I have a dataset of a hypothetical exam.
id <- c(1,1,3,4,5,6,7,7,8,9,9)
test_date <- c("2012-06-27","2012-07-10","2013-07-04","2012-03-24","2012-07-22", "2013-09-16","2012-06-21","2013-10-18", "2013-04-21", "2012-02-16", "2012-03-15")
result_date <- c("2012-07-29","2012-09-02","2013-08-01","2012-04-25","2012-09-01","2013-10-20","2012-07-01","2013-10-31", "2013-05-17", "2012-03-17", "2012-04-20")
data1 <- as_data_frame(id)
data1$test_date <- test_date
data1$result_date <- result_date
colnames(data1)[1] <- "id"
"id" indicates the ID of the students who have taken a particular exam. "test_date" is the date the students took the test and "result_date" is the date when the students' results are posted. I'm interested in finding out which students retook the exam BEFORE the result of that exam session was released, e.g. students who knew that they have underperformed and retook the exam without bothering to find out their scores. For example, student with "id" 1 took the exam for the second time on "2012-07-10" which was before the result date for his first exam - "2012-07-29".
I tried to:
data1%>%
group_by(id) %>%
arrange(id, test_date) %>%
filter(n() >= 2) %>% #To only get info on students who have taken the exam more than once and then merge it back in with the original data set using a join function
So essentially, I want to create a new column called "re_test" where it would equal 1 if a student retook the exam BEFORE receiving the result of a previous exam and 0 otherwise (those who retook after seeing their marks or those who did not retake).
I have tried to mutate in order to find cases where dates are either positive or negative by subtracting the 2nd test_date from the 1st result_date:
mutate(data1, re_test = result_date - lead(test_date, default = first(test_date)))
However, this leads to mixing up students with different id's. I tried to split but mutate won't work on a list of dataframes so now I'm stuck:
split(data1, data1$id)
Just to add on, this is a part of the desired result:
data2 <- as_data_frame(id <- c(1,1,3,4))
data2$test_date_result <- c("2012-06-27","2012-07-10", "2013-07-04","2012-03-24")
data2$result_date_result <- c("2012-07-29","2012-09-02","2013-08-01","2012-04-25")
data2$re_test <- c(1, 0, 0, 0)
Apologies for the verbosity and hope I was clear enough.
Thanks a lot in advance!
library(reshape2)
library(dplyr)
# first melt so that we can sequence by date
data1m <- data1 %>%
melt(id.vars = "id", measure.vars = c("test_date", "result_date"), value.name = "event_date")
# any two tests in a row is a flag - use dplyr::lag to comapre the previous
data1mc <- data1m %>%
arrange(id, event_date) %>%
group_by(id) %>%
mutate (multi_test = (variable == "test_date" & lag(variable == "test_date"))) %>%
filter(multi_test)
# id variable event_date multi_test
# 1 1 test_date 2012-07-10 TRUE
# 2 9 test_date 2012-03-15 TRUE
## join back to the original
data1 %>%
left_join (data1mc %>% select(id, event_date, multi_test),
by=c("id" = "id", "test_date" = "event_date"))
I have a piecewise answer that may work for you. I first create a data.frame called student that contains the re-test information, and then join it with the data1 object. If students re-took the test multiple times, it will compare the last test to the first, which is a flaw, but I'm unsure if students have the ability to re-test multiple times?
student <- data1 %>%
group_by(id) %>%
summarise(retest=(test_date[length(test_date)] < result_date[1]) == TRUE)
Some re-test values were NA. These were individuals that only took the test once. I set these to FALSE here, but you can retain the NA, as they do contain information.
student$retest[is.na(student$retest)] <- FALSE
Join the two data.frames to a single object called data2.
data2 <- left_join(data1, student, by='id')
I am sure there are more elegant ways to approach this. I did this by taking advantage of the structure of your data (sorted by id) and the lag function that can refer to the previous records while dealing with a current record.
