Question

我有一个鸟类数据的数据框（data_test），其中同一物种的多个计数可以在同一位置（池塘或横断面）发生。我想汇总数据，因此每个地点每个物种有一个总计数。我缺少某些位置坐标的值，但我不希望这些记录从数据集中删除。这是数据：

# Create data table
location <- c("pondA","pondA","transect1","pondB","pondB","transect2","pondC","transect3","pondD","transect4")
type <- c("ground","ground","air","ground","ground","air","ground","air","ground","air")
easting <- c(NA,NA,18264,NA,NA,46378,NA,86025,NA,46295)
northing <-c(NA,NA,96022,NA,NA,85766,NA,21233,NA,23090)
species <- c("NOPI","NOPI","SCAU","GWTE","GWTE","RUDU","NOPI","GADW","NOPI","MALL")
count <- c(10,23,50,1,2,43,12,3,7,9)
data_test <- data.frame(location=location,type=type,easting=easting,northing=northing,species=species,count=count)
data_test

当我使用聚合函数时，它会删除缺少东向和北向值的记录：

aggregate(count ~ species + location + type + easting + northing, data=data_test, FUN=sum)

结果：

  species  location type easting northing count
1    GADW transect3  air   86025    21233     3
2    MALL transect4  air   46295    23090     9
3    RUDU transect2  air   46378    85766    43
4    SCAU transect1  air   18264    96022    50

使用na.action = NULL不起作用，因为求和不在东向或北向字段上运行。我想要的是：

  species  location   type easting northing count
1    NOPI     pondA ground      NA       NA    33
2    SCAU transect1    air   18264    96022    50
3    GWTE     pondB ground      NA       NA     3    
4    RUDU transect2    air   46378    85766    43
5    NOPI     pondC ground      NA       NA    12
6    GADW transect3    air   86025    21233     3
7    NOPI     pondD ground      NA       BA     7
8    MALL transect4    air   46295    23090     9

任何帮助非常感谢。

Answer 1

一条data.table路线是将剩余的名称与c()

一起使用

library(data.table)

dt <- as.data.table(data_test)
dt[, .(count = sum(count)), by = c(names(dt)[-6])]
#     location   type easting northing species count
# 1:     pondA ground      NA       NA    NOPI    33
# 2: transect1    air   18264    96022    SCAU    50
# 3:     pondB ground      NA       NA    GWTE     3
# 4: transect2    air   46378    85766    RUDU    43
# 5:     pondC ground      NA       NA    NOPI    12
# 6: transect3    air   86025    21233    GADW     3
# 7:     pondD ground      NA       NA    NOPI     7
# 8: transect4    air   46295    23090    MALL     9

Answer 2

使用aggregate一个选项是将分组列转换为＆＃39; factor＆＃39;并添加＆＃34; NA＆＃34;作为addNA

的其中一个级别

  nm1 <- setdiff(names(data_test), 'count')
  data_test[nm1] <- lapply(data_test[nm1], addNA)
  aggregate(count~., data_test, FUN=sum)
  #    location   type easting northing species count
  #1 transect3    air   86025    21233    GADW     3
  #2     pondB ground    <NA>     <NA>    GWTE     3
  #3 transect4    air   46295    23090    MALL     9
  #4     pondA ground    <NA>     <NA>    NOPI    33
  #5     pondC ground    <NA>     <NA>    NOPI    12
  #6     pondD ground    <NA>     <NA>    NOPI     7
  #7 transect2    air   46378    85766    RUDU    43
  #8 transect1    air   18264    96022    SCAU    50

或使用dplyr

 library(dplyr)
 data_test %>%
     group_by_(.dots=nm1) %>% 
     summarise(count=sum(count))

如何聚合数据框并保留缺失值

2 个答案: