自动（循环）R中的欧氏距离测量
AIM:我想自动（循环）下面的代码，而不必为每个示例手动运行它。我有一个很糟糕的习惯，就是用base写长篇大论，需要开始使用循环，我发现这很难实现 数据：我有两个数据框：一个是样本数据（样本），另一个是参考数据（ref）。 它们都包含相同的变量（x，y，z） 代码描述：对于每个样本（样本$sample\u name），我想计算它到参考数据中每个案例的欧氏距离。然后使用结果对参考数据进行重新排序，以显示在欧几里德（三维）空间中哪些点与样本数据点“最近” 我当前的代码允许我简单地替换示例名称（即“s1”），然后重新运行代码，对.csv文件的文件名进行最后一次更改。输出是参考数据列表，按最接近样本的顺序排列（在欧几里德空间中） 我想自动化这个过程（进入循环？），这样我就可以使用示例名称列表（samples$sample_name）在两个数据帧上运行它，并希望能够自动导出到.csv文件 任何帮助都将不胜感激自动（循环）R中的欧氏距离测量,r,loops,automation,export-to-csv,euclidean-distance,R,Loops,Automation,Export To Csv,Euclidean Distance,AIM:我想自动（循环）下面的代码，而不必为每个示例手动运行它。我有一个很糟糕的习惯，就是用base写长篇大论，需要开始使用循环，我发现这很难实现 数据：我有两个数据框：一个是样本数据（样本），另一个是参考数据（ref）。 它们都包含相同的变量（x，y，z） 代码描述：对于每个样本（样本$sample\u name），我想计算它到参考数据中每个案例的欧氏距离。然后使用结果对参考数据进行重新排序，以显示在欧几里德（三维）空间中哪些点与样本数据点“最近” 我当前的代码允许我简单地替换示例名称（即“s
# Reference data
country<-c("Austria","Austria","Italy","Italy","Turkey","Romania","France")
x<-c(18.881,18.881,18.929,19.139,19.008,19.083,18.883)
y<-c(15.627,15.627,15.654,15.772,15.699,15.741,15.629)
z<-c(38.597,38.597,38.842,39.409,39.048,39.224,38.740)
pb_age<-c(-106,-106,-87,-6,-55,-26,-104)
ref<-data.frame(country,x,y,z,pb_age) # Reference data
# Sample data (for euclidean measurements against Reference data)
sample_name<-c("s1","s2","s3")
x2<-c(18.694,18.729,18.731)
y2<-c(15.682,15.683,15.677)
z2<-c(38.883,38.989,38.891)
pb_age2<-c(120,97,82)
samples<-data.frame(sample_name,x2,y2,z2,pb_age2) # Sample data
colnames(samples)<-c("sample_name","x","y","z","pb_age") # To match Reference data headings
# Euclidean distance measurements
library(fields) # Need package for Euclidean distances
# THIS IS WHAT I WANT TO AUTOMATE/LOOP (BELOW)...
# Currently, I have to update the 'id' for each sample to get a result (for each sample)
id<-"s1" # Sample ID - this is simply changed so the following code can be re-run for each sample
# The code
x1<-samples[which(samples$sample_name==id),c("x","y","z")]
x2<-ref[,c("x","y","z")]
result_distance<-rdist(x1,x2) # Computing the Euclidean distance
result_distance<-as.vector(result_distance) # Saving the results as a vector
euclid_ref<-data.frame(result_distance,ref) # Creating a new data.frame adding the Euclidean distances to the original Reference data
colnames(euclid_ref)[1]<-"euclid_distance" # Updating the column name for the result
# Saving and exporting the results
results<-euclid_ref[order(euclid_ref$euclid_distance),] # Re-ordering the data.frame by the euclide distances, smallest to largest
write.csv(results, file="s1.csv") # Ideally, I want the file name to be the same as the SAMPLE id, i.e. s1, s2, s3...
country循环足够简单，但更类似R的解决方案是利用矢量化和应用函数系列：
result_distances <- data.frame(t(rdist(samples[, 2:4], ref[, 2:4])), ref)
colnames(result_distances)[1:3] <- rep("euclid_distance", 3)
# str(result_distances)
# 'data.frame': 7 obs. of 8 variables:
# $ euclid_distance: num 0.346 0.346 0.24 0.695 0.355 ...
# $ euclid_distance: num 0.424 0.424 0.25 0.594 0.286 ...
# $ euclid_distance: num 0.334 0.334 0.205 0.666 0.319 ...
# $ country : chr "Austria" "Austria" "Italy" "Italy" ...
# $ x : num 18.9 18.9 18.9 19.1 19 ...
# $ y : num 15.6 15.6 15.7 15.8 15.7 ...
# $ z : num 38.6 38.6 38.8 39.4 39 ...
# $ pb_age : num -106 -106 -87 -6 -55 -26 -104
这将创建3个文件，“s1.csv”、“s2.csv”、“s3.csv”。这里有一个循环，使用原始输入数据和代码的关键部分，计算参考数据位置的所有样本的欧氏距离。它比矢量化的apply解决方案要详细一点，但可能更容易阅读，因为它没有那么简洁和嵌套。最终输出为单个数据帧
# prepare an empty list object to store the results
output <- vector("list", length = nrow(samples))
