random assignments in r

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How to randomly assign participants to groups in R?

To randomly assign participants to groups, we can use sample function.

For example, if we have a data frame called df that contains a column say Employee_ID and we want to create five groups that are stored in a vector say Grp then random assignment of participants to values in Grp can be done by using the command given below −

Consider the below data frame and vector group −

The following dataframe is created −

Add the following code to the above snippet −

In order to randomly assign student ID’s to groups in Group vector, add the following code to the above snippet −

If you execute all the above given snippets as a single program, it generates the following Output −

In order to randomly assign employee ID’s to groups in Category vector, add the following code to the above snippet −

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Introduction to probability and statistics, r tutorial b: random numbers.

In order to run simulations of experiments with random outcomes we make use of R’s ability to generate random numbers. More precisely it generates a ‘pseudo-random’ number, which behaves in many ways like a truly random number.

The Function Sample(x, k, replace = TRUE)

Allowing repeated elements         # Sometimes you want to allow repeats. For example when  we roll a die repeatedly we expect to see numbers repeating. We can think of this as picking a random element from a set and then putting it back, i.e. replacing it, so it can be drawn again.

# In R we do this by setting the optional argument replace=TRUE

# Now there is no problem asking for more than 5 things from a set of 5 elements.  

# To generate an m x n array of random values we can use the function sample function followed by the matrix function.

# Let’s simulate rolling a die.          # We use 1:6 to make vector (1,2,3,4,5,6).

Simulation  

# First let’s simulate rolling 1 die 3 times and checking if one of the rolls was a 6.

# First we use sample to generate 3 samples from 1:6  

# To check if any of the 3 rolls are 6 we use the following command. It returns a vector of TRUE or FALSE depending on whether that entry of x is 6 or not. Note the use of the double equal sign. We can’t use a single equal sign because that would mean ‘set the value of x to 6’. Compare the result with the value of x above.  

#  We can also see which elements of x are less than 6  

#  Now let’s roll the die 1000 times and see what fraction of the rolls give 6. We expect that about 1/6 of them will be.

# Simulate 1000 rolls  

# x == 6 gives a vector of TRUE or FALSE. R is clever, when we sum the vector: each TRUE counts as 1 and each FALSE counts as 0. So the sum is the number of TRUE’s. In this case that means the number of 6’s, which happens to be 168.   

# Divide by 1000 to get the fraction of 6’s.  

# Compare that with the theoretical value of 1/6.  

# Now let’s estimate the probability of getting at least one 6 in 4 rolls.

# Goal: estimate the probability of getting at least one 6 in 4 rolls.         # Experiment: roll 1 die 4 times and check for a 6.          # Repeat the experiment 10 times and see what fraction of times this happens.

# So you can see all the commands at once, we’ll show them all and then explain them later. For commenting, we’ll put a command number before each ‘>’   

# Command 1: Generate a 4 by 10 random array. Each column represents one experimental trial of 4 rolls. The 10 columns represent the 10 trials.      # Command 2: Display x      # Command 3: See which of the rolls are 6’s. Note the double equals to check which rolls were 6. The result y is an array of TRUE or FALSE. You can check that it picks out the 6’s.      # Command 4: Display y      # Command 5: Sum each of the columns. The result is then number of 6’s in each trial (column).      # Command 6: Display z      # Command 7: If the column sum is greater than 0 there was at least one TRUE in the column, that is at least one 6. This command prints a vector of TRUE or FALSE representing  whether or not the experiment yielded a 6.      # command 8: sum the vector in command 7 to get the number of trials that yielded a 6. We see that 5 out of 10 trials did. (This is random. Your answer may be different.)      # Command 9: Display s      # command 10: The mean function is just the sum divided by the number of trials. This is just 5/10 = .5. Half the trials yielded a 10.      # command 11: Disply the mean

# Let’s repeat this but with 1000 trials instead of 10. Without all the comments it’s pretty short.  

Our estimate of the probability of at least one 6 in 4 rolls is .525. This is pretty close to the theoretical value of .518.

The dim() function          # We can always check that x has 4 rows and 1000 columns using the dim() function.  

One More Simulation  

# Goal: estimate the probability of getting a sum of 7 when rolling two dice.  

# Command 1: We assign the number of trials to the variable ntrials. Writing code like this makes it easier to modify later. If we want to change the number of trials to 7 we just have to change this one line of code.         # Command 2: we create 10000 columns with 2 rows. That is, we run 10000 experiments of rolling 2 dice.         # Command 3: we sum each of the columns, i.e., we sum the two dice.         # Command 4: we find the fraction of sums that are 7.

# Note, this is essentially the exact answer of 1/6.

Exercise: Try to estimate the probability of two sixes when rolling two dice.

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randomizr: Easy-to-Use Tools for Common Forms of Random Assignment and Sampling

Generates random assignments for common experimental designs and random samples for common sampling designs.

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Methodology

• Random Assignment in Experiments | Introduction & Examples

Random Assignment in Experiments | Introduction & Examples

Published on March 8, 2021 by Pritha Bhandari . Revised on June 22, 2023.

In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomization.

With simple random assignment, every member of the sample has a known or equal chance of being placed in a control group or an experimental group. Studies that use simple random assignment are also called completely randomized designs .

Random assignment is a key part of experimental design . It helps you ensure that all groups are comparable at the start of a study: any differences between them are due to random factors, not research biases like sampling bias or selection bias .

Table of contents

Why does random assignment matter, random sampling vs random assignment, how do you use random assignment, when is random assignment not used, other interesting articles, frequently asked questions about random assignment.

Random assignment is an important part of control in experimental research, because it helps strengthen the internal validity of an experiment and avoid biases.

In experiments, researchers manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. To do so, they often use different levels of an independent variable for different groups of participants.

This is called a between-groups or independent measures design.

You use three groups of participants that are each given a different level of the independent variable:

• a control group that’s given a placebo (no dosage, to control for a placebo effect ),
• an experimental group that’s given a low dosage,
• a second experimental group that’s given a high dosage.

Random assignment to helps you make sure that the treatment groups don’t differ in systematic ways at the start of the experiment, as this can seriously affect (and even invalidate) your work.

If you don’t use random assignment, you may not be able to rule out alternative explanations for your results.

• participants recruited from cafes are placed in the control group ,
• participants recruited from local community centers are placed in the low dosage experimental group,
• participants recruited from gyms are placed in the high dosage group.

With this type of assignment, it’s hard to tell whether the participant characteristics are the same across all groups at the start of the study. Gym-users may tend to engage in more healthy behaviors than people who frequent cafes or community centers, and this would introduce a healthy user bias in your study.

Although random assignment helps even out baseline differences between groups, it doesn’t always make them completely equivalent. There may still be extraneous variables that differ between groups, and there will always be some group differences that arise from chance.

Most of the time, the random variation between groups is low, and, therefore, it’s acceptable for further analysis. This is especially true when you have a large sample. In general, you should always use random assignment in experiments when it is ethically possible and makes sense for your study topic.

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Random sampling and random assignment are both important concepts in research, but it’s important to understand the difference between them.

Random sampling (also called probability sampling or random selection) is a way of selecting members of a population to be included in your study. In contrast, random assignment is a way of sorting the sample participants into control and experimental groups.

While random sampling is used in many types of studies, random assignment is only used in between-subjects experimental designs.

Some studies use both random sampling and random assignment, while others use only one or the other.

Random sampling enhances the external validity or generalizability of your results, because it helps ensure that your sample is unbiased and representative of the whole population. This allows you to make stronger statistical inferences .

You use a simple random sample to collect data. Because you have access to the whole population (all employees), you can assign all 8000 employees a number and use a random number generator to select 300 employees. These 300 employees are your full sample.

