I am new to R and having trouble figuring out to go about this. I have data on tree growth rates from dead trees, organized by year. So, my first column is year and the columns to the right are growth rates for individual trees, ending in the year each tree died. After the tree died, the values are "NA" for the remaining years in the dataset. I need to take the mean growth for the 10 years preceding each tree's death, but each tree died in a different year. Does anyone have an idea for how to do this? Here is an example of what a dataset might look like:
Year Tree1 Tree2 Tree3
1989 53.00 84.58 102.52
1990 63.68 133.16 146.07
1991 90.37 103.10 233.58
1992 149.24 127.61 245.69
1993 96.20 54.78 417.96
1994 230.64 60.92 125.31
1995 150.81 60.98 100.43
1996 124.25 42.73 75.43
1997 173.42 67.20 50.34
1998 119.60 73.40 32.43
1999 179.97 61.24 NA
2000 114.88 67.43 NA
2001 82.23 55.23 NA
2002 49.40 NA NA
2003 93.46 NA NA
2004 104.67 NA NA
2005 44.14 NA NA
2006 88.40 NA NA
So, the averages I need to calculate are:
Tree1: mean(1997-2006) = 105.01
Tree2: mean(1992-2001) = 67.15
Tree3: mean(1989-1998) = 152.98
Since I need to do this for a large number of trees, it would be helpful to have a method of automating the calculation. Thank you very much for any help! Katie
You can use sapply and tail together with na.omit as follows:
sapply(mydf[-1], function(x) mean(tail(na.omit(x), 10)))
# Tree1 Tree2 Tree3
# 105.017 67.152 152.976
mydf[-1] says to drop the first column. tail has an argument, n, that lets you specify how many values you want from the end (tail) of your data. Here, we've set it to "10" since you want the last 10 values. Then, assuming that there are no NA values in your actual data from while the trees are alive, you can safely use na.omit on your data.
Related
I have a dataset that includes a column called BirthYear that includes lots of years in which people were born and I need to create a new column that prints "young" if their BirthYear is > 1993 and to print "old" if their BirthYear is < 1993. I've tried using the if function but I cant seem to achieve it, I would appreciate if u let me know how to do it, thanks!
I also like cut() for this, especially if you want the result to be a factor.
year <- sample(1989:1999, size=20, replace=T) # Arbitrary vector of years
breaks <- c(-Inf, 1993, Inf) # The 3 bounds of the 2 intervals
labels <- c("old", "young") # The 2 labels of the 2 intervals
binary <- cut(x=year, breaks=breaks, labels=labels, right=F)
# Inspect
data.frame(year, binary)
The result:
year binary
1 1993 young
2 1997 young
3 1989 old
4 1998 young
5 1999 young
6 1989 old
7 1994 young
8 1991 old
9 1991 old
10 1991 old
...
This is close to a duplicate, but involves custom labels.
If you have to inspect more than one variable eventually, look at dplyr::case_when().
Another option could be use dplyr::recode_factor as below:
set.seed(1)
year <- sample(1970:2005, size=10, replace=T)
> year
#[1] 2001 1975 1979 1994 1974 1973 1985 1994 1975 1981
recode_factor(as.factor(year > 1993), 'TRUE' = "Old", 'FALSE' = "Young")
#[1] Old Young Young Old Young Young Young Old Young Young
#Levels: Old Young
I did quite some searching on how to simplify the code for the problem below but was not successful. I assume that with some kind of apply-magic one could speed things up a little, but so far I still have my difficulties with these kind of functions ....
I have an data.frame data, structured as follows:
year iso3c gdpppc elec solid liquid heat
2010 USA 1567 1063 1118 835 616
2015 USA 1571 NA NA NA NA
2020 USA 1579 NA NA NA NA
... USA ... NA NA NA NA
2100 USA 3568 NA NA NA NA
2010 ARG 256 145 91 85 37
2015 ARG 261 NA NA NA NA
2020 ARG 270 NA NA NA NA
... ARG ... NA NA NA NA
2100 ARG 632 NA NA NA NA
As you can see, I have a historical starting value for 2010 and a complete scenario for gdppc up to 2100. I want to let values for elec, solid, liquid and heat grow according to some elasticity with respect to the development of gdppc, but separately for each country (coded in iso3c).
I have the elasticities defined in a separate data.frame parameters:
item value
elec 0.5
liquid 0.2
solid -0.1
heat 0.1
So far I am using a nested for loop:
for (e in 1:length(levels(parameters$item)){
for (c in 1:length(levels(data$iso3c)){
tmp <- subset(data, select=c("year", "iso3c", "gdppc", parameters[e, "item"]), subset=("iso3c" == levels(data$iso3c)[c]))
tmp[tmp$year %in% seq(2015, 2100, 5), parameters[e, "item"]] <-
tmp[tmp$year == 2010, parameters[e, "item"]] *
cumprod((1 + (tmp[tmp$year %in% seq(2015, 2100, 5), "gdppc"] /
tmp[tmp$year %in% seq(2010, 2095, 5), "gdppc"] - 1) * parameters[e, "value"]))
data[data$iso3c == levels(data$iso3c)[i] & data$year %in% seq(2015, 2100, 5), parameters[e, "item"]] <- tmp[tmp$year > 2010, parameters[e, "item"]]
}
}
The outer loop loops over the columns and the inner one over the countries. The inner loop runs for every country (I have 180+ countries). First, a subset containing data on one single country and on the variable of interest is selected. Then I let the respective variable grow with a certain elasticity to growth in gdppc and finally put the subset back into place in data.
