I have a data base with 121 rows and like 10 columns. One of these columns corresponds to Station, another to depth and the rest to chemical variables (temperature, salinity, etc.). I want to calculate the integrated value of these chemical properties by station, using the function oce::integrateTrapezoid. It's my first time doing a loop, so i dont know how. Could you help me?
dA<-matrix(data=NA, nrow=121, ncol=3)
for (Station in unique(datos$Station))
{dA[Station, cd] <- integrateTrapezoid(cd, Profundidad..m., "cA")
}
Station
Depth
temp
1
10
28
1
50
25
1
100
15
1
150
10
2
9
27
2
45
24
2
98
14
2
152
11
3
11
28.7
3
48
23
3
102
14
3
148
9
Related
I have the following table in R:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
162 148 108 93 67 83 44 53 37 47 25 34 17 22 11 11 5
I want to divide in into 7 parts had title of 1 2 3 4 5 6 7&greater, where it needs to combine all the number after 7 and merge it into the last one.
I have looked at aggregate & tapply but doesn't seem like the right function I need.
x <- c(x[1:6], "7 and above"=sum(x[-(1:6)]))
1 2 3 4 5 6 7 and above
162 148 108 93 67 83 306
data
x <- table(rep(c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17), c(162,148,108,93,67,83,44,53,37,47,25,34,17,22,11,11,5)))
If you are using table to generate the output above you can use pmin to keep minimum between the values in your data and 7 and then use table to count the frequency.
Assuming your dataframe is called df and column name is col_name you can do.
tab <- table(pmin(df$col_name, 7))
The values under 7 would include all the 7 & above values together. You can rename it to make it more clear.
names(tab)[7] <- '7&above'
I have a dataset with the reports from a local shop, where each line has a client's ID, date of purchase and total value per purchase.
I want to create a new plot where for each client ID I have all the purchases in the last month or even just sample purchases in a range of dates I choose.
The main problem is that certain customers might buy once a month, while others can come daily - so the number of observations per period of time can vary.
I have tried subsetting my dataset to a specific range of time, but either I choose a specific date - and then I only get a small % of all customers, or I choose a range and get multiple observations for certain customers.
(In this case - I wouldn't mind getting the earliest observation)
An important note: I know how to create a for loop to solve this problem, but since the dataset is over 4 million observations it isn't practical since it would take an extremely long time to run.
A basic example of what the dataset looks like:
ID Date Sum
1 1 1 234
2 1 2 45
3 1 3 1
4 2 4 223
5 3 5 546
6 4 6 12
7 2 1 20
8 4 3 30
9 6 2 3
10 3 5 45
11 7 6 456
12 3 7 65
13 8 8 234
14 1 9 45
15 3 2 1
16 4 3 223
17 6 6 546
18 3 4 12
19 8 7 20
20 9 5 30
21 11 6 3
22 12 6 45
23 14 9 456
24 15 10 65
....
And the new data set would look something like this:
ID 1Date 1Sum 2Date 2Sum 3Date 3Sum
1 1 234 2 45 3 1
2 1 20 4 223 NA NA
3 2 1 5 546 5 45
...
Thanks for your help!
I think you can do this with a bit if help from dplyr and tidyr
library(dplyr)
library(tidyr)
dd %>% group_by(ID) %>% mutate(seq=1:n()) %>%
pivot_wider("ID", names_from="seq", values_from = c("Date","Sum"))
Where dd is your sample data frame above.
So I have a csv file with column headers ID, Score, and Age.
So in R I have,
data <- read.csv(file.choose(), header=T)
attach(data)
I would like to create two new vectors with people's scores whos age are below 70 and above 70 years old. I thought there was a nice a quick way to do this but I cant find it any where. Thanks for any help
Example of what data looks like
ID, Score, Age
1, 20, 77
2, 32, 65
.... etc
And I am trying to make 2 vectors where it consists of all peoples scores who are younger than 70 and all peoples scores who are older than 70
Assuming data looks like this:
Score Age
1 1 29
2 5 39
3 8 40
4 3 89
5 5 31
6 6 23
7 7 75
8 3 3
9 2 23
10 6 54
.. . ..
