purrr

问题 I have a data.frame containing parties in government. These parties are nested in a list column grouped by period (= each year). I want to compare the overlap between each government and previous governments. library(tidyverse) df <- tibble::tribble( ~period, ~party, ~seats, 1, "A", 2, 1, "B", 3, 1, "C", 3, 2, "A", 2, 2, "C", 3, 3, "C", 4, 3, "E", 1, 3, "F", 3 ) df1 <- df %>% group_by(period) %>% nest() %>% mutate(gov=map(data, "party") %>% map(.,list)) %>% mutate(prev.govs=map(data, "party")

问题 The following produces a data frame with <int[]> and <list[]> fields: library(tidyverse) set.seed(123) s <- 4 data <- data.frame( lamda = c(5, 2, 3), meanlog = c(9, 10, 11), sdlog = c(2, 2.1, 2.2) ) data2 <- data %>% mutate( freq = map(lamda, ~rpois(s, .x)), freqsev = map(freq, ~map(.x, function(k) rlnorm(k, meanlog, sdlog))) ) Output: as_tibble(data2) lamda meanlog sdlog freq freqsev <dbl> <dbl> <dbl> <list> <list> 1 5 9 2 <int [4]> <list [4]> 2 2 10 2.1 <int [4]> <list [4]> 3 3 11 2.2 <int

问题 I am trying to scrape football(soccer) statistics for a project i'm working on and i'm trying to utilise rvest and purrr to loop through the numeric values at the end of the url. I'm not sure what i'm missing but i have a snippet of the code as well as the error message that keeps coming up. library(xml2) library(rvest) library(purrr) wins_URL <- "https://www.premierleague.com/stats/top/clubs/wins?se=%d" map_df(1:15, function(i){ cat(".") page <- read_html(sprintf(wins_URL, i)) data.frame

问题 Anomaly detection methods published and now abandoned by twitter have been separately forked and maintained in the anomalize package and the hrbrmstr/AnomalyDetection fork. Both have implemented features that are 'tidy'. Working static versions tidyverse_cran_downloads %>% filter(package == "tidyr") %>% ungroup() %>% select(-package) -> one_package_only one_package_only %>% anomalize::time_decompose(count, merge = TRUE, method = "twitter", frequency = "7 days") -> one_package_only_decomp one

问题 I have two functions: date_diff and group_stat. So I have read this article tidyverse and I try so create simple functions and use the pipe. The first function creates a difftime and names them timex_minus_timey but when I pipe this result into the next function I have to look at the name so I can fill in summary_var. Is there a better way to do this? library(tidyverse) # set.seed(42) data <- dplyr::bind_rows( tibble::tibble(Hosp = rep("A", 1000), drg = sample(letters[1:5], 1000, replace =

问题 I read a similar question that helped me get to the point I'm at now, but am struggling with the next step. I have a dataframe similar to below - Product = c("Apple", "Apple", "Banana", "Banana", "Banana", "Carrot", "Carrot") Category = c(1, 2, 1, 2, 3, 1, 2) Slope = c(2.988, 2.311, 2.181, 6.387, 2.615, 7.936, 3.267) df = data.frame(Product, Category, Slope) My objective is to have a Word report with a table for each product. To do this, I create a list with the data and flextables, as below

问题 I'd like to get chi-square output matrices (e.g., standardized residuals, expected values) by group using elements of the tidyverse. Using the mtcars data set, here's where I've started: mtcars %>% dplyr::select(vs, am) %>% table() %>% chisq.test(.) Which produces the chi-square test statistic. In order to get standardized residuals, for example, my only successful code is this: mtcars %>% dplyr::select(vs, am) %>% table() %>% chisq.test(.) -> chi.out chi.out$stdres vs am Freq 1 0 0 0.9523038

问题 This question already has answers here : R/tidyverse: calculating standard deviation across rows (5 answers) Calculating standard deviation of each row (1 answer) Closed 8 months ago . library(tidyverse) df <- tibble(col1 = c(5, 2), col2 = c(6, 4), col3 = c(9, 9)) # # A tibble: 2 x 3 # col1 col2 col3 # <dbl> <dbl> <dbl> # 1 5 6 9 # 2 2 4 9 df %>% mutate(col4 = apply(.[, c(1, 3)], 1, sum)) df %>% mutate(col4 = rowSums(.[c(1, 3)], na.rm = TRUE)) Lately R's apply() function has been trouble for

问题 I'd like to make some purrr code more concise. I have a df with one dependent variable (y) and 4 independent variables (x1, x2, x3, x4). I also have one conditioning variable that takes 2 levels (z, is either zero or 1). I'd like to run 8 t-tests: y ~ x1 [z==0], y ~ x2 [z==0] ... y ~ x1 [z==1], y ~ x2 [z==1] etc. I'd like to return a single dataframe with the tidy tests stacked on top of each other. I really want to generalize this method for more combinations of predictors, so building a

问题 Below are two simple data frames. I would like to re-code (collapse) the Sat1 and Sat2 columns so that all degrees of satisfied are coded simply as Satisfied , and all degrees of Dissatisfied are coded as Dissatisfied . Neutral will remain as Neutral. These factors will therefore have three levels - Satisfied, Dissatisfied, and Neutral . I would normally accomplish this by binding the data frames, and using lapply along with re-code from the car package, such as: DF1[2:3] <- lapply(DF1[2:3],