I have created the following simple function in R:
fun <- function(a,b,c,d,e){b+(c-a)*((e-b)/(d-a))}
That I want to apply this function to a data.frame that looks something like:
> data.frame("x1"=seq(55,75,5),"x2"=round(rnorm(5,50,10),0),"x3"=seq(30,10,-5))
x1 x2 x3
1 55 51 30
2 60 45 25
3 65 43 20
4 70 57 15
5 75 58 10
I want to apply fun to each separate row to create a new variable x4, but now comes the difficult part (to me at least..): for the arguments d and e I want to use the values x2 and x3 from the next row. So for the first row of the example that would mean: fun(a=55,b=51,c=30,d=45,e=25). I know that I can use mapply() to apply a function to each row, but I have no clue on how to tell mapply that it should use some values from the next row, or whether I should be looking for a different approach than mapply()?
Many thanks in advance!
Use mapply, but shift the fourth and fifth columns by one row. You can do it manually, or use taRifx::shift.
> dat
x1 x2 x3
1 55 25 30
2 60 58 25
3 65 59 20
4 70 68 15
5 75 43 10
library(taRifx)
> shift(dat$x2)
[1] 58 59 68 43 25
> mapply( dat$x1, dat$x2, dat$x3, shift(dat$x2), shift(dat$x3) , FUN=fun )
[1] 25.00000 -1272.00000 719.00000 -50.14815 26.10000
If you want the last row to be NA rather than wrapping, use wrap=FALSE,pad=TRUE:
> shift(dat$x2,wrap=FALSE,pad=TRUE)
[1] 58 59 68 43 NA
Related
Suppose, I have a dataframe, df, and I want to create a new column called "c" based on the addition of two existing columns, "a" and "b". I would simply run the following code:
df$c <- df$a + df$b
But I also want to do this for many other columns. So why won't my code below work?
# Reproducible data:
martial_arts <- data.frame(gym_branch=c("downtown_a", "downtown_b", "uptown", "island"),
day_boxing=c(5,30,25,10),day_muaythai=c(34,18,20,30),
day_bjj=c(0,0,0,0),day_judo=c(10,0,5,0),
evening_boxing=c(50,45,32,40), evening_muaythai=c(50,50,45,50),
evening_bjj=c(60,60,55,40), evening_judo=c(25,15,30,0))
# Creating a list of the new column names of the columns that need to be added to the martial_arts dataframe:
pattern<-c("_boxing","_muaythai","_bjj","_judo")
d<- expand.grid(paste0("martial_arts$total",pattern))
# Creating lists of the columns that will be added to each other:
e<- names(martial_arts %>% select(day_boxing:day_judo))
f<- names(martial_arts %>% select(evening_boxing:evening_judo))
# Writing a function and using mapply:
kick_him <- function(d,e,f){d <- rowSums(martial_arts[ , c(e, f)], na.rm=T)}
mapply(kick_him,d,e,f)
Now, mapply produces the correct results in terms of the addition:
> mapply(ff,d,e,f)
Var1 <NA> <NA> <NA>
[1,] 55 84 60 35
[2,] 75 68 60 15
[3,] 57 65 55 35
[4,] 50 80 40 0
But it doesn't add the new columns to the martial_arts dataframe. The function in theory should do the following
martial_arts$total_boxing <- martial_arts$day_boxing + martial_arts$evening_boxing
...
...
martial_arts$total_judo <- martial_arts$day_judo + martial_arts$evening_judo
and add four new total columns to martial_arts.
So what am I doing wrong?
The assignment is wrong here i.e. instead of having martial_arts$total_boxing as a string, it should be "total_boxing" alone and this should be on the lhs of the Map/mapply. As the OP already created the 'martial_arts$' in 'd' dataset as a column, we are removing the prefix part and do the assignment
kick_him <- function(e,f){rowSums(martial_arts[ , c(e, f)], na.rm=TRUE)}
martial_arts[sub(".*\\$", "", d$Var1)] <- Map(kick_him, e, f)
-check the dataset now
> martial_arts
gym_branch day_boxing day_muaythai day_bjj day_judo evening_boxing evening_muaythai evening_bjj evening_judo total_boxing total_muaythai total_bjj total_judo
1 downtown_a 5 34 0 10 50 50 60 25 55 84 60 35
2 downtown_b 30 18 0 0 45 50 60 15 75 68 60 15
3 uptown 25 20 0 5 32 45 55 30 57 65 55 35
4 island 10 30 0 0 40 50 40 0 50 80 40 0
I created a vector list, aa, with 50 elements. And I need to split aa into two vector lists called bb and cc. bb has the first 20 elements of aa while cc has the last 30 elements of aa. How do I do it?
Creation of original vector list
aa <- list (sample (1:50))
aa
#[[1]]
# [1] 29 30 39 45 17 11 43 14 24 34 3 1 28 2 21 23 6 31 5 27 44 7 4 46 49 22 33 38 50 36 15 48 8 16 25 42 13 41 47
#[40] 37 26 32 35 9 18 10 20 40 19 12
Sorry all, I know my question is really basic. Maybe it is because the question is too simple and the solution is thus not easily found from the internet.
Since I couldn't a direct question answering this adding an answer. We can first subset the list using [[ and then select individual elements in it with [.
bb <- aa[[1]][1:20]
cc <- aa[[1]][21:50]
We can also use head and tail to select first 20 and last 30 elements respectively.
bb <- head(aa[[1]], 20)
cc <- tail(aa[[1]], 30)
We can use split to create a list of vectors
lst1 <- split(aa[[1]], rep(1:2, c(20, 30)))
and extract the vector with [[
lst[[1]]
lst1[[2]]
It can be extended to any number of splits (i.e. generalized version) where we just need to change the rep
I am new to R. I have a data frame like following
>df=data.frame(Id=c("Entry_1","Entry_1","Entry_1","Entry_2","Entry_2","Entry_2","Entry_3","Entry_4","Entry_4","Entry_4","Entry_4"),Start=c(20,20,20,37,37,37,68,10,10,10,10),End=c(50,50,50,78,78,78,200,94,94,94,94),Pos=c(14,34,21,50,18,70,101,35,2,56,67),Hits=c(12,34,17,89,45,87,1,5,6,3,26))
Id Start End Pos Hits
Entry_1 20 50 14 12
Entry_1 20 50 34 34
Entry_1 20 50 21 17
Entry_2 37 78 50 89
Entry_2 37 78 18 45
Entry_2 37 78 70 87
Entry_3 68 200 101 1
Entry_4 10 94 35 5
Entry_4 10 94 2 6
Entry_4 10 94 56 3
Entry_4 10 94 67 26
For each entry I would like to iterate the data.frame in 3 different modes. For an example, for Entry_1 mode_1 =seq(20,50,3)and mode_2=seq(21,50,3) and mode_3=seq(22,50,3). I would like sum all the Values in Column "Hits" whose corresponding values in Column "Pos" that falls in mode_1 or_mode_2 or mode_3 and generate a data.frame like follow:
Id Mode_1 Mode_2 Mode_3
Entry_1 0 17 34
Entry_2 87 89 0
Entry_3 1 0 0
Entry_4 26 8 0
I tried the following code:
mode_1=0
mode_2=0
mode_3=0
mode_1_sum=0
mode_2_sum=0
mode_3_sum=0
for(i in dim(df)[1])
{
if(df$Pos[i] %in% seq(df$Start[i],df$End[i],3))
{
mode_1_sum=mode_1_sum+df$Hits[i]
print(mode_1_sum)
}
mode_1=mode_1_sum+counts
print(mode_1)
ifelse(df$Pos[i] %in% seq(df$Start[i]+1,df$End[i],3))
{
mode_2_sum=mode_2_sum+df$Hits[i]
print(mode_2_sum)
}
mode_2_sum=mode_2_sum+counts
print(mode_2)
ifelse(df$Pos[i] %in% seq(df$Start[i]+2,df$End[i],3))
{
mode_3_sum=mode_3_sum+df$Hits[i]
print(mode_3_sum)
}
mode_3_sum=mode_3_sum+counts
print(mode_3_sum)
}
But the above code only prints 26. Can any one guide me how to generate my desired output, please. I can provide much more details if needed. Thanks in advance.
It's not an elegant solution, but it works.
m <- 3 # Number of modes you want
foo <- ((df$Pos - df$Start)%%m + 1) * (df$Start < df$Pos) * (df$End > df$Pos)
tab <- matrix(0,nrow(df),m)
for(i in 1:m) tab[foo==i,i] <- df$Hits[foo==i]
aggregate(tab,list(df$Id),FUN=sum)
# Group.1 V1 V2 V3
# 1 Entry_1 0 17 34
# 2 Entry_2 87 89 0
# 3 Entry_3 1 0 0
# 4 Entry_4 26 8 0
-- EXPLANATION --
First, we find the indices of df$Pos That are both bigger than df$Start and smaller than df$End. These should return 1 if TRUE and 0 if FALSE. Next, we take the difference between df$Pos and df$Start, we take mod 3 (which will give a vector of 0s, 1s and 2s), and then we add 1 to get the right mode. We multiply these two things together, so that the values that fall within the interval retain the right mode, and the values that fall outside the interval become 0.
