{r setup, echo=FALSE}库(LearnBioconductor) stopifnot(BiocInstaller::biocVersion() == "3.0")BiocStyle::markdown() knitr::opts_chunk$set(tidy=FALSE)R·马丁·摩根简介
2014年10月27日## R语言和环境统计计算和图形 - 全功能编程语言 - 互动和*解释* - 方便，宽容 - 连贯，广泛的文档 - 统计，例如，`factor（）`，`na` - 可扩展 - cran，biocomadond，github，...矢量，类，对象 - 效率_vectorized_'原子'vectors`逻辑`，`整数`，`数字`，`complex`，`字符'，`byte` - 原子矢量是用于更复杂的_objects_ - `矩阵` - 具有'dim'属性的原子矢量 - `data.frame` - 相等长度原子矢量列出 - 正式_classes_代表complicated combinations of vectors, e.g., the return value of `lm()`, below Function, generic, method - Functions transform inputs to outputs, perhaps with side effects, e.g., `rnorm(1000)` - Argument matching first by name, then by position - Functions may define (some) arguments to have default values - _Generic_ functions dispatch to specific _methods_ based on class of argument(s), e.g., `print()`. - Methods are functions that implement specific generics, e.g., `print.factor`; methods are invoked _indirectly_, via the generic. Introspection - General properties, e.g., `class()`, `str()` - Class-specific properties, e.g., `dim()` Help - `?print`: help on the generic print - `?print.data.frame`: help on print method for objects of class data.frame. Example ```{r} x <- rnorm(1000) # atomic vectors y <- x + rnorm(1000, sd=.5) df <- data.frame(x=x, y=y) # object of class 'data.frame' plot(y ~ x, df) # generic plot, method plot.formula fit <- lm(y ~x, df) # object of class 'lm' methods(class=class(fit)) # introspection ``` ## Lab ### 1. _R_ data manipulation This exercise servers as a refresher / tutorial on basic input and manipulation of data. Input a file that contains ALL (acute lymphoblastic leukemia) patient information ```{r echo=TRUE, eval=FALSE} fname <- file.choose() ## "ALLphenoData.tsv" stopifnot(file.exists(fname)) pdata <- read.delim(fname) ``` ```{r echo=FALSE} fname <- system.file("extdata", "ALLphenoData.tsv", package="LearnBioconductor") stopifnot(file.exists(fname)) pdata <- read.delim(fname) ``` Check out the help page `?read.delim` for input options, and explore basic properties of the object you've created, for instance... ```{r ALL-properties} class(pdata) colnames(pdata) dim(pdata) head(pdata) summary(pdata$sex) summary(pdata$cyto.normal) ``` Remind yourselves about various ways to subset and access columns of a data.frame ```{r ALL-subset} pdata[1:5, 3:4] pdata[1:5, ] head(pdata[, 3:5]) tail(pdata[, 3:5], 3) head(pdata$age) head(pdata$sex) head(pdata[pdata$age > 21,]) ``` It seems from below that there are 17 females over 40 in the data set, but when sub-setting `pdata` to contain just those individuals 19 rows are selected. Why? What can we do to correct this? ```{r ALL-subset-NA} idx <- pdata$sex == "F" & pdata$age > 40 table(idx) dim(pdata[idx,]) ``` Use the `mol.biol` column to subset the data to contain just individuals with 'BCR/ABL' or 'NEG', e.g., ```{r ALL-BCR/ABL-subset} bcrabl <- pdata[pdata$mol.biol %in% c("BCR/ABL", "NEG"),] ``` The `mol.biol` column is a factor, and retains all levels even after subsetting. How might you drop the unused factor levels? ```{r ALL-BCR/ABL-drop-unused} bcrabl$mol.biol <- factor(bcrabl$mol.biol) ``` The `BT` column is a factor describing B- and T-cell subtypes ```{r ALL-BT} levels(bcrabl$BT) ``` How might one collapse B1, B2, ... to a single type B, and likewise for T1, T2, ..., so there are only two subtypes, B and T ```{r ALL-BT-recode} table(bcrabl$BT) levels(bcrabl$BT) <- substring(levels(bcrabl$BT), 1, 1) table(bcrabl$BT) ``` Use `xtabs()` (cross-tabulation) to count the number of samples with B- and T-cell types in each of the BCR/ABL and NEG groups ```{r ALL-BCR/ABL-BT} xtabs(~ BT + mol.biol, bcrabl) ``` Use `aggregate()` to calculate the average age of males and females in the BCR/ABL and NEG treatment groups. ```{r ALL-aggregate} aggregate(age ~ mol.biol + sex, bcrabl, mean) ``` Use `t.test()` to compare the age of individuals in the BCR/ABL versus NEG groups; visualize the results using `boxplot()`. In both cases, use the `formula` interface. Consult the help page `?t.test` and re-do the test assuming that variance of ages in the two groups is identical. What parts of the test output change? ```{r ALL-age} t.test(age ~ mol.biol, bcrabl) boxplot(age ~ mol.biol, bcrabl) ``` ## Resources - [StackOverflow](http://stackoverflow.com/questions/tagged/r) for _R_ programming questions; also [R-help]() mailing list. Publications (General _R_)(R): http://r-project.org