The following is what I have:
ID Year Score
1 1999 10
1 2000 11
1 2001 14
1 2002 22
2 2000 19
2 2001 17
2 2002 22
3 1998 10
3 1999 12
The following is what I would like to do:
ID Year Score Total
1 1999 10 10
1 2000 11 21
1 2001 14 35
1 2002 22 57
2 2000 19 19
2 2001 17 36
2 2002 22 48
3 1998 10 10
3 1999 12 22
The amount of years and the specific years vary for each Id.
I have a feeling that it's some advanced options in ddply but I have not been able to find the answer. I've also tried working with for/while loops but since these are dreadfully slow in R and my data-set is large, it's not working all that well.
Thanks in advance!
You can use the sumsum function and apply it with ave to all subgroups.
transform(dat, Total = ave(Score, ID, FUN = cumsum))
ID Year Score Total
1 1 1999 10 10
2 1 2000 11 21
3 1 2001 14 35
4 1 2002 22 57
5 2 2000 19 19
6 2 2001 17 36
7 2 2002 22 58
8 3 1998 10 10
9 3 1999 12 22
If your data is large, then ddply will be slow.
data.table is the way to go.
library(data.table)
DT <- data.table(dat)
# create your desired column in `DT`
DT[, agg.Score := cumsum(Score), by = ID]
Related
I have a time-series dataset with yearly values for 30 years for >200,000 study units that all start off as the same value of 'healthy==1' and can transition to 3 classes - 'exposed==2', 'infected==3' and 'recover==4'; some units also remain as 'healthy' throughout the time series. The dataset is in long format.
I would like to manipulate the dataset that keeps all 30 years for each unit but collapsed to only 'heathy==1' and 'infected==3' i.e. I would classify 'exposed==2' as 'healthy==1' and the first time a 'healthy' unit gets 'infected==3', it remains as infected for the remaining of the time-series even though it might 'recover==4'/change state again (gets infected and recover again).
Healthy units that never transition to another class will remain classified as healthy throughout the time series.
I am kinda stumped on how to code this out in r; any ideas would be greatly appreciated
example of dataset for two units; one remains health throughout the time series and another has multiple transitions.
UID annual_change_val year
1 control1 1 1990
4 control1 1 1991
5 control1 1 1992
7 control1 1 1993
9 control1 1 1994
12 control1 1 1995
13 control1 1 1996
16 control1 1 1997
18 control1 1 1998
20 control1 1 1999
22 control1 1 2000
24 control1 1 2001
26 control1 1 2002
28 control1 1 2003
30 control1 1 2004
31 control1 1 2005
33 control1 1 2006
35 control1 1 2007
38 control1 1 2008
40 control1 1 2009
42 control1 1 2010
44 control1 1 2011
46 control1 1 2012
48 control1 1 2013
50 control1 1 2014
52 control1 1 2015
53 control1 1 2016
55 control1 1 2017
57 control1 1 2018
59 control1 1 2019
61 control1 1 2020
2 control64167 1 1990
3 control64167 1 1991
6 control64167 1 1992
8 control64167 2 1993
10 control64167 2 1994
11 control64167 2 1995
14 control64167 2 1996
15 control64167 2 1997
17 control64167 3 1998
19 control64167 3 1999
21 control64167 4 2000
23 control64167 4 2001
25 control64167 4 2002
27 control64167 4 2003
29 control64167 3 2004
32 control64167 4 2005
34 control64167 4 2006
36 control64167 4 2007
37 control64167 4 2008
39 control64167 4 2009
41 control64167 4 2010
43 control64167 4 2011
45 control64167 4 2012
47 control64167 4 2013
49 control64167 4 2014
51 control64167 4 2015
54 control64167 4 2016
56 control64167 4 2017
58 control64167 4 2018
60 control64167 4 2019
62 control64167 4 2020
If for some reason you only want to use base R,
df$annual_change_val[df$annual_change_val == 2] <- 1
df$annual_change_val[df$annual_change_val == 4] <- 3
The first line means: take the annual_change_val column from ($) dataframe df, subset it ([) so that you're only left with values equal to 2, and re-assign (<-) to those a value of 1 instead. Similarly for the second line.
Update, based on comment/clarification.
Here, I replace the values as before, and then I create a temp variable called max_inf which holds the maximum year that the UID was "infected" (status=3). I then replace the status to 3 for any year that is beyond that year (within UID).
d %>%
mutate(status = if_else(annual_change_val %in% c(1,2),1,3)) %>%
group_by(UID) %>%
mutate(max_inf = max(year[which(status==3)],na.rm=T),
status = if_else(!is.na(max_inf) & year>max_inf & status==1,3,status)) %>%
select(!max_inf)
You can simply change the values from 2 to 1, and from 4 to 3, as Andrea mentioned in the comments. If d is your data, then
library(dplyr)
d %>% mutate(status = if_else(annual_change_val %in% c(1,2),1,3))
library(data.table)
setDT(d)[, status:=fifelse(annual_change_val %in% c(1,2),1,3)]
I have a list of dataframes that I need to be combined into a single one.
year<-1990:2000
v1<-1:11
v2<-20:30
df1<-data.frame(year,v1)
df2<-data.frame(year,v2)
ldf<-list(df1,df2)
I now want to unlist this dataframe and get
> head(df)
year v1 v2
1 1990 1 20
2 1991 2 21
3 1992 3 22
4 1993 4 23
Note that my question is different from the solution provided in a similar question, where the solution to that question was: `df <- ldply(ldf, data.frame)
Because what I am essentially looking for, is a more automatic way of doing this: df<-merge(df1,df2, by="year")
With more number of list elements, a convenient option is reduce with one of the join functions
library(tidyverse)
ldf %>%
reduce(inner_join, by = "year")
# year v1 v2
#1 1990 1 20
#2 1991 2 21
#3 1992 3 22
#4 1993 4 23
#5 1994 5 24
#6 1995 6 25
#7 1996 7 26
#8 1997 8 27
#9 1998 9 28
#10 1999 10 29
#11 2000 11 30
Is there anything wrong with:
df <- merge(ldf[[1]], ldf[[2]], by="year")
Or for a long list:
df1 <- ldf[[1]]
for (x in 2:length(ldf)) {
df1 <- merge(df1, ldf[[x]])
}
# year v1 v2
# 1 1990 1 20
# 2 1991 2 21
# 3 1992 3 22
# 4 1993 4 23
# 5 1994 5 24
# 6 1995 6 25
# 7 1996 7 26
# 8 1997 8 27
# 9 1998 9 28
# 10 1999 10 29
# 11 2000 11 30
This question already has answers here:
Add ID column by group [duplicate]
(4 answers)
How to create a consecutive group number
(13 answers)
Closed 5 years ago.
I have a dataframe (df) that looks like this:
School Student Year
A 10 1999
A 10 2000
A 20 1999
A 20 2000
A 20 2001
B 10 1999
B 10 2000
And I would like to create a person ID column so that df looks like this:
ID School Student Year
1 A 10 1999
1 A 10 2000
2 A 20 1999
2 A 20 2000
2 A 20 2001
3 B 10 1999
3 B 10 2000
In other words, the ID variable indicates which person it is in the dataset, accounting for both Student number and School membership (here we have 3 students total).
I did df$ID <- df$Student and tried to request the value +1 if c("School", "Student) was unique. It isn't working. Help appreciated.
We can do this in base R without doing any group by operation
df$ID <- cumsum(!duplicated(df[1:2]))
df
# School Student Year ID
#1 A 10 1999 1
#2 A 10 2000 1
#3 A 20 1999 2
#4 A 20 2000 2
#5 A 20 2001 2
#6 B 10 1999 3
#7 B 10 2000 3
NOTE: Assuming that 'School' and 'Student' are ordered
Or using tidyverse
library(dplyr)
df %>%
mutate(ID = group_indices_(df, .dots=c("School", "Student")))
# School Student Year ID
#1 A 10 1999 1
#2 A 10 2000 1
#3 A 20 1999 2
#4 A 20 2000 2
#5 A 20 2001 2
#6 B 10 1999 3
#7 B 10 2000 3
As #radek mentioned, in the recent version (dplyr_0.8.0), we get the notification that group_indices_ is deprecated, instead use group_indices
df %>%
mutate(ID = group_indices(., School, Student))
Group by School and Student, then assign group id to ID variable.
library('data.table')
df[, ID := .GRP, by = .(School, Student)]
# School Student Year ID
# 1: A 10 1999 1
# 2: A 10 2000 1
# 3: A 20 1999 2
# 4: A 20 2000 2
# 5: A 20 2001 2
# 6: B 10 1999 3
# 7: B 10 2000 3
Data:
df <- fread('School Student Year
A 10 1999
A 10 2000
A 20 1999
A 20 2000
A 20 2001
B 10 1999
B 10 2000')
I am using a national survey to run a regression: the survey is conducted every two years and some individual are repeatedly interviewed while others just one time.
