Everything else is same, just the last element now returns TRUE (because of the equal to condition). > #compare if Stock A's returns are greater than or equal to Stock B's returnsĬompare this with the Greater Than (>) operator. > # The following are two vectors containing returns from two stocks over the past five days. The Greater Than Equal To (>=) operator checks if each element of the first vector is greater than or equal to the corresponding element of the second vector. > #compare if Stock A's returns are not equal to Stock B's returns It returns TRUE if one of the statement is TRUE. It returns TRUE if both elements are TRUE. The Not Equal (!=) operator checks if each element of the first vector is unequal to the corresponding element of the second vector. The less than or equal to symbol is used in math to express the relationship between two quantities or as a boolean logical operator. Logical operators are used to combine conditional statements: Element-wise Logical AND operator. > #compare if Stock A's returns are equal to Stock B's returns > #compare if Stock A's returns are less than Stock B's returns Since the condition was exclusively checking for returns being greater, the condition is FALSE here. Specially observe the last day where the returns were same for both the stocks (12 and 12). So, for these days, the resulting vector contains TRUE, while for the rest of the days, it is FALSE. The result will be as follows: TRUE FALSE FALSE TRUE FALSEĪs you can see, Stock A's returns were higher than Stock B on day 1 (10 > 8) and day 4 (11 > 10). The following are two vectors containing returns from two stocks over the past five days. The result will be a vector with logical values (TRUE or FALSE) depending on whether the condition is true or not. The greater than (>) symbol checks if each element of the first vector is greater than the corresponding element of the second vector. Let's look at each of these operators in detail. The result of comparison is a Boolean value. There are six relational operators: OperatorĮach element of the first vector is compared with the corresponding element of the second vector. R – str_replace() to Replace Matched Patterns in a String.Relational Operators are used to compare values in R objects.How to Replace String with Another String or Character.R dplyr::mutate() – Replace Column Values.How to Replace Column Value with Another Column.How to Replace NA with Empty String in a R Dataframe? 1 Hi Karthik, I am trying to create a new data frame to only include rows/ids whereby the value of column'aged' is less than the value of column 'laclength' Bazon at 5:34 Add a comment 3 Answers Sorted by: 134 df dfaged How to Replace NA with 0 in Multiple R Dataframe Columns?.Also covered using data.table and dplyr packages. In this article, I have explained how to replace values based on a single logical condition, multiple conditions, conditions on numeric and character columns e.t.c. # Example 4 - Replace all DataFrame columns by condition # Example 3 - Replace Column Value by Checking Multiple Conditionsĭf$id <- "60" # Example 2 - Replace by Checking Condition on Character Column # Example 1 - Replace Column Value Based on Condition We read these symbols as equal to or less than and equal to or greater than. Complete Examples of Replace Values Based on Conditionįollowing is a complete example of how to replace column values based on conditions in R DataFrame. Notice in this example that r was left on the right side and thus the. # Replace using mutate() function and checking conditionĦ. Let’s see how we can write the above examples using dplyr::mutate() The dplyr package provides a set of functions to work with strings as easily as possible.Īll previous examples use the Base R built-in functions that can be used on a smaller dataset but, for bigger data sets, you have to use methods from dplyr package as they perform 30% faster. In case you don’t have this package, install it using install.packages("dplyr"). In order to use this mutate() method, first, you need to load its library using library("dplyr"). Replace Column Based on Condition Using dplyr Package # Replace conditionally using data.table.ĥ. In case you don’t have this package, install it using install.packages("data.table“). This performs much faster than the traditional approach.įirst, you need to load the library using library("data.table“). If you have data.table, then use the following approach to replace values Conditionally. Using data.table to Replace Values Conditionally Here, marks1 and marks2 have 99 value hence, these two values are updated with 95.Ĥ. The below example updates all column values in a DataFrame to 95 when the existing value is 99.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |