I learnt that in R you can pass a variable number of parameters to your function with ...
I'm now trying to create a function and loop through ..., for example to create a dataframe.
create_df <- function(...) {
for(i in ...){
... <- data.frame(...=c(1,2,3,4),column2=c(1,2,3,4))
}
}
create_df(hello,world)
I would like to create two dataframes with this code one named hello and the other world. One of the columns should also be named hello and world respectively. Thanks
This is my error:
Error in create_df(hello, there) : '...' used in an incorrect context
It's generally not a good idea for function to inject variables into their calling environment. It's better to return values from functions and keep related values in a list. In this case you could do this instead
create_df <- function(...) {
names <- sapply(match.call(expand.dots = FALSE)$..., deparse)
Map(function(x) {
setNames(data.frame(a1=c(1,2,3,4),a2=c(1,2,3,4)), c(x, "column2"))
}, names)
}
create_df(hello,world)
# $hello
# hello column2
# 1 1 1
# 2 2 2
# 3 3 3
# 4 4 4
# $world
# world column2
# 1 1 1
# 2 2 2
# 3 3 3
# 4 4 4
This returns a named list which is much easier to work with in R. We use match.call to turn the ... names into strings and then use those strings with functions that expect them like setNames() to change data.frame column names. Map is also a great helper for generating lists. It's often easier to use map style functions rather than bothering with explicit for loops.
Related
I am trying to write a function that will add a new column to a data frame, when I call it, without doing any explicit assignment.
i.e I just want to call the function with arguments and have it modify the data frame:
input_data:
x y
1 2
2 6
column_creator<-function(data,column_name,...){
data$column_name <- newdata ...}
column_creator(input_data,new_col,...)
x y new_col
1 2 5
2 6 9
As opposed to:
input_data$new_col <- column_creator(input_data,new_col,...)
However doing assignment inside the function is not modifying the global variable.
I am working around this by having the function return a statement of assignment (temp in the function below), however is there another way to do this?
Here is my function for reference, it should create a column of 1s inbetween the supplied start and end date with the name dummy_name.
dummy_creator<-function(data,date,dummy_name,start,end){
temp<-paste(data,"['",dummy_name,"'] <- ifelse(",data,"['",date,"'] > as.Date (","'" , start,"'" , ", format= '%Y-%m-%d') & ",data,"['",date,"'] < as.Date(", "'", end,"'" ,",format='%Y-%m-%d') ,1,0)",sep="")
print(temp)
return()
}
Thanks
I also tried:
dummy_creator<-function(data,date,dummy_name,start,end){
data[dummy_name] <<- ifelse(data[,date] > as.Date (start, format= "%Y-%m-%d") & data[,date] < as.Date(end,format="%Y-%m-%d") ,1,0)
}
But that attempt gave me error object of type closure is not subsettable.
It’s generally a bad idea to modify global data or data passed into a function: R objects are immutable, and using tricks to modify them inside a function breaks the user’s expectations and makes it harder to reason about the program’s state.
It is good form to return the modified object instead:
input_data = column_creator(input_data, new_col, …)
That said, you have a few options. Generally, R has several mechanisms to allow modifiable objects. I recommend you look into R6 classes for this.
You could also use non-standard evaluation to capture the passed object and modify it at the caller’s site. However, this is rarely advisable. I’m posting an example of this here because the mechanism is interesting and worth knowing, but I’ll reiterate that you shouldn’t use it here.
function (df, new_col, new_data) {
# Get the unevaluated expression representing the data frame
df_obj = substitute(df)
new_col = substitute(new_col)
# Ensure that the input is valid
stopifnot(is.name(df_obj))
stopifnot(is.name(new_col))
stopifnot(is.data.frame(df))
# Add new column to data frame
df[[deparse(new_col)]] = new_data
# Assign back to object in caller scope
assign(deparse(df_obj), df, parent.frame())
invisible(df)
}
test = data.frame(A = 1 : 5, B = 1 : 5)
column_creator(test, C, 6 : 10)
test
# A B C
# 1 1 1 6
# 2 2 2 7
# 3 3 3 8
# 4 4 4 9
# 5 5 5 10
I have searched quite a bit and not found a question that addresses this issue--but if this has been answered, forgive me, I am still quite green when it comes to coding in general. I have a data frame with a large number of variables that I would like to combine & create new variables from based on names I've put in a 2nd data frame in a loop. The data frame formulas should create & call columns from the main data frame data
USDb = c(1,2,3)
USDc = c(4,5,6)
EURb = c(7,8,9)
EURc = c(10,11,12)
data = data.frame(USDb, USDc, EURb, EURc)
Now I'd like to create a new column data$USDa as defined by
data$USDa = data$USDb - data$USDc
and so on for EUR and other variables. This is easy enough to do manually, but I'd like to create a loop that pulls the names from formulas, something like this:
a = c("USDa", "EURa")
b = c("USDb", "EURb")
c = c("USDc", "EURc")
formulas = data.frame(a,b,c)
for (i in 1:length(formulas[,a])){
data$formulas[i,a] = data$formulas[i,b] - data$formulas[i,c]
}
Obviously data$formulas[i,a] this returns NULL, so I tried data$paste0(formulas[i,a]) and that returns Error: attempt to apply non-function
How can I get these strings to be recognized as variables in this way? Thanks.
