Server Side Programming Articles - Page 1508 of 2646

Sometimes we want to figure out which value lies at some position in an R data frame column, this helps us to understand the data collection or data simulation process. For example, if we have a data frame df that contain columns x, y, and z each with 5000 values then we can use df$x[[253]] to find which values lies at 253rd row in column x of data frame df.ExampleConsider the below data frame − Live Demoset.seed(987) x

To create a point chart for cumulative sums using ggplot2, we need to use cumsum function for the dependent variable inside the aes function for aesthetic mapping that describes how the variable will be plotted. For example, if we have a data frame df that contain columns x and y where y is the dependent variable then the point chart for cumulative sums can be created as ggplot(df,aes(1:20,y=cumsum(y)))+geom_point().ExampleConsider the below data frame − Live Demoset.seed(666) x

If we have two lists of same size then we can create a data frame using those lists and this can be easily done with the help of expand.grid function. The expand.grid function create a data frame from all combinations of the provided lists or vectors or factors. For example, if we have two lists defined as List1 and List2 then we can create a data frame using the code expand.grid(List1,List2).Example Live DemoConsider the below lists −List1

In mathematics, when two vectors are multiplied the output is a scalar quantity which is the sum of the product of the values. For example, if we have two vectors x and y each containing 1 and 2 then the multiplication of the two vectors will be 5. In R, we can do it by using t(x)%*%y.Example1 Live Demox1

It is known that the random sample can be created by using sample function in R. If we want to create a random sample with values 0 and 1 only then there are three different ways to pass them inside the sample function −Creating a vector of 0 and 1Using 0:1Using c(1,2) directly inside the function.Also, we can set replace argument to TRUE or FALSE based on our requirement.Examples Live Demox1

To find the mean of list elements we need to unlist those elements. For example, if we have a list named as List that contains three elements of equal or different sizes such element1, element2, and element3 then we can find the mean of all the list elements by using mean(unlist(List)).Example1List1

Filling of blanks is not an easy task in data analysis, especially if the vector contains numerical or integer values. Suppose we have a vector x that contains 1, , 2, 3, 4, 5 and we want to put 1 in place of blank after first value then cummax function along with seq_along function can be used as x[cummax(seq_along(x)*(x!=""))].Example1 Live Demox1

If we want to remove rows containing missing values based on a particular column then we should select that column by ignoring the missing values. This can be done by using is.na function. For example, if we have a data frame df that contains column x, y, z and each of the columns have some missing values then rows of x without missing values can be selected as df[!is.na(df$x),].ExampleConsider the below data frame − Live Demox1

The concatenation of vectors can be done by using combination function c. For example, if we have three vectors x, y, z then the concatenation of these vectors can be done as c(x,y,z). Also, we can concatenate different types of vectors at the same time using the same same function.Example1 Live Demoset.seed(999) x1

The calculation of total for integer column based on two different character columns simply means that we need to create a contingency table for the available data. For this purpose, we can use with and tapply function. For example, if we have a data frame df that contains two categorical columns defined as gender and ethnicity and an integer column defined as Package then the contingency table can be created as:with(df,tapply(Package,list(gender,ethnicity),sum))ExampleConsider the below data frame − Live Demoset.seed(777) Class