plyr

问题 I have a df : dput(head(data)) structure(list(company_code = c(1L, 1L, 1L, 1L, 1L, 11L, 11L, 11L, 12L, 13L, 13L), company_name = c("AB Billingsfors-Långed", "AB Billingsfors-Långed", "AB Billingsfors-Långed", "AB Billingsfors-Långed", "AB Billingsfors-Långed", "AB Iggesunds B", "AB Iggesunds B", "AB Iggesunds B", "AB Industripapp", "AB Klippans FinpB", "AB Klippans FinpB" ), year_cg_code = c(11920L, 11920L, 11920L, 11920L, 11920L, 111929L, 111929L, 111929L, 121929L, 131929L, 131929L), plant

问题 I have this dataset df=structure(list(Dt = structure(1:39, .Label = c("2018-02-20 00:00:00.000", "2018-02-21 00:00:00.000", "2018-02-22 00:00:00.000", "2018-02-23 00:00:00.000", "2018-02-24 00:00:00.000", "2018-02-25 00:00:00.000", "2018-02-26 00:00:00.000", "2018-02-27 00:00:00.000", "2018-02-28 00:00:00.000", "2018-03-01 00:00:00.000", "2018-03-02 00:00:00.000", "2018-03-03 00:00:00.000", "2018-03-04 00:00:00.000", "2018-03-05 00:00:00.000", "2018-03-06 00:00:00.000", "2018-03-07 00:00:00

问题 I have dataset mydat=structure(list(code = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "52382МСК", class = "factor"), item = c(11709L, 11709L, 11709L, 11709L, 1170L, 1170L, 1170L, 1170L), sales = c(30L, 10L, 20L, 15L, 8L, 10L, 2L, 15L), action = c(0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L)), .Names = c("code", "item", "sales", "action" ), class = "data.frame", row.names = c(NA, -8L)) it has two groups by code and item code item 52382МСК 11709 52382МСК 1170 Also i have action column. It can

问题 Sample data: Date <- as.Date(c('1-01-2008','2-01-2008', '3-01-2008','4-01-2008', '5-01-2008', '1-01-2008','2-01-2008', '3-01-2008','4-01-2008', '5-01-2008'), format = "%m-%d-%Y") Country <- c('US', 'US','US','US', 'US', 'JP', 'JP', 'JP', 'JP', 'JP') Category <- c('Apple', 'Apple', 'Apple', 'Apple', 'Apple', 'Apple', 'Apple', 'Apple', 'Apple', 'Apple') Value <- c(runif(10, -0.5, 10)) df <- data.frame(Date, Country, Category, Value) I am using the following piece to calculate the lagged growth

问题 This question already has answers here : How to join (merge) data frames (inner, outer, left, right) (13 answers) Closed 4 years ago . What I would like to do is combine 2 dataframes, keeping all columns (which is not done in the example below) and input zeros where there are gaps in the dataframe from uncommon variables. This seems like a plyr or dplyr theme. However, a full join in plyr does not keep all of the columns, whilst a left or a right join does not keep all the rows I desire.

问题 This question is in the context where I have a lot Model types, each of the same class, but the amount of data for each Model is small and I want to spline to get a fuller dataset. I'm hoping to find a way to do this without having to individually spline every Model once at a time. So I have the following df: mydf<- data.frame(c("a","a","b","b","c","c"),c("e","e","e","e","e","e") ,as.numeric(c(1,2,3,10,20,30)), as.numeric(c(5,10,20,20,15,10))) Give some names: colnames(mydf)<-c("Model",

问题 I would like some help coloring a ggplot2 histogram generated from summarized data in a data.frame. The dataset I'm using is the [R] build in (USArrests) dataset. I'm trying to adapt the solution that was given to this question by arun. The desired result is to make a histogram of "Crime" and color each bar according to the relative contribution of c("Assault", "Rape", "Murder"). The code: attach(USArrests) #Create vector SUM arrests per state Crime <- with(USArrests, Murder+ Rape+ Assault)

问题 I have the below data frame where I am trying to merge multiple transactions of the one customer into one single record. Input: ST_DATE ND_DATE LO_NO ACTV_CODE ACTV_AMT AB_NO FEATURE_CODE L_NU 7/27/16 7/27/16 265 O 15 1 INTEREST 855 7/27/16 7/27/16 265 O 14 1 INSTALLMENT 855 Expected Output: ST_DATE ND_DATE LO_NO ACTV_CODE ACTV_AMT AB_NO FEATURE_INTEREST FEATURE_INSTALLMENT L_NU 7/27/16 7/27/16 265 O 29 1 1 1 855 Tried: install1 <- install %>% group_by(LO_NO,AB_NO,L_NU) %>% slice(which.min(as

问题 I have a series of annual incident counts per category, with no rows for years in which the category did not see an incident. I would like to add a column that shows, for each year, how many incidents occurred in the previous three years. One way to handle this is to add empty rows for all years with zero incidents, then use rollapply() with a left-aligned four year window, but that would expand my data set more than I want to. Surely there's a way to use ddply() and transform for this? The

问题 This question already has answers here : Aggregate / summarize multiple variables per group (e.g. sum, mean) (6 answers) Closed 3 years ago . I have the following dataframe for which I want merge together binary values from an amount of rows. df =data.frame(ID=c(rep("A",5),rep("B",5)), nr=c(rep("2",5),rep("3",5)), replicate(10,sample(0:1,10,rep=TRUE))) eg: # ID nr X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 # A 2 0 0 1 1 1 1 1 1 1 0 # A 2 1 0 0 0 0 0 0 1 0 1 # A 2 0 0 1 1 1 0 0 0 0 1 # A 2 0 0 0 0 0 1 1 1