Question

mydat=structure(list(Variable_16 = c(1474L, 1392L, 951L, 830L, 624L, 
2429L, 2270L, 925L, 704L, 972L, 1081L), Variable_17 = c(0.78, 
0.78, 0.55, 0.49, 0.4, 1.61, 1.52, 0.64, 0.49, 0.7, 0.8), Variable_18 = c(20175L, 
16780L, 16459L, 16173L, 13291L, 14681L, 14574L, 14949L, 12904L, 
12684L, 12544L), Variable_19 = c(10.65, 9.45, 9.5, 9.46, 8.5, 
9.7, 9.73, 10.37, 9.05, 9.12, 9.32), Variable_20 = c(111.31, 
110, 88, 86, 86, 89, 100, 65, 98, 78, 75), Variable_21 = c(597L, 
554L, 508L, 588L, 441L, 422L, 423L, 475L, 420L, 388L, 377L), 
    Variable_22 = c(337L, 294L, 359L, 310L, 253L, 255L, 243L, 
    272L, 274L, 266L, 246L), Variable_23 = c(286L, 250L, 278L, 
    232L, 214L, 189L, 190L, 213L, 208L, 210L, 193L), Variable_24 = c(251L, 
    221L, 238L, 214L, 169L, 153L, 149L, 176L, 183L, 163L, 156L
    ), Variable_25 = c(241L, 191L, 213L, 178L, 125L, 136L, 128L, 
    148L, 151L, 143L, 136L), Variable_26 = c(0.315039578, 0.311936937, 
    0.29330254, 0.344060854, 0.282149712, 0.278916061, 0.282376502, 
    0.329403606, 0.294530154, 0.278936017, 0.280089153), Variable_27 = c(0.177836412, 
    0.165540541, 0.207274827, 0.181392627, 0.161868202, 0.168539326, 
    0.162216288, 0.188626907, 0.192145863, 0.191229331, 0.182763744
    ), Variable_28 = c(0.150923483, 0.140765766, 0.160508083, 
    0.135751902, 0.136916187, 0.124917383, 0.126835781, 0.147711512, 
    0.145862553, 0.150970525, 0.143387816), Variable_29 = c(0.132453826, 
    0.124436937, 0.137413395, 0.125219427, 0.1081254, 0.101123596, 
    0.099465955, 0.122052705, 0.128330996, 0.117181884, 0.11589896
    ), Variable_30 = c(0.127176781, 0.107545045, 0.122979215, 
    0.104154476, 0.079974408, 0.08988764, 0.085447263, 0.102635229, 
    0.105890603, 0.102803738, 0.101040119), Variable.binary._31 = c(0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Variable.binary._32 = c(0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Variable.binary._33 = c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), Target = c(56.79, 
    21.3, 61.69, 25.32, 26.61, 30.74, 12.27, 28.28, 14.06, 60.78, 
    47.08)), .Names = c("Variable_16", "Variable_17", "Variable_18", 
"Variable_19", "Variable_20", "Variable_21", "Variable_22", "Variable_23", 
"Variable_24", "Variable_25", "Variable_26", "Variable_27", "Variable_28", 
"Variable_29", "Variable_30", "Variable.binary._31", "Variable.binary._32", 
"Variable.binary._33", "Target"), class = "data.frame", row.names = c(NA, 
-11L))

我有很多变量，我需要查看所有变量的异常值。例如如果我这样做

#check outliers
boxplot(mydat$Variable_16)

我只看到Variable_16的一个图。所以我的问题是：如何使用lapply函数同时为所有变量创建具有异常值的框线图，而不是分别为每个变量创建框线图？然后如何将标记为离群值的值写入CSV文件？我想要的输出

  row.number.16 Variable_16 row.number.17 Variable_17 row.number.18 Variable_18 row.number.19
1            30        1474            45        0.78           456       20175           567
2            45        1392            54        0.78            88       16780           234
3            78         951           678        0.55            42       16459           432
4           110         830           123        0.49          1234       16173           123
  Variable_19
1       10.65
2        9.45
3        9.50
4        9.46

第16行是variable_16包含异常值并且在variable_16中指示该值的行数

Answer 1

只需从宽到长（几乎是大多数分析方法的首选格式）重塑数据，然后相应地绘制数据即可。为了确定每个变量中哪些值是离群值，请使用与ave和ifelse相同的默认算法，用boxplot + boxplot.stats计算新列的指标。然后，将长数据导出为CSV。

# RESHAPE DATA WIDE TO LONG
rdf <- reshape(mydat, varying = list(names(mydat)), times = names(mydat),
               v.names = "value", timevar = "variable", 
               new.row.names = 1:1E5, direction = "long")

# FLAG OUTLIER WITHIN EACH VARIABLE
rdf$outlier <- with(rdf, ave(value, variable, FUN=function(v) 
                             ifelse(v <= -1.5 * quantile(v)[2] | v >= 1.5 * quantile(v)[4], 1, 0)))

head(rdf, 10)
#       variable value id outlier
# 1  Variable_16  1474  1       0
# 2  Variable_16  1392  2       0
# 3  Variable_16   951  3       0
# 4  Variable_16   830  4       0
# 5  Variable_16   624  5       0
# 6  Variable_16  2429  6       1
# 7  Variable_16  2270  7       1
# 8  Variable_16   925  8       0
# 9  Variable_16   704  9       0
# 10 Variable_16   972 10       0

# RUN BOXPLOT WITH FORMULA STYLE
boxplot(value ~ variable, rdf)

# EXPORT OUTLIER FLAGGED, LONG DATA TO CSV
write.csv(rdf, "/path/to/BoxplotOutliers.csv")

调整箱形图的可读性

par(mar=c(10,5,4,1))
boxplot(value ~ variable, rdf, las=3, 
        xlab = "", main="Outlier BoxPlots by Variable")

要删除任何非典型变量，请使用整形数据的 variable 字段进行子设置：

par(mar=c(10,5,4,1))
boxplot(value ~ variable, subset(rdf, variable != "Variable_18"), las=3, 
        xlab = "", main="Outlier BoxPlots by Variable")

在R中使用离群值创建多个离群图和数据框

1 个答案: