R/incomeSrc.R
In ColoradoDemography/codemogLib:
Defines functions
incomeSrc
#' incomeSrc Displays a table showinf sources of incomes
#'
#' @param level the data level from input$level
#' @param listID the list containing place id and Place names
#' @param ACS Specifies the ACS data set to be used, reads curACS from Shiny program
#' @param oType output type html table or latex table
#' @return kable formatted table and data file
#' @export
incomeSrc <- function(level, listID, ACS, oType) {
# Collecting place ids from idList, setting default values
# Currently Output Counties
if(level == "Municipalities") {
if(listID$PlFilter == "F") {
fipslist <- listID$plNum
fipsname <- listID$plName
} else {
fipslist <- listID$ctyNum
fipsname <- listID$ctyName
}
}
if(level == "Counties") {
fipslist <- listID$ctyNum
fipsname <- listID$ctyName
}
hhSQL <- paste0("SELECT * FROM data.incomesrc_hh WHERE (geonum = 108", fipslist," and acs = '",ACS,"');")
incSQL <- paste0("SELECT * FROM data.incomesrc_inc WHERE (geonum = 108", fipslist," and acs = '",ACS,"');")
# Call to Postrgres
pw <- {
"demography"
}
# loads the PostgreSQL driver
drv <- dbDriver("PostgreSQL")
# creates a connection to the postgres database
# note that "con" will be used later in each connection to the database
con <- dbConnect(drv, dbname = "dola",
host = "104.197.26.248", port = 5433,
user = "codemog", password = pw)
rm(pw) # removes the password
f.Households <- dbGetQuery(con,hhSQL)
f.Incomes <- dbGetQuery(con,incSQL)
#closing the connections
dbDisconnect(con)
dbUnloadDriver(drv)
rm(con)
rm(drv)
f.Households[f.Households == -9999] <- NA
f.Incomes[f.Incomes == -9999] <- NA
#County Level Household Income Counts
incC51 <- f.Households[c(3:5)]
incC54 <- f.Households[c(6:8)]
incC55 <- f.Households[c(9:11)]
incC56 <- f.Households[c(12:14)]
incC57 <- f.Households[c(15:17)]
incC59 <- f.Households[c(18:20)]
names(incC51)[c(1,2,3)] <- c("total", "in", "out")
names(incC54)[c(1,2,3)] <- c("total", "in", "out")
names(incC55)[c(1,2,3)] <- c("total", "in", "out")
names(incC56)[c(1,2,3)] <- c("total", "in", "out")
names(incC57)[c(1,2,3)] <- c("total", "in", "out")
names(incC59)[c(1,2,3)] <- c("total", "in", "out")
incCty <- do.call("rbind", list(incC51, incC54, incC55, incC56, incC57, incC59))
incCty$ID <- seq.int(nrow(incCty))
incCty$type <- c("With earnings","With interest, dividends or net rental income",
"With Social Security income", "With Supplemental Security Income (SSI)","With cash public assistance income",
"With retirement income")
# County Level MOE Counts
incC51_moe <- f.Households[c(21:23)]
incC54_moe <- f.Households[c(24:26)]
incC55_moe <- f.Households[c(27:29)]
incC56_moe <- f.Households[c(30:32)]
incC57_moe <- f.Households[c(33:35)]
incC59_moe <- f.Households[c(36:38)]
names(incC51_moe)[c(1,2,3)] <- c("total_moe", "in_moe", "out_moe")
names(incC54_moe)[c(1,2,3)] <- c("total_moe", "in_moe", "out_moe")
names(incC55_moe)[c(1,2,3)] <- c("total_moe", "in_moe", "out_moe")
