search for: meanpric

Like this? # example using qplot library(ggplot2) meanprice <- tapply(diamonds$price, diamonds$cut, mean);meanprice cut <- factor(levels(diamonds$cut), levels = levels(diamonds$cut)) qplot(cut, meanprice, geom="bar", stat="identity", fill = I("grey50")) dev.new() # create a new graph to compare with qplot # Example usin...

...a by 3 variables as follows # group by lat (StoreX)/long (StoreY) priceStore <- LapTopSales[,c(4,5,15,16)] priceStore <- priceStore[complete.cases(priceStore), ] # keep only non NA records priceStore_Grps <- priceStore %>% group_by(StorePC, StoreX, StoreY) %>% summarize(meanPrice=(mean(RetailPrice))) which results in . > priceStore_Grps # A tibble: 15 x 4 # Groups: StorePC, StoreX [?] StorePC StoreX StoreY meanPrice <fct> <int> <int> <dbl> 1 CR7 8LE 532714 168302 472. 2 E2 0RY 535652 182961 520. 3 E7...

...ong (StoreY) > > priceStore <- LapTopSales[,c(4,5,15,16)] > > priceStore <- priceStore[complete.cases(priceStore), ] # keep only non NA > records > > priceStore_Grps <- priceStore %>% > > group_by(StorePC, StoreX, StoreY) %>% > > summarize(meanPrice=(mean(RetailPrice))) > > > > which results in . > > > >> priceStore_Grps > > # A tibble: 15 x 4 > > # Groups: StorePC, StoreX [?] > > StorePC StoreX StoreY meanPrice > > <fct> <int> <int> <dbl...

...apTopSales[,c(4,5,15,16)] >> >> priceStore <- priceStore[complete.cases(priceStore), ]? # keep only >> non NA >> records >> >> priceStore_Grps <- priceStore %>% >> >> ?? group_by(StorePC, StoreX, StoreY) %>% >> >> ?? summarize(meanPrice=(mean(RetailPrice))) >> >> >> which results in . >> >> >>> priceStore_Grps >> >> # A tibble: 15 x 4 >> >> # Groups:?? StorePC, StoreX [?] >> >> ??? StorePC? StoreX StoreY meanPrice >> >> ??? <fct>???? <...

Hi, I'm trying to create a stacked bar plot using ggplot2. Rather than plotting the count of each of the 13 "Bar" factors on the Y axis, I would like to represent the sum of the Values associated with each of the 13 "Bar" factors. Is there a way to do that? Given the following data, that would obviously mean that there would be some negative sums represented. Here's

Dear All, I have a large data set that looks like this: CVX 20070201 9 30 51 73.25 81400 0 CVX 20070201 9 30 51 73.25 100 0 CVX 20070201 9 30 51 73.25 100 0 CVX 20070201 9 30 51 73.25 300 0 First, I would like to import it by merging column 3 4 and 5, since that is the timestamp. Then, I would like to aggregate the data by splitting them in bins of 5 minutes size, therefore from 93000 up to