Data Shape Transformation With Reshape()

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1280px-Origami-cranereshape() is an R function that accesses “observations” in grouped dataset columns and “records” in dataset rows, in order to programmatically transform the dataset shape into “long” or “wide” format.

Required dataframe:
data1 <- data.frame(id=c("ID.1", "ID.2", "ID.3"), sample1=c(5.01, 79.40, 80.37), sample2=c(5.12, 81.42, 83.12), sample3=c(8.62, 81.29, 85.92))

Answers to the exercises are available here.

Exercise 1
Using the reshape() parameter “direction=“, “varying=” columns are stacked according to the new records created by the “idvar=” column.

Therefore, convert “data1” to long format, by stacking columns 2 through 4. The new row names are from column “id“. The new time variable is called, “TIME“. The column name of the stacked data is called “Sample“. Set a new dataframe variable called, “data2“.

Exercise 2
Use direction="wide" to convert “data2” back to the shape of “data1“. Setting a new variable isn’t needed. (Note that rownames from “data2” are retained.)

Exercise 3
Time Variables:
Script a reshape() operation, where “timevar=” is set to the variable within “data2” that differentiates multiple records.

Exercise 4
New Row Names:
Script a reshape() operation, where “data2” is converted to “wide” format, and “new.row.names=” is set to unique “data2$id” names.

Exercise 5
Convert “data2” to wide format. Set “v.names=” to the “data2” column with observations.

Exercise 6
Set sep = "" in order to reshape “data1” to long format.

Exercise 7
Reshape “data2” to “wide“. Use the “direction =” parameter. Setting a new dataframe variable isn’t required.

Exercise 8
Use the most basic reshape command possible, in order to reshape
data2” to wide format.

Exercise 9
Reshape “data2” to “wide“, with column names for the reshaped data of “TIME” and “Sample“.

Exercise 10
Reshape “data1” by varying “sample1“, “sample2“, and “sample3“.

Image by Andreas Bauer (Own work) [CC-BY-SA-2.5], via Wikimedia Commons.

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