我尝试了多种方法通过匹配另一个数据帧或向量来对数据帧（IMD15）进行子集化。我确信它简单！在发布之前我一直在找几个小时，但我对R很新，我似乎无法找到解决方案:(
## Import the English Indices of Deprivation 2015 - LSOA Level
IMD15 <- read.csv(url("https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/467774/File_7_ID_2015_All_ranks__deciles_and_scores_for_the_Indices_of_Deprivation__and_population_denominators.csv"))
LSOA2011 <- read.csv("LSOA2011.csv")
LSOA2011.csv是20个LSOA代码（见下文）我想用来查询IMD15数据。
（“E01019602”，“E01019614”，“E01019615”，“E01019631”，“E01019600”， “E01019604”，“E01019606”， “E01019618”，“E01019599”，“E01019601”，“E01019613”，“E01019617”，“E01019632”，“E01019635”， “E01019636”，“E01019611”，“E01019597”，“E01019737”，“E01029801”，“E01029817”）
## Format the column names
names(IMD15) [1] <- "LSOA code (2011)"
names(IMD15) [2] <- "LSOA name (2011)"
names(IMD15) [3] <- "Local Authority District code (2013)"
names(IMD15) [4] <- "Local Authority District name (2013)"
names(IMD15) [5] <- "Index of Multiple Deprivation (IMD) Score"
names(IMD15) [6] <- "Index of Multiple Deprivation (IMD) Rank (where 1 is most deprived)"
names(IMD15) [7] <- "Index of Multiple Deprivation (IMD) Decile (where 1 is most deprived 10% of LSOAs)"
## Subset the data columns
IMD15 <- IMD15[ , 1:7]
> str(IMD15)
'data.frame': 32844 obs. of 7 variables:
$ LSOA code (2011) : Factor w/ 32844 levels "E01000001","E01000002",..: 30557 30558 30559 30560 30578 30582 30546 30547 30548 30573 ...
$ LSOA name (2011) : Factor w/ 32844 levels "Adur 001A","Adur 001B",..: 1 2 3 4 5 6 7 8 9 10 ...
$ Local Authority District code (2013) : Factor w/ 326 levels "E06000001","E06000002",..: 241 241 241 241 241 241 241 241 241 241 ...
$ Local Authority District name (2013) : Factor w/ 326 levels "Adur","Allerdale",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Index of Multiple Deprivation (IMD) Score : num 12.4 28.6 11.7 16.4 18.3 ...
$ Index of Multiple Deprivation (IMD) Rank (where 1 is most deprived) : int 21352 8864 22143 17252 15643 21176 27934 28249 27569 18352 ...
$ Index of Multiple Deprivation (IMD) Decile (where 1 is most deprived 10% of LSOAs): int 7 3 7 6 5 7 9 9 9 6 ...
> str(LSOA2011)
'data.frame': 20 obs. of 1 variable:
$ LSOA2011: Factor w/ 20 levels "E01019597","E01019599",..: 5 10 11 14 3 6 7 13 2 4 ...
当我尝试方法（a）时
IMD15a <- IMD15[c("E01019602", "E01019614", "E01019615", "E01019631", "E01019600", "E01019604", "E01019606",
"E01019618", "E01019599", "E01019601", "E01019613", "E01019617", "E01019632", "E01019635",
"E01019636", "E01019611", "E01019597", "E01019737", "E01029801", "E01029817"), ]
结果是NA值（7个变量的20个obs）
> str(IMD15a)
'data.frame': 20 obs. of 7 variables:
$ LSOA code (2011) : Factor w/ 32844 levels "E01000001","E01000002",..: NA NA NA NA NA NA NA NA NA NA ...
$ LSOA name (2011) : Factor w/ 32844 levels "Adur 001A","Adur 001B",..: NA NA NA NA NA NA NA NA NA NA ...
$ Local Authority District code (2013) : Factor w/ 326 levels "E06000001","E06000002",..: NA NA NA NA NA NA NA NA NA NA ...
$ Local Authority District name (2013) : Factor w/ 326 levels "Adur","Allerdale",..: NA NA NA NA NA NA NA NA NA NA ...
$ Index of Multiple Deprivation (IMD) Score : num NA NA NA NA NA NA NA NA NA NA ...
$ Index of Multiple Deprivation (IMD) Rank (where 1 is most deprived) : int NA NA NA NA NA NA NA NA NA NA ...
$ Index of Multiple Deprivation (IMD) Decile (where 1 is most deprived 10% of LSOAs): int NA NA NA NA NA NA NA NA NA NA ...
或方法（b）
IMD15b <- cbind(IMD15[match(names(LSOA2011), IMD15$`LSOA code (2011)` ),], LSOA2011)
这也导致NA值（8个变量的20个obs）
> str(IMD15b)
'data.frame': 20 obs. of 8 variables:
$ LSOA code (2011) : Factor w/ 32844 levels "E01000001","E01000002",..: NA NA NA NA NA NA NA NA NA NA ...
$ LSOA name (2011) : Factor w/ 32844 levels "Adur 001A","Adur 001B",..: NA NA NA NA NA NA NA NA NA NA ...
$ Local Authority District code (2013) : Factor w/ 326 levels "E06000001","E06000002",..: NA NA NA NA NA NA NA NA NA NA ...
$ Local Authority District name (2013) : Factor w/ 326 levels "Adur","Allerdale",..: NA NA NA NA NA NA NA NA NA NA ...
$ Index of Multiple Deprivation (IMD) Score : num NA NA NA NA NA NA NA NA NA NA ...
$ Index of Multiple Deprivation (IMD) Rank (where 1 is most deprived) : int NA NA NA NA NA NA NA NA NA NA ...
$ Index of Multiple Deprivation (IMD) Decile (where 1 is most deprived 10% of LSOAs): int NA NA NA NA NA NA NA NA NA NA ...
$ LSOA2011 : Factor w/ 20 levels "E01019597","E01019599",..: 5 10 11 14 3 6 7 13 2 4 ...
答案 0 :(得分：1)
试试这个：
LSOA <- ("E01019602", "E01019614", "E01019615", "E01019631", "E01019600", "E01019604", "E01019606", "E01019618", "E01019599", "E01019601", "E01019613", "E01019617", "E01019632", "E01019635", "E01019636", "E01019611", "E01019597", "E01019737", "E01029801", "E01029817")
df <- IMD15[IMD15[['LSOA code (2011)']] %in% LSOA, ]
str(df)
'data.frame': 20 obs. of 7 variables:
$ LSOA code (2011) : Factor w/ 32844 levels "E01000001","E01000002",..: 19057 19069 19070 19086 19055 19059 19061 19073 19054 19056 ...
$ LSOA name (2011) : Factor w/ 32844 levels "Adur 001A","Adur 001B",..: 8566 8567 8568 8569 8570 8571 8572 8573 8574 8575 ...
$ Local Authority District code (2013) : Factor w/ 326 levels "E06000001","E06000002",..: 75 75 75 75 75 75 75 75 75 75 ...
$ Local Authority District name (2013) : Factor w/ 326 levels "Adur","Allerdale",..: 77 77 77 77 77 77 77 77 77 77 ...
$ Index of Multiple Deprivation (IMD) Score : num 10.19 6.55 7.83 8.22 8.62 ...
$ Index of Multiple Deprivation (IMD) Rank (where 1 is most deprived) : int 23963 28526 26942 26470 25943 30038 31052 24720 28159 20152 ...
$ Index of Multiple Deprivation (IMD) Decile (where 1 is most deprived 10% of LSOAs): int 8 9 9 9 8 10 10 8 9 7 ...