rCh02.html

<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">source</span>(<span class="st">&#39;runDir.R&#39;</span>)</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">runDir</span>(<span class="st">&#39;../CodeExamples/c02_Loading_data_into_R&#39;</span>,
       <span class="st">&#39;../Statlog&#39;</span>,<span class="dt">last=</span><span class="dv">12</span>)</code></pre></div>
<pre><code>[1] &quot;############################### start  5 Fri Jun 17 10:32:27 2016&quot;
[1] &quot;#####  running  ../CodeExamples/c02_Loading_data_into_R/00005_example_2.1_of_section_2.1.1.R&quot;
[1] &quot;#####   in directory ../Statlog&quot;

&gt; # example 2.1 of section 2.1.1 
&gt; # (example 2.1 of section 2.1.1)  : Loading data into R : Working with data from files : Working with well-structured data from files or URLs 
&gt; # Title: Reading the UCI car data 
&gt; 
&gt; uciCar &lt;- read.table(     # Note: 1 
    &#39;http://www.win-vector.com/dfiles/car.data.csv&#39;,    # Note: 2 
    sep=&#39;,&#39;,    # Note: 3 
    header=T    # Note: 4 
    )

&gt; # Note 1: 
&gt; #   Command to read from a file or URL and store the result in a new data frame object 
&gt; #   called 
&gt; #   uciCar. 
&gt; 
&gt; # Note 2: 
&gt; #   Filename or URL to get the data from. 
&gt; 
&gt; # Note 3: 
&gt; #   Specify the column or field separator as a 
&gt; #   comma. 
&gt; 
&gt; # Note 4: 
&gt; #   Tell R to expect a header line that defines 
&gt; #   the data column names. 
&gt; 
[1] &quot;############################### end  5 Fri Jun 17 10:32:27 2016&quot;
[1] &quot;############################### start  6 Fri Jun 17 10:32:27 2016&quot;
[1] &quot;#####  running  ../CodeExamples/c02_Loading_data_into_R/00006_example_2.2_of_section_2.1.1.R&quot;
[1] &quot;#####   in directory ../Statlog&quot;

&gt; # example 2.2 of section 2.1.1 
&gt; # (example 2.2 of section 2.1.1)  : Loading data into R : Working with data from files : Working with well-structured data from files or URLs 
&gt; # Title: Exploring the car data 
&gt; 
&gt; class(uciCar)
[1] &quot;data.frame&quot;

&gt; ## [1] &quot;data.frame&quot;   # Note: 1 
&gt; summary(uciCar)
   buying      maint       doors     persons     lug_boot    safety   
 high :432   high :432   2    :432   2   :576   big  :576   high:576  
 low  :432   low  :432   3    :432   4   :576   med  :576   low :576  
 med  :432   med  :432   4    :432   more:576   small:576   med :576  
 vhigh:432   vhigh:432   5more:432                                    
   rating    
 acc  : 384  
 good :  69  
 unacc:1210  
 vgood:  65  

&gt; ##    buying      maint       doors
&gt; ##  high :432   high :432   2    :432
&gt; ##  low  :432   low  :432   3    :432
&gt; ##  med  :432   med  :432   4    :432
&gt; ##  vhigh:432   vhigh:432   5more:432
&gt; ##
&gt; ##  persons     lug_boot    safety
&gt; ##  2   :576   big  :576   high:576
&gt; ##  4   :576   med  :576   low :576
&gt; ##  more:576   small:576   med :576
&gt; ##
&gt; ##    rating
&gt; ##  acc  : 384
&gt; ##  good :  69
&gt; ##  unacc:1210
&gt; ##  vgood:  65
&gt; 
&gt; dim(uciCar)
[1] 1728    7

