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Wednesday 13 September 2017

ADF Basics: Filter ViewObject data using getFilteredRows and RowQualifier


Sometimes we need to get filtered data from ViewObject using one or multiple conditions,
Though this is the very basic of framework yet new developers find it confusing.

There are two ways of filtering ViewObject

 1. In this we apply WHERE clause on ViewObject and it affects resultSet data, Suppose we have Department ViewObject and we want to see data of DepartmentId 4 on page after filtering, for this viewCritera, bind variables comes in action
ADF Basics: Apply and Change WHERE Clause of ViewObject at runtime programmatically

2. In this user want to get filtered data (Rows) in code only without any effect on ViewObject resultSet (page data), Here I am discussing this point

We can get filtered data from view object using two methods-

Wednesday 23 August 2017

Add new row and copy existing row to HTML table using JavaScript


Hello All

This post is about adding new row in HTML table using form entry or copy existing row to table using javascript

So here I have created a HTML page, added a form, two buttons, and a table on page and it looks like this


Sunday 23 July 2017

R Data Types - Vectors, Matrices, Lists, Factors, Data Frames

Like other programming languages, R supports many different data types. You must have seen that variables are used to store data in a program and a data type is assigned to a variable and that variable can hold only that type of data. In this post, we'll learn about R data types and R objects. Basic data types in R programming are Numeric, Integer, Character, Logical and Complex and other than this R has some unique data types that are called R Objects.

Vectors in R

A Vector is basically a set of values of the same basic data type like numeric character etc. A vector in R is created using the c() function that represents a combination of elements. See this example
# A Numeric Vector
numeric_vector <- c(10, 20, 30)

# A Character Vector
character_vector <- c("a", "b", "c")

# A Boolean Vector
boolean_vector <-c(TRUE,FALSE,TRUE)
The output on R Console is 


Matrices in R

R supports Matrices and a Matrix is a collection of data values in 2 dimensions of the same basic data type, R creates a matrix of values using a matrix() function. See this example Here c(1,2,3,4,5,6,7,8,9) is a numeric vector nrow is the number of rows in the matrix ncol is the number of columns in the matrix
#Create a matrix using Vector
test_matrix<-matrix(c(1,2,3,4,5,6,7,8,9), nrow=3, ncol=3)
#Print matrix on console
print(test_matrix)
The output on R Console is 



Arrays in R

Arrays are the same as any other programming language and in R array is same as a matrix but it can have more than two dimensions. Array in R is created using array() function and uses a vector as input and dim value to create arrays. Here dim =c(2,2,4) means that 4 arrays will be created of 2x2.
#We have two vectors here
v1 <- c(1,2,3,7,8,9)
v2 <- c(4,5,6,10,11,12,13,14,15,16)

#Create array using vectors
test_array <- array(c(v1,v2),dim = c(2,2,4))

#print the array on the console
print(test_array)
The output on R Console is

  

Lists in R

A List is a set of values that can have the different basic data type, In R List is created using list() function.
#A list with different data types
#Declare a numeric vector
numeric_vector<-c(1,2,3)

#Create list 
test_list<- list("Ashish Awasthi", numeric_vector, 5.3)

#Print list
print(test_list)
The output on R Console

  

Factors in R

Factors are created using vectors as base and stores unique values as levels, In R Factor object is created using factor() function.
# Create a vector with duplicate values
emp_names <- c('James','Ram','James','Ashish','Ram','Ashish','James','Ram')

# Create a factor object using vector
factor_emp <- factor(emp_names)

# Print the factor object
print(factor_emp)
The output on R Console is 



Data Frames in R

Data Frame is used for storing data in tables, and this tabular data can have multiple types of vectors like numeric, characters etc. Data Frame can be created using data.frame() function.
#A Character Vector
string_vector <-c("Ashish", "Awasthi","R")

#A Numeric Vector
numeric_vector <- c(10, 20, 30.5)

#A Boolean Vector
boolean_vector<- c(TRUE, FALSE, TRUE)

# Create the data frame using all 3 vectors
test_df <- 	data.frame(string_vector,numeric_vector,boolean_vector)

#Print result
print(test_df)
The output on R Console 




Though this post gives an idea of using variables, In the next post, we'll learn more about using variables in R programming. 

  Cheers :) Happy Learning