Both esProc and R language are typical data processing and analysis languages with two-dimensional structured data objects. They are all good at multi-step complex computations. However their two-dimensional structured data objects are quite different from each other in the underlying mechanism. As a result, esProc is better at computation with structured data, and especially suitable for developers to do business computing. R is better at matrix computation and more suitable for scientists to do scientific or engineering computation.
esProc's two-dimensional structured data type is sequence table object (TSeq). Sequence table is based on records, with multiple records forming a row-styled two-dimensional table. In combination with the column name, this two-dimensional table can form a complete data structure. R language is based on vector, with multiple vectors forming a column-styled two-dimensional table. In combination with the column name, the two-dimensional table can form a complete data structure.
These underlying mechanisms affect actual user experience. In the following part we will compare the difference in practical use between sequence table object and data frame, in terms of basic functions, advanced features, actual use cases and test results.
Note: Primitive functions of development language are to be used in the following comparisons, the third party extension packages won’t be involved.
Basic functions
Example 1:retrieve two-dimensional structured data from the file, and access the value of the second column in the first row by coordinates.
Data frame:
data<-read.table("e:/sales.txt",header=TRUE,sep="\t")
result<-data[1,2]
Sequence table:
=data=file("e:/sales.txt").import@t()
=data(1).#2
Comparison: there is no significant difference in the most basic functions.
Note: the sales.txt file is tab separated structured data, and the first few lines are as following:
OrderID Client SellerId Amount OrderDate
1 WVF 5 440.00 2009-2-3 0:00:00
2 UFS 13 1863.40 2009-7-5 0:00:00
3 SWFR 2 1813.00 2009-7-8 0:00:00
4 JFS 27 670.80 2009-7-8 0:00:00
5 DSG 15 3730.00 2009-7-9 0:00:00
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Example 2: access the value of the second column in the first row, by row number and by field name.
Data frame:
Result1<-data$Client[1]
Result2<-data[1,]$Client
Sequence table:
=data(1).(Client)
=data.(Client)(1)
Comparison: there is no significant difference between the two.
Example 3: Access column data. There are two scenarios, and each falls into two situations: access by column number and column names:retrieve only the second column, or retrieve a combination of the second column and the fourth column.
Data frame:
Result1<-data[2]
Result2<-data[,c(2,4)]
Result3<-data$Client
Result4<-data[,c("Client","Amount")]
Sequence table:
=data.(#2)
=data.new(#2,#4)
=data.(Client)
=data.new(Client,Amount)
Comparison: Both can access the column data. The only difference is in the syntax for retrieving multiple column data. Data frame is retrieving the number directly, while with sequence table a new sequence table will be build with the new function. Although the syntax is different, the actual methods used are the same: both are duplicating two columns of data from the original objects to new objects.
Example 4: record manipulation. Includes: retrieve the first two records, appending records, inserting record in the second row, deleting the record in the second row.
Data frame：
Record1<-data[c(1,2),]
append<- data.frame(OrderID=152, Client="CA", SellerId=5, Amount=2961.40, OrderDate="2010-12-5 0:00:00")
data<- rbind(data, append)
insert<-data.frame(OrderID=153, Client="RA", SellerId=4, Amount=1931.20, OrderDate="2009-11-5 0:00:00")
data<-rbind(data[1,], insert,data[2:151,])
data<-data[-2,]
Sequence table:
=data([1,2])
=data.insert(0,152:OrderID,"CA":Client,5:SellerId,2961.40:Amount,"2010-12-5 0:00:00":OrderDate)
=data.insert(2,153:OrderID,"RA":Client,4:SellerId,1931.20:Amount,"2009-11-5 0:00:00":OrderDate)
=data.delete(2)
Comparison: record manipulation is possible in both ways. esProc is relatively more convenient. It can use insert function to append or insert records directly to sequence table, while in R language we need to split the data frame and then merge them again to achieve the same result in an indirect way.
Summary:
As both sequence table and data frame are structured, two-dimensional data object, no significant difference exists in basic functions for data reading/writing,data access and maintenance.
Advanced features
Example 5: modifying the association. A1, A2 are two-dimensional structured data object with the same field ID. We now need to add the bonus field values of A2 to the salary field values in A1 according to ID.
Sequence table:
A1=db.query("select id,name,salary from salary order by id")
A2=db.query("select id,bonus from bonus order by id")
A1.modify(1:A2,salary+bonus:salary)
Data frame has no functions to modify the association. We need to do manual coding for this, which is omitted here.
Example 6: merging associations. A1, A2, A3 are two-dimensional structured data objects with the same field sequence number. Please associate them with left join. As the data is sorted by sequence number, please leverage merging methods to improve the speed for association.
Sequence Table：join@m1(A1:salary,id; A2:bonus,id; A3,attendance,id)
Data frame supports association of two tables, such as: merge(A1,A2,by.x="id",by.y="id",all=TRUE).
In this case three tables are associated, which can be achieved indirectly through two two-table associations.
In this case three tables are associated, which can be achieved indirectly through two two-table associations.
In addition, the data frame does not support merging of association, and therefore no speed improvement is possible. In other words, data frame cannot use ordered sequence data to improve performance, not only with association, but also with other operations.
Example 7: Record lookup. Four scenarios: retrieving records with the Amount greater than 1000; retrieving the sequence number or records with the Amount greater than 1000;return records with primary key value of “v”, return the sequence number for records with primary key value of “v”.
Sequence table:
=data.select(Amount>1000)
= data.pselect(Amount>1000)
= data.find(v)
= data.pfind(v)
Data frame：only the first two scenarios can be achieved, which is done with following code:
newdata<- data [data $ Amount>1000,]
which(data $ Amount >1000)
Data frame hasn't the concept of major key, so we need to do manual coding for other 2 scenarios as indirect methods, or employ a third party package (i.e. data.table). The codes are omitted here.
Example 8: Group sum. The data is grouped by Client and SellerId. Then the other two fields are aggregated: do a sum for Amount field, and do a count for OrderID field.
Sequence table：
=data.groups(Client,SellerId;sum(Amount),count(OrderID))
Data frame：only support single field aggregation, such as the sum of Amount. As following：
To do aggregation of two fields at the same time with data frame, we can only use two separate aggregate statements and then merge the results. Codes are omitted here.
Example 9: Reuse grouping. Group data by Client. Complete multiple subsequent computations on group result. Including: aggregation by amount, and count after grouping by SellerId.
Sequence table:
A2=data.group(Client)
=A2.(~.sum(Amount))
=A2.(~.groups(SellerId;count(OrderID)))
Data frame does not support reuse of grouping directly. Grouping and aggregation usually need to be done in one step. This means we need to do two identical grouping operations to accomplish the same purpose. As following:
result<-aggregate(data[,2],sum)
result<-aggregate(data[,2],data[,3],count)
If we want to reuse grouping, we must use split function and loop to achieve this. The code is both lengthy and with low performance.
Summary:
Sequence tables and data frame are quite different in terms of advanced features. This is mainly demonstrated in the following five ways:
1. Richness of features. Sequence table has rich functions, and is very convenient to do structured data computation. Data frame originates from matrix, with less support for structured data and lack of many features. Use of the third party packages can in some degree supplement the functions data frame lacks, but these packages are no match for R’s primitive library function in muturity and stability.
2. Difficulty in syntax. The function names of sequence table are more intuitive.For example, select means to find; pselect is to find the location (position). With data frame the syntax is relatively obscure. For example, “find by field” is data [data $ Amount> 1000,], and retrieve value by field is data[,"Amount"]. These two are confusing and difficult for the programmer to understand. One must have some knowledge on vector to grasp it.
3. Memory consumption. Basically sequence table function only returns a reference, with very little memory occupation. Data frame must copied record from the original object. If we need to do multiple search, association and grouping operations on large amounts of data, data frame’s memory consumption will be very large. It will impact the whole system.
4. Code workload and code performance. The functions supported by data frame are not rich enough. We need to do hand-coding to achieve this indirectly. This means more workload. The R interpreter is known to be very slow. With hand-coding the performance is much lower than library functions.
5. Library function performance. Sequence table has many functions to improve computing performance, such as merging association, grouping functions, binary search, hash lookup. Although data frame supports association, aggregation and search, it’s hard to improve the performance.
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