{"id":75429,"date":"2020-01-13T15:52:45","date_gmt":"2020-01-13T10:22:45","guid":{"rendered":"https:\/\/techvidvan.com\/tutorials\/?p=75429"},"modified":"2020-01-13T15:52:45","modified_gmt":"2020-01-13T10:22:45","slug":"r-factors","status":"publish","type":"post","link":"https:\/\/techvidvan.com\/tutorials\/r-factors\/","title":{"rendered":"R Factors – Operating on Factors and Factor Levels"},"content":{"rendered":"

Factors are data structures in R that store<\/strong> categorical<\/strong>\u00a0data<\/strong>. In datasets, there are often fields that take only a few predefined values. For example – gender, availability, country, marital status, etc. Such data is called categorical data.<\/p>\n

R factors only allow values that are among a predefined set of possible values known as levels<\/strong>.<\/p>\n

Today, we are going to learn everything about factors in the R programming language.<\/p>\n

Get ready for an exciting tutorial, because here it comes!<\/p>\n

Categorical vs. Continuous Variables<\/h3>\n

In a dataset, there are two types of variables:<\/p>\n

    \n
  1. Continuous variables<\/li>\n
  2. Categorical variables<\/li>\n<\/ol>\n

    Continuous variables<\/strong> can take any values. They can be numerics, integers or characters. Examples of such variables would be names, addresses, phone numbers, etc..<\/p>\n

    Categorical variables<\/strong> can only take values from a finite group of possible values. They can be logical, characters, numerics or integers. Examples of categorical variables would be gender, marital status, logical values, country, etc.<\/p>\n

    Factors in R<\/h2>\n

    Factors are data structures in R that store categorical data. They have a levels<\/strong> attribute that holds all the possible values that elements of the factor can take.<\/p>\n

    R factors can be of any type. They only allow values permitted by the levels. Factors can have NA values, if a value that is not in the levels of a factor is entered into it.<\/p>\n

    Creating a Factor<\/h3>\n

    We use the factor()<\/code><\/strong> function to create factors. The following is the syntax of the factor()<\/code><\/strong> function:<\/p>\n

    factor_name=factor(x=character(),levels,labels,exclude,ordered,nmax)<\/code><\/strong><\/span><\/p>\n

    Where x<\/code><\/strong> is a vector with the data for the factor,<\/p>\n

    levels<\/code><\/strong>is an optional vector with unique values that x might take,<\/p>\n

    labels<\/code><\/strong> is an optional vector of labels for the levels in the factor,<\/p>\n

    exclude<\/code><\/strong> is a set of values that are excluded from the levels of the factor,<\/p>\n

    ordered<\/code><\/strong> is a logical value that determines whether the factor is an ordered or unordered factor,<\/p>\n

    nmax<\/code><\/strong> is an upper limit on the number of levels.<\/p>\n

    Code<\/strong>:<\/p>\n

    > gender <- c(\"male\",\"male\",\"female\",\"male\",\"female\",\"male\")\n> fac1 <- factor(gender)\n> fac1<\/pre>\n
    Output<\/strong>:<\/p>\n
    \"creating<\/a><\/strong><\/p>\n
    Indexing R Factor<\/h3>\n
    We can use the same indexing techniques as a vector to access the elements of a factor.<\/p>\n
    Forgot the basic data structure in R? Revis<\/strong>e <\/strong>the concept of R vector<\/a><\/strong><\/em>.<\/p>\n
    1. Using positive integers<\/h4>\n
    We can index factors by using positive integers or vectors of positive integers.<\/p>\n
    Code:<\/strong><\/p>\n
    > fac1[3]<\/pre>\n
    Code:<\/strong><\/p>\n
    > fac1[c(2:4)]<\/pre>\n
    Output:<\/strong><\/p>\n
    \"indexing<\/a><\/p>\n
    2. Using negative integers<\/h4>\n
    We can use negative integers or vectors of negative integers to exclude certain elements from R factor.<\/p>\n
    Code:<\/strong><\/p>\n
    > fac1[-2]<\/pre>\n
    Code:<\/strong><\/p>\n
    > fac1[c(-3,-4)]<\/pre>\n
    Output:<\/strong><\/p>\n
    \"indexing<\/a><\/strong><\/p>\n
    3. Using logical vectors<\/h4>\n
    We can index R factors by using logical vectors.<\/p>\n
    Code: <\/strong><\/p>\n
    > fac1[c(T,F,T,T,F,F)]<\/pre>\n
    Code:<\/strong><\/p>\n
    > fac1[c(T,F,T)] #logical index vector recycling<\/pre>\n
    Output:<\/strong><\/p>\n
    \"indexing<\/a><\/p>\n
    Modifying the R Factor<\/h3>\n
    We can modify existing values and add new ones as well by using reassignment<\/strong>. The new values must be within the level of the factor.<\/p>\n
    Initial state of factor:<\/strong><\/p>\n
    > fac1<\/pre>\n
    Output:<\/strong><\/p>\n
    [1] male male female male female male
    \nLevels: female male<\/div>\n
    Example 1:<\/strong><\/p>\n
    > fac1[2] <- \"female\"\n> fac1<\/pre>\n
    Output:<\/strong><\/p>\n
    [1] male female female male female male
    \nLevels: female male<\/div>\n
    Example 2:<\/strong><\/p>\n
    > fac1[7] <- \"other\"<\/pre>\n
    Output:<\/strong><\/p>\n
    Warning message:<\/span>
    \nIn `[<-.factor`(`*tmp*`, 7, value = “other”) :<\/span>
    \ninvalid factor level, NA generated<\/span><\/div>\n
    Note<\/strong>: a factor does not allow values that are not in its level.<\/p>\n
    Final state of factor:<\/strong><\/p>\n
    > fac1<\/pre>\n
    Output:<\/strong><\/p>\n
    [1] male female female male female male <NA>
    \nLevels: female male<\/div>\n
    \"modifying<\/a><\/strong><\/p>\n
    Adding and Dropping Levels of R Factor<\/h3>\n
    To add a value that does not exist in the level of a factor, we must add it to the level first.<\/p>\n
    Code:<\/strong><\/p>\n
    > levels(fac1) <- c(\"female\",\"male\",\"other\")\n> fac1<\/pre>\n
    Output:<\/strong><\/p>\n
    \"adding<\/a><\/strong><\/p>\n
    We can also remove levels from a factor by using the droplevels()<\/code><\/strong> function. This function removes unused levels from a factor.<\/p>\n
    Code:<\/strong><\/p>\n
    > droplevels(fac1)<\/pre>\n
    Output:<\/strong><\/p>\n
    \"dropping<\/a><\/strong><\/p>\n
    Ordered Factors in R<\/h3>\n
    We can classify R factors as ordered or unordered<\/strong>. By default, the levels are arranged in alphabetical order and are all considered equal irrespective of their arrangement. For comparison purposes, these levels can be ordered according to increasing weight or value.<\/p>\n
    Here is an example of an unordered<\/strong> R factor:<\/p>\n
    Code:<\/strong><\/p>\n
    > sizes <- c(\"s\",\"m\",\"s\",\"l\",\"m\",\"xs\",\"l\",\"m\",\"xl\",\"xxl\",\"s\", \"l\",\"xs\",\"xl\",\"m\",\"l\")\n> sizef <- factor(sizes)\n> sizef<\/pre>\n
    Output:<\/strong><\/p>\n
    \"unordered<\/a><\/strong><\/p>\n
