R：更快设置缺失值的算法
我在设置数据帧中缺少的值时遇到问题。在前3列中有产品ID、门店ID和周数。还有28列，从4到31，对应于销售商品的最后28天（最后7天是我们一周的天数）。我想通过比较第一列和第二列相同但周数不同的两条记录来设置缺少的值R：更快设置缺失值的算法,r,algorithm,missing-data,R,Algorithm,Missing Data,我在设置数据帧中缺少的值时遇到问题。在前3列中有产品ID、门店ID和周数。还有28列，从4到31，对应于销售商品的最后28天（最后7天是我们一周的天数）。我想通过比较第一列和第二列相同但周数不同的两条记录来设置缺少的值 corrections <- function(x,y){ #the functions changes vector y if the difference between weeks is not greeter than 3 if (x[1]==y[1] &a
corrections <- function(x,y){
#the functions changes vector y if the difference between weeks is not greeter than 3
if (x[1]==y[1] && x[2]==y[2] && -(x[3]-y[3])<=3){
t=y[3]-x[3]
t=as.integer(t)
a=x[(4+ (t*7) ):31]
b=y[4:(31- (t*7)) ]
c= a-b
for (i in 1:(28-(t*7))){
if (is.na(c[i]))
{
if (!(is.na(a[i]) && is.na(b[i])))
{
if (is.na(b[i]))
b[i]=a[i]
else
a[i]=b[i]
}
}
}
y[4:(31- t*7)]=b
}
return(y)
}
for (i in 2:(dim(salesTraining)[1]) {
salesTraining[i,]=corrections(salesTraining[i-1,], salesTraining[i,])
}
如果此代码按预期运行，并且只是慢的，则应考虑将其张贴在CoDeEvIEW.StAccExchange
productID storeID weekInData dailySales1 dailySales2 dailySales3 dailySales4 dailySales5
1 1 1 37 0 0 0 0 0
2 1 1 38 0 0 0 0 0
3 1 1 39 0 0 0 0 0
4 1 1 40 0 NA 0 NA 2
5 1 1 41 NA 0 NA 0 0
6 1 1 42 0 0 0 NA 0
7 1 1 43 0 0 NA 0 NA
8 1 1 44 0 2 1 NA 0
9 1 1 45 NA 0 0 NA 0
10 1 1 46 NA 0 0 NA NA
dailySales6 dailySales7 dailySales8 dailySales9 dailySales10 dailySales11 dailySales12 dailySales13
1 NA NA 0 NA 0 0 0 0
2 0 NA NA 0 0 0 0 0
3 0 NA 0 0 0 NA 2 NA
4 0 NA 0 NA 0 NA 0 0
5 0 0 NA 0 0 0 0 0
6 NA 0 NA 0 0 0 0 0
7 0 0 0 2 NA 0 0 0
8 0 NA 0 NA 0 NA 0 1
9 1 0 0 0 0 0 1 0
10 0 0 0 NA 0 NA 0 0
dailySales14 dailySales15 dailySales16 dailySales17 dailySales18 dailySales19 dailySales20
1 0 0 0 0 0 0 0
2 0 0 0 0 5 2 NA
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0
6 0 0 2 1 0 0 NA
7 0 0 0 0 0 0 1
8 0 0 0 0 0 1 0
9 0 0 -1 0 0 0 0
10 0 0 0 0 0 0 0
dailySales21 dailySales22 dailySales23 dailySales24 dailySales25 dailySales26 dailySales27
1 NA 0 0 0 5 2 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 NA 1 0 0 0
6 0 0 0 0 0 0 1
7 0 0 0 0 0 1 0
8 0 0 NA 0 0 0 0
9 NA 0 0 0 NA 0 0
10 0 1 0 0 0 0 0
dailySales28 daysStoreClosed_series daysStoreClosed_target dayOfMonth dayOfYear weekOfYear month
1 0 5 2 23 356 51 12
2 0 6 2 30 363 52 12
3 0 6 1 6 5 1 1
4 0 6 1 13 12 2 1
5 0 6 1 19 18 3 1
6 0 5 1 26 25 4 1
7 0 4 1 2 32 5 2
8 0 4 1 9 39 6 2
9 0 4 1 16 46 7 2
10 0 4 1 23 53 8 2
quarter
1 4
2 4
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1