Random assignment enhances the internal validity of the study, because it ensures that there are no systematic differences between the participants in each group. This helps you conclude that the outcomes can be attributed to the independent variable .

• a control group that receives no intervention.
• an experimental group that has a remote team-building intervention every week for a month.

You use random assignment to place participants into the control or experimental group. To do so, you take your list of participants and assign each participant a number. Again, you use a random number generator to place each participant in one of the two groups.

To use simple random assignment, you start by giving every member of the sample a unique number. Then, you can use computer programs or manual methods to randomly assign each participant to a group.

• Random number generator: Use a computer program to generate random numbers from the list for each group.
• Lottery method: Place all numbers individually in a hat or a bucket, and draw numbers at random for each group.
• Flip a coin: When you only have two groups, for each number on the list, flip a coin to decide if they’ll be in the control or the experimental group.
• Use a dice: When you have three groups, for each number on the list, roll a dice to decide which of the groups they will be in. For example, assume that rolling 1 or 2 lands them in a control group; 3 or 4 in an experimental group; and 5 or 6 in a second control or experimental group.

This type of random assignment is the most powerful method of placing participants in conditions, because each individual has an equal chance of being placed in any one of your treatment groups.

Random assignment in block designs

In more complicated experimental designs, random assignment is only used after participants are grouped into blocks based on some characteristic (e.g., test score or demographic variable). These groupings mean that you need a larger sample to achieve high statistical power .

For example, a randomized block design involves placing participants into blocks based on a shared characteristic (e.g., college students versus graduates), and then using random assignment within each block to assign participants to every treatment condition. This helps you assess whether the characteristic affects the outcomes of your treatment.

In an experimental matched design , you use blocking and then match up individual participants from each block based on specific characteristics. Within each matched pair or group, you randomly assign each participant to one of the conditions in the experiment and compare their outcomes.

Sometimes, it’s not relevant or ethical to use simple random assignment, so groups are assigned in a different way.

When comparing different groups

Sometimes, differences between participants are the main focus of a study, for example, when comparing men and women or people with and without health conditions. Participants are not randomly assigned to different groups, but instead assigned based on their characteristics.

In this type of study, the characteristic of interest (e.g., gender) is an independent variable, and the groups differ based on the different levels (e.g., men, women, etc.). All participants are tested the same way, and then their group-level outcomes are compared.

When it’s not ethically permissible

When studying unhealthy or dangerous behaviors, it’s not possible to use random assignment. For example, if you’re studying heavy drinkers and social drinkers, it’s unethical to randomly assign participants to one of the two groups and ask them to drink large amounts of alcohol for your experiment.

When you can’t assign participants to groups, you can also conduct a quasi-experimental study . In a quasi-experiment, you study the outcomes of pre-existing groups who receive treatments that you may not have any control over (e.g., heavy drinkers and social drinkers). These groups aren’t randomly assigned, but may be considered comparable when some other variables (e.g., age or socioeconomic status) are controlled for.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

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Statistics Made Easy

How to Generate Random Numbers in R (With Examples)

You can use the following methods to generate random numbers in R:

Method 1: Generate One Random Number in Range

Method 2: generate multiple random numbers in range, method 3: generate one random integer in range, method 4: generate multiple random integers in range.

The following examples show how to use each of these methods in practice.

The following code shows how to generate one random number between 1 and 20:

This function generates 8.651919 as the random number between 1 and 20.

The following code shows how to generate five random numbers between 1 and 20:

This function generates five random numbers between 1 and 20.

The following code shows how to generate one random integer between 1 and 20:

This function generates 7 as the random integer between 1 and 20.

The following code shows how to generate five random integers between 1 and 20:

Note that if we use replace=TRUE  then we allow the same integer to be generated more than once.

However, if we use replace=FALSE then we do not allow the same integer to be generated more than once.

Additional Resources

The following tutorials explain how to perform other common tasks in R:

How to Create a Data Frame with Random Numbers in R How to Create a Matrix with Random Numbers in R How to Create a Vector with Random Numbers in R

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Design and Analysis of Experiments with randomizr (Stata)

Alexander coppock.

randomizr is a small package for Stata that simplifies the design and analysis of randomized experiments. In particular, it makes the random assignment procedure transparent, flexible, and most importantly reproduceable. By the time that many experiments are written up and made public, the process by which some units received treatments is lost or imprecisely described. The randomizr package makes it easy for even the most forgetful of researchers to generate error-free, reproduceable random assignments.

A hazy understanding of the random assignment procedure leads to two main problems at the analysis stage. First, units may have different probabilities of assignment to treatment. Analyzing the data as though they have the same probabilities of assignment leads to biased estimates of the treatment effect. Second, units are sometimes assigned to treatment as a cluster. For example, all the students in a single classroom may be assigned to the same intervention together. If the analysis ignores the clustering in the assignments, estimates of average causal effects and the uncertainty attending to them may be incorrect.

A Hypothetical Experiment

Throughout this vignette, we’ll pretend we’re conducting an experiment among the 592 individuals in R’s HairEyeColor dataset. As we’ll see, there are many ways to randomly assign subjects to treatments. We’ll step through five common designs, each associated with one of the five randomizr functions: simple_ra , complete_ra , block_ra , cluster_ra , and block_and_cluster_ra .

Typically, researchers know some basic information about their subjects before deploying treatment. For example, they usually know how many subjects there are in the experimental sample (N), and they usually know some basic demographic information about each subject.

Our new dataset has 592 subjects. We have three pretreatment covariates, Hair, Eye, and Sex, which describe the hair color, eye color, and gender of each subject. We also have potential outcomes. We call the untreated outcome Y0 and we call the treated outcome Y1.

Imagine that in the absence of any intervention, the outcome (Y0) is correlated with out pretreatment covariates. Imagine further that the effectiveness of the program varies according to these covariates, i.e., the difference between Y1 and Y0 is correlated with the pretreatment covariates.

If we were really running an experiment, we would only observe either Y0 or Y1 for each subject, but since we are simulating, we have both. Our inferential target is the average treatment effect (ATE), which is defined as the average difference between Y0 and Y1.

Simple Random Assignment

Simple random assignment assigns all subjects to treatment with an equal probability by flipping a (weighted) coin for each subject. The main trouble with simple random assignment is that the number of subjects assigned to treatment is itself a random number - depending on the random assignment, a different number of subjects might be assigned to each group.

The simple_ra function has no required arguments. If no other arguments are specified, simple_ra assumes a two-group design and a 0.50 probability of assignment.

To change the probability of assignment, specify the prob argument:

If you specify num_arms without changing prob_each, simple_ra will assume equal probabilities across all arms.

You can also just specify the probabilities of your multiple arms. The probabilities must sum to 1.

You can also name your treatment arms.

Complete Random Assignment

Complete random assignment is very similar to simple random assignment, except that the researcher can specify exactly how many units are assigned to each condition.

The syntax for complete_ra is very similar to that of simple_ra . The argument m is the number of units assigned to treatment in two-arm designs; it is analogous to simple_ra ’s prob. Similarly, the argument m_each is analogous to prob_each.

If you specify no arguments in complete_ra , it assigns exactly half of the subjects to treatment.

To change the number of units assigned, specify the m argument:

If you specify multiple arms, complete_ra will assign an equal (within rounding) number of units to treatment.

You can also specify exactly how many units should be assigned to each arm. The total of m_each must equal N.

Simple and Complete Random Assignment Compared

When should you use simple_ra versus complete_ra ? Basically, if the number of units is known beforehand, complete_ra is always preferred, for two reasons: 1. Researchers can plan exactly how many treatments will be deployed. 2. The standard errors associated with complete random assignment are generally smaller, increasing experimental power. See this guide on EGAP for more on experimental power.

Since you need to know N beforehand in order to use simple_ra , it may seem like a useless function. Sometimes, however, the random assignment isn’t directly in the researcher’s control. For example, when deploying a survey experiment on a platform like Qualtrics, simple random assignment is the only possibility due to the inflexibility of the built-in random assignment tools. When reconstructing the random assignment for analysis after the experiment has been conducted, simple_ra provides a convenient way to do so.

To demonstrate how complete_ra is superior to simple_ra , let’s conduct a small simulation with our HairEyeColor dataset.

The standard error of an estimate is defined as the standard deviation of the sampling distribution of the estimator. When standard errors are estimated (i.e., by using the summary() command on a model fit), they are estimated using some approximation. This simulation allows us to measure the standard error directly, since the vectors simple_ests and complete_ests describe the sampling distribution of each design.

In this simulation complete random assignment led to a 6% decrease in sampling variability. This decrease was obtained with a small design tweak that costs the researcher essentially nothing.

Block Random Assignment

Block random assignment (sometimes known as stratified random assignment) is a powerful tool when used well. In this design, subjects are sorted into blocks (strata) according to their pre-treatment covariates, and then complete random assignment is conducted within each block. For example, a researcher might block on gender, assigning exactly half of the men and exactly half of the women to treatment.

Why block? The first reason is to signal to future readers that treatment effect heterogeneity may be of interest: is the treatment effect different for men versus women? Of course, such heterogeneity could be explored if complete random assignment had been used, but blocking on a covariate defends a researcher (somewhat) against claims of data dredging. The second reason is to increase precision. If the blocking variables are predictive of the outcome (i.e., they are correlated with the outcome), then blocking may help to decrease sampling variability. It’s important, however, not to overstate these advantages. The gains from a blocked design can often be realized through covariate adjustment alone.

Blocking can also produce complications for estimation. Blocking can produce different probabilities of assignment for different subjects. This complication is typically addressed in one of two ways: “controlling for blocks” in a regression context, or inverse probability weights (IPW), in which units are weighted by the inverse of the probability that the unit is in the condition that it is in.

The only required argument to block_ra is block_var, which is a variable that describes which block a unit belongs to. block_var can be a string or numeric variable. If no other arguments are specified, block_ra assigns an approximately equal proportion of each block to treatment.

For multiple treatment arms, use the num_arms argument, with or without the conditions argument

block_ra provides a number of ways to adjust the number of subjects assigned to each conditions. The prob_each argument describes what proportion of each block should be assigned to treatment arm. Note of course, that block_ra still uses complete random assignment within each block; the appropriate number of units to assign to treatment within each block is automatically determined.

For finer control, use the block_m_each argument, which takes a matrix with as many rows as there are blocks, and as many columns as there are treatment conditions. Remember that the rows are in the same order as seen in tab block_var, a command that is good to run before constructing a block_m_each matrix. The matrix can either be defined using the matrix define command or be inputted directly into the block_m_each option.

Clustered Assignment

Clustered assignment is unfortunate. If you can avoid assigning subjects to treatments by cluster, you should. Sometimes, clustered assignment is unavoidable. Some common situations include:

• Housemates in households: whole households are assigned to treatment or control
• Students in classrooms: whole classrooms are assigned to treatment or control
• Residents in towns or villages: whole communities are assigned to treatment or control

Clustered assignment decreases the effective sample size of an experiment. In the extreme case when outcomes are perfectly correlated with clusters, the experiment has an effective sample size equal to the number of clusters. When outcomes are perfectly uncorrelated with clusters, the effective sample size is equal to the number of subjects. Almost all cluster-assigned experiments fall somewhere in the middle of these two extremes.

The only required argument for the cluster_ra function is the clust_var argument, which indicates which cluster each subject belongs to. Let’s pretend that for some reason, we have to assign treatments according to the unique combinations of hair color, eye color, and gender.

This shows that each cluster is either assigned to treatment or control. No two units within the same cluster are assigned to different conditions.

