Data science

Courses, books

• 10 Free Must-Read Books for Machine Learning and Data Science
• STAT 430: Topics in Applied Statistics by Dirk Eddelbuettel
• Introduction to Data Science Data Analysis and Prediction Algorithms with R, by Rafael A Irizarry (free)
• https://jhu-advdatasci.github.io/2018/ Johns Hopkins SPH
• http://datasciencelabs.github.io/2016/ Harvard SPH
• https://cs109.github.io/2014 Harvard CS
• https://r4ds.had.co.nz/ R for Data Science

How to prepare data for collaboration

How to share data for collaboration. Especially Page 7 has some (raw data) variable coding guidelines.

• naming variables: using meaning variable names, no spacing in column header, avoiding separator (except an underscore)
• coding variables: be consistent, no spelling error
• date and time: YYYY-MM-DD (ISO 8601 standard). A gene symbol "Oct-4" will be interpreted as a date and reformatted in Excel.
• missing data: "NA". Not leave any cells blank.
• using a code book file (*.docx for example): any lengthy explanation about variables should be put here. See p5 for an example.

Five types of data:

• continuous
• oridinal
• categorical
• missing
• censored

Some extra from Data organization in spreadsheets (the paper appears in American Statistician)

• No empty cells
• Put one thing in a cell
• Make a rectangle
• No calculation in the raw data files
• Create a data dictionary (same as code book)

complete.cases()

Count the number of rows in a data frame that have missing values with

sum(!complete.cases(dF))

> tmp <- matrix(1:6, 3, 2)
> tmp
     [,1] [,2]
[1,]    1    4
[2,]    2    5
[3,]    3    6
> tmp[2,1] <- NA
> complete.cases(tmp)
[1]  TRUE FALSE  TRUE

Wrangling categorical data in R

https://peerj.com/preprints/3163.pdf

Some approaches:

• options(stringAsFactors=FALSE)
• Use the tidyverse package

Base R approach:

GSS <- read.csv("XXX.csv")
GSS$BaseLaborStatus <- GSS$LaborStatus
levels(GSS$BaseLaborStatus)
summary(GSS$BaseLaborStatus)
GSS$BaseLaborStatus <- as.character(GSS$BaseLaborStatus)
GSS$BaseLaborStatus[GSS$BaseLaborStatus == "Temp not working"] <- "Temporarily not working"
GSS$BaseLaborStatus[GSS$BaseLaborStatus == "Unempl, laid off"] <- "Unemployed, laid off"
GSS$BaseLaborStatus[GSS$BaseLaborStatus == "Working fulltime"] <- "Working full time"
GSS$BaseLaborStatus[GSS$BaseLaborStatus == "Working parttime"] <- "Working part time"
GSS$BaseLaborStatus <- factor(GSS$BaseLaborStatus)

Tidyverse approach:

GSS <- GSS %>%
    mutate(tidyLaborStatus =
        recode(LaborStatus,
            `Temp not working` = "Temporarily not working",
            `Unempl, laid off` = "Unemployed, laid off",
            `Working fulltime` = "Working full time",
            `Working parttime ` = "Working part time"))

NIH CBIIT

http://datascience.cancer.gov/

Reproducibility

Bioinformatics advice I wish I learned 10 years ago from NIH

Container

Data Science for Startups: Containers Building reproducible setups for machine learning