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Articles on Trending Technologies
Technical articles with clear explanations and examples
How to create two line charts in the same plot in R?
We can do this by using lines function after plotting the first chart.Example> x X1 X2 plot(x, X1, type="l",col="red", ylab="X") > lines(x, X2, col="green")
Read MoreHow to wrap a JSON using flexjson in Java?
The Flexjson library is a lightweight Java library for serializing and de-serializing java beans, maps, arrays, and collections in a JSON format. A JSONSerializer is the main class for performing serialization of Java objects to JSON and by default performs a shallow serialization. We can wrap a JSON object using the rootName() method of JSONSerializer class, this method wraps the resulting JSON in a javascript object that contains a single field named rootName.Syntaxpublic JSONSerializer rootName(String rootName)Exampleimport flexjson.JSONSerializer; public class JSONRootNameTest { public static void main(String[] args) { JSONSerializer serializer = new ...
Read MoreHow to convert a factor that is represented by numeric values to integer or numericnvariable in R?
We can convert a factor to integer or numeric variable by using as.numeric function with defining the levels of the factor or by defining the characters of the factorExample> f f [1] 0.323049098020419 0.916131897130981 0.271536672720686 0.462429489241913 [5] 0.657008627429605 0.462429489241913 0.462429489241913 0.212830029195175 [9] 0.271536672720686 0.497305172728375 7 Levels: 0.212830029195175 0.271536672720686 ... 0.916131897130981Using as.numeric> as.numeric(levels(f))[f] [1] 0.3230491 0.9161319 0.2715367 0.4624295 0.6570086 0.4624295 0.4624295 [8] 0.2128300 0.2715367 0.4973052 > Using as.numeric(as.character( )) > as.numeric(as.character(f)) [1] 0.3230491 0.9161319 0.2715367 0.4624295 0.6570086 0.4624295 0.4624295 [8] 0.2128300 0.2715367 0.4973052
Read MoreHow to replace NA values with zeros in an R data frame?
We can replace all NA values by using is.na functionExample> Data df df V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 1 9 7 3 0 3 7 7 3 9 9 2 9 2 3 0 2 0 1 4 6 7 3 5 0 9 2 4 8 8 7 NA 5 4 7 3 1 2 6 NA 7 1 1 8 5 3 2 9 6 4 7 0 5 6 1 6 8 5 6 5 3 9 6 0 7 0 7 8 3 4 NA NA 0 2 4 2 NA 8 6 9 9 9 4 0 6 1 7 NA 9 5 5 NA 8 1 NA 0 9 9 3 10 1 1 0 7 1 1 4 1 2 1Replacing NA’s by 0’s> df[is.na(df)] df V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 1 9 7 3 0 3 7 7 3 9 9 2 9 2 3 0 2 0 1 4 6 7 3 5 0 9 2 4 8 8 7 0 5 4 7 3 1 2 6 0 7 1 1 8 5 3 2 9 6 4 7 0 5 6 1 6 8 5 6 5 3 9 6 0 7 0 7 8 3 4 0 0 0 2 4 2 0 8 6 9 9 9 4 0 6 1 7 0 9 5 5 0 8 1 0 0 9 9 3 10 1 1 0 7 1 1 4 1 2 1
Read MoreHow to drop data frame columns in R by using column name?
Columns of a data frame can be dropped by creating an object of columns that we want to drop or an object of columns that we want to keep.Example> df keeps df[keeps] Var3 Var4 1 21 31 2 22 32 3 23 33 4 24 34 5 25 35 6 26 36 7 27 37 8 28 38 9 29 39 10 30 40
Read MoreHow to extract p-value and R-squared from a linear regression in R?
We can use regression model object name with $r.squared to find the R-squared and a user defined function to extract the p-value.ExampleExtracting R-Squared> x y LinearRegression summary(LinearRegression)$r.squared [1] 0.2814271Extracting p-value> Regressionp
Read MoreHow to sort a data frame in R by multiple columns together?
We can sort a data frame by multiple columns using order function.ExampleConsider the below data frame −> df df x1 x2 x3 x4 1 Hi A 4 9 2 Med B 7 5 3 Hi D 5 7 4 Low C 3 4Let’s say we want to sort the data frame by column x4 in descending order then by column x1 in ascending order.It can be done follows −> df[with(df, order(-x4, x1)), ] x1 x2 x3 x4 1 Hi A 4 9 3 Hi D 5 7 2 Med B 7 5 4 Low C 3 4We can do ...
Read MoreHow can we serialize a list of objects using flexjson in Java?
The Flexjson is a lightweight library for serializing and deserializing Java objects into and from JSON format. We can serialize a list of objects using the serialize() method of JSONSerializer class. This method can perform a shallow serialization of the target instance. We need to pass a list of objects of List type as an argument to the serialize() method.Syntaxpublic String serialize(Object target)Exampleimport flexjson.JSONSerializer; import java.util.*; public class JsonSerializeListTest { public static void main(String[] args) { JSONSerializer serializer = new JSONSerializer().prettyPrint(true); // pretty print JSON ...
Read MoreWhen can we call @JsonAnyGetter and @JsonAnySetter annotations in Java?
The @JsonAnyGetter annotation enables to use a Map as a container for properties that we want to serialize to JSON and @JsonAnySetter annotation instructs Jackson to call the same setter method for all unrecognized fields in the JSON object, which means that all fields that are not already mapped to a property or setter method in the Java object.Syntaxpublic @interface JsonAnyGetter public @interface JsonAnyGetterExampleimport java.io.*; import java.util.*; import com.fasterxml.jackson.core.*; import com.fasterxml.jackson.databind.*; import com.fasterxml.jackson.annotation.*; public class JsonAnyGetterAndJsonAnySetterTest { public static void main(String args[]) throws JsonGenerationException, JsonMappingException, IOException { Employee emp1 = new ...
Read MoreConvert CSV to JSON using the Jackson library in Java?
A Jackson is a Java JSON API that provides several different ways to work with JSON. We can convert CSV data to JSON data using the CsvMapper class, it is specialized ObjectMapper, with extended functionality to produce CsvSchema instances out of POJOs. We can use the reader() method for constructing ObjectReader with default settings. In order to convert this, we need to import the com.fasterxml.jackson.dataformat.csv package.In the below example, convert a CSV to JSON.Exampleimport java.io.*; import java.util.*; import com.fasterxml.jackson.databind.*; import com.fasterxml.jackson.dataformat.csv.*; public class CsvToJsonTest { public static void main(String args[]) throws Exception { ...
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