you can use
df_old <- data[data$Age >= 70,]
df_young <- data[data$Age < 70,]
which gives you
> df_old
Score Age
4 3 89
7 7 75
11 7 97
13 3 101
16 5 89
18 5 89
19 4 96
20 3 97
21 8 75
and
> df_young
Score Age
1 1 29
2 5 39
3 8 40
5 5 31
6 6 23
8 3 3
9 2 23
10 6 54
12 4 23
14 2 23
15 4 45
17 7 53
PS: if you only want the scores and not the age, you could use this:
df_old <- data[data$Age >= 70, "Score"]
df_young <- data[data$Age < 70, "Score"]
I have been trying to join scatterplots together in one table of data:
The data is:
row Var1 Var2 Freq
1 0 Good 17
2 1 Good 479
3 2 Good 455
4 3 Good 273
5 4 Good 155
6 5 Good 9
7 0 Average 81
8 1 Average 2449
9 2 Average 4627
10 3 Average 3261
11 4 Average 3142
12 5 Average 110
13 0 Bad 74
14 1 Bad 1472
15 2 Bad 3881
16 3 Bad 3399
17 4 Bad 5431
18 5 Bad 188
Joining together in the following format is a problem since I can't join lines and get a chart as attached. I want the different scatter plots linked together
(edit)The code for the plot is below:
ggplot(dfs, aes(Var1, Freq, colour=Var2))+geom_line()+geom_point()
The result should look something like (that I have successfully joined - the same data but using another Var1 as the x axis):
I am doing simulations and am trying to add error to a column repeatedly, specifically to the column titled Ao. In my output, the first 30 rows are correct; we have the initial data, the first year of altered data (error added to Ao), but then afterwards, where I would like to have 30 years of added error, I get repeats of Year 2 for Ao up to year 30. My goal is that I add error after each year of sampling. Ie. Year 2 is Year 1 Ao + error. Year 3 is Year 2 Ao + error, so on and so forth. Any helpers? Cheers.
for(t in 1:30){
Error<-rnorm(1000,0,1)
m<-rep(year1data$m,30)
r<-rep(year1data$r,30)
a<-rep(year1data$a,30)
g<-rep(year1data$g,30)
Year<-rep(2:31, each=TotSpecies)
Species<-1:TotSpecies
Ao<-year1data$Ao+sample(Error,TotSpecies,replace=FALSE)
TotSpeciesdata<-data.frame(Species,Year,Ao,m,r,a,g)
TotSpeciesdata<-rbind(year1data,TotSpeciesdata)
}
> TotSpeciesdata
Species Year Ao m r a g
1 1 1 25.770783 43 119.110786 3.2305180 2.6526471
2 2 1 53.908914 138 161.894541 0.7342070 0.1151602
3 3 1 2.010732 226 193.820489 2.2890904 3.6248105
4 4 1 23.742254 332 17.315335 1.4009572 2.0037931
5 5 1 4.291080 63 187.591209 0.2563995 2.1553908
6 6 1 4.691113 343 116.267867 0.3899113 3.3950085
7 7 1 604.133044 224 132.240197 3.0410743 0.7985524
8 8 1 13.332567 166 5.367118 0.7921644 1.7861011
9 9 1 3.759268 141 212.340970 2.8733737 2.7123141
10 10 1 3.647390 209 259.400858 0.1249936 0.6594659
11 11 1 23.731109 10 114.171147 2.2437372 0.9867591
12 12 1 85.116996 69 167.412993 0.8306823 2.8905148
13 13 1 31.684280 277 177.025460 2.7618332 2.9245554
14 14 1 30.657523 205 21.710438 2.7661347 1.5911379
15 15 1 12.240410 85 210.121109 2.8827455 3.0418454
16 1 2 27.038097 43 119.110786 3.2305180 2.6526471
17 2 2 54.251600 138 161.894541 0.7342070 0.1151602
18 3 2 2.010636 226 193.820489 2.2890904 3.6248105
19 4 2 22.699369 332 17.315335 1.4009572 2.0037931
20 5 2 4.542589 63 187.591209 0.2563995 2.1553908
21 6 2 3.607833 343 116.267867 0.3899113 3.3950085
22 7 2 604.480756 224 132.240197 3.0410743 0.7985524
23 8 2 13.663513 166 5.367118 0.7921644 1.7861011
24 9 2 2.138715 141 212.340970 2.8733737 2.7123141
25 10 2 3.642769 209 259.400858 0.1249936 0.6594659
26 11 2 22.897993 10 114.171147 2.2437372 0.9867591
27 12 2 85.490897 69 167.412993 0.8306823 2.8905148
