---标题：“A.1 - r”介绍作者：马丁摩根 日期：“2016年5月16日 - 2016年5月17日”输出：Biocstyle :: html_document：toc：true toc_depth：2 vignette：>％\ vignetteindexentry {a.1 - r}％\ gignetteengine {knitr :: Rarkndown} -- ```{R样式，Echo = False，结果='ASIS'}选项（宽度= 100）KNITR :: OPTS_CHUNK $ SET（eval = AS.LOGICY（SYS.GETENV（“KNITR_EVAL”，“TRUE”）），缓存= as.logical（sys.geteNv（“knitr_cache”，“true”）））```＃first extware键入_r_'s命令提示符的值和数学公式，进入_r_的命令提示符```{r plus} 1 + 1````符号的值（变量）```{r值 - 符号} x = 1 x + x```hevoke函数，例如`c（）`，它占用任何数量的值并返回一个_vector_``` {rVector} x = c（1,2,3）x``_r_函数，例如`sqrt（）`，通常在向量上有效地运行```{r vcasived} y = sqrt（x）y`````are often several ways to accomplish a task in _R_ ```{r skinning-the-cat} x = c(1, 2, 3) x x <- c(4, 5, 6) x x <- 7:9 x 10:12 -> x x ``` Sometimes _R_ does 'surprising' things that can be fun to figure out ```{r surprise} x <- c(1, 2, 3) -> y x y ``` # _R_ Data types: vector and list 'Atomic' vectors - Types include integer, numeric (float-point; real), complex, logical, character, raw (bytes) ```{r atomic-vectors} people <- c("Brian", "Jim", "Herve", "Dan", "Val", "Martin") people ``` - Atomic vectors can be named ```{r named-vectors} population <- c(Buffalo=259000, Rochester=210000, `New York`=8400000) population log10(population) ``` - Statistical concepts like `NA` (not available) ```{r NA-concept} truthiness <- c(TRUE, FALSE, NA) truthiness ``` - Logical concepts like 'and' (`&`), 'or' (`|`), and 'not' (`!`) ```{r logical-concept} !truthiness truthiness | !truthiness truthiness & !truthiness ``` - Numerical concepts like infinity (`Inf`) or not-a-number (`NaN`, e.g., 0 / 0) ```{r numerical-concept} undefined_numeric_values <- c(NA, 0/0, NaN, Inf, -Inf) undefined_numeric_values sqrt(undefined_numeric_values) ``` - Common string manipulations ```{r string-manipulation} toupper(people) substr(people, 1, 3) ``` - _R_ is a green consumer -- recylcing short vectors to align with long vectors ```{r greenery} x <- 1:3 x * 2 # '2' (vector of length 1) recycled to c(2, 2, 2) truthiness | NA truthiness & NA ``` - It's very common to nest operations, which can be simultaneously compact, confusing, and expressive (`[`: subset; `<`: less than) ```{r nested-operations} substr(tolower(people), 1, 3) population[population < 1000000] ``` Lists - The list type can contain other vectors, including other lists ```{r lists} frenemies = list( friends=c("Larry", "Richard", "Vivian"), enemies=c("Dick", "Mik") ) frenemies ``` - `[` subsets one list to create another list, `[[` extracts a list element ```{r list-subset} frenemies[1] frenemies[c("enemies", "friends")] frenemies[["enemies"]] ``` Factors - Character-like vectors, but with values restricted to specific levels ```{r factors} sex = factor(c("Male", "Male", "Female"), levels=c("Female", "Male", "Hermaphrodite")) sex sex == "Female" table(sex) sex[sex == "Female"] ``` # Classes: data.frame and beyond Variables are often related to one another in a highly structured way, e.g., two 'columns' of data in a spreadsheet ```{r related-variables} x = rnorm(1000) # 1000 random normal deviates y = x + rnorm(1000) # another 1000 deviates, as a function of x plot(y ~ x) # relationship bewteen x and y ``` Convenient to manipulate them together - `data.frame()`: like columns in a spreadsheet ```{r data.frame} df = data.frame(X=x, Y=y) head(df) # first 6 rows plot(Y ~ X, df) # same as above ``` - See all data with `View(df)`. Summarize data with `summary(df)` ```{r data.frame-summary} summary(df) ``` - Easy to manipulate data in a coordinated way, e.g., access column `X` with `$` and subset for just those values greater than 0 ```{r data.frame-subset} positiveX = df[df$X > 0,] head(positiveX) plot(Y ~ X, positiveX) ``` - _R_ is introspective -- ask it about itself ```{r introspection} class(df) dim(df) colnames(df) ``` - `matrix()` a related class, where all elements have the same type (a `data.frame()` requires elements within a column to be the same type, but elements between columns can be different types). A scatterplot makes one want to fit a linear model (do a regression analysis) - Use a _formula_ to describe the relationship between variables - Variables found in the second argument ```{r lm-formula} fit <- lm(Y ~ X, df) ``` - Visualize the points, and add the regression line ```{r lm-plot} plot(Y ~ X, df) abline(fit, col="red", lwd=3) ``` - Summarize the fit as an ANOVA table ```{r anova} anova(fit) ``` - Introspection -- what class is `fit`? What _methods_ can I apply to an object of that class? ```{r class-method-introspection} class(fit) methods(class=class(fit)) ``` # Help! Help available in _Rstudio_ or interactively - Check out the help page for `rnorm()` ```{r, eval=FALSE} ?rnorm ``` - 'Usage' section describes how the function can be used ``` rnorm(n, mean = 0, sd = 1) ``` - Arguments, some with default values. Arguments matched first by name, then position - 'Arguments' section describes what the arguments are supposed to be - 'Value' section describes return value - 'Examples' section illustrates use - Often include citations to relevant technical documentation, reference to related functions, obscure details - Can be intimidating, but in the end actually _very_ useful