Dselby86
commented
2 years ago A function can only return a single value or object. It looks like what is happening is in your analyze data function you are returning a graph and a Table.
Try commenting out this line:
build_graph(t.salary, unit = i)
And if it now displays tables you
need to write a second function
that just returns a table.
On Thu, Feb 24, 2022, 12:20 AM dholford @.***> wrote:
Hello,
I successfully (mostly) created a loop to run through each unit (I'm still struggling to figure out the best way to collect the graph and table for each unit), but for some reason when I knit the file, it only prints the graphs in the html doc and doesn't include the tables. I'm going to only include the two functions I'm referencing, along with a function I packaged into one, and my loop since that is what seems to be off.
add_position <- function( t, position, y, xmax, scale.f=8 ) {
t.original <- t t <- filter( t.original, title==position ) dot.size <- 2 + scale.fsum(t$p) offset.n <- 1 + sum(t$p)2
male.median <- NA n.male <- NA t <- filter( t.original, title==position & gender == "male" ) if( nrow(t) > 0 ) { male.median <- t$q50 n.male <- t$n }
female.median <- NA n.female <- NA t <- filter( t.original, title==position & gender == "female" ) if( nrow(t) > 0 ) { female.median <- t$q50 n.female <- t$n }
dumbell plots
segments( x0=female.median, x1=male.median, y0=y, col=gray(0.3,0.5), lwd=7 ) points( male.median, y, col=adjustcolor( "darkblue", alpha.f = 0.5), pch=19, cex=dot.size ) points( female.median, y, col=adjustcolor( "firebrick", alpha.f = 0.5), pch=19, cex=dot.size )
pos.f <- 2 pos.m <- 4 if( ! ( is.na(female.median) | is.na(male.median) ) ) { pos.f <- ifelse( female.median > male.median, 4, 2 ) pos.m <- ifelse( female.median > male.median, 2, 4 ) }
add salaries to right and left
text( female.median, y, paste0("$",round(female.median/1000,0),"k"), col=adjustcolor( "firebrick", alpha.f = 0.7), cex=1.2, pos=pos.f, offset=offset.n ) text( male.median, y, paste0("$",round(male.median/1000,0),"k"), col=adjustcolor( "darkblue", alpha.f = 0.7), cex=1.2, pos=pos.m, offset=offset.n )
add faculty counts
n.female <- ifelse( is.na(n.female), 0, n.female ) n.female <- ifelse( nchar(n.female)==1, paste0( " ", n.female), n.female ) n.male <- ifelse( is.na(n.male), 0, n.male ) n.male <- ifelse( nchar(n.male)==1, paste0( " ", n.male), n.male ) text( xmax-0.1xmax, y+0.14, paste0( "f = ", n.female), col="gray50", cex=1.1, pos=4 ) text( xmax-0.1xmax, y-0.14, paste0( "m = ", n.male), col="gray50", cex=1.1, pos=4 )
axis( side=2, at=y, labels=position, las=2, tick=F, cex.axis=1.5, col.axis="gray50" ) }
build_graph <- function( t.salary, unit ) { unique.titles <- unique( t.salary$title ) ymax <- length(unique.titles) xmax <- round( max(t.salary$q50), -3 ) + 50000 color.key.pos <- 40000 + ( xmax - 40000 ) / 2 color.key.inc <- ( xmax - 40000 ) / 10
t.mf <- filter( t.salary, gender %in% c("male","female") ) N <- sum( t.mf$n )
par( mar=c(6,15,4.1,0) ) plot.new() plot.window( xlim=c(40000-10000,xmax), ylim=c(0,ymax+1) )
abline( v=seq(40000,xmax-40000,20000), lwd=1.5, lty=2, col=gray(0.5,0.5) ) axis( side=1, at=seq(40000,xmax-40000,20000), labels=paste0("$",seq(40,(xmax-40000)/1000,20),"k"), cex.axis=1.1, col.axis="gray40", tick=FALSE )
y <- ymax
if( "Full Professor" %in% unique.titles ) { add_position( t.salary, position="Full Professor", y, xmax ) y <- y-1 } if( "Associate Professor" %in% unique.titles ) { add_position( t.salary, position="Associate Professor", y, xmax ) y <- y-1 } if( "Assistant Professor" %in% unique.titles ) { add_position( t.salary, position="Assistant Professor", y, xmax ) y <- y-1 } if( "Teaching Faculty" %in% unique.titles ) { add_position( t.salary, position="Teaching Faculty", y, xmax ) y <- y-1 } if( "Researcher" %in% unique.titles ) { add_position( t.salary, position="Researcher", y, xmax ) y <- y-1 }
