compute a Means variable for a specific value in another variable - r

I would like to compute the mean age for every value from 1-7 in another variable called period.
This is how my data looks like:
work1 <- read.table(header=T, text="ID dead age gender inclusion_year diagnosis surv agrp period
87 0 25 2 2006 1 2174 1 5
396 0 19 2 2003 1 3077 1 3
446 0 23 2 2003 1 3144 1 3
497 0 19 2 2011 1 268 1 7
522 1 57 2 1999 1 3407 2 1
714 0 58 2 2003 1 3041 2 3
741 0 27 2 2004 1 2587 1 4
767 0 18 1 2008 1 1104 1 6
786 0 36 1 2005 1 2887 3 4
810 0 25 1 1998 1 3783 4 2")
This is a subset of a data with more then 1500 observations
This is what I'm trying to achieve:
sim <- read.table(header=T, text="Period diagnosis dead surv age
1 1 50 50000 35.5
2 1 80 70000 40.3
3 1 100 80000 32.8
4 1 120 100000 39.8
5 1 140 1200000 28.7
6 1 150 1400000 36.2
7 1 160 1600000 37.1")
In this data set I would like to group by period and diagnosis while all deaths(dead) and surv(survival time in days) is summarised in period time. I would also like for a mean value of the age in every period.
Have tried everything, still can't create the data set I'm striving for.
All help is appreciated!

You could try data.table
library(data.table)
as.data.table(work1)[, .(dead_sum=sum(dead),
surv_sum=sum(surv),
age_mean=mean(age)), keyby=.(period, diagnosis)]
Or dplyr
library(dplyr)
work1 %>% group_by(period, diagnosis) %>%
summarise(dead_sum=sum(dead), surv_sum=sum(surv), age_mean=mean(age))
# result
period diagnosis dead_sum surv_sum age_mean
1: 1 1 1 3407 57.00000
2: 2 1 0 3783 25.00000
3: 3 1 0 9262 33.33333
4: 4 1 0 5474 31.50000
5: 5 1 0 2174 25.00000
6: 6 1 0 1104 18.00000
7: 7 1 0 268 19.00000

Related

Counting the number of changes of a categorical variable during repeated measurements within a category

I'm working with a dataset about migration across the country with the following columns:
i birth gender race region urban wage year educ
1 58 2 3 1 1 4620 1979 12
1 58 2 3 1 1 4620 1980 12
1 58 2 3 2 1 4620 1981 12
1 58 2 3 2 1 4700 1982 12
.....
i birth gender race region urban wage year educ
45 65 2 3 3 1 NA 1979 10
45 65 2 3 3 1 NA 1980 10
45 65 2 3 4 2 11500 1981 10
45 65 2 3 1 1 11500 1982 10
i = individual id. They follow a large group of people for 25 years and record changes in 'region' (categorical variables, 1-4) , 'urban' (dummy), 'wage' and 'educ'.
How do I count the aggregate number of times 'region' or 'urban' has changed (eg: from region 1 to region 3 or from urban 0 to 1) during the observation period (25 year period) within each subject? I also have some NA's in the data (which should be ignored)
A simplified version of expected output:
i changes in region
1 1
...
45 2
i changes in urban
1 0
...
45 2
I would then like to sum up the number of changes for region and urban.
I came across these answers: Count number of changes in categorical variables during repeated measurements and Identify change in categorical data across datapoints in R but I still don't get it.
Here's a part of the data for i=4.
i birth gender race region urban wage year educ
4 62 2 3 1 1 NA 1979 9
4 62 2 3 NA NA NA 1980 9
4 62 2 3 4 1 0 1981 9
4 62 2 3 4 1 1086 1982 9
4 62 2 3 1 1 70 1983 9
4 62 2 3 1 1 0 1984 9
4 62 2 3 1 1 0 1985 9
4 62 2 3 1 1 7000 1986 9
4 62 2 3 1 1 17500 1987 9
4 62 2 3 1 1 21320 1988 9
4 62 2 3 1 1 21760 1989 9
4 62 2 3 1 1 0 1990 9
4 62 2 3 1 1 0 1991 9
4 62 2 3 1 1 30500 1992 9
4 62 2 3 1 1 33000 1993 9
4 62 2 3 NA NA NA 1994 9
4 62 2 3 4 1 35000 1996 9
Here, output should be:
i change_reg change_urban
4 3 0
Here is something I hope will get your closer to what you need.
First you group by i. Then, you can then create a column that will indicate a 1 for each change in region. This compares the current value for the region with the previous value (using lag). Note if the previous value is NA (when looking at the first value for a given i), it will be considered no change.
Same approach is taken for urban. Then, summarize totaling up all the changes for each i. I left in these temporary variables so you can examine if you are getting the results desired.
Edit: If you wish to remove rows that have NA for region or urban you can add drop_na first.
library(dplyr)
library(tidyr)
df_tot <- df %>%
drop_na(region, urban) %>%
group_by(i) %>%
mutate(reg_change = ifelse(region == lag(region) | is.na(lag(region)), 0, 1),
urban_change = ifelse(urban == lag(urban) | is.na(lag(urban)), 0, 1)) %>%
summarize(tot_region = sum(reg_change),
tot_urban = sum(urban_change))
# A tibble: 3 x 3
i tot_region tot_urban
<int> <dbl> <dbl>
1 1 1 0
2 4 3 0
3 45 2 2
Edit: Afterwards, to get a grand total for both tot_region and tot_urban columns, you can use colSums. (Store your earlier result as df_tot as above.)
colSums(df_tot[-1])
tot_region tot_urban
6 2

