python查找指定数据，用R绘图
> meanfunction (x, ...)
UseMethod("mean")
<bytecode: 0x00000000111edea8>
<environment: namespace:base>
> mean.default
function (x, trim = 0, na.rm = FALSE, ...)
{
if (!is.numeric(x) && !is.complex(x) && !is.logical(x)) {
warning("argument is not numeric or logical: returning NA")
return(NA_real_)
}
if (na.rm)
x <- x[!is.na(x)]
if (!is.numeric(trim) || length(trim) != 1L)
stop("'trim' must be numeric of length one")
n <- length(x)
if (trim > 0 && n) {
if (is.complex(x))
stop("trimmed means are not defined for complex data")
if (anyNA(x))
return(NA_real_)
if (trim >= 0.5)
return(stats::median(x, na.rm = FALSE))
lo <- floor(n * trim) + 1
hi <- n + 1 - lo
x <- sort.int(x, partial = unique(c(lo, hi)))
}
.Internal(mean(x))
}
<bytecode: 0x00000000111ee3c0>
<environment: namespace:base>
/////////////////////////////////////////////////////////////////////
> mt <- function(v){
+ n <- length(v)
+ t1 <- v
+ tp <- sum(v)
+ t2 <- v
+ if (tp > 35){
+ max_index = which(t2==max(t2))
+ t2 = max(t2)- tp + 35
+ }
+ return(c(t1,t2))
+ }
> mt(1:10)
1 2 3 4 5 6 7 8 9 5
> a <- 1:10
> b <- c(2,5,6,7, 466, 466,466, 34,5,2)
> c <- c(2,5,6,7, 466, 89,14, 56,7,2)
> data1 <- data.frame(a,b,c)
> data1
a b c
1 1 2 2
2 2 5 5
3 3 6 6
4 4 7 7
5 5 466 466
6 6 46689
7 7 46614
8 83456
9 9 5 7
10 10 2 2
> apply(data1, 2, mt)
a b c
1 2 2
2 5 5
3 6 6
4 7 7
5466 466
6 -472 -44
746614
8 3456
9 5 7
5 2 2
> data1 <- data.frame(rbind(a,b,c) )
> data1
X1 X2 X3 X4X5X6X7 X8 X9 X10
a1234 5 6 78910
b2567 466 466 466 345 2
c2567 4668914 567 2
> apply(data1, 1, mt)
a b c
X11 2 2
X22 5 5
X33 6 6
X44 7 7
X55466 466
X66 -472 -44
X7746614
X88 3456
X99 5 7
X10 5 2 2
///////////////////////////////////////////////////////////////////////
> data <- read.table("test_plot.txt", skip=1, stringsAsFactors =F)
> c("A","B",3)
"A" "B" "3"
> mt <- function(v){
n <- length(v)
t1 <- v
t2 <- as.numeric(v)
tp <- sum(t2)
if (tp > 1){
max_index = which(t2==max(t2))
t2 = max(t2)- tp + 1
} else if (tp < 1){
min_index = which(t2==min(t2))
t2 = min(t2) + 1 - tp
}
return(c(t1,t2))
}
>
> apply(data, 1, mt)
1 2 3
V1 "1" "2" "3"
V2 "100" "101" "102"
V3 "(0)" "(0)" "(0)"
V4 "1" "1" "1"
V5 ":" ":" ":"
"0.663" "0.767" "0.651"
"0.185" "0.042" "0.083"
"0.039" "0.033" "0.062"
"0.113" "0.158" "0.204"
> t(apply(data, 1, mt))
V1 V2 V3 V4V5
"1" "100" "(0)" "1" ":" "0.663" "0.185"
"2" "101" "(0)" "1" ":" "0.767" "0.042"
"3" "102" "(0)" "1" ":" "0.651" "0.083"
"4" "103" "(0)" "1" ":" "0.358" "0.078"
"5" "104" "(0)" "1" ":" "0.062" "0.096"
"6" "105" "(0)" "1" ":" "0.058" "0.098"
> t <- sprintf( "%.7f", 1.44*1.21)
> t
"1.7424000"
> t <- sprintf( "%.3f", 1.44*1.21)
> t
"1.742"
> mt <- function(v){
+ n <- length(v)
+ t1 <- v
+ t2 <- as.numeric(v)
+ tp <- sum(t2)
+
+ if (tp > 1){
+ max_index <- which(t2==max(t2))
+ t2 <- sprintf("%.3f", max(t2)-tp+1)
+ } else if (tp < 1){
+ min_index <- which(t2==min(t2))
+ t2 <- sprintf("%.3f", min(t2) + 1 - tp)
+ }
+ return(c(t1,t2))
+ }
> head(data)
V1V2V3 V4 V5 V6 V7 V8 V9
11 100 (0)1: 0.663 0.185 0.038 0.113
22 101 (0)1: 0.767 0.042 0.033 0.158
33 102 (0)1: 0.651 0.083 0.062 0.204
44 103 (0)1: 0.358 0.078 0.185 0.379
55 104 (0)1: 0.062 0.096 0.487 0.355
66 105 (0)1: 0.058 0.098 0.516 0.329
> head(data[,6:9])
V6 V7 V8 V9
1 0.663 0.185 0.038 0.113
2 0.767 0.042 0.033 0.158
3 0.651 0.083 0.062 0.204
4 0.358 0.078 0.185 0.379
5 0.062 0.096 0.487 0.355
6 0.058 0.098 0.516 0.329
> head(t(apply(data, 1, mt)))
V1 V2 V3 V4V5
"1" "100" "(0)" "1" ":" "0.663" "0.185" "0.039" "0.113"
"2" "101" "(0)" "1" ":" "0.767" "0.042" "0.033" "0.158"
"3" "102" "(0)" "1" ":" "0.651" "0.083" "0.062" "0.204"
"4" "103" "(0)" "1" ":" "0.358" "0.078" "0.185" "0.379"
"5" "104" "(0)" "1" ":" "0.062" "0.096" "0.487" "0.355"
"6" "105" "(0)" "1" ":" "0.058" "0.098" "0.515" "0.329"
> apply(data[,6:9], 1 , sum)
0.999 1.000 1.000 1.000 1.000 1.001 1.000 1.000 1.000 1.000 1.000 1.000
1.001 1.000 1.000 1.001 0.999 0.999 1.000 1.000 1.000 1.000 1.000 1.000
1.001 1.000 1.001 1.000 0.999 1.000 1.000 1.000 1.000 1.000 1.001 1.000
1.000 1.000 0.999 1.000 0.999 1.001 1.000 0.999 1.000 1.000 1.000 1.000
1.000 0.999 1.000 1.000 1.001 1.000 1.001 1.000 1.000 1.001 1.000 1.000
1.000 1.000 1.000 1.001 0.999 1.000 0.999 1.001 0.999 0.999 1.000 1.000
0.999 1.000 1.000 1.000 0.999 1.000 1.000 1.001 1.000 1.000 1.000 1.001
1.000 1.001 1.000 1.000 1.000 1.001 1.000 1.000 1.000 1.000 1.000 1.001
1.001 1.000 1.000 1.000 1.000 1.000 1.001 1.001 1.000 1.001 1.000 1.000
1.000 1.000 0.999 0.999 1.001 1.000 1.000 1.000 0.999 1.001 1.000 1.000
1.000 1.000 1.000 1.000 1.000 0.999 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.001 1.001 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.001 0.999 1.000 0.999 1.000 1.000 0.999 1.000 0.999 1.000 0.999 1.000
0.999 0.999 1.000 1.001 1.000 1.001 1.000 1.000 1.000 0.999 1.000 1.001
1.000 1.000 0.999 1.000 1.000 1.000 0.999
> x <- t(apply(data, 1, mt))
> head(x)
V1 V2 V3 V4V5
"1" "100" "(0)" "1" ":" "0.663" "0.185" "0.039" "0.113"
"2" "101" "(0)" "1" ":" "0.767" "0.042" "0.033" "0.158"
"3" "102" "(0)" "1" ":" "0.651" "0.083" "0.062" "0.204"
"4" "103" "(0)" "1" ":" "0.358" "0.078" "0.185" "0.379"
"5" "104" "(0)" "1" ":" "0.062" "0.096" "0.487" "0.355"
"6" "105" "(0)" "1" ":" "0.058" "0.098" "0.515" "0.329"
> head(x[,6:9])
"0.663" "0.185" "0.039" "0.113"
"0.767" "0.042" "0.033" "0.158"
"0.651" "0.083" "0.062" "0.204"
"0.358" "0.078" "0.185" "0.379"
"0.062" "0.096" "0.487" "0.355"
"0.058" "0.098" "0.515" "0.329"
> m<- apply(data.frame(x[,6:9]), 1, as.numeric)
> apply(m,2,sum)
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
> n<- apply(data.frame(x[,6:9]), 2, as.numeric)
> apply(n,1,sum)
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
>
/////////////////////////////////////////////////////////////////////
> x <- t(apply(data, 1, mt))
> head(x)
V1 V2 V3 V4V5
"1" "100" "(0)" "1" ":" "0.663" "0.185" "0.039" "0.113"
"2" "101" "(0)" "1" ":" "0.767" "0.042" "0.033" "0.158"
"3" "102" "(0)" "1" ":" "0.651" "0.083" "0.062" "0.204"
"4" "103" "(0)" "1" ":" "0.358" "0.078" "0.185" "0.379"
"5" "104" "(0)" "1" ":" "0.062" "0.096" "0.487" "0.355"
"6" "105" "(0)" "1" ":" "0.058" "0.098" "0.515" "0.329"
> x1 <- as.data.frame(x)
> head(x1)
V1V2V3 V4 V5 V6 V7 V8 V9
1 1 100 (0)1: 0.663 0.185 0.039 0.113
2 2 101 (0)1: 0.767 0.042 0.033 0.158
3 3 102 (0)1: 0.651 0.083 0.062 0.204
4 4 103 (0)1: 0.358 0.078 0.185 0.379
5 5 104 (0)1: 0.062 0.096 0.487 0.355
6 6 105 (0)1: 0.058 0.098 0.515 0.329
> data_p <- x1, decreasing=T),]
> pdf("graph.pdf", height=5, width=15)
> barplot(as.matrix(t(data_p[,6:9])), names.arg=data_p$V2, col=rainbow(4), border= rainbow(4), las=2, cex.names=0.35, cex.axis =0.65)
> dev.off()
null device
1
>
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