search for: summarise

Dear group, Is there a way I could avoid the sort of duplication illustrated below? i.e., I have the same dplyr::summarise function on different group_by arguments. So I'd like to create a single summarise function that could be applied to both. My attempt below fails. df <- data.frame(matrix(rnorm(40), 10, 4), f1 = gl(3, 10, labels = letters[1:3]), f2 = gl(3, 10, labels = lett...

Hi,I am using the amazing package 'plyr". I have one problem. I would appreciate help to fix the following error: Thanks. ______________________________ > library(plyr) > data(baseball) > summarise(baseball, + duration = max(year) - min(year), + nteams = length(unique(team))) Error: could not find function "summarise" > ddply(baseball, "id", summarise, + duration = max(year) - min(year), + nteams = length(unique(team))) Error in llply(.data = .data, .fun = .fun, ..., .p...

...ig hilsen/ Best regards Edjabou Maklawe Essonanawe Vincent Mobile: +45 31 95 99 33 On Sat, Sep 9, 2017 at 12:30 PM, Lars Bishop <lars52r at gmail.com> wrote: > Dear group, > > Is there a way I could avoid the sort of duplication illustrated below? > i.e., I have the same dplyr::summarise function on different group_by > arguments. So I'd like to create a single summarise function that could be > applied to both. My attempt below fails. > > df <- data.frame(matrix(rnorm(40), 10, 4), > f1 = gl(3, 10, labels = letters[1:3]), >...

Hi Lars, Two comments: 1. You can achieve what you want with a slight modification of your definition of s(), using the hint from the error message that you need an argument '.': s <- function(.) { dplyr::summarise(., x1m = mean(X1), x2m = mean(X2), x3m = mean(X3), x4m = mean(X4)) } 2. You have not given a great test case in how you set your two factors because the two group_by()'s will give the identical groupings, An alternative which confirms th...

I have some summarised data from a 2D pivot table which I want to visualise in R. How can I read in the data as a R table so I can use mosaicplot()? Dirk -- View this message in context: http://r.789695.n4.nabble.com/read-in-summarised-data-as-table-tp3442283p3442283.html Sent from the R help mailing list archive at N...

I am currently summarising a data set by collapsing data based on common identifiers in a column. I am using the 'aggregate' function to summarise numeric columns, i.e. "aggregate(dat[,3], list(dat$gene), mean)". I also wish to summarise text columns e.g. by concatenating values in a comma separated list, but the aggregate function can only return scalar values and so something like "aggregate(dat[,3], list(dat$gene), cat)&quo...

Dear list, For a reason it would take me long to explain, I need to do something along the lines of what's shown below -- i.e., create an object from dplyr::summarise, and then evaluate it on a data frame. I know I could directly do: df %>% dplyr::summarise(x1_mean = mean(x1)) but this is not what I'm looking for. library(dplyr) df <- data.frame(x1 = rnorm(100), x2 = rnorm(100)) foo <- function(df) { mySummary <- quote(dplyr::summarise...

...) Fi = percentual frequency of occurrence of a food item Vi = percentual volume of a food item So, using ddply (plyr) function, I was able to calculate the total frequency of occurrence and total volume of each food item, using: Frequency = ddply (dieta, c('Specie','Fooditem') , summarise, Frequency = sum (Occurrence)) Volume = ddply (dieta, c('Specie','Fooditem') , summarise, Volume = sum (Volume)) and calculate total frequency and total volume for a given specie: TFrequency = ddply (Frequency, 'Specie' , summarise, TF = sum (Frequency)) TVolume = ddply...

...quot;F") Edad<-c(25,36,25,25,25,19,36,39,36,65,54,25,28,28) Ingreso<-c(125,365,265,987,690,369,325,369,789,854,254,268,698,258) Aporte <- c(3,6,3,6,9,6,9,7,9,7,4,8,2,8) datos<-data.frame(distrito=distrito,Sex=Sex,Edad=Edad,Ingreso=Ingreso,Aporte=Aporte) Quiero aplicar la function *summarise *del paquete *dplyr *a las 3 variables númericas. Para la variable Aporte por ejemplo: descrip<-function(data) { grupos <- group_by(data, distrito) result <- summarise(grupos, media = mean(Aporte), maximo = max(Aporte), minimo = min(Apor...

...results$finalreading [1] -1.4 6.9 1.1 3.4 0.0 3.6 -3.8 0.1 -0.1 0.9 1.2 -3.4 -1.5 0.1 5.6 [16] -3.3 -1.9 0.9 -3.1 1.5 0.7 -1.6 -0.3 1.1 -0.1 -0.6 1.5 0.2 0.8 -1.0 [31] 0.8 -0.5 1.9 -4.0 -3.3 3.1 2.8 -0.6 1.2 2.0 -1.9 -1.6 -1.1 -3.9 NA ... Aims: - Summarise these by groups (I can't work out how to use tapply...) - Produce a sensible 'typification' of each group's change in relation to the projected figure. I assume this would use a statistical algorithm to exclude exceptions. - Plot the 3 'typifications' in...