I have already tried to let the outer loop run in parallel using foreach but was not succesful recombining the results. Since I have to run similar calculations quite often I would be very grateful for any help.
Thanks
Here's one way. Note I renamed your parameters data.frame to p
library(data.table)
library(reshape2)
dt <- data.table(data)
dt.melt = melt(dt,id=1:3)
dt.melt[,value:=as.numeric(value)] # coerce value column to numeric
dt.melt[,value:=head(value,1)+(gdpppc-head(gdpppc,1))*p[p$item==variable,]$value,
by="iso3c,variable"]
result <- dcast(dt.melt,iso3c+year+gdpppc~variable)
result
# iso3c year gdpppc elec solid liquid heat
# 1 ARG 2010 256 145.0 91.0 85.0 37.0
# 2 ARG 2015 261 147.5 90.5 86.0 37.5
# 3 ARG 2020 270 152.0 89.6 87.8 38.4
# 4 ARG 2100 632 333.0 53.4 160.2 74.6
# 5 USA 2010 1567 1063.0 1118.0 835.0 616.0
# 6 USA 2015 1571 1065.0 1117.6 835.8 616.4
# 7 USA 2020 1579 1069.0 1116.8 837.4 617.2
# 8 USA 2100 3568 2063.5 917.9 1235.2 816.1
The basic idea is to use the melt(...) function to reshape your original data into "long" format, where the values in the four columns solid, liquid, elec, and heat are all in one column, value, and the column variable indicates which metric value refers to. Now, using data tables, you can fill in the values easily. Then, reshape the result back into wide format using dcast(...).
So what I have is data of cod weights at different ages. This data is taken at several locations over time.
What I would like to create is "weight at age", basically a mean value of weights at a certain age. I want do this for each location at each year.
However, the ages are not sampled the same way (all old fish caught are measured, while younger fish are sub sampled), so I can't just create a normal average, I would like to bootstrap samples.
The bootstrap should take out 5 random values of weight at an age, create a mean value and repeat this a 1000 times, and then create an average of the means. The values should be able to be used again (replace). This should be done for each age at every AreaCode for every year. Dependent factors: Year-location-Age.
So here's an example of what my data could look like.
df <- data.frame( Year= rep(c(2000:2008),2), AreaCode = c("39G4", "38G5","40G5"), Age = c(0:8), IndWgt = c(rnorm(18, mean=5, sd=3)))
> df
Year AreaCode Age IndWgt
1 2000 39G4 0 7.317489899
2 2001 38G5 1 7.846606144
3 2002 40G5 2 0.009212455
4 2003 39G4 3 6.498688035
5 2004 38G5 4 3.121134937
6 2005 40G5 5 11.283096043
7 2006 39G4 6 0.258404136
8 2007 38G5 7 6.689780137
9 2008 40G5 8 10.180511929
10 2000 39G4 0 5.972879108
11 2001 38G5 1 1.872273650
12 2002 40G5 2 5.552962065
13 2003 39G4 3 4.897882549
14 2004 38G5 4 5.649438631
15 2005 40G5 5 4.525012587
16 2006 39G4 6 2.985615831
17 2007 38G5 7 8.042884181
18 2008 40G5 8 5.847629941
AreaCode contains the different locations, in reality I have 85 different levels. The time series stretches 1991-2013, the ages 0-15. IndWgt contain the weight. My whole data frame has a row length of 185726.
Also, every age does not exist for every location and every year. Don't know if this would be a problem, just so the scripts isn't based on references to certain row number. There are some NA values in the weight column, but I could just remove them before hand.
I was thinking that I maybe should use replicate, and apply or another plyr function. I've tried to understand the boot function but I don't really know if I would write my arguments under statistics, and in that case how. So yeah, basically I have no idea.
I would be thankful for any help I can get!
How about this with plyr. I think from the question you wanted to bootstrap only the "young" fish weights and use actual means for the older ones. If not, just replace the ifelse() statement with its last argument.
require(plyr)
#cod<-read.csv("cod.csv",header=T) #I loaded your data from csv
bootstrap<-function(Age,IndWgt){
ifelse(Age>2, # treat differently for old/young fish
res<-mean(IndWgt), # old fish mean
res<-mean(replicate(1000,sample(IndWgt,5,replace = TRUE))) # young fish bootstrap
)
return(res)
}
ddply(cod,.(Year,AreaCode,Age),summarize,boot_mean=bootstrap(Age,IndWgt))
Year AreaCode Age boot_mean
1 2000 39G4 0 6.650294
2 2001 38G5 1 4.863024
3 2002 40G5 2 2.724541
4 2003 39G4 3 5.698285
5 2004 38G5 4 4.385287
6 2005 40G5 5 7.904054
7 2006 39G4 6 1.622010
8 2007 38G5 7 7.366332
9 2008 40G5 8 8.014071
PS: If you want to sample all ages in the same way, no need for the function, just:
ddply(cod,.(Year,AreaCode,Age),
summarize,
boot_mean=mean(replicate(1000,mean(sample(IndWgt,5,replace = TRUE)))))
Since you don't provide enough code, it's too hard (lazy) for me to test it properly. You should get your first step using the following code. If you wrap this into replicate, you should get your end result that you can average.
