Execute an operation on all variables begining by the same name

General
1

I have data with a number of test. In fonction of the data, number of test can be different and I want to automate my code as much as I can. I have some steps of importations or things like that for example, and I don't know how to do to do it in fonction of my number of tests.

  memo <-paste(BITR$num_ref,BITR$prenom,BITR$nom,BITR$njf,BITR$cp,BITR$ddn,BITR$sexe,
               BITR$adresse1,BITR$adresse2,BITR$localite,
               BITR$date_test_1,BITR$type_test_1, BITR$resultat_1, BITR$resul_test_1, BITR$date_1, BITR$resul_1,
               BITR$date_test_2,BITR$type_test_2, BITR$resultat_2, BITR$resul_test_2, BITR$date_2, BITR$resul_2,
               BITR$date_test_3,BITR$type_test_3, BITR$resultat_3, BITR$resul_test_3, BITR$date_3, BITR$resul_3,
               BITR$date_test_4,BITR$type_test_4, BITR$resultat_4, BITR$resul_test_4, BITR$date_4, BITR$resul_4,
               BITR$date_test_5,BITR$type_test_5, BITR$resultat_5, BITR$resul_test_5, BITR$date_5, BITR$resul_5,
               BITR$date_test_6,BITR$type_test_6, BITR$resultat_6, BITR$resul_test_6, BITR$date_6, BITR$resul_6,
               BITR$date_test_7,BITR$type_test_7, BITR$resultat_7, BITR$resul_test_7, BITR$date_7, BITR$resul_7,
               sep="_")

data<- data.frame(id, prenom, nom, nomjf, ddn, sexe,cpNaiss,cp,ville,
                      date_testD1,type_testD1,resultatD1,result_testD1,date_D1,resul_D1,
                      date_testD2,type_testD2,resultatD2,result_testD2,date_D2,resul_D2,
                      date_testD3,type_testD3,resultatD3,result_testD3,date_D3,resul_D3,
                      date_testD4,type_testD4,resultatD4,result_testD4,date_D4,resul_D4,
                      date_testD5,type_testD5,resultatD5,result_testD5,date_D5,resul_D5,
                      date_testD6,type_testD6,resultatD6,result_testD6,date_D6,resul_D6,
                      date_testD7,type_testD7,resultatD7,result_testD7,date_D7,resul_D7,Class)

These are examples of my instructions, and I want my program to do it easily, without the user need for example to add lines in the code. For example with "start_with ='date_testD'" or I don't know how.

Thank you for your time

2

Can you describe your data type here? like BITR and variables in the below dataframe?

Selection helpers from tidyselect shall be used with select or else tidyselect functions, for example:

library(tidyverse)
BITR %>% select(
  num_ref,prenom,nom,njf,cp,ddn,sexe,adresse1,adresse2,localite,
  starts_with(c("date_test","type_test","resultat","resul_test","date","resul")))
3

My date type is all in chr. Your solution is good but it doesn't keep my variables order. It's good also to create a datframe like in my second instruction but how can I do with the paste of the first condition?

4

In the case of memo, when you repeat the same information, thats a clue you arent using enough abstraction. With all the data for memo coming from 'BITR' its best to mention BITR only once and not however many times.
R has a function with() that supports that.
Similarly, when the names have a pattern i.e. different only by integer, you can generate the name in a loop. however, taking the time to craft that level of solution might be a loss if the data wont be changing, as copy and paste and changing numbers is probably quite fast.

One thing I would emphasise is the reproducible example, that I provided. This almost certainly took more effort for me to provide than for you to have provided, so in the future, where you can do so, please try.


#set up 
library(tidyverse)
library(glue)
BITR <- tibble(as.data.frame(matrix(1:(52*5),nrow=5,ncol=52)))
names(BITR) <- c("num_ref","prenom","nom","njf","cp","ddn","sexe",
"adresse1","adresse2","localite",
"date_test_1","type_test_1", "resultat_1", "resul_test_1", "date_1", "resul_1",
"date_test_2","type_test_2", "resultat_2", "resul_test_2", "date_2", "resul_2",
"date_test_3","type_test_3", "resultat_3", "resul_test_3", "date_3", "resul_3",
"date_test_4","type_test_4", "resultat_4", "resul_test_4", "date_4", "resul_4",
"date_test_5","type_test_5", "resultat_5", "resul_test_5", "date_5", "resul_5",
"date_test_6","type_test_6", "resultat_6", "resul_test_6", "date_6", "resul_6",
"date_test_7","type_test_7", "resultat_7", "resul_test_7", "date_7", "resul_7")

BITR

#original code

memo <-paste(BITR$num_ref,BITR$prenom,BITR$nom,BITR$njf,BITR$cp,BITR$ddn,BITR$sexe,
             BITR$adresse1,BITR$adresse2,BITR$localite,
             BITR$date_test_1,BITR$type_test_1, BITR$resultat_1, BITR$resul_test_1, BITR$date_1, BITR$resul_1,
             BITR$date_test_2,BITR$type_test_2, BITR$resultat_2, BITR$resul_test_2, BITR$date_2, BITR$resul_2,
             BITR$date_test_3,BITR$type_test_3, BITR$resultat_3, BITR$resul_test_3, BITR$date_3, BITR$resul_3,
             BITR$date_test_4,BITR$type_test_4, BITR$resultat_4, BITR$resul_test_4, BITR$date_4, BITR$resul_4,
             BITR$date_test_5,BITR$type_test_5, BITR$resultat_5, BITR$resul_test_5, BITR$date_5, BITR$resul_5,
             BITR$date_test_6,BITR$type_test_6, BITR$resultat_6, BITR$resul_test_6, BITR$date_6, BITR$resul_6,
             BITR$date_test_7,BITR$type_test_7, BITR$resultat_7, BITR$resul_test_7, BITR$date_7, BITR$resul_7,
             sep="_")

