在 R 中的 dplyr 管道中按组进行线性插值（近似）
linear interpolation (approx) by group in a dplyr pipe in R
我有一个问题，我觉得用 MRE 很难用简单的方式解释
回答的方式，主要是因为我没有完全理解问题所在
我。因此，对于前言含糊不清，我深表歉意。
我有很多样本和参考测量值，我想要
为每个样本做一些线性插值。我现在通过取出来做到这一点
所有参考测量值，将它们重新缩放为样本测量值，使用
approx，然后把它打回去。但是因为我先把它拿出来了，所以我
不能用 group_by dplyr 管道方式很好地完成。现在我用
非常丑陋的解决方法，我将新创建的空（NA）列添加到
采样 tibble，然后使用 for 循环进行。
所以我的问题真的是：如何在组内实现近似部分
进入管道，以便我可以在组内做所有事情？我试验过
dplyr::do() 和 运行 进入 "programming with dplyr" 的插图，但是
搜索主要给我 broom::augment 和 lm 我认为有用的东西
不同的......（例如见
). This thread also seems promising:
irc 上有人推荐使用条件变异，case_when，但我
还不完全了解在此上下文中的位置和方式。
我觉得问题出在我想过滤掉部分数据
对于以下变异操作，但变异操作依赖于
我刚刚过滤掉的分组数据，如果这有意义的话。
这是一个 MWE：
library(tidyverse) # or just dplyr, tibble
# create fake data
data <- data.frame(
# in reality a dttm with the measurement time
timestamp = c(rep("a", 7), rep("b", 7), rep("c", 7)),
# measurement cycle, normally 40 for sample, 41 for reference
cycle = rep(c(rep(1:3, 2), 4), 3),
# wheather the measurement is a reference or a sample
isref = rep(c(rep(FALSE, 3), rep(TRUE, 4)), 3),
# measurement intensity for mass 44
r44 = c(28:26, 30:26, 36, 33, 31, 38, 34, 33, 31, 18, 16, 15, 19, 18, 17)) %>%
# measurement intensity for mass 45, normally also masses up to mass 49
mutate(r45 = r44 + rnorm(21, 20))
# of course this could be tidied up to "intensity" with a new column "mass"
# (44, 45, ...), but that would make making comparisons even harder...
# overview plot
data %>%
ggplot(aes(x = cycle, y = r44, colour = isref)) +
geom_line() +
geom_line(aes(y = r45), linetype = 2) +
geom_point() +
geom_point(aes(y = r45), shape = 1) +
facet_grid(~ timestamp)
# what I would like to do
data %>%
group_by(timestamp) %>%
do(target_cycle = approx(x = data %>% filter(isref) %>% pull(r44),
y = data %>% filter(isref) %>% pull(cycle),
xout = data %>% filter(!isref) %>% pull(r44))$y) %>%
unnest()
# immediately append this new column to the original dataframe for all the
# samples (!isref) and then apply another approx for those values.
# here's my current attempt for one of the timestamps
matchref <- function(dat) {
# split the data into sample gas and reference gas
ref <- filter(dat, isref)
smp <- filter(dat, !isref)
# calculate the "target cycle", the points at which the reference intensity
# 44 matches the sample intensity 44 with linear interpolation
target_cycle <- approx(x = ref$r44,
y = ref$cycle, xout = smp$r44)
# append the target cycle to the sample gas
smp <- smp %>%
group_by(timestamp) %>%
mutate(target = target_cycle$y)
# linearly interpolate each reference gas to the target cycle
ref <- ref %>%
group_by(timestamp) %>%
# this is needed because the reference has one more cycle
mutate(target = c(target_cycle$y, NA)) %>%
# filter out all the failed ones (no interpolation possible)
filter(!is.na(target)) %>%
# calculate interpolated value based on r44 interpolation (i.e., don't
# actually interpolate this value but shift it based on the 44
# interpolation)
mutate(r44 = approx(x = cycle, y = r44, xout = target)$y,
r45 = approx(x = cycle, y = r45, xout = target)$y) %>%
select(timestamp, target, r44:r45)
# add new reference gas intensities to the correct sample gasses by the target cycle
left_join(smp, ref, by = c("time", "target"))
}
matchref(data)
# and because now "target" must be length 3 (the group size) or one, not 9
# I have to create this ugly for-loop
# for which I create a copy of data that has the new columns to be created
mr <- data %>%
# filter the sample gasses (since we convert ref to sample)
filter(!isref) %>%
# add empty new columns
mutate(target = NA, r44 = NA, r45 = NA)
# apply matchref for each group timestamp
for (grp in unique(data$timestamp)) {
mr[mr$timestamp == grp, ] <- matchref(data %>% filter(timestamp == grp))
}
这是一种将引用和样本分布到新列的方法。为了简单起见，我在这个例子中删除了 r45。
data %>%
select(-r45) %>%
mutate(isref = ifelse(isref, "REF", "SAMP")) %>%
spread(isref, r44) %>%
group_by(timestamp) %>%
mutate(target_cycle = approx(x = REF, y = cycle, xout = SAMP)$y) %>%
ungroup
给予，
# timestamp cycle REF SAMP target_cycle
# <fct> <dbl> <dbl> <dbl> <dbl>
# 1 a 1 30 28 3
# 2 a 2 29 27 4
# 3 a 3 28 26 NA
# 4 a 4 27 NA NA
# 5 b 1 31 26 NA
# 6 b 2 38 36 2.5
# 7 b 3 34 33 4
# 8 b 4 33 NA NA
# 9 c 1 15 31 NA
# 10 c 2 19 18 3
# 11 c 3 18 16 2.5
# 12 c 4 17 NA NA
编辑以解决下面的评论
要保留 r45，您可以使用这样的聚集-联合-传播方法：
df %>%
mutate(isref = ifelse(isref, "REF", "SAMP")) %>%
gather(r, value, r44:r45) %>%
unite(ru, r, isref, sep = "_") %>%
spread(ru, value) %>%
group_by(timestamp) %>%
mutate(target_cycle_r44 = approx(x = r44_REF, y = cycle, xout = r44_SAMP)$y) %>%
ungroup
给予，
# # A tibble: 12 x 7
# timestamp cycle r44_REF r44_SAMP r45_REF r45_SAMP target_cycle_r44
# <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 a 1 30 28 49.5 47.2 3
# 2 a 2 29 27 48.8 48.7 4
# 3 a 3 28 26 47.2 46.8 NA
# 4 a 4 27 NA 47.9 NA NA
# 5 b 1 31 26 51.4 45.7 NA
# 6 b 2 38 36 57.5 55.9 2.5
# 7 b 3 34 33 54.3 52.4 4
# 8 b 4 33 NA 52.0 NA NA
# 9 c 1 15 31 36.0 51.7 NA
# 10 c 2 19 18 39.1 37.9 3
# 11 c 3 18 16 39.2 35.3 2.5
# 12 c 4 17 NA 39.0 NA NA
我有一个问题，我觉得用 MRE 很难用简单的方式解释 回答的方式，主要是因为我没有完全理解问题所在 我。因此，对于前言含糊不清，我深表歉意。
我有很多样本和参考测量值，我想要
为每个样本做一些线性插值。我现在通过取出来做到这一点
所有参考测量值，将它们重新缩放为样本测量值，使用
approx，然后把它打回去。但是因为我先把它拿出来了，所以我
不能用 group_by dplyr 管道方式很好地完成。现在我用
非常丑陋的解决方法，我将新创建的空（NA）列添加到
采样 tibble，然后使用 for 循环进行。
所以我的问题真的是：如何在组内实现近似部分
进入管道，以便我可以在组内做所有事情？我试验过
dplyr::do() 和 运行 进入 "programming with dplyr" 的插图，但是
搜索主要给我 broom::augment 和 lm 我认为有用的东西
不同的......（例如见
irc 上有人推荐使用条件变异，case_when，但我
还不完全了解在此上下文中的位置和方式。
我觉得问题出在我想过滤掉部分数据 对于以下变异操作，但变异操作依赖于 我刚刚过滤掉的分组数据，如果这有意义的话。
这是一个 MWE：
library(tidyverse) # or just dplyr, tibble
# create fake data
data <- data.frame(
# in reality a dttm with the measurement time
timestamp = c(rep("a", 7), rep("b", 7), rep("c", 7)),
# measurement cycle, normally 40 for sample, 41 for reference
cycle = rep(c(rep(1:3, 2), 4), 3),
# wheather the measurement is a reference or a sample
isref = rep(c(rep(FALSE, 3), rep(TRUE, 4)), 3),
# measurement intensity for mass 44
r44 = c(28:26, 30:26, 36, 33, 31, 38, 34, 33, 31, 18, 16, 15, 19, 18, 17)) %>%
# measurement intensity for mass 45, normally also masses up to mass 49
mutate(r45 = r44 + rnorm(21, 20))
