I am trying to apply different functions to different rows based on the value of a string in an adjacent column. My dataframe looks like this:
type size
A 1
B 3
A 4
C 2
C 5
A 4
B 32
C 3
and I want to apply different functions to types A, B, and C, to give a third column column "size2." For example, let's say the following functions apply to A, B, and C:
for A: size2 = 3*size
for B: size2 = size
for C: size2 = 2*size
I'm able to do this for each type separately using this code
df$size2 <- ifelse(df$type == "A", 3*df$size, NA)
df$size2 <- ifelse(df$type == "B", 1*df$size, NA)
df$size2 <- ifelse(df$type == "C", 2*df$size, NA)
However, I can't seem to do it for all of the types without erasing all of the other values. I tried to use this code to limit the application of the function to only those values that were NA (i.e., keep existing values and only fill in NA values), but it didn't work using this code:
df$size2 <- ifelse(is.na(df$size2), ifelse(df$type == "C", 2*df$size, NA), NA)
Does anyone have any ideas? Is it possible to use some kind of AND statement with "is.na(df$size2)" and "ifelse(df$type == "C""?
Many thanks!
This might be a might more R-ish (and I called my dataframe 'dat' instead of 'df' since df is a commonly used function.
> facs <- c(3,1,2)
> dat$size2= dat$size* facs[ match( dat$type, c("A","B","C") ) ]
> dat
type size size2
1 A 1 3
2 B 3 3
3 A 4 12
4 C 2 4
5 C 5 10
6 A 4 12
7 B 32 32
8 C 3 6
The match function is used to construct indexes to supply to the extract function [.
if you want you can nest the ifelses:
df$size2 <- ifelse(df$type == "A", 3*df$size,
ifelse(df$type == "B", 1*df$size,
ifelse(df$type == "C", 2*df$size, NA)))
# > df
# type size size2
#1 A 1 3
#2 B 3 3
#3 A 4 12
#4 C 2 4
#5 C 5 10
#6 A 4 12
#7 B 32 32
#8 C 3 6
This could do it like this, creating separate logical vectors for each type:
As <- df$type == 'A'
Bs <- df$type == 'B'
Cs <- df$type == 'C'
df$size2[As] <- 3*df$size[As]
df$size2[Bs] <- df$size[Bs]
df$size2[Cs] <- 2*df$size[Cs]
but a more direct approach would be to create a separate lookup table like this:
df$size2 <- c(A=3,B=1,C=2)[as.character(df$type)] * df$size
Related
Suppose I have a vector of values, such as:
A C A B A C C B B C C A A A B B B B C A
I would like to create a new vector that, for each element, contains the number of elements since that element was last seen. So, for the vector above,
NA NA 2 NA 2 4 1 4 1 3 1 7 1 1 6 1 1 1 8 6
(where NA indicates that this is the first time the element has been seen).
For example, the first and second A are in position 1 and 3 respectively, a difference of 2; the third and fourth A are in position 4 and 11, a difference of 7, and so on.
Is there a pre-built pipe-compatible function that does this?
I hacked together this function to demonstrate:
# For reproducibility
set.seed(1)
# Example vector
x = sample(LETTERS[1:3], size = 20, replace = TRUE)
compute_lag_counts = function(x, first_time = NA){
# return vector to fill
lag_counts = rep(-1, length(x))
# values to match
vals = unique(x)
# find all positions of all elements in the target vector
match_list = grr::matches(vals, x, list = TRUE)
# compute the lags, then put them in the appropriate place in the return vector
for(i in seq_along(match_list))
lag_counts[x == vals[i]] = c(first_time, diff(sort(match_list[[i]])))
# return vector
return(lag_counts)
}
compute_lag_counts(x)
Although it seems to do what it is supposed to do, I'd rather use someone else's efficient, well-tested solution! My searching has turned up empty, which is surprising to me given that it seems like a common task.
