Say I have the following data.table:
dt <- data.table("x1"=c(1:10), "x2"=c(1:10),"y1"=c(10:1),"y2"=c(10:1), desc = c("a","a","a","b","b","b","b","b","c","c"))
I want to sum columns starting with an 'x', and sum columns starting with an 'y', by desc. At the moment I do this by:
dt[,.(Sumx=sum(x1,x2), Sumy=sum(y1,y2)), by=desc]
which works, but I would like to refer to all columns with "x" or "y" by their column names, eg using grepl().
Please could you advise me how to do so? I think I need to use with=FALSE, but cannot get it to work in combination with by=desc?
One-liner:
melt(dt, id="desc", measure.vars=patterns("^x", "^y"), value.name=c("x","y"))[,
lapply(.SD, sum), by=desc, .SDcols=x:y]
Long version (by #Frank):
First, you probably don't want to store your data like that. Instead...
m = melt(dt, id="desc", measure.vars=patterns("^x", "^y"), value.name=c("x","y"))
desc variable x y
1: a 1 1 10
2: a 1 2 9
3: a 1 3 8
4: b 1 4 7
5: b 1 5 6
6: b 1 6 5
7: b 1 7 4
8: b 1 8 3
9: c 1 9 2
10: c 1 10 1
11: a 2 1 10
12: a 2 2 9
13: a 2 3 8
14: b 2 4 7
15: b 2 5 6
16: b 2 6 5
17: b 2 7 4
18: b 2 8 3
19: c 2 9 2
20: c 2 10 1
Then you can do...
setnames(m[, lapply(.SD, sum), by=desc, .SDcols=x:y], 2:3, paste0("Sum", c("x", "y")))[]
# desc Sumx Sumy
#1: a 12 54
#2: b 60 50
#3: c 38 6
For more on improving the data structure you're working with, read about tidying data.
Use mget with grep is an option, where grep("^x", ...) returns the column names starting with x and use mget to get the column data, unlist the result and then you can calculate the sum:
dt[,.(Sumx=sum(unlist(mget(grep("^x", names(dt), value = T)))),
Sumy=sum(unlist(mget(grep("^y", names(dt), value = T))))), by=desc]
# desc Sumx Sumy
#1: a 12 54
#2: b 60 50
#3: c 38 6
Related
I am trying to call different columns of a data.table inside a loop, to get unique values of each column.
Consider the simple data.table below.
> df <- data.table(var_a = rep(1:10, 2),
+ var_b = 1:20)
> df
var_a var_b
1: 1 1
2: 2 2
3: 3 3
4: 4 4
5: 5 5
6: 6 6
7: 7 7
8: 8 8
9: 9 9
10: 10 10
11: 1 11
12: 2 12
13: 3 13
14: 4 14
15: 5 15
16: 6 16
17: 7 17
18: 8 18
19: 9 19
20: 10 20
My code works when I call for a specific column outside a loop,
> unique(df$var_a)
[1] 1 2 3 4 5 6 7 8 9 10
> unique(df[, var_a])
[1] 1 2 3 4 5 6 7 8 9 10
> unique(df[, "var_a"])
var_a
1: 1
2: 2
3: 3
4: 4
5: 5
6: 6
7: 7
8: 8
9: 9
10: 10
but not when I do so within a loop that goes through different columns of the data.table.
> for(v in c("var_a","var_b")){
+ print(v)
+ df$v
+ unique(df[, .v])
+ unique(df[, "v"])
+ }
[1] "var_a"
Error in `[.data.table`(df, , .v) :
j (the 2nd argument inside [...]) is a single symbol but column name '.v' is not found. Perhaps you intended DT[, ...v]. This difference to data.frame is deliberate and explained in FAQ 1.1.
>
> unique(df[, ..var_a])
Error in `[.data.table`(df, , ..var_a) :
Variable 'var_a' is not found in calling scope. Looking in calling scope because you used the .. prefix.
