使用現有因子為具有非現有值的組分配和創建值 R dataframe

Question

我有一個巨大的甲蟲計數實驗數據集，具有以下示例結構：

species_name1 <- c("A", "A", "A", "A", "B") # two factors for name1
species_name2 <- c("a", "a", "b", "b", "c") # three factors for name2
date <- c("2021-06-02", "2021-08-20", "2021-06-15", "2021-08-20", "2021-08-20") # three date factors
number <- c("30", "30", "11", "15", "40") # number of encountered beetles for the "date"

df <- data.frame(species_name1, species_name2, date, number) # create dataframe

df$species_full_name <- gsub(" ", " ", paste(df$species_name1, df$species_name2)) # new column with merged data of the first two columns

df$date <- as.Date(df$date, format ="%Y-%m-%d") 
df$number  <- as.numeric(df$number)
df$species_name1 <- as.factor(df$species_name1)
df$species_name2 <- as.factor(df$species_name2)
df$species_full_name <- as.factor(df$species_full_name)

str(df)

總體而言，存在三個日期因素（2021-06-02、2021-06-15、2021-08-20），但並非針對每個“species_full_name”。 我需要創建一個 dataframe，其中包含“species_full_name”列因素的三個日期中的每一個。 對於“species_full_name”-在最初的 dataframe 日期 R 中不存在“日期”的因素應該在“數字”列中寫入“0”。

我找到了一個代碼，它幾乎是我的目標 dataframe 的解決方案。問題是其他列（“species_name1”和...“_name2”）將消失：

as.data.frame(xtabs(number ~ species_full_name+date, df)) # create every factor "date" for every factor "species_full_name" and give counting data in column "Freq"

我需要一個 dataframe，它類似於這個 output，但每一列都來自原始 dataframe“df”。 假設列“species_name1”和“species_name2”的值也很重要。

謝謝你的幫助！

Answer 1

您可以使用tidyr中的complete()

complete(df, species_full_name,date) %>%
  mutate(number=if_else(is.na(number),0,number))

Output：

  species_full_name date       species_name1 species_name2 number
  <fct>             <date>     <fct>         <fct>          <dbl>
1 A a               2021-06-02 A             a                 30
2 A a               2021-06-15 NA            NA                 0
3 A a               2021-08-20 A             a                 30
4 A b               2021-06-02 NA            NA                 0
5 A b               2021-06-15 A             b                 11
6 A b               2021-08-20 A             b                 15
7 B c               2021-06-02 NA            NA                 0
8 B c               2021-06-15 NA            NA                 0
9 B c               2021-08-20 B             c                 40

然而，data.table 方法會更快。 您可以按如下方式使用data.table和CJ() ：

# load library
library(data.table)

# set df as data.table
setDT(df)

# get unique values of species_full_name and date
species_full_name = unique(df$species_full_name)
date = unique(df$date)

# merge (and update number to 0 if NA, and the name1 and name2 columns)
merge(CJ(date,species_full_name),df,by=c('date','species_full_name'),all.x =  T) %>% 
  .[, number:=fifelse(is.na(number),0,as.double(number))] %>% 
  .[, c("species_name1","species_name2"):=tstrsplit(species_full_name, " ")] %>% 
  .[]

Output：

         date species_full_name species_name1 species_name2 number
       <Date>            <fctr>        <char>        <char>  <num>
1: 2021-06-02               A a             A             a     30
2: 2021-06-02               A b             A             b      0
3: 2021-06-02               B c             B             c      0
4: 2021-06-15               A a             A             a      0
5: 2021-06-15               A b             A             b     11
6: 2021-06-15               B c             B             c      0
7: 2021-08-20               A a             A             a     30
8: 2021-08-20               A b             A             b     15
9: 2021-08-20               B c             B             c     40