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R Programming Articles
Page 93 of 174
Working with Dates and Times in R with lubridate
The dates and times appear simple and easy at first impression as we deal with them in our day-to-day life. But a lot of complexity involves when we work with dates and times objects in R. This article focuses on working with dates and times using the lubridate package in R. You can install this package locally by using the following command in the CRAN’s terminal − install.packages("lubridate") Types of data/time objects in R There are three types of data/time objects and are listed below − Date () object − Prints the date. Time () object − Prints ...
Read MoreParallel Programming in R
Parallel programming is a software development practice that involves dividing a computation or task into smaller parts that can be executed concurrently or in parallel. Parallel programming can help improve the performance and efficiency of your R code by utilizing multiple processors or cores in a computer or cluster. The main concept of parallel programming is, if one operation can be performed in S seconds using a single processor, then it should be able to get executed in S / N seconds when N processors are involved. Need for Parallel Programming in R Most of the time the code in ...
Read MoreManipulating Time Series Data in R with xts & zoo
The xts and zoo are two R packages that provide tools and functions for manipulating time series data. Both packages offer functions for reading, writing, and manipulating time series data stored in various formats, such as CSV, Excel, and other data sources. We shall start by introducing xts and zoo classes, basic manipulations, merging and modifying time series, and by the end, we will be discussing applying and aggregating by time. XTS and Zoo class Syntax In R, xts extends the zoo class. An xts object is similar to a matrix of observations that are indexed by a time object. ...
Read MoreJoining Data in R with data.table
In this article, we will discuss joining data in R using data.table package. By the term “joining data” we mean to say that performing different types of joins operations like INNER JOIN, LEFT OUTER JOIN, RIGHT OUTER JOIN, AND FULL OUTER JOIN between two or more tables. The main purpose of doing join operations between tables is to access data from multiple tables on the basis of some attribute (or column) condition. R provides us data.table package with the help of which we can handle tabular data (having rows and columns) very efficiently. This package was launched as an alternative ...
Read MoreDefensive R Programming
Defensive programming is a software development practice that involves designing and implementing code in a way that anticipates and prevents errors and vulnerabilities. In R programming, defensive programming involves using techniques and strategies to ensure that your R code is robust, reliable, and secure. By the word “Defensive” in defensive programming, most of you might be confused about whether it means writing such a code that doesn’t fail at all. But the actual definition of “Defensive programming” is writing such a code that fails properly. By “failing properly”, we mean − If the code fails, then it should be ...
Read MoreHow to find the moving standard deviation in an R matrix?
To find the moving standard deviation in a matrix is done in the same way as in a data frame, we just need to use the matrix object name in place of data frame name. Hence, we can make use of rollapply function of zoo package for this purpose.For example, if we have a matrix called M and we want to find the 2 moving standard deviations then we can use the below given command −rollapply(M,width=2,FUN=sd,fill=0,align="r")Example 1Following snippet creates a matrix −M1
Read MoreHow to round the summary output in R?
To round the output of summary function in R, we can use digits argument while applying the summary function.For example, if we have a data frame called df then to find the summary statistics with two digits in the output we can use the below given command −summary(df, digits=2)Example 1Following snippet creates a dataframe −head(iris, 20) The following dataframe is created − Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 ...
Read MoreHow to find the row wise sum for n number of columns in R?
To find the row wise sum of n number of columns can be found by using the rowSums function along with subsetting of the columns with single square brackets.For example, if we have a data frame called df that contains five columns and we want to find the row sums for last three columns then we can use the following command −df$Sum_3
Read MoreHow to extract the maximum value from named vector in R?
To extract the maximum value from named vector in R, we can use which.max function.For example, if we have a vector called X which is a named vector then we can use the following command to find the maximum value in X.X[which.max(X)]Check out the below examples to understand how it works.Example 1Following snippet creates a vector −x1
Read MoreHow to reduce the space between Y-axis value and ticks using ggplot2 in R?
To reduce the space between axis value and ticks using ggplot2, we can use theme function of ggplot2 package with margin set to 0.For example, if we have a data frame called df that contains two columns say x and y then the scatterplot between x and y with reduced space between Y-axis value and ticks can be created by using the following command −ggplot(df,aes(x,y))+geom_point()+theme(axis.text.y=element_text(margin=margin(r=0)))ExampleFollowing snippet creates a sample data frame −x
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