tidyverse

问题 structure(list(group = c(NA, "A", "B", NA, "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", NA, NA, "B", "B", "A", "A", NA, NA, "B", "B", "B", NA, "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", NA, NA, "B", "B", NA, "A"), seq_break = c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE,

问题 Using R and tidyverse library I am trying to achieve pivot like result. Here it sample data set: zz <- " Date ParAB ParCD 1 2017-05-27 A C 2 2017-05-27 B D 3 2017-05-27 A D 4 2017-05-27 B C 5 2017-05-27 B C 6 2017-05-28 A D 7 2017-05-28 A C 8 2017-05-28 A C 9 2017-05-28 A D" Data <- read.table(text=zz, header = TRUE)} I would like transform the data to look like this with number of occurrences per day: Date A B C D 2017-05-27 2 3 3 2 2017-05-28 2 0 1 1 I tired spread function that works great

问题 I have a function which takes multiple inputs and creates multiple outputs. For example: example_fun = function(a,b){ x = a+b y = a-b return(list(x=x, y=y)) } How can I use dplyr::mutate to evaluate this function on each row of a dataframe? Turn df = expand.grid(a=c(7,8), b=c(9,10)) df a b 1 7 9 2 8 9 3 7 10 4 8 10 into a b x y 1 7 9 16 -2 2 8 9 17 -1 3 7 10 17 -3 4 8 10 18 -2 this following code almost accomplishes it: df = df %>% mutate(outputs = pmap(list(a,b), example_fun)) %>% unnest()

问题 I am trying to read in and work with a horribly formatted debug log. There are no consistent delimeters and it does not appear line breaks are encoded either. What I'd like to do is read in and parse the data to have a new line for each date (YYYY-MM-DD format). I am trying to work within the tidyverse but cannot seem to get something that will parse the file correctly. Is there a way to force lines to be delimited by a date pattern? None of these work: library(tidyverse) Log_File <- read

问题 I have defined a list of expressions containing arguments I want to pass to a dplyr::filter call. library(tidyverse) # using tidyr 1.0.0 cond_filter <- list(expr(1 > 0), # condition to select all rows expr(Species == "setosa"), expr(Species != "virginica")) I further have a data frame that I put into a list-column and which I then expand by the number of filter expressions in said list. iris_nest <- iris %>% nest(data = everything()) %>% expand_grid(., filters = cond_filter) In a last step I

问题 How would you merge values between rows that have identical values in id_3 ? I'm sure there's a better name for the question title but I'm struggling to find the appropriate operation/function name(s) for this procedure. library(tidyverse) id_1 <- c("x12", NA, "a_bc", NA) id_2 <- c(NA, "gye", NA, "ab_c") id_3 <- c("qwe", "ert", "abc", "abc") param_1 <- c(0.21, 1.5, 0.23, NA) param_12 <- c(0.05, 4.4, NA, 6.3) df <- data.frame(id_1, id_2, id_3, param_1, param_12) as_tibble(df) # id_1 id_2 id_3

问题 To illustrate what I'm trying to do, I'm using diamond dataset as an example. After group_by(cut), I want to do lm on each group, depending on the mean depth of each group, and then save the model in the dataframe. diamonds %>% group_by(cut) %>% mutate(mean.depth=mean(depth)) %>% {if (.$mean.depth>60) do(model=lm(price~x, data=.)) else do(model=lm(price~y, data=.))} This is what I got: Error in function_list[[k]](value) : object 'mean.depth' not found Spent an hour to fix it but failed.

问题 I've identified, if not myself created, a difficult bug to resolve in some nice code received from a generous respondent here on StackOverflow a few weeks ago, and I could use some new assistance today. Sample data (called object eh below): ID 2013-03-20 2013-04-09 2013-04-11 2013-04-17 2013-04-25 2013-05-15 2013-05-24 2013-05-25 2013-05-26 5167f 0 0 0 0 0 0 0 0 0 1214m 0 0 0 0 0 0 0 0 0 1844f 0 0 0 0 0 0 0 0 0 2113m 0 0 0 0 0 0 0 0 0 2254m 0 0 0 0 0 0 0 0 0 2721f 0 0 0 0 0 0 0 0 0 3121f 0 0

问题 allcsvs = list.files(pattern = "*.csv$", recursive = TRUE) library(tidyverse) ##LOOP to redact the snow data csvs## for(x in 1:length(allcsvs)) { df = read.csv(allcsvs[x], check.names = FALSE) newdf = df %>% gather(COL_DATE, SNOW_DEPTH, -PT_ID, -DATE) %>% mutate( DATE = as.Date(DATE,format = "%m/%d/%Y"), COL_DATE = as.Date(COL_DATE, format = "%Y.%m.%d") ) %>% filter(DATE == COL_DATE) %>% select(-COL_DATE) ####TURN DATES UNAMBIGUOUS HERE#### df$DATE = lubridate::mdy(df$DATE) finaldf = merge

问题 I am trying to copy sets of rows into columns using dplyr. Following is my data frame. df <- data.frame( hid=c(1,1,1,1,2,2,2,2,2,3,3,3,3), mid=c(1,2,3,4,1,2,3,4,5,1,2,3,4), tmid=c("010","01010","010","01020", "010","0120","010","010","020", "010","01010","010","01020"), thid=c("010","02020","010","02020", "000","0120","010","010","010", "010","02020","010","02020"), ) It is printed in the following format: > df hid mid tmid thid 1 1 1 010 010 2 1 2 01010 02020 3 1 3 010 010 4 1 4 01020 02020