rank)
})
alphaSummaryByCountryForMap = reactive({
m1 = metaTableSummary()
m = m1[m1$TERRITORY_TYPE == "Country",]
m$count = 1
keep = aggregate(m[, "count"], unique(list(m$CNTR_ID)), FUN = sumNum)
keep = subset(keep, x >= 5)
keep = subset(keep, Group.1 != "")
m = m[m$CNTR_ID %in% keep[, 1],]
d = aggregate(m[, "ALPHA"], unique(list(m$CNTR_ID)), FUN = stdDev)
ds = d[order(-d$x),]
top = as.data.frame(ds)
top$x = round(top$x, 3)
mn = aggregate(m[, "ALPHA"], unique(list(m$CNTR_ID)), FUN = mean)
mean = as.data.frame(mn)
mean$x = round(mean$x, 3)
topC = join(top, mean, by = "Group.1")
topC = join(topC, keep, by = "Group.1")
m$ALPHA_REFERENCE = paste(m$REFERENCE, round(m$ALPHA, 3), sep = ": ")
mRefs = aggregate(m[, "ALPHA_REFERENCE"], unique(list(m$CNTR_ID)), FUN = collapseRefs)
mRefs = as.data.frame(mRefs)
topC = join(topC, mRefs, by = "Group.1")
rownames(topC) = topC$Group.1
colnames(topC) = c("CNTR_ID", "a", "b", "Estimations", "vals")
topCN = merge(
topC,
lookupCNTR_ID,
by = "CNTR_ID",
all.x = T,
all.y = F
)
topCN$Estimations = as.integer(topCN$Estimations)
finalTop = topCN[, c("CNTR_ID", "TERRITORY", "a", "b", "Estimations", "vals")]
colnames(finalTop) = c("CNTR_ID",
"Country",
"Alpha Diversity*",
"Mean Alpha",
"Estimations",
"Values")
finalTop = finalTop[finalTop$Country != "Taiwan",]
return(finalTop)
})
alphaSummaryByCountryForTable = reactive({
meta = metaPaperSelection()
m = meta[meta$TERRITORY_TYPE == "Country",]
if (input$alpha == "Lotka")
m$ALPHA = m$ALPHALOTKA
if (input$alpha == "Pareto")
m$ALPHA = m$ALPHAPARETO
m$count = 1
keep = aggregate(m[, "count"], unique(list(m$CNTR_ID)), FUN = sumNum)
keep = subset(keep, x >= 5)
keep = subset(keep, Group.1 != "")
m = m[m$CNTR_ID %in% keep[, 1],]
d = aggregate(m[, "ALPHA"], unique(list(m$CNTR_ID)), FUN = stdDev)
ds = d[order(-d$x),]
top = as.data.frame(ds)
top$x = round(top$x, 3)
mn = aggregate(m[, "ALPHA"], unique(list(m$CNTR_ID)), FUN = mean)
mean = as.data.frame(mn)
mean$x = round(mean$x, 3)
topC = join(top, mean, by = "Group.1")
topC = join(topC, keep, by = "Group.1")
rownames(topC) = topC$Group.1
colnames(topC) = c("CNTR_ID", "a", "b", "Estimations")
topCN = merge(
topC,
lookupCNTR_ID,
by = "CNTR_ID",
all.x = T,
all.y = F
)
topCN$Estimations = as.integer(topCN$Estimations)
topCN = topCN[order(-topCN[, 2]),]
finalTop = topCN[, c("CNTR_ID", "TERRITORY", "a", "b", "Estimations")]
colnames(finalTop) = c("CNTR_ID",
"Country",
"Alpha Diversity*",
"Mean Alpha",
"Estimations")
finalTop = finalTop[finalTop$Country != "Taiwan",]
finalTop$CNTR_ID = NULL
return(finalTop)
})
DARIUSmodel <- reactive({
zipf = DARIUSzipf()
if (input$alpha == "Lotka") {
model = lm(log(size) ~ log(rank), data = zipf)
}
if (input$alpha == "Pareto") {
model = lm(log(rank) ~ log(size), data = zipf)
}
m = summary(model)
alpha = round(-m$coefficients[2, 1], 3)
beta = round(-m$coefficients[1, 1], 3)
sd = round(m$coefficients[2, 2], 3)
r2 = round(m$r.squared, 3) * 100
ci9low = round(-confint(model)[2, 2], 3)
ci9high = round(-confint(model)[2, 1], 3)
n = m$df[1] + m$df[2]
summary = data.frame(alpha, sd, r2, n, beta)
colnames(summary) = c("Alpha", "Standard Deviation", "R2", "N Cities", "beta")
return(summary)
})
metaPaperSelection = reactive({
tab = meta
if (input$reproducePaperSelection == "arxiv") tab = subset(tab, ARXIV == 1)
if (input$reproducePaperSelection == "plosone") tab = subset(tab, PLOSONE == 1)
if (input$reproducePaperSelection == "current"){
refList = input$references
if('All' %in% refList | is.null(refList)){
tab = tab
} else {
tab = subset(tab, REFERENCE %in% refList)
}
}
return(tab)
})
refsPaperSelection = reactive({
tab = refs
if (input$reproducePaperSelection == "arxiv"){tab = subset(tab, ARXIV == 1)}
if (input$reproducePaperSelection == "plosone"){tab = subset(tab, PLOSONE == 1)}
return(tab)
})
metaTableSelected <- reactive({
tab = metaPaperSelection()
if (input$alpha == "Lotka")
tab$ALPHA = tab$ALPHALOTKA
if (input$alpha == "Pareto")
tab$ALPHA = tab$ALPHAPARETO
terr = input$territory
dec = input$decade
def = input$scale
if (length(terr) >= 1)
tab = tab[tab$TERRITORY %in% terr,]
if (length(dec) >= 1)
tab = tab[tab$DECADE %in% dec,]
if (length(def) >= 1)
tab = tab[tab$URBANSCALE %in% def,]
tab = tab[order(tab$DATE),]
tab$ALPHA = round(tab$ALPHA, 3)
return(tab)
})
metaTableSummary <- reactive({
tab = metaPaperSelection()
if (input$alpha == "Lotka")
tab$ALPHA = tab$ALPHALOTKA
if (input$alpha == "Pareto")
tab$ALPHA = tab$ALPHAPARETO
terr = input$territorys
dec = input$decades
def = input$scales
if (length(terr) >= 1)
tab = tab[tab$CONTINENT %in% terr,]
if (length(dec) >= 1)
tab = tab[tab$DECADE %in% dec,]
if (length(def) >= 1)
tab = tab[tab$URBANSCALE %in% def,]
return(tab)
})
tableForTrajectoryMaps <- reactive({
tab = metaPaperSelection()
if (input$alpha == "Lotka")tab$ALPHA = tab$ALPHALOTKA
if (input$alpha == "Pareto") tab$ALPHA = tab$ALPHAPARETO
terr = unique(as.character(tab$TERRITORY))
longitudinalAlphas = tab[tab$TRUNCATION != "" &
tab$TERRITORY %in% terr, ]
longitudinalAlphas$SAME_SPECIFICATIONS = ifelse(
longitudinalAlphas$TRUNCATION == "sample size",
paste(
longitudinalAlphas$REFID,
longitudinalAlphas$TERRITORY,
longitudinalAlphas$URBANSCALE,
longitudinalAlphas$ESTIMATION,
longitudinalAlphas$TRUNCATION,
longitudinalAlphas$N,
sep = "_"
),
paste(
longitudinalAlphas$REFID,
longitudinalAlphas$TERRITORY,
longitudinalAlphas$URBANSCALE,
longitudinalAlphas$ESTIMATION,
longitudinalAlphas$TRUNCATION_POINT,
sep = "_"
)
)
numberOfDates = ddply(longitudinalAlphas,
~ SAME_SPECIFICATIONS,
summarise,
N_DATES = length(unique(DATE)))
longitudinalAlphas = data.frame(longitudinalAlphas, numberOfDates[match(longitudinalAlphas$SAME_SPECIFICATIONS,
numberOfDates$SAME_SPECIFICATIONS),])
longitudinalAlphas = longitudinalAlphas[!is.na(longitudinalAlphas$REGRESSION) &
longitudinalAlphas$N_DATES > 1, ]
longitudinalAlphas = longitudinalAlphas[order(longitudinalAlphas$SAME_SPECIFICATIONS,
longitudinalAlphas$DATE),]
n = dim(longitudinalAlphas)[1] - 1
for (i in 1:n) {
if (longitudinalAlphas[i, "SAME_SPECIFICATIONS"] == longitudinalAlphas[i +
1, "SAME_SPECIFICATIONS"]) {
fv = longitudinalAlphas[i + 1, "ALPHA"]
iv = longitudinalAlphas[i, "ALPHA"]
ye = longitudinalAlphas[i + 1, "DATE"] - longitudinalAlphas[i, "DATE"]
median_date = (longitudinalAlphas[i, "DATE"] + longitudinalAlphas[i +
1, "DATE"]) / 2
longitudinalAlphas[i, "PCT_GROWTH_ALPHA"] = AAGR_pct(initVal = iv,
finalVal = fv,
nPeriods = ye)
longitudinalAlphas[i, "GROWTH_DECADE"] = paste0(substr(as.character(median_date), 1, 3), '0s')
if (!is.na(as.numeric(longitudinalAlphas[i, "N"]))) {
fvn = as.numeric(longitudinalAlphas[i + 1, "N"])
ivn = as.numeric(longitudinalAlphas[i, "N"])
longitudinalAlphas[i, "GN"] = AAGR_pct(initVal = ivn,
finalVal = fvn,
nPeriods = ye)
} else {
longitudinalAlphas[i, "GN"] = NA
}
} else {
longitudinalAlphas[i, "PCT_GROWTH_ALPHA"] = NA
longitudinalAlphas[i, "GROWTH_DECADE"] = NA
longitudinalAlphas[i, "GN"] = NA
}
}
longitudinalAlphas = longitudinalAlphas[is.finite(longitudinalAlphas$PCT_GROWTH_ALPHA),]
return(longitudinalAlphas)
})
tableForTrajectories <- reactive({
tab = metaPaperSelection()
if (input$alpha == "Lotka") tab$ALPHA = tab$ALPHALOTKA
if (input$alpha == "Pareto") tab$ALPHA = tab$ALPHAPARETO
terr = input$territory_3
if (length(terr) >= 1) {
tab = tab[tab$TERRITORY %in% terr,]
} else {
terr = unique(as.character(tab$TERRITORY))
}
longitudinalAlphas = tab[tab$TRUNCATION != "" &
tab$TERRITORY %in% terr, ]
longitudinalAlphas$SAME_SPECIFICATIONS = ifelse(
longitudinalAlphas$TRUNCATION == "sample size",
paste(
longitudinalAlphas$REFID,
longitudinalAlphas$TERRITORY,
longitudinalAlphas$URBANSCALE,
longitudinalAlphas$ESTIMATION,
longitudinalAlphas$TRUNCATION,
longitudinalAlphas$N,
sep = "_"
),
paste(
longitudinalAlphas$REFID,
longitudinalAlphas$TERRITORY,
longitudinalAlphas$URBANSCALE,
longitudinalAlphas$ESTIMATION,
longitudinalAlphas$TRUNCATION_POINT,
sep = "_"
)
)
numberOfDates = ddply(longitudinalAlphas,
~ SAME_SPECIFICATIONS,
summarise,
N_DATES = length(unique(DATE)))
longitudinalAlphas = data.frame(longitudinalAlphas, numberOfDates[match(longitudinalAlphas$SAME_SPECIFICATIONS,
numberOfDates$SAME_SPECIFICATIONS),])
longitudinalAlphas = longitudinalAlphas[!is.na(longitudinalAlphas$REGRESSION) &
longitudinalAlphas$N_DATES > 1, ]
n = dim(longitudinalAlphas)[1] - 1
for (i in 1:n) {
if (longitudinalAlphas[i, "SAME_SPECIFICATIONS"] == longitudinalAlphas[i +
1, "SAME_SPECIFICATIONS"]) {
fv = longitudinalAlphas[i + 1, "ALPHA"]
iv = longitudinalAlphas[i, "ALPHA"]
ye = longitudinalAlphas[i + 1, "DATE"] - longitudinalAlphas[i, "DATE"]
median_date = (longitudinalAlphas[i, "DATE"] + longitudinalAlphas[i +
1, "DATE"]) / 2
longitudinalAlphas[i, "PCT_GROWTH_ALPHA"] = AAGR_pct(initVal = iv,
finalVal = fv,
nPeriods = ye)
longitudinalAlphas[i, "GROWTH_DECADE"] = paste0(substr(as.character(median_date), 1, 3), '0s')
} else {
longitudinalAlphas[i, "PCT_GROWTH_ALPHA"] = NA
longitudinalAlphas[i, "GROWTH_DECADE"] = NA