### Ensure Data are sorted by ID ###
data1 <- arrange(data1,id)
### Create Flag for those that repeated ###
data1$repeater <- ifelse(lag(data1$id) == data1$id,1,0)
### I chose to do this on all data, you could filter on repeater flag first ###
data1$timegap <- as.Date(data1$result_date) - as.Date(data1$test_date)
data1$lagdate <- as.Date(data1$test_date) - lag(as.Date(data1$result_date))
### Display results where your repeater flag is 1 and there is negative time lag ###
data1[data1$repeater==1 & !is.na(data1$repeater) & as.numeric(data1$lagdate) < 0,]
# A tibble: 2 × 6
id test_date result_date repeater timegap lagdate
<dbl> <chr> <chr> <dbl> <time> <time>
1 1 2012-07-10 2012-09-02 1 54 days -19 days
2 9 2012-03-15 2012-04-20 1 36 days -2 days
I went with a simple shift comparison. 1 line of code.
data1 <- data.frame(id = c(1,1,3,4,5,6,7,7,8,9,9), test_date = c("2012-06-27","2012-07-10","2013-07-04","2012-03-24","2012-07-22", "2013-09-16","2012-06-21","2013-10-18", "2013-04-21", "2012-02-16", "2012-03-15"), result_date = c("2012-07-29","2012-09-02","2013-08-01","2012-04-25","2012-09-01","2013-10-20","2012-07-01","2013-10-31", "2013-05-17", "2012-03-17", "2012-04-20"))
data1$re_test <- unlist(lapply(split(data1,data1$id), function(x)
ifelse(as.Date(x$test_date) > c(NA, as.Date(x$result_date[-nrow(x)])), 0, 1)))
data1
id test_date result_date re_test
1 1 2012-06-27 2012-07-29 NA
2 1 2012-07-10 2012-09-02 1
3 3 2013-07-04 2013-08-01 NA
4 4 2012-03-24 2012-04-25 NA
5 5 2012-07-22 2012-09-01 NA
6 6 2013-09-16 2013-10-20 NA
7 7 2012-06-21 2012-07-01 NA
8 7 2013-10-18 2013-10-31 0
9 8 2013-04-21 2013-05-17 NA
10 9 2012-02-16 2012-03-17 NA
11 9 2012-03-15 2012-04-20 1
I think there is benefit in leaving NAs but if you really want all others as zero, simply:
data1$re_test <- ifelse(is.na(data1$re_test), 0, data1$re_test)
data1
id test_date result_date re_test
1 1 2012-06-27 2012-07-29 0
2 1 2012-07-10 2012-09-02 1
3 3 2013-07-04 2013-08-01 0
4 4 2012-03-24 2012-04-25 0
5 5 2012-07-22 2012-09-01 0
6 6 2013-09-16 2013-10-20 0
7 7 2012-06-21 2012-07-01 0
8 7 2013-10-18 2013-10-31 0
9 8 2013-04-21 2013-05-17 0
10 9 2012-02-16 2012-03-17 0
11 9 2012-03-15 2012-04-20 1
Let me know if you have any questions, cheers.
Related
I'm not used to using R. I already asked a question on stack overflow and got a great answer.
I'm sorry to post a similar question, but I tried many times and got the output that I didn't expect.
This time, I want to do slightly different from my previous question.
Merge two data with respect to date and week using R
I have two data. One has a year_month_week column and the other has a date column.
