This question already has answers here:
Split dataframe by levels of a factor and name dataframes by those levels
(3 answers)
Closed 5 years ago.
this is my first post.
I have this dataframe of the Nhl draft.
What I would like to do is to use some sort of recursive function to create 10 objects.
So, I want to create these 10 objects by subsetting the Nhl dataframe by Year.
Here are the first 6 rows of the data set (nhl_draft)
Year Overall Team
1 2000 1 New York Islanders
2 2000 2 Atlanta Thrashers
3 2000 3 Minnesota Wild
4 2000 4 Columbus Blue Jackets
5 2000 5 New York Islanders
6 2000 6 Nashville Predators
Player PS
1 Rick DiPietro 49.3
2 Dany Heatley 95.2
3 Marian Gaborik 103.6
4 Rostislav Klesla 34.5
5 Raffi Torres 28.4
6 Scott Hartnell 74.5
I want to create 10 objects by subsetting out the Years, 2000 ~ 2009.
I tried,
for (i in 2000:2009) {
nhl_draft.i <- subset(nhl_draft, Year == "i")
}
BUT this doesn't do anything. What's the problem with this for-loop? Can you suggest any other ways?
Please tell me if this is confusing after all, this is my first post......
The following code may fix your error.
# Create an empty list
nhl_list <- list()
for (i in 2000:2009) {
# Subset the data frame based on Year
nhl_draft_temp <- subset(nhl_draft, Year == i)
# Assign the subset to the list
nhl_list[[as.character(i)]] <- nhl_draft_temp
}
But you can consider split, which is more concise.
nhl_list <- split(nhl_draft, f = nhl_draft$Year)
Related
I am new to R and to programming in general and am looking for feedback on how to approach what is probably a fairly simple problem in R.
I have the following dataset:
df <- data.frame(county = rep(c("QU","AN","GY"), 3),
park = (c("Downtown","Queens", "Oakville","Squirreltown",
"Pinhurst", "GarbagePile","LottaTrees","BigHill",
"Jaynestown")),
hectares = c(12,42,6,18,92,6,4,52,12))
df<-transform(df, parkrank = ave(hectares, county,
FUN = function(x) rank(x, ties.method = "first")))
Which returns a dataframe looking like this:
county park hectares parkrank
1 QU Downtown 12 2
2 AN Queens 42 1
3 GY Oakville 6 1
4 QU Squirreltown 18 3
5 AN Pinhurst 92 3
6 GY GarbagePile 6 2
7 QU LottaTrees 4 1
8 AN BigHill 52 2
9 GY Jaynestown 12 3
I want to use this to create a two-column data frame that lists each county and the park name corresponding to a specific rank (e.g. if when I call my function I add "2" as a variable, shows the second biggest park in each county).
I am very new to R and programming and have spent hours looking over the built in R help files and similar questions here on stack overflow but I am clearly missing something. Can anyone give a simple example of where to begin? It seems like I should be using split then lapply or maybe tapply, but everything I try leaves me very confused :(
Thanks.
Try,
df2 <- function(A,x) {
# A is the name of the data.frame() and x is the rank No
df <- A[A[,4]==x,]
return(df)
}
> df2(df,2)
county park hectares parkrank
1 QU Downtown 12 2
6 GY GarbagePile 6 2
8 AN BigHill 52 2
I am new to R and started learning two weeks ago. I want to take a list of tropical cyclone counts for various years (where some years are absent, because there were no tropical cyclones) and create a list with a column of every year from 1907-2013 and a column of the number of tropical cyclones.
In the example I include the list of occurrences to 1973 (before 1912 there were none).
Year Count
1 1912 1
2 1913 1
3 1921 1
4 1940 1
5 1953 1
6 1958 1
7 1959 1
8 1960 1
9 1966 1
10 1969 1
11 1971 1
12 1973 2
I tried using a for loop and if/else statement, but it does not work. I get the message "longer object length is not a multiple of shorter object length" and "the condition has length > 1 and only the first element will be used."
tc.SP=matrix(0,len.tc.yr,2)
tc.SP[,1]=tc.year.list
for (i in 1:len.tc.yr) #107 yrs (1907-2013)
{
if (tc.SP5.count[,1] == tc.SP[,1]) #tc.SP5.count is various years of TC occ.
{tc.SP[,2]= tc.SP5.count[,2]}
else
{tc.SP[,2]= 0}
}
Thank you for any help in advance.
When you say list, i'm going to assume you want to create a data.frame. Let's say the data above is in a data.frame called cyclone. The easiest way to create a data.frame for every year is just to merge it with a complete list. For example
cyclone.full <- merge(cyclone, data.frame(Year=1907:2013), all=T)
Here the data.frames will automatically merge on the Year column because both sets have that column. This will put NA values in all the missing years. If you want the default to be 0, you can do
cyclone.full$Count[is.na(cyclone.full$Count)] <- 0
Then yo uget
head(cyclone.full)
# Year Count
# 1 1907 0
# 2 1908 0
# 3 1909 0
# 4 1910 0
# 5 1911 0
# 6 1912 1
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 question already has answers here:
simple data.frame reshape
(3 answers)
Reshaping data frame with duplicates
(4 answers)
Faster ways to calculate frequencies and cast from long to wide
(4 answers)
Closed 9 years ago.
I have a data.frame like this
VAR1 VAR2
1999 USA
1999 USA
1999 UK
2000 GER
2000 USA
2000 GER
2000 USA
2001 USA
How do I count any level of VAR2 for each year?
What I want is a plot, where the x-axe is the year, and the y-axe is the count of any level in VAR2
The data.table solution
library(data.table)
new.dat = data.table(dat)[,length(unique(var2)),by=var1]
new.dat=as.matrix(new.dat)
plot(x=new.dat[,1],y=new.dat[,2])
The simplest way I can think of:
let dat = your data frame
with(dat,table(VAR1,VAR2))
The output will look something like this:
VAR2
VAR1 GER UK USA
1999 0 1 2
2000 2 0 2
2001 0 0 1
Hope this helps.
There are a large number of ways and this question is undoubtedly a duplicate. What have you tried? You can use dcast in the reshape2 pacakge.
require(reshape2)
dcast( df , Country ~ Year , length )
# Country 1999 2000 2001
#1 GER 0 2 0
#2 UK 1 0 0
#3 USA 2 2 1
I have the following data read into R as a data frame named "data_old":
yes year month
1 15 2004 5
2 9 2005 6
3 15 2006 3
4 12 2004 5
5 14 2005 1
6 15 2006 7
. . ... .
. . ... .
I have written a small loop which goes through the data and sums up the yes variable for each month/year combination:
year_f <- c(2004:2006)
month_f <- c(1:12)
for (i in year_f){
for (j in month_f){
x <- subset(data_old, month == j & year == i, select="yes")
if (nrow(x) > 0){
print(sum(x))
}
else{print("Nothing")}
}
}
My question is this: I can print the sum for each month/year combination in the terminal, but how do i store it in a vector? (the nested loop is giving me headaches trying to figure this out).
Thomas
Another way,
library(plyr)
ddply(data_old,.(year,month),function(x) sum(x[1]))
year month V1
1 2004 5 27
2 2005 1 14
3 2005 6 9
4 2006 3 15
5 2006 7 15
Forget the loops, you want to use an aggregation function. There's a recent discussion of them in this SO question.
with(data_old, tapply(yes, list(year, month), sum))
is one of many solutions.
Also, you don't need to use c() when you aren't concatenating anything. Plain 1:12 is fine.
Just to add a third option:
aggregate(yes ~ year + month, FUN=sum, data=data_old)