For the first problem, when you're referencing a column name indirectly, you can either use double-dot ..v syntax, or add with=FALSE in the data.table::[ construct:
for (v in c("var_a", "var_b")) {
print(v)
print(df$v)
### either one of these will work:
print(unique(df[, ..v]))
# print(unique(df[, v, with = FALSE]))
}
# [1] "var_a"
# NULL
# var_a
# <int>
# 1: 1
# 2: 2
# 3: 3
# 4: 4
# 5: 5
# 6: 6
# 7: 7
# 8: 8
# 9: 9
# 10: 10
# [1] "var_b"
# NULL
# var_b
# <int>
# 1: 1
# 2: 2
# 3: 3
# 4: 4
# 5: 5
# 6: 6
# 7: 7
# 8: 8
# 9: 9
# 10: 10
# 11: 11
# 12: 12
# 13: 13
# 14: 14
# 15: 15
# 16: 16
# 17: 17
# 18: 18
# 19: 19
# 20: 20
# var_b
But this just prints it without changing anything. If all you want to do is look at unique values within each column (and not change the underlying frame), then I'd likely go with
lapply(df[,.(var_a, var_b)], unique)
# $var_a
# [1] 1 2 3 4 5 6 7 8 9 10
# $var_b
# [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
which shows the name and unique values. The use of lapply (whether on df as a whole or a subset of columns) is also preferable to another recommendation to use apply(df, 2, unique), though in this case it returns the same results.
Use .subset2 to refer to a column by its name:
for(v in c("var_a","var_b")) {
print(unique(.subset2(df, v)))
}
following the information on the first error, this would be the correct way to call in a loop:
for(v in c("var_a","var_b")){
print(unique(df[, ..v]))
}
# won't print all the lines
as for the second error you have not declared a variable called "var_a", it looks like you want to select by name.
# works as you have shown
unique(df[, "var_a"])
# works once the variable is declared
var_a <- "var_a"
unique(df[, ..var_a])
You may also be interested in the env param of data.table (see development version); here is an illustration below, but you could use this in a loop too.
v="var_a"
df[, v, env=list(v=v)]
Output:
[1] 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
I have two tables FDate and Task as follows:
FDate
Date Cycle Task
1: 1 90 D
2: 2 100 A
3: 3 130 B
4: 3 130 C
5: 4 180 <NA>
6: 5 200 A
7: 5 200 D
8: 6 230 <NA>
Task
Date Task
1 NA A
2 NA B
3 NA C
4 6 D
I want to write the Task name of same Date from table Task to table FDate. This is the code I try
for (i in 1:nrow(Task)) {
FDate$Task[FDate$Date %in% Task$Date[i]]<-Task$Task[i]
}
This is the output
Date Cycle Task
1: 1 90 D
2: 2 100 A
3: 3 130 B
4: 3 130 C
5: 4 180 <NA>
6: 5 200 A
7: 5 200 D
8: 6 230 4
I expect the output is D, not 4. I can't find what is wrong?
The issue is that the column is factor which gets coerced to integer storage mode value. Convert it to character before looping
FDate$Task <- as.character(FDate$Task)
Task$Task <- as.character(Task$Task)
Better, would be to use stringsAsFactors = FALSE either while reading (read.csv/read.table) or if we are creating with data.frame as in both cases, the default option is stringsAsFactors = TRUE and it can create some issues similar to this.
