获得R问题的良好帮助的关键是提供最低限度工作的可重复示例（MWRE）。使用R制作MWRE非常简单，它将有助于确保那些帮助您识别错误来源的人，并理想地提交给您，以修复错误，而不是向您发送有用的代码。要拥有MWRE，您需要以下项目：
- 产生错误的最小数据集
- 生成数据所需的最小可运行代码，在提供的数据集上运行
- 有关已使用的软件包，R版本和系统的必要信息
- 一个
seed值，如果随机特性是代码的一部分
让我们看看R中可用的工具，以帮助我们快速，轻松地创建这些组件。
生成最小数据集
这里有三个不同的选项：
- 使用内置R数据集
- 从头开始创建一个新的vector / data.frame
- 以可共享的方式输出您当前正在处理的数据
让我们依次看看每一个，看看R帮助我们做的工具。
内置数据集
R数据集中有一些规范的buit非常适合在帮助请求中使用。
- mtcars
- 鸢尾花
要查看R中的所有可用数据集，只需键入：data()。要加载任何这些数据集，只需使用以下内容：
data(mtcars) head(mtcars) # to look at the data mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
此选项适用于您知道在R中遇到命令时遇到问题的问题。如果您无法理解为什么您熟悉的命令无法处理数据，则此选项不是一个很好的选择。
data(stulevel)names(stulevel) [1] "X" "school" "stuid" "grade" "schid" [6] "dist" "white" "black" "hisp" "indian" [11] "asian" "econ" "female" "ell" "disab" [16] "sch_fay" "dist_fay" "luck" "ability" "measerr" [21] "teachq" "year" "attday" "schoolscore" "district" [26] "schoolhigh" "schoolavg" "schoollow" "readSS" "mathSS" [31] "proflvl" "race"
创建自己的数据
让我们创建一个学生考试成绩和人口统计学的模拟数据框架。
head(Data) id gender mathSS readSS race 1 1 female 396.6 349.2 H 2 2 male 369.5 330.7 W 3 3 female 423.3 354.3 B 4 4 male 348.7 333.1 W 5 5 male 299.7 353.4 H 6 6 female 338.0 422.1 I
我们模拟了学生数据。让我们使用快速绘图来查看变量之间的关系：
qplot(mathSS, readSS, data = Data, color = race) + theme_bw()
它看起来像比赛是相当均匀的分布和存在之间没有任何关系mathSS和readSS。对于某些应用程序，此数据已足够，但对于其他应用程序，我们可能希望获得更实际的数据。
table(Data$race) A B H I W 192 195 202 203 208 cor(Data$mathSS, Data$readSS) [1] -0.01236
输出您当前的数据
这里的最佳实践是创建您正在处理的数据的子集，然后使用该dput命令输出它。
dput(head(stulevel, 5)) structure(list(X = c(44L, 53L, 116L, 244L, 274L), school = c(1L, 1L, 1L, 1L, 1L), stuid = c(149995L, 13495L, 106495L, 45205L, 142705L), grade = c(3L, 3L, 3L, 3L, 3L), schid = c(495L, 495L, 495L, 205L, 205L), dist = c(105L, 45L, 45L, 15L, 75L), white = c(0L, 0L, 0L, 0L, 0L), black = c(1L, 1L, 1L, 1L, 1L), hisp = c(0L, 0L, 0L, 0L, 0L), indian = c(0L, 0L, 0L, 0L, 0L), asian = c(0L, 0L, 0L, 0L, 0L), econ = c(0L, 1L, 1L, 1L, 1L), female = c(0L, 0L, 0L, 0L, 0L), ell = c(0L, 0L, 0L, 0L, 0L), disab = c(0L, 0L, 0L, 0L, 0L), sch_fay = c(0L, 0L, 0L, 0L, 0L), dist_fay = c(0L, 0L, 0L, 0L, 0L), luck = c(0L, 1L, 0L, 1L, 0L), ability = c(87.8540493076978, 97.7875614875502, 104.493033823157, 111.671512686787, 81.9253913501755 ), measerr = c(11.1332639734731, 6.8223938284885, -7.85615858883968, -17.5741522573204, 52.9833376218976), teachq = c(39.0902471213577, 0.0984819168655733, 39.5388526976972, 24.1161227728637, 56.6806130368238 ), year = c(2000L, 2000L, 2000L, 2000L, 2000L), attday = c(180L, 180L, 160L, 168L, 156L), schoolscore = c(29.2242722609726, 55.9632592971956, 55.9632592971956, 55.9632592971956, 55.9632592971956), district = c(3L, 3L, 3L, 3L, 3L), schoolhigh = c(0L, 0L, 0L, 0L, 0L), schoolavg = c(1L, 1L, 1L, 1L, 1L), schoollow = c(0L, 0L, 0L, 0L, 0L), readSS = c(357.286464546893, 263.904581222636, 369.672179143784, 346.595665384202, 373.125445669888 ), mathSS = c(387.280282915207, 302.572371332695, 365.461432571883, 344.496386434725, 441.15810279391), proflvl = structure(c(2L, 3L, 2L, 2L, 2L), .Label = c("advanced", "basic", "below basic", "proficient"), class = "factor"), race = structure(c(2L, 2L, 2L, 2L, 2L), .Label = c("A", "B", "H", "I", "W"), class = "factor")), .Names = c("X", "school", "stuid", "grade", "schid", "dist", "white", "black", "hisp", "indian", "asian", "econ", "female", "ell", "disab", "sch_fay", "dist_fay", "luck", "ability", "measerr", "teachq", "year", "attday", "schoolscore", "district", "schoolhigh", "schoolavg", "schoollow", "readSS", "mathSS", "proflvl", "race"), row.names = c(NA, 5L), class = "data.frame")
