Your SQL CTE syntax is incorrect: you have a comma after the last CTE’s close-paren. The only time you need a comma there is if you have another CTE following it. Try changing to with unit as ( select t.RefDate, … ), — yes comma here, between CTEs agg as ( select u.RefDate, … ) … Read more
There is nothing wrong that I can see. Your code works. Some times such errors arise due to not flowing the ggplot expression properly. Try it like this: ggplot(train, aes(x= Item_Visibility, y = Item_Outlet_Sales)) + geom_point(size = 2.5, color=”navy”) + xlab(“Item Visibility”) + ylab(“Item Outlet Sales”) + ggtitle(“Item Visibility vs Item Outlet Sales”) Assuming you … Read more
apriori returns rules object, not a vector: data(“Adult”) ## Mine association rules. rules <- apriori(Adult, parameter = list(supp = 0.5, conf = 0.9, target = “rules”)) class(rules) # [1] “rules” if you want to compare lists of rules you will need to transform this object to a data.frame, e.g.: rules.df <- as(rules, “data.frame”) is.subset(rules.df$rules, rules.df$rules) … Read more
Actually figured it out – was very simple. NewDataFrame <- data.frame(colnames(Original)) solved R: create new dataframe rows are columns from another dataframe
dataset <- dataset[!(dataset %in% c(‘A’,’B’))] solved Removing contents from a dataset in R
Plyr solution: library(plyr) ddply(df,.(ID),transform,AGE_HEAD=head(AGE,1)) OR ddply(df,.(ID),transform,AGE_HEAD=AGE[PERNO==1]) ID AGE PERNO AGE_HEAD 1 1 30 1 30 2 1 25 2 30 3 2 25 1 25 4 2 24 2 25 5 2 3 3 25 6 3 65 1 65 7 3 55 2 65 data.table solution: library(data.table) DT<-data.table(df) DT[, AGE_HEAD := AGE[PERNO==1], by=”ID”] ID … Read more
A possible solution: library(lubridate) # example time data time = c( “2015-10-05 15:44:41.986797”, “2015-10-05 15:59:23.389583”, “2015-10-05 16:59:44.99402”, “2015-10-05 16:59:44.99402”, “2015-10-05 16:59:44.99402”, “2015-10-05 16:59:44.99402”, “2015-10-05 17:59:59.594524”, “2015-10-05 17:59:59.594524”, “2015-10-05 18:00:05.555564” ) # transform time strings to POSIXct objects for count time <- ymd_hms(time) # count by second event <- data.frame(table(time)) # transform time factors to POSIXct … Read more
Based on your description: data2[] <- lapply(data2, function(x) vapply(x, paste, collapse = “, “, character(1L))) Or data2[] <- lapply(data2, function(x) vapply(x, toString, character(1L))) 2 solved how do you apply a function to all of the columns in data frame? [closed]
You could try using the GenomicRanges package. library(dplyr) library(GenomicRanges) Here we load in the the example input data. (This is an inelegant way to do this — I know… but I was lazy and the sublime multiline edit made it easy.) Note: I don’t know where the “1” column means, but I kept it in … Read more
The problem appears to be here: sieve <- c(sieve[(sieve %% i) ! = 0], i). I’m assuming that you want “is not equal to”, so should be sieve <- c(sieve[(sieve %% i) != 0], i). Here’s the formatted code that works for me: Eratosthenes <- function(n) { if (n >= 2) { sieve <- seq(2,n) … Read more
Just write df1-df2[colnames(df1)] # a b c # 1 1 -3 1 # 2 2 -5 8 # 3 3 -6 -1 2 solved Subtract two data sets where column names are same [closed]
One approach is to use the tidy function from the broom package. It is versatile and can organize most R statistical results into a data frame. library(broom) set.seed(1) x <- rnorm(1000) x[sample(1:length(x), 100)] <- NA df <- tidy(summary(x)) df minimum q1 median mean q3 maximum NA’s 1 -3.008 -0.6834 -0.01371 0.00106 0.6978 3.81 100 As … Read more
This is what I was looking for avgDelay <- function(carrier, dest) { TempTotal = 0 TempCount = 0 for(i in 1:dim(delays)[1]) { if(delays$CARRIER[i] == carrier & delays$ARR_DELAY[i] >0 & is.na(delays$ARR_DELAY[i]) == FALSE & delays$DEST[i] == dest) { TempTotal <-TempTotal + delays$ARR_DELAY[i] TempCount <-TempCount + 1 # keeps count of the number of delays } } … Read more
You defined one and only one object when you executed: tino=read.delim(“clipboard”) The column names of that object are not handled as other objects. If you wnated to create a new object from that dataframe you could do this: x <- with(tino, cbind(DEX, GRW , Debt, Life) ) It’s possible this might do violence to the … Read more
As Gregor points out, you need a more fine-grained representation of your time variable if you want to look at how temperature changes by time. There are two solutions. The best option is if your data are ordered in the order they were recorded (so the first row was the first measurement, second row second … Read more