I have a dataframe that looks like
day.of.week count
1 0 3
2 3 1
3 4 1
4 5 1
5 6 3
and another like
day.of.week count
1 0 17
2 1 6
3 2 1
4 3 1
5 4 5
6 5 1
7 6 13
I want to add the values from df1 to df2 based on day.of.week. I was trying to use ddply
total=ddply(merge(total, subtotal, all.x=TRUE,all.y=TRUE),
.(day.of.week), summarize, count=sum(count))
which almost works, but merge combines rows that have a shared value. For instance in the example above for day.of.week=5. Rather than being merged to two records each with count one, it is instead merged to one record of count one, so instead of total count of two I get a total count of one.
day.of.week count
1 0 3
2 0 17
3 1 6
4 2 1
5 3 1
6 4 1
7 4 5
8 5 1
9 6 3
10 6 13
There is no need to merge. You can simply do
ddply(rbind(d1, d2), .(day.of.week), summarize, sum_count = sum(count))
I have assumed that both data frames have identical column names day.of.week and count
In addition to the suggestion Ben gave you about using merge, you could also do this simply using subsetting:
d1 <- read.table(textConnection(" day.of.week count
1 0 3
2 3 1
3 4 1
4 5 1
5 6 3"),sep="",header = TRUE)
d2 <- read.table(textConnection(" day.of.week count1
1 0 17
2 1 6
3 2 1
4 3 1
5 4 5
6 5 1
7 6 13"),sep = "",header = TRUE)
d2[match(d1[,1],d2[,1]),2] <- d2[match(d1[,1],d2[,1]),2] + d1[,2]
> d2
day.of.week count1
1 0 20
2 1 6
3 2 1
4 3 2
5 4 6
6 5 2
7 6 16
This assumes no repeated day.of.week rows, since match will return only the first match.
Related
I have the following dataframes:
A<-data.frame(AgentNo=c(1,2,3,4,5,6),
N=c(2,5,6,1,9,0),
Rarity=c(1,2,1,1,2,2))
AgentNo N Rarity
1 1 2 1
2 2 5 2
3 3 6 1
4 4 1 1
5 5 9 2
6 6 0 2
B<-data.frame(Rank=c(1,5),
AgentNo.x=c(2,5),
AgentNo.y=c(1,4),
N=c(3,1),
Rarity=c(1,2))
Rank AgentNo.x AgentNo.y N Rarity
1 1 2 1 3 1
2 5 5 4 1 2
I would like to left join B onto A by columns "AgentNo"="AgentNo.y" and "N"="N" but rather than add new columns to A from B I want the same columns from A but where joined values have been updated and taken from B.
For any joined rows I want A.AgentNo to now be B.AgentNo.x, A.N to be B.N and A.Rarity to be B.Rarity. I would like to drop B.Rank and B.Agent.y completely.
The result should be:
Result<-data.frame(AgentNo=c(2,2,3,5,5,6), N=c(2,5,6,1,9,0), Rarity=c(1,2,1,1,2,2))
AgentNo N Rarity
1 2 3 1
2 2 5 2
3 3 6 1
4 5 1 2
5 5 9 2
6 6 0 2
After some data wrangling, you can use rows_update to update the rows of A by the values of B:
library(dplyr)
A <- A %>%
mutate(AgentNo.y = AgentNo)
B <- select(B, AgentNo = AgentNo.x, AgentNo.y, N, Rarity)
rows_update(A, B, by = "AgentNo.y") %>%
select(-AgentNo.y)
output
AgentNo N Rarity
1 2 3 1
2 2 5 2
3 3 6 1
4 5 1 1
5 5 9 2
6 6 0 2
This question already has an answer here:
Incrementing an ID number each time a condition is met
(1 answer)
Closed 1 year ago.
I have a data.frame ordered by ID with a column of numeric values that I would like to bin into groups, increasing the group number only when a certain target value/trigger is surpassed. I haven't had success with seq(), seq_along(), or data.table cumsum(), but I'm sure there must be a way
Example data.frame with desired group column below. In this example, the sequence generating the group column should increase only when a number >= 300 appears in the value column.
dat = data.frame(ID=1:10, value=c(0,2,1,12,68,300,41,0,72959,51), group=c(1,1,1,1,1,2,2,2,3,3))
> dat
ID value group
1 1 0 1
2 2 2 1
3 3 1 1
4 4 12 1
5 5 68 1
6 6 300 2
7 7 41 2
8 8 0 2
9 9 72959 3
10 10 51 3
We may use cumsum on a logical vector to create the group
library(dplyr)
dat %>%
mutate(group2 = cumsum(value >=300)+ 1)
-output
ID value group group2
1 1 0 1 1
2 2 2 1 1
3 3 1 1 1
4 4 12 1 1
5 5 68 1 1
6 6 300 2 2
7 7 41 2 2
8 8 0 2 2
9 9 72959 3 3
10 10 51 3 3
I am trying to create duplicate rows by group. The number of duplicate rows I want to create varies by group and I want to fix the value of one column Attended = 0.