Next, we create an empty matrix that will contain the values. Then, we use a for-loop to fill in the matrix. Finally, we aggregate the matrix.
I tried looking for a quicker solution, but the main problem I cannot work around is the varying intervals for each row.
I hope you are doing very well. I would like to know how to calculate the cumulative sum of a data set with certain conditions. A simplified version of my data set would look like:
t id
A 22
A 22
R 22
A 41
A 98
A 98
A 98
R 98
A 46
A 46
R 46
A 46
A 46
A 46
R 46
A 46
A 12
R 54
A 66
R 13
A 13
A 13
A 13
A 13
R 13
A 13
Would like to make a new data set where, for each value of "id", I would have the cumulative number of times that each id appears , but when t=R I need to restart the counting e.g.
t id count
A 22 1
A 22 2
R 22 0
A 41 1
A 98 1
A 98 2
A 98 3
R 98 0
A 46 1
A 46 2
R 46 0
A 46 1
A 46 2
A 46 3
R 46 0
A 46 1
A 12 1
R 54 0
A 66 1
R 13 0
A 13 1
A 13 2
A 13 3
A 13 4
R 13 0
A 13 1
Any ideas as to how to do this? Thanks in advance.
Using rle:
out <- transform(df, count = sequence(rle(do.call(paste, df))$lengths))
out$count[out$t == "R"] <- 0
If your data.frame has more than these two columns, and you want to check only these two columns, then, just replace df with df[, 1:2] (or) df[, c("t", "id")].
If you find do.call(paste, df) dangerous (as #flodel comments), then you can replace that with:
as.character(interaction(df))
I personally don't find anything dangerous or clumsy with this setup (as long as you have the right separator, meaning you know your data well). However, if you do find it as such, the second solution may help you.
Update:
For those who don't like using do.call(paste, df) or as.character(interaction(df)) (please see the comment exchanges between me, #flodel and #HongOoi), here's another base solution:
idx <- which(df$t == "R")
ww <- NULL
if (length(idx) > 0) {
ww <- c(min(idx), diff(idx), nrow(df)-max(idx))
df <- transform(df, count = ave(id, rep(seq_along(ww), ww),
FUN=function(y) sequence(rle(y)$lengths)))
df$count[idx] <- 0
} else {
df$count <- seq_len(nrow(df))
}
I try to apply a function over all rows and columns of two dataframes but I don't know how to solve it with apply.
I think the following script explains what I intend to do and the way i tried to solve it. Any advice would be warmly appreciated! Please note, that the simplefunction is only intended to be an example function to keep it simple.
# some data and a function
df1<-data.frame(name=c("aa","bb","cc","dd","ee"),a=sample(1:50,5),b=sample(1:50,5),c=sample(1:50,5))
df2<-data.frame(name=c("aa","bb","cc","dd","ee"),a=sample(1:50,5),b=sample(1:50,5),c=sample(1:50,5))
simplefunction<-function(a,b){a+b}
# apply on a single row
simplefunction(df1[1,2],df2[1,2])
# apply over all colums
apply(?)
## apply over all columns and rows
# create df to receive results
df3<-df2
# loop it
for (i in 2:5)df3[i]<-apply(?)
My first mapply answer!! For your simple example you have...
mapply( FUN = `+` , df1[,-1] , df2[,-1] )
# a b c
# [1,] 60 35 75
# [2,] 57 39 92
# [3,] 72 71 48
# [4,] 31 19 85
# [5,] 47 66 58
You can extend it like so...
mapply( FUN = function(x,y,z,etc){ simplefunctioncodehere} , df1[,-1] , df2[,-1] , ... other dataframes here )
The dataframes will be passed in order to the function, so in this example df1 would be x, df2 would be y and z and etc would be some other dataframes that you specify in that order. Hopefully that makes sense. mapply will take the first row, first column values of all dataframes and apply the function, then the first row, second column of all data frames and apply the function and so on.
You can also use Reduce:
set.seed(45) # for reproducibility
Reduce(function(x,y) { x + y}, list(df1[, -1], df2[,-1]))
# a b c
# 1 53 22 23
# 2 64 28 91
# 3 19 56 51
# 4 38 41 53
# 5 28 42 30
You can just do :
df1[,-1] + df2[,-1]
Which gives :
a b c
1 52 24 37
2 65 63 62
3 31 90 89
4 90 35 33
5 51 33 45