Change indent to 4 (#4)

* Change indent to 4

* Format code with JuliaFormatter

.JuliaFormatter.toml

@@ -1,2 +1,2 @@
-indent = 2
+indent = 4
 format_docstrings = true

01-files.jl

@@ -90,7 +90,7 @@ DataFrame(readstat("iv_bolus_sd.xpt"))
 ##############################################################################################
 # Optional: run this to delete all the files created in the examples
 begin
-  root_files = filter(contains("new"), readdir())
-  data_files = joinpath.("data", filter(contains("new"), readdir("data")))
-  foreach(rm, vcat(root_files, data_files))
+    root_files = filter(contains("new"), readdir())
+    data_files = joinpath.("data", filter(contains("new"), readdir("data")))
+    foreach(rm, vcat(root_files, data_files))
 end

02-select_subset.jl

@@ -24,9 +24,9 @@ using DataFramesMeta # You don't need to import DataFrames if you import DataFra
 @select(df, :AGE, :WEIGHT) # We can also call it in a similar way to functions
 
 @select df begin # block syntax, probably the best alternative for multiple columns
-  :ID
-  :AGE
-  :WEIGHT
+    :ID
+    :AGE
+    :WEIGHT
 end
 
 ## Tip: select columns the other way around
@@ -43,16 +43,16 @@ df[4:16, All()] # Get rows 4 to 16 for all columns
 
 # You can also have multiple conditions
 @subset df begin
-  :AGE .> 60
-  :ISMALE .== 1 # Get males only
-  :WEIGHT .< 50 # Get subjects that weigh less than 50 kg
+    :AGE .> 60
+    :ISMALE .== 1 # Get males only
+    :WEIGHT .< 50 # Get subjects that weigh less than 50 kg
 end
 
 ## Tip: use @rsubset instead of broadcasting everything (.>, .==, etc.)
 @rsubset df begin
-  :AGE > 60
-  :ISMALE == 1
-  :WEIGHT < 50
+    :AGE > 60
+    :ISMALE == 1
+    :WEIGHT < 50
 end
 
 ## You don't always want to use @rsubset

03-transform.jl

@@ -12,16 +12,16 @@ df
 
 # You can also apply multiple transformations at once
 @rtransform df begin
-  :ISMALE = :ISMALE == 0 ? " Female" : "Male"
-  :AGE = Int(round(:AGE, digits = 0)) # Round age to an integer
-  :AGE_months = :AGE * 12 # Calculate age in months
+    :ISMALE = :ISMALE == 0 ? " Female" : "Male"
+    :AGE = Int(round(:AGE, digits = 0)) # Round age to an integer
+    :AGE_months = :AGE * 12 # Calculate age in months
 end
 
 # Notice that our age in months was not computed from the rounded version of the AGE column
 ## We have to use @astable to be able to use intermediate results
 @rtransform df @astable begin
-  :AGE = Int(round(:AGE, digits = 0))
-  :AGE_months = :AGE * 12
+    :AGE = Int(round(:AGE, digits = 0))
+    :AGE_months = :AGE * 12
 end
 
 # Modify the original DataFrame

04-grouping.jl

@@ -26,14 +26,14 @@ combined_df = @combine groupby(df, [:WEIGHT_cat, :ISMALE]) :AGE = mean(:AGE)
 
 ## Tip: you can include multiple calculations inside of @combine
 @combine grouped_df begin
-  :AGE = mean(:AGE)
-  :WEIGHT = mean(:WEIGHT)
-  :n = length(:AGE) # Calculate the number of subjects for each group
+    :AGE = mean(:AGE)
+    :WEIGHT = mean(:WEIGHT)
+    :n = length(:AGE) # Calculate the number of subjects for each group
 end
 
 # the @by macro: groupby + @combine in one call
 @by df :ISMALE begin
-  :AGE = mean(:AGE)
-  :WEIGHT = mean(:WEIGHT)
-  :n = length(:AGE)
+    :AGE = mean(:AGE)
+    :WEIGHT = mean(:WEIGHT)
+    :n = length(:AGE)
 end

05-chaining.jl

@@ -3,28 +3,28 @@ df = CSV.read("demographics.csv", DataFrame)
 
 # Get ages for all female subjects
 @chain df begin
-  @rsubset :ISMALE == 0
-  @select :ID :AGE # We didn't have to pass df as an argument
+    @rsubset :ISMALE == 0
+    @select :ID :AGE # We didn't have to pass df as an argument
 end
 
 # More complicated example
 @chain df begin
 
-  @rtransform begin
-    :SEX = :ISMALE == 0 ? "Female" : "Male" # Create the new sex column
-    :WEIGHT_cat = :WEIGHT > 70 ? "Over 70 kg" : "Under 70 kg" # Create weight categories
-  end
+    @rtransform begin
+        :SEX = :ISMALE == 0 ? "Female" : "Male" # Create the new sex column
+        :WEIGHT_cat = :WEIGHT > 70 ? "Over 70 kg" : "Under 70 kg" # Create weight categories
+    end
 
-  @by [:SEX, :WEIGHT_cat] begin # Calculate mean values for each column
-    :AGE = mean(:AGE)
-    :SCR = mean(:SCR)
-    :eGFR = mean(:eGFR)
-    :n = length(:AGE)
-  end
+    @by [:SEX, :WEIGHT_cat] begin # Calculate mean values for each column
+        :AGE = mean(:AGE)
+        :SCR = mean(:SCR)
+        :eGFR = mean(:eGFR)
+        :n = length(:AGE)
+    end
 
-  @orderby :SEX :WEIGHT_cat # Fix ordering
+    @orderby :SEX :WEIGHT_cat # Fix ordering
 
-  # Make column names more readable
-  rename(Dict(:SEX => :Sex, :WEIGHT_cat => :Weight, :AGE => :Age))
+    # Make column names more readable
+    rename(Dict(:SEX => :Sex, :WEIGHT_cat => :Weight, :AGE => :Age))
 
 end