I want to create a column with sequential values but it gets its value from input from two other columns in the df. I want the value to sequentially count if either Team changes (between 1 and 2) or Event = x. Any help would be appreciated! See example below:
Team Event Value
1 1 a 1
2 1 a 1
3 2 a 2
4 2 x 3
5 2 a 3
6 1 a 4
7 1 x 5
8 1 a 5
9 2 x 6
10 2 a 6
This will do it...
df$Value <- cumsum(df$Event=="x" | c(1, diff(df$Team))!=0)
It takes the cumulative sum (i.e. of TRUE values) of those elements where either Event=="x" or the difference in successive values of Team is non-zero. An extra element is added at the start of the diff term to keep it the same length as the original.
Related
I have a data.frame with 1200 rows and 5 columns, where each row contains 5 values of one person. now i need to sort one column by size but I want the remaining columns to sort with the column, so that one column is sorted by increasing values and the other columns contain the values of the right persons. ( So that one row still contains data from one and the same person)
colnames(BAPlotDET) = c("fsskiddet", "fspiddet","avg", "diff","absdiff")
these are the column names of my data.frame and I wanna sort it by the column called "avg"
First of all, please always provide us with a reproducible example such as below. The sorting of a data frame by default sorts all columns.
vector <- 1:3
BAPlotDET <- data.frame(vector, vector, vector, vector, vector)
colnames(BAPlotDET) = c("fsskiddet", "fspiddet","avg", "diff","absdiff")
fsskiddet fspiddet avg diff absdiff
1 1 1 1 1 1
2 2 2 2 2 2
3 3 3 3 3 3
BAPlotDET <- BAPlotDET[order(-BAPlotDET$avg),]
> BAPlotDET
fsskiddet fspiddet avg diff absdiff
3 3 3 3 3 3
2 2 2 2 2 2
1 1 1 1 1 1
New to SO, but can't figure out how to get this code to work. I have a dataframe that is very large, and is set up like this:
Number Year Type Amount
1 1 A 5
1 2 A 2
1 3 A 7
1 4 A 1
1 1 B 5
1 2 B 11
1 3 B 0
1 4 B 2
This goes onto multiple for multiple numbers. I want to take this dataframe and make a new dataframe that has two of the rows together, but it would be nested (for example, row 1 and row 2, row 1 and row 3, row 1 and row 4, row 2 and row 3, row 2 and row 4) where each combination of each year is together within types and numbers.
Example output:
Number Year Type Amount Number Year Type Amount
1 1 A 5 1 2 A 2
1 1 A 5 1 3 A 7
1 1 A 5 1 4 A 1
1 2 A 2 1 3 A 7
1 2 A 2 1 4 A 1
1 3 A 7 1 4 A 1
I thought that I would do a for loop to loop within number and type, but I do not know how to make the rows paste from there, or how to ensure that I am only getting the combinations of the rows once. For example:
for(i in 1:n_number){
for(j in 1:n_type){
....}}
Any tips would be appreciated! I am relatively new to coding, so I don't know if I should be using a for loop at all. Thank you!
df <- data.frame(Number= rep(1,8),
Year = rep(c(1:4),2),
Type = rep(c('A','B'),each=4),
Amount=c(5,2,7,1,5,11,0,2))
My interpretation is that you want to create a dataframe with all row combinations, where Number and Type are the same and Year is different.
First suggestion - join on Number and Type, then remove rows that have different Year. I added an index to prevent redundant matches (1 with 2 and 2 with 1).
df$index <- 1:nrow(df)
out <- merge(df,df,by=c("Number","Type"))
out <- out[which(out$index.x>out$index.y & out$Year.x!=out$Year.y),]
Second suggestion - if you want to see a version using a loop.
out2 <- NULL
for (i in c(1:(nrow(df)-1))){
for (j in c((i+1):nrow(df))){
if(df[i,"Year"]!=df[j,"Year"] & df[i,"Number"]==df[j,"Number"] & df[i,"Type"]==df[j,"Type"]){
out2 <- rbind(out2,cbind(df[i,],df[j,]))
}
}
}
I have a table like the one below with 100's of rows of data.
ID RANK
1 2
1 3
1 3
2 4
2 8
3 3
3 3
3 3
4 6
4 7
4 7
4 7
4 7
4 7
4 6
I want to try to find a way to group the data by ID so that I can ReRank each group separately. The ReRank column is based on the Rank column and basically renumbering it starting at 1 from least to greatest, but it's important to note that the the number in the ReRank column can be put in more than once depending on the numbers in the Rank column .
In other words, the output needs to look like this
ID Rank ReRANK
1 3 2
1 2 1
1 3 2
2 4 1
2 8 2
3 3 1
3 3 1
3 3 1
For the life of me, I can't figure out how to be able to ReRank the the columns by the grouped columns and the value of the Rank columns.
This has been my best guess so far, but it definitely is not doing what I need it to do
ReRANK = mat.or.vec(length(RANK),1)
ReRANK[1] = counter = 1
for(i in 2:length(RANK)) {
if (RANK[i] != RANK[i-1]) { counter = counter + 1 }
ReRANK[i] = counter
}
Thank you in advance for the help!!
Here is a base R method using ave and rank:
df$ReRank <- ave(df$Rank, df$ID, FUN=function(i) rank(i, ties.method="min"))
The min argument in rank assures that the minimum ranking will occur when there are ties. the default is to take the mean of the ranks.
