So, as you can see, not having a reproducible example can call the hounds on you. Nevertheless, I feel that your problem was well-described, if not well researched, and thus deserves an answer (if not too time consuming). In this case, there are some basic data manipulation functions that you will have to familiarize yourself … Read more
Ok, still not perfectly clear, but I THINK that presence is now an adjacency matrix, where the columns represent users and the rows represent events, so presence[i,j] indicates that user i attended event j. If I’m interpreting it correctly then counts seems to be the co-occurrence matrix, correct? count[i,j] should record the number of events … Read more
Suppose you have a list of lists, and each element is named: ll <- list(jan = list(1:3), feb = list(4:6), mar = list(7:9)) ls() # [1] “ll” You can use list2env to assign the list components into the global environment: list2env(ll, globalenv()) ls() # [1] “feb” “jan” “ll” “mar” jan # [[1]] # [1] 1 … Read more
I fixed it by: adding this line df1$yr <- strftime(df1$Close.Date, “%Y”) and then replacing Close.Date with yr in the ggplot line. solved ggplot2: Group histogram data by year
Here’s a solution without loops. # some artificial data set.seed(1) daf <- data.frame(species = factor(paste0(“species”, c(rep(1:3, 10)))), year = rep(2000:2009, 3), x = sample(1:100, 30)) library(dplyr) library(broom) lm_fit <- daf %>% group_by(species) %>% do(fit = lm(x ~ year, .)) tidy(lm_fit, fit) # or as.data.frame(tidy(lm_fit, fit)) to get a data.frame # # A tibble: 6 x … Read more
The crucial point in OP’s approach is the staggered aggregation (see the related question row not consolidating duplicates in R when using multiple months in Date Filter). The OP wants to aggregate data across a number of files which apparently are too large to be loaded altogether and combined into a large data.table. Instead, each … Read more
I have managed to do it by s <- c(1,2,3) powerSet(s)[-length(powerSet(s))] Thanks! 2 solved How to generate a list of certain subsets of x in r?
An easy way to understand how loops work in any programming language is to follow, step by step what it is doing. Here a simple example of how you can “debug” in paper. This could be useful in the future. The table below represents each iteration of your loops and the values of each operation. … Read more
You don’t. REvo R’s latest uses R 2.13.2. If you want R 2.14.0, install it directly. It comes free, so there is no reason not to. 5 solved How to Create My Own Installation of High Performance R which Has all Packages of Revo. R and can Beat it on Performance Tests
We can use na.aggregate from zoo. Loop through the columns of dataset (assuming all the columns are numeric ), apply the na.aggregate to replace the NA with mean values (by default) and assign it back to the dataset. library(zoo) df[] <- lapply(df, na.aggregate) By default, the FUN argument of na.aggregate is mean: Default S3 method: … Read more
I had a discussion with the OP using his data. One of his issues was to make scale_colour_gradient2() work. The solution was to set up a midpoint value. By default, it is set at 0 in the function. In his case, he has a continuous variable that has about 50 as median. library(ggmap) library(ggplot2) library(RColorBrewer) … Read more
Perhaps the reason that you are having problems is that you are using empty strings when you should be using NAs. This is what I would assume is the idiomatic code. df <- data.frame(unique_id = c(rep(1,3),rep(2,3)), school = c(rep(‘great’,3),rep(‘spring’,3)), subject = rep(c(“Math”, “English”, “History”),2), grade = c(88,78,98,65,72,84), sex = c(NA,NA, “male”, NA, “female”, NA)) r2 … Read more
You should use the absolute path of the .csv file. eg. Desktop:\R\lung_cancer.csv Also use double\ instead of \ or use / . solved How to read csv file in R? [closed]
Seems your code needs to be cleaned up a bit: if(lastFlag!=1){ lastDay <<- rawData$Date[nrow(rawData)] # last complete day rawData <<- subset.data.frame(rawData,rawData$Date < lastDay) } I think the “rawData” could be an empty dataframe here but not be checked; Let’s suppose: rawData <- data.frame(x=c(), y=c()) tProcRows <- 100 So: tProcRows <- tProcRows + as.numeric(row.names(rawData)[nrow(rawData)]) print(tProcRows) Output: … Read more
Here’s a tidyverse approach: library(dplyr) library(tidyr) # put data in a data.frame data_frame(string = unlist(data)) %>% # add ID column so we can recombine later add_rownames(‘id’) %>% # add a lagged column to compare against mutate(string2 = lag(string)) %>% # break strings into words separate_rows(string) %>% # evaluate the following calls rowwise (until regrouped) rowwise() … Read more