R 中死亡率指标的 7 天滚动平均值
7 day rolling average of mortality rate metric in R
我有一个医院患者的数据集，包括他们的入院日期和结果（死亡或出院）。 对于每一天，我想计算死亡率的 7 天滚动平均值。具体来说，我希望代码能够
a) 添加没有患者入院的缺失日期（到目前为止，我一直手动完成 --> 是在此处创建第二个数据集的最佳选择，left_join()?）
b) 对于每一天，确定在过去 7 天内入院的所有患者（因此过去 6 天加上手头的具体日期）
c) 根据结果计算死亡率（=（死亡患者数/所有患者数）对于这 7 天内入院的患者window
示例：如果我查看 4 月 8 日，我想知道这一天和之前六天收治的患者有哪些。然后我想让代码意识到该样本中有 x 个死亡患者和 y 个出院患者，并根据 x/x+y.
计算死亡率
之前我将患者分配到第 1 周、第 2 周、第 3 周等，并使用 dplyr 的 group_by() 和 summarize() 来计算每周的死亡率。现在，我必须做一个滚动平均数，但我不知道该怎么做。我使用 R 已经有一段时间了，但有时仍然觉得自己像个初学者 ://
这是一些数据：
structure(list(Summary = c("Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Dead", "Discharged", "Discharged",
"Discharged", "Dead", "Dead", "Dead", "Dead", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Dead", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Dead", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Dead", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Dead", "Discharged", "Discharged", "Dead", "Discharged",
"Discharged", "Discharged", "Dead", "Dead", "Discharged", "Discharged",
"Dead", "Discharged", "Dead", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Dead",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Dead", "Discharged", "Discharged", "Dead", "Discharged", "Discharged",
"Dead", "Dead", "Discharged", "Discharged", "Discharged", "Dead",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Dead", "Discharged", "Discharged", "Dead", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Dead", "Discharged", "Dead", "Dead", "Dead", "Discharged", "Discharged",
"Discharged", "Dead", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Dead", "Discharged", "Dead", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Dead", "Dead", "Discharged", "Discharged",
"Discharged", "Dead", "Discharged", "Dead", "Discharged", "Discharged",
"Dead", "Discharged", "Dead", "Discharged", "Discharged", "Dead",
"Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Dead", "Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Discharged", "Discharged", "Dead", "Discharged", "Dead", "Dead",
"Discharged", "Dead", "Dead", "Discharged", "Discharged", "Dead",
"Dead", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Dead", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Dead", "Discharged", "Dead", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Dead", "Discharged", "Dead", "Discharged", "Discharged", "Discharged",
"Dead", "Dead", "Dead", "Discharged", "Discharged", "Dead", "Dead",
"Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Dead", "Dead", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Dead",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Discharged", "Dead", "Discharged", "Dead", "Discharged", "Dead",
"Dead", "Discharged", "Discharged", "Discharged", "Dead", "Dead",
"Discharged", "Dead", "Dead", "Discharged", "Dead", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Dead",
"Discharged", "Discharged", "Discharged", "Discharged", "Dead",
"Dead", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Dead", "Discharged", "Discharged",
