> head(df)
               Empst Gender Age Agegroup   Marst                         Education State Year Month
1           Employed Female  58    50-60 Married  Some college or associate degree    AL 2008    12
2 Not in labor force   Male  63      61+ Married   Less than a high school diploma    AL 2008    12
3           Employed   Male  60    50-60  Single  Some college or associate degree    AL 2008    12
4 Not in labor force   Male  55    50-60  Single High school graduates, no college    AL 2008    12
5           Employed   Male  36    30-39  Single  Some college or associate degree    AL 2008    12
6           Employed Female  42    40-49 Married       Bachelor's degree or higher    AL 2008    12
  YYYYMM   Weight
1 200812 1876.356
2 200812 2630.503
3 200812 2763.981
4 200812 2693.110
5 200812 2905.784
6 200812 3511.313

sum(df[df$Empst=="Unemployed",]$Weight) /
    sum(df[df$Empst %in% c("Employed","Unemployed"),]$Weight)

UnR<-vector()
for(i in levels(factor(df$YYYYMM))){
  temp<-sum(df[df$Empst=="Unemployed" & df$YYYYMM == i,]$Weight) /
        sum(df[df$Empst %in% c("Employed","Unemployed") & df$YYYYMM == i,]$Weight)
  UnR<-append(UnR,temp)
  rm(temp)
}

Empst            Gender             Age         Agegroup          Marst
 Not in universe   :  11423   Male  :1266475   Min.   :16.00   16-19:187734   Married:1441114
 Employed          :1600882   Female:1377638   1st Qu.:31.00   20-29:422699   Married:      0
 Unemployed        : 132344                    Median :45.00   30-39:431298   Single :1202999
 Not in labor force: 899464                    Mean   :45.81   40-49:490533   Single :      0
                                               3rd Qu.:59.00   50-60:518633   Single :      0
                                               Max.   :85.00   61+  :593216   Single :      0

                             Education          State              Year          Month
 Less than a high school diploma  :418636   CA     : 221244   Min.   :2008   Min.   : 1.000
 High school graduates, no college:802141   TX     : 132650   1st Qu.:2008   1st Qu.: 4.000
 Some college or associate degree :719492   NY     : 114282   Median :2009   Median : 6.000
 Bachelor's degree or higher      :703844   FL     : 106116   Mean   :2009   Mean   : 6.385
                                            PA     :  82482   3rd Qu.:2009   3rd Qu.: 9.000
                                            IL     :  80816   Max.   :2010   Max.   :12.000
                                            (Other):1906523
     YYYYMM           Weight
 Min.   :200804   Min.   :    0
 1st Qu.:200810   1st Qu.: 1176
 Median :200904   Median : 2496
 Mean   :200887   Mean   : 2226
 3rd Qu.:200910   3rd Qu.: 3139
 Max.   :201004   Max.   :16822

unemployment_rate.df <- ddply(.data = df,
                              .variables = "YYYYMM",
                              .fun = function(x){
                                return(sum(x$weight[x$Empst== "unemployed"])/sum(x$weight[|x$Empst== "Not in labor force"]))

YYYYMM V1
200812 0.13
200901 0.1
200902 0.43

#Create an output vector. We can specify length, because we know there'll
#be one entry for each unique value in the YYYYMM column.
#That saves time because it means R just modifies the vector in place.
UnR <- numeric(length(unique(df$YYYYMM))

#And now, the for loop.
for(i in levels(factor(df$YYYYMM))){

  #Instead of creating a temporary object (which takes time), and then appending
  #(which takes time), we can just assign the result to the Ith element of the
  #output vector.
  UnR[i]<-sum(df[df$Empst=="Unemployed" & df$YYYYMM == i,]$Weight) /
        sum(df[df$Empst %in% c("Employed","Unemployed") & df$YYYYMM == i,]$Weight)
}

require(dplyr)
df %.%
    group_by(YYYYMM) %.%
    summarize(UnR = sum(Weight[Empst == "Employed"]) /
                    sum(Weight[Empst %in% c("Employed", "Unemployed")]))