There are simpler ways to do this, but I'll stick to most of your code as a means of explanation. Your code should work so long as you edit your for loop to the following:
for (i in 1:length(formulas[,"a"])){
data[formulas[i,"a"]] = data[formulas[i,"b"]] - data[formulas[i,"c"]]
}
formulas[,a] won't work because you have a variable defined as a already that is not appropriate inside an index. Use formulas[, "a"] instead if you want all rows from column "a" in data.frame formulas.
data$formulas is literally searching for the column called "formulas" in the data.frame data. Instead you want to write data[formulas](of course, knowing that you need to index formulas in order to make it a proper string)
logic : iterate through each of the formulae, using a apply which is a for loop internally, and do calculation based on the formula
x = apply(formulas, 1, function(x) data[[x[3]]] - data[[x[2]]])
colnames(x) = formulas$a
x
# USDa EURa
#[1,] 3 3
#[2,] 3 3
#[3,] 3 3
cbind(data, x)
# USDb USDc EURb EURc USDa EURa
#1 1 4 7 10 3 3
#2 2 5 8 11 3 3
#3 3 6 9 12 3 3
Another option is split with sapply
sapply(setNames(split.default(as.matrix(formulas[-1]),
row(formulas[-1])), formulas$a), function(x) Reduce(`-`, data[rev(x)]))
# USDa EURa
#[1,] 3 3
#[2,] 3 3
#[3,] 3 3
I am trying to write a function that will add a new column to a data frame, when I call it, without doing any explicit assignment.
i.e I just want to call the function with arguments and have it modify the data frame:
input_data:
x y
1 2
2 6
column_creator<-function(data,column_name,...){
data$column_name <- newdata ...}
column_creator(input_data,new_col,...)
x y new_col
1 2 5
2 6 9
As opposed to:
input_data$new_col <- column_creator(input_data,new_col,...)
However doing assignment inside the function is not modifying the global variable.
I am working around this by having the function return a statement of assignment (temp in the function below), however is there another way to do this?
Here is my function for reference, it should create a column of 1s inbetween the supplied start and end date with the name dummy_name.
dummy_creator<-function(data,date,dummy_name,start,end){
temp<-paste(data,"['",dummy_name,"'] <- ifelse(",data,"['",date,"'] > as.Date (","'" , start,"'" , ", format= '%Y-%m-%d') & ",data,"['",date,"'] < as.Date(", "'", end,"'" ,",format='%Y-%m-%d') ,1,0)",sep="")
print(temp)
return()
}
Thanks
I also tried:
dummy_creator<-function(data,date,dummy_name,start,end){
data[dummy_name] <<- ifelse(data[,date] > as.Date (start, format= "%Y-%m-%d") & data[,date] < as.Date(end,format="%Y-%m-%d") ,1,0)
}
But that attempt gave me error object of type closure is not subsettable.
It’s generally a bad idea to modify global data or data passed into a function: R objects are immutable, and using tricks to modify them inside a function breaks the user’s expectations and makes it harder to reason about the program’s state.
It is good form to return the modified object instead:
input_data = column_creator(input_data, new_col, …)
That said, you have a few options. Generally, R has several mechanisms to allow modifiable objects. I recommend you look into R6 classes for this.