memo2 <-with(data = BITR,
            expr = paste(num_ref,prenom,nom,njf,cp,ddn,sexe,
             adresse1,adresse2,localite,
             date_test_1,type_test_1, resultat_1, resul_test_1, date_1, resul_1,
             date_test_2,type_test_2, resultat_2, resul_test_2, date_2, resul_2,
             date_test_3,type_test_3, resultat_3, resul_test_3, date_3, resul_3,
             date_test_4,type_test_4, resultat_4, resul_test_4, date_4, resul_4,
             date_test_5,type_test_5, resultat_5, resul_test_5, date_5, resul_5,
             date_test_6,type_test_6, resultat_6, resul_test_6, date_6, resul_6,
             date_test_7,type_test_7, resultat_7, resul_test_7, date_7, resul_7,
             sep="_"))

identical(memo,memo2)

# manufactor the paste expression;  its shortened because of the 1:7 which saves us repeating text
myreps <- function(n) glue("date_test_{n},type_test_{n}, resultat_{n}, resul_test_{n}, date_{n}, resul_{n}") %>%
  paste0(collapse=",\n")

(myexpr <- parse(text=glue(("paste(num_ref,prenom,nom,njf,cp,ddn,sexe,
      adresse1,adresse2,localite,\n {myreps(1:7)} ,
sep='_')"))))


memo3 <-with(data = BITR,
             expr = eval(myexpr))

identical(memo,memo3)
5

you can try unite to paste columns:

library(tidyverse)
BITR %>% select(
  num_ref,prenom,nom,njf,cp,ddn,sexe,adresse1,adresse2,localite,
  starts_with(c("date_test","type_test","resultat","resul_test","date","resul"))) %>%
  unite("memo", everything(),sep = "_")

as to the column order, you shall rather change another structure of your data, or you can generate a vector of names of ideal order of columns, and use all_of inside select to select them:

vars <- expand_grid(
  A = c('date_test_',"type_test_","resultat_","resul_test_","date_","resul_"),
  B = 1:7
) %>% arrange(B) %>% str_glue_data("{A}{B}")

BITR %>% select(
  num_ref,prenom,nom,njf,cp,ddn,sexe,adresse1,adresse2,localite,all_of(vars)) %>%
  unite('memo',everything(),sep = "_")
6

that's great, and then i imagine I can reuse "var"? For example now I have a separate procedure :

final<-separate(paires, memo, into = c('idR','prenomR','nomR','nomjfR','cpNaissR',
                                       'ddn_','sexe_','ad1_','ad2_','ville_',
                                       'date_test_1','type_test_1','resultat_1','result_test_1','date__1','resul__1',
                                       'date_test_2','type_test_2','resultat_2','result_test_2','date__2','resul__2',
                                       'date_test_3','type_test_3','resultat_3','result_test_3','date__3','resul__3',
                                       'date_test_4','type_test_4','resultat_4','result_test_4','date__4','resul__4',
                                       'date_test_5','type_test_5','resultat_5','result_test_5','date__5','resul__5',
                                       'date_test_6','type_test_6','resultat_6','result_test_6','date__6','resul__6',
                                       'date_test_7','type_test_7','resultat_7','result_test_7','date__7','resul__7'),sep='_')

why something like that doesn't work ? :

final<-paires%>% separate(memo, into(
  num_ref,prenom,nom,njf,cp,ddn,sexe,adresse1,adresse2,localite,all_of(vars))
7

all_of() must be used with tidyselect functions.

in your next case, the param into just needs a vector of names rather than some cols which requires tidyselect syntax, so it would be:

separate(
  paires,
  memo,
  sep = "_",
  into = c(
    'num_ref',
    'prenom',
    'nom',
    'njf',
    'cp',
    'ddn',
    'sexe',
    'adresse1',
    'adresse2',
    'localite',
    vars
  )
)
8

Thank you! Then I have the same to create a dataframe. But if I put this :

df<- data.frame(id, prenom, nom, ddnR,  vars,Clas_SGRC)

it doesn't work because there is not the same row lenght. Indeed, it doesn't take my values but the col names who are stock in 'vars'. How can I deal with it?

9

If you're sure that you are going to construct a dataframe with variables from the global environment, the function get() might be of help. And we also need map() function from purrr to do the loop.
In addition, I used add_column() rather than bind_cols here to control the position of added cols.

data.frame(id, prenom, nom, ddnR, Clas_SGRC) %>% add_column(
  vars %>% map_df(~ data.frame(get(.x)) %>% `names<-`(.x)), .before = Clas_SGRC
)
10

Yes my variable are from values in the global environnement. I don't know why but I have this error :

Error:
! New columns must be compatible with `.data`.
x New columns have 14742 rows.
i `.data` has 351 rows.

Unless my values in the global environnement have all 351 rows

11

Sry for my bad, we shall use map_dfc to bind columns here:

data.frame(id, prenom, nom, ddnR, Clas_SGRC) %>% add_column(
  vars %>% map_dfc(~ data.frame(get(.x)) %>% `names<-`(.x)), .before = Clas_SGRC
)
12

:sweat_smile:

Error:
! Can't find columns ``, ``, ``, ``, ``, and 346 more in `.data`.
13

sry again for my mistake, I didn't test the code very rigorously :face_with_hand_over_mouth:
try this?

data.frame(id, prenom, nom, ddnR, Clas_SGRC) %>% add_column(
  vars %>% map_dfc(~ data.frame(get(.x)) %>% `names<-`(.x)), .before = "Clas_SGRC"
)

or if Clas_SGRC is a dataframe, replace .before = "Clas_SGRC" with .after = "ddnR"

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