Or
ave(seq.int(x), x, FUN = function(x) c(NA, diff(x)))
# [1] NA NA 2 NA 2 4 1 4 1 3 1 7 1 1 6 1 1 1 8 6
We calculate the first difference of the indices for each group of x.
A data.table option thanks to #Henrik
library(data.table)
dt = data.table(x)
dt[ , d := .I - shift(.I), x]
dt
Here's a function that would work
compute_lag_counts <- function(x) {
seqs <- split(seq_along(x), x)
unsplit(Map(function(i) c(NA, diff(i)), seqs), x)
}
compute_lag_counts (x)
# [1] NA NA 2 NA 2 4 1 4 1 3 1 7 1 1 6 1 1 1 8 6
Basically you use split() to separate the indexes where values appear by each unique value in your vector. Then we use the different between the index where they appear to calculate the distance to the previous value. Then we use unstack to put those values back in the original order.
An option with dplyr by taking the difference of adjacent sequence elements after grouping by the original vector
library(dplyr)
tibble(v1) %>%
mutate(ind = row_number()) %>%
group_by(v1) %>%
mutate(new = ind - lag(ind)) %>%
pull(new)
#[1] NA NA 2 NA 2 4 1 4 1 3 1 7 1 1 6 1 1 1 8 6
data
v1 <- c("A", "C", "A", "B", "A", "C", "C", "B", "B", "C", "C", "A",
"A", "A", "B", "B", "B", "B", "C", "A")
I am looking to do the following in a more elegant manner in R. I believe there is a way but just cant wrap my head around it. Following is the problem.
I have a df which contains NAs. However, I want to make the NAs into zeros where if the sum of the NA is not equal to zero and if the sum is NA then leave as NA. The example below should make it clear.
A<-c("A", "A", "A", "A",
"B","B","B","B",
"C","C","C","C")
B<-c(1,NA,NA,1,NA,NA,NA,NA,2,1,2,3)
data<-data.frame(A,B)
Following is how the data looks like
A B
1 A 1
2 A NA
3 A NA
4 A 1
5 B NA
6 B NA
7 B NA
8 B NA
9 C 2
10 C 1
11 C 2
12 C 3
And am looking to get a result as per the following
A B
1 A 1
2 A 0
3 A 0
4 A 1
5 B NA
6 B NA
7 B NA
8 B NA
9 C 2
10 C 1
11 C 2
12 C 3
I know I can use inner join by creating a table first and and then making an IF statement based on that table but I was wondering if there is a way to do it in one or two lines of code in R.
Following is the solution related to the inner join I was referring to
sum_NA <- function(x) if(all(is.na(x))) NA_integer_ else sum(x, na.rm=TRUE)
data2 <- data %>% group_by(A) %>% summarize(x = sum_NA(B), Y =
ifelse(is.na(x), TRUE, FALSE))
data2
data2_1 <- right_join(data, data2, by = "A")
data <- mutate(data2_1, B = ifelse(Y == FALSE & is.na(B), 0,B))
data <- select(data, - Y,-x)
data
Maybe solution like this would work:
data[is.na(B) & A %in% unique(na.omit(data)$A), ]$B <- 0
Here you're asking:
if B is NA
if A is within letters that have non-NA values
Then make those values 0.
Or similarly, with ifelse():
data$B <- ifelse(is.na(data$B) & data$A %in% unique(na.omit(data)$A), 0, data$B)
or with dplyr its:
library(dplyr)
data %>%
mutate(B=ifelse(is.na(B) & A %in% unique(na.omit(data)$A), 0, B))
Say I have a dataframe:
df <- data.frame(rbind(c(10,1,5,4), c(6,0,3,10), c(7,1,10,10)))
colnames(df) <- c("a", "b", "c", "d")
df
a b c d
10 1 5 4
6 0 3 10
7 1 10 10
And a vector of numbers (which correspond to the four column names a,b,c,d)
threshold <- c(7,1,5,8)
I need to compare each row in the data frame to the vector. When the value in the data frame meets or exceeds that in the vector, I need to return the column name. The output would be:
a b c d cols
10 1 5 4 a,b,c #10>7, 1>=1, 5>=5
6 0 3 10 d #10>8
7 1 10 10 a,b,c,d ##7>=7, 1>=1, 10>=5, 10>-8
The column cols can be a string that simply lists the columns where the value is exceeded.
