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Articles on Trending Technologies
Technical articles with clear explanations and examples
Robin-Hood Hashing in Data Structure
In this section we will see what is Robin-Hood Hashing scheme. This hashing is one of the technique of open addressing. This attempts to equalize the searching time of element by using the fairer collision resolution strategy. While we are trying to insert, if we want to insert element x at position xi, and there is already an element y is placed at yj = xi, then the younger of two elements must move on. So if i ≤ j, then we will try to insert x at position xi+1, xi+2 and so on. Otherwise we will store x at ...
Read MoreLCFS Hashing in Data Structure
In this section we will see what is LCFS Hashing. This is one of the open-addressing strategy, that changes the collision resolution strategy. If we check the algorithms for the hashing in open address scheme, we can find that if two elements collide, then whose priority is higher, will be inserted into the table, and subsequent element must move on. So we can tell that the hashing in open addressing scheme is FCFS criteria.With the LCFS (Last Come First Serve) scheme. The task is performed exactly in opposite way. When we insert one element, that will be placed at position ...
Read MoreAsymmetric Hashing in Data Structure
In this section we will see what is Asymmetric Hashing technique. In this technique, the hash table is split into d number of blocks. Each split is of length n/d. The probe value xi, 0 ≤ i ≤ d, is drawn uniformly from $$\lbrace\frac{i*n}{d}, ..., \frac{(i+1)*n}{d-1}\rbrace$$. As with multiple choice hashing, to insert x, the algorithm checks the length of the list A[x0], A[x1], . . ., A[xd – 1]. Then appends x to the shortest of these lists. If there is a tie, then it inserts x to the list with smallest index.According to Vocking, the expected length of ...
Read MoreHow to split a big data frame into smaller ones in R?
Dealing with big data frames is not an easy task therefore we might want to split that into some smaller data frames. These smaller data frames can be extracted from the big one based on some criteria such as for levels of a factor variable or with some other conditions. This can be done by using split function.ExampleConsider the below data frame −> set.seed(1) > Grades Age Category df head(df, 20) Grades Age Category 1 A 25 6 2 B 4 ...
Read MoreHow to add a column between columns or after last column in an R data frame?
Since no one is perfect, people might forget to add all columns that are necessary for the analysis but this problem can be solved. If a column is missing in our data frame and we came to know about it later then it can be added easily with the help of reordering the columns.ExampleConsider the below data frame −> x1 x2 x3 df df x1 x2 x3 1 1 a 1 2 2 b 2 3 3 c 1 4 4 d 2 5 5 e 1 ...
Read MoreHow to delete a row from an R data frame?
While doing the analysis, we might come across with data that is not required and we want to delete it. This data can be a whole row or multiple rows. For example, if a row contains values greater than, less than or equal to a certain threshold then it might not be needed, therefore we can delete it. In R, we achieve this with the help of subsetting through single square brackets.ExampleConsider the below data frame −> set.seed(99) > x1 x2 x3 x4 x5 df df ...
Read MoreHow to replace missing values recorded with blank spaces in R with NA or any other value?
Sometimes when we read data in R, the missing values are recorded as blank spaces and it is difficult to replace them with any value. The reason behind this is we need to know how many spaces we have used in place of missing values. If we know that then assigning any value becomes easy.ExampleConsider the below data frame of vectors x and y.> x y df df x y 1 1 2 3 2 3 2 4 1 43 5 2 2 6 3 7 2 3 ...
Read MoreHow to find the correlation matrix in R using all variables of a data frame?
Correlation matrix helps us to determine the direction and strength of linear relationship among multiple variables at a time. Therefore, it becomes easy to decide which variables should be used in the linear model and which ones could be dropped. We can find the correlation matrix by simply using cor function with data frame name.ExampleConsider the below data frame of continuous variable −> set.seed(9) > x1 x2 x3 x4 x5 df df x1 x2 ...
Read MoreHow to change the order of columns in an R data frame?
Ordering columns might be required when we want to manipulate the data. Manipulation can have several reasons such as cross verification, visualisation, etc. We should also be careful when we change anything in the original data because that might affect our processing. To change the order of columns we can use the single square brackets.ExampleConsider the below data frame −> set.seed(1) > Class Grade Score df df Class Grade Score 1 a A 68 2 b B 39 3 c C 1 4 ...
Read MoreHow to create bar chart using ggplot2 with chart sub-title in R?
There are different ways to express any chart. The more information we can provide in a chart, the better it is because a picture says thousand words. Since nobody likes to read a long-reports, we should have better reporting of charts. Therefore, we can add a chart title as well as chart sub-title in ggplot2 to help the readers.ExampleConsider the below data −> set.seed(1) > x table(x) x 2 3 4 5 6 7 8 9 11 1 3 4 2 4 2 2 1 1 > df library(ggplot2)Creating a simple bar chart −> ggplot(df, aes(x))+ + geom_bar()OutputCreating a ...
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