问题
I have a big data.frame
of flowers and fruits in a plant for a 30 years survey. I want to add zeros (0) in some rows which represent individuals in specific months where the plant did not have flowers
or fruits
(because it is a seasonal species).
Example:
Year Month Flowers Fruits
2004 6 25 2
2004 7 48 4
2005 7 20 1
2005 8 16 1
I want to add the months that are not included with values of zero so I was thinking in a function that recognize the missing months and fill them with 0.
Thanks.
回答1:
## x is the data frame you gave in the question
x <- data.frame(
Year = c(2004, 2004, 2005, 2005),
Month = c(6, 7, 7, 8),
Flowers = c(25, 48, 20, 16),
Fruits = c(2, 4, 1, 1)
)
## y is the data frame that will provide the missing values,
## so you can replace 2004 and 2005 with whatever your desired
## time interval is
y <- expand.grid(Year = 2004:2005, Month = 1:12)
## this final step fills in missing dates and replaces NA's with zeros
library(tidyr)
x <- merge(x, y, all = TRUE) %>%
replace_na(list(Flowers = 0, Fruits = 0))
## if you don't want to use tidyr, you can alternatively do
x <- merge(x, y, all = TRUE)
x[is.na(x)] <- 0
It looks like this:
head(x, 10)
# Year Month Flowers Fruits
# 1 2004 1 0 0
# 2 2004 2 0 0
# 3 2004 3 0 0
# 4 2004 4 0 0
# 5 2004 5 0 0
# 6 2004 6 25 2
# 7 2004 7 48 4
# 8 2004 8 0 0
# 9 2004 9 0 0
# 10 2004 10 0 0
回答2:
Here is another option using expand
and left_join
library(dplyr)
library(tidyr)
expand(df1, Year, Month = 1:12) %>%
left_join(., df1) %>%
replace_na(list(Flowers=0, Fruits=0))
# Year Month Flowers Fruits
# <int> <int> <dbl> <dbl>
#1 2004 1 0 0
#2 2004 2 0 0
#3 2004 3 0 0
#4 2004 4 0 0
#5 2004 5 0 0
#6 2004 6 25 2
#7 2004 7 48 4
#8 2004 8 0 0
#9 2004 9 0 0
#10 2004 10 0 0
#.. ... ... ... ...
来源:https://stackoverflow.com/questions/37579756/adding-rows-to-data-frame-conditionally