Reshape 似乎正在失去观察结果

问题描述 投票：0回答：1

我正在 dplyr 中使用 reshape 将长数据表变成宽数据表。但当我这样做时，我似乎失去了一些观察结果。名为“数据”的数据表记录了20年来6种不同鸟类的观察结果。我想通过组合每个位置/日期来使其成为一个广泛的数据表，以在调查期间未观察到每个物种时包含 0。

头（数据） 通用名称 地点.ID 观察.日期 观察.计数 1 博内利斯_鹰 L1210237 12/17/2007 1 2 北方猫头鹰 L11834228 9/3/2020 1 3 猎鹰_猎鹰 L12137171 2021 年 6 月 27 日 1 4 Saker_Falcon L1218263 2004 年 4 月 27 日 1 5 棕色_鱼_猫头鹰 L13864707 2/26/2021 1 6 博内利斯_鹰 L16000115 8/6/2021 2

表（数据$COMMON.NAME，数据$OBSERVATION.COUNT）

                      1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  17  20  21  22  26  27  28  31  35  39  40  41  42  50  51  60  62  64  94 100

博内利斯_鹰 137 51 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 北方猫头鹰 18 2 2 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 棕色_鱼_猫头鹰 51 29 11 6 4 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 大鸨 38 13 7 9 5 2 2 6 2 5 1 3 0 2 2 1 3 1 1 2 3 1 1 0 1 1 1 1 1 1 1 1 1 1 0 Lanner_Falcon 56 12 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pin_tailed_沙鸡 17 7 2 6 2 4 3 5 1 0 0 1 1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 Saker_Falcon 61 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

我总共应该有 69 只隼隼（61:1、2:2 和 1:4）

然后我使用 LOCALITY.ID 和 OBSERVATION.DATE 重塑数据表，并使用 time.var“COMMON.NAME” 获取所有 6 个物种的宽数据表：

d_sens = reshape(data, idvar=c('LOCALITY.ID','OBSERVATION.DATE'), timevar = 'COMMON.NAME', Direction='wide')

最后，我将 NA 替换为 0

colnames(d_sens) = c('location','date','BE','BO','SF','BFO','GB','PS','LF') 
d_sens$BE[is.na(d_sens$BE)] = 0
d_sens$BO[is.na(d_sens$BO)] = 0
d_sens$SF[is.na(d_sens$SF)] = 0
d_sens$BFO[is.na(d_sens$BFO)] = 0
d_sens$GB[is.na(d_sens$GB)] = 0
d_sens$PS[is.na(d_sens$PS)] = 0
d_sens$LF[is.na(d_sens$LF)] = 0

头（d_sens） 地点 日期 BE BO SF BFO GB PS LF 1 L1210237 2007年12月17日 1 0 0 0 0 0 0 2 L11834228 2020年9月3日 0 1 0 0 0 0 0 3 L12137171 2021年6月27日 0 0 1 0 0 0 0 4 L1218263 2004年4月27日 0 0 1 0 0 0 0 5 L13864707 2/26/2021 0 0 0 1 0 0 0 6 L16000115 2021 年 8 月 6 日 2 0 0 0 0 0 0

这就是我想要的数据表类型。

表（d_sens$SF）

0 1 2 4 545 57 2 1

当我比较 Saker Falcon 观测次数（在宽“d_sens”和长“data”文件之间）时，我丢失了 4 次观测到的次数 1。我的“数据”中没有任何缺失值。 任何建议将不胜感激。

我尝试使用 dplyr reshape 命令：

d_sens = reshape(data, idvar=c('LOCALITY.ID','OBSERVATION.DATE'), timevar = 'COMMON.NAME', Direction='wide')

d_sens 看起来像我想要的表类型，但缺少值。

我还尝试了 tidyrivot_wider 函数（在之前的帖子中建议过），但我无法让它工作。

数据%>% hub_wider(names_from=c(LOCALITY.ID,OBSERVATION.DATE),values_from=OBSERVATION.COUNT)

但是我收到这个警告： 警告信息： 

OBSERVATION.COUNT
中的值不是唯一标识的；输出将包含列表列。

pivot-table reshape
1个回答
0
投票

这可能是原始数据中存在重复项的问题。 例如，对于同一地点、同一天和同一货币：

COMMON.NAME     LOCALITY.ID OBSERVATION.DATE    OBSERVATION.COUNT
Bonellis_Eagle  L1218263    03/01/2023          1
Bonellis_Eagle  L1218263    03/01/2023          3