&gt; ## [1] 1728    7      # Note: 2
&gt; 
&gt; # Note 1: 
&gt; #   The loaded object uciCar is of type data.frame. 
&gt; 
&gt; # Note 2: 
&gt; #   The [1] is just an output sequence 
&gt; #   marker. The actual information is this: uciCar has 
&gt; #   1728 rows and 7 columns. Always try to confirm you 
&gt; #   got a good parse by at least checking that the 
&gt; #   number of rows is exactly one fewer than the 
&gt; #   number of lines of text in the original file. The 
&gt; #   difference of one is because the column header 
&gt; #   counts as a line, but not as a data row. 
&gt; 
[1] &quot;############################### end  6 Fri Jun 17 10:32:27 2016&quot;
[1] &quot;############################### start  8 Fri Jun 17 10:32:27 2016&quot;
[1] &quot;#####  running  ../CodeExamples/c02_Loading_data_into_R/00008_example_2.3_of_section_2.1.2.R&quot;
[1] &quot;#####   in directory ../Statlog&quot;

&gt; # example 2.3 of section 2.1.2 
&gt; # (example 2.3 of section 2.1.2)  : Loading data into R : Working with data from files : Using R on less-structured data 
&gt; # Title: Loading the credit dataset 
&gt; 
&gt; d &lt;- read.table(paste(&#39;http://archive.ics.uci.edu/ml/&#39;,
    &#39;machine-learning-databases/statlog/german/german.data&#39;,sep=&#39;&#39;),
    stringsAsFactors=F,header=F)

&gt; print(d[1:3,])
   V1 V2  V3  V4   V5  V6  V7 V8  V9  V10 V11  V12 V13  V14  V15 V16  V17
1 A11  6 A34 A43 1169 A65 A75  4 A93 A101   4 A121  67 A143 A152   2 A173
2 A12 48 A32 A43 5951 A61 A73  2 A92 A101   2 A121  22 A143 A152   1 A173
3 A14 12 A34 A46 2096 A61 A74  2 A93 A101   3 A121  49 A143 A152   1 A172
  V18  V19  V20 V21
1   1 A192 A201   1
2   1 A191 A201   2
3   2 A191 A201   1
[1] &quot;############################### end  8 Fri Jun 17 10:32:28 2016&quot;
[1] &quot;############################### start  9 Fri Jun 17 10:32:28 2016&quot;
[1] &quot;#####  running  ../CodeExamples/c02_Loading_data_into_R/00009_example_2.4_of_section_2.1.2.R&quot;
[1] &quot;#####   in directory ../Statlog&quot;

&gt; # example 2.4 of section 2.1.2 
&gt; # (example 2.4 of section 2.1.2)  : Loading data into R : Working with data from files : Using R on less-structured data 
&gt; # Title: Setting column names 
&gt; 
&gt; colnames(d) &lt;- c(&#39;Status.of.existing.checking.account&#39;,
    &#39;Duration.in.month&#39;,  &#39;Credit.history&#39;, &#39;Purpose&#39;,
    &#39;Credit.amount&#39;, &#39;Savings account/bonds&#39;,
    &#39;Present.employment.since&#39;,
    &#39;Installment.rate.in.percentage.of.disposable.income&#39;,
    &#39;Personal.status.and.sex&#39;, &#39;Other.debtors/guarantors&#39;,
    &#39;Present.residence.since&#39;, &#39;Property&#39;, &#39;Age.in.years&#39;,
    &#39;Other.installment.plans&#39;, &#39;Housing&#39;,
    &#39;Number.of.existing.credits.at.this.bank&#39;, &#39;Job&#39;,
    &#39;Number.of.people.being.liable.to.provide.maintenance.for&#39;,
    &#39;Telephone&#39;, &#39;foreign.worker&#39;, &#39;Good.Loan&#39;)

&gt; d$Good.Loan &lt;- as.factor(ifelse(d$Good.Loan==1,&#39;GoodLoan&#39;,&#39;BadLoan&#39;))

