r

问题 This question already has answers here : How to combine two lists in R (3 answers) Closed 12 months ago . I have list 1: l1 <- list(letters[1:26]) I have list 2: l2 <- list(c(1:26)) How would I combine l1 and l2 into a new list l4 with 52 elements: the first 26 elements are the elements of l1 and the next 26 are the elements of l2 ? 回答1: You could use c to combine them l4 <- c(l1[[1]], l2[[1]]) Or with unlist l4 <- c(unlist(l1), unlist(l2)) Maybe you want to wrap it in list l4 <- list(c(l1[[1

问题 I'm trying to define a function which returns a graphical object in R. The idea is that I can then call this function with different arguments multiple times using an for loop or lapply function, then plotting the list of grobs in gridExtra::grid.arrange . However, I did not get that far yet. I'm having trouble with r recognising the arguments as being part of the call. I've made some code to show you my problem. I have tried quoting and unquoting the arguments, using unqoute() in the

问题 I found several questions on Stackoverflow regarding this topic (some of them without any answer) but nothing related (so far) with this error in regression. I'm, running a probit model in r with (I'm guessing) too many fixed effects (year and places): myprobit <- glm(factor(Y) ~ factor(T) + factor(X1) + factor(X2) + factor(X3) + factor(YEAR) + factor(PLACE), family = binomial(link = "probit"), data = DT) The PLACE variable has about 1000 unique values and YEAR 8 values. The dataset DT has 13

问题 I'm trying to sort out functional programming with dplyr/ggplot. In my first couple of weeks of R I mostly went by trial and error following snippets found on the web, but I'm trying to understand this better so it comes more natural. I'm playing around with mtcars as an example: library(tidyverse) data <- mtcars data$carb <- as.factor(data$carb) My sample code w/o using variables looks like this: data %>% filter(carb != 4) %>% ggplot() + geom_point(aes(x = mpg, y = hp, color = carb)) +

问题 I'm trying to sort out functional programming with dplyr/ggplot. In my first couple of weeks of R I mostly went by trial and error following snippets found on the web, but I'm trying to understand this better so it comes more natural. I'm playing around with mtcars as an example: library(tidyverse) data <- mtcars data$carb <- as.factor(data$carb) My sample code w/o using variables looks like this: data %>% filter(carb != 4) %>% ggplot() + geom_point(aes(x = mpg, y = hp, color = carb)) +

问题 i´m currently working with a large dataframe of 75 columns and round about 9500 rows. This dataframe contains observations for every day from 1995-2019 for several observation points. Edit: The print from dput(head(df)) > dput(head(df)) structure(list(date = structure(c(9131, 9132, 9133, 9134, 9135, 9136), class = "Date"), x1 = c(50.75, 62.625, 57.25, 56.571, 36.75, 39.125), x2 = c(62.25, 58.714, 49.875, 56.375, 43.25, 41.625), x3 = c(90.25, NA, 70.125, 75.75, 83.286, 98.5), x4 = c(60, 72, 68

问题 I'm trying to sort out functional programming with dplyr/ggplot. In my first couple of weeks of R I mostly went by trial and error following snippets found on the web, but I'm trying to understand this better so it comes more natural. I'm playing around with mtcars as an example: library(tidyverse) data <- mtcars data$carb <- as.factor(data$carb) My sample code w/o using variables looks like this: data %>% filter(carb != 4) %>% ggplot() + geom_point(aes(x = mpg, y = hp, color = carb)) +

问题 i´m currently working with a large dataframe of 75 columns and round about 9500 rows. This dataframe contains observations for every day from 1995-2019 for several observation points. Edit: The print from dput(head(df)) > dput(head(df)) structure(list(date = structure(c(9131, 9132, 9133, 9134, 9135, 9136), class = "Date"), x1 = c(50.75, 62.625, 57.25, 56.571, 36.75, 39.125), x2 = c(62.25, 58.714, 49.875, 56.375, 43.25, 41.625), x3 = c(90.25, NA, 70.125, 75.75, 83.286, 98.5), x4 = c(60, 72, 68

问题 I have reading all post about scaling the y-axis and in all of them there are some extra steps in order to control the y-axis limits when using sec.axis in ggplot. I have the following df. structure(list(day = c(1, 3, 5, 7, 9), mean = c(0.000452620431539136, 0.000244953967091816, 0.000409529176828165, 0.000621566432113383, 0.000975471413145951), sd = c(0.000145928952108396, 7.48403498938327e-05, 8.70694523628839e-05, 0.000265199022927143, 0.00076194983870935 ), group = c("pi", "pi", "pi", "pi

问题 I'm trying to fit data with nls() function where the nature of data gives me bounds for one coefficient and for sum of two coefficients. Let me introduce short example to see where is the problem. I want parameter b1 to be between 0 and 1 and I want sum of parameters b1 and b2 to be between 0 and 1 as well. set.seed(123) # example where everything is OK x <- 1:200 g <- rbinom(200, 1, 0.5) y <- 3 + (0.7 + 0.2 * g) * x yeps <- y + rnorm(length(y), sd = 0.1) # both parameter b1 and sum of