In the case that you have ties lower down in the groups, rank will count those lower values and then add continue with the next lowest value as the count of the lower values + 1. These values wil still be ordered and distinct. If you really want to have the count be 1, 2, 3, and so on rather than 1, 3, 6 or whatever depending on the number of duplicate values, here is a little hack using factor:
df$ReRank <- ave(df$Rank, df$ID, FUN=function(i) {
as.integer(factor(rank(i, ties.method="min"))))
Here, we use factor to build values counting from upward for each level. We then coerce it to be an integer.
For example,
temp <- c(rep(1, 3), 2,5,1,4,3,7)
[1] 2.5 2.5 2.5 5.0 8.0 2.5 7.0 6.0 9.0
rank(temp, ties.method="min")
[1] 1 1 1 5 8 1 7 6 9
as.integer(factor(rank(temp, ties.method="min")))
[1] 1 1 1 2 5 1 4 3 6
data
df <- read.table(header=T, text="ID Rank
1 2
1 3
1 3
2 4
2 8
3 3
3 3
3 3 ")
I have a few large data sets with many variables. There is a "key" variable that is the ID for the research participant. In these data sets, there are some IDs that are duplicated. I have written code to extract all data for duplicated IDs, but I would like a way to check if the remainder of the variables for those IDs are equal or not. Below is a simplistic example:
ID X Y Z
1 2 3 4
1 2 3 5
2 5 5 4
2 5 5 4
3 1 2 3
3 2 2 3
3 1 2 3
In this example, I would like to be able to identify that the rows for ID 1 and ID 3 are NOT all equal. Is there any way to do this in R?
You can use duplicated for this:
d <- read.table(text='ID X Y Z
1 2 3 4
1 2 3 5
2 5 5 4
2 5 5 4
3 1 2 3
3 2 2 3
3 1 2 3
4 1 1 1', header=TRUE)
tapply(duplicated(d), d[, 1], function(x) all(x[-1]))
## 1 2 3 4
## FALSE TRUE FALSE TRUE
Duplicated returns a vector indicating, for each row of a dataframe, whether it has been encountered earlier in the dataframe. We use tapply over this logical vector, splitting it in to groups based on ID and applying a function to each of these groups. The function we apply is all(x[-1]), i.e. we ask whether all rows for the group, other than the initial row, are duplicated?
Note that I added a group with a single record to ensure that the solution works in these cases as well.
Alternatively, you can reduce the dataframe to unique records with unique, and then split by ID and check whether each split has only a single row:
sapply(split(unique(d), unique(d)[, 1]), nrow) == 1
## 1 2 3 4
## FALSE TRUE FALSE TRUE
(If it's a big dataframe it's worth calculating unique(d) in advance rather than calling it twice.)
I need help simulating a dataset.
It is supposed to simulate all possible outcomes on a signal detection theory task (participants are presented with trials and have to decide whether or not they detected given signal). Now, I need a dataset of all possible values for varying number of trials.
Say, there are 6 trials, 5 with the signal present, 5 with the signal absent. I am only interested in correct detections (hits) and false alarms (Type I errors). A participant can correctly detect between 1 (I don't need 0's) and 5 and make the same number of false alarms. With all possible combinations, that would be dataset containing two variables with 5^2 cases each. To make things more complicated, even the number of trials is variable. The number of both signal and non-signal trials can vary between 1 and 20 but the total number of trials cannot be less than 3 (either 1 S trial and 2 Non-S trials, or the other way around). And for each possible combination of trials, there is a group of possible combinations of hits and false alarms.
What I need is a dataset with 5 variables (total N, N of S trials, N of Non-S trials, N of Hits, and N of False Alarms) with all the possible values.
EXAMPLE
Here are all possible data for total N of 4. Note that Signal + Noise = N_total and that N_Hit seq(1:Signal) and N_FA seq(1:Noise)
N_total Signal Noise N_Hit N_FA
4 1 3 1 1
4 1 3 1 2
4 1 3 1 3
4 2 2 1 1
4 2 2 1 2
4 2 2 2 1
4 2 2 2 2
4 3 1 1 1
4 3 1 2 1
4 3 1 3 1
I'm an R novice so any help at all would be much appreciated!
Hope the description is clear.
I created a function, which uses the number of trials as parameter.
myfunc <- function(n) {
# create a data frame of all combinations
grid <- expand.grid(rep(list(seq_len(n - 1)), 4))
# remove invalid combinations (keep valid ones)
grid <- grid[grid[3] <= grid[1] & # number of hits <= number of signals
grid[4] <= grid[2] & # false alarms <= noise
(grid[1] + grid[2]) == n , ] # signal and noise sum to total n
# remove signal and noise > 20
grid <- grid[!rowSums(grid[1:2] > 20), ]
# sort rows
grid <- grid[order(grid[1], grid[3], grid[4]), ]
# add total number of trials
res <- cbind(n, grid)
# remove row names, add column names and return the object
return(setNames("rownames<-"(res, NULL),
c("N_total", "Signal", "Noise", "N_Hit", "N_FA")))
}
Use the function:
> myfunc(4)
N_total Signal Noise N_Hit N_FA
1 4 1 3 1 1
2 4 1 3 1 2
3 4 1 3 1 3
4 4 2 2 1 1
5 4 2 2 1 2
6 4 2 2 2 1
7 4 2 2 2 2
8 4 3 1 1 1
9 4 3 1 2 1
10 4 3 1 3 1
How to apply this function to the values 3-40:
lapply(3:40, myfunc)
This will return a list of data frames.