"Discharged", "Discharged", "Dead", "Dead", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Dead", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Dead",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Dead", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Dead",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Dead", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Dead", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Dead", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Dead", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged"), Admission = structure(c(1578586420.224,
1580147094.528, 1580467041.28, 1580482376.704, 1581576565.76,
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1602851124.224, 1602856629.248, 1602964501.504, 1602974594.048,
1603078140.928), class = c("POSIXct", "POSIXt"), tzone = "UTC")), row.names = c(NA,
-398L), class = c("tbl_df", "tbl", "data.frame"))
这是一个data.table方法。代码的解释在代码的注释中
library( data.table )
#set data to data.table format
setDT( mydata )
#summarise by day
DT <- mydata[, .(total = .N), by = .(date = as.IDate( Admission ), Summary ) ]
#cast to wide
DT <- dcast(DT, date ~ Summary, value.var = "total", fill = 0 )
#create final based on min/max data in DT
final <- data.table( date = seq( min( DT$date ), max( DT$date ), by = 1 ) )
#initialise columns with zero-value
final[, `:=`( Dead = 0, Discharged = 0 ) ]
#update column values based on date
final[ DT, `:=`( Dead = i.Dead, Discharged = i.Discharged ), on = .(date) ][]
#calculate rolling 7 day mortality-rate
final[, mortality.rate := frollsum( Dead, n = 7 ) / ( frollsum( (Dead + Discharged), n = 7 ) ) ]
这里有一个 tidyverse 方法。我也在使用 zoo 包。
正在计算每天的入院人数和死亡人数
library(tidyverse)
summaries <- df %>%
mutate(date = as.Date(Admission)) %>%
group_by(date) %>%
summarise(total_adm = n(),
deaths = sum(Summary == "Dead"))
summaries
#> # A tibble: 129 x 3
#> date total_adm deaths
#> * <date> <int> <int>
#> 1 2020-01-09 1 0
#> 2 2020-01-27 1 0
#> 3 2020-01-31 2 0
#> 4 2020-02-13 1 0
#> 5 2020-02-21 1 1
#> 6 2020-02-22 1 0
#> 7 2020-02-23 1 0
#> 8 2020-02-24 1 0
#> 9 2020-02-28 1 1
#> 10 2020-03-01 1 1
#> # … with 119 more rows
计算滚动总和和死亡率
对于日期，我先创建一个序列，然后 left_join()。在 rollsum() 中使用 align = "right" 意味着你得到前六天。
# sequence fro min to max date
date_seq <- seq.Date(from = min(summaries$date),
to = max(summaries$date),
by = 1)
tibble(date = date_seq) %>%
left_join(summaries) %>%
# Calculating rolling sums for admission and deaths, and mortality rate
mutate(
adm_7_days = zoo::rollsum(total_adm, k = 7, fill = NA, align = "right", na.rm = TRUE),
deaths_7_days = zoo::rollsum(deaths, k = 7, fill = NA, align = "right", na.rm = TRUE),
mortality_rate = deaths_7_days/adm_7_days
) %>%
print(n = 20)
#> Joining, by = "date"
#> # A tibble: 285 x 6
#> date total_adm deaths adm_7_days deaths_7_days mortality_rate
#> <date> <int> <int> <int> <int> <dbl>
#> 1 2020-01-09 1 0 NA NA NA
#> 2 2020-01-10 NA NA NA NA NA
#> 3 2020-01-11 NA NA NA NA NA
#> 4 2020-01-12 NA NA NA NA NA
#> 5 2020-01-13 NA NA NA NA NA
#> 6 2020-01-14 NA NA NA NA NA
#> 7 2020-01-15 NA NA 1 0 0
#> 8 2020-01-16 NA NA 0 0 NaN
#> 9 2020-01-17 NA NA 0 0 NaN
#> 10 2020-01-18 NA NA 0 0 NaN
#> 11 2020-01-19 NA NA 0 0 NaN
#> 12 2020-01-20 NA NA 0 0 NaN
#> 13 2020-01-21 NA NA 0 0 NaN
#> 14 2020-01-22 NA NA 0 0 NaN
#> 15 2020-01-23 NA NA 0 0 NaN
#> 16 2020-01-24 NA NA 0 0 NaN
#> 17 2020-01-25 NA NA 0 0 NaN
