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$count <- seq_len(nrow(df))
}
Related
Suppose, I have a dataframe, df, and I want to create a new column called "c" based on the addition of two existing columns, "a" and "b". I would simply run the following code:
df$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 am trying to assign a column name to a variable using mutate.
df <-data.frame(x = sample(1:100, 50), y = rnorm(50))
new <- function(name){
df%>%mutate(name = ifelse(x <50, "small", "big"))
}
When I run
new(name = "newVar")
it doesn't work. I know mutate_() could help but I'm struggling in using it together with ifelse.
Any help would be appreciated.
Using dplyr 0.7.1 and its advances in NSE, you have to UQ the argument to mutate and then use := when assigning. There is lots of info on programming with dplyr and NSE here: https://cran.r-project.org/web/packages/dplyr/vignettes/programming.html
I've changed the name of the function argument to myvar to avoid confusion. You could also use case_when from dplyr instead of ifelse if you have more categories to recode.
df <- data.frame(x = sample(1:100, 50), y = rnorm(50))
new <- function(myvar){
df %>% mutate(UQ(myvar) := ifelse(x < 50, "small", "big"))
}
new(myvar = "newVar")
This returns
x y newVar
1 37 1.82669 small
2 63 -0.04333 big
3 46 0.20748 small
4 93 0.94169 big
5 83 -0.15678 big
6 14 -1.43567 small
7 61 0.35173 big
8 26 -0.71826 small
9 21 1.09237 small
10 90 1.99185 big
11 60 -1.01408 big
12 70 0.87534 big
13 55 0.85325 big
14 38 1.70972 small
15 6 0.74836 small
16 23 -0.08528 small
17 27 2.02613 small
18 76 -0.45648 big
19 97 1.20124 big
20 99 -0.34930 big
21 74 1.77341 big
22 72 -0.32862 big
23 64 -0.07994 big
24 53 -0.40116 big
25 16 -0.70226 small
26 8 0.78965 small
27 34 0.01871 small
28 24 1.95154 small
29 82 -0.70616 big
30 77 -0.40387 big
31 43 -0.88383 small
32 88 -0.21862 big
33 45 0.53409 small
34 29 -2.29234 small
35 54 1.00730 big
36 22 -0.62636 small
37 100 0.75193 big
38 52 -0.41389 big
39 36 0.19817 small
40 89 -0.49224 big
41 81 -1.51998 big
42 18 0.57047 small
43 78 -0.44445 big
44 49 -0.08845 small
45 20 0.14014 small
46 32 0.48094 small
47 1 -0.12224 small
48 66 0.48769 big
49 11 -0.49005 small
50 87 -0.25517 big
Following the dlyr programming vignette, define your function as follows:
new <- function(name)
{
nn <- enquo(name) %>% quo_name()
df %>% mutate( !!nn := ifelse(x <50, "small", "big"))
}
enquo takes its expression argument and quotes it, followed by quo_name converting it into a string. Since nn is now quoted, we need to tell mutate not to quote it a second time. That's what !! is for. Finally, := is a helper operator to make it valid R code. Note that with this definition, you can simply pass newVar instead of "newVar" to your function, maintaining dplyr style.
> new( newVar ) %>% head
x y newVar
1 94 -1.07642088 big
2 85 0.68746266 big
3 80 0.02630903 big
4 74 0.18323506 big
5 86 0.85086915 big
6 38 0.41882858 small
Base R solution
df <-data.frame(x = sample(1:100, 50), y = rnorm(50))
new <- function(name){
df[,name]='s'
df[,name][df$x>50]='b'
return(df)
}
I am using dplyr 0.5 so i just combine base R with mutate
new <- function(Name){
df=mutate(df,ifelse(x <50, "small", "big"))
names(df)[3]=Name
return(df)
}
new("newVar")
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$Start, we take mod 3 (which will give a vector of 0s, 1s and 2s), and then we add 1 to get the right mode. We multiply these two things together, so that the values that fall within the interval retain the right mode, and the values that fall outside the interval become 0.
Next, we create an empty matrix that will contain the values. Then, we use a for-loop to fill in the matrix. Finally, we aggregate the matrix.
I tried looking for a quicker solution, but the main problem I cannot work around is the varying intervals for each row.
