plyr

问题 Below is my scenerio. Scenerio I have two dataframe. 1st dataframe contains data about system usage and another dataframe contains data about System location. I would like to track instrument usage based on date the system was used and also the location where the instrument is located. For this I am performing outer join on dataframes using dplyr library. Next, I would like to get frequency count of the systems based on date. For this I am using groupby on System and Locations. If the system

问题 I cannot figure out how to label each vline with year. can someone help? Below is an example of my dataset and code. I would like to label the year on the mean length vline and/or have the same colour code as the year in the figure legend. Sector2 Family Year Length BUN Acroporidae 2010 332.1300496 BUN Poritidae 2011 141.1467966 BUN Acroporidae 2012 127.479 BUN Acroporidae 2013 142.5940556 MUR Faviidae 2010 304.0405 MUR Faviidae 2011 423.152 MUR Pocilloporidae 2012 576.0295 MUR Poritidae 2013

问题 In R, to apply some function to a column, you can do: df$col <- someFunction(df$col) Now my question is, how do you the similar task when you have data frames in a nested list? Say I have a following list like this, where I have data frames in the second level from the root. +------+------+ type1 | id | name | +----------->|------|------| | | | | | | | | year1 | +------+------+ +------------------+ | | | | +------+------+-----+ | | type2 | meta1|meta2 | name| | +----------> |------|------|---

问题 This question builds upon a previous one which was nicely answered for me here. R: Grouped rolling window linear regression with rollapply and ddply Wouldn't you know that the code doesn't quite work when extended to the real data rather than the example data? I have a somewhat large dataset with the following characteristics. str(T0_satData_reduced) 'data.frame': 45537 obs. of 5 variables: $ date : POSIXct, format: "2014-11-17 08:47:35" "2014-11-17 08:47:36" "2014-11-17 08:47:37" ... $ trial

问题 I have a set of spatial coordinate (x,y) data that has a response variable for each coordinate over the course of several years. The following code generates a similar data frame: df <- data.frame( id = rep(1:2, 2), x = rep(c(25, 30),10), y = rep(c(100, 200), 10), year = rep(1980:1989, 2), response = rnorm(20) ) The resulting data frame: head(df) id x y year response 1 1 25 100 1980 0.1707431 2 2 30 200 1981 1.3562263 3 1 25 100 1982 -0.4590506 4 2 30 200 1983 1.3238410 5 1 25 100 1984 1

问题 I have a dataframe similar to the one generated below. Some individuals have more than one observation for a particular variable and each variable has an associated standard error (SE) for the estimate. I would like to create a new dataframe that contains only a single row for each individual. For individuals with more than one observation, such as Kim or Bob, I need to calculate a precision weighted average based on the standard errors of the estimates along with a variance for the newly

问题 I have dataframe df with x,y,and monthly.year data for each x,y point. I am trying to get the seasonal aggregate. I need to calculate seasonal means i.e. For winter mean of (December,January,February); for Spring mean of (March,April,May), for Summer mean of (June,July,August) and for autumn mean of (September,October,November). The data looks similar to: set.seed(1) df <- data.frame(x=1:3,y=1:3, matrix(rnorm(72),nrow=3) ) names(df)[3:26] <- paste(month.abb,rep(2009:2010,each=12),sep=".") x y

问题 Here is some sample data on my problem: mydf <- data.frame(A = rnorm(20, 1, 5), B = rnorm(20, 2, 5), C = rnorm(20, 3, 5), D = rnorm(20, 4, 5), E = rnorm(20, 5, 5)) Now I'd like to run a one-sample t-test on each column of the data.frame, to prove if it differs significantly from zero, like t.test(mydf$A) , and then store the mean of each column, the t-value and the p-value in a new data.frame. So the result should look something like this: A B C D E mean x x x x x t x x x x x p x x x x x I

问题 I would like to take a set of intervals, possibly overlapping, within categories of an identifier and create new intervals that are either exactly overlapping (ie same start/end values) or completely non-overlapping. These new intervals should collectively span the range of the original intervals and not include any ranges not in the original intervals. This needs to be a relatively fast operation because I'm working with lots of data. Here is some example data: library(data.table) set.seed

问题 Here are two artificial but I hope pedagogical examples of my problem. 1) When running this code: > dat0 <- data.frame(A=c("a","a","b"), B="") > data.table(dat0)[, lapply(.SD, function(x) length(A)) , by = "A"] A B 1: a 1 2: b 1 I expected the output A B 1: a 2 2: b 1 (similarly to plyr::ddply(dat0, .(A), nrow) ). Update to question 1) Let me give a less artificial example. Consider the following dataframe: dat0 <- data.frame(A=c("a","a","b"), x=c(1,2,3), y=c(9,8,7)) > dat0 A x y 1 a 1 9 2 a