#> 18 2020-01-26 NA NA 0 0 NaN
#> 19 2020-01-27 1 0 1 0 0
#> 20 2020-01-28 NA NA 1 0 0
#> # … with 265 more rows
这里包runner也有帮助
library(dplyr)
library(tidyr)
library(runner)
df %>%
group_by(date = as.Date(strptime(Admission, format = "%Y-%m-%d"))) %>%
pivot_wider(id_cols = date,
names_from = Summary,
values_from = Admission,
values_fn = length,
values_fill = 0) %>%
ungroup() %>%
complete(date = seq.Date(min(.$date), max(.$date), by = 'day'),
fill = list(Discharged = 0, Dead = 0)) %>%
mutate(Total = Discharged + Dead) %>%
mutate(mortality_rate = sum_run(Dead, k = 7)/sum_run(Total, k = 7))
# A tibble: 285 x 5
date Discharged Dead Total mortality_rate
<date> <dbl> <dbl> <dbl> <dbl>
1 2020-01-09 1 0 1 0
2 2020-01-10 0 0 0 0
3 2020-01-11 0 0 0 0
4 2020-01-12 0 0 0 0
5 2020-01-13 0 0 0 0
6 2020-01-14 0 0 0 0
7 2020-01-15 0 0 0 0
8 2020-01-16 0 0 0 NaN
9 2020-01-17 0 0 0 NaN
10 2020-01-18 0 0 0 NaN
# ... with 275 more rows
我有一个医院患者的数据集，包括他们的入院日期和结果（死亡或出院）。 对于每一天，我想计算死亡率的 7 天滚动平均值。具体来说，我希望代码能够 a) 添加没有患者入院的缺失日期（到目前为止，我一直手动完成 --> 是在此处创建第二个数据集的最佳选择，left_join()?） b) 对于每一天，确定在过去 7 天内入院的所有患者（因此过去 6 天加上手头的具体日期） c) 根据结果计算死亡率（=（死亡患者数/所有患者数）对于这 7 天内入院的患者window
示例：如果我查看 4 月 8 日，我想知道这一天和之前六天收治的患者有哪些。然后我想让代码意识到该样本中有 x 个死亡患者和 y 个出院患者，并根据 x/x+y.
计算死亡率之前我将患者分配到第 1 周、第 2 周、第 3 周等，并使用 dplyr 的 group_by() 和 summarize() 来计算每周的死亡率。现在，我必须做一个滚动平均数，但我不知道该怎么做。我使用 R 已经有一段时间了，但有时仍然觉得自己像个初学者 ://
这是一些数据：
structure(list(Summary = c("Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Dead", "Discharged", "Discharged",
"Discharged", "Dead", "Dead", "Dead", "Dead", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Dead", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Dead", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Dead", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Dead", "Discharged", "Discharged", "Dead", "Discharged",
"Discharged", "Discharged", "Dead", "Dead", "Discharged", "Discharged",
"Dead", "Discharged", "Dead", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Dead",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Dead", "Discharged", "Discharged", "Dead", "Discharged", "Discharged",
"Dead", "Dead", "Discharged", "Discharged", "Discharged", "Dead",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Dead", "Discharged", "Discharged", "Dead", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Dead", "Discharged", "Dead", "Dead", "Dead", "Discharged", "Discharged",
"Discharged", "Dead", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Dead", "Discharged", "Dead", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Discharged", "Discharged", "Discharged",
"Discharged", "Discharged", "Dead", "Dead", "Discharged", "Discharged",
"Discharged", "Dead", "Discharged", "Dead", "Discharged", "Discharged",
"Dead", "Discharged", "Dead", "Discharged", "Discharged", "Dead",
"Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Dead", "Discharged", "Discharged", "Discharged", "Dead", "Discharged",
"Discharged", "Discharged", "Dead", "Discharged", "Dead", "Dead",
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这是一个data.table方法。代码的解释在代码的注释中
library( data.table )
#set data to data.table format
setDT( mydata )
#summarise by day
DT <- mydata[, .(total = .N), by = .(date = as.IDate( Admission ), Summary ) ]
