purrr

问题 I have created a function where the objective is to create a series of plots in a vectorzation way. The functions partially does what I want which is update update the plot based on the selected variables. However, I am not able to pass the label argument (i.e. label_x and label_y) so that the xlab and ylab are updated consistently. library(tidyverse) plot_scatter_with_label <- function(df, var_x, var_y, label_x, label_y, geom_smooth = FALSE, point_shape = 16, point_color = "#EB3300", point

问题 Representative sample data (list of lists): l <- list(structure(list(a = -1.54676469632688, b = "s", c = "T", d = structure(list(id = 5L, label = "Utah", link = "Asia/Anadyr", score = -0.21104594634643), .Names = c("id", "label", "link", "score")), e = 49.1279871269422), .Names = c("a", "b", "c", "d", "e")), structure(list(a = -0.934821052832427, b = "k", c = "T", d = list(structure(list(id = 8L, label = "South Carolina", link = "Pacific/Wallis", score = 0.526540892113734, externalId = -6

问题 I know that there are many related questions here on SO, but I am looking for a purrr solution, please, not one from the apply list of functions or cbind/rbdind (I want to take this opportunity to get to know purrr better). I have a list of dataframes and I would like to add a new column to each dataframe in the list. The value of the column will be the name of the dataframe, i.e. the name of each element in the list. There is something similar here, but it involves the use of a function and

问题 I have a dataframe with one column of a response variable, and several columns of predictor variables. I want to fit models for the response variable using each of the predictor variables separately, finally creating a dataframe that contains the coefficients of the model. Previously, I would have done this: data(iris) iris_vars <- c("Sepal.Width", "Petal.Length", "Petal.Width") fits.iris <- lapply(iris_vars, function(x) {lm(substitute(Sepal.Length ~ i, list(i = as.name(x))), data = iris)}) #

问题 I'm comparing two vectors (data_A_score, data_B_score) with another vector K1, using ks.test(), which I get this result: score_ref_k1 <- c(0.09651, 0.09543, 0.09122, 0.09458, 0.09382, 0.10158, 0.10339, 0.13594, 0.09458, 0.09296) data_A_score_src <- c(0.09293, 0.09838, 0.09866, 0.10866, 0.09726, 0.10731, 0.09866, 0.09398, 0.10007, 0.10408) data_B_score_src <- c(0.04741, 0.0621, 0.09606, 0.08851, 0.05063, 0.39775, 0.05509, 0.10784, 0.0468, 0.04782) ks.test(data_A_score_src, score_ref_k1, exact

问题 I suspect that this is a bug in the purrr package, but would like to check my logic in StackOverflow first, please. It seems to me that the possibly function is not working inside the map2_chr function. I'm using purrr version 0.2.5 Consider this example: library(dplyr) library(purrr) lets <- tibble(posn = 2:0, lets_list = list(letters[1:5], letters[1:5], letters[1:5])) %>% glimpse() returns Observations: 3 Variables: 2 $ posn <int> 2, 1, 0 $ lets_list <list> [<"a", "b", "c", "d", "e">, <"a",

问题 I have a dataframe df1 with an ID column and a lubridate time interval column, and I want to filter (subsample) a dataframe df2, which has ID and DateTime columns, so that only df2 rows with DateTime fitting the corresponding ID interval in df1 are kept. I want to do so in a tidyverse framework. It can easily be done using a join (see example below), but I would like to know whether there would be a more direct solution (maybe purrr-based) that would avoid joining and then removing the time

问题 Aim: I want to use the map() function to do a linear model that involves categorical variables. Problem: I am getting the following error but I know the categorical variable included, borrower_genders has five levels. Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels What I've done: The following code works perfectly fine when removing borrower_genders , not with it, why? kiva_country%>% dplyr::group_by(country

问题 I am using pmap to run the same function across multiple dataframes and wish to assign each element of the output list to a separate object with a name that has a pattern. But I can't figure out how to do this. For example, here is a minimal example where I am computing quantiles for three different variables- # function call purrr::pmap(.l = list( x = list(iris$Sepal.Length, mtcars$wt, anscombe$y4), probs = list(seq(0, 1, 0.10)), na.rm = list(TRUE) ), .f = stats::quantile) # output #> [[1]]

问题 I am working on a web scraping project, which aims to extract Google + reviews from a set of children's hospitals. My methodology is as follows: 1) Define a list of Google + urls to navigate to for review scraping. The urls are in a dataframe along with other variables defining the hospital. 2) Scrape reviews, number of stars, and post time for all reviews related to a given url. 3) Save these elements in a dataframe, and name the dataframe after another variable in the dataframe