As with all functions in randomizr, you can specify multiple treatment arms in a variety of ways:

…or using conditions.

… or using m_each, which describes how many clusters should be assigned to each condition. m_each must sum to the number of clusters.

Block and Clustered Assignment

The power of clustered experiments can sometimes be improved through blocking. In this scenario, whole clusters are members of a particular block – imagine villages nested within discrete regions, or classrooms nested within discrete schools.

As an example, let’s group our clusters into blocks by size

We're sorry, but some features of Research Randomizer require JavaScript. If you cannot enable JavaScript, we suggest you use an alternative random number generator such as the one available at Random.org .

RESEARCH RANDOMIZER

Random sampling and random assignment made easy.

Research Randomizer is a free resource for researchers and students in need of a quick way to generate random numbers or assign participants to experimental conditions. This site can be used for a variety of purposes, including psychology experiments, medical trials, and survey research.

GENERATE NUMBERS

In some cases, you may wish to generate more than one set of numbers at a time (e.g., when randomly assigning people to experimental conditions in a "blocked" research design). If you wish to generate multiple sets of random numbers, simply enter the number of sets you want, and Research Randomizer will display all sets in the results.

Specify how many numbers you want Research Randomizer to generate in each set. For example, a request for 5 numbers might yield the following set of random numbers: 2, 17, 23, 42, 50.

Specify the lowest and highest value of the numbers you want to generate. For example, a range of 1 up to 50 would only generate random numbers between 1 and 50 (e.g., 2, 17, 23, 42, 50). Enter the lowest number you want in the "From" field and the highest number you want in the "To" field.

Selecting "Yes" means that any particular number will appear only once in a given set (e.g., 2, 17, 23, 42, 50). Selecting "No" means that numbers may repeat within a given set (e.g., 2, 17, 17, 42, 50). Please note: Numbers will remain unique only within a single set, not across multiple sets. If you request multiple sets, any particular number in Set 1 may still show up again in Set 2.

Sorting your numbers can be helpful if you are performing random sampling, but it is not desirable if you are performing random assignment. To learn more about the difference between random sampling and random assignment, please see the Research Randomizer Quick Tutorial.

Place Markers let you know where in the sequence a particular random number falls (by marking it with a small number immediately to the left). Examples: With Place Markers Off, your results will look something like this: Set #1: 2, 17, 23, 42, 50 Set #2: 5, 3, 42, 18, 20 This is the default layout Research Randomizer uses. With Place Markers Within, your results will look something like this: Set #1: p1=2, p2=17, p3=23, p4=42, p5=50 Set #2: p1=5, p2=3, p3=42, p4=18, p5=20 This layout allows you to know instantly that the number 23 is the third number in Set #1, whereas the number 18 is the fourth number in Set #2. Notice that with this option, the Place Markers begin again at p1 in each set. With Place Markers Across, your results will look something like this: Set #1: p1=2, p2=17, p3=23, p4=42, p5=50 Set #2: p6=5, p7=3, p8=42, p9=18, p10=20 This layout allows you to know that 23 is the third number in the sequence, and 18 is the ninth number over both sets. As discussed in the Quick Tutorial, this option is especially helpful for doing random assignment by blocks.

Please note: By using this service, you agree to abide by the SPN User Policy and to hold Research Randomizer and its staff harmless in the event that you experience a problem with the program or its results. Although every effort has been made to develop a useful means of generating random numbers, Research Randomizer and its staff do not guarantee the quality or randomness of numbers generated. Any use to which these numbers are put remains the sole responsibility of the user who generated them.

Note: By using Research Randomizer, you agree to its Terms of Service .

• radiant.data
• radiant.design
• radiant.basics
• radiant.model
• radiant.multivariate

Design > Random assignment

Vincent r. nijs, rady school of management (ucsd).

Randomly assign respondents to experimental conditions

To use the random assignment tool, select a data set where each row in the data set is unique (i.e., no duplicates). A dataset that fits these requirements is bundled with Radiant and is available through the Data > Manage tab (i.e., choose Examples from the Load data of type drop-down and press Load ). Select rndnames from the Datasets dropdown.

