Initial Data combination and cleaning

I combined The CSI and NOE data by creating a Mutant factor and Residue factor. There were a few Mutants from NOE that I did not have CSI measurement for so they were excluded. Residues are positions 2:30.

I created factors for CSI and NOE as well. Based on the cutoffs we discussed. For the factor Structure I used CSI \(\geq\) 0 are considerd Loop, while -0.2 \(\leq\) CSI \(\leq\) 0 \(\Rightarrow\) No Structure and CSI \(\leq\) -0.2 \(\Rightarrow\) Helix. And for the factor Dynamics I used NOE \(\geq\) 0.6 as More and NOE \(\leq\) 0.6 as Less.

All of these cutoffs can be changes very easily. Here is a preview of what the data look like now.

##      Mutant Residue      CSI       NOE    Structure Dynamics
## 1       AFA       2 -0.14000 0.5558715 No Structure     More
## 2  pS16_PLN       2 -0.06947 0.3769380 No Structure     More
## 3      S16E       2 -0.14841 0.5061962 No Structure     More
## 4      P21G       2 -0.14500 0.5874758 No Structure     More
## 5 S16E_P21G       2 -0.07300 0.4986210 No Structure     More
## 6      P21A       2 -0.11000 0.4802768 No Structure     More

Some basic counts of the new factor variables for Structure and Dynamics

Now some more interesting plots

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

And Correlations

First the overall correlation between CSI and NOE

## 
##  Pearson's product-moment correlation
## 
## data:  data$CSI and data$NOE
## t = -11.233, df = 404, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5586514 -0.4099949
## sample estimates:
##        cor 
## -0.4878522

And then the correlations by both Mutant and Residue