}
}
return(longitudinalAlphas)
})
GrowthCountries <- reactive({
c_shp <-
full_countries#[full_countries$CNTR_ID == input$Country_name, ]
tab = tableForTrajectoryMaps()
# periods = c("1950s", "1960s", "1970s")
periods = as.character(input$decade_3)
tab$GROWTH_DECADE = as.character(tab$GROWTH_DECADE)
tab = tab[tab$TERRITORY_TYPE == "Country" &
!is.na(tab$PCT_GROWTH_ALPHA) &
tab$GROWTH_DECADE %in% periods,]
tab$count = 1
data = ddply(
tab,
~ TERRITORY,
summarise,
MEAN_GROWTH_ALPHA = mean(PCT_GROWTH_ALPHA),
SD_GROWTH_ALPHA = sd(PCT_GROWTH_ALPHA),
N_GROWTH_ALPHA = sumNum(count)
)
data = merge(
data,
lookupCNTR_ID,
by = "TERRITORY",
all.x = T,
all.y = F
)
c_shp@data = data.frame(c_shp@data, data[match(c_shp@data$CNTR_ID, data$CNTR_ID), ])
rate_gdp = gdps[, c("CNTR_ID", paste0('Pct_GDP_', c(1960, 1970, 1980, 1990, 2000, 2010), 's'))]
c_shp@data = data.frame(c_shp@data, rate_gdp[match(c_shp@data$CNTR_ID, rate_gdp$CNTR_ID), ])
rate_pop = pops[, c("CNTR_ID", paste0('Pct_POP_', c(1950, 1960, 1970, 1980, 1990, 2000, 2010), 's'))]
c_shp@data = data.frame(c_shp@data, rate_pop[match(c_shp@data$CNTR_ID, rate_pop$CNTR_ID), ])
return(c_shp)
})
ZipfCountries <- reactive({
c_shp <-
full_countries
data = alphaSummaryByCountryForMap()
colnames(data) = c("CNTR_ID",
"Name",
"Diversity",
"Alpha",
"Estimation",
"Values")
c_shp@data = data.frame(c_shp@data, data[match(c_shp@data$CNTR_ID, data$CNTR_ID), ])
c_shp@data = data.frame(c_shp@data, data[match(c_shp@data$CNTR_ID, data$CNTR_ID), ])
c_shp@data = data.frame(c_shp@data, data[match(c_shp@data$CNTR_ID, data$CNTR_ID), ])
return(c_shp)
})
datasetInput <- reactive({
refsToAdd = refsToAdd
if (is.null(input$addest) || input$addest == 0)
return(NULL)
line = dim(refsToAdd)[1]
refsToAdd[1, 1] = input$author
refsToAdd[1, 2] = input$year
refsToAdd[1, 3] = input$page
if (input$type != "Thesis")
refsToAdd[1, 4] = input$journal
if (input$type == "Thesis")
refsToAdd[1, 4] = "Dissertation"
refsToAdd[1, 5] = input$nestimates
refsToAdd[1, 6] = input$regression
colnames(refsToAdd) = c("author", "year", "p", "journal", "n", "form")
refName = paste(input$author, input$year, "p.", input$page, sep = "")
r = input$nestimates
estimToAdd <-
lapply(1:r, function(i) {
c(input[[paste0("alphaestim_", i)]],
input[[paste0("territoryestim_", i)]], input[[paste0("urbandefestim_", i)]],
input[[paste0("truncestim_", i)]], input[[paste0("dateestim_", i)]],
input[[paste0("nCitiesestim_", i)]], input[[paste0("r2estim_", i)]])
})
metaToAdd = as.data.frame(t(as.data.frame(estimToAdd)))
if (input$alpha == "Lotka") {
colnames(metaToAdd) = c("alphaLOTKA",
"where",
"what",
"truncation",
"when",
"n",
"r2")
metaToAdd$alphaPARETO = 1 / metaToAdd$alphaLOTKA
} else {
colnames(metaToAdd) = c("alphaPARETO",
"where",
"what",
"truncation",
"when",
"n",
"r2")
metaToAdd$alphaLOTKA = 1 / metaToAdd$alphaPARETO
}
rownames(metaToAdd) = 1:r
metaToAdd$ref = refName
return(metaToAdd)
})
metaModelOLS <- reactive({
tab = metaPaperSelection()
