I have a data.table dt:
names <- c("john","mary","mary","mary","mary","mary","mary","tom","tom","tom","mary","john","john","john","tom","tom")
dates <- c(as.Date("2010-06-01"),as.Date("2010-06-01"),as.Date("2010-06-05"),as.Date("2010-06-09"),as.Date("2010-06-13"),as.Date("2010-06-17"),as.Date("2010-06-21"),as.Date("2010-07-09"),as.Date("2010-07-13"),as.Date("2010-07-17"),as.Date("2010-06-01"),as.Date("2010-08-01"),as.Date("2010-08-05"),as.Date("2010-08-09"),as.Date("2010-09-03"),as.Date("2010-09-04"))
shifts_missed <- c(2,11,11,11,11,11,11,6,6,6,1,5,5,5,0,2)
shift <- c("Day","Night","Night","Night","Night","Night","Night","Day","Day","Day","Day","Night","Night","Night","Night","Day")
df <- data.frame(names=names, dates=dates, shifts_missed=shifts_missed, shift=shift)
dt <- as.data.table(df)
names dates shifts_missed shift
john 2010-06-01 2 Day
mary 2010-06-01 11 Night
mary 2010-06-05 11 Night
mary 2010-06-09 11 Night
mary 2010-06-13 11 Night
mary 2010-06-17 11 Night
mary 2010-06-21 11 Night
tom 2010-07-09 6 Day
tom 2010-07-13 6 Day
tom 2010-07-17 6 Day
mary 2010-06-01 1 Day
john 2010-08-01 5 Night
john 2010-08-05 5 Night
john 2010-08-09 5 Night
tom 2010-09-03 0 Night
tom 2010-09-04 2 Day
Ultimately, what I want is to get the following:
names dates shifts_missed shift count
john 2010-06-01 2 Day 1
mary 2010-06-01 11 Night 1
mary 2010-06-05 11 Night 1
mary 2010-06-09 11 Night 1
mary 2010-06-13 11 Night 1
mary 2010-06-17 11 Night 1
mary 2010-06-21 11 Night 1
tom 2010-07-09 6 Day 1
tom 2010-07-13 6 Day 1
tom 2010-07-17 6 Day 1
mary 2010-06-01 1 Day 1
john 2010-08-01 5 Night 1
john 2010-08-05 5 Night 1
john 2010-08-09 5 Night 1
tom 2010-09-03 0 Night 0
tom 2010-09-04 2 Day 1
john 2010-06-01 2 Night 1
mary 2010-06-05 11 Day 1
mary 2010-06-09 11 Day 1
mary 2010-06-13 11 Day 1
mary 2010-06-17 11 Day 1
mary 2010-06-21 11 Day 1
tom 2010-07-09 6 Night 1
tom 2010-07-13 6 Night 1
tom 2010-07-17 6 Night 1
john 2010-08-05 5 Day 1
john 2010-08-09 5 Day 1
tom 2010-09-04 2 Night 1
As you can see, the second half of the data is almost a duplicate of the first half. However, if shifts_missed = 0, it should not be duplicated, and if shifts_missed is odd, the first row should not be duplicated but the remaining rows should. It should then add a 1 in the count column for all except when shifts_missed = 0.
I've seen some answers that speak about !duplicate or unique, but these values in shifts_missed are not unique. I'm sure this isn't overly complicated and is probably a multi-step process, but I can't figure out how to isolate the first rows of the odd shifts_missed column.
dt[, is.in := if(shifts_missed[1] %% 2 == 0) T else c(F, rep(T, .N-1))
, by = .(names, shift)]
rbind(dt, dt[is.in & shifts_missed != 0])
Adding the extra column part should be obvious.