You could also use non-standard evaluation to capture the passed object and modify it at the caller’s site. However, this is rarely advisable. I’m posting an example of this here because the mechanism is interesting and worth knowing, but I’ll reiterate that you shouldn’t use it here.
function (df, new_col, new_data) {
# Get the unevaluated expression representing the data frame
df_obj = substitute(df)
new_col = substitute(new_col)
# Ensure that the input is valid
stopifnot(is.name(df_obj))
stopifnot(is.name(new_col))
stopifnot(is.data.frame(df))
# Add new column to data frame
df[[deparse(new_col)]] = new_data
# Assign back to object in caller scope
assign(deparse(df_obj), df, parent.frame())
invisible(df)
}
test = data.frame(A = 1 : 5, B = 1 : 5)
column_creator(test, C, 6 : 10)
test
# A B C
# 1 1 1 6
# 2 2 2 7
# 3 3 3 8
# 4 4 4 9
# 5 5 5 10
I have, for example, this three datasets (in my case, they are many more and with a lot of variables):
data_frame1 <- data.frame(a=c(1,5,3,3,2), b=c(3,6,1,5,5), c=c(4,4,1,9,2))
data_frame2 <- data.frame(a=c(6,0,9,1,2), b=c(2,7,2,2,1), c=c(8,4,1,9,2))
data_frame2 <- data.frame(a=c(0,0,1,5,1), b=c(4,1,9,2,3), c=c(2,9,7,1,1))
on each data frame I want to add a variable resulting from a transformation of an existing variable on that data frame. I would to do this by a loop. For example:
datasets <- c("data_frame1","data_frame2","data_frame3")
vars <- c("a","b","c")
for (i in datasets){
for (j in vars){
# here I need a code that create a new variable with transformed values
# I thought this would work, but it didn't...
get(i)$new_var <- log(get(i)[,j])
}
}
Do you have some valid suggestions about that?
Moreover, it would be great for me if it were possible also to assign the new column names (in this case new_var) by a character string, so I could create the new variables by another for loop nested in the other two.
Hope I've not been too tangled in explain my problem.
Thanks in advance.
You can put your dataframes in a list and use lapply to process them one by one. So no need to use a loop in this case.
For example you can do this :
data_frame1 <- data.frame(a=c(1,5,3,3,2), b=c(3,6,1,5,5), c=c(4,4,1,9,2))
data_frame2 <- data.frame(a=c(6,0,9,1,2), b=c(2,7,2,2,1), c=c(8,4,1,9,2))
data_frame3 <- data.frame(a=c(0,0,1,5,1), b=c(4,1,9,2,3), c=c(2,9,7,1,1))
ll <- list(data_frame1,data_frame2,data_frame3)
lapply(ll,function(df){
df$log_a <- log(df$a) ## new column with the log a
df$tans_col <- df$a+df$b+df$c ## new column with sums of some columns or any other
## transformation
### .....
df
})
the dataframe1 becomes :
[[1]]
a b c log_a tans_col
1 1 3 4 0.0000000 8
2 5 6 4 1.6094379 15
3 3 1 1 1.0986123 5
4 3 5 9 1.0986123 17
5 2 5 2 0.6931472 9
I had the same need and wanted to change also the columns in my actual list of dataframes.
I found a great method here (the purrr::map2 method in the question works for dataframes with different columns), followed by
list2env(list_of_dataframes ,.GlobalEnv)
I have to call a function in R that takes "2 or more objects" as an input, so the function definition is:
function(..., all = TRUE, <other named parameters>)
where ... is defined as 2 or more objects
The issue is I have is that my objects are in a list, and I am working with a different number of objects according to what I want to do. So if my list has 3 elements for example I would have to do:
function(list[[1]], list [[2]], list[[3]])
How can I do that generically, regardless of the number of element in my list ?
You can use do.call, as that takes a list of arguments and applies them on function. Eg for rbind :
X <- list(A=1:3,B=4:6,C=7:9)
do.call(rbind,X)
[,1] [,2] [,3]
A 1 2 3
B 4 5 6
C 7 8 9
Mind you, if you need extra arguments, you should add them to the list as well. See eg :
X <- list(A=list(A1=1:2,A2=3:4),B=list(B1=5:6,B2=7:8))
do.call(c,X) # Returns a list
do.call(c,X,recursive=TRUE) # Gives an error
do.call(c,c(X,list(recursive=TRUE)))
A.A11 A.A12 A.A21 A.A22 B.B11 B.B12 B.B21 B.B22
1 2 3 4 5 6 7 8
An example would be helpful, but I'm pretty sure you're looking for do.call:
do.call(function, c(list, list(all=TRUE, <other named parameters>)))