&gt; print(d[1:3,])
  Status.of.existing.checking.account Duration.in.month Credit.history
1                                 A11                 6            A34
2                                 A12                48            A32
3                                 A14                12            A34
  Purpose Credit.amount Savings account/bonds Present.employment.since
1     A43          1169                   A65                      A75
2     A43          5951                   A61                      A73
3     A46          2096                   A61                      A74
  Installment.rate.in.percentage.of.disposable.income
1                                                   4
2                                                   2
3                                                   2
  Personal.status.and.sex Other.debtors/guarantors Present.residence.since
1                     A93                     A101                       4
2                     A92                     A101                       2
3                     A93                     A101                       3
  Property Age.in.years Other.installment.plans Housing
1     A121           67                    A143    A152
2     A121           22                    A143    A152
3     A121           49                    A143    A152
  Number.of.existing.credits.at.this.bank  Job
1                                       2 A173
2                                       1 A173
3                                       1 A172
  Number.of.people.being.liable.to.provide.maintenance.for Telephone
1                                                        1      A192
2                                                        1      A191
3                                                        2      A191
  foreign.worker Good.Loan
1           A201  GoodLoan
2           A201   BadLoan
3           A201  GoodLoan
[1] &quot;############################### end  9 Fri Jun 17 10:32:28 2016&quot;
[1] &quot;############################### start  10 Fri Jun 17 10:32:28 2016&quot;
[1] &quot;#####  running  /Users/johnmount/Documents/work/PracticalDataScienceWithR/zmPDSwR/RunExamples/replacements/00010_example_2.5_of_section_2.1.2.R&quot;
[1] &quot;#####   in directory ../Statlog&quot;

