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In Data Analysis, we often need to look for less than, less than equal to, greater than, or greater than equal to values to compare them with some threshold. Sometimes we also require the frequency of these values. Therefore, we can use sum function for this purpose. For example, if a vector x has 10 integer values then to check how many of them are greater than or equal to 10, we can use the command sum(x>=10).Example1 Live Demox1=5)Output[1] 83Example2 Live Demox2=5)Output[1] 8Example3 Live Demox3=0.25)Output[1] 38Example4 Live Demox4=10)Output[1] 49Example5 Live Demox5=4)Output[1] 21
If we have two categorical columns in an R data frame then we can find the frequency/count of each category with respect to each category in the other column. This will help us to compare the frequencies for all categories. To find the counts of categories, we can use table function as shown in the below examples.Example1 Live DemoConsider the below data frame −x1
If a variable is numerical then it can be converted into a categorical variable by defining the lower and upper limits. For example, age starting from 21 and ending at 25 can be converted into a category say 21−25. To convert an R data frame column into a categorical variable, we can use cut function.Example1 Live DemoConsider the below data frame −set.seed(141) x1
Sometimes the string vector contains unnecessary characters at the end or at the starting and do not make sense, it is also possible that the string makes sense but nor required there is a spelling mistake. In such type of cases, we need to remove the unnecessary characters. This can be done by using gsub function.Example1 Live Demox1
If we have a vector that contains values with less than, equal to, and greater than 2 and the value 2 is the threshold. If this threshold value is defined for lower values and we want to replace the values that are less than 2 with 2 then pmax function can be used. For example, for a vector x, it will be done as pmax(x,2).Example1 Live Demox1
To count the number of duplicate rows in an R data frame, we would first need to convert the data frame into a data.table object by using setDT and then count the duplicates with Count function. For example, if we have a data frame called df then the duplicate rows will be counted by using the command − setDT(df)[,list(Count=.N),names(df)].Example1 Live DemoConsider the below data frame −x1
To remove the first and last character in a string, we can use str_sub function of stringr package. For example, if a word say tutorialspoint is mistakenly typed as ttutorialspointt and stored in a vector called x then to remove the first and last “t”, we can use the command str_sub(x,2,−2).Example1library(stringr) x1
We can split the screen to assign the plots manually in the plot area. The split.screen function can be used for this purpose. For example, if we want to create 4 plots in the plot window then we can use split.screen(c(2, 2)). Now to create the plot in first screen the command will be screen(1) then plot will be created. If we again create the plot then the original plot in screen(1) will be replaced with the new one. To create the plot in 3rd screen that is screen(3), we first need to use the command screen(3) and then create ... Read More
To convert multiple columns into single column in an R data frame, we can use unlist function. For example, if we have data frame defined as df and contains four columns then the columns of df can be converted into a single by using data.frame(x=unlist(df)).Example1 Live DemoConsider the below data frame −x1
There are many ways to define an outlying value and it can be manually set by the researchers as well as technicians. Also, we can use 5th percentile for the lower outlier and the 95th percentile for the upper outlier. For this purpose, we can use squish function of scales package as shown in the below examples.Example1library(scales) x1