Composing a data.frame from loop-generated sequences - r

Question

I have a data set which is made up of observations of the weights of fish, the julian dates they were captured on, and their names. I am seeking to assess what the average growth rate of these fish is according to the day of the year (julian date). I believe the best method to do this is to compose a data.frame with two fields: "Julian Date" and "Growth". The idea is this: for a fish which is observed on January 1 (1) at weight 100 and a fish observed again on April 10 (101) at weight 200, the growth rate would be 100g/100days, or 1g/day. I would represent this in a data.frame as 100 rows in which the "Julian Date" column is composed of the Julian date sequence (1:100) and the "Growth" column is composed of the average growth rate (1g/day) over all days.
I have attempted to compose a for loop which passes through each fish, calculates the average growth rate, then creates a list in which each index contains the sequence of Julian dates and the growth rate (repeated the number of times equal to the length of the Julian date sequence). I would then utilize the function to compose my data.frame.
growth_list <- list() # initialize empty list
p <- 1 # initialize increment count
# Looks at every other fish ID beginning at 1 (all even-number observations are the same fish at a later observation)
for (i in seq(1, length(df$FISH_ID), by = 2)){
rate <- (df$growth[i+1]-df$growth[i])/(as.double(df$date[i+1])-as.double(df$date[i]))
growth_list[[p]] <- list(c(seq(as.numeric(df$date[i]),as.numeric(df$date[i+1]))), rep(rate, length(seq(from = as.numeric(df$date[i]), to = as.numeric(df$date[i+1])))))
p <- p+1 # increase to change index of list item in next iteration
}
# Converts list of vectors (the rows which fulfill above criteria) into a data.frame
growth_df <- do.call(rbind, growth_list)
My expected results can be illustrated here: https://imgur.com/YXKLkpK
My actual results are illustrated here: https://imgur.com/Zg4vuVd
As you can see, the actual results appear to be a data.frame with two columns specifying the type of the object, as well as the length of the original list item. That is, row 1 of this dataset contained 169 days between observations, and therefore contained 169 julian dates and 169 repetitions of the growth rate.

Answer 1

Instead of list(), use data.frame() with named columns to build a list of data frames to be row binded at the end:
growth_list <- vector(mode="list", length=length(df$FISH_ID)/2)
for (i in seq(1, length(df$FISH_ID), by=2)){
rate <- with(df, (growth[i+1]-growth[i])/(as.double(date[i+1])-as.double(date[i])))
date_seq <- seq(as.numeric(df$date[i]), as.numeric(df$date[i+1]))
growth_list[[p]] <- data.frame(Julian_Date = date_seq,
Growth_Rate = rep(rate, length(date_seq))
p <- p + 1
}
growth_df <- do.call(rbind, growth_list)

Answer 2

Welcome to stackoverflow
Couple things about your code:
I recommend using the apply function instead of the for loop. You can set parameters in apply to perform row-wise functions. It makes you code run faster. The apply family of functions also creates a list for you, which reduces the code you write to make the list and populate it.
It is common to supply users with a snippet example of your initial data to work with. Sometimes the way we describe our data is not representative of our actual data. This tradition is necessary to alleviate any communication errors. If you can, please manufacture a dummy dataset for us to use.
Have you tried using as.data.frame(growth_list), or data.frame(growth_list)?
Another option is to use an if else statement within your for loop that performs the rbind function. This would look something like this:
#make a row-wise for loop
for(x in 1:nrow(i)){
#insert your desired calculations here. You can turn the rows into their own dataframe by using this, which may make it easier to perform your calculations:
dataCurrent <- data.frame(i[x,])
#finish with something like this to turn your calculations for each row into an output dataframe of your choice.
outFish <- cbind(date, length, rate)
#make your final dataframe as follows
if(exists("finalFishOut") == FALSE){
finalFishOut <- outFish
}else{
finalFishOut <- rbind(finalFishOut, outFish)
}
}
Please update with a snippet of data and I'll update this answer with your exact solution.

Answer 3

Here is a solution using dplyr and plyr with some toy data. There are 20 fish, with a random start and end time, plus random weights at each time. Find the growth rate over time, then create a new df for each fish with 1 row per day elapsed and the daily average growth rate, and output a new df containing all fish.
df <- data.frame(fish=rep(seq(1:20),2),weight=sample(c(50:100),40,T),
time=sample(c(1:100),40,T))
df1 <- df %>% group_by(fish) %>% arrange(time) %>%
mutate(diff.weight=weight-lag(weight),
diff.time=time-lag(time)) %>%
mutate(rate=diff.weight/diff.time) %>%
filter(!is.na(rate)) %>%
ddply(.,.(fish),function(x){
data.frame(time=seq(1:x$diff.time),rate=x$rate)
})
head(df1)
fish time rate
1 1 1 -0.7105263
2 1 2 -0.7105263
3 1 3 -0.7105263
4 1 4 -0.7105263
5 1 5 -0.7105263
6 1 6 -0.7105263
tail(df1)
fish time rate
696 20 47 -0.2307692
697 20 48 -0.2307692
698 20 49 -0.2307692
699 20 50 -0.2307692
700 20 51 -0.2307692
701 20 52 -0.2307692