[英]Fill in missing values (NAs) with values from another dataframe in R
How do I subset missing values in one dataframe with values from another? 如何将一个数据框中的缺失值与另一个数据框中的值子集化?
Let's say I have two datasets: 假设我有两个数据集:
dataset 1 shows the amount of food that is produced by a country each day. 数据集1显示了一个国家每天生产的食物数量。
country day tonnes of food
## 1 china 1 6
## 2 china 1 NA
## 3 china 2 2
## 4 china 2 NA
dataset2 is the average amount of food by day 数据集2是每天的平均食物量
country day average tonnes of food
## 1 china 1 6
## 3 china 2 2
How can I fill in the NAs of dataset1 with the averages from dataset2. 如何用数据集2的平均值填写数据集1的NA。
Ie IF is.na(dataset1$tonnes)
is TRUE then fill in with average for day from dataset2$averagetonnes
即如果IF
is.na(dataset1$tonnes)
为TRUE,则用数据dataset2$averagetonnes
日期进行dataset2$averagetonnes
We can use join in data.table
我们可以在
data.table
使用join
library(data.table)
setDT(df1)[df2, on =c("country", "day")][is.na(tonnes.of.food),
tonnes.of.food:= average.tonnes.of.food][, average.tonnes.of.food:=NULL][]
# country day tonnes.of.food
#1: china 1 6
#2: china 1 6
#3: china 2 2
#4: china 2 2
If I understand you correctly using the match
function will solve your problem. 如果我正确理解您的信息,则可以使用
match
功能解决您的问题。 Data: 数据:
df1 <- data.frame(country=c(rep('china1',2),rep('china2',2)),day=c(1,1,2,2),tof = c(6,NA,2,NA),stringsAsFactors = F)
df2 <- data.frame(country=c('china1','china2'),day=c(1,2),atof = c(6,2),stringsAsFactors = F)
df1
country day tof
#1 china1 1 6
#2 china1 1 NA
#3 china2 2 2
#4 china2 2 NA
This line will replace the NAs with the averages of the corresponding country of the second data.frame df2. 该行将用第二个data.frame df2对应国家/地区的平均值替换NA。 The
match
function results in a vector of positions of matches and [which(is.na(df1$tof))]
selects the indices where there is a NA
in the “tof” column. match
函数产生match
位置的向量, [which(is.na(df1$tof))]
选择“ tof”列中不存在NA
的索引。
df1$tof[is.na(df1$tof)] <- df2$atof[match(df1$country,df2$country)][which(is.na(df1$tof))]
df1
country day tof
#1 china1 1 6
#2 china1 1 6
#3 china2 2 2
#4 china2 2 2
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