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An Activation Record is a data structure that is activated/ created when a procedure/function is invoked, and it includes the following data about the function.Activation Record in 'C' language consist ofActual ParametersNumber of ArgumentsReturn AddressReturn ValueOld Stack Pointer (SP)Local Data in a function or procedureHere, Old SP stores the value of stack pointer of Activation Record of procedure which has called this procedure which leads to the generation of this Activation Record, i.e., It is a pointer to the activation record of the caller.In Stack Allocation Scheme, when procedure A calls Procedure B, the activation record for B will be ... Read More
To subset an R data frame by specifying columns that contains NA, we can follow the below steps −First of all, create a data frame with some columns containing NAs.Then, use is.na along with subset function to subset the data frame by specifying columns that contains NA.ExampleCreate the data frameLet’s create a data frame as shown below −x
ListsIt is conceptually simplest and easy to implement a data structure for the symbol table in a linear list of records, as shown below −It can use an individual array to store the name and its associated information. New Names are inserted to the list in the order in which they are encountered. It can retrieve data about a name we search from the starting of the array up to the position marked by the pointer AVAILABLE which indicates the beginning of the empty portion of the array.When the name is placed, the associated data can be discovered in the ... Read More
To find the common elements between two columns of an R data frame, we can use intersect function.For Example, if we have a data frame called df that contains two columns say X and Y then we can find the common elements between X and Y by using the below command −intersect(df$X,df$Y)Example 1Following snippet creates a sample data frame −x1
To find the number of non-missing values in each group of an R data frame, we can convert the data frame to data.table object and then use the sum function with negation of is.na.For Example, if we have a data frame called df that contains a grouping column say Group and a numerical column with few NAs say Num then we can find the number of non-missing values in each Group by using the below given command −setDT(df)[,sum(!is.na(df)),by=.(Group)]Example 1Following snippet creates a sample data frame −Grp
Search TreeA more effective technique to symbol table organization is to add two link fields, LEFT and RIGHT, to every record. We use these fields to link the records into a binary search tree.This tree has the property that all names NAME (j) accessible from NAME (i) by following the link LEFT (i) and then following any sequence of links will precede NAME (i) in alphabetical order (symbolically, NAME (j) < NAME (i))Similarly, all names NAME (k) accessible starting with RIGHT (i) will have the property that NAME (i) < NAME (k). Thus, if we are searching for NAME and ... Read More
An access link is a pointer to each activation record that obtains a direct implementation of lexical scope for nested procedures. In other words, an access link is used to implement lexically scoped language. An “access line” can be required to place data required by the called procedure.An improved scheme for handling static links defined at various lexical levels is the usage of a data structure called display. A display is an array of pointers to the activation records. Display [0] contains a pointer to the activation record of the most recent activation of a procedure defined at lexical level ... Read More
To find the column variance if some columns are categorical in R data frame, we can follow the below steps −First of all, create a data frame.Then, use numcolwise function from plyr package to find the column variance if some columns are categorical.ExampleCreate the data frameLet’s create a data frame as shown below −Group
To create stacked bar chart with percentages on Y-axis using ggplot2 in R, we can use fill argument inside geom_bar and put the second categorical variable with position set to fill.For Example, if we have a data frame called with two categorical columns say C1 and C2 then we can create stacked bar chart with percentages on Y-axis using the below mentioned command −ggplot(df,aes(C1))+geom_bar(aes(fill=C2),position="fill")ExampleFollowing snippet creates a sample data frame −f1
Representing scope information is a concept in which the scope of each variable name is preserved in the symbol table so that we can use the same name in different blocks and different locations. Representing name in symbol table along with an indicator of the block in which it appears.Suppose we have a variable name 'a' in block A and the same variable in block B. Suppose it can store 'a' in the symbol table without block information. In that case, it will only keep the first instance of 'a' which it encounters, hence to overcome this problem names are ... Read More