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After getting some experience with data frame people generally move on to data.table object because it is easy to play with a data.table object as compared to a data frame. We also need to create a subset of a data.table object and it can be easily done with the help of square brackets. For example, if we have a data.table object called DT that contains 10 columns and we want to create a subset of 1 to 8 columns then we can use DT[,−c(9,10),with=FALSE] to create that subset.Examplelibrary(data.table) x1
To combine two factor vectors, we can extract the unique levels of both the vectors then combine those levels. This can be done by using unique function. Also, we can set the levels of the original vectors to the combination of the levels, in this way, we can complete both the vectors with missing levels. Check out the examples below to understand how it works.Example1x1
We can use %in% to check whether a matrix element is present in another matrix or not. For example, suppose we have two matrices defined as − M1 1 2 3 1 2 3 1 2 3 M2 1 2 3 4 5 6 7 8 9Then M1%in%M2 will return −[1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEBut M2%in%M1 will return −[1] TRUE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSEExample1 Live DemoM1
The xtable function of xtable package creates a Latex table. We can use digits argument with negative sign to convert the values in the original table into scientific form. For example, if we have a data frame defined as df then we can read it with xtable as xtable(df,digits=-10).Loading xtable package −library(xtable)Example1data1
To convert a list into an array then firstly we need to unlist the list elements and then use the array function. For example, if we have a list defined as List then it can be converted into an array using the command array(unlist(List)). Suppose the list contain the elements as shown below −1 2 3 4 5 1 2 3 4 5 1 2 3 4 5If we convert this list into an array then the output will be 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5.Example1List1
To create a character vector in R we can enclose the vector values in double quotation marks but if we want to use a data frame row values to create a character vector then as.character function can be used. For example, if we have a data frame df then the values in first row of the df can form a character vector using as.character(df[1,]).Example Live DemoConsider the below data frame −set.seed(3232) x
If integer vector is read as strings and we want to find the position of the minimum value in that vector then we need to use as.numeric along with the vector to read it as a numeric vector then use which function for finding the position of the minimum value. For example, if we have a vector x that contains first ten integers as strings then to find the position of the minimum we can use which(as.numeric(x)==min(as.numeric(x))).Example1x1
While dealing with text data, we sometimes need to extract values between two words. These words can be close to each other, at the end sides or on random sides. If we want to extract the strings between two words then str_extract_all function of stringr package can be used.Loading stringr package −library(stringr)Example1 Live Demox1
In data analysis, we encounter many problems with a lot of variations among them. One such problem is we have some information in a place that needs to be checked through a different place and these places can be data frames. Therefore, we can to find a data frame’s column value based on a column value of another data frame. In R, we can easily do it with the help of which function.Example Live DemoConsider the below data frame −set.seed(12121) x1
A set in mathematics is defined as the collection of unique elements and the order of the elements does not matter. In R, we can create sets using set_power function of sets package. For example, if we have a vector x that contains A, B, C then the sets using the vector x can be created by using set_power(x).Loading sets package −library(sets)Examplesx1