My data looks something like this:
ID CSEX MID CMOB CYRB 1ST 2ND
1 1 1 1 1991 0 1
2 1 1 7 1989 1 0
3 2 2 1 1985 1 0
4 2 2 11 1985 0 1
5 1 2 9 1994 0 0
6 2 3 4 1992 1 0
7 2 4 2 1992 0 1
8 1 4 10 1983 1 0
With ID = child ID, CSEX = child sex, MID = mother ID, CMOB = month of birth and CYRB = year of birth, 1st = first born dummy, 2nd = second born dummy.
And I'm trying to make a dummy variable that takes the value 1 if the first two children born into a family (i.e. with the same MID) are the same sex.
I tried
Identifiers_age <- Identifiers_age %>% group_by(MPUBID) %>%
mutate(samesex =
as.numeric(((first == 1 & CSEX == 1) & (second == 1 & CSEX == 1))
| (first == 1 & CSEX == 2) & (second == 1 & CSEX ==2))))
But clearly this still only check the condition for each individual ID rather than by MID so returns a dummy which always takes value = 0.
Thanks
Edit for expected output:
ID CSEX MID CMOB CYRB 1ST 2ND SAMESEX
1 1 1 1 1991 0 1 1
2 1 1 7 1989 1 0 1
3 2 2 1 1985 1 0 1
4 2 2 11 1985 0 1 1
5 1 2 9 1994 0 0 1
6 2 3 4 1992 1 0 0
7 2 4 2 1992 0 1 0
8 1 4 10 1983 1 0 0
i.e. for any individual that is in a family where the first two children born are of the same sex, the dummy SAMESEX = 1
Edit2 (What I showed before was just an example I made, for the true dataset calling structure gives):
CPUBID MPUBID CSEX CMOB CYRB first second
<int> <int> <int> <int> <int> <dbl> <dbl>
1 201 2 2 3 1993 1 0
2 202 2 2 11 1994 0 1
3 301 3 2 6 1981 1 0
4 302 3 2 10 1983 0 1
5 303 3 2 4 1986 0 0
6 401 4 1 8 1980 1 0
7 403 4 2 3 1997 0 1
8 801 8 2 3 1976 1 0
9 802 8 1 5 1979 0 1
10 803 8 2 9 1982 0 0
and str:
Classes ‘grouped_df’, ‘tbl_df’, ‘tbl’ and 'data.frame': 11512 obs. of 7 variables:
$ CPUBID : int 201 202 301 302 303 401 403 801 802 803 ...
$ MPUBID : int 2 2 3 3 3 4 4 8 8 8 ...
$ CSEX : int 2 2 2 2 2 1 2 2 1 2 ...
$ CMOB : int 3 11 6 10 4 8 3 3 5 9 ...
$ CYRB : int 1993 1994 1981 1983 1986 1980 1997 1976 1979 1982 ...
$ first : num 1 0 1 0 0 1 0 1 0 0 ...
$ second : num 0 1 0 1 0 0 1 0 1 0 ...
May be this helps
library(dplyr)
Identifiers_age %>%
group_by(MID) %>%
mutate(ind1 = CSEX *`1ST`,
ind2 = CSEX *`2ND`,
SAMESEX = as.integer(n_distinct(c(ind1[ind1!=0],
ind2[ind2!=0]))==1 & sum(ind1) >0 & sum(ind2) > 0)) %>%
select(-ind1, -ind2)
# ID CSEX MID CMOB CYRB 1ST 2ND SAMESEX
# <int> <int> <int> <int> <int> <int> <int> <int>
#1 1 1 1 1 1991 0 1 1
#2 2 1 1 7 1989 1 0 1
#3 3 2 2 1 1985 1 0 1
#4 4 2 2 11 1985 0 1 1
#5 5 1 2 9 1994 0 0 1
#6 6 2 3 4 1992 1 0 0
#7 7 2 4 2 1992 0 1 0
#8 8 1 4 10 1983 1 0 0
Or it can be made slightly compact with
Identifiers_age %>%
group_by(MID) %>%
mutate(SAMESEX = as.integer(n_distinct(c(CSEX * NA^!`1ST`, CSEX * NA^!`2ND`),
na.rm = TRUE)==1 & sum(`1ST`) > 0 & sum(`2ND`) > 0))
data
Identifiers_age <- structure(list(ID = 1:8, CSEX = c(1L, 1L, 2L, 2L, 1L,
2L, 2L,
1L), MID = c(1L, 1L, 2L, 2L, 2L, 3L, 4L, 4L), CMOB = c(1L, 7L,
1L, 11L, 9L, 4L, 2L, 10L), CYRB = c(1991L, 1989L, 1985L, 1985L,
1994L, 1992L, 1992L, 1983L), `1ST` = c(0L, 1L, 1L, 0L, 0L, 1L,
0L, 1L), `2ND` = c(1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L)), .Names = c("ID",
"CSEX", "MID", "CMOB", "CYRB", "1ST", "2ND"), class = "data.frame",
row.names = c(NA, -8L))
Related
I have a dataframe as follows
head(data)
subject block trial timeLeft timeRight stim1 stim2 Chosen
1 1 13 0 0 0 2 1 2
2 1 13 1 0 1 3 2 2
3 1 13 3 0 0 3 1 1
4 1 13 4 2 0 2 3 3
5 1 13 6 1 1 1 3 1
6 1 13 7 2 2 2 1 1
...
454 1006 14 0 0 0 6 5 5
455 1006 14 1 0 0 6 4 6
456 1006 14 3 0 1 4 5 4
457 1006 14 4 1 1 4 5 4
458 1006 14 6 1 2 6 4 6
my objective is to group by subject and block and to only keep rows prior and including where both timeLeft and timeRight =0
in this case the output would be
subject block trial timeLeft timeRight stim1 stim2 Chosen
1 1 13 0 0 0 2 1 2
2 1 13 1 0 1 3 2 2
3 1 13 3 0 0 3 1 1
...
454 1006 14 0 0 0 6 5 5
455 1006 14 1 0 0 6 4 6
Thank you in advance!
here is the structure of the data
'data.frame': 64748 obs. of 8 variables:
$ subject : num 1 1 1 1 1 1 1 1 1 1 ...
$ block : int 13 13 13 13 13 13 13 13 13 13 ...
$ trial : int 0 1 3 4 6 7 9 10 12 13 ...
$ timeLeft : int 0 0 0 2 1 2 2 1 3 4 ...
$ timeRight: int 0 1 0 0 1 2 1 3 4 4 ...
$ stim1 : int 2 3 3 2 1 2 2 3 2 2 ...
$ stim2 : int 1 2 1 3 3 1 3 1 1 1 ...
$ Chosen : int 2 2 1 3 1 1 2 1 2 2 ...
You may do this with the help of custom function -
library(dplyr)
select_rows <- function(timeLeft, timeRight) {
inds <- which(timeLeft == 0 & timeRight == 0)
if(length(inds) >= 2) inds[1]:inds[2]
else 0
}
data %>%
group_by(subject, block) %>%
slice(select_rows(timeLeft, timeRight)) %>%
ungroup
# subject block trial timeLeft timeRight stim1 stim2 Chosen
# <int> <int> <int> <int> <int> <int> <int> <int>
#1 1 13 0 0 0 2 1 2
#2 1 13 1 0 1 3 2 2
#3 1 13 3 0 0 3 1 1
#4 1006 14 0 0 0 6 5 5
#5 1006 14 1 0 0 6 4 6
If the data is huge you may also do this with data.table -
library(data.table)
setDT(data)[, .SD[select_rows(timeLeft, timeRight)], .(subject, block)]
data
It is easier to help if you provide data in a reproducible format
data <- structure(list(subject = c(1L, 1L, 1L, 1L, 1L, 1L, 1006L, 1006L,
1006L, 1006L, 1006L), block = c(13L, 13L, 13L, 13L, 13L, 13L,
14L, 14L, 14L, 14L, 14L), trial = c(0L, 1L, 3L, 4L, 6L, 7L, 0L,
1L, 3L, 4L, 6L), timeLeft = c(0L, 0L, 0L, 2L, 1L, 2L, 0L, 0L,
0L, 1L, 1L), timeRight = c(0L, 1L, 0L, 0L, 1L, 2L, 0L, 0L, 1L,
1L, 2L), stim1 = c(2L, 3L, 3L, 2L, 1L, 2L, 6L, 6L, 4L, 4L, 6L
), stim2 = c(1L, 2L, 1L, 3L, 3L, 1L, 5L, 4L, 5L, 5L, 4L), Chosen = c(2L,
2L, 1L, 3L, 1L, 1L, 5L, 6L, 4L, 4L, 6L)), class = "data.frame", row.names =
c("1", "2", "3", "4", "5", "6", "454", "455", "456", "457", "458"))
If you want to keep all rows before timeLeft and timeRight are 0, you can try this way.
Data
subject block trial timeLeft timeRight stim1 stim2 Chosen
1 1 13 0 0 0 2 1 2
2 1 13 1 0 1 3 2 2
3 1 13 3 0 0 3 1 1
4 1 13 4 2 0 2 3 3
5 1 13 6 1 1 1 3 1
6 1 13 7 2 2 2 1 1
7 1006 14 0 0 1 6 5 5
8 1006 14 0 0 0 6 5 5
9 1006 14 1 0 0 6 4 6
10 1006 14 3 0 1 4 5 4
11 1006 14 4 1 1 4 5 4
12 1006 14 6 1 2 6 4 6
I add one more row for subject:1006, to make first row is not 0,0.
Code
df %>%
group_by(subject) %>%
mutate(key = max(which((timeLeft == 0 & timeRight ==0)))) %>%
slice(1:key)
subject block trial timeLeft timeRight stim1 stim2 Chosen key
<int> <int> <int> <int> <int> <int> <int> <int> <int>
1 1 13 0 0 0 2 1 2 3
2 1 13 1 0 1 3 2 2 3
3 1 13 3 0 0 3 1 1 3
4 1006 14 0 0 1 6 5 5 3
5 1006 14 0 0 0 6 5 5 3
6 1006 14 1 0 0 6 4 6 3
You can filter for only rows that meet the condition and then group
data %>%
filter(timeLeft > 0 & timeRight > 0) %>%
group_by(subject, block)
I'm having a trouble figuring out how to group variables to achieve the desired result from dplyr. I have an experimental dataset set up like this:
subject task_phase block_number trial_number ResponseCorrect
<chr> <chr> <dbl> <dbl> <dbl>
1 268301377 1 1 2 1
2 268301377 1 1 3 1
3 268301377 1 1 4 1
4 268301377 1 2 2 -1
5 268301377 1 2 3 1
6 268301377 1 2 4 1
7 268301377 1 3 2 1
8 268301377 1 3 3 -1
9 268301377 1 3 4 1
10 268301377 2 1 50 1
11 268301377 2 1 51 1
12 268301377 2 1 52 1
13 268301377 2 2 37 -1
14 268301377 2 2 38 1
15 268301377 2 2 39 1
16 268301377 2 3 41 -1
17 268301377 2 3 42 -1
18 268301377 2 3 43 1
I'm hoping to sum the consecutive "correct" responses, and to have this tally "reset" each time there was an incorrect response:
subject task_phase block_number trial_number ResponseCorrect ConsecutiveCorrect
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 268301377 1 1 1 1 1
2 268301377 1 1 2 1 2
3 268301377 1 1 3 1 3
4 268301377 1 2 1 -1 0
5 268301377 1 2 2 1 1
6 268301377 1 2 3 1 2
7 268301377 1 3 1 1 1
8 268301377 1 3 2 -1 0
9 268301377 1 3 3 1 1
10 268301377 2 1 1 1 1
11 268301377 2 1 2 1 2
12 268301377 2 1 3 1 3
13 268301377 2 2 1 -1 0
14 268301377 2 2 2 1 1
15 268301377 2 2 3 1 2
16 268301377 2 3 1 -1 0
17 268301377 2 3 2 -1 0
18 268301377 2 3 3 1 1
I originally thought I could do something along the lines of df %>% group_by(subject, task_phase, block_number, ResponseCorrect) %>% mutate(ConsecutiveCorrect = cumsum(ResponseCorrect), and that almost works. But, it doesn't give a consecutive value: it just sums up the total number of correct responses per block (. I'm essentially trying to use the -1s as break points that start the summation over again.
Is there a grouping function (Tidyverse or otherwise) that I'm not aware of that could do something along these lines?
You could try
library(dplyr)
data %>%
group_by(
subject,
task_phase,
block_number,
grp = lag(cumsum(ResponseCorrect == -1), default = 0)
) %>%
mutate(ConsecutiveCorrect = ifelse(ResponseCorrect == -1, 0, cumsum(ResponseCorrect))) %>%
ungroup() %>%
select(-grp)
which returns
# A tibble: 18 x 6
subject task_phase block_number trial_number ResponseCorrect ConsecutiveCorrect
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 268301377 1 1 2 1 1
2 268301377 1 1 3 1 2
3 268301377 1 1 4 1 3
4 268301377 1 2 2 -1 0
5 268301377 1 2 3 1 1
6 268301377 1 2 4 1 2
7 268301377 1 3 2 1 1
8 268301377 1 3 3 -1 0
9 268301377 1 3 4 1 1
10 268301377 2 1 50 1 1
11 268301377 2 1 51 1 2
12 268301377 2 1 52 1 3
13 268301377 2 2 37 -1 0
14 268301377 2 2 38 1 1
15 268301377 2 2 39 1 2
16 268301377 2 3 41 -1 0
17 268301377 2 3 42 -1 0
18 268301377 2 3 43 1 1
An option with data.table. Grouped by 'subject', 'task_phase', 'block_number', get the run-length-id (rleid) of 'ResponseCorrect', return with rowid of that sequence, multiply with a logical vector so that elements that corresponds to -1 (FALSE -> 0 will return 0 and TRUE -> 1 returns the element)
library(data.table)
setDT(df)[, ConsecutiveCorrect := rowid(rleid(ResponseCorrect)) *
(ResponseCorrect == 1), by = .(subject, task_phase, block_number)]
-output
df
subject task_phase block_number trial_number ResponseCorrect ConsecutiveCorrect
1: 268301377 1 1 2 1 1
2: 268301377 1 1 3 1 2
3: 268301377 1 1 4 1 3
4: 268301377 1 2 2 -1 0
5: 268301377 1 2 3 1 1
6: 268301377 1 2 4 1 2
7: 268301377 1 3 2 1 1
8: 268301377 1 3 3 -1 0
9: 268301377 1 3 4 1 1
10: 268301377 2 1 50 1 1
11: 268301377 2 1 51 1 2
12: 268301377 2 1 52 1 3
13: 268301377 2 2 37 -1 0
14: 268301377 2 2 38 1 1
15: 268301377 2 2 39 1 2
16: 268301377 2 3 41 -1 0
17: 268301377 2 3 42 -1 0
18: 268301377 2 3 43 1 1
data
df <- structure(list(subject = c(268301377L, 268301377L, 268301377L,
268301377L, 268301377L, 268301377L, 268301377L, 268301377L, 268301377L,
268301377L, 268301377L, 268301377L, 268301377L, 268301377L, 268301377L,
268301377L, 268301377L, 268301377L), task_phase = c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L),
block_number = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L,
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), trial_number = c(2L, 3L,
4L, 2L, 3L, 4L, 2L, 3L, 4L, 50L, 51L, 52L, 37L, 38L, 39L,
41L, 42L, 43L), ResponseCorrect = c(1L, 1L, 1L, -1L, 1L,
1L, 1L, -1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, -1L, 1L)),
class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18"))
I'd like to insert missing numbers in the index column following these two conditions:
Partitioned by multiple columns
The minimum value is always 1
The maximum value is always the maximum for the group and type
Current Data:
group type index vol
A 1 1 200
A 1 2 244
A 1 5 33
A 2 2 66
A 2 3 2
A 2 4 199
A 2 10 319
B 1 4 290
B 1 5 188
B 1 6 573
B 1 9 122
Desired Data:
group type index vol
A 1 1 200
A 1 2 244
A 1 3 0
A 1 4 0
A 1 5 33
A 2 1 0
A 2 2 66
A 2 3 2
A 2 4 199
A 2 5 0
A 2 6 0
A 2 7 0
A 2 8 0
A 2 9 0
A 2 10 319
B 1 1 0
B 1 2 0
B 1 3 0
B 1 4 290
B 1 5 188
B 1 6 573
B 1 7 0
B 1 8 0
B 1 9 122
I've just added in spaces between the partitions for clarity.
Hope you can help out!