Related
I would like to calculate the number of days which have passed since the first event. There are different groups, so each group's starting date for an event is different and I want to calculate each groups number of days passed since their own first event.
names = c('Ben',"Ben","Ben","Ben","Ben","Ben" ,'Dan',"Dan","Dan","Dan", 'Peter',"Peter","Peter","Peter","Peter","Peter","Peter",'Betty',"Betty","Betty",'Betty', "Betty")
dates = c('2000-02-01','2000-02-02',"2000-02-03","2000-02-04",'2000-02-05','2000-02-05', '2000-01-11','2000-01-12',"2000-01-13",'2000-01-14',
'2000-09-10','2000-09-11',"2000-09-12",'2000-09-13','2000-09-14','2000-09-15','2000-09-16','2000-11-13','2000-11-14', "2000-11-15",'2000-11-16','2000-11-17')
events = c(0,0,1,4,5,11,0,0,2,6,0,0,1,2,3,4,5,0,0,1,2,3)
newd = data.frame(names,dates,events)
newd
so the data frame looks like this:
> newd
names dates events
1 Ben 2000-02-01 0
2 Ben 2000-02-02 0
3 Ben 2000-02-03 1
4 Ben 2000-02-04 4
5 Ben 2000-02-05 5
6 Ben 2000-02-05 11
7 Dan 2000-01-11 0
8 Dan 2000-01-12 0
9 Dan 2000-01-13 2
10 Dan 2000-01-14 6
11 Peter 2000-09-10 0
12 Peter 2000-09-11 0
13 Peter 2000-09-12 1
14 Peter 2000-09-13 2
15 Peter 2000-09-14 3
16 Peter 2000-09-15 4
17 Peter 2000-09-16 5
18 Betty 2000-11-13 0
19 Betty 2000-11-14 0
20 Betty 2000-11-15 1
21 Betty 2000-11-16 2
22 Betty 2000-11-17 3
This is just an example I am using, the 'events' are not in a specific order and are totally random, there are also many other dates with the event of 0. So I would like to only start counting days where: event > 0.
So if there's a 0 at 'event' than there should also be a 0 days counted.
Convert the dates to actual date and you can then subtract minimum dates for each names.
newd$dates <- as.Date(newd$dates)
library(dplyr)
newd %>% group_by(names) %>% mutate(events = as.integer(dates - min(dates)))
# names dates events
# <chr> <date> <int>
# 1 Ben 2000-02-02 0
# 2 Ben 2000-02-03 1
# 3 Ben 2000-02-04 2
# 4 Ben 2000-02-05 3
# 5 Ben 2000-02-05 3
# 6 Dan 2000-01-12 0
# 7 Dan 2000-01-13 1
# 8 Dan 2000-01-14 2
# 9 Peter 2000-09-11 0
#10 Peter 2000-09-12 1
#11 Peter 2000-09-13 2
#12 Peter 2000-09-14 3
#13 Peter 2000-09-15 4
#14 Peter 2000-09-16 5
#15 Betty 2000-11-14 0
#16 Betty 2000-11-15 1
#17 Betty 2000-11-16 2
#18 Betty 2000-11-17 3
In base R :
newd$events <- with(newd, dates - ave(dates, names, FUN = min))
and data.table :
library(data.table)
setDT(newd)[, events := dates - min(dates), names]
I'm trying to create a column that identifies if the row meets two conditions. For example, I have a table similar to this:
> dat <- data.frame(Date = c(rep(c("2019-01-01", "2019-02-01","2019-03-01", "2019-04-01"), 4)),
+ Rep = c(rep("Mike", 4), rep("Tasha", 4), rep("Dane", 4), rep("Trish", 4)),
+ Manager = c(rep("Amber", 2), rep("Michelle", 2), rep("Debbie", 4), rep("Brian", 4), rep("Tim", 3), "Trevor"),
+ Sales = floor(runif(16, min = 0, max = 10)))
> dat
Date Rep Manager Sales
1 2019-01-01 Mike Amber 6
2 2019-02-01 Mike Amber 3
3 2019-03-01 Mike Michelle 9
4 2019-04-01 Mike Michelle 2
5 2019-01-01 Tasha Debbie 9
6 2019-02-01 Tasha Debbie 6
7 2019-03-01 Tasha Debbie 0
8 2019-04-01 Tasha Debbie 4
9 2019-01-01 Dane Brian 3
10 2019-02-01 Dane Brian 6
11 2019-03-01 Dane Brian 6
12 2019-04-01 Dane Brian 1
13 2019-01-01 Trish Tim 6
14 2019-02-01 Trish Tim 7
15 2019-03-01 Trish Tim 6
16 2019-04-01 Trish Trevor 1
