sample function in R

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I've only recently started using RStudio to learn R, so I may have some fundamental questions. Regarding the "sample" function, one of them. My dataset contains 402224 observations across 147 different variables, to be more precise. My job is to create a dataframe from a sample of 50 observations, then go on. However, y = sample(mydata, 50, replace = TRUE, prob = NULL) results in a dataset with 40224 observations over 50 variables when the function sample is called. That is, variables rather than objectives are sampled.

Have you thought about why it occurs? I want to say thank you.
Jul 20, 2022 in Data Science by avinash
• 1,840 points
506 views

1 answer to this question.

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It seems like you are experiencing an issue with the sample function in R. The problem you described, where you get 40224 observations over 50 variables instead of 50 observations, suggests that the function is sampling columns (variables) instead of rows (observations). This can happen if you are not specifying the correct data frame or if there's a misunderstanding of how sample should be used.

The sample function in R is used to randomly sample elements from a vector or a data frame. To sample rows from your data frame (mydata), you should set the size argument to 50 and specify replace = FALSE if you don't want duplicates:

# Sample 50 rows from your data frame sampled_data <- mydata[sample(nrow(mydata), 50, replace = FALSE), ] Here's what this code does:

  1. nrow(mydata) calculates the number of rows in your data frame, which is used as the population size for sampling.

  2. sample(nrow(mydata), 50, replace = FALSE) samples 50 unique row indices from your data frame.

  3. mydata[sampled_indices, ] extracts the sampled rows from your data frame.

Now, sampled_data should contain 50 observations from your original data frame mydata.

Make sure you are using this approach to sample rows, not columns, from your data frame.

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answered Sep 8, 2023 by anonymous
• 1,380 points

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