1/6/2024 0 Comments Error in taplyIn this example we decided to set it to 0. To override this behavior you can set the default argument to the value you want, instead of NA. Note that as there were no food sold in the Store 4, the corresponding cell returns a NA value. Tapply(price, list(type, store), mean) Store 1 Store 2 Store 3 Store 4 In this example, we are going to apply the tapply function to the type and store factors to calculate the mean price of the objects by type and store. You can apply the tapply function to multiple columns (or factor variables) passing them through the list function. In this case, you can access the output elements with the $ sign and the element name. Mean_prices_list <- tapply(price, type, mean, simplify = FALSE) However, you can modify the output class to list if you set the simplify argument to FALSE. The basic idea is that if you can execute a computation in X X seconds on a single. Generally, parallel computation is the simultaneous execution of different pieces of a larger computation across multiple computing processors or cores. Hence, if needed, you can access each element of the output specifying the desired index in square brackets. Many computations in R can be made faster by the use of parallel computation. It also should be noticed that the default output is of class “array”. You can verify it with the length function. Note that the tapply arguments must have the same length. Labels = c("toy", "food", "electronics", "drinks"))įinally, you can use the tapply function to calculate the mean by type of object of the stores as follows: # Mean price by product type Second, store the values as variables and convert the column named type to factor. Type = sample(1:4, size = 25, replace = TRUE), First, consider the following example dataset, that represents the price of some objects, its type and the store where they were sold. Complete both parts before comparing your answers to those at the link below.The tapply function is very easy to use in R. Using the data in the table above, a) compute the incidence rate ratio and the incidence rate difference for moderate activity compared to the least active subjects, and b) write an interpretation of your findings. I can do this using the ifelse() function, which has the following format: Suppose my data set has a continuously distributed variable called "birthwgt", which is each child's weight in grams at birth, but I wish to create a new variable that categorizes children as having Low Birth Weight (lowBW), i.e. Creating a Dichotomous Variable from a Continuous Variable Using the double equal sign (=) basically means "only if DrugExp equals 1". Currently I am in the process of trying to run a normality test, and to run a regression to test possible relationships to the things I. The project entailed the students to gather data and analyses them. > t.test(Birthwt) # 1-sample t-test to get 95% CI for those unexposed to drugs Hello, Recently I have been using R for an assessment item for a university project. > t.test(Birthwt) # 1-sample t-test to get 95% CI for those exposed to drugs > sd(Birthwt) sd(Birthwt) # standard deviation for each exposure group > mean(Birthwt) mean(Birthwt) # means for each exposure group Getting descriptive statistics by category can also be achieved as follows: > tapply(Birthwt,DrugExp,t.test) # Gives 95% confidence interval for exposed and unexposed in one outpu t An Alternate Method of Subset Analysis > tapply(Ppregwt,DrugExp,sd) # Gives the standard deviations of pre-pregnancy weight by drug > tapply(Ppregwt,DrugExp,mean) # Gives the means of pre-pregnancy weight by drug exposur e > tapply(Dubow,DrugExp,sd) # Gives the standard deviations of Dubowitz score by drug exposur e > tapply(Dubow,DrugExp,mean) # Gives means of Dubowitz score by drug exposure My goal is to sort the data set by DrugExp and then compute the mean and standard deviation of Dubow Scores and Pre-pregnancy weights for each category of DrugExp. įor example, suppose I have a data set with continuous variables Dubow (Dubow Score), DrugExp (Drug Exposure) and Ppregwt (Pre-pregnancy weight). Where is the variable that you want to analyze, is the variable that you want to subset by, and is the function or computation that you want to apply to. The basic structure of the tapply command is: For categorical variables you should use the table() function to get counts of categorical variables and use the prop.table() function to get proportions. Note that tapply() is used for descriptive statistics (e.g., mean, sd, summary) for continuously distributed variables. In effect this enables you to subset the data by one or more classifying factors and then performing some function (e.g., computing the mean and standard deviation of a given variable) by subset. The tapply() function is useful for performing functions (e.g., descriptive statistics) on subsets of a data set. Analyzing Data in Subsets Using R The tapply() command
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |