Cross-tabulation is a statistical technique that finds an interdependent relationship between two tables of values. The ore.crosstab function enables cross-column analysis of an ore.frame. This function is a sophisticated variant of the R table function.
You must use ore.crosstab function before performing frequency analysis using ore.freq.
If the result of the ore.crosstab function invocation is a single cross-tabulation, the function returns an ore.frame object. If the result is multiple cross-tabulations, then the function returns a list of ore.frame objects.
For details about function arguments, invoke help(ore.crosstab).
The examples of ore.corr use the NARROW data set; for more information, see "About the NARROW Data Set for Examples".
The most basic use case is to create a single-column frequency table, as shown in Example 3-26.
Example 3-26 Creating a Single Column Frequency Table
This example filters the NARROW ore.frame, grouping by GENDER.
ct <- ore.crosstab(~AGE, data=NARROW) head(ct)Listing for Example 3-26
R> ct <- ore.crosstab(~AGE, data=NARROW) R> head(ct) AGE ORE$FREQ ORE$STRATA ORE$GROUP 17 17 14 1 1 18 18 16 1 1 19 19 30 1 1 20 20 23 1 1 21 21 22 1 1 22 22 39 1 1
Example 3-27 Analyzing Two Columns
This example analyses AGE by GENDER and AGE by CLASS.
ct <- ore.crosstab(AGE~GENDER+CLASS, data=NARROW) head(ct)Listing for Example 3-27
R> ct <- ore.crosstab(AGE~GENDER+CLASS, data=NARROW)
R> head(ct)
$`AGE~GENDER`
AGE GENDER ORE$FREQ ORE$STRATA ORE$GROUP
17|F 17 F 5 1 1
17|M 17 M 9 1 1
18|F 18 F 6 1 1
18|M 18 M 7 1 1
19|F 19 F 15 1 1
19|M 19 M 13 1 1
# The remaining output is not shown.
Example 3-28 Weighting Rows
To weight rows, include a count based on another column as shown in this example. The example weights values in AGE and GENDER using values in YRS_RESIDENCE.
ct <- ore.crosstab(AGE~GENDER*YRS_RESIDENCE, data=NARROW) head(ct)Listing for Example 3-28
R> ct <- ore.crosstab(AGE~GENDER*YRS_RESIDENCE, data=NARROW)
R> head(ct)
AGE GENDER ORE$FREQ ORE$STRATA ORE$GROUP
17|F 17 F 1 1 1
17|M 17 M 8 1 1
18|F 18 F 4 1 1
18|M 18 M 10 1 1
19|F 19 F 15 1 1
19|M 19 M 17 1 1
Example 3-29 Ordering Cross-Tabulated Data
There are several possibilities for ordering rows in a cross-tabulated table, such as the following:
Default or NAME orders by the columns being analyzed
FREQ orders by frequency counts
-NAME or -FREQ does reverse ordering
INTERNAL bypasses ordering
This example orders by frequency count and then by reverse order by frequency count.
ct <- ore.crosstab(AGE~GENDER|FREQ, data=NARROW) head(ct) ct <- ore.crosstab(AGE~GENDER|-FREQ, data=NARROW) head(ct)Listing for Example 3-29
R> ct <- ore.crosstab(AGE~GENDER|FREQ, data=NARROW)
R> head(ct)
AGE GENDER ORE$FREQ ORE$STRATA ORE$GROUP
66|F 66 F 1 1 1
70|F 70 F 1 1 1
73|M 73 M 1 1 1
74|M 74 M 1 1 1
76|F 76 F 1 1 1
77|F 77 F 1 1 1
R> ct <- ore.crosstab(AGE~GENDER|-FREQ, data=NARROW)
R> head(ct)
AGE GENDER ORE$FREQ ORE$STRATA ORE$GROUP
27|M 27 M 33 1 1
35|M 35 M 28 1 1
41|M 41 M 27 1 1
34|M 34 M 26 1 1
37|M 37 M 26 1 1
28|M 28 M 25 1 1
Example 3-30 Analyzing Three or More Columns
This example demonstrates analyzing three or more columns. The result is similar to what the SQL GROUPING SETS clause accomplishes.
ct <- ore.crosstab(AGE+COUNTRY~GENDER, NARROW) head(ct)Listing for Example 3-30
R> ct <- ore.crosstab(AGE+COUNTRY~GENDER, NARROW)
R> head(ct)
$`AGE~GENDER`
AGE GENDER ORE$FREQ ORE$STRATA ORE$GROUP
17|F 17 F 5 1 1
17|M 17 M 9 1 1
18|F 18 F 6 1 1
18|M 18 M 7 1 1
19|F 19 F 15 1 1
19|M 19 M 13 1 1
# The rest of the output is not shown.
$`COUNTRY~GENDER`
COUNTRY GENDER ORE$FREQ ORE$STRATA ORE$GROUP
Argentina|F Argentina F 14 1 1
Argentina|M Argentina M 28 1 1
Australia|M Australia M 1 1 1
# The rest of the output is not shown.
Example 3-31 Specifying a Range of Columns
You can specify a range of columns instead of having to type all the column names, as demonstrated in this example.
names(NARROW) # Because AGE, MARITAL_STATUS and COUNTRY are successive columns, # you can simply do the following: ct <- ore.crosstab(AGE-COUNTRY~GENDER, NARROW) # An equivalent invocation is the following: ct <- ore.crosstab(AGE+MARITAL_STATUS+COUNTRY~GENDER, NARROW)Listing for Example 3-31
R> names(NARROW) [1] "ID" "GENDER" "AGE" "MARITAL_STATUS" [5] "COUNTRY" "EDUCATION" "OCCUPATION" "YRS_RESIDENCE" [9] "CLASS" R> # Because AGE, MARITAL_STATUS and COUNTRY are successive columns, R> # you can simply do the following: R> ct <- ore.crosstab(AGE-COUNTRY~GENDER, NARROW) R> # An equivalent invocation is the following: R> ct <- ore.crosstab(AGE+MARITAL_STATUS+COUNTRY~GENDER, NARROW)
Example 3-32 Producing One Cross-Tabulation Table for Each Value of Another Column
This example produces one cross-tabulation table (AGE, GENDER) for each unique value of another column COUNTRY.
ct <- ore.crosstab(~AGE/COUNTRY, data=NARROW) head(ct)Listing for Example 3-32
R> ct <- ore.crosstab(~AGE/COUNTRY, data=NARROW)
R> head(ct)
AGE ORE$FREQ ORE$STRATA ORE$GROUP
Argentina|17 17 1 1 1
Brazil|17 17 1 1 3
United States of America|17 17 12 1 19
United States of America|18 18 16 1 19
United States of America|19 19 30 1 19
United States of America|20 20 23 1 19
Example 3-33 Producing One Cross-Tabulation Table for Each Set of Value of Two Columns
You can extend the cross-tabulation to more than one column, as shown in this example, which produces one (AGE, EDUCATION) table for each unique combination of (COUNTRY, GENDER).
ct <- ore.crosstab(AGE~EDUCATION/COUNTRY+GENDER, data=NARROW) head(ct)Listing for Example 3-33
R> ct <- ore.crosstab(AGE~EDUCATION/COUNTRY+GENDER, data=NARROW)
R> head(ct)
AGE EDUCATION ORE$FREQ ORE$STRATA ORE$GROUP
United States of America|F|17|10th 17 10th 3 1 33
United States of America|M|17|10th 17 10th 5 1 34
United States of America|M|17|11th 17 11th 1 1 34
Argentina|M|17|HS-grad 17 HS-grad 1 1 2
United States of America|M|18|10th 18 10th 1 1 34
United States of America|F|18|11th 18 11th 2 1 33
Example 3-34 Augmenting Cross-Tabulation with Stratification
All of the cross-tabulation tables in the previous examples can be augmented with stratification, as shown in this example.
ct <- ore.crosstab(AGE~GENDER^CLASS, data=NARROW) head(ct) R> head(ct) # The previous function invocation is the same as the following: ct <- ore.crosstab(AGE~GENDER, NARROW, strata="CLASS")Listing for Example 3-34
R> ct <- ore.crosstab(AGE~GENDER^CLASS, data=NARROW)
R> head(ct)
R> head(ct)
AGE GENDER ORE$FREQ ORE$STRATA ORE$GROUP
0|17|F 17 F 5 1 1
0|17|M 17 M 9 1 1
0|18|F 18 F 6 1 1
0|18|M 18 M 7 1 1
0|19|F 19 F 15 1 1
0|19|M 19 M 13 1 1
# The previous function invocation is the same as the following:
ct <- ore.crosstab(AGE~GENDER, NARROW, strata="CLASS")
Example 3-35 Binning Followed by Cross-Tabulation
This example does a custom binning by AGE and then calculates the cross-tabulation for GENDER and the bins.
NARROW$AGEBINS <- ifelse(NARROW$AGE<20, 1, ifelse(NARROW$AGE<30,2,
ifelse(NARROW$AGE<40,3,4)))
ore.crosstab(GENDER~AGEBINS, NARROW)
Listing for Example 3-35
R> NARROW$AGEBINS <- ifelse(NARROW$AGE<20, 1, ifelse(NARROW$AGE<30,2,
+ ifelse(NARROW$AGE<40,3,4)))
R> ore.crosstab(GENDER~AGEBINS, NARROW)
GENDER AGEBINS ORE$FREQ ORE$STRATA ORE$GROUP
F|1 F 1 26 1 1
F|2 F 2 108 1 1
F|3 F 3 86 1 1
F|4 F 4 164 1 1
M|1 M 1 29 1 1
M|2 M 2 177 1 1
M|3 M 3 230 1 1
M|4 M 4 381 1 1