names(incC56_moe)[c(1,2,3)] <- c("total_moe", "in_moe", "out_moe")
names(incC57_moe)[c(1,2,3)] <- c("total_moe", "in_moe", "out_moe")
names(incC59_moe)[c(1,2,3)] <- c("total_moe", "in_moe", "out_moe")
incCty_moe <- do.call("rbind", list(incC51_moe, incC54_moe, incC55_moe, incC56_moe, incC57_moe, incC59_moe))
incCty_moe$ID <- seq.int(nrow(incCty_moe))
incCty_moe$type <- c("With earnings","With interest, dividends or net rental income",
"With Social Security income", "With Supplemental Security Income (SSI)","With cash public assistance income",
"With retirement income")
ctyHH <- as.numeric(unique(incCty$total))
ctyHH_moe <- as.numeric(unique(incCty_moe$total_moe))
#combining files
incCty2 <- incCty[,c(4,5,2)]
incCty_moe2 <- incCty_moe[,c(4,5,2)]
incCtyHH <- merge(incCty2, incCty_moe2, by="ID")
incCtyHH <- incCtyHH[,c(1:3,5)]
names(incCtyHH) <- c("ID","type","total","total_moe")
totRow <- data.frame(ID = 0,
type="All Households",
total=ctyHH,
total_moe=ctyHH_moe)
incCtyHH <- rbind(totRow,incCtyHH)
incCtyHH$pct <- percent((as.numeric(incCtyHH$total)/as.numeric(ctyHH))*100)
incCtyHH$pct_moe <- percent((as.numeric(incCtyHH$total_moe)/as.numeric(ctyHH))*100)
# County HH Income Value
mincC61 <- f.Incomes[c(2,10)]
mincC64 <- f.Incomes[c(2,11)]
mincC65 <- f.Incomes[c(2,12)]
mincC66 <- f.Incomes[c(2,13)]
mincC67 <- f.Incomes[c(2,14)]
mincC69 <- f.Incomes[c(2,15)]
names(mincC61)[2] <- "Agg_Income"
names(mincC64)[2] <- "Agg_Income"
names(mincC65)[2] <- "Agg_Income"
names(mincC66)[2] <- "Agg_Income"
names(mincC67)[2] <- "Agg_Income"
names(mincC69)[2] <- "Agg_Income"
mincCty <- do.call("rbind", list(mincC61, mincC64, mincC65, mincC66, mincC67, mincC69))
mincCty$ID <- seq.int(nrow(mincCty))
mincCty$type <- c("With earnings","With interest, dividends or net rental income",
"With Social Security income", "With Supplemental Security Income (SSI)","With cash public assistance income",
"With retirement income")
mincC61_moe <- f.Incomes[c(2,23)]
mincC64_moe <- f.Incomes[c(2,24)]
mincC65_moe <- f.Incomes[c(2,25)]
mincC66_moe <- f.Incomes[c(2,26)]
mincC67_moe <- f.Incomes[c(2,27)]
mincC69_moe <- f.Incomes[c(2,28)]
names(mincC61_moe)[2] <- "Agg_Income_moe"
names(mincC64_moe)[2] <- "Agg_Income_moe"
names(mincC65_moe)[2] <- "Agg_Income_moe"
names(mincC66_moe)[2] <- "Agg_Income_moe"
names(mincC67_moe)[2] <- "Agg_Income_moe"
names(mincC69_moe)[2] <- "Agg_Income_moe"
mincCty_moe <- do.call("rbind", list(mincC61_moe, mincC64_moe, mincC65_moe, mincC66_moe, mincC67_moe, mincC69_moe))
mincCty_moe$ID <- seq.int(nrow(mincCty_moe))
mincCty_moe$type <- c("With earnings","With interest, dividends or net rental income",
"With Social Security income", "With Supplemental Security Income (SSI)","With cash public assistance income",
"With retirement income")
# Creating Summary values
mincCTot <- as.numeric(f.Incomes[1,3])
mincCTot_moe <- as.numeric(f.Incomes[1,16])
#combining files
mincCty2 <- mincCty[,c(3,4,2)]