Dividing proportionally row values based on common identifier and specific column in a data frame

After a merging process, I got a data frame that looks like:
df <- data.frame(trip=c(315,328,422,422,458,652,652,652,699),
catch_kg=c(10,8,12,2,26,4,18,14,11),
age_1=c(0,0,0,0,0,0,0,0,0),
age_2=c(2,1,7.5,7.5,8,11,11,11,13),
id=c(1,2,3,3,4,5,5,5,6))
trip catch_kg age_1 age_2 id
315 10 0 2 1
328 8 0 1 2
422 12 0 7.5 3
422 2 0 7.5 3
458 26 0 8 4
652 4 0 11 5
652 18 0 11 5
652 14 0 11 5
699 11 0 13 6
where trips represents the fishing trip, catch_kg the amount of caught fish (in kg), age_1 & age_2 is the number of individuals in each trip and per age group, and id represents the haul identity in each trip.
In some fishing trips I have more than 1 haul - this can be accessed in the id column, where trips with more than 1 haul have the same id number. For instance: trip number 422 has two hauls (id=3).
At this very moment, for a trip with more than 1 haul, I have that the number of individuals within each age group is equally divided by the number of hauls that appears within that specific trip. For example, in trip 422 I have a total of 15 individuals, but since there are 2 hauls, this number was divided by 2 leading to 7.5 individuals per haul.
What I would like, however, is to compute the number of individuals within each age group as a proportion of the total catch in each haul group.
Thus, at the end I would like to have a data frame that looks like:
trip catch_kg age_1 age_2 id
315 10 0 2 1
328 8 0 1 2
422 12 0 13 3
422 2 0 2 3
458 26 0 8 4
652 4 0 4 5
652 18 0 16 5
652 14 0 13 5
699 11 0 13 6
This is basically a rule of three calculation, where for trip 422 (2 hauls), for instance, I would have the following calculation:
haul1: 12*(7.5 + 7.5)/(12 + 2) = 13 individuals
haul2: 2*(7.5 + 7.5)/(12 + 2) = 2 individuals
Is there an easy way to compute these calculations?
Any help would be much appreciated.
-M
You could use dplyr to help with this
library(dplyr)
df %>% group_by(trip) %>%
mutate(age_2=catch_kg/sum(catch_kg)*sum(age_2))
# trip catch_kg age_1 age_2 id
# <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 315 10 0 2.000000 1
# 2 328 8 0 1.000000 2
# 3 422 12 0 12.857143 3
# 4 422 2 0 2.142857 3
# 5 458 26 0 8.000000 4
# 6 652 4 0 3.666667 5
# 7 652 18 0 16.500000 5
# 8 652 14 0 12.833333 5
# 9 699 11 0 13.000000 6
Not sure exactly what rounding rule you were using to get to integer counts of people, but you'd likely run into trouble with parts not adding up to wholes in more complicated scenarios.
Another solution using data.table:
library(data.table)
setDT(df)
df[, age_2 := catch_kg * sum(age_2) / sum(catch_kg), trip]
# trip catch_kg age_1 age_2 id
#1: 315 10 0 2.000000 1
#2: 328 8 0 1.000000 2
#3: 422 12 0 12.857143 3
#4: 422 2 0 2.142857 3
#5: 458 26 0 8.000000 4
#6: 652 4 0 3.666667 5
#7: 652 18 0 16.500000 5
#8: 652 14 0 12.833333 5
#9: 699 11 0 13.000000 6
If you want you can round age_2 with round(): age_2 := round(catch_kg * sum(age_2) / sum(catch_kg))

