r - Use of list in for loop -

i in trouble part of code. i'm beginner , tried make loop list construct different data.frame. let's see

df<-data  head(data)  col1    col2    col3       1       13       2       34       2       46 b       1       23 d       3       56   b       2       31   df_a<-data[which(data$col1=="a") df_b<-data[which(data$col1=="b") df_c<-data[which(data$col1=="c") df_d<-data[which(data$col1=="d")  list<-c("_a","_b","_c","_d") (i in list){     paste0("df",i,"1")<-data(which(paste0("df",i)$col2==1))     paste0("df",i,"2")<-data(which(paste0("df",i)$col2==2))     paste0("df",i,"3")<-data(which(paste0("df",i)$col2==3)) 

in case aim construct different dataframe original dataframe. in context little tricky i'm not using way if syntax similar.

the problem adjunction of paste name of data frame , "$". r return error message :

error in which(paste0("df", i, "1")$col2 == 1) :    erreur d'évaluation de l'argument 'x' lors de la sélection d'une  méthode pour la fonction 'which' : error in paste0("df",  i, "1")$col2 :    $ operator invalid atomic vectors 

do have idea resolve problem ?

if i'm interpreting question correctly, in order need exactly, need combination of assign, eval, , parse:

df <- data.frame(col1 = c("a", "a", "a", "b", "d", "b"),                   col2 = c(1, 2, 2, 1, 3, 2),                  col3 = c(13 ,34, 46, 23, 56, 31))  df_a<-df[which(df$col1=="a"), ] df_b<-df[which(df$col1=="b"), ] df_c<-df[which(df$col1=="c"), ] df_d<-df[which(df$col1=="d"), ]  list <- c("_a","_b","_c","_d")  (i in list) {   assign(paste0("df", i, "1"), df[eval(parse(text = paste0("which(df", i, "$col2 == 1)"))), ])   assign(paste0("df", i, "2"), df[eval(parse(text = paste0("which(df", i, "$col2 == 2)"))), ])   assign(paste0("df", i, "3"), df[eval(parse(text = paste0("which(df", i, "$col2 == 3)"))), ]) } 

if goal perform operations on these different groups of data, may want package dplyr's group_by(), provides cleaner way perform grouped operations.

another, cleaner, way using split() function store each of splits in 1 list:

split_dfs <- split(df, df$col1) split_dfs <- lapply(split_dfs, function(x) split(x, x$col2))