Data science

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Courses, books

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/