Server Side Programming Articles - Page 1726 of 2646

We can use match %in% to check whether a vector contains a given value of notExample> x 1%in%x [1] TRUE > 10%in%x [1] TRUE > 99%in%x [1] TRUE > 1024%in%x [1] TRUE > 100%in%x [1] FALSEWe can also do this for checking the common values between two vectors.Example> x y x%in%y [1] FALSE TRUE FALSE FALSE

There are two ways to do drop the factor levels in a subset of a data frame, first one is by using factor function and another is by using lapply.Example> df levels(df$alphabets) [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" > subdf levels(subdf$alphabets) [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j"Although we have created a subset but the level of factor variable alphabets still showing 10 levels. If we want to drop the factor levels then it can be done byUsing factor function> subdf$alphabets levels(subdf$alphabets) [1] "a" "b" "c" "d" "e" ... Read More

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")

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

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

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

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

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 More

In this tutorial, we are going to write a program that combines elements of the same indices different lists into a single list. And there is one constraint here. All the lists must be of the same length. Let's see an example to understand it more clearly.Input[[1, 2, 3], [4, 5, 6], [7, 8, 9]]Output[[1, 4, 7], [2, 5, 8], [3, 6, 9]]We can solve it in different ways. Let's see how to solve with normal loops.Initialize the list with lists.Initialize an empty list.Initialize a variable index to 0.Iterate over the sub list length timesAppend an empty list to the ... Read More

In this tutorial, we are going to write a program that groups all anagrams in a list. First, let's see what are anagrams.Any two strings that have the same character in a different order are known as anagrams.Before diving into the solution, let's see an example.Input['cat', 'dog', 'fired', 'god', 'pat', 'tap', 'fried', 'tac']Output[['cat', 'tac'], ['dog', 'god'], ['fried', 'fired'], ['pat', 'tap']]We will breakdown the problem into two pieces. First, we will write a function that checks two strings are anagrams or not. Follow the below steps to write code to check anagrams.Initialize the strings.Sort both the strings.If both sorted strings are ... Read More