###Tidy data format I have came across the original tidyr paper recently, and start to understand more about tidy data format. In fact, in my previous post, the cheatsheet of dplyr also showed a the tidy data format. As Hadley Wickham pointed out in the paper, each observation should occupy one row, and each column should store the value of a variable.
library(dplyr)
library(tidyr)
## Warning: package 'tidyr' was built under R version 3.1.2
data(sleep)
This is a clean data, observation gathered to one column:
sleep # clean data
## extra group ID
## 1 0.7 1 1
## 2 -1.6 1 2
## 3 -0.2 1 3
## 4 -1.2 1 4
## 5 -0.1 1 5
## 6 3.4 1 6
## 7 3.7 1 7
## 8 0.8 1 8
## 9 0.0 1 9
## 10 2.0 1 10
## 11 1.9 2 1
## 12 0.8 2 2
## 13 1.1 2 3
## 14 0.1 2 4
## 15 -0.1 2 5
## 16 4.4 2 6
## 17 5.5 2 7
## 18 1.6 2 8
## 19 4.6 2 9
## 20 3.4 2 10
This is a messy data with observations in two columns:
sleep %>%
spread(group,extra) # messy data
## ID 1 2
## 1 1 0.7 1.9
## 2 2 -1.6 0.8
## 3 3 -0.2 1.1
## 4 4 -1.2 0.1
## 5 5 -0.1 -0.1
## 6 6 3.4 4.4
## 7 7 3.7 5.5
## 8 8 0.8 1.6
## 9 9 0.0 4.6
## 10 10 2.0 3.4
###Situation that tidy data may not work I found this format very useful in analysis, especially when using dplyr and ggplot in my research. However, I have encountered one particular situation that tidy data may not be the best choice, which is machine learning. Since predictive models in R requires each row representing a sample or class label, and each columns contain observation or contributors, models can not be applied tidy data format. But as I am still new to the field, maybe there’s a way to get around or I misunderstood the table.