Out of the Reps that have switched manager, I would like to identify weather this manager is the first or the second manager with respect to the date. The ideal output would look something like:
Date Rep Manager Sales New_Column
1 2019-01-01 Mike Amber 6 1
2 2019-02-01 Mike Amber 3 1
3 2019-03-01 Mike Michelle 9 2
4 2019-04-01 Mike Michelle 2 2
5 2019-01-01 Trish Tim 6 1
6 2019-02-01 Trish Tim 7 1
7 2019-03-01 Trish Tim 6 1
8 2019-04-01 Trish Trevor 1 2
I have tried a few things but they're not quite working out. I have created two separate data frames where one consists of the first instance of that Rep and associated manager (df1) and the other one consists of the last instance of that rep and associated manager (df2). The code that I have tried that has gotten the closest is:
dat$New_Column <- ifelse(dat$Rep %in% df1$Rep & dat$Manager %in% df1$Manager, 1,
ifelse(dat$Rep %in% df2$Rep & dat$Manager %in% df2$Manager, 2, NA))
However this reads as two separate conditions, rather than having a condition of a condition (i.e. If Mike exists in the first instance and Amber exists in the first instance assign 1 rather than If Mike exists with the manager Amber in the first instance assign 1). Any help would be really appreciated. Thank you!
An option is to first grouped by 'Rep' filter the rows where the number of unique 'Manager' is 2, and then add a column by matching the 'Manager' with the unique elements of 'Manager' to get the indices
library(dplyr)
dat %>%
group_by(Rep) %>%
filter(n_distinct(Manager) == 2) %>%
mutate(New_Column = match(Manager, unique(Manager)))
# A tibble: 8 x 5
# Groups: Rep [2]
# Date Rep Manager Sales New_Column
# <chr> <chr> <chr> <int> <int>
#1 2019-01-01 Mike Amber 6 1
#2 2019-02-01 Mike Amber 3 1
#3 2019-03-01 Mike Michelle 9 2
#4 2019-04-01 Mike Michelle 2 2
#5 2019-01-01 Trish Tim 6 1
#6 2019-02-01 Trish Tim 7 1
#7 2019-03-01 Trish Tim 6 1
#8 2019-04-01 Trish Trevor 1 2
I inherited a data set coded in an unusual way. I would like to learn a less verbose way of reshaping it. The data frame looks like this:
# Input.
participant = c(rep("John",6), rep("Mary",6))
day = c(rep(1,3), rep(2,3), rep(1,3), rep(2,3))
likes = c("apples", "apples", "18", "apples", "apples", "7", "bananas", "bananas", "24", "bananas", "bananas", "3")
question = rep(c(1,1,0),4)
number = c(rep(18,3), rep(7,3), rep(24,3), rep(3,3))
df = data.frame(participant, day, question, likes)
participant day question likes
1 John 1 1 apples
2 John 1 1 apples
3 John 1 0 18
4 John 2 1 apples
5 John 2 1 apples
6 John 2 0 7
7 Mary 1 1 bananas
8 Mary 1 1 bananas
9 Mary 1 0 24
10 Mary 2 1 bananas
11 Mary 2 1 bananas
12 Mary 2 0 3
As you can see, the column likes is heterogeneous. When question equals 0, likes conveys a number chosen by the participants, not their preferred fruit. So I would like to re-code it in a new column as follows:
participant day question likes number
1 John 1 1 apples 18
2 John 1 1 apples 18
3 John 1 0 18 18
4 John 2 1 apples 7
5 John 2 1 apples 7
6 John 2 0 7 7
7 Mary 1 1 bananas 24
8 Mary 1 1 bananas 24
9 Mary 1 0 24 24
10 Mary 2 1 bananas 3
11 Mary 2 1 bananas 3
12 Mary 2 0 3 3
My current solution with base R involves subsetting the initial data frame, creating a lookup table, changing the column names and then merging the lookup table with the original data frame. But this involves several steps and I worry that there should be a simpler solution. I think that tidyr might be the answer, but I don't know how to use it to spread values in one column (likes) conditional other columns (day and question).
