您如何在mutate_at()中使用rox()? [英] How do you use approx() inside of mutate_at()?
问题描述
我在让rox()在mutate_at()内部工作时遇到了问题.我确实使用一个很长的mutate()函数来获取想要的东西,但是为了将来参考,我想知道是否有一种更优雅,更少粘贴粘贴的mutate_at()方式来实现.
I'm having issues getting approx() to work inside of a mutate_at(). I did manage to get what I want using a very long mutate() function, but for future reference I was wondering if there was a more graceful and less copy-pasting mutate_at() way to do this.
总体问题是将数据集从1年间隔的数据合并到3年间隔的数据,并以3年间隔对没有数据的年份进行插值.在几年之间,以及一年之间需要某种形式的推断的缺失值.
The overarching problem is merging a dataset with data from 1 year intervals to one with 3 year intervals, and interpolating years with no data in the dataset with 3 year intervals. There are missing values in between the years, and one year that requires some form of extrapolation.
library("tidyverse")
demodf <- data.frame(groupvar = letters[rep(1:15, each = 6)],
timevar = c(2000, 2003, 2006, 2009, 2012, 2015),
x1 = runif(n = 90, min = 0, max = 3),
x2 = runif(n = 90, min = -1, max = 4),
x3 = runif(n = 90, min = 1, max = 12),
x4 = runif(n = 90, min = 0, max = 30),
x5 = runif(n = 90, min = -2, max = 5),
x6 = runif(n = 90, min = 20, max = 50),
x7 = runif(n = 90, min = 1, max = 37),
x8 = runif(n = 90, min = 0.3, max = 0.5))
demotbl <- tbl_df(demodf)
masterdf <- data.frame(groupvar = letters[rep(1:15, each = 17)],
timevar = 2000:2016,
z1 = runif(n = 255, min = 0, max = 1E6))
mastertbl <- tbl_df(masterdf)
joineddemotbls <- mastertbl %>% left_join(demotbl, by = c("groupvar", "timevar"))
View(joineddemotbls)
joineddemotblswithinterpolation <- joineddemotbls %>% group_by(groupvar) %>%
mutate(x1i = approx(timevar, x1, timevar, rule = 2, f = 0, ties = mean, method = "linear")[["y"]],
x2i = approx(timevar, x2, timevar, rule = 2, f = 0, ties = mean, method = "linear")[["y"]],
x3i = approx(timevar, x3, timevar, rule = 2, f = 0, ties = mean, method = "linear")[["y"]],
x4i = approx(timevar, x4, timevar, rule = 2, f = 0, ties = mean, method = "linear")[["y"]],
x5i = approx(timevar, x5, timevar, rule = 2, f = 0, ties = mean, method = "linear")[["y"]],
x6i = approx(timevar, x6, timevar, rule = 2, f = 0, ties = mean, method = "linear")[["y"]],
x7i = approx(timevar, x7, timevar, rule = 2, f = 0, ties = mean, method = "linear")[["y"]],
x8i = approx(timevar, x8, timevar, rule = 2, f = 0, ties = mean, method = "linear")[["y"]])
View(joineddemotblswithinterpolation)
# this is what I want
效果很好.但是我已经尝试了所有这些mutate_at()变体,但还没有使它们起作用.我确定某个地方的语法有误...
That works pretty well. But I've tried all these mutate_at() variants and have not gotten them to work. I am sure there is an error in the syntax somewhere...
joineddemotblswithinterpolation2 <- joineddemotblswithinterpolation %>% group_by(groupvar) %>%
mutate_at(vars(x1, x2, x3, x4, x5, x6, x7, x8), approx(timevar, ., timevar, rule = 2, f = 0, ties = mean, method = "linear")[["y"]])
# error
joineddemotblswithinterpolation2 <- joineddemotblswithinterpolation %>% group_by(groupvar) %>%
mutate_at(vars(x1, x2, x3, x4, x5, x6, x7, x8), approxfun(timevar, ., timevar, rule = 2, f = 0, ties = mean, method = "linear")[["y"]])
# error
joineddemotblswithinterpolation2 <- joineddemotblswithinterpolation %>% group_by(groupvar) %>%
mutate_at(vars(x1, x2, x3, x4, x5, x6, x7, x8), funs(approxfun(timevar, ., timevar, rule = 2, f = 0, ties = mean, method = "linear")[["y"]]))
# error
joineddemotblswithinterpolation2 <- joineddemotblswithinterpolation %>% group_by(groupvar) %>%
mutate_at(vars(x1, x2, x3, x4, x5, x6, x7, x8), funs(approxfun(timevar, ., rule = 2, f = 0, ties = mean, method = "linear")[["y"]]))
我什至尝试了na.approx(),但也无济于事...
I even tried na.approx(), but also to no avail...
library("zoo")
joineddemotblswithinterpolation2 <- joineddemotblswithinterpolation %>% group_by(groupvar) %>%
mutate_at(vars(x1, x2, x3, x4, x5, x6, x7, x8), na.approx(., timevar, na.rm = FALSE))
我已经从以下相关问题构建了这些不同的试验:
I've kind of constructed these different trials from the following related questions:
感谢您的帮助!
推荐答案
您非常亲密.这对我有用:
You're very close. This works for me:
joineddemotblswithinterpolation <- joineddemotbls %>%
group_by(groupvar) %>%
mutate_at(vars(starts_with("x")), # easier than listing each column separately
funs("i" = approx(timevar, ., timevar, rule = 2, f = 0, ties = mean,
method = "linear")[["y"]]))
这将创建带有插值的列 x1_i
, x2_i
等.
This will create columns x1_i
, x2_i
etc. with the interpolated values.
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