df1<-data.frame(id=c(1,1,1,2,2,2,2),
year_month_week=c(2022051,2022052,2022053,2022041,2022042,2022043,2022044),
points=c(65,58,47,21,25,27,43))
df2<-data.frame(id=c(1,1,1,2,2,2),
date=c(20220503,20220506,20220512,20220401,20220408,20220409),
temperature=c(36.1,36.3,36.6,34.3,34.9,35.3))
For df1, 2022051 means 1st week of May,2022. Likewise, 2022052 means 2nd week of May,2022. For df2,20220503 means May 3rd, 2022. What I want to do now is merge df1 and df2 with respect to year_month_week. In this case, 20220503 and 20220506 are 1st week of May,2022.If more than one date are in year_month_week, I will just include the first of them. Now, here's the different part. Even if there is no date inside year_month_week,just leave it NA. So my expected output has a same number of rows as df1 which includes the column year_month_week.So my expected output is as follows:
df<-data.frame(id=c(1,1,1,2,2,2,2),
year_month_week=c(2022051,2022052,2022053,2022041,2022042,2022043,2022044),
points=c(65,58,47,21,25,27,43),
temperature=c(36.1,36.6,NA,34.3,34.9,NA,NA))
First we can convert the dates in df2 into year-month-date format, then join the two tables:
library(dplyr);library(lubridate)
df2$dt = ymd(df2$date)
df2$wk = day(df2$dt) %/% 7 + 1
df2$year_month_week = as.numeric(paste0(format(df2$dt, "%Y%m"), df2$wk))
df1 %>%
left_join(df2 %>% group_by(year_month_week) %>% slice(1) %>%
select(year_month_week, temperature))
Result
Joining, by = "year_month_week"
id year_month_week points temperature
1 1 2022051 65 36.1
2 1 2022052 58 36.6
3 1 2022053 47 NA
4 2 2022041 21 34.3
5 2 2022042 25 34.9
6 2 2022043 27 NA
7 2 2022044 43 NA
You can build off of a previous answer here by taking the function to count the week of the month, then generate a join key in df2. See here
df1 <- data.frame(
id=c(1,1,1,2,2,2,2),
year_month_week=c(2022051,2022052,2022053,2022041,2022042,2022043,2022044),
points=c(65,58,47,21,25,27,43))
df2 <- data.frame(
id=c(1,1,1,2,2,2),
date=c(20220503,20220506,20220512,20220401,20220408,20220409),
temperature=c(36.1,36.3,36.6,34.3,34.9,35.3))
# Take the function from the previous StackOverflow question
monthweeks.Date <- function(x) {
ceiling(as.numeric(format(x, "%d")) / 7)
}
# Create a year_month_week variable to join on
df2 <-
df2 %>%
mutate(
date = lubridate::parse_date_time(
x = date,
orders = "%Y%m%d"),
year_month_week = paste0(
lubridate::year(date),
0,
lubridate::month(date),
monthweeks.Date(date)),
year_month_week = as.double(year_month_week))
# Remove duplicate year_month_weeks
df2 <-
df2 %>%
arrange(year_month_week) %>%
distinct(year_month_week, .keep_all = T)
# Join dataframes
df1 <-
left_join(
df1,
df2,
by = "year_month_week")
Produces this result
id.x year_month_week points id.y date temperature
1 1 2022051 65 1 2022-05-03 36.1
2 1 2022052 58 1 2022-05-12 36.6
3 1 2022053 47 NA <NA> NA
4 2 2022041 21 2 2022-04-01 34.3
5 2 2022042 25 2 2022-04-08 34.9
6 2 2022043 27 NA <NA> NA
7 2 2022044 43 NA <NA> NA
>
Edit: forgot to mention that you need tidyverse loaded
library(tidyverse)
Have a look at the simplified table below. I want for each product a vector containing the quantities sold within each delivery time. A delivery time is defined as 4 days. So if we look at product A, we see that it starts at 03/12/15 and within the first delivery term (until 07/12/15) it has sold a quantity of 4. The second delivery term starts at 08/12/15 and ends at 12/12/15. So for this period there is 1 quantity sold. The following delivery term starts at 13/12/15 and ends at 17/12/15. During these period there are no quantities sold and thus for this period the vector must have a value of 0. In the last period, finally, 2 products are sold. So basically the problem here is that information regarding the periods were no products are sold is missing.
Any ideas on how the vector I want can be created using R? I've been thinking of for or while loops, but these do not seem to give the requested results. Note that the code must be applicable on a real dataset containing over 1000 product categories, so it has to be 'automatized' in one way.
I would be very gratefull if somebody could point me in the right direction.
