我希望单独计算'oldvar'的7天import java.util.List;
import org.reactivestreams.Publisher;
import org.springframework.cloud.gateway.filter.GatewayFilter;
import org.springframework.cloud.gateway.filter.GatewayFilterChain;
import org.springframework.http.HttpStatus;
import org.springframework.util.CollectionUtils;
import org.springframework.util.PatternMatchUtils;
import org.springframework.web.server.ServerWebExchange;
import org.springframework.http.server.reactive.ServerHttpResponse;
import org.springframework.http.server.reactive.ServerHttpResponseDecorator;
import org.springframework.core.io.buffer.DataBuffer;
import org.springframework.core.io.buffer.DataBufferFactory;
import org.springframework.core.Ordered;
import org.springframework.stereotype.Component;
import reactor.core.publisher.Mono;
public class AuthorizationFilter implements GatewayFilter {
@Override
public Mono<Void> filter(
ServerWebExchange exchange, GatewayFilterChain chain) {
List<String> authorization = exchange.getRequest().getHeaders().get("Authorization");
if (CollectionUtils.isEmpty(authorization) &&
!PatternMatchUtils.simpleMatch(URL_WITHOUT_AUTH, exchange.getRequest().getURI().toString())) {
exchange.getResponse().setStatusCode(HttpStatus.UNAUTHORIZED);
ServerHttpResponse originalResponse = exchange.getResponse();
DataBufferFactory bufferFactory = originalResponse.bufferFactory();
ServerHttpResponseDecorator decoratedResponse = new ServerHttpResponseDecorator(originalResponse) {
@Override
public Mono<Void> writeWith(Publisher<? extends DataBuffer> body) {
if (body instanceof Flux) {
Flux<? extends DataBuffer> fluxBody = (Flux<? extends DataBuffer>) body;
return super.writeWith(fluxBody.map(dataBuffer -> {
// probably should reuse buffers
byte[] content = new byte[dataBuffer.readableByteCount()];
dataBuffer.read(content);
byte[] uppedContent = new String(content, Charset.forName("UTF-8")).toUpperCase().getBytes();
return bufferFactory.wrap(uppedContent);
}));
}
return super.writeWith(body); // if body is not a flux. never got there.
}
};
return chain.filter(exchange.mutate().response(decoratedResponse).build()); // replace response with decorator
}
String token = authorization.get(0).split(" ")[1];
// token validation
return chain.filter(exchange);
}
}
和7天moving average。
我真诚的道歉，我没有在原帖中添加以下详细信息。这些是对每个id的重复观察，其可以从每个id的至少3个观察值到每个id的100个观察值。对于不同的ID，开始日期可能不同，为了使事情变得复杂，日期不是等间隔的，因此有些ID会丢失几天。
这是数据结构。请注意，“平均值”是我尝试创建的变量，作为每个ID的7天平均值：
moving slope此外，我还希望了解如何使用相同的方法计算id day outcome average
1 1 15 100 NA
2 1 16 110 NA
3 1 17 190 NA
4 1 18 130 NA
5 1 19 140 NA
6 1 20 150 NA
7 1 21 160 140
8 1 22 100 140
9 1 23 180 150
10 1 24 120 140
12 2 16 90 NA
13 2 17 110 NA
14 2 18 120 NA
12 2 20 130 NA
15 3 16 110 NA
16 3 18 200 NA
17 3 19 180 NA
18 3 21 170 NA
19 3 22 180 168
20 3 24 210 188
21 3 25 160 180
22 3 27 200 184
。
再次感谢你和第一次不清楚的道歉。
答案 0 :(得分：0)
真正的挑战是在完成缺失的行后创建data.frame。一种解决方案可能是使用zoo库。 rollapply函数将提供为初始行分配NA值的方法。
按原样使用OP的数据，解决方案可能是：
library(zoo)
library(dplyr)
# Data from OP
df <- structure(list(id = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L),
day = c(15L,16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 16L, 17L, 18L, 20L,
16L, 18L, 19L, 21L, 22L, 24L, 25L, 27L),
outcome = c(100L, 110L,190L, 130L, 140L, 150L, 160L, 100L, 180L, 120L, 90L, 110L, 120L,
130L, 110L, 200L, 180L, 170L, 180L, 210L, 160L, 200L)),
.Names = c("id", "day", "outcome"), row.names = c(NA, -22L), class = "data.frame")
# Make a list without missing day for each id
df_complete <- merge(
expand.grid(id=unique(df$id), day=min(df$day):max(df$day)),
df, all=TRUE)
# Valid range of day for each ID group
df_id_wise_range <- df %>% group_by(id) %>%
summarise(min_day = min(day), max_day = max(day)) %>% as.data.frame()
# id min_day max_day
# 1 1 15 24
# 2 2 16 20
# 3 3 16 27
# Join original df and df_complete and then use df_id_wise_range to
# filter it for valid range of day for each group
df_final <- df_complete %>%
left_join(df, by=c("id","day")) %>%
select(-outcome.y) %>%
inner_join(df_id_wise_range, by="id") %>%
filter(day >= min_day & day <= max_day) %>%
mutate(outcome = outcome.x) %>%
select( id, day, outcome) %>%
as.data.frame()
# Now apply mean to get average
df_average <- df_final %>% group_by(id) %>%
mutate(average= rollapply(outcome, 7, mean, na.rm = TRUE, by = 1,
fill = NA, align = "right", partial = 7)) %>% as.data.frame()
df_average
# The result
# id day outcome average
#1 1 15 100 NA
#2 1 16 110 NA
#3 1 17 190 NA
#4 1 18 130 NA
#5 1 19 140 NA
#6 1 20 150 NA
#7 1 21 160 140.0
#8 1 22 100 140.0
#9 1 23 180 150.0
#10 1 24 120 140.0
#11 2 16 90 NA
#12 2 17 110 NA
#13 2 18 120 NA
#....
#....
#19 3 19 180 NA
#20 3 20 NA NA
#21 3 21 170 NA
#22 3 22 180 168.0
#23 3 23 NA 182.5
#24 3 24 210 188.0
#25 3 25 160 180.0
#26 3 26 NA 180.0
#27 3 27 200 184.0
计算moving slope的步骤如下：
首先创建一个返回斜率的函数
使用函数作为rollapplyr
#Function to calculate slope
slop_e <- function(z) coef(lm(b ~ a, as.data.frame(z)))[[2]]
#Apply function
z2$slope <- rollapplyr(zoo(z2), 7, slop_e , by.column = FALSE, fill = NA, align = "right")
z2
a b mean_a slope
1 1 21 NA NA
2 2 22 NA NA
3 3 23 NA NA
4 4 24 NA NA
5 5 25 NA NA
6 6 26 NA NA
7 7 27 4 1
8 8 28 5 1
9 9 29 6 1
10 10 30 7 1
11 11 31 8 1
12 12 32 9 1
13 13 33 10 1
14 14 34 11 1
15 15 35 12 1
16 16 36 13 1
17 17 37 14 1
18 18 38 15 1
19 19 39 16 1
20 20 40 17 1