A minimal working example of the data set DF I am working with is:
ID Demo Attended t
1 3 1 1
1 3 1 3
1 3 0 4
1 3 1 5
2 5 1 2
2 5 1 4
3 7 0 1
For the example above, suppose I want every person (ID) to have 5 rows, with Demo the same across all rows for each individual. Thus, I have to create 1 row for ID = 1, 3 for ID = 2 and 4 for ID = 4 (I would like to calculate these dynamically for each subgroup). For the new rows I generate I want Attended = 0 and t to take on the value of a missing index, so that the final output is:
ID Demo Attended t
1 3 1 1
1 3 1 3
1 3 0 4
1 3 1 5
1 3 0 2
2 5 1 2
2 5 1 4
2 5 0 1
2 5 0 3
2 5 0 5
3 7 0 1
3 7 0 2
3 7 0 3
3 7 0 4
3 7 0 5
I have been able to create duplicate rows by group, but haven't been able to figure out how to create different number of duplicates by participant and correctly fill in the index column t.
Here is what I have working:
DF %>%
group_by(ID) %>%
rbind(., mutate(., t = row_number()))
I have been trying to create the right number of duplicates using slice() and trying to get the t value to be exactly what I want but to no avail.
Any help would be appreciated!
One tidyverse possibility could be:
df %>%
complete(t, nesting(ID), fill = list(Attended = 0)) %>%
arrange(ID)
t ID Demo Attended
<int> <int> <int> <dbl>
1 1 1 3 1
2 2 1 3 0
3 3 1 3 1
4 4 1 3 0
5 5 1 3 1
6 1 2 5 0
7 2 2 5 1
8 3 2 5 0
9 4 2 5 1
10 5 2 5 0
11 1 3 7 0
12 2 3 7 0
13 3 3 7 0
14 4 3 7 0
15 5 3 7 0
For each ID, I want to return the value in the 'distance' column where the value becomes negative for the first time. If the value does not become negative at all, return the value 99 (or some other random number) for that ID. A sample data frame is given below.
df <- data.frame(ID=c(rep(1, 4),rep(2,4),rep(3,4),rep(4,4),rep(5,4)),distance=rep(1:4,5), value=c(1,4,3,-1,2,1,-4,1,3,2,-1,1,-4,3,2,1,2,3,4,5))
> df
ID distance value
1 1 1 1
2 1 2 4
3 1 3 3
4 1 4 -1
5 2 1 2
6 2 2 1
7 2 3 -4
8 2 4 1
9 3 1 3
10 3 2 2
11 3 3 -1
12 3 4 1
13 4 1 -4
14 4 2 3
15 4 3 2
16 4 4 1
17 5 1 2
18 5 2 3
19 5 3 4
20 5 4 5
The desired output is as follows
> df2
ID first_negative_distance
1 1 4
2 2 3
3 3 3
4 4 1
5 5 99
I tried but couldn't figure out how to do it through dplyr. Any help would be much appreciated. The actual data I'm working on has thousands of ID's with 30 different distance levels for each. Bear in mind that for any ID, there could be multiple instances of negative values. I just need the first one.
Edit:
Tried the solution proposed by AntonoisK.
> df%>%group_by(ID)%>%summarise(first_neg_dist=first(distance[value<0]))
first_neg_dist
1 4
This is the result I am getting. Does not match what Antonois got. Not sure why.
library(dplyr)
df %>%
group_by(ID) %>%
summarise(first_neg_dist = first(distance[value < 0]))
# # A tibble: 5 x 2
# ID first_neg_dist
# <dbl> <int>
# 1 1 4
# 2 2 3
# 3 3 3
# 4 4 1
# 5 5 NA
If you really prefer 99 instead of NA you can use
summarise(first_neg_dist = coalesce(first(distance[value < 0]), 99L))
instead.
For instance, I have a data frame like this, there is no duplicated number for each row, numbers are sorted by each row.
W1 W2 W3 W4
1 1 3 4 7
2 4 5 6 7
3 1 2 5 8
4 2 5 9 10
5 4 7 10 13
6 1 2 6 9
I want to get the row ID when 1/2/3.... appears, since 1 in row 1,3,6; 2 in row 3,4,6; 3 in row 1 only, ...; So my result would like this
1 1 3 6
2 3 4 6
3 1
4 1 2 5
5 2 3 4
......
I would do:
split(t(row(df)), unlist(t(df)))
And if you need empty levels to show up:
split(t(row(df)), factor(unlist(t(df)), 1:max(df)))
This should be a lot faster than looping via for example:
lapply(1:max(df), function(i) which(rowSums(df == i) > 0))