#cast to wide
DT <- dcast(DT, date ~ Summary, value.var = "total", fill = 0 )
#create final based on min/max data in DT
final <- data.table( date = seq( min( DT$date ), max( DT$date ), by = 1 ) )
#initialise columns with zero-value
final[, `:=`( Dead = 0, Discharged = 0 ) ]
#update column values based on date
final[ DT, `:=`( Dead = i.Dead, Discharged = i.Discharged ), on = .(date) ][]
#calculate rolling 7 day mortality-rate
final[, mortality.rate := frollsum( Dead, n = 7 ) / ( frollsum( (Dead + Discharged), n = 7 ) ) ]
这里有一个 tidyverse 方法。我也在使用 zoo 包。
正在计算每天的入院人数和死亡人数
library(tidyverse)
summaries <- df %>%
mutate(date = as.Date(Admission)) %>%
group_by(date) %>%
summarise(total_adm = n(),
deaths = sum(Summary == "Dead"))
summaries
#> # A tibble: 129 x 3
#> date total_adm deaths
#> * <date> <int> <int>
#> 1 2020-01-09 1 0
#> 2 2020-01-27 1 0
#> 3 2020-01-31 2 0
#> 4 2020-02-13 1 0
#> 5 2020-02-21 1 1
#> 6 2020-02-22 1 0
#> 7 2020-02-23 1 0
#> 8 2020-02-24 1 0
#> 9 2020-02-28 1 1
#> 10 2020-03-01 1 1
#> # … with 119 more rows
计算滚动总和和死亡率
对于日期，我先创建一个序列，然后 left_join()。在 rollsum() 中使用 align = "right" 意味着你得到前六天。
# sequence fro min to max date
date_seq <- seq.Date(from = min(summaries$date),
to = max(summaries$date),
by = 1)
tibble(date = date_seq) %>%
left_join(summaries) %>%
# Calculating rolling sums for admission and deaths, and mortality rate
mutate(
adm_7_days = zoo::rollsum(total_adm, k = 7, fill = NA, align = "right", na.rm = TRUE),
deaths_7_days = zoo::rollsum(deaths, k = 7, fill = NA, align = "right", na.rm = TRUE),
mortality_rate = deaths_7_days/adm_7_days
) %>%
print(n = 20)
#> Joining, by = "date"
#> # A tibble: 285 x 6
#> date total_adm deaths adm_7_days deaths_7_days mortality_rate
#> <date> <int> <int> <int> <int> <dbl>
#> 1 2020-01-09 1 0 NA NA NA
#> 2 2020-01-10 NA NA NA NA NA
#> 3 2020-01-11 NA NA NA NA NA
#> 4 2020-01-12 NA NA NA NA NA
#> 5 2020-01-13 NA NA NA NA NA
#> 6 2020-01-14 NA NA NA NA NA
#> 7 2020-01-15 NA NA 1 0 0
#> 8 2020-01-16 NA NA 0 0 NaN
#> 9 2020-01-17 NA NA 0 0 NaN
#> 10 2020-01-18 NA NA 0 0 NaN
#> 11 2020-01-19 NA NA 0 0 NaN
#> 12 2020-01-20 NA NA 0 0 NaN
#> 13 2020-01-21 NA NA 0 0 NaN
#> 14 2020-01-22 NA NA 0 0 NaN
#> 15 2020-01-23 NA NA 0 0 NaN
#> 16 2020-01-24 NA NA 0 0 NaN
#> 17 2020-01-25 NA NA 0 0 NaN
#> 18 2020-01-26 NA NA 0 0 NaN
#> 19 2020-01-27 1 0 1 0 0
#> 20 2020-01-28 NA NA 1 0 0
#> # … with 265 more rows
这里包runner也有帮助
library(dplyr)
library(tidyr)
library(runner)
df %>%
group_by(date = as.Date(strptime(Admission, format = "%Y-%m-%d"))) %>%
pivot_wider(id_cols = date,
names_from = Summary,
values_from = Admission,
values_fn = length,
values_fill = 0) %>%
ungroup() %>%
complete(date = seq.Date(min(.$date), max(.$date), by = 'day'),
fill = list(Discharged = 0, Dead = 0)) %>%
mutate(Total = Discharged + Dead) %>%
mutate(mortality_rate = sum_run(Dead, k = 7)/sum_run(Total, k = 7))
# A tibble: 285 x 5
date Discharged Dead Total mortality_rate
<date> <dbl> <dbl> <dbl> <dbl>
1 2020-01-09 1 0 1 0
2 2020-01-10 0 0 0 0
3 2020-01-11 0 0 0 0
4 2020-01-12 0 0 0 0
5 2020-01-13 0 0 0 0
6 2020-01-14 0 0 0 0
7 2020-01-15 0 0 0 0
8 2020-01-16 0 0 0 NaN
9 2020-01-17 0 0 0 NaN
10 2020-01-18 0 0 0 NaN
# ... with 275 more rows