Create data frame by iteratively adding rows

Question

I am trying to create a data frame (BOS.df) in order to explore the structure of a future analysis I will perform prior to receiving the actual data. In this scenario, lets say that there are 4 restaurants looking to run ad campaigns (the "Restaurant" variable). The total number of days that the campaign will last is cmp.lngth. I want random numbers for how much they are billing for the ads (ra.num). The ad campaigns start on StartDate. ultimately, I want to create a data frame the cycles through each restaurant, and adds a random billing number for each day of the ad campaign by adding rows.

#Create Data Placeholders
set.seed(123)
Restaurant <- c('B1', 'B2', 'B3', 'B4')
cmp.lngth <- 42
ra.num <- rnorm(cmp.lngth, mean = 100, sd = 10)
StartDate <- as.Date("2017-07-14")


BOS.df <- data.frame(matrix(NA, nrow =0, ncol = 3))
colnames(BOS.df) <- c("Restaurant", "Billings", "Date")


for(i in 1:length(Restaurant)){
  for(z in 1:cmp.lngth){
    BOS.row <- c(as.character(Restaurant[i]),ra.num[z],StartDate + 
    cmp.lngth[z]-1)
    BOS.df <- rbind(BOS.df, BOS.row)
  }
}

My code is not functioning correctly right now. The column names are incorrect, and the data is not being placed correctly if at all. The output comes through as follows:

  X.B1. X.94.3952435344779. X.17402.
1    B1    94.3952435344779    17402
2    B1                <NA>     <NA>
3    B1                <NA>     <NA>
4    B1                <NA>     <NA>
5    B1                <NA>     <NA>
6    B1                <NA>     <NA>

How can I obtain the correct output? Is there a more efficient way than using a for loop?

Answer 1

Using expand.grid:

cmp.lngth <- 2
StartDate <- as.Date("2017-07-14")

set.seed(1)
df1 <- data.frame(expand.grid(Restaurant, seq(cmp.lngth) + StartDate))
colnames(df1) <- c("Restaurant", "Date")
df1$Billings <- rnorm(nrow(df1), mean = 100, sd = 10)
df1 <- df1[ order(df1$Restaurant, df1$Date), ]

df1
#   Restaurant       Date  Billings
# 1         B1 2017-07-15  93.73546
# 5         B1 2017-07-16 103.29508
# 2         B2 2017-07-15 101.83643
# 6         B2 2017-07-16  91.79532
# 3         B3 2017-07-15  91.64371
# 7         B3 2017-07-16 104.87429
# 4         B4 2017-07-15 115.95281
# 8         B4 2017-07-16 107.38325