[英]R: loop through data frame extracting subset of data depending on date
I have a large data frame that consists of data that looks something like this: 我有一个大型数据框,包含如下所示的数据:
date w x y z region
1 2012 01 21 43 12 3 NORTH
2 2012 02 32 54 21 16 NORTH
3 2012 03 14 32 65 32 NORTH
4 2012 04 65 33 75 21 NORTH
: : : : : : :
: : : : : : :
12 2012 12 32 58 53 17 NORTH
13 2012 01 12 47 43 23 SOUTH
14 2012 02 87 43 21 76 SOUTH
: : : : : : :
25 2012 01 12 46 84 29 EAST
26 2012 02 85 29 90 12 EAST
: : : : : : :
: : : : : : :
I want to extract section of the data that have the same date
value, for example to do this just for 2012 01
I would just create a subset of data 我想提取具有相同date
值的数据部分,例如仅为2012 01
执行此操作我将创建一个数据子集
data_1 <- subset(data, date == "2012 01")
and this gives me all the data for 2012 01
but I then go on to apply a function to this data. 这给了我2012 01
所有数据,但我继续将函数应用于这些数据。 I would like to be able to apply my function to all possible subsets of my data, so ideally I would be looping through my large data frame and extracting the data for 2012 01, 2012 02, 2012 03, 2012 04...
and applying a function to each of these subsets of data separately. 我希望能够将我的函数应用于我的所有可能的数据子集,所以理想情况下我将遍历我的大数据框并提取2012 01, 2012 02, 2012 03, 2012 04...
并应用分别对这些数据子集中的每一个的函数。
But I would like to be able to apply this to my data frame even if my data frames length were to change, so it may not always go from 2012 01 - 2012 12
, the range of dates may vary so that sometimes it may be used on data from for example 2011 03 - 2013 01
. 但是我希望能够将这个应用到我的数据框中,即使我的数据帧长度发生变化,因此它可能并不总是从2012 01 - 2012 12
,日期范围可能会有所不同,因此有时可能会被使用来自例如2011 03 - 2013 01
。
Loop through each unique date and build the subset. 遍历每个唯一日期并构建子集。
uniq <- unique(unlist(data$Date))
for (i in 1:length(uniq)){
data_1 <- subset(data, date == uniq[i])
#your desired function
}
is this what you want ? 这是你想要的吗 ? df_list <- split(data, as.factor(data$date))
After sub-setting your dataset by date, imagine that the function you would like to apply to each subset is to find the mean of the column x
. 在按日期对数据集进行子设置之后,假设您要应用于每个子集的函数是查找列x
的平均值。 You could do it this way: (df is your dataframe) 你可以这样做:( df是你的数据帧)
library(plyr)
ddply(df, .(date), summarize, mean = mean(x))
您可以将data.frame
拆分为data.frames
list
,如下所示:
list.of.dfs<-by(data,data$date)
This is a perfect situation for the plyr
package: 这是plyr
包的完美情况:
require(plyr)
ddply(my_df, .(date), my_function, extra_arg_1, extra_arg_2)
where my_function
is the function you want to perform on the split data frames, and extra_arg
s are any extra arguments that need to go to that function. 其中my_function
是您要对拆分数据帧执行的函数,而extra_arg
是需要转到该函数的任何额外参数。
ddply
( d
ata frame -> d
ata frame) is the form you want if you want your results in a data frame; 如果你想在数据框中得到结果, ddply
( d
ata frame - > d
ata frame)就是你想要的形式; dlply
returns a list. dlply
返回一个列表。
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