Is there any clever way to do this? I'm migrating an old Excel function and I can write a loop or something, but I thought there almost had to be a better way.
You do not need which and the desired output is for comma separated values:
df$cols <- apply(df[-1], 1, function(x) toString(names(df)[-1][x >= threshold]))
df
id a b c d cols
1 aa 10 1 5 4 a, b, c
2 bb 6 0 3 10 d
3 cc 7 1 10 10 a, b, c, d
We can also try
i1 <- which(df >=threshold[col(df)], arr.ind=TRUE)
df$cols <- unname(tapply(names(df)[i1[,2]], i1[,1], toString))
df$cols
#[1] "a, b, c" "d" "a, b, c, d"
You can try this:
df$cols <- apply(df[, 2:5], 1, function(x) names(df[, 2:5])[which(x >= threshold)])
Ok, I have a matrix of values with certain identifiers, such as:
A 2
B 3
C 4
D 5
E 6
F 7
G 8
I would like to pull out a subset of these values (using R) based on a list of the identifiers ("B", "D", "E") for example, so I would get the following output:
B 3
D 5
E 6
I'm sure there's an easy way to do this (some sort of apply?) but I can't seem to figure it out. Any ideas? Thanks!
If the letters are the row names, then you can just use this:
m <- matrix(2:8, dimnames = list(LETTERS[1:7], NULL))
m[c("B","D","E"),]
# B D E
# 3 5 6
Note that there is a subtle but very important difference between: m[c("B","D","E"),] and m[rownames(m) %in% c("B","D","E"),]. Both return the same rows, but not necessarily in the same order.
The former uses the character vector c("B","D","E") as in index into m. As a result, the rows will be returned in the order of character vector. For instance:
# result depends on order in c(...)
m[c("B","D","E"),]
# B D E
# 3 5 6
m[c("E","D","B"),]
# E D B
# 6 5 3
The second method, using %in%, creates a logical vector with length = nrow(m). For each element, that element is T if the row name is present in c("B","D","E"), and F otherwise. Indexing with a logical vector returns rows in the original order:
# result does NOT depend on order in c(...)
m[rownames(m) %in% c("B","D","E"),]
# B D E
# 3 5 6
m[rownames(m) %in% c("E","D","B"),]
# B D E
# 3 5 6
This is probably more than you wanted to know...
Your matrix:
> m <- matrix(2:8, dimnames = list(LETTERS[1:7]))
You can use %in% to filter out the desired rows. If the original matrix only has a single column, using drop = FALSE will keep the matrix structure. Otherwise it will be converted to a named vector.
> m[rownames(m) %in% c("B", "D", "E"), , drop = FALSE]
# [,1]
# B 3
# D 5
# E 6
I have a DF where I want to add a new variable called "B" into the 2nd position.
A C D
1 1 5 2
2 3 3 7
3 6 2 3
4 6 4 8
5 1 1 2
Anyone have an idea?
The easiest way would be to add the columns you want and then reorder them:
dat$B <- 1:5
newdat <- dat[, c("A", "B", "C", "D")]
Another way:
newdat <- cbind(dat[1], B=1:5, dat[,2:3])
If you're concerned about overhead, perhaps a data.table solution? (With help from this answer):
library(data.table)
dattable <- data.table(dat)
dattable[,B:=1:5]
setcolorder(dattable, c("A", "B", "C", "D"))
dat$B <- 1:5
ind <- c(1:which(names(data) == "A"),ncol(data),(which(names(data) == "A")+1):ncol(data)-1)
data <- data[,ind]
Create the variable at the end of the data.frame and then using an indicator vector signaling how to reorder the columns. ind is just a vector of numbers