過濾15個列的值3個SD與100 +行

Question

我有一個數據集，包含15列col1到col15是數字。作爲一個因素，我有100行數據與每行連接的名稱。我想爲所有15列的每一行做一個總結。

head(df2phcl[,c(1:16)]) 
col1 col2 col3 col4 col5 col6 col7 col8 col9 col10 col11 col12 col13 col14 col15    NAME 
78 95 101 100 84 93 93 85 81 97 80 94 81 79 87   R04-001 
100 61 96 75 98 92 99 99 102 83 84 NA 101 93 96   R04-002 
81 84 82 83 77 86 90 92 92 78 86 91 59 80 84   R04-003 
91 84 87 95 103 93 92 95 86 92 107 96 94 87 97   R04-004 
72 79 66 98 84 75 85 83 75 80 91 65 90 81 73   R04-005 
72 75 68 44 79 64 83 71 81 82 85 63 87 94 60   R04-006 

我的代碼是這樣的。

library(dplyr) 
####Rachis 
SUMCL <- df2phcl %>% 
    group_by(name) %>% 
summarise(CL = mean(c(col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,col12,col13,col14,col15), na.rm=T), 
     CLMAX = max(c(col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,col12,col13,col14,col15), na.rm=T), 
     CLMIN = min(c(col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,col12,col13,col14,col15), na.rm=T), 
     CLSTD = sd(c(col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,col12,col13,col14,col15), na.rm=T), 
     OUT = outliers(c(col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,col12,col13,col14,col15), na.rm=T)) 
head(SUMCL) 
tail(SUMCL) 

我的分析結果出來的......

Error: 
    Evaluation error: missing value where TRUE/FALSE needed. 

我也試過這個...

df2phcl$col1+col2+col3+col4+col5+col6+col7+col8+col9+col10+col11+col12+col13+co114+col15[!df2phcl$col1+col2+col3+col4+col5+col6+col7+col8+col9+col10+col11+col12+col13+col14+col15%in%boxplot.stats(df2phcl$col1+col2+col3+col4+col5+col6+col7+col8+col9+col10+co111+col12+col13+col14+col15)$out] 

This returns .... 
    Error: object 'col2' not found 

不知道我做錯了這一點也適用mean，max，min和sd。

> head(SUMCL) 
# A tibble: 6 x 11 
# Groups: ENTRY, NAME, HEADCODE, RHTGENES, HEAD, PL [6] 
    ENTRY    NAME HEADCODE RHTGENES HEAD PL PH  CL CLMAX CLMIN  CLSTD 
    <int>   <fctr> <fctr> <fctr> <fctr> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> 
1  1   R04-001  CAW  Rht1 Club 319 83 88.53333 101 78 7.989875 
2  2   R04-002  LBW Wildtype Common 330 102 91.35714 102 61 11.770936 
3  3   R04-003  CBW  Rht2 Club 230 82 83.00000 92 59 8.220184 
4  4   R04-004  LBW  Rht1 Common 328 117 93.26667 107 84 6.192930 
5  5   R04-005  CBW  Rht1 Club 280 97 79.80000 98 65 9.182281 
6  6   R04-006  LAW  Rht1 Common 310 92 73.86667 94 44 12.749603 

我只是想在3 SD或多個過濾異常值，然後使用dplyr打包做我的統計......

Answer 1

我不太確定你想要什麼要做的，讓我知道下面的代碼是否在正確的軌道上。

下面的方法是將數據從寬格式轉換爲長格式，這使得對name的每個級別的摘要變得更容易。

library(tidyverse) 

# Fake data 
set.seed(2) 
dat = as.data.frame(replicate(15, rnorm(100))) 
names(dat) = paste0("col", 1:15) 
dat$name = paste0(rep(LETTERS[1:10], each=10), rep(letters[1:10], 10)) 

# Convert data to long format, remove outliers and summarize 
dat %>% 
    gather(column, value, -name) %>% # reshape from wide to long 
    group_by(name) %>%    # summarize by name 
    mutate(value = replace(value, abs(value - mean(value)) > 2*sd(value), NA)) %>% # set outliers to NA 
    summarise(mean = mean(value, na.rm=TRUE), 
      max = max(value, na.rm=TRUE), 
      sd = sd(value, na.rm=TRUE)) 

name   mean  max  sd 
1 Aa 0.007848188 1.238744 0.8510016 
2 Ab -0.208536464 1.980401 1.2764606 
3 Ac -0.152986713 1.587845 0.8443106 
4 Ad -0.413543054 0.965692 0.7225872 
5 Ae -0.112648322 1.178716 0.7269527 
6 Af 0.442268890 2.048040 1.0350119 
7 Ag 0.390627994 1.978260 0.8716681 
8 Ah 0.080505879 2.396349 1.3128403 
9 Ai 0.257925059 1.984474 1.0196722 
10 Aj 0.137469703 1.470177 0.7192616 
# ... with 90 more rows 

Answer 2

我設法弄到一些山坳性病。開發。改變;但是，我不確定它拿出了多少觀察結果。我想要從發行量的頂部和底部以均勻的價格取出。就像一個修剪的手段，它會佔用20％的obs。從分佈的頂部和底部開始。我很好奇的是隻留下分佈的頂部和底部（±3 SD）的觀測值。

> SUMCL <- df2phcl %>% 
+ gather(column, value, -c(ENTRY, NAME, HEADCODE, RHTGENES, HEAD,PL,PH)) %>% # reshape from wide to long 
+ group_by(ENTRY, NAME, HEADCODE, RHTGENES, HEAD,PL,PH) %>%    # summarize by name 
+ mutate(value = replace(value, abs(value - mean(value)) > 2*sd(value), NA)) %>% # set outliers to NA 
+ summarise(CL = mean(value, na.rm=TRUE), 
+    CLMAX = max(value, na.rm=TRUE), 
+    CLMIN = min(value, na.rm=TRUE), 
+    N = sum(!is.na(value), na.rm=TRUE), 
+    CLSTD= sd(value, na.rm=TRUE), 
+    CLSE = (CLSTD/sqrt(N))) 
> head(SUMCL) 
# A tibble: 6 x 13 
# Groups: ENTRY, NAME, HEADCODE, RHTGENES, HEAD, PL [6] 
    ENTRY    NAME HEADCODE RHTGENES HEAD PL PH  CL CLMAX CLMIN  N  CLSTD  CLSE 
    <int>   <fctr> <fctr> <fctr> <fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>  <dbl> <dbl> 
1  1   R04-001  CAW  Rht1 Club 319 83 88.53333 101 78 15 7.989875 2.062977 
2  2   R04-002  LBW Wildtype Common 330 102 91.35714 102 61 14 11.770936 3.145915 
3  3   R04-003  CBW  Rht2 Club 230 82 84.71429 92 77 14 5.029583 1.344213 
4  4   R04-004  LBW  Rht1 Common 328 117 92.28571 103 84 14 5.075258 1.356420 
5  5   R04-005  CBW  Rht1 Club 280 97 79.80000 98 65 15 9.182281 2.370855 
6  6   R04-006  LAW  Rht1 Common 310 92 76.00000 94 60 14 10.076629 2.693093