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R Programming Articles
Page 41 of 174
How to create combinations of 0 and 1 with fixed number of 1s in each row in an R data frame?
To create the combinations of 0 and 1 data frame, we can use expand.grid function along with the rep function. If we want to create combination of 0 and 1 with fixed number of 1’s in each row then rowSums functions can be used with the appropriate sum value. For example, to have rows containing less than three 1’s, the rowSums will be extracted from grid for the same.Example1First
Read MoreHow to add a new value to each element of list in R?
Suppose we have a list that contain two elements and we get a new value for both of these elements then the problem of adding those values to the original list arises. This can be done with the help of mapply function. We can append the new values in the original easily but a vector of the new values needs to be created first.Example1List1
Read MoreHow to remove rows for categorical columns that has three or less combination of duplicates in an R data frame?
In Data Analysis, we sometimes decide the size of the data or sample size based on our thoughts and this might result in removing some part of the data. One such thing could be removing three or less duplicate combinations of categorical columns and it can be done with the help of filter function of dplyr package by grouping with group_by function.Example1y1
Read MoreHow to create a large vector with repetitive elements of varying size in R?
To create a large vector of repetitive elements of varying size we can use the rep function along with the logical vector as an index. The logical vector that contains TRUE or FALSE will define the selection or omission of the values in the vector created with the help of rep function as shown in the below examples. If the vector created by using rep is larger than the logical vector then the logical vector will be recycled.Example1x1
Read MoreHow to find the percentage of missing values in an R data frame?
To find the percentage of missing values in an R data frame, we can use sum function with the prod function. For example, if we have a data frame called df that contains some missing values then the percentage of missing values can be calculated by using the command: (sum(is.na(df))/prod(dim(df)))*100Example1y1
Read MoreHow to replace the outliers with 5th and 95th percentile values in R?
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
Read MoreHow to convert multiple columns into single column in an R data frame?
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)).Example1y1
Read MoreHow to count the number of duplicate rows in an R data frame?
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)].Example1y1
Read MoreHow to create a categorical variable using a data frame column in R?
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.Example1y1
Read MoreHow to find the counts of categories in categorical columns in an R data frame?
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.Example1y1
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