CODEWITHSUNDEEP
rdplyrsummary
Lonewolf
Lonewolf

Reputation: 197

Unable to create a grouped summary dataset in R

I am having trouble in creating a grouped summary statistics.

Below is the code that I'm using to create this summary dataset

library(dplyr)

#sample dataset 
D           A                 B             C        VAL        PD
Agriculture Services    Bought with Cash 01OCT2014   10      0.4435714
Agriculture Grain       Bought with Cash 01OCT2014   10      0.7266667
Agriculture Livestock   Bought with Cash 01OCT2014   10      1.1372414
Agriculture Fr, ve      Bought with Cash 01OCT2014   10      1.5170370
Agriculture Livestock   Financed         01OCT2014   76      1.1372414
Agriculture Fr, ve      Financed         01OCT2014   76      1.5170370
Agriculture Grain       Financed         01OCT2014   76      0.7266667
Agriculture Services    Financed         01OCT2014   76      0.4435714
Agriculture Services    Insurance        01OCT2014   10      0.4435714
Agriculture Livestock   Insurance        01OCT2014   10      1.1372414

groupDF<-select.other %>% 
   group_by(.dots=c("A","B","C")) %>% 
   summarize(PD=mean(PD),VAL=mean(VAL))

I'm expecting the dataset to have the mean PD and mean VAL grouped by A, B, and C 

    A       B                 C         PD      VAL     
Services  Bought with Cash   01OCT2017   1      10

Instead I am getting 

PD           VAL
0.8574816   6059877

Any help or guidance will be appreciated.

Upvotes: 1

Views: 58

Answers (1)

akrun
akrun

Reputation: 887193

We can use group_by_at if it is a string

library(dplyr)
select.other %>% 
      group_by_at(vars(c("A","B","C"))) %>% 
       summarize(PD=mean(PD),VAL=mean(VAL))
# A tibble: 10 x 5
# Groups:   A, B [10]
#   A         B                C            PD   VAL
#   <chr>     <chr>            <chr>     <dbl> <dbl>
# 1 Fr, ve    Bought with Cash 01OCT2014 1.52     10
# 2 Fr, ve    Financed         01OCT2014 1.52     76
# 3 Grain     Bought with Cash 01OCT2014 0.727    10
# 4 Grain     Financed         01OCT2014 0.727    76
# 5 Livestock Bought with Cash 01OCT2014 1.14     10
# 6 Livestock Financed         01OCT2014 1.14     76
# 7 Livestock Insurance        01OCT2014 1.14     10
# 8 Services  Bought with Cash 01OCT2014 0.444    10
# 9 Services  Financed         01OCT2014 0.444    76
#10 Services  Insurance        01OCT2014 0.444    10

or another option is to convert to symbols and then do the evaluation (!!!)

select.other %>% 
      group_by(!!! rlang::syms(c("A","B","C"))) %>% 
       summarize(PD=mean(PD),VAL=mean(VAL))

data

select.other <- structure(list(D = c("Agriculture", "Agriculture", "Agriculture", 
"Agriculture", "Agriculture", "Agriculture", "Agriculture", "Agriculture", 
"Agriculture", "Agriculture"), A = c("Services", "Grain", "Livestock", 
"Fr, ve", "Livestock", "Fr, ve", "Grain", "Services", "Services", 
"Livestock"), B = c("Bought with Cash", "Bought with Cash", "Bought with Cash", 
"Bought with Cash", "Financed", "Financed", "Financed", "Financed", 
"Insurance", "Insurance"), C = c("01OCT2014", "01OCT2014", "01OCT2014", 
"01OCT2014", "01OCT2014", "01OCT2014", "01OCT2014", "01OCT2014", 
"01OCT2014", "01OCT2014"), VAL = c(10L, 10L, 10L, 10L, 76L, 76L, 
76L, 76L, 10L, 10L), PD = c(0.4435714, 0.7266667, 1.1372414, 
1.517037, 1.1372414, 1.517037, 0.7266667, 0.4435714, 0.4435714, 
1.1372414)), class = "data.frame", row.names = c(NA, -10L))

Upvotes: 4

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