You can do the following
library(dplyr)
library(tidyr)
my_df %>%
group_by(group, type) %>%
complete(index = 1:max(index), fill = list(vol = 0))
# group type index vol
# 1 A 1 1 200
# 2 A 1 2 244
# 3 A 1 3 0
# 4 A 1 4 0
# 5 A 1 5 33
# 6 A 2 1 0
# 7 A 2 2 66
# 8 A 2 3 2
# 9 A 2 4 199
# 10 A 2 5 0
# 11 A 2 6 0
# 12 A 2 7 0
# 13 A 2 8 0
# 14 A 2 9 0
# 15 A 2 10 319
# 16 B 1 1 0
# 17 B 1 2 0
# 18 B 1 3 0
# 19 B 1 4 290
# 20 B 1 5 188
# 21 B 1 6 573
# 22 B 1 7 0
# 23 B 1 8 0
# 24 B 1 9 122
With group_by you specify the groups you indicated withed the white spaces. With complete you specify which columns should be complete and then what values should be filled in for the remaining column (default would be NA)
Data
my_df <-
structure(list(group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("A", "B"), class = "factor"),
type = c(1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L),
index = c(1L, 2L, 5L, 2L, 3L, 4L, 10L, 4L, 5L, 6L, 9L),
vol = c(200L, 244L, 33L, 66L, 2L, 199L, 319L, 290L, 188L, 573L, 122L)),
class = "data.frame", row.names = c(NA, -11L))
One dplyr and tidyr possibility could be:
df %>%
group_by(group, type) %>%
complete(index = full_seq(1:max(index), 1), fill = list(vol = 0))
group type index vol
<fct> <int> <dbl> <dbl>
1 A 1 1 200
2 A 1 2 244
3 A 1 3 0
4 A 1 4 0
5 A 1 5 33
6 A 2 1 0
7 A 2 2 66
8 A 2 3 2
9 A 2 4 199
10 A 2 5 0
11 A 2 6 0
12 A 2 7 0
13 A 2 8 0
14 A 2 9 0
15 A 2 10 319
16 B 1 1 0
17 B 1 2 0
18 B 1 3 0
19 B 1 4 290
20 B 1 5 188
21 B 1 6 573
22 B 1 7 0
23 B 1 8 0
24 B 1 9 122
I have a data.frame as below and I want to add a variable describing the longest consecutive count of 1 in the VALUE variable observed in the group (i.e. longest consecutive rows with 1 in VALUE per group).
GROUP_ID VALUE
1 0
1 1
1 1
1 1
1 1
1 0
2 1
2 1
2 0
2 1
2 1
2 1
3 1
3 0
3 1
3 0
So the output would look like this:
GROUP_ID VALUE CONSECUTIVE
1 0 4
1 1 4
1 1 4
1 1 4
1 1 4
1 0 4
2 1 3
2 1 3
2 0 3
2 1 3
2 1 3
2 1 3
3 1 1
3 0 1
3 1 1
3 0 1
Any help would be greatly appreciated!
Using dplyr:
library(dplyr)
dat %>%
group_by(GROUP_ID) %>%
mutate(CONSECUTIVE = {rl <- rle(VALUE); max(rl$lengths[rl$values == 1])})
which gives:
# A tibble: 16 x 3
# Groups: GROUP_ID [3]
GROUP_ID VALUE CONSECUTIVE
<int> <int> <int>
1 1 0 4
2 1 1 4
3 1 1 4
4 1 1 4
5 1 1 4
6 1 0 4
7 2 1 3
8 2 1 3
9 2 0 3
10 2 1 3
11 2 1 3
12 2 1 3
13 3 1 1
14 3 0 1
15 3 1 1
16 3 0 1
Or with data.table:
library(data.table)
setDT(dat) # convert to a 'data.table'
dat[, CONSECUTIVE := {rl <- rle(VALUE); max(rl$lengths[rl$values == 1])}
, by = GROUP_ID][]
We can use ave with rle and get maximum occurrence of consecutive 1's for each group. (GROUP_ID)
df$Consecutive <- ave(df$VALUE, df$GROUP_ID, FUN = function(x) {
y <- rle(x == 1)
max(y$lengths[y$values])
})
df
# GROUP_ID VALUE Consecutive
#1 1 0 4
#2 1 1 4
#3 1 1 4
#4 1 1 4
#5 1 1 4
#6 1 0 4
#7 2 1 3
#8 2 1 3
#9 2 0 3
#10 2 1 3
#11 2 1 3
#12 2 1 3
#13 3 1 1
#14 3 0 1
#15 3 1 1
#16 3 0 1
Here is another option with data.table
library(data.table)
library(dplyr)
setDT(df1)[, CONSECUTIVE := max(table(na_if(rleid(VALUE)*VALUE, 0))), .(GROUP_ID)]
df1
# GROUP_ID VALUE CONSECUTIVE
# 1: 1 0 4
# 2: 1 1 4
# 3: 1 1 4
# 4: 1 1 4
# 5: 1 1 4
# 6: 1 0 4
# 7: 2 1 3
# 8: 2 1 3
# 9: 2 0 3
#10: 2 1 3
#11: 2 1 3
#12: 2 1 3
#13: 3 1 1
#14: 3 0 1
#15: 3 1 1
#16: 3 0 1
data
df1 <- structure(list(GROUP_ID = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L), VALUE = c(0L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L)), class = "data.frame", row.names = c(NA,
-16L))
I want to create a count variable with the number of peoples with Z==0 in each of the given years. As Illustrated below:
PersonID Year Z Count*
1 1990 0 1
2 1990 1 1
3 1990 1 1
4 1990 2 1
5 1990 1 1
1 1991 1 3
2 1991 0 3
3 1991 1 3
4 1991 0 3
5 1991 0 3
1 1992 NA 1
2 1992 2 1
3 1992 2 1
4 1992 0 1
5 1993 1 0
1 1993 1 0
2 1993 2 0
3 1993 NA 0
4 1993 1 0
5 1994 0 5
1 1994 0 5
2 1994 0 5
3 1994 0 5
4 1994 0 5
I looked at my previous R-scripts and found this
library(dplyr)
sum_data <- data %>% group_by(PersonID) %>% summarise(Count = sum(Z, na.rm=T))
Can someone help me get this right? The count variable should basically count a total number of persons with Z==0, in the same format as I illustrated above. Thanks!!
