我想使用dplyr，piping和NA插入缺失值。
数据：
NA Q1：如何使用以下功能代码，但限制在每个变量的第一个非var1值和最后一个非wetland值之间进行插值的范围。例如，它只应在深度-8为NA插入0，并为深度-14和library(tidyverse)
test_int <- test %>%
group_by(site) %>%
mutate_at(vars(c(var1, var2)),
funs("i" = if(sum(!is.na(.)) > 1)
spline(x=depth, y=., xout=depth)[["y"]]
else
NA))
返回0。
Inf Q2：有没有办法将我的插值从smooth绑定到loess？或者这不适合样条曲线（例如，我应该使用其他插值方法，例如const Parent = {
data() {
return {
transitionName: "slide-left",
transitionDuration: 2.5
};
},
computed: {
transitionStyle() {
return {
'transition-duration': this.transitionDuration +'s'
}
}
},
beforeRouteUpdate(to, from, next) {
const toDepth = to.path.split("/").length;
const fromDepth = from.path.split("/").length;
///////////////
// this is where you set the transition duration
// replace with your own implementation of setting duration
this.transitionDuration = Math.random()*1.5+0.5;
///////////////
this.transitionName = toDepth < fromDepth ? "slide-right" : "slide-left";
next();
},
template: `
<div class="parent">
<h2>Parent</h2>
{{transitionStyle}}
<transition :name="transitionName">
<router-view class="child-view" :style="transitionStyle"></router-view>
</transition>
</div>
`
};
或beforeRouteUpdate）？
答案 0 :(得分：0)
不漂亮，但能够过滤掉多余的值。
副作用是它还会过滤掉超出min和max限制的插值。
test_clean <-
test %>%
group_by(site) %>%
mutate_at(vars(c(var1, var2)),
funs(c("c" = if(sum(!is.na(.)) > 1)
spline(x=depth, y=., xout=depth)[["y"]]
else NA),
"min" = min(., na.rm = TRUE),
"max" = max(., na.rm = TRUE)
)
) %>%
mutate(var1_i = if_else(var1_c >= var1_min & var1_c <= var1_max, var1_c, NA_real_),
var2_i = if_else(var2_c >= var2_min & var2_c <= var2_max, var2_c, NA_real_)) %>%
select(site:var2, ends_with("i"))
test_clean
# A tibble: 14 x 6
# Groups: site [3]
site depth var1 var2 var1_i var2_i
<fctr> <int> <int> <int> <dbl> <dbl>
1 wetland 0 NA 1 NA 1.000000
2 wetland -3 1 NA 1.0 3.078125
3 wetland -4 3 3 3.0 3.000000
4 wetland -8 NA 4 NA 4.000000
5 wetland -10 6 8 6.0 8.000000
6 wetland -14 NA NA NA NA
7 lake 0 1 NA 1.0 NA
8 lake -1 2 NA 2.0 NA
9 lake -3 NA NA 3.4 NA
10 lake -5 4 NA 4.0 NA
11 stream 0 1 NA 1.0 NA
12 stream -2 NA 2 2.0 NA
13 stream -4 NA NA 3.0 NA
14 stream -6 4 NA 4.0 NA
并且为了帮助每个人改进这一点或校对在最终数据帧的路上发生的步骤，这里包含了包含中间步骤的数据框：
# A tibble: 14 x 12
# Groups: site [3]
site depth var1 var2 var1_c var2_c var1_min var2_min var1_max var2_max var1_i var2_i
<fctr> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 wetland 0 NA 1 -7.5714286 1.000000 1 1 6 8 NA 1.000000
2 wetland -3 1 NA 1.0000000 3.078125 1 1 6 8 1.0 3.078125
3 wetland -4 3 3 3.0000000 3.000000 1 1 6 8 3.0 3.000000
4 wetland -8 NA 4 6.7142857 4.000000 1 1 6 8 NA 4.000000
5 wetland -10 6 8 6.0000000 8.000000 1 1 6 8 6.0 8.000000
6 wetland -14 NA NA -0.5714286 30.750000 1 1 6 8 NA NA
7 lake 0 1 NA 1.0000000 NA 1 Inf 4 -Inf 1.0 NA
8 lake -1 2 NA 2.0000000 NA 1 Inf 4 -Inf 2.0 NA
9 lake -3 NA NA 3.4000000 NA 1 Inf 4 -Inf 3.4 NA
10 lake -5 4 NA 4.0000000 NA 1 Inf 4 -Inf 4.0 NA
11 stream 0 1 NA 1.0000000 NA 1 2 4 2 1.0 NA
12 stream -2 NA 2 2.0000000 NA 1 2 4 2 2.0 NA
13 stream -4 NA NA 3.0000000 NA 1 2 4 2 3.0 NA
14 stream -6 4 NA 4.0000000 NA 1 2 4 2 4.0 NA