# of course this could be tidied up to "intensity" with a new column "mass"
# (44, 45, ...), but that would make making comparisons even harder...
# overview plot
data %>%
ggplot(aes(x = cycle, y = r44, colour = isref)) +
geom_line() +
geom_line(aes(y = r45), linetype = 2) +
geom_point() +
geom_point(aes(y = r45), shape = 1) +
facet_grid(~ timestamp)
# what I would like to do
data %>%
group_by(timestamp) %>%
do(target_cycle = approx(x = data %>% filter(isref) %>% pull(r44),
y = data %>% filter(isref) %>% pull(cycle),
xout = data %>% filter(!isref) %>% pull(r44))$y) %>%
unnest()
# immediately append this new column to the original dataframe for all the
# samples (!isref) and then apply another approx for those values.
# here's my current attempt for one of the timestamps
matchref <- function(dat) {
# split the data into sample gas and reference gas
ref <- filter(dat, isref)
smp <- filter(dat, !isref)
# calculate the "target cycle", the points at which the reference intensity
# 44 matches the sample intensity 44 with linear interpolation
target_cycle <- approx(x = ref$r44,
y = ref$cycle, xout = smp$r44)
# append the target cycle to the sample gas
smp <- smp %>%
group_by(timestamp) %>%
mutate(target = target_cycle$y)
# linearly interpolate each reference gas to the target cycle
ref <- ref %>%
group_by(timestamp) %>%
# this is needed because the reference has one more cycle
mutate(target = c(target_cycle$y, NA)) %>%
# filter out all the failed ones (no interpolation possible)
filter(!is.na(target)) %>%
# calculate interpolated value based on r44 interpolation (i.e., don't
# actually interpolate this value but shift it based on the 44
# interpolation)
mutate(r44 = approx(x = cycle, y = r44, xout = target)$y,
r45 = approx(x = cycle, y = r45, xout = target)$y) %>%
select(timestamp, target, r44:r45)
# add new reference gas intensities to the correct sample gasses by the target cycle
left_join(smp, ref, by = c("time", "target"))
}
matchref(data)
# and because now "target" must be length 3 (the group size) or one, not 9
# I have to create this ugly for-loop
# for which I create a copy of data that has the new columns to be created
mr <- data %>%
# filter the sample gasses (since we convert ref to sample)
filter(!isref) %>%
# add empty new columns
mutate(target = NA, r44 = NA, r45 = NA)
# apply matchref for each group timestamp
for (grp in unique(data$timestamp)) {
mr[mr$timestamp == grp, ] <- matchref(data %>% filter(timestamp == grp))
}
这是一种将引用和样本分布到新列的方法。为了简单起见，我在这个例子中删除了 r45。
data %>%
select(-r45) %>%
mutate(isref = ifelse(isref, "REF", "SAMP")) %>%
spread(isref, r44) %>%
group_by(timestamp) %>%
mutate(target_cycle = approx(x = REF, y = cycle, xout = SAMP)$y) %>%
ungroup
给予，
# timestamp cycle REF SAMP target_cycle
# <fct> <dbl> <dbl> <dbl> <dbl>
# 1 a 1 30 28 3
# 2 a 2 29 27 4
# 3 a 3 28 26 NA
# 4 a 4 27 NA NA
# 5 b 1 31 26 NA
# 6 b 2 38 36 2.5
# 7 b 3 34 33 4
# 8 b 4 33 NA NA
# 9 c 1 15 31 NA
# 10 c 2 19 18 3
# 11 c 3 18 16 2.5
# 12 c 4 17 NA NA
编辑以解决下面的评论
要保留 r45，您可以使用这样的聚集-联合-传播方法：
df %>%
mutate(isref = ifelse(isref, "REF", "SAMP")) %>%
gather(r, value, r44:r45) %>%
unite(ru, r, isref, sep = "_") %>%
spread(ru, value) %>%
group_by(timestamp) %>%
mutate(target_cycle_r44 = approx(x = r44_REF, y = cycle, xout = r44_SAMP)$y) %>%
ungroup
给予，
# # A tibble: 12 x 7
# timestamp cycle r44_REF r44_SAMP r45_REF r45_SAMP target_cycle_r44
# <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 a 1 30 28 49.5 47.2 3
# 2 a 2 29 27 48.8 48.7 4
# 3 a 3 28 26 47.2 46.8 NA
# 4 a 4 27 NA 47.9 NA NA
# 5 b 1 31 26 51.4 45.7 NA
# 6 b 2 38 36 57.5 55.9 2.5
# 7 b 3 34 33 54.3 52.4 4
# 8 b 4 33 NA 52.0 NA NA
# 9 c 1 15 31 36.0 51.7 NA
# 10 c 2 19 18 39.1 37.9 3
# 11 c 3 18 16 39.2 35.3 2.5
# 12 c 4 17 NA 39.0 NA NA