if (input$subsetData == T){
terr = input$territory
dec = input$decade
def = input$scale
if (length(terr) >= 1)
tab = tab[tab$TERRITORY %in% terr,]
if (length(dec) >= 1)
tab = tab[tab$DECADE %in% dec,]
if (length(def) >= 1)
tab = tab[tab$URBANSCALE %in% def,]
}
if (input$alpha == "Lotka")
tab$ALPHA = tab$ALPHALOTKA
if (input$alpha == "Pareto")
tab$ALPHA = tab$ALPHAPARETO
TechnicalSpecs = input$technicalSpecs
TopicalSpecs = input$topicalSpecs
OtherSpecs = input$otherSpecs
if ('alltech' %in% TechnicalSpecs == "TRUE")
TechnicalSpecs = c("truncation4model", "N4model", "scale4model", "regForm", "olsOrnot")
if ('alltop' %in% TopicalSpecs == "TRUE")
TopicalSpecs = c("time",
"urbanisation4model",
"countrySize",
"countryGDP",
"countryUrb")
if ('allother' %in% OtherSpecs == "TRUE")
OtherSpecs = c("yearOfPubli",
"studySize",
"studyPeriod",
"studyCoverage") #"discipline"
regressants = "ALPHA ~ 1"
columnsToKeep = c("ALPHA", "DATE")
if ('truncation4model' %in% TechnicalSpecs == "TRUE") {
tab$Population_Cutoff_ = as.factor(
ifelse(tab$TRUNCATION_POINT == "MissingInfo", "MissingInfo",
ifelse(tab$TRUNCATION_POINT <= input$truncVal[[1]],
"Low",
ifelse(
tab$TRUNCATION_POINT >= input$truncVal[[2]],
"High",
" Medium"
)
)))
regressants = paste0(regressants, " + Population_Cutoff_")
columnsToKeep = c(columnsToKeep, "Population_Cutoff_")
}
if ('N4model' %in% TechnicalSpecs == "TRUE") {
tab$Number_Of_Cities_ = as.factor(ifelse(
tab$N == "MissingInfo", "MissingInfo",
ifelse(tab$N <= input$NVal[[1]],
"Small",
ifelse(tab$N >= input$NVal[[2]], "Large", " Medium")
)))
regressants = paste0(regressants, " + Number_Of_Cities_")
columnsToKeep = c(columnsToKeep, "Number_Of_Cities_")
}
if ('time' %in% TopicalSpecs == "TRUE") {
tab$Date_of_Observation_ = as.factor(ifelse(
tab$DATE <= input$nTime[[1]],
"Early",
ifelse(tab$DATE >= input$nTime[[2]], "Late", " Intermediate")
))
regressants = paste0(regressants, " + Date_of_Observation_")
columnsToKeep = c(columnsToKeep, "Date_of_Observation_")
}
if ('regForm' %in% TechnicalSpecs == "TRUE") {
tab$Regression_Form_ = tab$REGRESSION
regressants = paste0(regressants, " + Regression_Form_")
columnsToKeep = c(columnsToKeep, "Regression_Form_")
}
if('olsOrnot' %in% TechnicalSpecs == "TRUE") {
tab$Estimation_Method_ = tab$ESTIMATION
regressants = paste0(regressants, " + Estimation_Method_")
columnsToKeep = c(columnsToKeep, "Estimation_Method_")
}
if ('scale4model' %in% TechnicalSpecs == "TRUE") {
tab$City_Definition_ = tab$URBANSCALE
regressants = paste0(regressants, " + City_Definition_")
columnsToKeep = c(columnsToKeep, "City_Definition_")
}
if ('urbanisation4model' %in% TopicalSpecs == "TRUE") {
# tab = subset(tab, URBANISATION != "")
tab$Urbanisation_Age_ = tab$URBANISATION
regressants = paste0(regressants, " + Urbanisation_Age_")
columnsToKeep = c(columnsToKeep, "Urbanisation_Age_")
}
if ('countrySize' %in% TopicalSpecs == "TRUE") {
# tab = subset(tab, TOTAL_POP > 0)
tab$Country_Size_ = as.factor(ifelse(
tab$TOTAL_POP == "MissingInfo", "MissingInfo",
ifelse(
tab$TOTAL_POP <= input$PopVal[[1]],
"Small",
ifelse(tab$TOTAL_POP >= input$PopVal[[2]], "Large", " Medium")
)))
regressants = paste0(regressants, " + Country_Size_")
columnsToKeep = c(columnsToKeep, "Country_Size_")
}
if ('countryUrb' %in% TopicalSpecs == "TRUE") {
# tab = subset(tab, URBP > 0)
tab$Country_Urbanization_ = as.factor(ifelse(
tab$URBP == "MissingInfo", "MissingInfo",
ifelse(
tab$URBP <= input$UrbVal[[1]],
"Low",
ifelse(tab$URBP >= input$UrbVal[[2]], "High", " Medium")
)))
regressants = paste0(regressants, " + Country_Urbanization_")
columnsToKeep = c(columnsToKeep, "Country_Urbanization_")
}
if ('countryGDP' %in% TopicalSpecs == "TRUE") {
# tab = subset(tab, GDPPC > 0)
tab$Country_GDP_ = as.factor(ifelse(
tab$GDPPC == "MissingInfo", "MissingInfo",
ifelse(
tab$GDPPC <= input$GDPVal[[1]],
"Low",
ifelse(tab$GDPPC >= input$GDPVal[[2]], "High", " Medium")
)))
regressants = paste0(regressants, " + Country_GDP_")
columnsToKeep = c(columnsToKeep, "Country_GDP_")
}
if ('yearOfPubli' %in% OtherSpecs == "TRUE") {
tab$Year_Of_Publication_ = as.factor(ifelse(
tab$YEARPUB <= input$yearOfP[[1]],
"Early",
ifelse(tab$YEARPUB >= input$yearOfP[[2]], "Recent", " Medium")
))
regressants = paste0(regressants, " + Year_Of_Publication_")
columnsToKeep = c(columnsToKeep, "Year_Of_Publication_")
}
if ('studySize' %in% OtherSpecs == "TRUE") {
tab$Study_Size_ = as.factor(ifelse(
tab$N_ESTIM == 1,
"Single",
ifelse(tab$N_ESTIM >= input$n_estim, "Large", " Small")
))
regressants = paste0(regressants, " + Study_Size_")
columnsToKeep = c(columnsToKeep, "Study_Size_")
}
if ('studyCoverage' %in% OtherSpecs == "TRUE") {
tab$Territorial_Coverage_ = as.factor(ifelse(
tab$N_COUNTRIES == 1,
"Single",
ifelse(tab$N_COUNTRIES >= input$n_territories, "Large", " Small")
))
regressants = paste0(regressants, " + Territorial_Coverage_")
columnsToKeep = c(columnsToKeep, "Territorial_Coverage_")
}
if ('studyPeriod' %in% OtherSpecs == "TRUE") {
tab$Period_Analysed_ = as.factor(ifelse(
tab$StudyPeriod == 0,
"Cross_section",
ifelse(tab$StudyPeriod >= input$s_period, "Long", " Short")
))
regressants = paste0(regressants, " + Period_Analysed_")
columnsToKeep = c(columnsToKeep, "Period_Analysed_")
}
#
#
# sameSample = input$sameSample
# if (sameSample == T) {
# tab = subset(tab, TRUNCATION_POINT >= 0)
# tab = subset(tab, N >= 0)
# tab = subset(tab, URBANISATION != "")
# tab = subset(tab, URBP > 0)
# tab = subset(tab, TOTAL_POP > 0)
# tab = subset(tab, GDPPC > 0)
# tab = tab[, columnsToKeep]
# tab = tab[complete.cases(tab),]
# }
#
model = lm(regressants, data = tab, na.action = na.omit)
return(model)
})
metaModelFixed <- reactive({
tab = metaPaperSelection()
if (input$subsetData == T){
terr = input$territory
dec = input$decade
def = input$scale
if (length(terr) >= 1)
tab = tab[tab$TERRITORY %in% terr,]
if (length(dec) >= 1)
tab = tab[tab$DECADE %in% dec,]
if (length(def) >= 1)
tab = tab[tab$URBANSCALE %in% def,]
}