Now I want to make the df a panel one (have only the individual that appears more than one time). The df is like this:
year nquest nord nordp sex age
2000 10 1 1 F 40
2000 10 2 2 M 43
2000 30 1 1 M 30
2002 10 1 1 F 42
2002 10 2 2 M 45
2002 10 3 NA F 15
2002 30 1 1 M 32
2004 10 1 1 F 44
2004 10 2 2 M 47
2004 10 3 3 F 17
2004 50 1 NA M 66
where nquest is the code number of the family, nord is the code number of the individual and nordp is the code number that the individual had in the previous survey; when a new individual is interviewed the value in nordp is "missing" (R automatically insert NA). For example the individual 3 of family 10 has nordp=NA in 2002 because it is the first time that she is interviewed, while in 2004 nordp is 3 (because 3 was the number that she had in 2002).
I can't use nord to filter the df because the composition of the family may change (for example in 2002 in family x the mother has nordp=2 (it means that in 2000 nord was 2) and nord=2 but the next year nord could be 1 (for example if she gets divorced) but nordp is still 2).
I tried to filter using this command:
df <- df %>%
group_by(nquest, nordp)
filter(n()>1)
but I don't get the right df because if for the same family there are more than one individual insert (NA) they will be considered as the same person since nordp is NA the first time.
How can I consider also the individual that appears for the first time in a certain year (nordp=NA)? I tried to a create a command using age (the age in t shoul be equal to (age (in t-2) + 2; for example in 2000 age is 20, in 2002 is 22) but it didn't worked.
Consider that the df is composed by thousand observations and I can't check manually.
The final df should be:
year nquest nordp sex age
2000 10 1 F 40
2000 10 2 M 43
2000 30 1 M 30
2002 10 1 F 42
2002 10 2 M 45
2002 10 3 F 15
2002 30 1 M 32
2004 10 1 F 44
2004 10 2 M 47
2004 10 3 F 17
As you can see there are only the individual that appears more than one time and nquest=10 nordp=30 appears three times; with my command it appears just two times because in the first year nordp was NA.
We wish to assign unique IDs to individuals, then filter by the count of unique IDs. The main idea is to chain together the nordp and nord values within each family over years. Here's an idea inspired by Identify groups of linked episodes which chain together. First, load the igraph package, via library(igraph). Then the following function assigns IDs for a given family.
assignID <- function(d) {
fields <- names(d) # store original column names
d$nordp[is.na(d$nordp)] <- seq_len(sum(is.na(d$nordp))) + 100
d$nordp_x <- (d$year-2) * 1000 + d$nordp
d$nord_x <- d$year * 1000 + d$nord
dd <- d[, c("nordp_x", "nord_x")]
gr.test <- graph.data.frame(dd)
links <- data.frame(org_id = unique(unlist(dd)),
id = clusters(gr.test)$membership)
d <- merge(d, links, by.x = "nord_x", by.y = "org_id", all.x = TRUE)
d$uid <- d$nquest * 100 + d$id
d[, c(fields, "uid")]
}
The function can "tell", for example, that
year nordp nord
2000 1 1
2002 1 2
2004 2 3
is the same individual, by chaining together the nordp and nord over the years, and assigns the same unique ID to all 3 rows. So, for example,
assignID(subset(df, nquest == 10))
# year nquest nord nordp sex age dob uid
# 1 2000 10 1 1 F 40 1960 1001
# 2 2000 10 2 2 M 43 1957 1002
# 3 2002 10 1 1 F 42 1960 1001
# 4 2002 10 2 2 M 45 1957 1002
# 5 2002 10 3 101 F 15 1987 1003
# 6 2004 10 1 1 F 44 1960 1001
# 7 2004 10 2 2 M 47 1957 1002
# 8 2004 10 3 3 F 17 1987 1003
gives us an additional column with the uid for each individual.
