I have a dataset of repeated measurements (hb) for patients (record_id) over several days. I have included an example below
record_id Day hb
1 0 122
1 1 90
1 2 71
1 3 71
2 0 139
2 1 130
2 2 119
2 3 106
3 0 89
3 1 126
3 2 127
3 3 110
4 0 90
4 1 86
4 2 82
4 3 78
5 0 118
5 1 108
5 2 95
5 3 94
I wish to find out the minimum value for "hb" for each patient:
#nadir Hb by patient
x1 <- aggregate(hb~record_id, data=df, FUN=function(df) c(min=min(df), count=length(df)))
summary(x1)
Would anyone be kind enough to show me how I could plot a histogram of these minimum values? Thank you very much for your time, Annemarie
Related
I was not so sure how to ask this question. i am trying to answer what is the average tone when an initiative is mentioned and additionally when a topic, and a goal( or achievement) are mentioned. My dataframe (df) has many mentions of 70 initiatives (rows). meaning my df has 500+ rows of data, but only 70 Initiatives.
My data looks like this
> tabmean
Initiative Topic Goals Achievements Tone
1 52 44 2 2 2
2 294 42 2 2 2
3 103 31 2 2 2
4 52 41 2 2 2
5 87 26 2 1 1
6 52 87 2 2 2
7 136 81 2 2 2
8 19 7 2 2 1
9 19 4 2 2 2
10 0 63 2 2 2
11 0 25 2 2 2
12 19 51 2 2 2
13 52 51 2 2 2
14 108 94 2 2 1
15 52 89 2 2 2
16 110 37 2 2 2
17 247 25 2 2 2
18 66 95 2 2 2
19 24 49 2 2 2
20 24 110 2 2 2
I want to find what is the mean or average Tone when an Initiative is mentioned. as well as what is the Tone when an Initiative, a Topic and a Goal are mentioned at the same time. The code options for Tone are : positive(coded: 1), neutral(2), negative (coded:3), and both positive and negative(4). Goals and Achievements are coded yes(1) and no(2).
I have used this code:
GoalMeanTone <- tabmean %>%
group_by(Initiative,Topic,Goals,Tone) %>%
summarize(averagetone = mean(Tone))
With Solution output :
GoalMeanTone
# A tibble: 454 x 5
# Groups: Initiative, Topic, Goals [424]
Initiative Topic Goals Tone averagetone
<chr> <chr> <chr> <chr> <dbl>
1 0 104 2 0 NA
2 0 105 2 0 NA
3 0 22 2 0 NA
4 0 25 2 0 NA
5 0 29 2 0 NA
6 0 30 2 1 NA
7 0 31 1 1 NA
8 0 42 1 0 NA
9 0 44 2 0 NA
10 0 44 NA 0 NA
# ... with 444 more rows
note that for Initiative Value 0 means "other initiative".
and I've also tried this code
library(plyr)
GoalMeanTone2 <- ddply( tabmean, .(Initiative), function(x) mean(tabmean$Tone) )
with solution output
> GoalMeanTone2
Initiative V1
1 0 NA
2 1 NA
3 101 NA
4 102 NA
5 103 NA
6 104 NA
7 105 NA
8 107 NA
9 108 NA
10 110 NA
Note that in both instances, I do not get an average for Tone but instead get NA's
I have removed the NAs in the df from the column "Tone" also have tried to remove all the other mission values in the df ( its only about 30 values that i deleted).
and I have also re-coded the values for Tone :
tabmean<-Meantable %>% mutate(Tone=recode(Tone,
`1`="1",
`2`="0",
`3`="-1",
`4`="2"))
I still cannot manage to get the average tone for an initiative. Maybe the solution is more obvious than i think, but have gotten stuck and have no idea how to proceed or solve this.
i'd be super grateful for a better code to get this. Thanks!
I'm not completely sure what you mean by 'the average tone when an initiative is mentioned', but let's say that you'd want to get the average tone for when initiative=1, you could try the following:
tabmean %>% filter(initiative==1) %>% summarise(avg_tone=mean(tone, na.rm=TRUE)
Note that (1) you have to add na.rm==TRUE to the summarise call if you have missing values in the column that you are summarizing, otherwise it will only produce NA's, and (2) check that the columns are of type numeric (you could check that with str(tabmean) and for example change tone to numeric with tabmean <- tabmean %>% mutate(tone=as.numeric(tone)).
I am struggling with how to describe level 2 data in my Multilevel Model in R.
I am using the nlme package.
I have longitudinal data with repeated measures. I have repeated observations for every subject across many days.
The Goal:
Level 1 would be the individual observations within the subject ID
Level 2 would be the differences between overall means between subject IDs (Cluster).
I am trying to determine if Test scores are significantly affected by study time, and to see if it's significantly different within subjects and between subjects.
