Looping through csv and dumping to data frame in R - r

Question

Using R, I am trying to take a csv file, loop through it, extract values, and dump them into a data frame. There are four columns in the csv: ID, UG_inst, Freq, and Year. Specifically, I want to loop through the UG_inst column by institution name for each year (2010-11,2011-12,2012-13,and 2013-14) and put the value at that cell into the respective "cell" in the R data frame. Right now, the csv just has a Year column, but the data frame I've created has a column for each year. The ultimate idea is to be able to create bar graphs representing the frequency per institution per year. Currently, the code below throws up NO errors, but appears to do nothing to the R data frame "j".
A couple of caveats: 1) Doing a nested for loop was making my head spin, so I decided to just use 2010-11 for now and just loop through the institution name. Since there are only 4 years, I can rewrite this four times, each time with a different year. 2) Also, in the csv, there are repeat names. So, if an institution name appears twice (will be adjacent rows in the csv due to alphabetical arrangement), is there a way to dump the SUM of these into the data frame in R?
All relevant info is below. Thanks so much for any help!!!!
Here is a link to the .csv file: https://www.dropbox.com/s/9et7muchkrgtgz7/UG_inst_ALL.csv
And here is the R code I am trying:
abc <- read.csv(insert file path to above csv here)
inst_string <- unique(abc$UG_inst)
j <- data.frame("UG_inst"=inst_string,"2010-11"=NA,"2011-12"=NA,"2012-13"=NA,"2013-14"=NA)
for (i in inst_string) {
inst.index <- which(abc$UG_inst == i && abc$Year == "2010-11")
j$X2010.11[j$Ug_inst==i] <- abc$Freq[inst.index]
}

Answer 1

Instead of using a nested loop (or a loop at all) I suggest using the reshape() function in base R.
abc <- read.csv("UG_inst_ALL.csv")
abc <- abc[2:4]
reshape(data = abc,
v.names = "Freq",
timevar = "Year",
idvar = "UG_inst",
direction = "wide")

Answer 2

This is known as "reshaping" your data, and you are going from a "long" format to a "wide" format.
In addition to base R's reshape function, here are a few other options to consider.
I'll assume that we are starting with data read in like the following.
abc <- read.csv("~/Downloads/UG_inst_ALL.csv", row.names = 1)
head(abc)
# UG_inst Freq Year
# 1 Abilene Christian University 0 2010-11
# 2 Adams State University 0 2010-11
# 3 Adrian College 1 2010-11
# 4 Agnes Scott College 0 2010-11
# 5 Alabama A&M University 1 2010-11
# 6 Albion College 1 2010-11
Option 1: xtabs
out <- as.data.frame.matrix(xtabs(Freq ~ UG_inst + Year, abc))
head(out)
# 2010-11 2011-12 2012-13 2013-14
# Abilene Christian University 0 1 0 0
# Adams State University 0 0 0 1
# Adrian College 1 0 0 0
# Agnes Scott College 0 0 1 0
# Alabama A&M University 1 3 1 2
# Albion College 1 0 0 0
Option 2: dcast from "reshape2"
library(reshape2)
head(dcast(abc, UG_inst ~ Year, value.var = "Freq"))
Option 3: spread from "tidyr"
library(dplyr)
library(tidyr)
abc %>% select(-X) %>% group_by(UG_inst) %>% spread(Year, Freq)