if (input$alpha == "Lotka")
tab$ALPHA = tab$ALPHALOTKA
if (input$alpha == "Pareto")
tab$ALPHA = tab$ALPHAPARETO
TechnicalSpecs = input$technicalSpecs
TopicalSpecs = input$topicalSpecs
OtherSpecs = input$otherSpecs
if ('alltech' %in% TechnicalSpecs == "TRUE")
TechnicalSpecs = c("truncation4model", "N4model", "scale4model", "regForm", "olsOrnot")
if ('alltop' %in% TopicalSpecs == "TRUE")
TopicalSpecs = c("time",
"urbanisation4model",
"countrySize",
"countryGDP",
"countryUrb")
if ('allother' %in% OtherSpecs == "TRUE")
OtherSpecs = c("yearOfPubli",
"studySize",
"studyPeriod",
"studyCoverage") #"discipline"
regressants = "ALPHA ~ 1"
columnsToKeep = c("REFID", "CNTR_ID", "ALPHA" )
if ('truncation4model' %in% TechnicalSpecs == "TRUE") {
tab$Population_Cutoff_ = as.factor(ifelse(
tab$TRUNCATION_POINT == "MissingInfo", "MissingInfo",ifelse(
tab$TRUNCATION_POINT <= input$truncVal[[1]],
"Low",
ifelse(
tab$TRUNCATION_POINT >= input$truncVal[[2]],
"High",
" Medium"
)
)))
regressants = paste0(regressants, " + Population_Cutoff_")
columnsToKeep = c(columnsToKeep, "Population_Cutoff_")
}
if ('N4model' %in% TechnicalSpecs == "TRUE") {
tab$Number_Of_Cities_ = as.factor(ifelse(
tab$N == "MissingInfo", "MissingInfo",ifelse(
tab$N <= input$NVal[[1]],
"Small",
ifelse(tab$N >= input$NVal[[2]], "Large", " Medium")
)))
regressants = paste0(regressants, " + Number_Of_Cities_")
columnsToKeep = c(columnsToKeep, "Number_Of_Cities_")
}
if ('regForm' %in% TechnicalSpecs == "TRUE") {
tab$Regression_Form_ = tab$REGRESSION
regressants = paste0(regressants, " + Regression_Form_")
columnsToKeep = c(columnsToKeep, "Regression_Form_")
}
if ('olsOrnot' %in% TechnicalSpecs == "TRUE") {
tab$Estimation_Method_ = tab$ESTIMATION
regressants = paste0(regressants, " + Estimation_Method_")
columnsToKeep = c(columnsToKeep, "Estimation_Method_")
}
if ('scale4model' %in% TechnicalSpecs == "TRUE") {
tab$City_Definition_ = tab$URBANSCALE
regressants = paste0(regressants, " + City_Definition_")
columnsToKeep = c(columnsToKeep, "City_Definition_")
}
if ('urbanisation4model' %in% TopicalSpecs == "TRUE") {
# tab = subset(tab, URBANISATION != "")
tab$Urbanisation_Age_ = tab$URBANISATION
regressants = paste0(regressants, " + Urbanisation_Age_")
columnsToKeep = c(columnsToKeep, "Urbanisation_Age_")
}
if ('time' %in% TopicalSpecs == "TRUE") {
tab$Date_of_Observation_ = as.factor(ifelse(
tab$DATE <= input$nTime[[1]],
"Early",
ifelse(tab$DATE >= input$nTime[[2]], "Late", " Intermediate")
))
regressants = paste0(regressants, " + Date_of_Observation_")
columnsToKeep = c(columnsToKeep, "Date_of_Observation_")
}
if ('countrySize' %in% TopicalSpecs == "TRUE") {
# tab = subset(tab, TOTAL_POP > 0)
tab$Country_Size_ = as.factor(ifelse(
tab$TOTAL_POP == "MissingInfo", "MissingInfo",ifelse(
tab$TOTAL_POP <= input$PopVal[[1]],
"Small",
ifelse(tab$TOTAL_POP >= input$PopVal[[2]], "Large", " Medium")
)))
regressants = paste0(regressants, " + Country_Size_")
columnsToKeep = c(columnsToKeep, "Country_Size_")
}