mincCty_moe2 <- mincCty_moe[,c(3,4,2)]
mincCtyHH <- merge(mincCty2, mincCty_moe2, by="ID")
mincCtyHH <- mincCtyHH[,c(1:3,5)]
names(mincCtyHH) <- c("ID","type","Agg_Income","Agg_Income_moe")
totRow <- data.frame(ID = 0,
type="All Households",
Agg_Income=mincCTot,
Agg_Income_moe=mincCTot_moe)
mincCtyHH <- rbind(totRow,mincCtyHH)
# Producing final File
mincCtyF <- mincCtyHH[,c(1,3,4)]
incCtyFin <- merge(incCtyHH, mincCtyF, by ="ID")
incCtyFin$avg_income <- paste0("$",format(round(as.numeric(incCtyFin$Agg_Income)/as.numeric(incCtyFin$total),digits=0),big.mark=","))
incCtyFin$avg_income_moe <- paste0("$",format(round(as.numeric(incCtyFin$Agg_Income_moe)/as.numeric(incCtyFin$total),digits=0),big.mark=","))
# Building table
totalHH <- format(as.numeric(incCtyFin[1,3]),big.mark=",")
totalHH_moe <- format(as.numeric(incCtyFin[1,4]),big.mark=",")
m.IncFin <- as.matrix(incCtyFin)
for(i in 1:nrow(m.IncFin)) {
if(is.na(m.IncFin[i,7])){
m.IncFin[i,9] = ""
m.IncFin[i,10] = ""
}
}
m.IncFin <- m.IncFin[,c(2,5,6,9,10)]
m.IncFin[1,2] <- totalHH
m.IncFin[1,3] <- totalHH_moe
# table Heading
tblHead1 <- c(fipsname = 5)
# set vector names
names(tblHead1) <- c(fipsname)
tblHead2 <- c(" " = 1,"Total Households" = 2,"Mean Income" = 2)
names(tblHead2) <- c(" ","Total Households","Mean Income")
names_spaced <- c("Income Source","Estimate","MOE", "Estimate","MOE")
if(oType == "html") {
inc_tab <- m.IncFin %>%
kable(format='html', table.attr='class="cleanTable"',
digits=1,
row.names=FALSE,
align='lrrrr',
caption="Household Income Source(s)",
col.names = names_spaced,
escape = FALSE) %>%
kable_styling(bootstrap_options = "condensed",full_width = F,font_size = 12) %>%
row_spec(0, align = "c") %>%
column_spec(1, width="3in") %>%
column_spec(2, width="0.75in") %>%
column_spec(3, width="0.75in") %>%
column_spec(4, width="0.75in") %>%
column_spec(5, width="0.75in") %>%
add_indent(c(2:7)) %>%
add_header_above(header=tblHead2) %>%
add_header_above(header=tblHead1) %>%
footnote(captionSrc("ACS",ACS))
f.outData <- as.data.frame(m.IncFin)
names(f.outData) <- c(paste0("Income Source: ",fipsname),"Households: Estimate",
"Households: MOE", "Average Income: Estimate", "Average Income; MOE")
#FlexTable
Ft <- data.frame(m.IncFin)
names(Ft) <- c("V1","V2","V3","V4","V5")
FlexOut <- regulartable(Ft)
FlexOut <- set_header_labels(FlexOut, V1 = "Income Source(s)",
V2 = "Estimate", V3 = "Margin of Error",
V4 = "Estimate", V5 = "Margin of Error")
FlexOut <- add_header(FlexOut,V1 = "", V2 = "Total Households", V3 = "",
V4 = "Mean Income", V5 ="",
top=TRUE)
FlexOut <- add_header(FlexOut,V1 =paste0("Household Income Source(s): ",fipsname), top=TRUE)
FlexOut <- add_footer(FlexOut,V1=captionSrc("ACS",ACS))
FlexOut <- merge_at(FlexOut,i=1,j = 1:5,part="header")
FlexOut <- merge_at(FlexOut,i=2,j = 2:3, part="header")
FlexOut <- merge_at(FlexOut,i=2,j = 4:5, part="header")
FlexOut <- merge_at(FlexOut,i=1, j = 1:5, part = "footer")
FlexOut <- align(FlexOut,i=1,j = 1, align="left",part="header")