Average columns based on other column value and number of rows in R

I'm using R and am trying to create a new dataframe of averaged results from another dataframe based on the values in Column A. To demonstrate my goal here is some data:
set.seed(1981)
df <- data.frame(A = sample(c(0,1), replace=TRUE, size=100),
B=round(runif(100), digits=4),
C=sample(1:1000, 100, replace=TRUE))
head(df, 30)
A B C
0 0.6739 459
1 0.5466 178
0 0.154 193
0 0.41 206
1 0.7526 791
1 0.3104 679
1 0.739 434
1 0.421 171
0 0.3653 577
1 0.4035 739
0 0.8796 147
0 0.9138 37
0 0.7257 350
1 0.2125 779
0 0.1502 495
1 0.2972 504
0 0.2406 245
1 0.0325 613
0 0.8642 539
1 0.1096 630
1 0.2113 363
1 0.277 974
0 0.0485 755
1 0.0553 412
0 0.509 24
0 0.2934 795
0 0.0725 413
0 0.8723 606
0 0.3192 591
1 0.5557 177
I need to reduce the size of the data by calculating the average value for column B and column C for as many rows as the value in Column A stays consecutively the same, up to a maximum of 3 rows. If value A remains either 1, or 0 for more than 3 rows it would roll over into the next row in the new dataframe as you can see below.
The new dataframe requires the following columns:
Value of A B.Av C.Av No. of rows used
0 0.6739 459 1
1 0.5466 178 1
0 0.282 199.5 2
1 0.600666667 634.6666667 3
1 0.421 171 1
0 0.3653 577 1
1 0.4035 739 1
0 0.8397 178 3
1 0.2125 779 1
0 0.1502 495 1
1 0.2972 504 1
0 0.2406 245 1
1 0.0325 613 1
0 0.8642 539 1
1 0.1993 655.6666667 3
0 0.0485 755 1
1 0.0553 412 1
0 0.291633333 410.6666667 3
0 0.59575 598.5 2
1 0.5557 177 1
I haven't managed to find another similar scenario to mine whilst searching Stack Overflow so any help would be really appreciated.
Here is a base-R solution:
## define a function to split the run-length if greater than 3
split.3 <- function(l,v) {
o <- c(values=v,lengths=min(l,3))
while(l > 3) {
l <- l - 3
o <- rbind(o,c(values=v,lengths=min(l,3)))
}
return(o)
}
## compute the run-length encoding of column A
rl <- rle(df$A)
## apply split.3 to the run-length encoding
## the first column of vl are the values of column A
## the second column of vl are the corresponding run-length limited to 3
vl <- do.call(rbind,mapply(split.3,rl$lengths,rl$values))
## compute the begin and end row indices of df for each value of A to average
fin <- cumsum(vl[,2])
beg <- fin - vl[,2] + 1
## compute the averages
out <- do.call(rbind,lapply(1:length(beg), function(i) data.frame(`Value of A`=vl[i,1],
B.Av=mean(df$B[beg[i]:fin[i]]),
C.Av=mean(df$C[beg[i]:fin[i]]),
`No. of rows used`=fin[i]-beg[i]+1)))
## Value.of.A B.Av C.Av No..of.rows.used
##1 0 0.6739000 459.0000 1
##2 1 0.5466000 178.0000 1
##3 0 0.2820000 199.5000 2
##4 1 0.6006667 634.6667 3
##5 1 0.4210000 171.0000 1
##6 0 0.3653000 577.0000 1
##7 1 0.4035000 739.0000 1
##8 0 0.8397000 178.0000 3
##9 1 0.2125000 779.0000 1
##10 0 0.1502000 495.0000 1
##11 1 0.2972000 504.0000 1
##12 0 0.2406000 245.0000 1
##13 1 0.0325000 613.0000 1
##14 0 0.8642000 539.0000 1
##15 1 0.1993000 655.6667 3
##16 0 0.0485000 755.0000 1
##17 1 0.0553000 412.0000 1
##18 0 0.2916333 410.6667 3
##19 0 0.5957500 598.5000 2
##20 1 0.5557000 177.0000 1
Here is a data.table solution:
library(data.table)
setDT(df)
# create two group variables, consecutive A and for each consecutive A every three rows
(df[,rleid := rleid(A)][, threeWindow := ((1:.N) - 1) %/% 3, rleid]
# calculate the average of the columns grouped by the above two variables
[, c(.N, lapply(.SD, mean)), .(rleid, threeWindow)]
# drop group variables
[, `:=`(rleid = NULL, threeWindow = NULL)][])
# N A B C
#1: 1 0 0.6739000 459.0000
#2: 1 1 0.5466000 178.0000
#3: 2 0 0.2820000 199.5000
#4: 3 1 0.6006667 634.6667
#5: 1 1 0.4210000 171.0000
#6: 1 0 0.3653000 577.0000
#7: 1 1 0.4035000 739.0000
#8: 3 0 0.8397000 178.0000
#9: 1 1 0.2125000 779.0000
#10: 1 0 0.1502000 495.0000
#11: 1 1 0.2972000 504.0000
#12: 1 0 0.2406000 245.0000
#13: 1 1 0.0325000 613.0000
#14: 1 0 0.8642000 539.0000
#15: 3 1 0.1993000 655.6667
#16: 1 0 0.0485000 755.0000
#17: 1 1 0.0553000 412.0000
#18: 3 0 0.2916333 410.6667
#19: 2 0 0.5957500 598.5000
#20: 1 1 0.5557000 177.0000

