# Shortening lengthy code
**URL:** https://discourse.julialang.org/t/shortening-lengthy-code/139627
**Category:** New to Julia
**Created:** [September 23, 2026, 4:23pm UTC](https://discourse.julialang.org/t/shortening-lengthy-code/139627 "2026-09-23T16:23:18Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@omgplshelp](https://discourse.julialang.org/u/omgplshelp)
#### Post date: [September 23, 2026, 4:23pm UTC](https://discourse.julialang.org/t/shortening-lengthy-code/139627/1 "2026-09-23T16:23:19Z")
Hello, I am trying to perform a linear regression for two separate layers using the same variables. Right now, I have written everything twice, once for layer one and once for layer two. Is there an easy way to simplify the code in order to have it execute the regression for both layers separately without having to write it twice?
```julia-auto
using DataFrames, CSV, Statistics, GLMakie, DelimitedFiles, GLM
ResData = DataFrame(Layer = rand(1:2, 200), X = rand(200), Y = rand(200), Porosity = rand(200), Density = rand(200), Permeability = rand(200))
#renaming columns from sorted ResData to use later on
LayerNum = ResData[:, 1]
XCoord = ResData[:, 2]
YCoord = ResData[:, 3]
Porosity = ResData[:, 4]
Density = ResData[:, 5]
Permeability = ResData[:, 6]
#use mask to sort data into layers 1 and 2
ResLayer1_mask = LayerNum .== 1
ResLayer2_mask = LayerNum .== 2
l1x = XCoord[ResLayer1_mask]
l1y = YCoord[ResLayer1_mask]
l2x = XCoord[ResLayer2_mask]
l2y = YCoord[ResLayer2_mask]
#data from layers 1 and 2 separated
Por1 = Porosity[ResLayer1_mask]
Por2 = Porosity[ResLayer2_mask]
Dens1 = Density[ResLayer1_mask]
Dens2 = Density[ResLayer2_mask]
Perm1 = Permeability[ResLayer1_mask]
Perm2 = Permeability[ResLayer2_mask]
#create porosity-permeability model for both layers using log-log
Por1_log = log.(Por1[Perm1 .> 0.0])
Perm1_log = log.(Perm1[Perm1 .> 0.0])
Por2_log = log.(Por2[Perm2 .> 0.0])
Perm2_log = log.(Perm2[Perm2 .> 0.0])
PorPerm1 = DataFrame(n=Por1_log, k=Perm1_log)
PorPerm2 = DataFrame(n=Por2_log, k=Perm2_log)
Formula2 = @formula(k ~ 1 + n)
LRPP1 = lm(Formula2, PorPerm1)
LRPP2 = lm(Formula2, PorPerm2)
#get regression line values for porosity-permeability plot
n_plot1 = range(minimum(Por1_log), maximum(Por1_log), length = 100)
PorPerm1_pred = predict(LRPP1, DataFrame(n = n_plot1), interval = :prediction, level = 0.95)
n_plot2 = range(minimum(Por2_log), maximum(Por2_log), length = 100)
PorPerm2_pred = predict(LRPP2, DataFrame(n = n_plot2), interval = :prediction, level = 0.95)
#extract prediction results
predictionPP1 = Vector{Float64}(PorPerm1_pred.prediction)
lowerPP1 = Vector{Float64}(PorPerm1_pred.lower)
upperPP1 = Vector{Float64}(PorPerm1_pred.upper)
predictionPP2 = Vector{Float64}(PorPerm2_pred.prediction)
lowerPP2 = Vector{Float64}(PorPerm2_pred.lower)
upperPP2 = Vector{Float64}(PorPerm2_pred.upper)
#make plot
f = Figure()
ax1 = Axis(f[1, 1],
title = "Linear Regression 2 With Error Bounds",
xlabel = "log(Porosity)",
ylabel = "log(Permeability)")
#plot for porosity/permeability
band!(ax1, n_plot1, lowerPP1, upperPP1, alpha = 0.3)
GLMakie.scatter!(ax1, Por1_log, Perm1_log, markersize=4)
lines!(ax1, n_plot1, predictionPP1, linewidth = 2)
lines!(ax1, n_plot1, lowerPP1, linewidth = 1.5, linestyle =:dash)
lines!(ax1, n_plot1, upperPP1, linewidth = 1.5, linestyle =:dash)
band!(ax1, n_plot2, lowerPP2, upperPP2, alpha = 0.3)
GLMakie.scatter!(ax1, Por2_log, Perm2_log, markersize=4)
lines!(ax1, n_plot2, predictionPP2, linewidth = 2)
lines!(ax1, n_plot2, lowerPP2, linewidth = 1.5, linestyle =:dash)
lines!(ax1, n_plot2, upperPP2, linewidth = 1.5, linestyle =:dash)
display(f)
```
I am aware of “for loops” but am not sure how I would go about implementing one in the code. Can anyone help?
---
### Author:  [@Jeff\_Emanuel](https://discourse.julialang.org/u/Jeff_Emanuel)
#### Post date: [September 23, 2026, 4:32pm UTC](https://discourse.julialang.org/t/shortening-lengthy-code/139627/2 "2026-09-23T16:32:46Z")
```julia-auto
# First define a struct to hold your results here
struct Results
# Exercise for reader
end
results = Results[]
for ilayer=1:2
mask = LayerNum .== ilayer
lx = XCoord[mask]
ly = YCoord[mask]
# etc.
push!(results,Results(#=your regression data here=#))
end
# Plot or save results here
```
---
### Author:  [@mikmoore](https://discourse.julialang.org/u/mikmoore)
#### Post date: [September 23, 2026, 7:13pm UTC](https://discourse.julialang.org/t/shortening-lengthy-code/139627/3 "2026-09-23T19:13:34Z")
This would be a good place for a function. Something like this:
```julia
function compute_prediction(ResData)
lx = ResData.X
ly = ResData.Y
por = ResData.Porosity
perm = ResData.Permeability
Por_log = log.(por[perm .> 0])
Perm_log = log.(perm[perm .> 0])
# ... keep going
# return predictionPP, lowerPP, upperPP # can return as Tuple
return (; predictionPP, lowerPP, upperPP) # but a NamedTuple labels the outputs nicely
end
# compute layers chosen manually
predictionPP1, lowerPP1, upperPP1 = compute_prediction(ResData[ResData.Layer .== 1, :]) # only processes where Layer == 1
predictionPP2, lowerPP2, upperPP2 = compute_prediction(ResData[ResData.Layer .== 2, :]) # only processes where Layer == 2
# or can compute a range of layers in a single call
results = map(layer -> compute_prediction(ResData[ResData.Layer .== layer, :]), 1:2) # compute layers in range 1:2
predictionPP1, lowerPP1, upperPP1 = results[1] # can extract them this way (note: if you don't start your layer range from 1, results[1] will not correspond to Layer==1)
```
(There’s a chance I’ve made minor typos here. I have not attempted to run this because obviously I didn’t finish writing everything.)