FlexOut <- align(FlexOut,i=2:3,j = 1:5, align="center",part="header")
FlexOut <- align(FlexOut,i=1, align="left",part="footer")
FlexOut <- align(FlexOut, j=1, align="left", part="body")
FlexOut <- autofit(FlexOut)
FlexOut <- width(FlexOut,j = 1, width = 2)
FlexOut <- width(FlexOut, j = 2:5, width = 1)
outList <- list("table" = inc_tab, "data" = f.outData,"FlexTable"=FlexOut)
return(outList)
}
if(oType == "latex") {
inc_tab <- m.IncFin %>%
kable(
row.names=FALSE,
align='lrrrr',
caption="Household Income Source(s)",
col.names = names_spaced,
format="latex", booktabs=TRUE) %>%
kable_styling(latex_options="HOLD_position") %>%
row_spec(0, align = "c") %>%
column_spec(1, width="3in") %>%
column_spec(2, width="0.75in") %>%
column_spec(3, width="0.75in") %>%
column_spec(4, width="0.75in") %>%
column_spec(5, width="0.75in") %>%
add_indent(c(2:7)) %>%
add_header_above(header=tblHead2) %>%
add_header_above(header=tblHead1) %>%
footnote(captionSrc("ACS",ACS))
# Text
OutText <- paste0("The Houselold Income Source(s) Table shows household income sources and amounts for housholds in ", fipsname,".")
OutText <- paste0(OutText," Households will have multiple sources of income, so this table is not mutually exclusive.")
OutText <- paste0(OutText," Mean income values reflect values from the cited source.")
outList <- list("table" = inc_tab, "text" = OutText)
return(outList)
}
}
ColoradoDemography/codemogLib documentation built on Dec. 15, 2020, 2:42 a.m.
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Defines functions incomeSrc
#' incomeSrc Displays a table showinf sources of incomes
#'
#' @param level the data level from input$level
#' @param listID the list containing place id and Place names
#' @param ACS Specifies the ACS data set to be used, reads curACS from Shiny program
#' @param oType output type html table or latex table
#' @return kable formatted table and data file
#' @export
incomeSrc <- function(level, listID, ACS, oType) {
# Collecting place ids from idList, setting default values
# Currently Output Counties
if(level == "Municipalities") {
if(listID$PlFilter == "F") {
fipslist <- listID$plNum
fipsname <- listID$plName
} else {
fipslist <- listID$ctyNum
fipsname <- listID$ctyName
}
}
if(level == "Counties") {
fipslist <- listID$ctyNum
fipsname <- listID$ctyName
}
hhSQL <- paste0("SELECT * FROM data.incomesrc_hh WHERE (geonum = 108", fipslist," and acs = '",ACS,"');")
incSQL <- paste0("SELECT * FROM data.incomesrc_inc WHERE (geonum = 108", fipslist," and acs = '",ACS,"');")
# Call to Postrgres
pw <- {
"demography"
}
# loads the PostgreSQL driver
drv <- dbDriver("PostgreSQL")
# creates a connection to the postgres database
# note that "con" will be used later in each connection to the database
con <- dbConnect(drv, dbname = "dola",
host = "104.197.26.248", port = 5433,
user = "codemog", password = pw)
rm(pw) # removes the password
f.Households <- dbGetQuery(con,hhSQL)
