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in census-income grouped education, filtered occupation and removed native country variable;
added script to make release zip
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vendored
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@ -20,6 +20,7 @@ vignettes/*.pdf
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data-collection/*/original/*
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data-collection/*/preprocessed/*
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data-collection.zip
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# markdown outputs
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*.html
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23
README.md
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README.md
@ -3,14 +3,14 @@ Andrzej Wójtowicz
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Document generation date: 2016-08-11 18:12:19.
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Document generation date: 2016-08-19 21:47:14.
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This project preprocesses a few datasets from [UC Irvine Machine Learning
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Repository](https://archive.ics.uci.edu/ml/) into tidy R object files.
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It focuses on the binary classification datasets and saves only complete cases
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within a dataset.
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**R software**: [Microsoft R Open](https://mran.microsoft.com/open/) (3.2.5)
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**R software**: [Microsoft R Open](https://mran.microsoft.com/open/) (3.3.0)
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**Reproducibility library**: [checkpoint](https://github.com/RevolutionAnalytics/checkpoint)
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@ -18,7 +18,11 @@ within a dataset.
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1. Run *s1-download-data.R* to download original datasets.
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2. Run *s2-preprocess-data.R* to preprocess the datasets.
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3. Optionally knit s*3-make-readme.Rmd* to get an overview of the preprocessed datasets.
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Optionally:
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3. knit *s3-make-readme.Rmd* to get an overview of the preprocessed datasets,
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4. run *s4-make-release.sh* to create zip file with preprocessed datasets.
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# Table of Contents
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@ -302,20 +306,19 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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**Dataset**:
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```nohighlight
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'data.frame': 45222 obs. of 14 variables:
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'data.frame': 46018 obs. of 13 variables:
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$ age : int 39 50 38 53 28 37 49 52 31 42 ...
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$ workclass : Factor w/ 8 levels "federal.gov",..: 7 6 4 4 4 4 4 6 4 4 ...
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$ workclass : Factor w/ 7 levels "federal.gov",..: 6 5 3 3 3 3 3 5 3 3 ...
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$ fnlwgt : int 77516 83311 215646 234721 338409 284582 160187 209642 45781 159449 ...
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$ education : Ord.factor w/ 16 levels "preschool"<"x1st.4th"<..: 13 13 9 7 13 14 5 9 14 13 ...
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$ education : Ord.factor w/ 5 levels "school"<"highschool"<..: 4 4 2 1 4 5 1 2 5 4 ...
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$ marital.status: Factor w/ 7 levels "divorced","married.af.spouse",..: 5 3 1 3 3 3 4 3 5 3 ...
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$ occupation : Factor w/ 14 levels "adm.clerical",..: 1 4 6 6 10 4 8 4 10 4 ...
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$ occupation : Factor w/ 13 levels "adm.clerical",..: 1 3 5 5 9 3 7 3 9 3 ...
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$ relationship : Factor w/ 6 levels "husband","not.in.family",..: 2 1 2 1 6 6 2 1 2 1 ...
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$ race : Factor w/ 5 levels "amer.indian.eskimo",..: 5 5 5 3 3 5 3 5 5 5 ...
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$ sex : Factor w/ 2 levels "female","male": 2 2 2 2 1 1 1 2 1 2 ...
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$ capital.gain : int 2174 0 0 0 0 0 0 0 14084 5178 ...
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$ capital.loss : int 0 0 0 0 0 0 0 0 0 0 ...
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$ hours.per.week: int 40 13 40 40 40 40 16 45 50 40 ...
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$ native.country: Factor w/ 41 levels "cambodia","canada",..: 39 39 39 39 5 39 23 39 39 39 ...
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$ class : Factor w/ 2 levels "x..50k","x.50k": 1 1 1 1 1 1 1 2 2 2 ...
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```
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@ -324,7 +327,7 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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|Type | Frequency|
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|:--------------|---------:|
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|factor | 7|
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|factor | 6|
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|integer | 5|
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|ordered factor | 1|
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@ -333,7 +336,7 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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| class A | class B |
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|:-------:|:-------:|
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| 25 % | 75 % |
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| 11208 | 34014 |
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| 11417 | 34601 |
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---
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2
config.R
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config.R
@ -13,7 +13,7 @@ USER.INIT.FILE = "init.R.user"
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# checkpoint library
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CHECKPOINT.MRAN.URL = "https://mran.microsoft.com/"
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CHECKPOINT.SNAPSHOT.DATE = "2016-07-01"
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CHECKPOINT.SNAPSHOT.DATE = "2016-06-01"
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CHECKPOINT.QUICK.LOAD = TRUE # skip testing https and checking url
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# logging system
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@ -42,8 +42,20 @@ preprocess.dataset = function()
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dataset = dataset %>%
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mutate(education = factor(education, levels = education.ordered.levels,
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ordered = TRUE)) %>%
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select(-education.num) %>%
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filter(complete.cases(.))
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select(-education.num, -native.country) %>% # native.country is too much
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# biased into US
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filter(complete.cases(.) & occupation != "Armed-Forces") %>% # only few
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# cases of
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# Armed-Forces
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droplevels
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dataset$education = factor(combine_factor(dataset$education, # combine into
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c(1, 1, 1, 1, 1, # more numerous
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1, 1, 1, 2, 3, # groups
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3, 3, 4, 5, 5, 5)),
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ordered = TRUE)
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levels(dataset$education) = c("school", "highschool", "college",
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"university", "science")
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return(dataset)
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}
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1
init.R
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init.R
@ -47,6 +47,7 @@ library(RCurl)
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library(tools)
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library(yaml)
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library(reshape)
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library(plyr)
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library(dplyr)
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library(foreign)
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@ -26,7 +26,11 @@ within a dataset.
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1. Run *s1-download-data.R* to download original datasets.
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2. Run *s2-preprocess-data.R* to preprocess the datasets.
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3. Optionally knit s*3-make-readme.Rmd* to get an overview of the preprocessed datasets.
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Optionally:
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3. knit *s3-make-readme.Rmd* to get an overview of the preprocessed datasets,
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4. run *s4-make-release.sh* to create zip file with preprocessed datasets.
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```{r show-datasets, results='asis'}
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14
s4-make-release.sh
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s4-make-release.sh
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#!/bin/bash
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OUT_ZIP_FILE="data-collection.zip"
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rm -f $OUT_ZIP_FILE
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zip $OUT_ZIP_FILE $(find data-collection/*/preprocessed/*.rds)
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for f in $(find data-collection/*/preprocessed/*.rds) ; do
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dataset_name=$(echo "$f" | sed -e 's/data-collection\/\(.*\)\/preprocessed\/.*\.rds/\1/')
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echo "Renaming $f -> $dataset_name.rds"
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# https://stackoverflow.com/a/16710654
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printf "@ $f\n@=$dataset_name.rds\n" | zipnote -w $OUT_ZIP_FILE
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done
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