生成的代码可以复制并粘贴到R ，它将自动按照描述自动构建数据集。
匿名化您的数据
也可能是您想要dput数据的情况，但您希望保持数据内容的匿名性。谷歌搜索提出了一个体面的功能，以实现这一目标：
anonym <- function(df) { if (length(df) > 26) { LETTERS <- c(LETTERS, paste(LETTERS, LETTERS, sep = "")) }) } level.id.df <- function(df) { level.id <- function(i) { if (class(df[, i]) == "factor" | class(df[, i]) == "character") { sep = ".") } else if (is.numeric(df[, i])) { } else { column <- df[, i] } return(column) } DF <- data.frame(sapply(seq_along(df), level.id)) return(DF) } df <- level.id.df(df) return(df) } test <- anonym(stulevel) head(test[, c(2:6, 28:32)]) B C D 1 0.00217632592657076 1.51160611230132 0.551020408163265 2 0.00217632592657076 0.135998696526593 0.551020408163265 3 0.00217632592657076 1.07322572705443 0.551020408163265 4 0.00217632592657076 0.455562880806568 0.551020408163265 5 0.00217632592657076 1.43813960635994 0.551020408163265 6 0.00217632592657076 0.151115261535106 0.551020408163265
创建示例
一旦我们得到了最小的数据集，我们就需要在该数据集上重现我们的错误。
让我们看一个聚合数据的错误示例。
Data <- data.frame(id = seq(1, 1000), gender = sample(c("male", "female"), 1000, replace = TRUE), mathSS = rnorm(1000, mean = 400, sd = 60), readSS = rnorm(1000, mean = 370, sd = 58.3), race = sample(c("H", "B", "W", "I", "A"), 1000, replace = TRUE)) myAgg <- Data[, list(meanM = mean(mathSS)), by = race] Error: unused argument(s) (by = race) head(myAgg) Error: object 'myAgg' not found
为什么我会收到错误？如果您将上述代码发送给某人，他们可以快速评估错误，如果他们知道您正在尝试使用data.table包，请查看错误。
library(data.table) Data <- data.frame(id = seq(1, 1000), gender = sample(c("male", "female"), 1000, replace = TRUE), mathSS = rnorm(1000, mean = 400, sd = 60), readSS = rnorm(1000, mean = 370, sd = 58.3), race = sample(c("H", "B", "W", "I", "A"), 1000, replace = TRUE)) Data <- data.table(Data) myAgg <- Data[, list(meanM = mean(mathSS)), by = race] head(myAgg) race meanM 1: H 398.6 2: B 405.1 3: A 397.8 4: W 395.7 5: I 399.1
会话信息
但是，他们可能不知道这一点，所以我们需要提供最后一条信息。要诊断错误，必须知道您正在运行的系统，工作区中加载了哪些软件包，以及您使用的R版本和给定软件包。
只需添加sessionInfo()功能的输出 。这很容易复制和粘贴或包含在knitr文档中。
sessionInfo() R version 2.15.2 (2012-10-26) Platform: x86_64-w64-mingw32/x64 (64-bit) locale: [1] LC_COLLATE=English_United States.1252 [2] LC_CTYPE=English_United States.1252 [3] LC_MONETARY=English_United States.1252 [4] LC_NUMERIC=C [5] LC_TIME=English_United States.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] data.table_1.8.8 eeptools_0.2 ggplot2_0.9.3.1 knitr_1.2 loaded via a namespace (and not attached): [1] colorspace_1.2-2 dichromat_2.0-0 digest_0.6.3 [4] evaluate_0.4.3 formatR_0.7 grid_2.15.2 [7] gtable_0.1.2 labeling_0.1 MASS_7.3-23 [10] munsell_0.4 plyr_1.8 proto_0.3-10 [13] RColorBrewer_1.0-5 reshape2_1.2.2 scales_0.2.3 [16] stringr_0.6.2 tools_2.15.2