&gt; # Example in book was purposely elipsized, here is complete mapping table
&gt; 
&gt; mapping &lt;- list(&#39;A11&#39;=&#39;... &lt; 0 DM&#39;,
  &#39;A12&#39;=&#39;0 &lt;= ... &lt; 200 DM&#39;,
  &#39;A13&#39;=&#39;... &gt;= 200 DM / salary assignments for at least 1 year&#39;,
  &#39;A14&#39;=&#39;no checking account&#39;,
  &#39;A30&#39;=&#39;no credits taken/all credits paid back duly&#39;,
  &#39;A31&#39;=&#39;all credits at this bank paid back duly&#39;,
  &#39;A32&#39;=&#39;existing credits paid back duly till now&#39;,
  &#39;A33&#39;=&#39;delay in paying off in the past&#39;,
  &#39;A34&#39;=&#39;critical account/other credits existing (not at this bank)&#39;,
  &#39;A40&#39;=&#39;car (new)&#39;,
  &#39;A41&#39;=&#39;car (used)&#39;,
  &#39;A42&#39;=&#39;furniture/equipment&#39;,
  &#39;A43&#39;=&#39;radio/television&#39;,
  &#39;A44&#39;=&#39;domestic appliances&#39;,
  &#39;A45&#39;=&#39;repairs&#39;,
  &#39;A46&#39;=&#39;education&#39;,
  &#39;A47&#39;=&#39;(vacation - does not exist?)&#39;,
  &#39;A48&#39;=&#39;retraining&#39;,
  &#39;A49&#39;=&#39;business&#39;,
  &#39;A410&#39;=&#39;others&#39;,
  &#39;A61&#39;=&#39;... &lt; 100 DM&#39;,
  &#39;A62&#39;=&#39;100 &lt;= ... &lt; 500 DM&#39;,
  &#39;A63&#39;=&#39;500 &lt;= ... &lt; 1000 DM&#39;,
  &#39;A64&#39;=&#39;.. &gt;= 1000 DM&#39;,
  &#39;A65&#39;=&#39;unknown/ no savings account&#39;,
  &#39;A71&#39;=&#39;unemployed&#39;,
  &#39;A72&#39;=&#39;... &lt; 1 year&#39;,
  &#39;A73&#39;=&#39;1 &lt;= ... &lt; 4 years&#39;,
  &#39;A74&#39;=&#39;4 &lt;= ... &lt; 7 years&#39;,
  &#39;A75&#39;=&#39;.. &gt;= 7 years&#39;,
  &#39;A91&#39;=&#39;male : divorced/separated&#39;,
  &#39;A92&#39;=&#39;female : divorced/separated/married&#39;,
  &#39;A93&#39;=&#39;male : single&#39;,
  &#39;A94&#39;=&#39;male : married/widowed&#39;,
  &#39;A95&#39;=&#39;female : single&#39;,
  &#39;A101&#39;=&#39;none&#39;,
  &#39;A102&#39;=&#39;co-applicant&#39;,
  &#39;A103&#39;=&#39;guarantor&#39;,
  &#39;A121&#39;=&#39;real estate&#39;,
  &#39;A122&#39;=&#39;if not A121 : building society savings agreement/life insurance&#39;,
  &#39;A123&#39;=&#39;if not A121/A122 : car or other, not in attribute 6&#39;,
  &#39;A124&#39;=&#39;unknown / no property&#39;,
  &#39;A141&#39;=&#39;bank&#39;,
  &#39;A142&#39;=&#39;stores&#39;,
  &#39;A143&#39;=&#39;none&#39;,
  &#39;A151&#39;=&#39;rent&#39;,
  &#39;A152&#39;=&#39;own&#39;,
  &#39;A153&#39;=&#39;for free&#39;,
  &#39;A171&#39;=&#39;unemployed/ unskilled - non-resident&#39;,
  &#39;A172&#39;=&#39;unskilled - resident&#39;,
  &#39;A173&#39;=&#39;skilled employee / official&#39;,
  &#39;A174&#39;=&#39;management/ self-employed/highly qualified employee/ officer&#39;,
  &#39;A191&#39;=&#39;none&#39;,
  &#39;A192&#39;=&#39;yes, registered under the customers name&#39;,
  &#39;A201&#39;=&#39;yes&#39;,
  &#39;A202&#39;=&#39;no&#39;)
[1] &quot;############################### end  10 Fri Jun 17 10:32:28 2016&quot;
[1] &quot;############################### start  11 Fri Jun 17 10:32:28 2016&quot;
[1] &quot;#####  running  ../CodeExamples/c02_Loading_data_into_R/00011_example_2.6_of_section_2.1.2.R&quot;
[1] &quot;#####   in directory ../Statlog&quot;

&gt; # example 2.6 of section 2.1.2 
&gt; # (example 2.6 of section 2.1.2)  : Loading data into R : Working with data from files : Using R on less-structured data 
&gt; # Title: Transforming the car data 
&gt; 
&gt; for(i in 1:(dim(d))[2]) {                 # Note: 1 
    if(class(d[,i])==&#39;character&#39;) {
       d[,i] &lt;- as.factor(as.character(mapping[d[,i]]))     # Note: 2 
    }
 }

&gt; # Note 1: 
&gt; #   (dim(d))[2] is the number of columns 
&gt; #   in the data frame d. 
&gt; 
&gt; # Note 2: 
&gt; #   Note that the indexing operator [] is vectorized. Each step in the for loop remaps an 
&gt; #   entire column of data through our list. 
&gt; 
[1] &quot;############################### end  11 Fri Jun 17 10:32:28 2016&quot;
[1] &quot;############################### start  12 Fri Jun 17 10:32:28 2016&quot;
[1] &quot;#####  running  ../CodeExamples/c02_Loading_data_into_R/00012_example_2.7_of_section_2.1.2.R&quot;
[1] &quot;#####   in directory ../Statlog&quot;

&gt; # example 2.7 of section 2.1.2 
&gt; # (example 2.7 of section 2.1.2)  : Loading data into R : Working with data from files : Using R on less-structured data 
&gt; # Title: Summary of Good.Loan and Purpose 
&gt; 
&gt; table(d$Purpose,d$Good.Loan) 
                     