part.result <- aggregate(IndWgt ~ Year + AreaCode + Age, data = data, FUN = function(x) {
rws <- length(x)
get.em <- sample(x, size = 5, replace = TRUE)
out <- mean(get.em)
out
})
To handle any missing combination of year/age/location, you could probably add an if statement checking for NULL/NA and producing a warning and/or skipping the iteration.
This seems like a very simple problem, but I can't seem to sort it out. I have sought help from this forum, with the below topics being close, but don't seem to do exactly what I need. I have count data over several years. I want to obtain frequencies of the count value by year. It seems I need a table function with a grouping option, but I haven't found the proper syntax.
Data:
count year
1 15 1957
2 6 1957
3 23 1957
4 23 1957
5 2 1957
6 28 1980
7 15 1980
8 32 1980
9 18 1981
thank you in advance!
Counting the number of elements with the values of x in a vector
grouping data splitted by frequencies
Aggregate data in R
You're looking for the table function. Something like:
with(yourdata, table(Year, Count))
I have a question about creating lag variables depending on a time factor.
Basically I am working with a baseball dataset where there are lots of names for each player between 2002-2012. Obviously I only want lag variables for the same person to try and create a career arc to predict the current stat. Like for example I want to use lag 1 Average (2003) , lag 2 Average (2004) to try and predict the current average in 2005. So I tried to write a loop that goes through every row (the data frame is already sorted by name and then year, so the previous year is n-1 row), check if the name is the same, and if so then grab the value from the previous row.
Here is my loop:
i=2 #as 1 errors out with 1-0 row
for(i in 2:6264){
if(TS$name[i]==TS$name[i-1]){
TS$runvalueL1[i]=TS$Run_Value[i-1]
}else{
TS$runvalueL1 <- NA
}
i=i+1
}
Because each row is dependent on the name I cannot use most of the lag functions. If you have a better idea I am all ears!
Sample Data won't help a bunch but here is some:
edit: Sample data wasn't producing useable results so I just attached the first 10 people of my dataset. Thanks!
TS[(6:10),c('name','Season','Run_Value')]
name Season ARuns
321 Abad Andy 2003 -1.05
3158 Abercrombie Reggie 2006 27.42
1312 Abercrombie Reggie 2007 7.65
1069 Abercrombie Reggie 2008 5.34
4614 Abernathy Brent 2002 46.71
707 Abernathy Brent 2003 -2.29
1297 Abernathy Brent 2005 5.59
6024 Abreu Bobby 2002 102.89
6087 Abreu Bobby 2003 113.23
6177 Abreu Bobby 2004 128.60
Thank you!
Smth along these lines should do it:
names = c("Adams","Adams","Adams","Adams","Bobby","Bobby", "Charlie")
years = c(2002,2003,2004,2005,2004,2005,2010)
Run_value = c(10,15,15,20,10,5,5)
library(data.table)
dt = data.table(names, years, Run_value)
dt[, lag1 := c(NA, Run_value), by = names]
# names years Run_value lag1
#1: Adams 2002 10 NA
#2: Adams 2003 15 10
#3: Adams 2004 15 15
#4: Adams 2005 20 15
#5: Bobby 2004 10 NA
#6: Bobby 2005 5 10
#7: Charlie 2010 5 NA
An alternative would be to split the data by name, use lapply with the lag function of your choice and then combine the splitted data again:
TS$runvalueL1 <- do.call("rbind", lapply(split(TS, list(TS$name)), your_lag_function))
or
TS$runvalueL1 <- do.call("c", lapply(split(TS, list(TS$name)), your_lag_function))
But I guess there is also a nice possibility with plyr, but as you did not provide a reproducible example, that is all for the beginning.
Better:
TS$runvalueL1 <- unlist(lapply(split(TS, list(TS$name)), your_lag_function))
This is obviously not a problem where you want to create a matrix with cbind, so this is a better data structure:
full=data.frame(names, years, Run_value)
The ave function is quite useful for constructing new columns within categories of other columns:
full$Lag1 <- ave(full$Run_value, full$names,
FUN= function(x) c(NA, x[-length(x)] ) )
full
names years Run_value Lag1
1 Adams 2002 10 NA
2 Adams 2003 15 10
3 Adams 2004 15 15
4 Adams 2005 20 15
5 Bobby 2004 10 NA
6 Bobby 2005 5 10
7 Charlie 2010 5 NA
I thinks it's safer to cionstruct with NA, since that will help prevent errors in logic that using 0 for prior years in year 1 would not alert you to.