The following is an example of how I want to treat my data sets. It might be a bit different to understand how my data frame is structured, but I hope it makes sense:
First density must be calculated for columns A, B, and C using raw data from columns ADry, AEthanol, BDry ...... (Since these were earlier defined as vectors too, i used the vectors instead data frame columns as it was shorter - ADry_1_0 instead of Sample_1_0$ADry_1_0)
Sample_1_0$ADensi_1_0=(ADry_1_0/(ADry_1_0-AEthanol_1_0))*(peth-pair)+pair
Sample_1_0$BDensi_1_0=(BDry_1_0/(BDry_1_0-BEthanol_1_0))*(peth-pair)+pair
Sample_1_0$CDensi_1_0=(CDry_1_0/(CDry_1_0-CEthanol_1_0))*(peth-pair)+pair
This yields 10 densities for both A, B, and C. What's interesting is the mean density
Mean_1_0=apply(Sample_1_0[7:9],2,mean)
Next standard deviations are found. We are mainly interested in standard deviations for our raw data columns (ADry and AEthanol), as error propagation calculations are afterwards carried out to find out how the deviations sum up when calculating the densities
StdAfv_1_0=apply(Sample_1_0,2,sd)
Error propagation (same for B and C)
ASd_1_0=(sqrt((sd(Sample_1_0$ADry_1_0)/mean(Sample_1_0$ADry_1_0))^2+(sqrt((sd(Sample_1_0$ADry_1_0)^2+sd(Sample_1_0$AEthanol_1_0)^2))/(mean(Sample_1_0$ADry_1_0)-mean(Sample_1_0$AEthanol_1_0)))^2))*mean(Sample_1_0$ADensi_1_0)
In the end we semi manually gathered the end informations (mean density and deviation hereof) in a plot-able dataframe. Some of the codes might be a tad long and maybe we could have achieved equal results using shorter codes, but bear with us, we are rookies.
So now to the real actual problem
This was for A_1_0, B_1_0, and C_1_0. We would like to apply the same series of commands to 15 other data frames. The dimensions are the same, and they will be named A_1_1, A_1_2, A_2_0 and so on.
Is it possible to use some kind of loop function or make a loadable script containing x and y placeholders, where we can easily insert A_1_1 for instance??
Thanks in advance, i tried to keep the amount of confusion at a minimum, although it's tough!