f.Incomes <- dbGetQuery(con,incSQL)
#closing the connections
dbDisconnect(con)
dbUnloadDriver(drv)
rm(con)
rm(drv)
f.Households[f.Households == -9999] <- NA
f.Incomes[f.Incomes == -9999] <- NA
#County Level Household Income Counts
incC51 <- f.Households[c(3:5)]
incC54 <- f.Households[c(6:8)]
incC55 <- f.Households[c(9:11)]
incC56 <- f.Households[c(12:14)]
incC57 <- f.Households[c(15:17)]
incC59 <- f.Households[c(18:20)]
names(incC51)[c(1,2,3)] <- c("total", "in", "out")
names(incC54)[c(1,2,3)] <- c("total", "in", "out")
names(incC55)[c(1,2,3)] <- c("total", "in", "out")
names(incC56)[c(1,2,3)] <- c("total", "in", "out")
names(incC57)[c(1,2,3)] <- c("total", "in", "out")
names(incC59)[c(1,2,3)] <- c("total", "in", "out")
incCty <- do.call("rbind", list(incC51, incC54, incC55, incC56, incC57, incC59))
incCty$ID <- seq.int(nrow(incCty))
incCty$type <- c("With earnings","With interest, dividends or net rental income",
"With Social Security income", "With Supplemental Security Income (SSI)","With cash public assistance income",
"With retirement income")
# County Level MOE Counts
incC51_moe <- f.Households[c(21:23)]
incC54_moe <- f.Households[c(24:26)]
incC55_moe <- f.Households[c(27:29)]
incC56_moe <- f.Households[c(30:32)]
incC57_moe <- f.Households[c(33:35)]
incC59_moe <- f.Households[c(36:38)]
names(incC51_moe)[c(1,2,3)] <- c("total_moe", "in_moe", "out_moe")
names(incC54_moe)[c(1,2,3)] <- c("total_moe", "in_moe", "out_moe")
names(incC55_moe)[c(1,2,3)] <- c("total_moe", "in_moe", "out_moe")
names(incC56_moe)[c(1,2,3)] <- c("total_moe", "in_moe", "out_moe")
names(incC57_moe)[c(1,2,3)] <- c("total_moe", "in_moe", "out_moe")
names(incC59_moe)[c(1,2,3)] <- c("total_moe", "in_moe", "out_moe")
incCty_moe <- do.call("rbind", list(incC51_moe, incC54_moe, incC55_moe, incC56_moe, incC57_moe, incC59_moe))
incCty_moe$ID <- seq.int(nrow(incCty_moe))
incCty_moe$type <- c("With earnings","With interest, dividends or net rental income",
"With Social Security income", "With Supplemental Security Income (SSI)","With cash public assistance income",
"With retirement income")
ctyHH <- as.numeric(unique(incCty$total))
ctyHH_moe <- as.numeric(unique(incCty_moe$total_moe))
#combining files
incCty2 <- incCty[,c(4,5,2)]
incCty_moe2 <- incCty_moe[,c(4,5,2)]
incCtyHH <- merge(incCty2, incCty_moe2, by="ID")
incCtyHH <- incCtyHH[,c(1:3,5)]
names(incCtyHH) <- c("ID","type","total","total_moe")
totRow <- data.frame(ID = 0,
type="All Households",
total=ctyHH,
total_moe=ctyHH_moe)
incCtyHH <- rbind(totRow,incCtyHH)
incCtyHH$pct <- percent((as.numeric(incCtyHH$total)/as.numeric(ctyHH))*100)
incCtyHH$pct_moe <- percent((as.numeric(incCtyHH$total_moe)/as.numeric(ctyHH))*100)
# County HH Income Value
mincC61 <- f.Incomes[c(2,10)]
mincC64 <- f.Incomes[c(2,11)]
mincC65 <- f.Incomes[c(2,12)]
mincC66 <- f.Incomes[c(2,13)]
mincC67 <- f.Incomes[c(2,14)]
mincC69 <- f.Incomes[c(2,15)]
names(mincC61)[2] <- "Agg_Income"
names(mincC64)[2] <- "Agg_Income"
