Article Categories
- All Categories
-
Data Structure
-
Networking
-
RDBMS
-
Operating System
-
Java
-
MS Excel
-
iOS
-
HTML
-
CSS
-
Android
-
Python
-
C Programming
-
C++
-
C#
-
MongoDB
-
MySQL
-
Javascript
-
PHP
Articles by Nizamuddin Siddiqui
Page 196 of 196
How to check if a vector contains a given value in R?
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
Read MoreHow to drop factor levels in subset of a data frame in R?
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 MoreHow 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 convert a factor that is represented by numeric values to integer or numeric\\nvariable 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 More