28 13 2 31.689202 277 177.025460 2.7618332 2.9245554
29 14 2 30.644419 205 21.710438 2.7661347 1.5911379
30 15 2 12.050207 85 210.121109 2.8827455 3.0418454
31 1 3 27.038097 43 119.110786 3.2305180 2.6526471
32 2 3 54.251600 138 161.894541 0.7342070 0.1151602
33 3 3 2.010636 226 193.820489 2.2890904 3.6248105
34 4 3 22.699369 332 17.315335 1.4009572 2.0037931
35 5 3 4.542589 63 187.591209 0.2563995 2.1553908
36 6 3 3.607833 343 116.267867 0.3899113 3.3950085
37 7 3 604.480756 224 132.240197 3.0410743 0.7985524
38 8 3 13.663513 166 5.367118 0.7921644 1.7861011
39 9 3 2.138715 141 212.340970 2.8733737 2.7123141
40 10 3 3.642769 209 259.400858 0.1249936 0.6594659
41 11 3 22.897993 10 114.171147 2.2437372 0.9867591
42 12 3 85.490897 69 167.412993 0.8306823 2.8905148
43 13 3 31.689202 277 177.025460 2.7618332 2.9245554
44 14 3 30.644419 205 21.710438 2.7661347 1.5911379
45 15 3 12.050207 85 210.121109 2.8827455 3.0418454
The main problem you have with your approach is the line:
TotSpeciesdata<-data.frame(Species,Year,Ao,m,r,a,g)
Because Year is a 30 * TotSpecies vector, but all the others are just TotSpecies long. So in effect, you are recycling all columns except Year 30 times when you create the data frame, which will lead to the year 2 data repeated 30 times, among other things. If you just have Year <- rep(i + 1, TotSpecies) I think your logic will work fine. That said, here is an alternate approach:
This will, for each species, create an incrementing random walk starting with Ao for that species for 5 years (just did that for display purposes):
set.seed(1)
year1data <- data.frame(species=1:10, year=1, Ao=runif(10, 1, 700))
TotSpeciesData <- do.call(
rbind,
lapply(
split(year1data, year1data$species),
function(data)
with(
data,
data.frame(species=species, year=year, Ao=c(Ao, Ao + cumsum(rnorm(5)))
) ) ) )
head(TotSpeciesData, 15)
Note I excluded columns m-g since they don't seem directly relevant to your particular question, but you can add them relatively easily. I also only did 5 years in addition to year 1 so you can see the results here, but that is also easy to change:
species year Ao
1.1 1 1 186.5906
1.2 1 1 185.7701
1.3 1 1 186.2575
1.4 1 1 186.9958
1.5 1 1 187.5716
1.6 1 1 187.2662
2.1 2 1 261.1146
2.2 2 1 262.6264
2.3 2 1 263.0162
2.4 2 1 262.3950
2.5 2 1 260.1803
2.6 2 1 261.3052
3.1 3 1 401.4245
3.2 3 1 401.3796
3.3 3 1 401.3634
It has been pointed out that the code that you provided above, or at least that I have edited, repeats itself every 15 years, rather than being unique year year in a step-wise fashion. I edited it as shown below:
TotSpeciesData <- do.call(
rbind, #bind the table by rows
lapply( #applying the function in list form
split(year1data, year1data$Species), #splits data into groups by species
function(data)
with(
data,
data.frame(Species=Species, Year=1:Community, Ao=c(Ao, Ao + cumsum(rnorm((TotSpecies-1),0,2))),m=m, r=r, a=a, g=g) #data frame is Species, Year,
) ) )
TotSpeciesData$Ao[TotSpeciesData$Ao<0]<-0 #any values less than 0 go to 0
TotSpeciesData<-TotSpeciesData[order(TotSpeciesData$Year),] #orders the data frame by Year
When I do this code:
TotSpeciesData[TotSpeciesData$Species==1 & TotSpeciesData$Year %in% c(1,2,16,17),]
I end up with an output showing that the data is repeating itself.
Species Year Ao m r a g
1.1 1 1 48.49161 239 332.9625 3.791778 2.723104
1.2 1 2 49.62851 239 332.9625 3.791778 2.723104
1.16 1 16 48.49161 239 332.9625 3.791778 2.723104
1.17 1 17 49.62851 239 332.9625 3.791778 2.723104
Any comments toward this?