names(mincC65)[2] <- "Agg_Income"
names(mincC66)[2] <- "Agg_Income"
names(mincC67)[2] <- "Agg_Income"
names(mincC69)[2] <- "Agg_Income"
mincCty <- do.call("rbind", list(mincC61, mincC64, mincC65, mincC66, mincC67, mincC69))
mincCty$ID <- seq.int(nrow(mincCty))
mincCty$type <- c("With earnings","With interest, dividends or net rental income",
"With Social Security income", "With Supplemental Security Income (SSI)","With cash public assistance income",
"With retirement income")
mincC61_moe <- f.Incomes[c(2,23)]
mincC64_moe <- f.Incomes[c(2,24)]
mincC65_moe <- f.Incomes[c(2,25)]
mincC66_moe <- f.Incomes[c(2,26)]
mincC67_moe <- f.Incomes[c(2,27)]
mincC69_moe <- f.Incomes[c(2,28)]
names(mincC61_moe)[2] <- "Agg_Income_moe"
names(mincC64_moe)[2] <- "Agg_Income_moe"
names(mincC65_moe)[2] <- "Agg_Income_moe"
names(mincC66_moe)[2] <- "Agg_Income_moe"
names(mincC67_moe)[2] <- "Agg_Income_moe"
names(mincC69_moe)[2] <- "Agg_Income_moe"
mincCty_moe <- do.call("rbind", list(mincC61_moe, mincC64_moe, mincC65_moe, mincC66_moe, mincC67_moe, mincC69_moe))
mincCty_moe$ID <- seq.int(nrow(mincCty_moe))
mincCty_moe$type <- c("With earnings","With interest, dividends or net rental income",
"With Social Security income", "With Supplemental Security Income (SSI)","With cash public assistance income",
"With retirement income")
# Creating Summary values
mincCTot <- as.numeric(f.Incomes[1,3])
mincCTot_moe <- as.numeric(f.Incomes[1,16])
#combining files
mincCty2 <- mincCty[,c(3,4,2)]
mincCty_moe2 <- mincCty_moe[,c(3,4,2)]
mincCtyHH <- merge(mincCty2, mincCty_moe2, by="ID")
mincCtyHH <- mincCtyHH[,c(1:3,5)]
names(mincCtyHH) <- c("ID","type","Agg_Income","Agg_Income_moe")
totRow <- data.frame(ID = 0,
type="All Households",
Agg_Income=mincCTot,
Agg_Income_moe=mincCTot_moe)
mincCtyHH <- rbind(totRow,mincCtyHH)
# Producing final File
mincCtyF <- mincCtyHH[,c(1,3,4)]
incCtyFin <- merge(incCtyHH, mincCtyF, by ="ID")
incCtyFin$avg_income <- paste0("$",format(round(as.numeric(incCtyFin$Agg_Income)/as.numeric(incCtyFin$total),digits=0),big.mark=","))
incCtyFin$avg_income_moe <- paste0("$",format(round(as.numeric(incCtyFin$Agg_Income_moe)/as.numeric(incCtyFin$total),digits=0),big.mark=","))
# Building table
totalHH <- format(as.numeric(incCtyFin[1,3]),big.mark=",")
totalHH_moe <- format(as.numeric(incCtyFin[1,4]),big.mark=",")
m.IncFin <- as.matrix(incCtyFin)
for(i in 1:nrow(m.IncFin)) {
if(is.na(m.IncFin[i,7])){
m.IncFin[i,9] = ""
m.IncFin[i,10] = ""
}
}
m.IncFin <- m.IncFin[,c(2,5,6,9,10)]
m.IncFin[1,2] <- totalHH
m.IncFin[1,3] <- totalHH_moe
# table Heading
tblHead1 <- c(fipsname = 5)
# set vector names
names(tblHead1) <- c(fipsname)
tblHead2 <- c(" " = 1,"Total Households" = 2,"Mean Income" = 2)
names(tblHead2) <- c(" ","Total Households","Mean Income")
names_spaced <- c("Income Source","Estimate","MOE", "Estimate","MOE")
if(oType == "html") {
inc_tab <- m.IncFin %>%
kable(format='html', table.attr='class="cleanTable"',
digits=1,