dput(data)
structure(list(PersonID = c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L),
Year = c(1990L, 1990L, 1990L, 1990L, 1990L, 1991L, 1991L,
1991L, 1991L, 1991L, 1992L, 1992L, 1992L, 1992L, 1993L, 1993L,
1993L, 1993L, 1993L, 1994L, 1994L, 1994L, 1994L, 1994L),
Z = c(0L, 1L, 1L, 2L, 1L, 1L, 0L, 1L, 0L, 0L, NA, 2L, 2L,
0L, 1L, 1L, 2L, NA, 1L, 0L, 0L, 0L, 0L, 0L)), .Names = c("PersonID",
"Year", "Z"), class = "data.frame", row.names = c(NA, -24L))
Here's a simple solution :
library(dplyr)
sum_data <- df %>%
mutate(Z=replace(Z, is.na(Z), 1)) %>%
mutate(temp = ifelse(Z == 0, 1, 0)) %>%
group_by(Year) %>%
summarize(count = sum(temp))
basically this is what the code is doing :
mutate(Z=replace(Z, is.na(Z), 1)) replace the NA with 1 (optional)
mutate(temp = ifelse(Z == 0, 1, 0)) create a conditional temp
variable :
ifelse(Z == 0, 1, 0) say if Z == 0 then the value is 1
else 0
group_by(Year) pretty explicite :) it group the data frame by
Year
summarize(count = sum(temp)) create a count variable with the
sum of earlier generated temp
results :
Year count
<int> <int>
1 1990 5
2 1991 5
3 1992 4
4 1993 5
5 1994 5
and if you want to join this data to the original data frame just use join :
left_join(df, sum_data)
Joining, by = "Year"
PersonID Year Z count
1 1 1990 0 1
2 2 1990 1 1
3 3 1990 1 1
4 4 1990 2 1
5 5 1990 1 1
6 1 1991 1 3
7 2 1991 0 3
8 3 1991 1 3
9 4 1991 0 3
10 5 1991 0 3
11 1 1992 NA 1
12 2 1992 2 1
13 3 1992 2 1
14 4 1992 0 1
15 5 1993 1 0
16 1 1993 1 0
17 2 1993 2 0
18 3 1993 NA 0
19 4 1993 1 0
20 5 1994 0 5
21 1 1994 0 5
22 2 1994 0 5
23 3 1994 0 5
24 4 1994 0 5
Try this:
library(dplyr)
df <- left_join(data, data %>% filter(Z==0) %>% group_by(Year) %>% summarise(Count = n()))
df[is.na(df$Count),]$Count <- 0
PersonID Year Z Count
1 1 1990 0 1
2 2 1990 1 1
3 3 1990 1 1
4 4 1990 2 1
5 5 1990 1 1
6 1 1991 1 3
7 2 1991 0 3
8 3 1991 1 3
9 4 1991 0 3
10 5 1991 0 3
11 1 1992 NA 1
12 2 1992 2 1
13 3 1992 2 1
14 4 1992 0 1
15 5 1993 1 0
16 1 1993 1 0
17 2 1993 2 0
18 3 1993 NA 0
19 4 1993 1 0
20 5 1994 0 5
21 1 1994 0 5
22 2 1994 0 5
23 3 1994 0 5
24 4 1994 0 5