Server Side Programming Articles - Page 1724 of 2646

This can be easily done by using subset function.Example> df df x y z a 1 1 6 11 16 2 2 7 12 17 3 3 8 13 18 4 4 9 14 19 5 5 10 15 20To remove only one column> df df y z a 1 6 11 16 2 7 12 17 3 8 13 18 4 9 14 19 5 10 15 20To remove two columns> df df df z a 1 11 16 2 12 17 3 13 18 4 14 19 5 15 20To remove a range of columns> df df df a 1 16 2 17 3 18 4 19 5 20To remove separate columns> df df df y 1 6 2 7 3 8 4 9 5 10

We can use stri_sub function in stringi package.Example> x x [1] "TutorialsPoint is the largest online library for best tutorials" > library(stringi) > stri_sub(x,1,9) [1] "Tutorials" > stri_sub(x,1,-20) [1] "TutorialsPoint is the largest online library" > stri_sub(x,-14,-1) [1] "best tutorials" > stri_sub(x,-41,-1) [1] "largest online library for best tutorials"

To check whether a string is a subset of another string we can use grepl function.Example> Company Job grepl(Job, Company, fixed = TRUE) [1] TRUEHere we are getting TRUE because Tutor is a subset of TutorialsPoint.> grepl(Company, Job, fixed = TRUE) [1] FALSEHere we are getting FALSE because TutorialsPoint is not a subset of Tutor.

This can be done by using tail function.Example> x tail(x,n=1) [1] 1095 > data tail(data,n=1) Class 10 PhD df = data.frame(matrix(rnorm(20), nrow=5)) > tail(df,n=1) X1 X2 X3 X4 5 -0.3595053 0.9943738 0.959761 -0.6565688 > tail(df$X4,n=1) [1] -0.6565688

We can use options(scipen=999) to do this.Example> x t.test(x, mu=2000)One Sample t-testdata: x t = -14.212, df = 9, p-value = 1.801e-07 alternative hypothesis: true mean is not equal to 200095 percent confidence interval −151.3501 659.0499sample estimates −mean of x 405.2Here p-value is in scientific notation. Now we can deactivate it as follows −> options(scipen=999) > t.test(x, mu=2000)One Sample t-testdata: x t = -14.212, df = 9, p-value = 0.0000001801 alternative hypothesis: true mean is not equal to 200095 percent confidence interval −151.3501 659.0499sample estimates −mean of x 405.2If we want to activate scientific notation again then it be ... Read More

This can be done by setting the levels of the variable in the order we want.Example> data data ggplot(data, aes(x = Class)) + geom_bar()Setting the levels in increasing order> data

Reordering of columns can be done by using square brackets.Example> df = data.frame(matrix(rnorm(20), nrow=5)) > df       X1       X2       X3       X4 1 -0.3637644 2.0770246 0.48763128 -0.09019256 2 -3.1758515 2.3173075 0.86846761 0.38396459 3 1.1844641 0.3412267 1.90986295 -1.03493074 4 -0.5953466 1.7211738 -0.90686896 -0.71215313 5 -0.8732530 0.3256303 0.02312328 -0.36993899Let’s say we want to change the order of columns as X3, X2, X4, and X1 then it can be done as shown below −> df[,c(3,2,4,1)]       X3       X2       X4          X1 1  0.48763128 2.0770246 -0.09019256 -0.3637644 2  0.86846761 2.3173075  0.38396459 -3.1758515 3  1.90986295 0.3412267 -1.03493074  1.1844641 4 -0.90686896 1.7211738 -0.71215313 -0.5953466 5  0.02312328 0.3256303 -0.36993899 -0.8732530

There are three ways to find the index of an element in a vector.Example> x x [1] 8 10 9 6 2 1 4 7 5 3Using which> which(x == 6)[[1]] [1] 4Here we found the index of 6 in vector x.Using match> match(c(4,8),x) [1] 7 1Here we found the index of 4 and 8 in vector x.Using which with %in% > which(x %in% c(2,4)) [1] 5 7Here we found the index of 2 and 4 in vector x.

This can be done simply by using sample function.Example> df = data.frame(matrix(rnorm(20), nrow=5)) > df X1 X2 X3 X4 1 -0.3277833 -0.1810403 0.2844406 -2.9676440 2 0.8262923 0.4334449 0.4031084 -1.9278049 3 -0.1769219 -0.1583660 -0.2829540 -0.1962654 4 1.0357773 0.9326049 0.3250011 -1.8835882 5 -1.0682642 -0.6589731 -0.4783144 -0.2945062Let’s say we want to select 3 rows randomly then it can be done as follows −> df[sample(nrow(df), 3), ] X1 X2 X3 X4 2 0.8262923 0.4334449 0.4031084 -1.9278049 1 -0.3277833 -0.1810403 0.2844406 -2.9676440 5 -1.0682642 -0.6589731 -0.4783144 -0.2945062

We can do this by defining the newname as shown below −> Samp Samp sample.1.100..10. 1 47 2 63 3 57 4 16 5 53 6 7 7 54 8 2 9 13 10 14 > colnames(Samp) Samp Sampled Values 1 47 2 63 3 57 4 16 5 53 6 7 7 54 8 2 9 13 10 14 Since we only have one column in the data frame, so it is sufficient to use the object name.