Names is a unique identifier in this dataset. If we select this variable and specify two (or more) Conditions (e.g., “test” and “control”) a table will be shown with a columns .conditions that indicates to which condition each person was (randomly) assigned.

By default, the Random assignment tool will use equal probabilities for each condition. However, as can be seen in the screenshot below, it is also possible to specify the probabilities to use in assignment (e.g., 30% to “test” and 70% to the “control” condition).

If we expect that some variables are likely predictive of the outcome of our experiment then we can use blocking to decrease sampling variability. In block random assignment (or stratified random assignment) subjects are first sorted into blocks (or strata) based on one or more characteristics before being randomly assigned within each block. For example, if we select Gender as a Blocking variable the Random assignment tool will attempt to put exactly 30% of men and exactly 30% of women in the treatment condition based on the Probabilities we specified in advance. As we can see in the screenshot below, the assignment of men and women to the test and control condition turned out exactly as intended.

By default, the random seed is set to 1234 to ensure the sampling results are reproducible. If there is no input in Rnd. seed , the selected rows will change every time we generate a sample.

To download data with the assignments in the .conditions column in CSV format, click on the icon in the top-right of your screen. The same data can also be stored in Radiant by providing a name for the dataset and then clicking on the Store button.

Report > Rmd

Add code to Report > Rmd to (re)create the sample by clicking the icon on the bottom left of your screen or by pressing ALT-enter on your keyboard.

R-functions

For an overview of related R-functions used by Radiant for sampling and sample size calculations see Design > Sample

For more information see the vignette for the randomizr package that radiant uses for the Random assignment tool.

The key functions from the randomizr package used in the randomizer tool are complete_ra and block_ra .

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Select random element in a list of R?

Some languages have a picker -function -- choose one random number from a -- how in R?

• 11 On a side note: You seem like you're new to R and should probably just read some introductory materials. stackoverflow.com/questions/420296/… –  Dason Commented Feb 22, 2012 at 7:54
• On another side note: The example data you are providing is a vector, not a list. In case you want to know how to sample an element from a list, you may have a look here: statisticsglobe.com/select-random-element-from-list-in-r –  Joachim Schork Commented Jan 6, 2021 at 13:33

6 Answers 6

• 14 Be careful with sample! If a has length 1, is numeric (in the sense of is.numeric) and a >= 1, sampling via sample takes place from 1:a . Note that this convenience feature may lead to undesired behavior when a is of varying length. –  pomber Commented Dec 26, 2014 at 3:58
• Is there any function in R that sample the values and leaves unchosen values as NAs? –  hhh Commented Oct 10, 2018 at 15:40
• @hhh Only a few years late but... I don't really understand what you want with that –  Dason Commented Apr 5, 2023 at 21:35

the above answers are technically correct:

however, if you would like to repeat this process many times, let's say you would like to imitate throwing a dice, then you need to add:

Hope it helps.

Be careful when using sample!

sample(a, 1) works great for the vector in your example, but when the vector has length 1 it may lead to undesired behavior, it will use the vector 1:a for the sampling.

So if you are trying to pick a random item from a varying length vector, check for the case of length 1!

• 1 They should fix this bug. –  skan Commented Dec 8, 2017 at 19:54

This method doesn't produce an error when your vector is length one, and it's simple.

Read this article about generating random numbers in R.

http://blog.revolutionanalytics.com/2009/02/how-to-choose-a-random-number-in-r.html

You can use sample in this case

Second attribute is showing that you want to get only one random number. To generate number between some range runif function is useful.

An alternative is to select an item from the vector using runif. i.e

Lets say you want a function that picks one each time it is run (useful in a simulation for example). So

Unit: nanoseconds

sample_fun_a() - 4665

runif_fun_a() - 1400

runif seems to be quicker in this example.

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