row.names=FALSE,
align='lrrrr',
caption="Household Income Source(s)",
col.names = names_spaced,
escape = FALSE) %>%
kable_styling(bootstrap_options = "condensed",full_width = F,font_size = 12) %>%
row_spec(0, align = "c") %>%
column_spec(1, width="3in") %>%
column_spec(2, width="0.75in") %>%
column_spec(3, width="0.75in") %>%
column_spec(4, width="0.75in") %>%
column_spec(5, width="0.75in") %>%
add_indent(c(2:7)) %>%
add_header_above(header=tblHead2) %>%
add_header_above(header=tblHead1) %>%
footnote(captionSrc("ACS",ACS))
f.outData <- as.data.frame(m.IncFin)
names(f.outData) <- c(paste0("Income Source: ",fipsname),"Households: Estimate",
"Households: MOE", "Average Income: Estimate", "Average Income; MOE")
#FlexTable
Ft <- data.frame(m.IncFin)
names(Ft) <- c("V1","V2","V3","V4","V5")
FlexOut <- regulartable(Ft)
FlexOut <- set_header_labels(FlexOut, V1 = "Income Source(s)",
V2 = "Estimate", V3 = "Margin of Error",
V4 = "Estimate", V5 = "Margin of Error")
FlexOut <- add_header(FlexOut,V1 = "", V2 = "Total Households", V3 = "",
V4 = "Mean Income", V5 ="",
top=TRUE)
FlexOut <- add_header(FlexOut,V1 =paste0("Household Income Source(s): ",fipsname), top=TRUE)
FlexOut <- add_footer(FlexOut,V1=captionSrc("ACS",ACS))
FlexOut <- merge_at(FlexOut,i=1,j = 1:5,part="header")
FlexOut <- merge_at(FlexOut,i=2,j = 2:3, part="header")
FlexOut <- merge_at(FlexOut,i=2,j = 4:5, part="header")
FlexOut <- merge_at(FlexOut,i=1, j = 1:5, part = "footer")
FlexOut <- align(FlexOut,i=1,j = 1, align="left",part="header")
FlexOut <- align(FlexOut,i=2:3,j = 1:5, align="center",part="header")
FlexOut <- align(FlexOut,i=1, align="left",part="footer")
FlexOut <- align(FlexOut, j=1, align="left", part="body")
FlexOut <- autofit(FlexOut)
FlexOut <- width(FlexOut,j = 1, width = 2)
FlexOut <- width(FlexOut, j = 2:5, width = 1)
outList <- list("table" = inc_tab, "data" = f.outData,"FlexTable"=FlexOut)
return(outList)
}
if(oType == "latex") {
inc_tab <- m.IncFin %>%
kable(
row.names=FALSE,
align='lrrrr',
caption="Household Income Source(s)",
col.names = names_spaced,
format="latex", booktabs=TRUE) %>%
kable_styling(latex_options="HOLD_position") %>%
row_spec(0, align = "c") %>%
column_spec(1, width="3in") %>%
column_spec(2, width="0.75in") %>%
column_spec(3, width="0.75in") %>%
column_spec(4, width="0.75in") %>%
column_spec(5, width="0.75in") %>%
add_indent(c(2:7)) %>%
add_header_above(header=tblHead2) %>%
add_header_above(header=tblHead1) %>%
footnote(captionSrc("ACS",ACS))
# Text
OutText <- paste0("The Houselold Income Source(s) Table shows household income sources and amounts for housholds in ", fipsname,".")
OutText <- paste0(OutText," Households will have multiple sources of income, so this table is not mutually exclusive.")
OutText <- paste0(OutText," Mean income values reflect values from the cited source.")
outList <- list("table" = inc_tab, "text" = OutText)
return(outList)
}
}
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