Checking your samples

The new function check_data() will warn you if it finds some known issues with your data.

library("experDesign")
library("MASS")

If we take the survey dataset from the MASS package we can see that it has some issues:

data(survey, package = "MASS")
check_data(survey)
## Warning: Two categorical variables don't have all combinations.
## Warning: Some values are missing.
## Warning: There is a combination of categories with no replicates; i.e. just one
## sample.
## [1] FALSE

While if we fabricate our own dataset we might realize we have a problem

rdata <- expand.grid(sex = c("M", "F"), class = c("lower", "median", "high"))
stopifnot("Same samples/rows as combinations of classes" = nrow(rdata) == 2*3)
check_data(rdata)
## Warning: There is a combination of categories with no replicates; i.e. just one
## sample.
## [1] FALSE
# We create some new samples with the same conditions
rdata2 <- rbind(rdata, rdata)
check_data(rdata2)
## [1] TRUE

One might decide to go ahead with what is available or use only some of those samples or wait to collect more samples for the experiment

Follow up

Imagine you have 100 samples that you distribute in 4 batches of 25 samples each. Later, you collect 80 more samples to analyze. You want these new samples to be analyzed together with those previous 100 samples. Will it be possible? How should you distribute your new samples in groups of 25?

Using the same dataset from MASS imagine if we first collected 118 observations and later 119 more:

survey1 <- survey[1:118, ]
survey2 <- survey[119:nrow(survey), ]
# Using low number of iterations to speed the process 
# you should even use higher number than the default
fu <- follow_up(survey1, survey2, size_subset = 50, iterations = 10)
## Warning: There are some problems with the data.
## Warning: There are some problems with the new samples and the batches.
## Warning: There are some problems with the new data.
## Warning: There are some problems with the old data.

Following the previous new function it reports if there are problems with the observations. One can check each collection with check_data to know more about the problems found.

If you have already performed the experiment on your observations you can also check the distribution:

# Create the first batch
variables <- c("Sex", "Smoke", "Age")
survey1 <- survey1[, variables]
index1 <- design(survey1, size_subset = 50, iterations = 10)
## Warning: There might be some problems with the data use check_data().
r_survey <- inspect(index1, survey1)
# Create the second batch with "new" students
survey2 <- survey2[, variables]
survey2$batch <- NA
# Prepare the follow up
all_classroom <- rbind(r_survey, survey2)
fu2 <- follow_up2(all_classroom, size_subset = 50, iterations = 10)
## Warning: There are some problems with the data.
## Warning: There are some problems with the new samples and the batches.
## Warning: There are some problems with the new data.
## Warning: There are some problems with the old data.
tail(fu2)
## [1] "NewSubset2" "NewSubset2" "NewSubset2" "NewSubset2" "NewSubset2"
## [6] "NewSubset3"

Using this function will help to decide which new observations go to which new batches.

Closing remarks

The famous quote from Fisher goes:

“To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.”

This emphasizes the importance of involving a statistician early on in the experimental design process.
Unfortunately, in some cases, it may be too late to involve a statistician in the experimental design process or the reality of unforeseen circumstances messed the design of your carefully planned experiment.

My aim with this package is to provide practical tools for statisticians, bioinformaticians, and anyone who works with data. These tools are designed to be easy to use and can be used to analyze data in a variety of contexts. Let me know if it is helpful in your case.

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