# this is the start of the loop
for(i in seq_len(nrow(samples))){
# we can read this as 'for row i of the samples dataframe, do this...'
# get coords for sample i
sample_coords <- samples[i ,c("x","y","z")]
# get coords for all reference locations
# this line would be fine above the loop
# since it gives the same result for each
# iteration. I place it here to echo your
# original workflow
ref_coords <- ref[,c("x","y","z")]
# compute Euclidean distance and coerce to vector,
e_dist_vec <- as.vector(rdist(sample_coords, ref_coords))
# store in data frame
e_dist_ref_df <- data.frame(e_dist_vec, ref)
# update colname
colnames(e_dist_ref_df)[1] <- "euclid_distance"
# order df by euclid_distance values
results <- e_dist_ref_df[order(e_dist_ref_df$euclid_distance),]
# store results for sample i in the list
output[[i]] <- results
} # this is the end of the loop
# assign sample names to list items
names(output) <- samples$sample_name
# bind list dfs into one big data frame, not sure what the one-line equivalent in base R is
output_df <- dplyr::bind_rows(output, .id = "sample_id")
谢谢你。这是一种将结果合并到数据框中的简洁方法，用于快速重新排序/分析R-在代码方面也很紧凑！感谢这一点——这很好，也很容易理解，实际上对我理解循环和以前的解决方法有很大帮助。和您一样，用R打印输出对于快速查看最近的邻居非常有用-我经常这样做-因此非常有用！我愿意接受这两个答案都是有用的（两个答案都有各自的优点）——但我只想勾选一个@Rchaeologist不用担心，希望在以后的工作中帮助您编写其他循环！
# prepare an empty list object to store the results
output <- vector("list", length = nrow(samples))
# this is the start of the loop
for(i in seq_len(nrow(samples))){
# we can read this as 'for row i of the samples dataframe, do this...'
# get coords for sample i
sample_coords <- samples[i ,c("x","y","z")]
# get coords for all reference locations
# this line would be fine above the loop
# since it gives the same result for each
# iteration. I place it here to echo your
# original workflow
ref_coords <- ref[,c("x","y","z")]
# compute Euclidean distance and coerce to vector,
e_dist_vec <- as.vector(rdist(sample_coords, ref_coords))
# store in data frame
e_dist_ref_df <- data.frame(e_dist_vec, ref)
# update colname
colnames(e_dist_ref_df)[1] <- "euclid_distance"
# order df by euclid_distance values
results <- e_dist_ref_df[order(e_dist_ref_df$euclid_distance),]
# store results for sample i in the list
output[[i]] <- results
} # this is the end of the loop
# assign sample names to list items
names(output) <- samples$sample_name
> output
$s1
euclid_distance country x y z pb_age
3 0.2401874 Italy 18.929 15.654 38.842 -87
7 0.2428559 France 18.883 15.629 38.740 -104
1 0.3461069 Austria 18.881 15.627 38.597 -106
2 0.3461069 Austria 18.881 15.627 38.597 -106
5 0.3551197 Turkey 19.008 15.699 39.048 -55
6 0.5206563 Romania 19.083 15.741 39.224 -26
4 0.6948388 Italy 19.139 15.772 39.409 -6
$s2
euclid_distance country x y z pb_age
3 0.2499000 Italy 18.929 15.654 38.842 -87
5 0.2856186 Turkey 19.008 15.699 39.048 -55
7 0.2977129 France 18.883 15.629 38.740 -104
1 0.4241509 Austria 18.881 15.627 38.597 -106
2 0.4241509 Austria 18.881 15.627 38.597 -106
6 0.4288415 Romania 19.083 15.741 39.224 -26
4 0.5936506 Italy 19.139 15.772 39.409 -6
$s3
euclid_distance country x y z pb_age
3 0.2052657 Italy 18.929 15.654 38.842 -87
7 0.2195655 France 18.883 15.629 38.740 -104
5 0.3191583 Turkey 19.008 15.699 39.048 -55
1 0.3338203 Austria 18.881 15.627 38.597 -106
2 0.3338203 Austria 18.881 15.627 38.597 -106
6 0.4887627 Romania 19.083 15.741 39.224 -26
4 0.6661929 Italy 19.139 15.772 39.409 -6
# bind list dfs into one big data frame, not sure what the one-line equivalent in base R is
output_df <- dplyr::bind_rows(output, .id = "sample_id")
> output_df
sample_id euclid_distance country x y z pb_age
1 s1 0.2401874 Italy 18.929 15.654 38.842 -87
2 s1 0.2428559 France 18.883 15.629 38.740 -104
3 s1 0.3461069 Austria 18.881 15.627 38.597 -106
4 s1 0.3461069 Austria 18.881 15.627 38.597 -106
5 s1 0.3551197 Turkey 19.008 15.699 39.048 -55
6 s1 0.5206563 Romania 19.083 15.741 39.224 -26
7 s1 0.6948388 Italy 19.139 15.772 39.409 -6
8 s2 0.2499000 Italy 18.929 15.654 38.842 -87
9 s2 0.2856186 Turkey 19.008 15.699 39.048 -55
10 s2 0.2977129 France 18.883 15.629 38.740 -104
11 s2 0.4241509 Austria 18.881 15.627 38.597 -106
12 s2 0.4241509 Austria 18.881 15.627 38.597 -106
13 s2 0.4288415 Romania 19.083 15.741 39.224 -26
14 s2 0.5936506 Italy 19.139 15.772 39.409 -6
15 s3 0.2052657 Italy 18.929 15.654 38.842 -87
16 s3 0.2195655 France 18.883 15.629 38.740 -104
17 s3 0.3191583 Turkey 19.008 15.699 39.048 -55
18 s3 0.3338203 Austria 18.881 15.627 38.597 -106
19 s3 0.3338203 Austria 18.881 15.627 38.597 -106
20 s3 0.4887627 Romania 19.083 15.741 39.224 -26
21 s3 0.6661929 Italy 19.139 15.772 39.409 -6