group by and scale/normalize a column in r
The issue is that you are using the wrong dplyr verb. Summarize will create one result per group per variable. What you want is mutate. Mutate changes variables and returns a result of the same length as the original. See http://cran.rstudio.com/web/packages/dplyr/vignettes/dplyr.html. Below two approaches using dplyr:
df %>%
group_by(Store) %>%
mutate(Temperature = normalit(Temperature), Sum_Sales = normalit(Sum_Sales))
df %>%
group_by(Store) %>%
mutate_each(funs(normalit), Temperature, Sum_Sales)
Note: The Store variable is different between your data and desired result. I assumed that @jlhoward got the right data.
Here's a data.table solution. I changed your example a bit to have two type of store.
df <- read.table(header=T,text="Store Temperature Unemployment Sum_Sales
1 1 42.31 8.106 1643691
2 1 38.51 8.106 1641957
3 1 39.93 8.106 1611968
4 2 46.63 8.106 1409728
5 2 46.50 8.106 1554807
6 2 57.79 8.106 1439542")
library(data.table)
DT <- as.data.table(df)
DT[,list(Temperature=normalit(Temperature),Sum_Sales=normalit(Sum_Sales)),
by=list(Store,Unemployment)]
# Store Unemployment Temperature Sum_Sales
# 1: 1 8.106 1.00000000 1.0000000
# 2: 1 8.106 0.00000000 0.9453393
# 3: 1 8.106 0.37368421 0.0000000
# 4: 2 8.106 0.01151461 0.0000000
# 5: 2 8.106 0.00000000 1.0000000
# 6: 2 8.106 1.00000000 0.2055018
Note that your normalization will have problems if there is only 1 row for a stoer.