  • Reshape
    旋转时将仅保留第一个元素。
  • Pivot_wider
    将抛出错误（“XXX 中的值不是唯一的 已识别”）。另外，当您 称之为。

假设“data”是数据框的名称，在旋转之前，您可以使用以下命令删除重复项：

data = data %>%
  group_by(LOCALITY.ID, OBSERVATION.DATE, COMMON.NAME) %>%
  summarise(OBSERVATION.COUNT = n(),.groups = "drop")

或者你可以直接使用 

pivot_wider
来解决这个问题，并以 
value_fn=sum
作为参数。

玩具数据集：

data=structure(list(COMMON.NAME = c("Bonellis_Eagle", "Lanner_Falcon", 
"Saker_Falcon", "Great_Bustard", "Brown_Fish_Owl", "Pin_tailed_Sandgrouse", 
"Boreal_Owl", "Brown_Fish_Owl", "Lanner_Falcon", "Lanner_Falcon", 
"Brown_Fish_Owl", "Great_Bustard", "Saker_Falcon", "Saker_Falcon", 
"Brown_Fish_Owl", "Boreal_Owl", "Lanner_Falcon", "Bonellis_Eagle", 
"Lanner_Falcon", "Saker_Falcon", "Saker_Falcon", "Brown_Fish_Owl", 
"Bonellis_Eagle", "Pin_tailed_Sandgrouse", "Great_Bustard", "Pin_tailed_Sandgrouse", 
"Saker_Falcon", "Great_Bustard", "Boreal_Owl", "Brown_Fish_Owl", 
"Lanner_Falcon", "Bonellis_Eagle", "Bonellis_Eagle", "Great_Bustard", 
"Lanner_Falcon", "Boreal_Owl", "Bonellis_Eagle", "Lanner_Falcon", 
"Brown_Fish_Owl", "Boreal_Owl", "Saker_Falcon", "Boreal_Owl", 
"Pin_tailed_Sandgrouse", "Brown_Fish_Owl", "Saker_Falcon", "Bonellis_Eagle", 
"Pin_tailed_Sandgrouse", "Boreal_Owl", "Lanner_Falcon", "Lanner_Falcon", 
"Lanner_Falcon", "Pin_tailed_Sandgrouse", "Pin_tailed_Sandgrouse", 
"Boreal_Owl", "Lanner_Falcon", "Bonellis_Eagle", "Bonellis_Eagle", 
"Brown_Fish_Owl", "Pin_tailed_Sandgrouse", "Boreal_Owl", "Lanner_Falcon", 
"Saker_Falcon", "Pin_tailed_Sandgrouse", "Saker_Falcon", "Great_Bustard", 
"Saker_Falcon", "Boreal_Owl", "Saker_Falcon", "Great_Bustard", 
"Saker_Falcon", "Brown_Fish_Owl", "Pin_tailed_Sandgrouse", "Boreal_Owl", 
"Lanner_Falcon", "Pin_tailed_Sandgrouse", "Bonellis_Eagle", "Boreal_Owl", 
"Great_Bustard", "Saker_Falcon", "Bonellis_Eagle", "Pin_tailed_Sandgrouse", 
"Pin_tailed_Sandgrouse"), LOCALITY.ID = c("L1218263", "L11834228", 
"L12137171", "L1210237", "L16000115", "L13864707", "L1210237", 
"L11834228", "L16000115", "L1210237", "L1210237", "L16000115", 
"L16000115", "L13864707", "L16000115", "L12137171", "L13864707", 
"L16000115", "L1210237", "L1210237", "L1210237", "L13864707", 
"L13864707", "L11834228", "L12137171", "L1218263", "L11834228", 
"L13864707", "L13864707", "L1210237", "L13864707", "L1210237", 
"L12137171", "L1210237", "L1210237", "L1210237", "L16000115", 
"L1210237", "L11834228", "L1218263", "L1218263", "L1210237", 
"L13864707", "L12137171", "L13864707", "L11834228", "L1218263", 
"L1210237", "L16000115", "L1218263", "L13864707", "L1210237", 
"L1218263", "L13864707", "L16000115", "L1210237", "L11834228", 
"L12137171", "L16000115", "L16000115", "L13864707", "L12137171", 
"L12137171", "L13864707", "L13864707", "L16000115", "L11834228", 
"L1210237", "L1210237", "L1210237", "L11834228", "L12137171", 
"L12137171", "L16000115", "L13864707", "L12137171", "L11834228", 
"L1218263", "L11834228", "L12137171", "L11834228", "L11834228"
), OBSERVATION.DATE = c("03/01/2023", "06/01/2023", "05/01/2023", 
"06/01/2023", "07/01/2023", "06/01/2023", "06/01/2023", "06/01/2023", 
"03/01/2023", "07/01/2023", "01/01/2023", "06/01/2023", "02/01/2023", 
"02/01/2023", "03/01/2023", "01/01/2023", "02/01/2023", "05/01/2023", 
"06/01/2023", "03/01/2023", "02/01/2023", "07/01/2023", "02/01/2023", 
"03/01/2023", "06/01/2023", "01/01/2023", "02/01/2023", "01/01/2023", 
"02/01/2023", "06/01/2023", "05/01/2023", "06/01/2023", "03/01/2023", 
"03/01/2023", "04/01/2023", "01/01/2023", "04/01/2023", "03/01/2023", 
"04/01/2023", "04/01/2023", "07/01/2023", "07/01/2023", "03/01/2023", 
"06/01/2023", "01/01/2023", "01/01/2023", "02/01/2023", "05/01/2023", 
"06/01/2023", "04/01/2023", "01/01/2023", "01/01/2023", "03/01/2023", 
"03/01/2023", "02/01/2023", "02/01/2023", "06/01/2023", "07/01/2023", 
"01/01/2023", "04/01/2023", "07/01/2023", "04/01/2023", "05/01/2023", 
"04/01/2023", "05/01/2023", "01/01/2023", "04/01/2023", "05/01/2023", 
"05/01/2023", "06/01/2023", "02/01/2023", "04/01/2023", "06/01/2023", 
"04/01/2023", "02/01/2023", "05/01/2023", "02/01/2023", "01/01/2023", 
"05/01/2023", "04/01/2023", "06/01/2023", "01/01/2023"), OBSERVATION.COUNT = c(1, 
5, 2, 2, 4, 2, 5, 1, 1, 2, 0, 1, 1, 5, 3, 3, 1, 3, 1, 1, 0, 3, 
0, 0, 3, 5, 5, 5, 4, 2, 5, 4, 1, 4, 5, 1, 3, 4, 5, 0, 0, 0, 4, 
2, 5, 0, 2, 2, 0, 1, 0, 5, 3, 3, 3, 2, 0, 4, 4, 2, 1, 5, 0, 2, 
4, 1, 4, 2, 2, 3, 0, 5, 1, 4, 3, 2, 1, 3, 4, 0, 1, 2)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -82L))

旋转（+设置列名称、删除 NA、对 df 进行排序）。

library(dplyr)
library(tidyr)
output = data %>%
  pivot_wider(names_from = 'COMMON.NAME',values_from = 'OBSERVATION.COUNT',values_fn = sum) %>%
  setNames(c('location','date','BE','BO','SF','BFO','GB','PS','LF')) %>% 
  mutate(across(everything(),~replace_na(.x,0))) %>% 
  arrange(location,date)

输出：

# A tibble: 38 × 9
   location  date          BE    BO    SF   BFO    GB    PS    LF
   <chr>     <chr>      <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 L11834228 01/01/2023     0     0     0     0     0     2     0
 2 L11834228 02/01/2023     0     0     5     0     0     0     1
 3 L11834228 03/01/2023     0     0     0     0     0     0     0
 4 L11834228 04/01/2023     0     0     0     0     5     0     4
 5 L11834228 05/01/2023     0     0     4     0     0     0     0
 6 L11834228 06/01/2023     0     5     0     0     1     1     0
 7 L1210237  01/01/2023     0     0     0     0     0     5     1
 8 L1210237  02/01/2023     2     0     0     0     0     0     0
 9 L1210237  03/01/2023     0     4     1     4     0     0     0
10 L1210237  04/01/2023     0     5     0     0     0     0     0
# ℹ 28 more rows
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