Product Quantity Date
A 1 03/12/15
A 2 04/12/15
A 1 05/12/15
A 1 08/12/15
A 1 17/12/16
A 1 18/12/16
B 1 19/12/15
B 2 10/05/15
B 2 11/05/15
C 1 01/06/15
C 1 02/06/15
C 1 12/06/15
Assume that dt is the dataset you provided. You'll get a better understanding of the process if you run it step by step (and maybe with an even simpler dataset).
library(lubridate)
library(dplyr)
# create date time columns
dt$Date = dmy(dt$Date)
dt %>%
group_by(Product) %>%
do(data.frame(days = seq(min(.$Date), max(.$Date), by="1 day"))) %>% # create all combinations between product and days
mutate(dist = as.numeric(difftime(days,min(days), units="days"))) %>% # create distance of each day with min date
ungroup() %>%
left_join(dt, by=c("Product"="Product","days"="Date")) %>% # join info to get quantities for each day
mutate(Quantity = ifelse(is.na(Quantity), 0, Quantity), # replace NAs with 0s
id = floor(dist/5 + 1)) %>% # create the 4 period id
group_by(Product, id) %>%
summarise(Sum = sum(Quantity),
min_date = min(days),
max_date = max(days)) %>%
ungroup
# Product id Sum min_date max_date
# 1 A 1 4 2015-12-03 2015-12-07
# 2 A 2 1 2015-12-08 2015-12-12
# 3 A 3 0 2015-12-13 2015-12-17
# 4 A 4 0 2015-12-18 2015-12-22
# 5 A 5 0 2015-12-23 2015-12-27
# 6 A 6 0 2015-12-28 2016-01-01
# 7 A 7 0 2016-01-02 2016-01-06
# 8 A 8 0 2016-01-07 2016-01-11
# 9 A 9 0 2016-01-12 2016-01-16
# 10 A 10 0 2016-01-17 2016-01-21
# .. ... .. ... ... ...
First row of the output tells you that for product A in the first 4 days period (id = 1) you had 4 quantities in total and the period is from 3/12 to 7/12.
I would suggest {dplyr}'s summarise(),mutate() and group_by() functions. group_by() groups your data by desired variables (in your case - product and delivery term),mutate() allows operations on grouped columns, and summarise() applies a summarising function over these groups (in your case sum(Quantity)).
So this is how it will look:
convert date into proper format:
library(dplyr)
df=tbl_df(df)
df$Date=as.Date(df$Date,format="%d/%m/%y")
calculating delivery terms
df=group_by(df,Product) %>% arrange(Date)
df=mutate(df,term=1+unclass((Date-min(Date)))%/%4)
group by product and terms and calculate sum of quantity:
df=group_by(df,Product,term)
summarise(df,sum=sum(Quantity))
Here's a base R way:
df$groups <- ave(as.numeric(df$Date), df$Product, FUN=function(x) {
intrvl <- findInterval(x, seq(min(x), max(x),4))
as.numeric(factor(intrvl))
})
df
# Product Quantity Date groups
# 1 A 1 2015-12-03 1
# 2 A 2 2015-12-04 1
# 3 A 1 2015-12-05 1
# 4 A 1 2015-12-08 2
# 5 A 1 2016-12-17 3
# 6 A 1 2016-12-18 3
# 7 B 1 2015-12-19 2
# 8 B 2 2015-05-10 1
# 9 B 2 2015-05-11 1
# 10 C 1 2015-06-01 1
# 11 C 1 2015-06-02 1
# 12 C 1 2015-06-12 2
The dates should be converted to one of the date classes. I chose as.Date. When it converts to numeric, the output will be the number of days from a specified date. From there, we are able to group by 4 day increments.
Data
df$Date <- as.Date(df$Date, format="%d/%m/%y")
I have two tables, one with property listings and another one with contacts made for a property (i.e. is someone is interested in the property they will "contact" the owner).
Sample "listings" table below:
listings <- data.frame(id = c("6174", "2175", "9176", "4176", "9177"), city = c("A", "B", "B", "B" ,"A"), listing_date = c("01/03/2015", "14/03/2015", "30/03/2015", "07/04/2015", "18/04/2015"))
listings$listing_date <- as.Date(listings$listing_date, "%d/%m/%Y")
listings
# id city listing_date
#1 6174 A 01/03/2015
#2 2175 B 14/03/2015
#3 9176 B 30/03/2015
#4 4176 B 07/04/2015
#5 9177 A 18/04/2015
Sample "contacts" table below:
contacts <- data.frame (id = c ("6174", "6174", "6174", "6174", "2175", "2175", "2175", "9176", "9176", "4176", "4176", "9177"), contact_date = c("13/03/2015","14/04/2015", "27/03/2015", "13/04/2015", "15/03/2015", "16/03/2015", "17/03/2015", "30/03/2015", "01/06/2015", "08/05/2015", "09/05/2015", "23/04/2015" ))
contacts$contact_date <- as.Date(contacts$contact_date, "%d/%m/%Y")
contacts
# id contact_date
#1 6174 2015-03-13
#2 6174 2015-04-14
#3 6174 2015-03-27
#4 6174 2015-04-13
#5 2175 2015-03-15
#6 2175 2015-03-16
#7 2175 2015-03-17
#8 9176 2015-03-30
#9 9176 2015-06-01
#10 4176 2015-05-08
#11 4176 2015-05-09
#12 9177 2015-04-23
Problem
1. I need to count the number of contacts made for a property within 'x' days of listing. The output should be a new column added to "listings" with # contacts:
Sample ('x' = 30 days)
listings
# id city listing_date ngs
#1 6174 A 2015-03-01 2
#2 2175 B 2015-03-14 3
#3 9176 B 2015-03-30 1
#4 4176 B 2015-04-07 0
#5 9177 A 2015-04-18 1
I have done this with the for loop; it is horrible slow for live data:
n <- nrow(listings)
mat <- vector ("integer", n)
for (i in 1:n) {
mat[i] <- nrow (contacts[contacts$id==listings[i,"id"] & as.numeric (contacts$contact_date - listings[i,"listing_date"]) <=30,])
}
listings$ngs <- mat
I need to prepare a histogram of # contacts vs days with 'x' as variable - through manipulate function. I can't figure out a way to do all this inside the manipulate function.
Here's a possible solution using data.table rolling joins
library(data.table)
# key `listings` by proper columns in order perform the binary join
setkey(setDT(listings), id, listing_date)
# Perform a binary rolling join while extracting matched icides and counting them
indx <- data.table(listings[contacts, roll = 30, which = TRUE])[, .N, by = V1]
# Joining back to `listings` by proper rows while assigning the counts by reference
listings[indx$V1, ngs := indx$N]
# id city listing_date ngs
# 1: 2175 B 2015-03-14 3
# 2: 4176 B 2015-04-07 NA
# 3: 6174 A 2015-03-01 2
# 4: 9176 B 2015-03-30 1
# 5: 9177 A 2015-04-18 1
I'm not sure if your actual id values are factor, but I'll start by making those numeric. Using them as factors will cause you problems:
listings$id <- as.numeric(as.character(listings$id))
contacts$id <- as.numeric(as.character(contacts$id))
Then, the strategy is to calculate the "days since listing" value for each contact and add this to your contacts data.frame. Then, aggregate this new data.frame (in your example, sum of contacts within 30 days), and then merge the resulting count back into your original data.
contacts$ngs <- contacts$contact_date - listings$listing_date[match(contacts$id, listings$id)]
a <- aggregate(ngs ~ id, data = contacts, FUN = function(x) sum(x <= 30))
merge(listings, a)
# id city listing_date ngs
# 1 2175 B 2015-03-14 3
# 2 4176 B 2015-04-07 0
# 3 6174 A 2015-03-01 2
# 4 9176 B 2015-03-30 1
# 5 9177 A 2015-04-18 1
Or:
indx <- match(contacts$id, listings$id)
days_since <- contacts$contact_date - listings$listing_date[indx]
n <- with(contacts[days_since <= 30, ], tapply(id, id, length))
n[is.na(n)] <- 0
listings$n <- n[match(listings$id, names(n))]
It's similar to Thomas' answer but utilizes tapply and match instead of aggregate and merge.