Also, this can be done with a join (assuming the datasets are data.table
library(data.tabl)
FDate[na.omit(df2), Task := i.Task,on = .(Date)]
FDate
# Date Cycle Task
#1: 1 90 D
#2: 2 100 A
#3: 3 130 B
#4: 3 130 C
#5: 4 180 <NA>
#6: 5 200 A
#7: 5 200 D
#8: 6 230 D
NOTE: changed the second data.table identifier to 'df2' instead of 'Task' as there is a column 'Task' in each dataset
Finding the last position of a vector that is less than a given value is fairly straightforward (see e.g. this question
But, doing this line by line for a column in a data.frame or data.table is horribly slow. For example, we can do it like this (which is ok on small data, but not good on big data)
library(data.table)
set.seed(123)
x = sort(sample(20,5))
# [1] 6 8 15 16 17
y = data.table(V1 = 1:20)
y[, last.x := tail(which(x <= V1), 1), by = 1:nrow(y)]
# V1 last.x
# 1: 1 NA
# 2: 2 NA
# 3: 3 NA
# 4: 4 NA
# 5: 5 NA
# 6: 6 1
# 7: 7 1
# 8: 8 2
# 9: 9 2
# 10: 10 2
# 11: 11 2
# 12: 12 2
# 13: 13 2
# 14: 14 2
# 15: 15 3
# 16: 16 4
# 17: 17 5
# 18: 18 5
# 19: 19 5
# 20: 20 5
Is there a fast, vectorised way to get the same thing? Preferably using data.table or base R.
You may use findInterval
y[ , last.x := findInterval(V1, x)]
Slightly more convoluted using cut. But on the other hand, you get the NAs right away:
y[ , last.x := as.numeric(cut(V1, c(x, Inf), right = FALSE))]
Pretty simple in base R
x<-c(6L, 8L, 15L, 16L, 17L)
y<-1:20
cumsum(y %in% x)
[1] 0 0 0 0 0 1 1 2 2 2 2 2 2 2 3 4 5 5 5 5
I have the following data:
a <- data.frame(ID=c("A","B","Z","H"), a=c(0,1,2,45), b=c(3,4,5,22), c=c(6,7,8,3))
> a
ID a b c
1 A 0 3 6
2 B 1 4 7
3 Z 2 5 8
4 H 45 22 3
b <- data.frame(ID=c("A","B","E","W","Z","H"), a=c(9,10,11,39,5,0), b=c(4,2,7,54,12,34), c=c(12,0,34,23,13,14))
> b
ID a b c
1: A 9 4 12
2: B 10 2 0
3: E 11 7 34
4: W 39 54 23
5: Z 5 12 13
6: H 0 34 14
I want to merge both dataframes, keeping only rows of data.frame a and summarize the same columns, so at the end I get:
> z
ID a b c
1 A 9 7 18
2 B 11 6 7
3 Z 7 17 21
4 H 45 56 17
So far I have tried the following:
merge(a,b,by="ID",all.x=T,all.y=F)
> merge(a,b,by="ID",all.x=T,all.y=F)
ID a.x b.x c.x a.y b.y c.y
1 A 0 3 6 9 4 12
2 B 1 4 7 10 2 0
3 H 45 22 3 0 34 14
4 Z 2 5 8 5 12 13
> join(a,b,type="left",by="ID")
ID a b c a b c
1 A 0 3 6 9 4 12
2 B 1 4 7 10 2 0
3 Z 2 5 8 5 12 13
4 H 45 22 3 0 34 14
I cannot manage to summarize the columns.
My dataframe is pretty big so if the solution can speed up things that would even be better.