                      BadLoan GoodLoan
  business                 34       63
  car (new)                89      145
  car (used)               17       86
  domestic appliances       4        8
  education                22       28
  furniture/equipment      58      123
  others                    5        7
  radio/television         62      218
  repairs                   8       14
  retraining                1        8

&gt; ##                       BadLoan GoodLoan
&gt; ##   business                 34       63
&gt; ##   car (new)                89      145
&gt; ##   car (used)               17       86
&gt; ##   domestic appliances       4        8
&gt; ##   education                22       28
&gt; ##   furniture/equipment      58      123
&gt; ##   others                    5        7
&gt; ##   radio/television         62      218
&gt; ##   repairs                   8       14
&gt; ##   retraining                1        8
&gt; 
[1] &quot;############################### end  12 Fri Jun 17 10:32:28 2016&quot;</code></pre>
<p>Step 00016_example_2.11_of_section_2.2.2.R requires large files that we don't have the rights to redistributed in their entirety. So we are either running that step, or skipping it and just loading phsample.RDdata .</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">runDir</span>(<span class="st">&#39;../../CodeExamples/c02_Loading_data_into_R&#39;</span>,
       <span class="st">&#39;../PUMS/loadExample&#39;</span>,<span class="dt">first=</span><span class="dv">13</span>,<span class="dt">last=</span><span class="dv">16</span>)</code></pre></div>
<pre><code>[1] &quot;############################### start  16 Fri Jun 17 10:32:28 2016&quot;
[1] &quot;#####  running  ../../CodeExamples/c02_Loading_data_into_R/00016_example_2.11_of_section_2.2.2.R&quot;
[1] &quot;#####   in directory ../PUMS/loadExample&quot;

&gt; # example 2.11 of section 2.2.2 
&gt; # (example 2.11 of section 2.2.2)  : Loading data into R : Working with relational databases : Loading data from a database into R 
&gt; # Title: Loading data into R from a relational database 
&gt; 
&gt; options( java.parameters = &quot;-Xmx2g&quot; )     # Note: 1 

&gt; library(RJDBC)

Loading required package: DBI

Loading required package: rJava


&gt; drv &lt;- JDBC(&quot;org.h2.Driver&quot;,  # Note: 2 
    &quot;h2-1.3.176.jar&quot;,   # Note: 3 
    identifier.quote=&quot;&#39;&quot;)   # Note: 4 

&gt; options&lt;-&quot;;LOG=0;CACHE_SIZE=65536;LOCK_MODE=0;UNDO_LOG=0&quot;

&gt; conn &lt;- dbConnect(drv,paste(&quot;jdbc:h2:./H2DB&quot;,options,sep=&#39;&#39;),&quot;u&quot;,&quot;u&quot;)

&gt; dhus &lt;- dbGetQuery(conn,&quot;SELECT * FROM hus WHERE ORIGRANDGROUP&lt;=1&quot;)   # Note: 5 

&gt; dpus &lt;- dbGetQuery(conn,&quot;SELECT pus.* FROM pus WHERE pus.SERIALNO IN \
    (SELECT DISTINCT hus.SERIALNO FROM hus \
    WHERE hus.ORIGRANDGROUP&lt;=1)&quot;)   # Note: 6 