How would I write the script if I want to do "Between Subjects" ?
Here is my script for Level 1 Model
model1 <- lme(fixed = TestScore~Studytime, random =~1|SubjectID, data=dataframe, na.action=na.omit)
Below is my example dataframe
`Subject ID` Observations TestScore Studytime
1 1 1 50 600
2 1 2 72 900
3 1 3 82 627
4 1 4 90 1000
5 1 5 81 300
6 1 6 37 333
7 2 1 93 900
8 2 2 97 1000
9 2 3 99 1200
10 2 4 85 600
11 3 1 92 800
12 3 2 73 900
13 3 3 81 1000
14 3 4 96 980
15 3 5 99 1300
16 4 1 47 600
17 4 2 77 900
18 4 3 85 950
I appreciate the help!
I searched in google and SO but could not find any answer to my question.
I try to get a value from the first upcomming row if the condition is met.
Example:
Pupil participation bonus
2 55 6
2 33 3
2 88 9
2 0 -100
2 44 4
2 66 7
2 0 -33
to
Pupil participation bonus bonusAtNoParti sumBonusTillParticipation=0
2 55 6 -94 6+3+9 = 18
2 33 3 -97 3+9 = 12
2 88 9 -91 9
2 0 -100 0 0
2 44 4 -29 4+7=11
2 66 7 -26 7
2 0 -33 0 0
So I need to do this:
Iterate through the dataframe and check next rows till participation equals to 0 and get the bonus from that line and add the bonus from the current line and write it to bonusAtNoPati.
My problem here is the "check next rows till participation equals to 0 and get the bonus from that line"
I know how to Iterate through the whole list but not after the current point(row)
I would need to do this process to the whole list where i can get any random participation value in random order.
Has anyone any idea how to realize it?
Edit, I also added another column("sumBonusTillParticipation=0", only sum value is required) which is even harder to realize. R is such a hard to learn language =(
you can use which to get which row number participation is 0.
df <- read.table(text = 'Pupil participation bonus
2 55 6
2 33 3
2 88 9
2 0 -100
2 44 4
2 66 7
2 0 -33', header = T)
index <- c(0, which(df$participation == 0))
diffs <- diff(index)
df$tp <- rep(df$bonus[index], times = diffs)
df$bonusAtNoParti <- df$bonus + df$tp
df$bonusAtNoParti[index] <- 0
df$tp <- NULL
Pupil participation bonus bonusAtNoParti
1 2 55 6 -94
2 2 33 3 -97
3 2 88 9 -91
4 2 0 -100 0
5 2 44 4 -29
6 2 66 7 -26
7 2 0 -33 0
Consider a data frame df with an extract from a web server access log, with two fields (sample below, duration is in msec and to simplify the example, let's ignore the date).
time,duration
18:17:26.552,8
18:17:26.632,10
18:17:26.681,12
18:17:26.733,4
18:17:26.778,5
18:17:26.832,5
18:17:26.889,4
18:17:26.931,3
18:17:26.991,3
18:17:27.040,5
18:17:27.157,4
18:17:27.209,14
18:17:27.249,4
18:17:27.303,4
18:17:27.356,13
18:17:27.408,13
18:17:27.450,3
18:17:27.506,13
18:17:27.546,3
18:17:27.616,4
18:17:27.664,4
18:17:27.718,3
18:17:27.796,10
18:17:27.856,3
18:17:27.909,3
18:17:27.974,3
18:17:28.029,3
qplot(time, duration, data=df); gives me a graph of the duration. I'd like to add, superimposed a line showing the number of requests for each minute. Ideally, this line would have a single data point per minute, at the :30sec point. If that's too complicated, an acceptable alternative is to have a step line, with the same value (the count of request) during a minute.
One way is to trunc(df$time, units=c("mins")), then calculate the count of request per minute into a new column then graph it.
I'm asking if there is, perhaps, a more direct way to accomplish the above. Thanks.
