Hire Top R developers with ultraGenius

Here are the data structures available in R programming language -

• Vector - A vector is a sequence of homogeneous data elements like an Array in C++. The data elements in a vector are called Components.
• List - A list is a heterogeneous data type in R consisting of numbers, vectors, string or may be another list inside one list. 
• Matrix - A matrix is a two-dimensional data structure which is used to bind the vectors of same size. All the matrix elements of the matrix should be of same data type.
• DataFrame - A data frame is an extended form of matrix where different columns can have different data types like numeric, string, character, etc. A dataframe combines the matrices and lists feature such as a rectangular list.

R Markdown is a type of file format for designing dynamic documents with R programming language. Markdown is a light-weight text-to-HTML conversion tool which allows developers to write and read the document easily using a plain-text editor. R Markdown is designed and developed using some chunks of R code and Markdown.

R Markdown is a better choice than LaTex for R programmers as it allows the insertion of the results of R code inside the formatted documents directly.

A confusion matrix in R is an N x N matrix used for evaluating the performance of a classification model, where N represents the number of target classes. The confusion matrix compares the values predicted by the machine learning model with the actual values.

Data imputation in R is the mechanism of replacing the missed or inconsistent values with the predicted data values based on the information that is available to the R freelance developers and researchers. The estimated values are mean or median of the massive datasets, for example, if there are x1, x2, x3, x4, x5, x6, ....xk variables and the value of x1 goes missing then its value is regressed on variables from x2 to xk and substituted. Once all missing / inconsistent values are replaced with estimated values, then data analysis can be started using the standard methods and techniques. 

Here is the list of packages used for imputing data in R -

• Amelia
• MICE
• Hmisc
• missForest
• Mi
• imputeR

Here are the steps used to design and evaluate a R linear regression model -

• First, divide the data into train and test sets as the linear regression model is designed on train sets and evaluated on test sets. This can be done using function sample.split() which is available in "catools" package in R. It provides the option of split-ration which an R developer can set according to the requirements.
• Once the data is split into train and test sets, then start working on building the model over train set. The lm() function from DAAG package is used for building a model.
• Finally, for predicting the results on test sets, use predict() function.

Random Forest is a non-linear classification algorithm used in R for building and combining multiple decision trees to generate more accurate estimations. This approach is called "Random Forest" because it is used randomly at the training time and multiple trees are used to predict the results. The Random forest algorithm always gives more accurate results than decision trees as it combines multiple decision trees for predicting the results.

Random forest takes random sample from a set of observations and predicts the result in the following manner -

• Select any n samples from train data and draw a bootstrap sample.
• Make a decision tree from the bootstrap sample. Choose d features randomly at each node of the tree.
• Next, split the node according to an objective function, for example,  again maximize the information and using variables (or features). 
• If m is the number of trees that need to be created using subset of observation samples, then steps 1st and 2nd are repeated m times.
• Combine the prediction generated by each tree for assigning a new data point to the group that is selected by majority of trees.

The rbind() and cbind() functions combine matrices, vectors, or data frames of same size by rows and columns respectively. The cbind() function combines specified matrix, data frames, or vectors by columns whereas the rbind() function combines any specified matrix, vectors, or data frames by rows.

For example, v1 = 1:5, v2 = 6:10

cbind(v1,v2)

The output will be - 

[1, ] 1 6

[2, ] 2 7

[3, ] 3 8

[4, ] 4 9

[5, ] 5 1

On the same vectors' set, if rbind is applied -

rbind(v1,v2), then output will be - 

   [  ,1] [  ,2] [  ,3] [  ,4] [   ,5] 

v1  1       2       3      4     5

v2 6       7       8      9        10

Principal Component Analysis (PCA) in R is an important and useful technique to reduce the dimensionality of datasets, increasing the data interpretability without losing any information. PCA does this by creating new variables which are uncorrelated and maximize variance.

Steps to implement PCA - 

• Calculate the n-dimensional mean of the given data set.
• Calculate the co-variance matrix of the variables(or features).
• Calculate the eigenvalues and eigenvectors of the covariance matrix.
• Sort / Rank the eigenvectors in descending order by eigen values.
• Select x eigenvectors with the largest eigenvalues.

If you are trying to hire a R Developer. Here is the R Developer Job Description that you can use in your hiring.