I have a data sheet with 40 data columns (40 different nutrients), with additional columns for plot numbers and factors. I would like to automatically loop through each column name and produce a linear model and summary for each. The data columns begin at column 10.

for(i in 10:ncol(df)) {       # for-loop over columns
  mod2<-aov(i~block+tillage*residue+Error(subblock),data=df)
  summary(mod2)
}

This is currently producing the error Error in model.frame.default(formula = i ~ subblock, data = df, drop.unused.levels = TRUE) : variable lengths differ (found for 'subblock')
Variable lengths are consistent so I imagine I am looping incorrectly.

The data looks similar to below (with more categorical columns at the start), with the nutrient columns beginning at column 10.

>Solution :

In general it is helpful to post a sample of your data using dput(). In the absence of that I am going to use the built in dataset mtcars to show you how it is possible to do what you are doing with formula():

head(mtcars)

#                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
# Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
# Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
# Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
# Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
# Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
# Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

# Select columns
desired_columns  <- names(mtcars)[!names(mtcars)=="mpg"]

for (column in desired_columns){
    this_formula = formula(paste("mpg ~ ", column))
    print(summary(lm(this_formula, data = mtcars)))
}

This will output lm(mpg ~ var) for each var in the data. The key is the paste() statement which builds the expression into a string, and then formula() makes it into a formula object Hopefully you can see how this can be applied to your data.