How to remove rows based on distance from an average of column and max of another column

Consider this toy data frame. I would like to create a new data frame in which only rows that are below the average of "birds" and only rows that less than the two top values after the maximum value of "wolfs".So in this data frame I'll get only rows: 543,608,987,225,988,556.
I used this two lines of code for the first constrain but couldn't find a solution for the second constrain.
df$filt<-ifelse(df$birds<mean(df$birds),1,0)
df1<-df1[which(df1$filt==1),]
How can I create the second constrain ?
Here is the toy dataframe:
df <- read.table(text = "userid target birds wolfs
222 1 9 7
444 1 8 4
234 0 2 8
543 1 2 3
678 1 8 3
987 0 1 2
294 1 7 1
608 0 1 5
123 1 9 7
321 1 8 7
226 0 2 7
556 0 2 3
334 1 6 3
225 0 1 1
999 0 3 9
988 0 1 1 ",header = TRUE)
subset(df,birds < mean(birds) & wolfs < sort(unique(wolfs),decreasing=T)[3]);
## userid target birds wolfs
## 4 543 1 2 3
## 6 987 0 1 2
## 8 608 0 1 5
## 12 556 0 2 3
## 14 225 0 1 1
## 16 988 0 1 1
Here a solution but maybe some constraints are not clear to me because it is fit another row respect your desired output.
avbi <- mean(df$birds)
ttw <- sort(df$wolfs, decreasing = T)[3]
df[df$birds < avbi & df$wolfs < ttw , ]
userid target birds wolfs
4 543 1 2 3
6 987 0 1 2
8 608 0 1 5
12 556 0 2 3
14 225 0 1 1
16 988 0 1 1
or with dplyr
df %>% filter(birds < avbi & wolfs < ttw)

R: calculate and superimpose on a ggplot graph minute-based totals for an event series

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')

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