data <- "/home/wrk/iwrk/closed/kemri/Francis_Final_TregData_Jan2020/Data/"
knitr::opts_knit$set(echo = TRUE, root.dir=data)
## R Tidyverse
## Using R Tidyverse
The new way of analysing R data is the tidyverse https://www.tidyverse.org/ which includes the online book 'R for Data Science'.
The new (and hot) way of analysing data with R is the tidyverse https://www.tidyverse.org/ which includes the online book 'R for Data Science'. Please check it out!
Instead of dataframes we use Tibbles now. First import the data using the File menu in Rstudio - and make sure FACTORS is off and tables and columns show. This is the old way (remember) of plotting the individuals_attributes data frame
which shows that all high [ELISA](https://en.wikipedia.org/wiki/ELISA) values are for all late diagnosis only. ELISA uses a solid-phase enzyme immunoassay (EIA) to detect the presence of a ligand (commonly a protein) in a liquid sample using antibodies directed against the protein to be measured.
Let's try a simple correlation. This site has some
interesting [ideas](https://paulvanderlaken.com/2018/09/10/simpler-correlation-analysis-in-r-using-tidyverse-priciples/) which we may visit later. Let's correlate
using the pipes from dplyr:
cs = cbind(tb$Time_to_diagnosis,tb$ELISA,tb$Age)
From this it is clear that correlations between time to diagnosis, age and ELISA are low.