I am building a streambed hydrology calculator in R using multiple tables from an Access database. I am having trouble automating and calculating the same set of indices for multiple sites. The following sample dataset describes my data structure:
> Thalweg
StationID AB0 AB1 AB2 AB3 AB4 AB5 BC1 BC2 BC3 BC4 Xdep_Vdep
1 1AAUA017.60 47 45 44 55 54 6 15 39 15 11 18.29
2 1AXKR000.77 30 27 24 19 20 18 9 12 21 13 6.46
3 2-BGU005.95 52 67 62 42 28 25 23 26 11 19 20.18
4 2-BLG011.41 66 85 77 83 63 35 10 70 95 90 67.64
5 2-CSR003.94 29 35 46 14 19 14 13 13 21 48 6.74
where each column represents certain field-measured parameters (i.e. depth of a reach section) and each row represents a different site.
I have successfully used the apply functions to simultaneously calculate simple functions on multiple rows:
> Xdepth <- apply(Thalweg[, 2:11], 1, mean) # Mean Depth
> Xdepth
1 2 3 4 5
33.1 19.3 35.5 67.4 25.2
and appending the results back to the proper station in a dataframe.
However, I am struggling when I want to calculate and save variables that are subsequently used for further calculations. I cannot seem to loop or apply the same function to multiple columns on a single row and complete the same calculations over the next row without mixing variables and data.
I want to do:
Residual_AB0 <- min(Xdep_Vdep, Thalweg$AB0)
Residual_AB1 <- min((Residual_AB0 + other_variables), Thalweg$AB1)
Residual_AB2 <- min((Residual_AB1 + other_variables), Thalweg$AB2)
Residual_AB3 <- min((Residual_AB2 + other_variables), Thalweg$AB3)
# etc.
Depth_AB0 <- (Thalweg$AB0 - Residual_AB0)
Depth_AB1 <- (Thalweg$AB1 - Residual_AB1)
Depth_AB2 <- (Thalweg$AB2 - Residual_AB2)
# etc.
I have tried and subsequently failed at for loops such as:
for (i in nrow(Thalweg)){
Residual_AB0 <- min(Xdep_Vdep, Thalweg$AB0)
Residual_AB1 <- min((Residual_AB0 + Stacks_Equation), Thalweg$AB1)
Residual_AB2 <- min((Residual_AB1 + Stacks_Equation), Thalweg$AB2)
Residual_AB3 <- min((Residual_AB2 + Stacks_Equation), Thalweg$AB3)
Residuals <- data.frame(Thalweg$StationID, Residual_AB0, Residual_AB1, Residual_AB2, Residual_AB3)
}
Is there a better way to approach looping through multiple lines of data when I need unique variables saved for each specific row that I am currently calculating? Thank you for any suggestions.
your exact problem is still a mistery to me...
but it looks like you want a double for loop
for(i in 1:nrow(thalweg)){
residual=thalweg[i,"Xdep_Vdep"]
for(j in 2:11){
residual=min(residual,thalweg[i,j])
}
}
I have created the following simple function in R:
fun <- function(a,b,c,d,e){b+(c-a)*((e-b)/(d-a))}
That I want to apply this function to a data.frame that looks something like:
> data.frame("x1"=seq(55,75,5),"x2"=round(rnorm(5,50,10),0),"x3"=seq(30,10,-5))
x1 x2 x3
1 55 51 30
2 60 45 25
3 65 43 20
4 70 57 15
5 75 58 10
I want to apply fun to each separate row to create a new variable x4, but now comes the difficult part (to me at least..): for the arguments d and e I want to use the values x2 and x3 from the next row. So for the first row of the example that would mean: fun(a=55,b=51,c=30,d=45,e=25). I know that I can use mapply() to apply a function to each row, but I have no clue on how to tell mapply that it should use some values from the next row, or whether I should be looking for a different approach than mapply()?
Many thanks in advance!
Use mapply, but shift the fourth and fifth columns by one row. You can do it manually, or use taRifx::shift.
> dat
x1 x2 x3
1 55 25 30
2 60 58 25
3 65 59 20
4 70 68 15
5 75 43 10
library(taRifx)
> shift(dat$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