r-使用数据帧列中的下一个非na值进行计算
[英]r - calculate using next non-na value in data frame column
问题描述
I have some data in a dataframe, and I would like to calculate the percentage change between the month value. 
我在数据框中有一些数据，我想计算month值之间的百分比变化。 The problem is I have NA in some entries and it throws of the calculation. 问题是我在某些条目中不NA ，并且引发了计算。
irm code price pct.change
1 201807 511130F075A04 4.6600 2.192982
2 201806 511130F075A04 4.5600 1.333333
3 201805 511130F075A04 4.5000 -13.461538
4 201804 511130F075A04 5.2000 NA
5 201803 511130F075A04 NA NA
6 201802 511130F075A04 4.9100 1.867220
7 201801 511130F075A04 4.8200 -5.304519
8 201712 511130F075A04 5.0900 2.414487
9 201711 511130F075A04 4.9700 -3.307393
10 201710 511130F075A04 5.1400 NA
11 201709 511130F075A04 NA NA
12 201708 511130F075A04 5.2900 2.918288
13 201707 511130F075A04 5.1400 66.553255
14 201706 511130F075A04 3.0861 -10.664351
15 201705 511130F075A04 3.4545 -7.241824
The problem is in row 4 and row 10 in the pct.change column. 问题出在pct.change列的第4行和第10行。 They are NA but I would like them to be calculated using the latest value of price that is not NA . 它们是NA但我希望使用不是NA的最新price值来计算它们。 The desired output would be (see rows 4 and 10): 所需的输出将是（请参阅第4和10行）：
irm code price pct.change
1 201807 511130F075A04 4.6600 2.192982
2 201806 511130F075A04 4.5600 1.333333
3 201805 511130F075A04 4.5000 -13.461538
**4 201804 511130F075A04 5.2000 5.906314**
5 201803 511130F075A04 NA NA
6 201802 511130F075A04 4.9100 1.867220
7 201801 511130F075A04 4.8200 -5.304519
8 201712 511130F075A04 5.0900 2.414487
9 201711 511130F075A04 4.9700 -3.307393
**10 201710 511130F075A04 5.1400 -2.835539**
11 201709 511130F075A04 NA NA
12 201708 511130F075A04 5.2900 2.918288
13 201707 511130F075A04 5.1400 66.553255
14 201706 511130F075A04 3.0861 -10.664351
15 201705 511130F075A04 3.4545 -7.241824
I had tried the standard (x/lead(x) - 1)*100 and several variations using (x/lag(which(!is.na(lead(x)) but I seem to be missing something. Is there a straightforward way to do it in base or even dplyr ? I don't want to replace the NAs, I want to keep them. 我已经尝试过标准(x/lead(x) - 1)*100和使用(x/lag(which(!is.na(lead(x))几种变体，但我似乎缺少了一些东西。 base或什dplyr是dplyr ？ 我不想替换NA，我想保留它们。
2 个解决方案
解决方案1
2 已采纳 2018-08-16 16:11:33
@LAP's comment is probably the best way to do it. @LAP的评论可能是最好的方法。 The syntax is a little better with data.table 使用data.table的语法要好data.table
library(data.table)
setDT(df)
df[!is.na(price), pct.change := 100*(price/shift(price, type = 'lead') - 1)]
# irm code price pct.change
# 1: 201807 511130F075A04 4.6600 2.192982
# 2: 201806 511130F075A04 4.5600 1.333333
# 3: 201805 511130F075A04 4.5000 -13.461538
# 4: 201804 511130F075A04 5.2000 5.906314
# 5: 201803 511130F075A04 NA NA
# 6: 201802 511130F075A04 4.9100 1.867220
# 7: 201801 511130F075A04 4.8200 -5.304519
# 8: 201712 511130F075A04 5.0900 2.414487
# 9: 201711 511130F075A04 4.9700 -3.307393
# 10: 201710 511130F075A04 5.1400 -2.835539
# 11: 201709 511130F075A04 NA NA
# 12: 201708 511130F075A04 5.2900 2.918288
# 13: 201707 511130F075A04 5.1400 66.553255
# 14: 201706 511130F075A04 3.0861 -10.664351
# 15: 201705 511130F075A04 3.4545 NA
解决方案2
1 2018-08-16 16:34:37
in Base R you can decide to replace: 在Base R中，您可以决定替换：
a = which(is.na(df$price))-1
transform(df,pct.change=replace(pct.change,a,100*(price[a]/price[a+2]-1)))
irm code price pct.change
1 201807 511130F075A04 4.6600 2.192982
2 201806 511130F075A04 4.5600 1.333333
3 201805 511130F075A04 4.5000 -13.461538
4 201804 511130F075A04 5.2000 5.906314
5 201803 511130F075A04 NA NA
6 201802 511130F075A04 4.9100 1.867220
7 201801 511130F075A04 4.8200 -5.304519
8 201712 511130F075A04 5.0900 2.414487
9 201711 511130F075A04 4.9700 -3.307393
10 201710 511130F075A04 5.1400 -2.835539
11 201709 511130F075A04 NA NA
12 201708 511130F075A04 5.2900 2.918288
13 201707 511130F075A04 5.1400 66.553255
14 201706 511130F075A04 3.0861 -10.664351
15 201705 511130F075A04 3.4545 -7.241824
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