    We can provide a specific order of levels by using the levels =<\/code><\/strong> argument of the factor()<\/code><\/strong> function. However, the resultant factor is still considered unordered.<\/p>\n
    Code:<\/strong><\/p>\n
    > sizef <- factor(sizes,levels=c(\"xs\",\"s\",\"m\",\"l\",\"xl\",\"xxl\"))\n> sizef<\/pre>\n
    Output:<\/strong><\/p>\n
    \"ordered<\/a><\/strong><\/p>\n
    We can also use the ordered()<\/code><\/strong> function to create an ordered factor. This function has the same syntax as the factor()<\/code><\/strong> function.<\/p>\n
    Code:<\/strong><\/p>\n
    > sizeof <- ordered(sizes,levels=c(\"xs\",\"s\",\"m\",\"l\",\"xl\",\"xxl\"))\n> sizeof<\/pre>\n
    Output:<\/strong><\/p>\n
    \"ordered<\/a><\/strong><\/p>\n
    We can also convert unordered factors into ordered ones by using the as.ordered()<\/code><\/strong> function.<\/p>\n
    Code:<\/strong><\/p>\n
    > as.ordered(sizef)<\/pre>\n
    Output:<\/strong><\/p>\n
    \"converting<\/a><\/p>\n
    Functions of R Factors<\/h3>\n
    There are a few functions that give us information about the R factor variables we use. Functions like is.factor()<\/code><\/strong>, as.factor()<\/code><\/strong>, is.ordered()<\/code><\/strong>, etc.. This is what these functions do:<\/p>\n
    1. is.factor()<\/code><\/strong><\/h4>\n
    The is.factor()<\/code><\/strong> function checks if a variable is a factor or not. It returns a logical value of TRUE<\/code><\/strong> if the variable is a factor and FALSE<\/code><\/strong> if it is not.<\/p>\n
    Code:<\/strong><\/p>\n
    > is.factor(fac1)\n> is.factor(gender)<\/pre>\n
    \"R<\/a><\/p>\n
    2. as.factor()<\/code><\/strong><\/h4>\n
    The as.factor()<\/code><\/strong> function converts vector inputs into factors.<\/p>\n
    > as.factor(c(TRUE,FALSE,TRUE,TRUE,FALSE))<\/pre>\n
    \"R<\/a><\/p>\n
    3. is.ordered()<\/code><\/strong><\/h4>\n
    The is.ordered()<\/code><\/strong> function checks if the variable is an ordered factor or not. This function returns TRUE<\/code><\/strong> if the variable<\/a> is an ordered factor and FALSE<\/code><\/strong> if it is not.<\/p>\n
    > is.ordered(sizeof)\n> is.ordered(fac1)<\/pre>\n
    \"R<\/a><\/p>\n
    Summary<\/h2>\n
    R factors are data structures that store categorical data. They only allow observations of certain predefined values. Therefore, they are useful for fields that have a limited number of possible values like gender, marital status, availability, confirmation, etc..<\/p>\n
    In this article, we learned about R factors. We learned how to create them and how to modify them. We saw what they are used for. Finally, we looked at a few functions that provide more insights into factors and convert other variables into R factors.<\/p>\n
    Before start working with R you must have a basic understanding of data structures<\/a><\/strong><\/em> in R.<\/p>\n
    Still, have some doubts about the R factor? Ask us and our TechVidvan<\/strong> experts will be happy to help you.<\/p>\n
    Keep Learning ?<\/p>\n","protected":false},"excerpt":{"rendered":"
    Factors are data structures in R that store categorical\u00a0data. In datasets, there are often fields that take only a few predefined values. For example – gender, availability, country, marital status, etc. Such data is...<\/p>\n","protected":false},"author":1,"featured_media":75472,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1020],"tags":[1360,1361,1362,1363,1364,1365,1366,1367],"class_list":["post-75429","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-r","tag-droplevels-r","tag-factor-function-in-r","tag-factors-in-r-programming","tag-r-factor-functions","tag-r-factor-levels","tag-r-factor-levels-order","tag-r-factors","tag-r-ordered-factor"],"yoast_head":"\nR Factors - Operating on Factors and Factor Levels - TechVidvan<\/title>\n<meta name=\"description\" content=\"What is Factor in R? 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