...oduced several models, named model1, model2, model3, etc... I would like to extract several elements from each model's object, e.g. at minimum the estimates, SEs, and P values of each model's intercept and slopes, model R-squared, and AIC... ...and then produce a new object (a table) that summarises all of my models, with M\models in rows and extractd model elements in columns. Before reinventing the wheel, I wonder if there is a package or function that does what I need? Thank you! Mark Na [[alternative HTML version deleted]]

Hello, I have vector called test. And now I wish to measure the mean of the first 10 number, the second 10 numbers etc How does it work? Thanks Wim > dput (test) c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.71, 0.21875, 0,

Hi all - I'm on R 2.5.1 for XP. in the systemfit package, the summary is set to print the McElroy's measure of fit unless it's NULL. When the option saveMemory = TRUE, the McElroy isn't included, instead it defaults to NA. Thus I am unable to use summary.systemfit. > library(systemfit) > example(systemfit) > surfit2 <-

...chael Artz <michaeleartz at gmail.com> wrote: > Oh thanks for that clarification Bert! Hope you enjoyed your coffee! I > ended up just using the transform argument in the ddply function. It > worked and it repeated, then I called a mode function in another call to > ddply that summarised. Kinda hacky but oh well! > > On Tue, Apr 19, 2016 at 12:31 PM, Bert Gunter <bgunter.4567 at gmail.com> > wrote: > >> ... and I'm getting another cup of coffee... >> >> -- Bert >> Bert Gunter >> >> "The trouble with having an open min...

Hi, Here is what I am doing notGroupedAll <- ddply(data ,~groupColumn ,summarise ,col1_mean=mean(col1) ,col2_mode=Mode(col2) #Function I wrote for getting the mode shown below ,col3_Range=myIqr(col3) ) groupedAll <- ddply(data ,~groupColumn ,summarise ,col1...

Hello 1* I need to use logistic regression. But my data file is very huge( appx. 4 million line). R doesn't handle such a file. What can I do ? ------------------------ 2* So, I thought whether I could perform sta. analyses on summarised data (count of yes/no values) of the huge file. Normally, summarised data file short and R could handle it. Then I used this command. > lo <-glm(hey.count~as.factor(jeo)+as.factor(eg)+as.factor(kon)+ as.factor(yol)+ as.factor(aks)+as.factor(fay),family=poisson,data=dt2) as you see I used c...

...king 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 weight var (wt). I use Rcmdr's plotMeans to obtain a line chart. The following code works: attach(nes) obj1 = ddply(nes, .(partyid3), summarise, var = wtd.mean(environ.therm, wt)) print(obj1) plotMeans(obj1$var, obj1$partyid3, error.bars="none") Here is what happens when I write and run the function, meanN: meanN=function(data,x,y,w=NULL) + {obj1=ddply(data,.(x),summarise, var=wtd.mean(y,w)) + print(obj1) + plotMeans(obj1$...

I am trying to replicate the SAS proc univariate in R. I got most of the stats I needed for a by grouping in a data frame using: all1 <- ddply(all,"ACT_NAME", summarise, mean=mean(COUNTS), sd=sd(COUNTS), q25=quantile(COUNTS,.25),median=quantile(COUNTS,.50), q75=quantile(COUNTS,.75), q90=quantile(COUNTS,.90), q95=quantile(COUNTS,.95), q99=quantile(COUNTS,.99) ) So I got the mean, median std dev, quantiles etc. IS there any way I can add th...

...s (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Apr 19, 2016 at 4:25 PM, Michael Artz <michaeleartz at gmail.com> wrote: > Hi, > Here is what I am doing > > notGroupedAll <- ddply(data > ,~groupColumn > ,summarise > ,col1_mean=mean(col1) > ,col2_mode=Mode(col2) #Function I wrote for getting the > mode shown below > ,col3_Range=myIqr(col3) > ) > > groupedAll <- ddply(data > ,~groupColumn >...

...t1)) > dput(head(dt1))structure(c(16814, 16814, 16814, 16815, 16815, 16815, 4.680496, 6.272414, 1.032782, 11.004884, 10.096824, 6.658797), dim = c(6L, 2L), dimnames = list(NULL, c("date", "EnergykWh"))) Then I tried this: library(dplyr) dt1 %>% group_by(date) %>% summarise(EnergykWh.sum = sum(EnergykWh)) and got this errors dt1 %>%+ group_by(date) %>%+ summarise(EnergykWh.sum = sum(EnergykWh))Error in UseMethod("group_by") : no applicable method for 'group_by' applied to an object of class "c('matrix', 'array', '...