Do you have any suggestions? Thanks a lot!
Using the data set above, you can try the following. You group your data by participant and day and look for a row with question == 0 for each group.
library(dplyr)
group_by(df, participant, day) %>%
mutate(age = as.numeric(as.character(likes[which(question == 0)])))
Or as alistaire suggested, you can use grep() too.
group_by(df, participant, day) %>%
mutate(age = as.numeric(grep('\\d+', likes, value = TRUE)))
# participant day question likes age
# (fctr) (dbl) (dbl) (fctr) (dbl)
#1 John 1 1 apples 18
#2 John 1 1 apples 18
#3 John 1 0 18 18
#4 John 2 1 apples 7
#5 John 2 1 apples 7
#6 John 2 0 7 7
#7 Mary 1 1 bananas 24
#8 Mary 1 1 bananas 24
#9 Mary 1 0 24 24
#10 Mary 2 1 bananas 3
#11 Mary 2 1 bananas 3
#12 Mary 2 0 3 3
If you want to use data.table, you can do:
library(data.table)
setDT(df)[, age := as.numeric(as.character(likes[which(question == 0)])),
by = list(participant, day)]
NOTE
The present data set is a new one. Jota's answer works for the deleted data set.
Addressing the new example data:
# create a key column, overwrite it later
df$number <- paste0(df$participant, df$day) # use as a key
# create lookup table
lookup <- df[!is.na(as.numeric(as.character(df$likes))), c("number", "likes")]
# use lookup to overwrite df$number with the appropriate number
df$number <- lookup$likes[match(df$number, lookup$number)]
# participant day question likes number
#1 John 1 1 apples 18
#2 John 1 1 apples 18
#3 John 1 0 18 18
#4 John 2 1 apples 7
#5 John 2 1 apples 7
#6 John 2 0 7 7
#7 Mary 1 1 bananas 24
#8 Mary 1 1 bananas 24
#9 Mary 1 0 24 24
#10 Mary 2 1 bananas 3
#11 Mary 2 1 bananas 3
#12 Mary 2 0 3 3
The warning about NAs be introduced by coercion is expected due to converting characters to numeric (as.numeric(as.character(df$likes))),.
If you're data are ordered like in the example, you can use na.locf from the zoo package:
library(zoo)
df$age <- na.locf(as.numeric(as.character(df$likes)), fromLast = TRUE)
I have a df with two variables, one with IDs and one with a variable called numbers. I would like to excude individuals who do not start their sequence of numbers with the number 1.
I have managed to do this by creating a binary indicator and excluding if the person has this indicator. However, there must be a simpler more elegant way to do this?
Example data and the code I've used to achieve desired result are below.
Thank you.
