# File Input/Output

Many Julia programs involve the input and output of files. When analyzing a dataset, that dataset file will need to be pulled into your program (input). If you want to see the results of your analysis, your program will need an output. 

This section provides the syntax for inputing files (reading) and outputting results (writing) use base Julia (i.e., no packages such as CSV.jl).

### UC Irvine Machine Learning Repository: Adult Data Set

* Tabulate and report counts for sex in [Adult Data Set](https://archive.ics.uci.edu/dataset/2/adult) from the [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/).

***Dataset*** (example lines from `adult.data`) 

```julia
39, State-gov, 77516, Bachelors, 13, Never-married, Adm-clerical, Not-in-family, White, Male, 2174, 0, 40, United-States, <=50K
50, Self-emp-not-inc, 83311, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 13, United-States, <=50K
38, Private, 215646, HS-grad, 9, Divorced, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
53, Private, 234721, 11th, 7, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male, 0, 0, 40, United-States, <=50K
28, Private, 338409, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, Black, Female, 0, 0, 40, Cuba, <=50K
```

***Input*** (`process_file.jl`)

```julia
# process_file.jl
# Tabulate and report counts for sex in Adult Data Set
# https://archive.ics.uci.edu/ml/datasets/adult

# relative path of file
data_file = open("_data/adult/adult.data", "r")

# absolute path of file
# data_file = open("/Users/user/data/adult/adult.data", "r")

# initialize collection (dictionary for tabulating counts)
gender_dict = Dict()

# read each line, extract sex, and keep track of counts
for line in readlines(data_file)

  # skip empty lines
  if isempty(line)
      continue
   end

  # split line into array, based on delimiter (comma and space)
  line_array = split(line, ", ")

  # tabulate the counts for gender
  gender = line_array[10]
  if haskey(gender_dict, gender)
    gender_dict[gender] += 1
  else
    gender_dict[gender] = 1
  end
end

# report total counts
println("Sort by key (alphabetical):")
for gender in keys(gender_dict)
  println("  $gender = $(gender_dict[gender])")
end

# report total counts by key, in reverse order
println("Sort by key (reverse alphabetical):")
for gender in sort(collect(keys(gender_dict)), rev=true)
  println("  $gender = $(gender_dict[gender])")
end

# report total counts by value, in reverse order (send output to file)
output_file = open("process_file_output.txt", "w")
println("Sort by value (reverse numerical):")
for (count, gender) in sort(collect(zip(values(gender_dict),keys(gender_dict))), rev=true)
  println("  $gender = $(gender_dict[gender])")
  write(output_file, "$gender = $count\n")
end
```

***Output***

```julia
Sort by key (alphabetical):
  Female = 10771
  Male = 21790
Sort by key (reverse alphabetical):
  Male = 21790
  Female = 10771
Sort by value (reverse numerical):
  Male = 21790
  Female = 10771
```

***Terminal***

```julia
$ julia process_file.jl
Sort by key (alphabetical):
  Female = 10771
  Male = 21790
Sort by key (reverse alphabetical):
  Male = 21790
  Female = 10771
Sort by value (reverse numerical):
  Male = 21790
  Female = 10771

$ ls -1
process_file.jl
process_file_output.txt

$ more process_file_output.txt
Male = 21790
Female = 10771
```

## Exercises <a href="#documentation" id="documentation"></a>

* Analyze the MIMIC-IV Demo Files Using Julia - *<mark style="color:yellow;">Forthcoming!</mark>*
* Analyze the SyntheticRI Demo Files Using Julia - *<mark style="color:yellow;">Forthcoming!</mark>*

## Resources <a href="#documentation" id="documentation"></a>

* Julia Documentation: [Base - I/O and Network](https://docs.julialang.org/en/v1/base/io-network/)
* Think Julia: [Chapter 14 - Files](https://benlauwens.github.io/ThinkJulia.jl/latest/book.html#chap14)


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