&gt; dbDisconnect(conn)    # Note: 7 
[1] TRUE

&gt; save(dhus,dpus,file=&#39;phsample.RData&#39;)     # Note: 8

&gt; # Note 1: 
&gt; #   Set Java option for extra memory before DB 
&gt; #   drivers are loaded. 
&gt; 
&gt; # Note 2: 
&gt; #   Specify the name of the database driver, same 
&gt; #   as in our XML database configuration. 
&gt; 
&gt; # Note 3: 
&gt; #   Specify where to find the implementation of 
&gt; #   the database driver. 
&gt; 
&gt; # Note 4: 
&gt; #   SQL column names with mixed-case 
&gt; #   capitalization, special characters, or that 
&gt; #   collide with reserved words must be quoted. We 
&gt; #   specify single-quote as the quote we’ll use when 
&gt; #   quoting column names, which may different than the 
&gt; #   quote we use for SQL literals. 
&gt; 
&gt; # Note 5: 
&gt; #   Create a data frame called dhus from * 
&gt; #   (everything) from the database table hus, taking 
&gt; #   only rows where ORGINRANGGROUP &lt;= 1. The 
&gt; #   ORGINRANDGROUP column is a random integer from 0 
&gt; #   through 999 that SQL Screwdriver adds to the rows 
&gt; #   during data load to facilitate sampling. In this 
&gt; #   case, we’re taking 2/1000 of the data rows to get 
&gt; #   a small sample. 
&gt; 
&gt; # Note 6: 
&gt; #   Create a data frame called dpus from the 
&gt; #   database table pus, taking only records that have 
&gt; #   a household ID in the set of household IDs we 
&gt; #   selected from households table hus. 
&gt; 
&gt; # Note 7: 
&gt; #   Disconnect for the database. 
&gt; 
&gt; # Note 8: 
&gt; #   Save the two data frames into a file named 
&gt; #   phsample.RData, which can be read in with load(). 
&gt; #   Try help(&quot;save&quot;) or help(&quot;load&quot;) for more 
&gt; #   details. 
&gt; 
[1] &quot;############################### end  16 Fri Jun 17 10:32:31 2016&quot;</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># load(&#39;../PUMS/phsample.RData&#39;)</span>
<span class="kw">runDir</span>(<span class="st">&#39;../CodeExamples/c02_Loading_data_into_R&#39;</span>,
       <span class="st">&#39;../PUMS&#39;</span>,<span class="dt">first=</span><span class="dv">17</span>)</code></pre></div>
<pre><code>[1] &quot;############################### start  17 Fri Jun 17 10:32:31 2016&quot;
[1] &quot;#####  running  ../CodeExamples/c02_Loading_data_into_R/00017_example_2.12_of_section_2.2.3.R&quot;
[1] &quot;#####   in directory ../PUMS&quot;

&gt; # example 2.12 of section 2.2.3 
&gt; # (example 2.12 of section 2.2.3)  : Loading data into R : Working with relational databases : Working with the PUMS data 
&gt; # Title: Selecting a subset of the Census data 
&gt; 
&gt; load(&#39;phsample.RData&#39;)

&gt; psub = subset(dpus,with(dpus,(PINCP&gt;1000)&amp;(ESR==1)&amp;
    (PINCP&lt;=250000)&amp;(PERNP&gt;1000)&amp;(PERNP&lt;=250000)&amp;
    (WKHP&gt;=40)&amp;(AGEP&gt;=20)&amp;(AGEP&lt;=50)&amp;
    (PWGTP1&gt;0)&amp;(COW %in% (1:7))&amp;(SCHL %in% (1:24))))    # Note: 1

&gt; # Note 1: 
&gt; #   Subset of data rows matching detailed 
&gt; #   employment conditions 
&gt; 
[1] &quot;############################### end  17 Fri Jun 17 10:32:31 2016&quot;
[1] &quot;############################### start  18 Fri Jun 17 10:32:31 2016&quot;
[1] &quot;#####  running  ../CodeExamples/c02_Loading_data_into_R/00018_example_2.13_of_section_2.2.3.R&quot;
[1] &quot;#####   in directory ../PUMS&quot;

&gt; # example 2.13 of section 2.2.3 
&gt; # (example 2.13 of section 2.2.3)  : Loading data into R : Working with relational databases : Working with the PUMS data 
&gt; # Title: Recoding variables 
&gt; 
&gt; psub$SEX = as.factor(ifelse(psub$SEX==1,&#39;M&#39;,&#39;F&#39;))     # Note: 1 

