通过匹配来自另一个数据帧的列来子集数据帧

Question

我已经尝试了通过与另一个数据帧或向量进行匹配来对数据帧（IMD15）进行子集化的多种方法。我相信它很简单！我一直在寻找了几个小时，然后再发布，但我很新的RI似乎无法找到一个soloution :(

## 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") 

的LSOA2011.csv是20个LSOA代码（见下文），我想用它来查询IMD15数据。

（ “E01019602”， “E01019614”， “E01019615”， “E01019631”， “E01019600”， “E01019604”， “E01019606”， “E01019618”， “E01019599”， E01019613，E01019613，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 ... 

当我尝试方法的（a）

IMD15a <- IMD15[c("E01019602", "E01019614", "E01019615", "E01019631", "E01019600", "E01019604", "E01019606", 
      "E01019618", "E01019599", "E01019601", "E01019613", "E01019617", "E01019632", "E01019635", 
      "E01019636", "E01019611", "E01019597", "E01019737", "E01029801", "E01029817"), ] 

结果为NA的值（7个变量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 ... 

或者方法（b）

IMD15b <- cbind(IMD15[match(names(LSOA2011), IMD15$`LSOA code (2011)`),], LSOA2011) 

这也导致NA值（20 OBS的8个变量）

> 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 ... 

Answer 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 ...