similar to: ddply with mean and max...

I've got a problem with the sparseby command (reshape library), and I have reached the peak of my R knowledge (it isn't really that high). I have a small data frame of 23 rows and 15 columns, here is a subset, the first four columns are factors and the rest are numeric (only one, line54 is provided). bearID YEAR Season SEX line54 5 1900 8 3 0 16.3923519 11 2270

I want to use aggregate with the mean function on specific columns gender <- factor(c("m", "m", "f", "f", "m")) student <- c(0001, 0002, 0003, 0003, 0001) score <- c(50, 60, 70, 65, 60) basicSub <- data.frame(student, gender, score) basicSubMean <- aggregate(basicSub, by=list(basicSub$student), FUN=mean, na.rm=TRUE) This

My data frame consists of character variables, factors, and proportions, something like c1 <- c("A", "B", "C", "C") c2 <- factor(c(1, 1, 2, 2), labels = c("Y","N")) x <- c(0.5234, 0.6919, 0.2307, 0.1160) y <- c(0.9251, 0.7616, 0.3624, 0.4462) df <- data.frame(c1, c2, x, y) pct <- function(x) round(100*x, 1) I want to

Hi R-users, I want to apply a function to each column of a data frame that is numeric. Thus I tried to check it for each column first: > apply(df, 2, function(x) is.numeric(x)) A60 A64 A66a A67 A71 A75a A80 A85 A91 A95 A96 A97 A98 A99 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

Does anyone know if there is an R function that will take a matrix like this jim 1 0 0 0 0 0 jim 0 1 0 0 0 0 jim 0 0 1 0 0 0 bob 1 0 0 0 0 0 bob 0 0 1 0 0 0 harry 0 0 1 0 0 0 harry 0 0 0 1 0 0 harry 0 0 0 0 1 0 harry 0 0 0 0 0 1 and make it like this? (that is, add together rows that have the same name?) jim 1 1 1 0 0 0 bob

Hi, (Please use ?dput() to share the example dataset. Avoid using images to show dataset. Also, please read the posting guide esp. regarding home work, assignments etc.) res <- sapply(Gene[,-1],function(x) tapply(x,list(Gene$Genotype),mean)) #or res2 <-? aggregate(.~Genotype, data=Gene,mean) #or library(plyr) ?res3 <- ddply(Gene,.(Genotype),numcolwise(mean)) identical(res2,res3)

Hola ¿Alguno tiene un código para sumarizar medidas? my.df <- data.frame( "ID" = c( rep("A", 3 ), rep("B",2),"C", rep("D",3) ), "Obs.1" = rnorm( 9,0,1 ), "Obs.2" = rnorm( 9,0,3 ) ) Algo que quede mas bonito que esto: by( my.df, my.df$ID, mean ) Gracias un saludo! -- Patricia García González [[alternative HTML

I want to apply a more complicated function than what I use in my example, but the idea is the same: Suppose you have a data frame named x and you want to a function applied to each variable, we'll just use the quantile function for this example. I'm trying all sorts of apply functions, but not having luck. My best guess would be: sapply(x, FUN=quantile) -- View this message in

Hello, I've got a data frame with a mix of numeric, integer and factor columns. I'd like to pull out (or just operate only on) the numeric/integer columns. Every thing I've found in searches is about how to subset by rows, or how to operate assuming you have the column names.  I'd like to pull by type. Thanks! Barry [[alternative HTML version deleted]]

Hi R-helpers, I have a dataframe with 60columns and I would like to convert several columns to factor, others to numeric, and yet others to dates. Rather than having 60 lines like this: data$Var1<-as.factor(data$Var1) I wonder if it's possible to write one line of code (per data type, e.g. factor) that would apply a function (e.g., as.factor) to several (non-contiguous) columns. So, I

Hi, i have that table Thesis Day A B C 1 0 83.43 90.15 22.97 1 0 85.50 94.97 16.62 1 0 83.36 95.38 20.70 1 0 84.47 92.16 23.58 1 0 83.98 95.33 19.39 1 0 82.86 93.78 24.55 1 0 83.39 92.67 19.56 1 0 85.17 95.24 17.95 1 0 81.62 93.32 28.49 1 0 82.99 92.85 19.73 1 0 81.11 95.67 27.20 1 0 83.39 94.69 16.51 1 0 79.56 89.87 30.39 1 0 80.54 93.32 21.76 1 0 82.11 92.58 22.17 1 14 85.65 94.00 19.19 1 14

Dear R-Helpers: I am using trying to use *ddply* to extract min and max of a particular column in a data.frame. I am using two different forms of the function: ## var_name_to_split is a string -- something like "var1" which is the name of a column in data.frame ddply( df, .(as.name(var_name_to_split)), function(x) c(min(x[ , 3] , max(x[ , 3]))) ## fails with an error - case 1 ddply(

I have a straightforward application of ddply() and summarize(): ddply(MyFrame, .(Treatment, Week), summarize, MeanValue=mean(MyVar)) This works just fine: Treatment Week MeanValue 1 MyDrug BASELINE 5.91 2 MyDrug WEEK 1 4.68 3 MyDrug WEEK 2 4.08 4 MyDrug WEEK 3 3.67 5 MyDrug WEEK 4 2.96 6 MyDrug WEEK 5 2.57 7 MyDrug

Hi I've records like this df= x panel 4 1 93 2 21 3 83 4 75 1 87 2 87 3 78 4 50 1 76 2 86 3 65 4 84 1 40 2 39 3 26 4 i want to create histogram out of it . i want all the mid and count values for panel wise my code is histoutput = ddply(df,.(df[2]),hist) i'm not able to get the required result. please help me using for loop takes a lot of time if there are more records ----- Thanks

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Hello I'm new to R (one week) so please excuse any obvious mistakes in my code or posting. I am attempting to fit a non linear function defining the relationship between dependent variable A and the variables PAR and T grouped by the condition Di. The following steps are taken in the Rcode below: 1) load the data (not shown) 2) define the function to be fit 3) define the starting values

Hi, I am really confused how ddply work, so maybe you can help me. I created a function that sorts a vector etc. fn <- function(x){ x1 <- sort(x) x2 <- seq(length(x)) x3 <- x2/max(x2) df <- data.frame(x1,x2,x3) df } Probably this is not the best form of the function, but at least it produces what I want (data to plot a cumulative count curve). This function works on a

Hello, all. I'm new to R and just beginning to learn to write functions. I know I'm out of my depth posting here, and I'm sure my issue is mundane. But here goes. I'm analyzing the American National Election Study (nes), looking at mean values of a numeric dep_var (environ.therm) across values of a factor (partyid3). I use ddply from plyr and wtd.mean from Hmisc. The nes requires a

Hi, I am confused by results from: > ddply(aa, names(aa), colwise(sum)) I thought ddply was just calling colwise(sum)() with each column. However ddply() returns a 13 x 5 result !! The general result I expected is similar to that of apply() , or using colwise(sum)() alone. Shouldn't ddply() produce the same ? Thanks in advance for your help, - Stuart Andrews >

Hi, I have a data.frame that contains a variable act which records the duration (in seconds) of two states (wet-dry) for several individuals (identified by Ring) over a period of time. Since I want to work with daytime (i.e. from sunrise till sunset) and night time (i.e. from sunset till next sunrise), I have to split act from time[i] till sunset and from sunset until time[i+1], and from time[k]