sample df:
zz<-" names numbers
1 john 1
2 john 2
3 john 3
4 john 4
5 john 5
6 john 6
7 john 7
8 john 8
9 mary 4
10 mary 5
11 mary 6
12 mary 7
13 mary 8
14 mary 9
15 mary 10
16 mary 11
17 mary 12
18 pat 1
19 pat 2
20 pat 3
21 pat 4
22 pat 5
23 pat 6
24 pat 7
25 pat 8
26 pat 9
27 pat 10
28 sue 2
29 sue 3
30 sue 4
31 sue 5
32 sue 6
33 sue 7
34 sue 8
35 sue 9
36 tom 5
37 tom 6
38 tom 7
39 tom 8
40 tom 9
41 tom 10
42 tom 11
"
Data <- read.table(text=zz, header = TRUE)
Step 1 - add binary indicator
df$all<-ifelse(df$numbers==1, 1,0)
df$allperson<-ave(df$all, df$names, FUN=cumsum)
Step two - get rid of people who do not have 1 as their start number
df[!df$allperson==0,]
If you want elegance, I must recommend the package dplyr:
library(dplyr)
Data %>%
group_by(names) %>%
filter(min(numbers) != 1)
It means just what it appears to mean: filter only records where a group (defined by names) has a minimum numbers value inequal to 1.
names numbers
1 mary 4
2 mary 5
3 mary 6
4 mary 7
5 mary 8
6 mary 9
7 mary 10
8 mary 11
9 mary 12
10 sue 2
11 sue 3
You may also try:
zz1 <- zz[with(zz, names %in% unique(names)[!!table(zz)[,1]]),]
head(zz1,4)
# names numbers
#1 john 1
#2 john 2
#3 john 3
#4 john 4
My question is to improve the efficiency/elegance of my code. I have a df with a list of drugs. I want to identify the drugs that start with C09 and C10. If a person has these drugs, I want to give them a binary indicator (1=yes, 0=no) of whether they have these drugs. Binary indicator will be in a new column called "statins", in the same dataframe. I used this post as a guide: What's the R equivalent of SQL's LIKE 'description%' statement?.
Here is what I have done;
names<-c("tom", "mary", "mary", "john", "tom", "john", "mary", "tom", "mary", "tom", "john")
drugs<-c("C10AA05", "C09AA03", "C10AA07", "A02BC01", "C10AA05", "C09AA03", "A02BC01", "C10AA05", "C10AA07", "C07AB03", "N02AA01")
df<-data.frame(names, drugs)
df
names drugs
1 tom C10AA05
2 mary C09AA03
3 mary C10AA07
4 john A02BC01
5 tom C10AA05
6 john C09AA03
7 mary A02BC01
8 tom C10AA05
9 mary C10AA07
10 tom C07AB03
11 john N02AA01
ptn = '^C10.*?'
get_statin = grep(ptn, df$drugs, perl=T)
stats<-df[get_statin,]
names drugs
1 tom C10AA05
3 mary C10AA07
5 tom C10AA05
8 tom C10AA05
9 mary C10AA07
ptn2='^C09.*?'
get_other=grep(ptn2, df$drugs, perl=T)
other<-df[get_other,]
other
names drugs
2 mary C09AA03
6 john C09AA03
df$statins=ifelse(df$drugs %in% stats$drugs,1,0)
df
names drugs statins
1 tom C10AA05 1
2 mary C09AA03 0
3 mary C10AA07 1
4 john A02BC01 0
5 tom C10AA05 1
6 john C09AA03 0
7 mary A02BC01 0
8 tom C10AA05 1
9 mary C10AA07 1
10 tom C07AB03 0
11 john N02AA01 0
df$statins=ifelse(df$drugs %in% other$drugs,1,df$statins)
df
names drugs statins
1 tom C10AA05 1
2 mary C09AA03 1
3 mary C10AA07 1
4 john A02BC01 0
5 tom C10AA05 1
6 john C09AA03 1
7 mary A02BC01 0
8 tom C10AA05 1
9 mary C10AA07 1
10 tom C07AB03 0
11 john N02AA01 0
So, I can get what I want - but I feel there is probably a better, nicer way to do it and would appreciate any guidance here. An obvious solution that I can feel you all shouting at your screens is just use '^C' as a pattern - and therefore catch all the drugs beginning with C. I won't be able to do this in my main analysis as the 'C' will catch things that I don't want in some instances, so I need to make it as narrow as possible.
Here you go:
transform(df, statins=as.numeric(grepl('^C(10|09)', drugs)))