Following may be helpful. Create a data frame with steps and plot:
time duration sec sec2 diffsec2 step30s steps
1 18:17:26.552 8 26.552 552 0 0 0
2 18:17:26.632 10 26.632 632 80 1 1
3 18:17:26.681 12 26.681 681 49 0 0
4 18:17:26.733 4 26.733 733 52 1 1
5 18:17:26.778 5 26.778 778 45 0 0
6 18:17:26.832 5 26.832 832 54 1 1
7 18:17:26.889 4 26.889 889 57 1 2
8 18:17:26.931 3 26.931 931 42 0 0
9 18:17:26.991 3 26.991 991 60 1 1
10 18:17:27.040 5 27.040 040 -951 0 0
11 18:17:27.157 4 27.157 157 117 1 1
12 18:17:27.209 14 27.209 209 52 1 2
13 18:17:27.249 4 27.249 249 40 0 0
14 18:17:27.303 4 27.303 303 54 1 1
15 18:17:27.356 13 27.356 356 53 1 2
16 18:17:27.408 13 27.408 408 52 1 3
17 18:17:27.450 3 27.450 450 42 0 0
18 18:17:27.506 13 27.506 506 56 1 1
19 18:17:27.546 3 27.546 546 40 0 0
20 18:17:27.616 4 27.616 616 70 1 1
21 18:17:27.664 4 27.664 664 48 0 0
22 18:17:27.718 3 27.718 718 54 1 1
23 18:17:27.796 10 27.796 796 78 1 2
24 18:17:27.856 3 27.856 856 60 1 3
25 18:17:27.909 3 27.909 909 53 1 4
26 18:17:27.974 3 27.974 974 65 1 5
27 18:17:28.029 3 28.029 029 -945 0 0
>
> ggplot(ddf)+geom_point(aes(x=time, y=duration))+geom_line(aes(x=time, y=steps, group=1),color='red')
I am trying to rank multiple numeric variables ( around 700+ variables) in the data and am not sure exactly how to do this as I am still pretty new to using R.
I do not want to overwrite the ranked values in the same variable and hence need to create a new rank variable for each of these numeric variables.
From reading the posts, I believe assign and transform function along with rank maybe able to solve this. I tried implementing as below ( sample data and code) and am struggling to get it to work.
The output dataset in addition to variables xcount, xvisit, ysales need to be populated
With variables xcount_rank, xvisit_rank, ysales_rank containing the ranked values.
input <- read.table(header=F, text="101 2 5 6
102 3 4 7
103 9 12 15")
colnames(input) <- c("id","xcount","xvisit","ysales")
input1 <- input[,2:4] #need to rank the numeric variables besides id
for (i in 1:3)
{
transform(input1,
assign(paste(input1[,i],"rank",sep="_")) =
FUN = rank(-input1[,i], ties.method = "first"))
}
input[paste(names(input)[2:4], "rank", sep = "_")] <-
lapply(input[2:4], cut, breaks = 10)
The problem with this approach is that it's creating the rank values as (101, 230] , (230, 450] etc whereas I would like to see the values in the rank variable to be populated as 1, 2 etc up to 10 categories as per the splits I did. Is there any way to achieve this? input[5:7] <- lapply(input[5:7], rank, ties.method = "first")
The approach I tried from the solutions provided below is:
input <- read.table(header=F, text="101 20 5 6
102 2 4 7
103 9 12 15
104 100 8 7
105 450 12 65
109 25 28 145
112 854 56 93")
colnames(input) <- c("id","xcount","xvisit","ysales")
input[paste(names(input)[2:4], "rank", sep = "_")] <-
lapply(input[2:4], cut, breaks = 3)
Current output I get is:
id xcount xvisit ysales xcount_rank xvisit_rank ysales_rank
1 101 20 5 6 (1.15,286] (3.95,21.3] (5.86,52.3]
2 102 2 4 7 (1.15,286] (3.95,21.3] (5.86,52.3]
3 103 9 12 15 (1.15,286] (3.95,21.3] (5.86,52.3]
4 104 100 8 7 (1.15,286] (3.95,21.3] (5.86,52.3]
5 105 450 12 65 (286,570] (3.95,21.3] (52.3,98.7]
6 109 25 28 145 (1.15,286] (21.3,38.7] (98.7,145]
7 112 854 56 93 (570,855] (38.7,56.1] (52.3,98.7]
Desired output:
id xcount xvisit ysales xcount_rank xvisit_rank ysales_rank
1 101 20 5 6 1 1 1
2 102 2 4 7 1 1 1
3 103 9 12 15 1 1 1
4 104 100 8 7 1 1 1
5 105 450 12 65 2 1 2
6 109 25 28 145 1 2 3
Would like to see the records in the group they would fall under if I try to rank the interval values.
Using dplyr
library(dplyr)
nm1 <- paste("rank", names(input)[2:4], sep="_")
input[nm1] <- mutate_each(input[2:4],funs(rank(., ties.method="first")))
input
# id xcount xvisit ysales rank_xcount rank_xvisit rank_ysales
#1 101 2 5 6 1 2 1
#2 102 3 4 7 2 1 2
#3 103 9 12 15 3 3 3
Update
Based on the new input and using cut
input[nm1] <- mutate_each(input[2:4], funs(cut(., breaks=3, labels=FALSE)))
input
# id xcount xvisit ysales rank_xcount rank_xvisit rank_ysales
#1 101 20 5 6 1 1 1
#2 102 2 4 7 1 1 1
#3 103 9 12 15 1 1 1
#4 104 100 8 7 1 1 1
#5 105 450 12 65 2 1 2
#6 109 25 28 145 1 2 3
#7 112 854 56 93 3 3 2