You could use the dplyr package. First merge the data:
all.data <- merge(contacts,listings,by = "id")
Set a target number of days:
number.of.days <- 30
Then gather the data by ID (group_by), exclude the results that are not within the time frame (filter) and count the number of occurrences/rows (summarise).
result <- all.data %>% group_by(id) %>% filter(contact_date > listing_date + number.of.days) %>% summarise(count.of.contacts = length(id))
I think there are a number of ways this could be potentially solved but I have found dplyr to be very helpful in a lot circumstances.
EDIT:
Sorry should have thought about that a little more. Does this work,
result <- all.data %>% group_by(id,city,listing_date) %>% summarise(ngs = length(id[which(contact_date < listing_date + number.of.days)]))
I don't think zero results can be passed sensibly through the filter stage (understandably, the goal is usually the opposite). I'm not too sure what sort of impact the 'which' component will have on processing time, likely to be slower than using the 'filter' function but might not matter.
Using dplyr for your first problem:
left_join(contacts, listings, by = c("id" = "id")) %>%
filter(abs(listing_date - contact_date) < 30) %>%
group_by(id) %>% summarise(cnt = n()) %>%
right_join(listings)
And the output is:
id cnt city listing_date
1 6174 2 A 2015-03-01
2 2175 3 B 2015-03-14
3 9176 1 B 2015-03-30
4 4176 NA B 2015-04-07
5 9177 1 A 2015-04-18
I'm not sure I understand your second question to answer it.
I have an issue that I just cannot seem to sort out. I have a dataset that was derived from a raster in arcgis. The dataset represents every fire occurrence during a 10-year period. Some raster cells had multiple fires within that time period (and, thus, will have multiple rows in my dataset) and some raster cells will not have had any fire (and, thus, will not be represented in my dataset). So, each row in the dataset has a column number (sequential integer) and a row number assigned to it that corresponds with the row and column ID from the raster. It also has the date of the fire.
I would like to assign a unique ID (fire_ID) to all of the fires that are within 4 days of each other and in adjacent pixels from one another (within the 8-cell neighborhood) and put this into a new column.
To clarify, if there were an observation from row 3, col 3, Jan 1, 2000 and another from row 2, col 4, Jan 4, 2000, those observations would be assigned the same fire_ID.
Below is a sample dataset with "rows", which are the row IDs of the raster, "cols", which are the column IDs of the raster, and "dates" which are the dates the fire was detected.
rows<-sample(seq(1,50,1),600, replace=TRUE)
cols<-sample(seq(1,50,1),600, replace=TRUE)
dates<-sample(seq(from=as.Date("2000/01/01"), to=as.Date("2000/02/01"), by="day"),600, replace=TRUE)
fire_df<-data.frame(rows, cols, dates)
I've tried sorting the data by "row", then "column", then "date" and looping through, to create a new fire_ID if the row and column ID were within one value and the date was within 4 days, but this obviously doesn't work, as fires which should be assigned the same fire_ID are assigned different fire_IDs if there are observations in between them in the list that belong to a different fire_ID.
fire_df2<-fire_df[order(fire_df$rows, fire_df$cols, fire_df$date),]
fire_ID=numeric(length=nrow(fire_df2))
fire_ID[1]=1
for (i in 2:nrow(fire_df2)){
fire_ID[i]=ifelse(
fire_df2$rows[i]-fire_df2$rows[i-1]<=abs(1) & fire_df2$cols[i]-fire_df2$cols[i-1]<=abs(1) & fire_df2$date[i]-fire_df2$date[i-1]<=abs(4),
fire_ID[i-1],
i)
}
length(unique(fire_ID))
fire_df2$fire_ID<-fire_ID
Please let me know if you have any suggestions.
I think this task requires something along the lines of hierarchical clustering.
