Server Side Programming Articles - Page 1442 of 2646

Sometimes data is incorrectly entered into systems and that is the reason we must be careful while doing data cleaning before proceeding to analysis. A data collector or the sampled unit might enter blank to an answer if he or she does not find an appropriate option for the question. This also happens if the questionnaire is not properly designed or blank is filled by mistake. Also, if we have categorical variable then a control category might be filled with blank or we may want to have a blank category to use a new one at later stage. Whatever the ... Read More

The replacement of values in a vector with the values in the same vector can be done with the help of replace function. The replace function will use the index of the value that needs to be replaced and the index of the value that needs to be placed but the output will be the value in the vector.Example1Live Demo> x1 x1Output[1] 3 0 1 0 1 1 1 1 2 1Example> replace(x1, c(10), x1[c(1)])Output[1] 3 0 1 0 1 1 1 1 2 3 Example2Live Demo> x2 x2Output[1] 5 1 4 2 3 4 2 4 5 3 6 ... Read More

The standardization means converting a vector or column of an R data frame in a way such that the mean of the same becomes 0 and the standard deviation becomes 1, that is it should be converted to standard normal distribution. In R, it can be easily done with the help of scale function. Check out the below example to understand how it is done.ExampleConsider the below data frame:Live Demo> set.seed(3665) > x1 x2 x3 x4 x5 x6 df dfOutputx1 x2 x3 x4 x5 x6 1 1.3958185 49.39843 128.5224 3 4.183664 2.33406246 2 1.0467979 48.90103 120.5796 7 3.526731 0.02043217 3 ... Read More

The rgb colors are referred to red green and blue. This combination helps us to create many different colors. In R, we can use rgb function to create a plot using with different colors along with the image function. If we want to have a plot with rgb colors without any axes title or axes labels then the appropriate arguments should be used inside the image function as shown in the below example.ExampleConsider the below data frame:Live Demo> set.seed(9991) > x1 x2 x3 df dfOutput x1 x2 ... Read More

To create a random vector that sums to 1, we can use uniform distribution. The main thing that needs to be done cautiously is we should include 0 in the vector with randomly generating uniform distribution values. Check out the below examples to understand how it can be done.Example1Live Demo> x1 x1Output[1] 0.45490995 0.23826247 -0.07338489 -0.33361362 0.26125094 -0.45243689 [7] 0.05967125 0.43007076 0.04069027 0.37457976Example> sum(x1)Output[1] 1Example2Live Demo> x2 x2Output[1] 1.84330339 -0.11622911 -0.15001654 0.07803346 -0.17353612 0.23651847 [7] -0.21121933 -0.30938763 0.44503222 -0.64249881Example> sum(x2)Output[1] 1Example3Live Demo> x3 x3Output[1] 2.63249755 1.17230387 -0.28068787 0.58040911 -1.48530836 -0.04894802 [7] 0.66718009 0.13504265 -0.18253891 -0.49757615 1.63580429 -2.31002917 [13] 2.66256899 -2.40636756 ... Read More

We sometimes want to highlight the main title of a plot and one of the ways to do it is changing the font of the title to a unique or using a mixed font for the title. If we want to used mixed font then we need to use the appropriate font style for the title inside as shown in the below examples.Example1> plot(rpois(10,5),main=substitute(paste(italic("Point Chart"),": Poisson Distribution")))Output:Example2> plot(rpois(10,2),main=substitute(paste(bold("Point Chart"),": Poisson Distribution")))Output:

The range function in R provides the minimum and maximum values instead of the difference between the two. Hence, we can find the minimum and maximum by using range function then diff function can be used to find the actual range. For example, if we have a vector x then the range can be found by using diff(range(x)).ExampleLive Demo> x1 x1Output[1] 4 2 3 0 2 3 1 3 4 2Example> diff(range(x1))Output[1] 4 ExampleLive Demo> x2 x2Output[1] 4 5 3 10 2 4 2 4 8 7 3 1 5 6 7 3 7 3 4 5 3 7 ... Read More

The range function in R provides the minimum and maximum values instead of the difference between the two. Hence, we can find the minimum and maximum by using range function but for a data frame we cannot use it directly. Check out the below examples to understand how it works.Example1Live Demo> set.seed(974) > x1 x2 x3 df1 df1Output x1 x2 x3 1 0 6 10 2 0 7 10 3 3 3 11 4 2 7 9 5 3 2 5 6 3 4 7 7 2 7 7 8 2 8 5 9 0 4 9 10 2 2 ... Read More

If we find the mean of scientific numbers then the result will be also in scientific notation. We can get rid of this problem by using options(scipen=999), once we will use this code in R console all the inputs that are in scientific notation will be converted to normal numeric form, including any calculation and if we want to go back to the scientific notation then options(scipen=0) can be used.ExampleLive Demo> x1 mean(x1)Output[1] 4.436267e-22Example> options(scipen=999) > mean(x1)Output[1] 0.0000000000000000000004436267ExampleLive Demo> x2 x2Output[1] 0.000000000000000000000000000000000000001010964 [2] 0.000000000000000000000000000068291679999999998 [3] 0.000000000000000000000000006026013000000000181 [4] 0.000000000000000000000000002702241000000000107 [5] 0.000000000000000000000042258669999999998179163 [6] 0.000000000000000000000000000000091949710000000 [7] 0.000000000000000000000000000000000000107406400 [8] 0.000000000000000000000000000000091949710000000 [9] 0.000000000000000000000003463124999999999951636 [10] 0.000000000000000000004305051000000000103323794 ... Read More

The absolute distance can be found by calculating the difference between column values. And if we want the distance to be absolute then we would be need to use abs function. For example, suppose we have a data frame df that contain columns x and y then the absolute distance can be found by using df$Absolute_Distance set.seed(274) > x1 y1 df1 df1Output  x1 y1 1 6 11 2 1 4 3 4 2 4 7 12 5 4 5 6 6 10 7 6 14 8 6 8 9 2 11 10 3 8 11 3 8 12 2 6 ... Read More