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Amanda Goldberg's questions

Martin Hope
Amanda Goldberg
Asked: 2024-10-24 02:51:13 +0800 CST
  • 6

我有相机陷阱数据，我想删除可能重复的动物检测。我将间隔设置为 60 分钟（1 小时），这样如果在不到 60 分钟的时间内再次检测到同一物种的任何个体，则该个体是同一个体。我的数据是在多个区块收集的，每个区块内有多个站点。

#Data example:
Block<-c("a","a","a","a","a","b","b","b","b","b") #2 block
Site<-c("p1","p1","p2","p2","p2","p1","p1","p1","p2","p2") #two sites
Species<-c("c","c","c","c","e","d","d","c","c","c")
datetime<-c("2021-03-29 05:45:00","2021-03-29 06:40:00","2021-03-30 05:45:00","2021-03-30 07:45:00","2021-03-29 09:45:00","2021-03-29 05:45:00","2021-03-29 05:55:00","2021-03-29 08:45:00","2021-03-29 10:45:00","2021-03-30 10:59:00")
df<-data.frame(Block, Site, Species, datetime)

#what I want for the output: 

   Block Site Species            datetime
1      a   p1       c 2021-03-29 05:45:00
2      a   p2       c 2021-03-30 05:45:00
3      a   p2       c 2021-03-30 07:45:00
4      a   p2       e 2021-03-29 09:45:00
5      b   p1       d 2021-03-29 05:45:00
6      b   p1       c 2021-03-29 08:45:00
7      b   p2       c 2021-03-29 10:45:00
8      b   p2       c 2021-03-30 10:59:00

至少对我来说，棘手的部分是我需要删除在 1 小时内出现的 Block/Site/Species 的重复项（我不能只说保留一天中一小时内的第一个）。

感谢您的帮助。

Martin Hope
Amanda Goldberg
Asked: 2024-10-22 06:34:55 +0800 CST
  • 8

我正在创建一个文件，用于在模型中运行，其中包含我们多次访问的多个不同站点的物种计数。但是，并不是每次访问都会访问所有站点。

如果未访问过，我需要一列保留所有 NA，但如果该列来自已访问但未检测到物种的地块，则应为 0。以下是我当前格式的数据的迷你样本。所以我本质上需要一种方法将 p1 和 p2 列中的所有 NA 转换为 0，同时保留 p3 以供第二次访问所有 NA。我的真实数据中有数百列，所以我宁愿不必手动分配 NA 是否应转换为 0 或尽可能逐列保留 NA。

    ## creates example dataframe
library(dplyr)
library(tidyr)

Species<-c("a","b","c","a","d","d","c","a","b","c") # four species
visit<-c(1,1,2,2,1,2,2,1,2,1) #two visits
Site<-c("p1","p1","p1","p1","p2","p2","p2","p3","p2","p2") #three sites
TotalCount<-c(1,3,4,3,5,2,1,3,1,1) $number of individuals captured
df<-data.frame(Species, visit, Site, TotalCount)

df2 <- df %>% 
  dplyr::select("Species", "Site","TotalCount","visit") %>%
  pivot_wider(names_from = Site, values_from = TotalCount, 
             values_fill = NA)%>%
arrange(visit, Species)

最终的 df2 数据框应如下所示：

   Species  visit   p1    p2    p3
1  a           1     1     0     3
2  b           1     3     0     0
3  c           1     0     1     0
4  d           1     0     5     0
5  a           2     3     0    NA
6  b           2     0     1    NA
7  c           2     4     1    NA
8  d           2     0     2    NA
Martin Hope
Amanda Goldberg
Asked: 2024-09-04 09:09:26 +0800 CST
  • 5

我有几个月内每小时收集的水强度数据（以下是数据子集）。我需要第一次强度大于 900 的日期，但前提是强度大于或等于 900 的时间至少为 24 小时。此外，我需要在水位高于 900 至少 24 小时的最后一天之后，水位随后降至 900 以下的日期。然后，我需要在整个研究期间，对水位强度再次高于/低于 900 的所有后续 24 小时以上时间段重复此操作。我希望避免必须手动查看每个站点的所有数据。

## creates example dataframe
NoOfHours <- as.numeric(ymd_hms("2010-01-06 01:00:00") - ymd_hms("2010-01-01 07:00:00"))*24 
dt<-ymd_hms("2010-01-01 00:00:00") + hours(0:NoOfHours)
intensity<-c(rep(c(0),23),rep(c(901,904),12), rep(c(660,540),10), rep(c(905,3000),10), 550, rep(c(1000,1200),13),340)

df<-data.frame(dt, intensity)

因此我应该得到以下结论：

                  dt1 status
1 2010-01-01 23:00:00  start
2 2010-01-02 23:00:00   stop
3 2010-01-04 16:00:00  start
4 2010-01-05 18:00:00   stop
Martin Hope
Amanda Goldberg
Asked: 2024-08-15 00:02:27 +0800 CST
  • 6

我有一组包含每天降水总量 (Precip) 的数据。我在多个池子收集了数据。我需要计算自上次降水量超过 12 毫米以来的天数。如果当天的降雨量超过 12 毫米，则结果为“0”。

## creates example dataframe
Pool <- c("A","A","A","A","A","A","A","A","A","A",
        "B","B","B","B","B","B","B","B","B","B")
DATE <- as.Date(c("2005-01-01","2005-01-02","2005-01-03","2005-01-04","2005-01-05",
                        "2005-01-06","2005-01-07","2005-01-08","2005-01-09","2005-01-10",
                        "2005-01-01","2005-01-02","2005-01-03","2005-01-04","2005-01-05",
                        "2005-01-06","2005-01-07","2005-01-08","2005-01-09","2005-01-10"))
Precip <- c(0,0,3,18,4,3,13,8,3,0,13,0,3,13,0,3,10,8,13,0))

df <- data.frame(Pool, DATE, Precip)

我需要以下数据框：

   Pool       DATE Precip  Days_since12
1     A 2005-01-01      0     NA
2     A 2005-01-02      0     NA
3     A 2005-01-03      3     NA
4     A 2005-01-04     18     NA
5     A 2005-01-05      4     1
6     A 2005-01-06      3     2
7     A 2005-01-07     13     0
8     A 2005-01-08      8     1
9     A 2005-01-09      3     2
10    A 2005-01-10      0     3
11    B 2005-01-01     13     0
12    B 2005-01-02      0     1
13    B 2005-01-03      3     2
14    B 2005-01-04     13     0
15    B 2005-01-05      0     1
16    B 2005-01-06      3     2
17    B 2005-01-07     10     3
18    B 2005-01-08      8     4
19    B 2005-01-09     13     0
20    B 2005-01-10      0     1

我可以轻松添加一列来指示降水量是否超过 12：

df2 <- df %>%
  group_by(Pool) %>%
  mutate(pre12=ifelse(Precip>12,1,0))

但是我不确定当 pre12==1 时如何计算 DATE 和前一个日期之间的天数