text( color.key.pos + 3color.key.inc, 0, "MALE", col=adjustcolor( "darkblue", alpha.f = 0.7), cex=1.2 ) text( color.key.pos + 1.8color.key.inc, 0, "FEMALE", col="firebrick", cex=1.2 ) text( xmax - 0.1*xmax, 0, paste0("N = ",N), col="gray40", cex=1.2, pos=4 )
title( main="Median Salary by Rank and Gender", cex.main=1.5, col.main="gray30" ) title( xlab=unit, col.lab="gray50", cex.lab=1.5, line=5 ) title( xlab="dot size represents proportion of faculty at that rank", col.lab="gray50", cex.lab=0.9 )
return(NULL) }
Step 10: Top Five Salaries
dollarize <- function(salary) { paste0("$", format( round( salary, 0 ), big.mark="," ) ) }
get_top_5 <- function(Data){
t.top5 <- select(Data, c('Full.Name', 'gender', 'Job.Description', 'salary'))%>% arrange(desc(salary)) %>% slice(1:5)
t.top5$salary <-dollarize(t.top5$salary)
t.top5 <- pander(t.top5)
return(t.top5)
}
Step 11: Package Functions into Single Process
analyze_data <- function(Data){ Data <- add_gender(Data) Data <- add_titles(Data) Data$salary <- convert_currency(Data) Data$salary <- get_fte(Data) t.salary <- create_salary_table(Data) build_graph(t.salary, unit = i) get_top_5(Data)
}
URL <- 'https://docs.google.com/spreadsheets/d/1RoiO9bfpbXowprWdZrgtYXG9_WuK3NFemwlvDGdym7E/export?gid=1335284952&format=csv' d <- read.csv( URL )
academic.units <- c("CISA-Intrdisp Hum & Comm", "CISA-Science & Mathematics", "College of Health Solutions MS", "College of Health Solutions NT", "College of Health Solutions SH", "College Of Law", "English", "Hugh Downs School Of Comm", "Humanities Arts & Cultural", "Journalism & Mass Comm", "Ldrshp and Integrative Studies", "Math & Natural Sciences Div", "MDT Music", "Physics Department", "Psychology", "Sch Biological & Hlth Sys Engr", "Sch Compt Infor & Dec Sys Engr", "Sch Elect Comptr & Energy Engr", "Sch Engr Matter Trnsprt Energy", "Sch Future of Innov in Society", "Sch Sustain Engr & Built Envrn", "School Of Art", "School of Criminology & Crim J", "School Of Earth & Space Explor", "School of Geog Sci & Urban Pln", "School of Math & Stat Sciences", "School of Molecular Sciences", "School of Politics & Global St", "School Of Public Affairs", "School of Social Transform", "School Of Social Work", "SHPRS History Faculty", "Social & Behavioral Sciences", "Sols Administration & Faculty", "SOS Faculty & Researchers", "The Design School", "The Sanford School", "WPC Accountancy", "WPC Economics", "WPC Information Systems", "WPC Management", "WPC Supply Chain Management")
for( i in academic.units ) {
d2 <- filter( d, Department.Description == i ) if( nrow(d2) == 0 ) { next } else {
analyze_data(d2)}
}
— Reply to this email directly, view it on GitHub https://github.com/Watts-College/cpp-527-spr-2022/issues/34, or unsubscribe https://github.com/notifications/unsubscribe-auth/AB4EHB2V7DOX3AXHCQRWFITU4XL5TANCNFSM5PGRVCRA . Triage notifications on the go with GitHub Mobile for iOS https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Android https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.
You are receiving this because you are subscribed to this thread.Message ID: @.***>
dholford
Hello,
I successfully (mostly) created a loop to run through each unit (I'm still struggling to figure out the best way to collect the graph and table for each unit), but for some reason when I knit the file, it only prints the graphs in the html doc and doesn't include the tables. I'm going to only include the two functions I'm referencing, along with a function I packaged into one, and my loop since that is what seems to be off.
Step 10: Top Five Salaries
Step 11: Package Functions into Single Process