If your data.frame is very big, then you may consider this option:
library(data.table)
## convert data.frame to data.table
setDT(a)
## convert data.frame to data.table
setDT(b)
## merge the two data.tables
c <- merge(a,b,by='ID')
## extract names of all columns except the first one i.e. ID
col_names <- colnames(a)[-1]
## query building
col_1 <- paste0(col_names,'.x')
col_2 <- paste0(col_names,'.y')
cols <- paste(col_1,col_2,sep=',')
cols_2 <- paste0(col_names," = sum(",cols,")")
cols_3 <- paste(cols_2,collapse=',')
query <- paste0("z <- c[,.(",cols_3,"),by=ID]")
## query execution
eval(parse(text = query))
This works at least for your example:
a <- data.frame(ID=c("A","B","Z","H"), a=c(0,1,2,45), b=c(3,4,5,22), c=c(6,7,8,3))
b <- data.frame(ID=c("A","B","E","W","Z","H"), a=c(9,10,11,39,5,0), b=c(4,2,7,54,12,34), c=c(12,0,34,23,13,14))
match_a <- na.omit(match(b$ID, a$ID))
match_b <- na.omit(match(a$ID, b$ID))
df <- cbind(ID = a$ID[match_a], a[match_a, -1] + b[match_b, -1])
First, get matching rows from a in b and vice versa, so we can be sure that we only have those rows that appear in both data frames (and we now know their row-indices in both data frames). Then, simply use vectorized additions for those matching rows, but omit ID, as factor cannot be summed up; add ID back manually.
You cannot directly add both data frame is because both the data frames are of unequal size. To make them of equal size you can check for IDs in a which are present in b and then add them element wise.
new <- b[b$ID %in% a$ID, ]
cbind(ID = a$ID, a[-1] + new[-1])
# ID a b c
#1 A 9 7 18
#2 B 11 6 7
#3 Z 7 17 21
#4 H 45 56 17
i hope this one isn´t stupid.
I have two dataframes with Variables ID and gender/sex. In df1, there are NAs. In df2, the variable is complete. I want to complete the column in df1 with the values from df2.
(In df1 the variable is called "gender". In df2 it is called "sex".)
Here is what i tried so far:
#example-data
ID<-seq(1,30,by=1)
df1<-as.data.frame(ID)
df2<-df1
df1$gender<-c(NA,"2","1",NA,"2","2","2","2","2","2",NA,"2","1","1",NA,"2","2","2","2","2","1","2","2",NA,"2","2","2","2","2",NA)
df2$sex<-c("2","2","1","2","2","2","2","2","2","2","2","2","1","1","2","2","2","2","2","2","1","2","2","2","2","2","2","2","2","2")
#Approach 1:
NAs.a <- is.na(df1$gender)
df1$gender[NAs.a] <- df2[match(df1$ID[NAs.a], df2$ID),]$sex
#Approach 2 (i like dplyr a lot, perhaps there´s a way to use it):
library("dplyr")
temp<-df2 %>% select(ID,gender)
#EDIT:
#df<-left_join(df1$gender,df2$gender, by="ID")
df<-left_join(df1,df2, by="ID")
Thank you very much.
Here's a quick solution using data.tables binary join this will join only gender with sex and leave all the rest of the columns untouched
library(data.table)
setkey(setDT(df1), ID)
df1[df2, gender := i.sex][]
# ID gender
# 1: 1 2
# 2: 2 2
# 3: 3 1
# 4: 4 2
# 5: 5 2
# 6: 6 2
# 7: 7 2
# 8: 8 2
# 9: 9 2
# 10: 10 2
# 11: 11 2
# 12: 12 2
# 13: 13 1
# 14: 14 1
# 15: 15 2
# 16: 16 2
# 17: 17 2
# 18: 18 2
# 19: 19 2
# 20: 20 2
# 21: 21 1
# 22: 22 2
# 23: 23 2
# 24: 24 2
# 25: 25 2
# 26: 26 2
# 27: 27 2
# 28: 28 2
# 29: 29 2
# 30: 30 2
This would probably be the simplest with base R.
idx <- is.na(df1$gender)
df1$gender[idx] = df2$sex[idx]
You could do
df1 %>% select(ID) %>% left_join(df2, by = "ID")
# ID sex
#1 1 2
#2 2 2
#3 3 1
#4 4 2
#5 5 2
#6 6 2
#.. ..
This assumes - as in the example - that all ID's from df1 are also present in df2 and have a sex/gender information there.
If you have other columns in your data you could also try this instead:
df1 %>% select(-gender) %>% left_join(df2[c("ID", "sex")], by = "ID")