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)))
Related
In my data set, their is column of full names (eg: below) and I want to add the another column next to it mentioning if a name has appeared two one, two, three, four.... times using R. My output should look like the column below: Number of repetition.
Eg: Data set name: People
**Full name** **Number of repetition**
Peter 1
Peter 2
Alison
Warren
Jack 1
Jack 2
Jack 3
Jack 4
Susan 1
Susan 2
Henry 1
Walison
Tinder 1
Peter 3
Henry 2
Tinder 2
Thanks
Teena
Here is an alternative way solved with help from akrun: sum() condition in ifelse statement
library(dplyr)
df1 %>%
group_by(Fullname) %>%
mutate(newcol = row_number(),
newcol = if(sum(newcol)> 1) newcol else NA) %>%
ungroup
Fullname newcol
<chr> <int>
1 Peter 1
2 Peter 2
3 Alison NA
4 Warren NA
5 Jack 1
6 Jack 2
7 Jack 3
8 Jack 4
9 Susan 1
10 Susan 2
11 Henry 1
12 Walison NA
13 Tinder 1
14 Peter 3
15 Henry 2
16 Tinder 2
Here is one way. Do a group by 'Fullname', and create the sequence with row_number() if the number of rows is greater than 1. By default, case_when returns the other case as NA
library(dplyr)
df1 <- df1 %>%
group_by(Fullname) %>%
mutate(Number_of_repetition = case_when(n() > 1 ~ row_number())) %>%
ungroup
-output
df1
# A tibble: 16 × 2
Fullname Number_of_repetition
<chr> <int>
1 Peter 1
2 Peter 2
3 Alison NA
4 Warren NA
5 Jack 1
6 Jack 2
7 Jack 3
8 Jack 4
9 Susan 1
10 Susan 2
11 Henry 1
12 Walison NA
13 Tinder 1
14 Peter 3
15 Henry 2
16 Tinder 2
If we need to add a third column, use unite on the updated data from previous step
library(tidyr)
df1 %>%
unite(FullNameRep, Fullname, Number_of_repetition, sep="", na.rm = TRUE, remove = FALSE)
-output
# A tibble: 16 × 3
FullNameRep Fullname Number_of_repetition
<chr> <chr> <int>
1 Peter1 Peter 1
2 Peter2 Peter 2
3 Alison Alison NA
4 Warren Warren NA
5 Jack1 Jack 1
6 Jack2 Jack 2
7 Jack3 Jack 3
8 Jack4 Jack 4
9 Susan1 Susan 1
10 Susan2 Susan 2
11 Henry1 Henry 1
12 Walison Walison NA
13 Tinder1 Tinder 1
14 Peter3 Peter 3
15 Henry2 Henry 2
16 Tinder2 Tinder 2
data
df1 <- structure(list(Fullname = c("Peter", "Peter", "Alison", "Warren",
"Jack", "Jack", "Jack", "Jack", "Susan", "Susan", "Henry", "Walison",
"Tinder", "Peter", "Henry", "Tinder")), row.names = c(NA, -16L
), class = "data.frame")
I have a list of people and I want to examine the characteristics of their collaborative networks. However, I want to use all the edges of their networks, not just the edges that are directly connected to them. For example, let's say that I am looking at Bob's network. For Project1, Bob, Bill, and Jane worked together.
If I only cared about the edges connected to Bob, then my edge list would be: Bob--Bill, Bob--Jane. The network would have 0 triangles. However, if I looked at all the edges for Bob's network, the edge list would look like this: Bob--Bill, Bob--Jane, Bill--Jane. The network would have 1 triangle.
The only way to do this that I can think of is to create a "grouped" edge list. I'm just not sure how to tackle it.
My edge data looks like this:
collab <- data.frame(vertex1 = c("Bob","Bill","Bob","Jane","Bill","Jane","Bob","Jane","Bob","Bill","Bob"
,"Jane","Bill","Jane","Bob","Jane","Jane","Jill","Jane","Susan","Susan"),
edgeID = c(1,1,1,1,1,1,2,2,1,1,1,1,1,1,2,2,3,3,3,3,3),
vertex2 = c("Bill","Bob","Jane","Bob","Jane","Jill","Jane","Bob","Bill","Bob"
,"Jane","Bob","Jane","Bill","Jane","Bob","Jill","Jane","Susan","Jane","Jill"))
vertex1 edgeID vertex2
1 Bob 1 Bill
2 Bill 1 Bob
3 Bob 1 Jane
4 Jane 1 Bob
5 Bill 1 Jane
6 Jane 1 Jill
7 Bob 2 Jane
8 Jane 2 Bob
9 Jane 3 Jill
10 Jill 3 Jane
11 Jane 3 Susan
12 Susan 3 Jane
13 Susan 3 Jill
I have another vector that holds the names of my target people:
targets <- data.frame(name=c("Bob","Jane"))
What I'd like to do is group the relevant edges under the appropriate targets such that the result is something like:
group vertex1 edgeID vertex2
1 Bob Bob 1 Bill
2 Bob Bill 1 Bob
3 Bob Bob 1 Jane
4 Bob Jane 1 Bob
5 Bob Bill 1 Jane
6 Bob Jane 1 Jill
7 Bob Bob 2 Jane
8 Bob Jane 2 Bob
9 Jane Bob 1 Bill
10 Jane Bill 1 Bob
11 Jane Bob 1 Jane
12 Jane Jane 1 Bob
13 Jane Bill 1 Jane
14 Jane Jane 1 Bill
15 Jane Bob 2 Jane
16 Jane Jane 2 Bob
17 Jane Jane 3 Jill
18 Jane Jill 3 Jane
19 Jane Jane 3 Susan
20 Jane Susan 3 Jane
21 Jane Susan 3 Jill
I figure that if I can get here, I can make a for loop that cycles through each target, creates a graph with iGraph, and calculates the network metrics for Bob and Jane without much issue. Am I going about this the right way or does iGraph have a better way to do this?
Here might be one option
g <- graph_from_data_frame(collab[c(1, 3, 2)], directed = FALSE)
do.call(
rbind,
c(
make.row.names = FALSE,
lapply(
targets$name,
function(nm) {
z <- c(nm, V(g)$name[distances(g, nm) == 1])
cbind(group = nm, unique(subset(collab, vertex1 %in% z & vertex2 %in% z)))
}
)
)
)
which gives
group vertex1 edgeID vertex2
1 Bob Bob 1 Bill
2 Bob Bill 1 Bob
3 Bob Bob 1 Jane
4 Bob Jane 1 Bob
5 Bob Bill 1 Jane
6 Bob Bob 2 Jane
7 Bob Jane 2 Bob
8 Bob Jane 1 Bill
9 Jane Bob 1 Bill
10 Jane Bill 1 Bob
11 Jane Bob 1 Jane
12 Jane Jane 1 Bob
13 Jane Bill 1 Jane
14 Jane Jane 1 Jill
15 Jane Bob 2 Jane
16 Jane Jane 2 Bob
17 Jane Jane 1 Bill
18 Jane Jane 3 Jill
19 Jane Jill 3 Jane
20 Jane Jane 3 Susan
21 Jane Susan 3 Jane
22 Jane Susan 3 Jill
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 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.
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