我有一个简单的问题,我不知道。我有一个包含两个因素(distance
)和年份(years
)的数据框。我想将每个因子的所有years
值都补0。
即从这里:
distance years area
1 NPR 3 10
2 NPR 4 20
3 NPR 7 30
4 100 1 40
5 100 5 50
6 100 6 60
获取此内容:
distance years area
1 NPR 1 0
2 NPR 2 0
3 NPR 3 10
4 NPR 4 20
5 NPR 5 0
6 NPR 6 0
7 NPR 7 30
8 100 1 40
9 100 2 0
10 100 3 0
11 100 4 0
12 100 5 50
13 100 6 60
14 100 7 0
我尝试应用expand()
函数:
library(tidyr)
library(dplyr, warn.conflicts = FALSE)
expand(df, years = 1:7)
但是这只会产生一列数据框,而不会扩展原始数据框:
# A tibble: 7 x 1
years
<int>
1 1
2 2
3 3
4 4
5 5
6 6
7 7
或expand.grid()
都不起作用:
require(utils)
expand.grid(df, years = 1:7)
Error in match.names(clabs, names(xi)) :
names do not match previous names
In addition: Warning message:
In format.data.frame(x, digits = digits, na.encode = FALSE) :
corrupt data frame: columns will be truncated or padded with NAs
是否有一种简单的方法来expand
我的数据框?以及如何根据两个类别进行扩展:distance
和uniqueLoc
?
distance <- rep(c("NPR", "100"), each = 3)
years <-c(3,4,7, 1,5,6)
area <-seq(10,60,10)
uniqueLoc<-rep(c("a", "b"), 3)
df<-data.frame(uniqueLoc, distance, years, area)
> df
uniqueLoc distance years area
1 a NPR 3 10
2 b NPR 4 20
3 a NPR 7 30
4 b 100 1 40
5 a 100 5 50
6 b 100 6 60
答案 0 :(得分:5)
您可以使用complete
函数:
complete(df, distance, years = full_seq(years, period = 1), fill = list(area = 0))
# A tibble: 14 x 3
distance years area
<fct> <dbl> <dbl>
1 100 1. 40.
2 100 2. 0.
3 100 3. 0.
4 100 4. 0.
5 100 5. 50.
6 100 6. 60.
7 100 7. 0.
8 NPR 1. 0.
9 NPR 2. 0.
10 NPR 3. 10.
11 NPR 4. 20.
12 NPR 5. 0.
13 NPR 6. 0.
14 NPR 7. 30.
或更短:
complete(df, distance, years = 1:7, fill = list(area = 0))
答案 1 :(得分:0)
将tidyr :: pivot_wider()和tidyr :: pivot_longer()组合也可以使隐式缺失值显式显示。
# Load packages
library(tidyverse)
# Your data
df <- tibble(distance = c(rep("NPR",3), rep(100, 3)),
years = c(3,4,7,1,5,6),
area = seq(10, 60, by = 10))
# Solution
df %>%
pivot_wider(names_from = years,
values_from = area) %>% # pivot_wider() makes your implicit missing values explicit
pivot_longer(2:7, names_to = "years",
values_to = "area") %>% # Turn to your desired format (long)
mutate(area = replace_na(area, 0)) # Replace missing values (NA) with 0s