Server Side Programming Articles - Page 1510 of 2646

The boxplot can be created by using boxplot function in base R but the Y−axis labels are generated based on the vector we pass through the function. If we want to remove the axis labels then axes = FALSE argument can be used. For example, if we have a vector x then the boxplot for x without axes labels can be created by using boxplot(x,axes=FALSE).Example Live DemoConsider the below vector x and creating boxplot −set.seed(777) x

The t test is used to find the p−value for the correlation coefficient and on the basis of that we decide whether there exists a statistically significant relationship between two variables or not. In R, we can perform this test by using function cor.test. For example, if we have a vector x and y then we can find the p−value using cor.test(x,y).Example1 Live Demoset.seed(444) x1

In base R, we use boxplot function to create the boxplots but if we have categorical vector and the corresponding numerical vector then the boxplot can be easily created. For this purpose, we should save those vectors in a data frame and use the $ operator and las = 2 argument to create the boxplot as shown in the below example.ExampleConsider the below vectors:Countries Rate

In R, for the calculation of power we can simply use power operator ^ and this will be also used in case of generating a power sequence. For example, if we want to generate a power sequence from 1 to 5 of 2 then we can use the code 2^(1:5) this will result 2 4 8 16 32.Example Live Demo2^(0:2)Output[1] 1 2 4Example Live Demo2^(0:10)Output[1] 1 2 4 8 16 32 64 128 256 512 1024 Example Live Demo2^(0:50)Output[1] 1.000000e+00 2.000000e+00 4.000000e+00 8.000000e+00 1.600000e+01 [6] 3.200000e+01 6.400000e+01 1.280000e+02 2.560000e+02 5.120000e+02 [11] 1.024000e+03 2.048000e+03 4.096000e+03 8.192000e+03 1.638400e+04 [16] 3.276800e+04 6.553600e+04 1.310720e+05 2.621440e+05 5.242880e+05 [21] ... Read More

To convert columns of an R data frame from integer to numeric we can use lapply() function. For example, if we have a data frame df that contains all integer columns then we can use the code lapply(df,as.numeric) to convert all of the columns data type into numeric data type.Example1Consider the below data frame − Live Demoset.seed(871) x1

The point chart can be created by using geom_point function and if we want to create a point chart for single vector then we should pass the vector in both the places inside aes function. Also, by default the points are complete black circles and if we want to change the points to empty points then shape argument can be used.ExampleConsider the below data frame − Live Demoset.seed(171) x

Random samples can be generated in many ways such as using discrete and continuous distributions, using integer vectors, using numerical vectors, using character vectors and/or factor vectors, also with columns of a data set. If we have the sample that is continuous in nature then the values are likely to contain many values after decimal point and we can limit those values to 4 or use any other limit using round function.Example Live Demox1

If a vector contains string values and they are separated with a special character (This special character can be anything, also it is not necessarily to be a special character) and we want to extract only the values that exists before that special then we can use gsub function. For example, if we have a vector x that contain "UT/YT", "IST/IT", "PST/PT" then gsub can help us to extract UT, IST, PST.Examplex1

The relative frequency histogram can be created for the column of an R data frame or a vector that contains discrete data. For this purpose, we can use PlotRelativeFrequency function of HistogramTools package along with hist function to generate histogram. For example, if we have a vector x for which we want to create a histogram with relative frequencies then it can be done as PlotRelativeFrequency(hist(x)).ExampleConsider the below vector − Live Demox

Cumulative sums are mostly used in descriptive analysis of data but sometimes we might want to calculate them in understanding the time series analysis for moving sums but it is very rare. If we have a factor column in an R data frame then it would not make sense to find the cumulative sum for all factor levels together, we must find the cumulative sums for each level. This can be easily done by using ave function.ExampleConsider the below data frame − Live Demoset.seed(15) x1