&gt; psub$SEX = relevel(psub$SEX,&#39;M&#39;)  # Note: 2 

&gt; cowmap &lt;- c(&quot;Employee of a private for-profit&quot;,
    &quot;Private not-for-profit employee&quot;,
    &quot;Local government employee&quot;,
    &quot;State government employee&quot;,
    &quot;Federal government employee&quot;,
    &quot;Self-employed not incorporated&quot;,
    &quot;Self-employed incorporated&quot;)

&gt; psub$COW = as.factor(cowmap[psub$COW])    # Note: 3 

&gt; psub$COW = relevel(psub$COW,cowmap[1])

&gt; schlmap = c(      # Note: 4 
    rep(&quot;no high school diploma&quot;,15),
   &quot;Regular high school diploma&quot;,
   &quot;GED or alternative credential&quot;,
   &quot;some college credit, no degree&quot;,
   &quot;some college credit, no degree&quot;,
   &quot;Associate&#39;s degree&quot;,
   &quot;Bachelor&#39;s degree&quot;,
   &quot;Master&#39;s degree&quot;,
   &quot;Professional degree&quot;,
   &quot;Doctorate degree&quot;)

&gt; psub$SCHL = as.factor(schlmap[psub$SCHL])

&gt; psub$SCHL = relevel(psub$SCHL,schlmap[1])

&gt; dtrain = subset(psub,ORIGRANDGROUP &gt;= 500)    # Note: 5 

&gt; dtest = subset(psub,ORIGRANDGROUP &lt; 500)      # Note: 6

&gt; # Note 1: 
&gt; #   Reencode sex from 1/2 to M/F. 
&gt; 
&gt; # Note 2: 
&gt; #   Make the reference sex M, so F encodes a 
&gt; #   difference from M in models. 
&gt; 
&gt; # Note 3: 
&gt; #   Reencode class of worker info into a more 
&gt; #   readable form. 
&gt; 
&gt; # Note 4: 
&gt; #   Reencode education info into a more readable 
&gt; #   form and fewer levels (merge all levels below high 
&gt; #   school into same encoding). 
&gt; 
&gt; # Note 5: 
&gt; #   Subset of data rows used for model 
&gt; #   training. 
&gt; 
&gt; # Note 6: 
&gt; #   Subset of data rows used for model 
&gt; #   testing. 
&gt; 
[1] &quot;############################### end  18 Fri Jun 17 10:32:31 2016&quot;
[1] &quot;############################### start  19 Fri Jun 17 10:32:31 2016&quot;
[1] &quot;#####  running  ../CodeExamples/c02_Loading_data_into_R/00019_example_2.14_of_section_2.2.3.R&quot;
[1] &quot;#####   in directory ../PUMS&quot;

&gt; # example 2.14 of section 2.2.3 
&gt; # (example 2.14 of section 2.2.3)  : Loading data into R : Working with relational databases : Working with the PUMS data 
&gt; # Title: Summarizing the classifications of work 
&gt; 
&gt; summary(dtrain$COW)
Employee of a private for-profit      Federal government employee 
                             423                               21 
       Local government employee  Private not-for-profit employee 
                              39                               55 
      Self-employed incorporated   Self-employed not incorporated 
                              17                               16 
       State government employee 
                              24 

&gt; ## Employee of a private for-profit      Federal government employee
&gt; ##                              423                               21
&gt; ##        Local government employee  Private not-for-profit employee
&gt; ##                               39                               55
&gt; ##       Self-employed incorporated   Self-employed not incorporated
&gt; ##                               17                               16
&gt; ##        State government employee
&gt; ##                               24
&gt; 
[1] &quot;############################### end  19 Fri Jun 17 10:32:31 2016&quot;</code></pre>