Note, however, that there will be necessarily some degree of arbitrariness in the ids. This is because it is entirely possible that the cluster of fires itself is longer than 4 days yet every fire is less than 4 days away from some other fire in that cluster (and thus should have the same id).
library(dplyr)
# Create the distances
fire_dist <- fire_df %>%
# Normalize dates
mutate( norm_dates = as.numeric(dates)/4) %>%
# Only keep the three variables of interest
select( rows, cols, norm_dates ) %>%
# Compute distance using L-infinite-norm (maximum)
dist( method="maximum" )
# Do hierarchical clustering with "single" aggl method
fire_clust <- hclust(fire_dist, method="single")
# Cut the tree at height 1 and obtain groups
group_id <- cutree(fire_clust, h=1)
# First attach the group ids back to the data frame
fire_df2 <- cbind( fire_df, group_id ) %>%
# Then sort the data
arrange( group_id, dates, rows, cols )
# Print the first 20 records
fire_df2[1:10,]
(Make sure you have dplyr library installed. You can run install.packages("dplyr",dep=TRUE) if not installed. It is a really good and very popular library for data manipulations)
A couple of simple tests:
Test #1. The same forest fire moving.
rows<-1:6
cols<-1:6
dates<-seq(from=as.Date("2000/01/01"), to=as.Date("2000/01/06"), by="day")
fire_df<-data.frame(rows, cols, dates)
gives me this:
rows cols dates group_id
1 1 1 2000-01-01 1
2 2 2 2000-01-02 1
3 3 3 2000-01-03 1
4 4 4 2000-01-04 1
5 5 5 2000-01-05 1
6 6 6 2000-01-06 1
Test #2. 6 different random forest fires.
set.seed(1234)
rows<-sample(seq(1,50,1),6, replace=TRUE)
cols<-sample(seq(1,50,1),6, replace=TRUE)
dates<-sample(seq(from=as.Date("2000/01/01"), to=as.Date("2000/02/01"), by="day"),6, replace=TRUE)
fire_df<-data.frame(rows, cols, dates)
output:
rows cols dates group_id
1 6 1 2000-01-10 1
2 32 12 2000-01-30 2
3 31 34 2000-01-10 3
4 32 26 2000-01-27 4
5 44 35 2000-01-10 5
6 33 28 2000-01-09 6
Test #3: one expanding forest fire
dates <- seq(from=as.Date("2000/01/01"), to=as.Date("2000/01/06"), by="day")
rows_start <- 50
cols_start <- 50
fire_df <- data.frame(dates = dates) %>%
rowwise() %>%
do({
diff = as.numeric(.$dates - as.Date("2000/01/01"))
expand.grid(rows=seq(rows_start-diff,rows_start+diff),
cols=seq(cols_start-diff,cols_start+diff),
dates=.$dates)
})
gives me:
rows cols dates group_id
1 50 50 2000-01-01 1
2 49 49 2000-01-02 1
3 49 50 2000-01-02 1
4 49 51 2000-01-02 1
5 50 49 2000-01-02 1
6 50 50 2000-01-02 1
7 50 51 2000-01-02 1
8 51 49 2000-01-02 1
9 51 50 2000-01-02 1
10 51 51 2000-01-02 1
and so on. (All records identified correctly to belong to the same forest fire.)
This may be very complicated and I suspect requires advanced knowledge. I have now two different types of data.frames I need to combine:
The data:
Dataframe A:
lists all transfusion dates by patient ID. Every transfusion is represented by a separate row, patients can have multiple transfusions. Different patients can have transfusions on the same date.
Patient ID Transfusion.Date
1 01/01/2000
1 01/30/2000
2 04/01/2003
3 04/01/2003
Dataframes of Type B contain test results at other dates, also by patient ID:
Patient ID Test.Date Test.Value
1 11/30/1999 negative
1 01/15/2000 700 copies/uL
1 01/27/2000 900 copies/uL
2 03/30/2003 negative
What I would like to have is Dataframe A with the same number of rows (1 for each transfusion), and with the most recent Test.Value as a separate column. Each transfusion date should have the test result from the test performed most closely (prior) to the transfusion.
desired output:
-->
Patient ID Transfusion.Date Pre.Transfusion.Test
1 01/01/2000 negative
1 01/30/2000 900 copies/ul
2 04/01/2003 negative
3 04/01/2003 NA
I think the general strategy would be to subset the data.frames by patient IDs. Then take all transfusion dates for patient 1, check which result is closest to all available test_dates for each element and then return the value closest.
How can I explain R to do that?