The remaining steps are straightforward. We split the dataframe by nquest, apply assignID to each subset, and rbind the output:
dd <- do.call(rbind, by(df, df$nquest, assignID))
Then we can just group by uid and filter by count:
dd %>% group_by(uid) %>% filter(n()>1)
# Source: local data frame [10 x 8]
# Groups: uid [4]
# year nquest nord nordp sex age dob uid
# <int> <int> <int> <dbl> <fctr> <int> <int> <dbl>
# 1 2000 10 1 1 F 40 1960 1001
# 2 2000 10 2 2 M 43 1957 1002
# 3 2002 10 1 1 F 42 1960 1001
# 4 2002 10 2 2 M 45 1957 1002
# 5 2002 10 3 101 F 15 1987 1003
# 6 2004 10 1 1 F 44 1960 1001
# 7 2004 10 2 2 M 47 1957 1002
# 8 2004 10 3 3 F 17 1987 1003
# 9 2000 30 1 1 M 30 1970 3001
# 10 2002 30 1 1 M 32 1970 3001
I am trying to clean my data. One of the criteria is that I need an uninterrupted sequence of a variable "assets", but I have some NAs. However, I cannot simply delete the NA observations, but need to delete all subsequent observations following the NA event.
Here an example:
productreference<-c(1,1,1,1,2,2,2,3,3,3,3,4,4,4,5,5,5,5)
Year<-c(2000,2001,2002,2003,1999,2000,2001,2005,2006,2007,2008,1998,1999,2000,2000,2001,2002,2003)
assets<-c(2,3,NA,2,34,NA,45,1,23,34,56,56,67,23,23,NA,14,NA)
mydf<-data.frame(productreference,Year,assets)
mydf
# productreference Year assets
# 1 1 2000 2
# 2 1 2001 3
# 3 1 2002 NA
# 4 1 2003 2
# 5 2 1999 34
# 6 2 2000 NA
# 7 2 2001 45
# 8 3 2005 1
# 9 3 2006 23
# 10 3 2007 34
# 11 3 2008 56
# 12 4 1998 56
# 13 4 1999 67
# 14 4 2000 23
# 15 5 2000 23
# 16 5 2001 NA
# 17 5 2002 14
# 18 5 2003 NA
I have already seen that there is a way to carry out functions by group using plyr and I have also been able to create a column with 0-1, where 0 indicates that assets has a valid entry and 1 highlights missing values of NA.
mydf$missing<-ifelse(mydf$assets>=0,0,1)
mydf[c("missing")][is.na(mydf[c("missing")])] <- 1
I have a very large data set so cannot manually delete the rows and would greatly appreciate your help!
I believe this is what you want:
library(dplyr)
group_by(mydf, productreference) %>%
filter(cumsum(is.na(assets)) == 0)
# Source: local data frame [11 x 3]
# Groups: productreference [5]
#
# productreference Year assets
# (dbl) (dbl) (dbl)
# 1 1 2000 2
# 2 1 2001 3
# 3 2 1999 34
# 4 3 2005 1
# 5 3 2006 23
# 6 3 2007 34
# 7 3 2008 56
# 8 4 1998 56
# 9 4 1999 67
# 10 4 2000 23
# 11 5 2000 23
Here is the same approach using data.table:
library(data.table)
dt <- as.data.table(mydf)
dt[,nas:= cumsum(is.na(assets)),by="productreference"][nas==0]
# productreference Year assets nas
# 1: 1 2000 2 0
# 2: 1 2001 3 0
# 3: 2 1999 34 0
# 4: 3 2005 1 0
# 5: 3 2006 23 0
# 6: 3 2007 34 0
# 7: 3 2008 56 0
# 8: 4 1998 56 0
# 9: 4 1999 67 0
#10: 4 2000 23 0
#11: 5 2000 23 0
Here is a base R option
mydf[unsplit(lapply(split(mydf, mydf$productreference),
function(x) cumsum(is.na(x$assets))==0), mydf$productreference),]
# productreference Year assets
#1 1 2000 2
#2 1 2001 3
#5 2 1999 34
#8 3 2005 1
#9 3 2006 23
#10 3 2007 34
#11 3 2008 56
#12 4 1998 56
#13 4 1999 67
#14 4 2000 23
#15 5 2000 23
Or an option with data.table
library(data.table)
setDT(mydf)[, if(any(is.na(assets))) .SD[seq(which(is.na(assets))[1]-1)]
else .SD, by = productreference]
You can do it using base R and a for loop. This code is a bit longer than some of the code in the other answers. In the loop we subset mydf by productreference and for every subset we look for the first occurrence of assets==NA, and exclude that row and all following rows.
mydf2 <- NULL
for (i in 1:max(mydf$productreference)){
s1 <- mydf[mydf$productreference==i,]
s2 <- s1[1:ifelse(all(!is.na(s1$assets)), NROW(s1), min(which(is.na(s1$assets)==T))-1),]
mydf2 <- rbind(mydf2, s2)
mydf2 <- mydf2[!is.na(mydf2$assets),]
}
mydf2