Noob问题。我无法弄清楚这段代码有什么问题。我试图找到在400米半径圆圈内发生的观测数量。每次观察我都有纬度和长度。我正在尝试创建一个新列，该列将显示400米半径范围内的竞争餐馆数量。我包含了我正在使用的代码的数据样本以及数据帧的STR。提前谢谢。
for (i in seq(nrow(expandedDataFrame2)))
{
# circle's centre
xcentre <- df[i,'latitude']
ycentre <- df[i,'longitude']
# checking how many restaurants lie within 400 m of the above centre, noofcloserest column will contain this value
expandedDataFrame2[i,'noofcloserest'] <- sum(
(expandedDataFrame2[,'latitude'] - xcentre)^2 +
(expandedDataFrame2[,'longitude'] - ycentre)^2
<= 400^2
) - 1
# logging part for deeper analysis
cat(i,': ')
cat((expandedDataFrame2[,'latitude'] - xcentre)^2 +
(expandedDataFrame2[,'longitude'] - ycentre)^2
<= 400^2)
cat('\n')
}
样品：
business_id restaurantType full_address open city
1 --5jkZ3-nUPZxUvtcbr8Uw Greek 1336 N Scottsdale Rd\nScottsdale, AZ 85257 1 Scottsdale
2 --BlvDO_RG2yElKu9XA1_g Sushi Bars 14870 N Northsight Blvd\nSte 103\nScottsdale, AZ 85260 1 Scottsdale
3 -_Ke8q969OAwEE_-U0qUjw Beer, Wine & Spirits 18555 N 59th Ave\nGlendale, AZ 85308 0 Glendale
4 -_npP9XdyzILAjtFfX8UAQ Vietnamese 6025 N 27th Avenue\nSte 24\nPhoenix, AZ 85073 1 Phoenix
5 -2xCV0XGD9NxfWaVwA1-DQ Pizza 9008 N 99th Ave\nPeoria, AZ 85345 1 Peoria
6 -3WVw1TNQbPBzaKCaQQ1AQ Chinese 302 E Flower St\nPhoenix, AZ 85012 1 Phoenix
review_count name longitude state stars latitude type categories1 categories2
1 11 George's Gyros Greek Grill -111.9269 AZ 4.5 33.46337 business Greek <NA>
2 37 Asian Island -111.8983 AZ 4.0 33.62146 business Sushi Bars Hawaiian
3 6 Jug 'n Barrel Wine Shop -112.1863 AZ 4.5 33.65387 business <NA> Beer, Wine & Spirits
4 15 Thao's Sandwiches -112.0739 AZ 3.0 33.44990 business Vietnamese Sandwiches
5 4 Nino's Pizzeria 2 -112.2766 AZ 4.0 33.56626 business Pizza <NA>
6 145 China Chili -112.0692 AZ 3.5 33.48585 business Chinese <NA>
categories3 categories4 categories5 categories6 categories7 categories8 categories9 categories10 isRestaurant Freq
1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> TRUE 66
2 Chinese <NA> <NA> <NA> <NA> <NA> <NA> <NA> TRUE 58
3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> TRUE 8
4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> TRUE 44
5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> TRUE 166
6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> TRUE 166
avgRev avgStar duration delta
1 31.32836 3.694030 381 days 0
2 68.62712 3.661017 690 days 0
3 34.33333 3.555556 604 days 1
4 63.22222 3.577778 1916 days 0
5 30.84431 3.482036 226 days 0
6 23.79042 3.535928 2190 days 0
数据的结构是;
str(expandeddataframe2)
'data.frame': 2833 obs. of 28 variables:
$ business_id : chr "--5jkZ3-nUPZxUvtcbr8Uw" "--BlvDO_RG2yElKu9XA1_g" "-_Ke8q969OAwEE_-U0qUjw" "-_npP9XdyzILAjtFfX8UAQ" ...
$ restaurantType: chr "Greek" "Sushi Bars" "Beer, Wine & Spirits" "Vietnamese" ...
$ full_address : chr "1336 N Scottsdale Rd\nScottsdale, AZ 85257" "14870 N Northsight Blvd\nSte 103\nScottsdale, AZ 85260" "18555 N 59th Ave\nGlendale, AZ 85308" "6025 N 27th Avenue\nSte 24\nPhoenix, AZ 85073" ...
$ open : Factor w/ 2 levels "0","1": 2 2 1 2 2 2 2 2 2 2 ...
$ city : chr "Scottsdale" "Scottsdale" "Glendale" "Phoenix" ...
$ review_count : num 11 37 6 15 4 145 255 35 7 7 ...
$ name : chr "George's Gyros Greek Grill" "Asian Island" "Jug 'n Barrel Wine Shop" "Thao's Sandwiches" ...
$ longitude : num -112 -112 -112 -112 -112 ...
$ state : chr "AZ" "AZ" "AZ" "AZ" ...
$ stars : num 4.5 4 4.5 3 4 3.5 4.5 4 2.5 4.5 ...
$ latitude : num 33.5 33.6 33.7 33.4 33.6 ...
$ type : chr "business" "business" "business" "business" ...
$ categories1 : chr "Greek" "Sushi Bars" NA "Vietnamese" ...
$ categories2 : chr NA "Hawaiian" "Beer, Wine & Spirits" "Sandwiches" ...
$ categories3 : chr NA "Chinese" NA NA ...
$ categories4 : chr NA NA NA NA ...
$ categories5 : chr NA NA NA NA ...
$ categories6 : chr NA NA NA NA ...
$ categories7 : chr NA NA NA NA ...
$ categories8 : chr NA NA NA NA ...
$ categories9 : chr NA NA NA NA ...
$ categories10 : chr NA NA NA NA ...
$ isRestaurant : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ Freq : num 66 58 8 44 166 166 98 35 45 166 ...
$ avgRev : num [1:2833(1d)] 31.3 68.6 34.3 63.2 30.8 ...
..- attr(*, "dimnames")=List of 1
.. ..$ : chr "Greek" "Sushi Bars" "Beer, Wine & Spirits" "Vietnamese" ...
$ avgStar : num [1:2833(1d)] 3.69 3.66 3.56 3.58 3.48 ...
..- attr(*, "dimnames")=List of 1
.. ..$ : chr "Greek" "Sushi Bars" "Beer, Wine & Spirits" "Vietnamese" ...
$ duration :Class 'difftime' atomic [1:2833] 381 690 604 1916 226 ...
.. ..- attr(*, "units")= chr "days"
$ delta : num 0 0 1 0 0 0 0 0 0 0 ...
答案 0 :(得分：2)
所以这是一种方法，它使用包spDistsN1(...)中的函数sp。调用您的数据框df，
library(sp)
get.dists <- function(i) {
ref.pt <- with(df[i,],c(longitude,latitude))
points <- as.matrix(with(df[-i,],cbind(longitude,latitude)))
dists <- spDistsN1(points, ref.pt, longlat=T)
return(length(which(dists<0.4)))
}
df$count <- sapply(1:nrow(df),get.dists)
spDistsN1(points, ref.pt)计算从ref.pt到points中每个点的大圆距离。如果longlat=T以km为单位返回距离。因此，函数get.dists生成从参考行到每隔一行的距离向量，然后计算有多少＆lt;使用length(which(dists<0.4)) 0.4km。使用df为sapply(...)中的每一行调用此函数。
请注意，在您的样本数据集中，没有餐厅彼此相距400米。