Edit 1: Here is the R-code for these examples
df_A <- data.frame(MRN = c(1,1,2,3),
Transfusion.Date = as.Date(c('01/01/2000', '01/30/2000',
'04/01/2003','04/01/2003'),'%m/%d/%Y'))
df_B <- data.frame(MRN = c(1,1,1,2),
Test.Date = as.Date(c('11/30/1999', '01/15/2000', '01/27/2000',
'03/30/2003'),'%m/%d/%Y'), Test.Result = c('negative',
'700 copies/ul','900 copies/ul','negative'))
Edit 2:
To clarify, the resulting data should be: Patient A received transfusions on Day X and Day Y. (for df_A). Prior to the transfusion on day X, his most recent test result was X (closest test date to first transfusion, in df_B). Prior to the transfusion on day Y, his most recent test result was Y (prior to the second transfusion, also in df_B. df_B also contains a bunch of other test dates, which are not needed for the final output.
Here's using data.table's rolling joins:
require(data.table)
setkey(setDT(df_A), MRN, Transfusion.Date)
setkey(setDT(df_B), MRN, Test.Date)
df_B[df_A, roll=TRUE]
# MRN Test.Date Test.Result
# 1: 1 2000-01-01 negative
# 2: 1 2000-01-30 900 copies/ul
# 3: 2 2003-04-01 negative
# 4: 3 2003-04-01 NA
setDT converts data.frame to data.table by reference (without any additional copying). That'll result in df_A and df_B now being data.tables.
setkey sorts the data.table by the columns we provided, and marks those columns as key columns, which allows us to use binary search based joins.
We perform a join of the form x[i] on the key columns, where for each row of i, the matching rows of x (if any, else NA) along with i's rows are returned. This is what we call an equi-join. By adding roll = TRUE, in the event of a mismatch, the last observation is carried forward (LOCF). This is what we call a rolling join. The sorting in increasing order (due to setkey()) ensures that the last observation is the most recent date.
HTH
dfLast <- df_B[ df_B$Test.Date %in%
as.Date( tapply(df_B$Test.Date, df_B$MRN, tail,1),"1970-01-01"), ]
merge(df_A, dfLast, by=c(1:2,1:2) ,all.y=TRUE)
MRN Transfusion.Date Test.Result
1 1 2000-01-27 900 copies/ul
2 2 2003-03-30 negative
Edited. Had some logical errors and some sytactic errors. tapply returned the integer values of the Dates and as you pointed out I was using the wrong column name in the data reduction step.
OK thanks for everyone's help. It took me a lot of toil, blood, sweat, and tears, but this is the solution I came up with:
Merge both data frames:
df_AB <- merge(df_A, df_B, all.x = T)
df_AB:
MRN Transfusion.Date Test.Date Test.Result
1 1 2000-01-01 1999-11-30 negative
2 1 2000-01-01 2000-01-15 700 copies/ul
3 1 2000-01-01 2000-01-27 900 copies/ul
4 1 2000-01-30 1999-11-30 negative
5 1 2000-01-30 2000-01-15 700 copies/ul
6 1 2000-01-30 2000-01-27 900 copies/ul
7 2 2003-04-01 2003-03-30 negative
8 3 2003-04-01 <NA> <NA>
Using dplyr
df_tests <- df_AB %>%
group_by(MRN, Transfusion.Date) %>%
mutate(Time.Difference = Transfusion.Date - Test.Date) %>%
filter(Time.Difference > 0) %>%
arrange(Time.Difference) %>%
summarize(Test.Date = Test.Date[1], Test.Result = Test.Result[1])
df_tests:
MRN Transfusion.Date Test.Date Test.Result
1 1 2000-01-01 1999-11-30 negative
2 1 2000-01-30 1999-11-30 negative
3 2 2003-04-01 2003-03-30 negative
using merge again for MRN3:
df_desired <- merge(df_A, df_tests, all.x = T)
MRN Transfusion.Date Test.Date Test.Result
1 1 2000-01-01 1999-11-30 negative
2 1 2000-01-30 2000-01-27 900 copies/ul
3 2 2003-04-01 2003-03-30 negative
4 3 2003-04-01 <NA> <NA>