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We might want to convert categorical columns to numeric for reasons such as parametric results of the ordinal or nominal data. If we have categorical columns and the values are represented by using letters/words then the conversion will be based on the first character of the category. To understand the conversion, check out the below examples.Example1 Live DemoConsider the below data frame −set.seed(100) x1
The NaN values are referred to as the Not A Number in R. It is also called undefined or unrepresentable but it belongs to numeric data type for the values that are not numeric, especially in case of floating-point arithmetic. To remove rows from data frame in R that contains NaN, we can use the function na.omit.Example1 Live DemoConsider the below data frame −x1
If we have very large data set then it is highly that we forget the column names, therefore, we might want to check whether a particular column exists in the data frame or not if we know the column name. For this purpose, we can use grep function that will result the column name if exists in the data frame otherwise 0. To understand how it works check out the below examples.Example1 Live DemoConsider the below data frame −Gender
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