tidyr

问题 I have a data frame that contains a date column that is in integer type. I also want to divide price in range of 10,000 and then count frequency which falls in that month > df date values price 11/25/18 a 10000 11/30/18 b 30500 12/4/18 a 20000 12/5/18 b 65000 12/5/18 a 50000 12/6/18 b 35000 12/6/18 c 40000 12/6/18 a 45000 12/6/18 a 30000 12/7/18 b 80000 12/7/18 c 85000 12/7/18 a 90000 12/9/18 b 20000 12/12/18 a 32500 12/12/18 c 40200 12/13/18 b 56000 1/9/19 a 82000 1/9/19 c 63000 1/9/19 b

问题 library(tidyverse) iris %>% as_tibble() %>% select(everything()) #> # A tibble: 150 x 5 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <fct> #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa #> 7 4.6 3.4 1.4 0.3 setosa #> 8 5 3.4 1.5 0.2 setosa #> 9 4.4 2.9 1.4 0.2 setosa #> 10 4.9 3.1 1.5 0.1 setosa #> # ... with 140 more rows Say I want

问题 I have a long form dataframe that have multiple entries for same date and person. jj <- data.frame(month=rep(1:3,4), student=rep(c("Amy", "Bob"), each=6), A=c(9, 7, 6, 8, 6, 9, 3, 2, 1, 5, 6, 5), B=c(6, 7, 8, 5, 6, 7, 5, 4, 6, 3, 1, 5)) I want to convert it to wide form and make it like this: month Amy.A Bob.A Amy.B Bob.B 1 2 3 1 2 3 1 2 3 1 2 3 My question is very similar to this. I have used the given code in the answer : kk <- jj %>% gather(variable, value, -(month:student)) %>% unite(temp

问题 I have a data like this n <- 1e5 set.seed(24) df1 <- data.frame(query_string = sample(sprintf("%06d", 100:1000), n, replace=TRUE), id.x = sample(1:n), s_val = sample(paste0("F", 400:700), n, replace=TRUE), id.y = sample(100:3000, n, replace=TRUE), ID_col_n = sample(100:1e6, n, replace=TRUE), total_id = 1:n) I use the spread function to assign common strings using the following function library(tidyr) res <- spread(resNik,s_val,value=query_string,fill=NA) This works perfectly but when the data

问题 I'm using tidyr::complete() to include missing rows in a data frame with many columns, leading to NAs values. How can I instruct the fill option to replace the NA values with 0 if I don't have an explicit list of column names? Example: df <- data.frame(year = c(2010, 2013:2015), age.21 = runif(4, 0, 10), age.22 = runif(4, 0, 10), age.23 = runif(4, 0, 10), age.24 = runif(4, 0, 10), age.25 = runif(4, 0, 10)) # replaces missing values with NA - not what I want df.complete <- complete(df, year =

问题 I am struggling with renumbering result from intersection/ group_indices in R for a few days. Sample data frame is shown below: t <- data.frame(mid=c(102,102,102,102,102,102,102,103,103,103,103,103,103,103), aid=c(10201,10202,10203,10204,10205,10206,10207, 10301,10302,10303,10304,10305,10306,10307), dummy=c(0,1,0,1,0,1,0,0,1,0,1,0,1,0), location=c(0,2,0,4,0,1,0,0,2,0,2,0,3,0) ) I need to update numbers stored in "location" fiels to sequential number by a group of "mid" without changing its

问题 Suppose I have a data frame like this df <- data.frame(x=c("This script outputs 10 visualizations.", "This script outputs 1 visualization.", "This script outputs 5 data files.", "This script outputs 1 data file.", "This script doesn't output any visualizations or data files", "This script outputs 9 visualizations and 28 data files.", "This script outputs 1 visualization and 1 data file.")) It looks like this x 1 This script outputs 10 visualizations. 2 This script outputs 1 visualization. 3

问题 I have a dataframe that contains a column of ragged data: "topics" where each topic is a string of characters, and adjacent topics are separated from each other by a delimiter ("|" in this case): library(lubridate) events <- data.frame( date =dmy(c( "12/6/2012", "13/7/2012", "4/8/2012")), days = c( 1, 6, 0.5), name = c("Intro to stats", "Stats Winter school", "TidyR tools"), topics= c( "probability|R", "R|regression|ggplot", "tidyR|dplyr"), stringsAsFactors=FALSE ) The events dataframe looks

问题 I have some data grouped by let like so: events <- structure(list(let = c("A", "A", "A", "B", "B", "B"), age = c(0L, 4L, 16L, 0L, 8L, 7L), value = c(61L, 60L, 13L, 29L, 56L, 99L)), class = "data.frame", row.names = c("1", "2", "3", "4", "5", "6")) let age value 1 A 0 61 2 A 4 60 3 A 16 13 4 B 0 29 5 B 8 56 6 B 7 99 How can I cast the data frame so that: Age is multiple columns grouped into weeks. So for each column, take the value of the largest age that is less than or equal to 0, 7, 14, etc

问题 I have a data frame like this: X <- data.frame(value = c(1,2,3,4), variable = c("cost", "cost", "reed_cost", "reed_cost")) I'd like to split the variable column into two; one column to indicate if the variable is a 'cost' and another column to indicate whether or not the variable is "reed". I cannot seem to figure out the right regex for the split (e.g. using tidyr) If my data were something nicer, say: Y <- data.frame(value = c(1,2,3,4), variable = c("adjusted_cost", "adjusted_cost", "reed