tidyverse

问题 I have daily rainfall data which I have converted to yearwise cumulative value using following code library(seas) library(data.table) library(ggplot2) #Loading data data(mscdata) dat <- (mksub(mscdata, id=1108447)) dat$julian.date <- as.numeric(format(dat$date, "%j")) DT <- data.table(dat) DT[, Cum.Sum := cumsum(rain), by=list(year)] df <- cbind.data.frame(day=dat$julian.date,cumulative=DT$Cum.Sum) Then I want to apply segmented regression year-wise to have year-wise breakpoints. I could able

问题 I have a table that has a "membership type" column that includes a zillion different membership levels that we've used over the years. example <-data.frame(membership = c( "Legacy Payment ID #3564, Payment Record #0, Period Paid: 1 Flag: N", "Legacy Payment ID #3611, Payment Record #0, Period Paid: 2 Flag: N", "Legacy Payment ID #4105, Payment Record #0, Period Paid: 1 Flag: G", "Legacy Payment ID #4136, Payment Record #0, Period Paid: 1 Flag: N", "Legacy Payment ID #5191, Payment Record #0,

问题 I am trying to translate a pipeline of manipulations on a dataframe in R over to its Python equivalent. A basic example of the pipeline is as follows, incorporating a few mutate and filter calls: library(tidyverse) calc_circle_area <- function(diam) pi / 4 * diam^2 calc_cylinder_vol <- function(area, length) area * length raw_data <- tibble(cylinder_name=c('a', 'b', 'c'), length=c(3, 5, 9), diam=c(1, 2, 4)) new_table <- raw_data %>% mutate(area = calc_circle_area(diam)) %>% mutate(vol = calc

问题 I am trying to translate a pipeline of manipulations on a dataframe in R over to its Python equivalent. A basic example of the pipeline is as follows, incorporating a few mutate and filter calls: library(tidyverse) calc_circle_area <- function(diam) pi / 4 * diam^2 calc_cylinder_vol <- function(area, length) area * length raw_data <- tibble(cylinder_name=c('a', 'b', 'c'), length=c(3, 5, 9), diam=c(1, 2, 4)) new_table <- raw_data %>% mutate(area = calc_circle_area(diam)) %>% mutate(vol = calc

问题 I'd like to produce "wide" summary tables of data in this sort of format: ---- Centiles ---- Param Group Mean SD 25% 50% 75% Height 1 x.xx x.xxx x.xx x.xx x.xx 2 x.xx x.xxx x.xx x.xx x.xx 3 x.xx x.xxx x.xx x.xx x.xx Weight 1 x.xx x.xxx x.xx x.xx x.xx 2 x.xx x.xxx x.xx x.xx x.xx 3 x.xx x.xxx x.xx x.xx x.xx I can do that in dplyr 0.8.x. I can do it generically, with a function that can handle arbitrary grouping variables with arbitrary numbers of levels and arbitrary statistics summarising

问题 I'd like to produce "wide" summary tables of data in this sort of format: ---- Centiles ---- Param Group Mean SD 25% 50% 75% Height 1 x.xx x.xxx x.xx x.xx x.xx 2 x.xx x.xxx x.xx x.xx x.xx 3 x.xx x.xxx x.xx x.xx x.xx Weight 1 x.xx x.xxx x.xx x.xx x.xx 2 x.xx x.xxx x.xx x.xx x.xx 3 x.xx x.xxx x.xx x.xx x.xx I can do that in dplyr 0.8.x. I can do it generically, with a function that can handle arbitrary grouping variables with arbitrary numbers of levels and arbitrary statistics summarising

问题 I'd like to produce "wide" summary tables of data in this sort of format: ---- Centiles ---- Param Group Mean SD 25% 50% 75% Height 1 x.xx x.xxx x.xx x.xx x.xx 2 x.xx x.xxx x.xx x.xx x.xx 3 x.xx x.xxx x.xx x.xx x.xx Weight 1 x.xx x.xxx x.xx x.xx x.xx 2 x.xx x.xxx x.xx x.xx x.xx 3 x.xx x.xxx x.xx x.xx x.xx I can do that in dplyr 0.8.x. I can do it generically, with a function that can handle arbitrary grouping variables with arbitrary numbers of levels and arbitrary statistics summarising

问题 I'd like to produce "wide" summary tables of data in this sort of format: ---- Centiles ---- Param Group Mean SD 25% 50% 75% Height 1 x.xx x.xxx x.xx x.xx x.xx 2 x.xx x.xxx x.xx x.xx x.xx 3 x.xx x.xxx x.xx x.xx x.xx Weight 1 x.xx x.xxx x.xx x.xx x.xx 2 x.xx x.xxx x.xx x.xx x.xx 3 x.xx x.xxx x.xx x.xx x.xx I can do that in dplyr 0.8.x. I can do it generically, with a function that can handle arbitrary grouping variables with arbitrary numbers of levels and arbitrary statistics summarising

问题 df = data.frame( A = c(1, 4, 5, 13, 2), B = c("Group 1", "Group 3", "Group 2", "Group 1", "Group 2"), C = c("Group 3", "Group 2", "Group 1", "Group 2", "Group 3") ) df %>% group_by(B) %>% summarise(val = mean(A)) df %>% group_by(C) %>% summarise(val = mean(A)) Instead of writing a new chunck of code for each unique set of group_by I would like to create a loop that would iterate through the df data frame and save the results into a list or a data frame. I would like to see how the average

问题 I have 2 data frames. How I can make something like tidyr::complete with them using tidyverse ? My data: df <-data.frame(a=letters[1:2] ) df1<-data.frame(one=1:2) Expected Result: a 1 b 1 a 2 b 2 Thx! 回答1: With this particular example I think you can just use the merge function. As a standard its arguments all.x and all.y are set to TRUE, so it automatically creates all combinations since the dataframes do not have any variables or values in common. df <-data.frame(a=letters[1:10] ) df1<-data