data.table

问题 While preparing this answer, I've got the error message Error: value for ‘spine_hlfs’ not found from running setDT(giraffe)[rbindlist(mget(df_names), idcol = "df.name"), on = "runkey", project := df.name][] while df_list <- mget(df_names) setDT(giraffe)[rbindlist(df_list, idcol = "df.name"), on = "runkey", project := df.name][] works as expected. Before reporting an issue on github, I want to verify with the community that this indeed is a bug or if there is a simple explanation for the error

问题 Why in the code below dplyr's filter doesn't return the same data.frame as base R subsetting? In fact none of them works as expected. I'd like to remove observations/rows which, simultaneously, b==1 AND c==1 . That is, I'd like to remove only the third row. require(dplyr) df <- data.frame(a=c(0,0,0,0,1,1,1), b=c(0,0,1,1,0,0,1), c=c(1,NA,1,NA,1,NA,NA)) filter(df, !(b==1 & c==1)) df[!(df$b==1 & df$c==1),] 回答1: Or use complete.cases to convert NA to FALSE in the result logic vector so that you

问题 Why in the code below dplyr's filter doesn't return the same data.frame as base R subsetting? In fact none of them works as expected. I'd like to remove observations/rows which, simultaneously, b==1 AND c==1 . That is, I'd like to remove only the third row. require(dplyr) df <- data.frame(a=c(0,0,0,0,1,1,1), b=c(0,0,1,1,0,0,1), c=c(1,NA,1,NA,1,NA,NA)) filter(df, !(b==1 & c==1)) df[!(df$b==1 & df$c==1),] 回答1: Or use complete.cases to convert NA to FALSE in the result logic vector so that you

问题 Why in the code below dplyr's filter doesn't return the same data.frame as base R subsetting? In fact none of them works as expected. I'd like to remove observations/rows which, simultaneously, b==1 AND c==1 . That is, I'd like to remove only the third row. require(dplyr) df <- data.frame(a=c(0,0,0,0,1,1,1), b=c(0,0,1,1,0,0,1), c=c(1,NA,1,NA,1,NA,NA)) filter(df, !(b==1 & c==1)) df[!(df$b==1 & df$c==1),] 回答1: Or use complete.cases to convert NA to FALSE in the result logic vector so that you

问题 I have a data.table : groups <- data.table(group = c("A", "B", "C", "D", "E", "F", "G"), code_1 = c(2,2,2,7,8,NA,5), code_2 = c(NA,3,NA,3,NA,NA,2), code_3 = c(4,1,1,4,4,1,8)) group code_1 code_2 code_3 A 2 NA 4 B 2 3 1 C 2 NA 1 D 7 3 4 E 8 NA 4 F NA NA 1 G 5 2 8 What I would like to achieve, is for each group to find the immediate neighbors based on the available codes. For example: Group A has immediate neighbors groups B, C due to code_1 (code_1 is equal to 2 in all groups) and has

问题 I have a dataframe which has multiple values in a single file. I want to divide it into multiple files around 25 from the file. Pattern for the file is where there is one blank row and a header title is there , it is a new df. I Have tried this Splitting dataframes in R based on empty rows but this does not take care of any blank row within the new df (V1 column 9th row). I want the data to be divided on empty row and a header title my data and code i have tried is given below . Also how can

问题 I have a dataframe which has multiple values in a single file. I want to divide it into multiple files around 25 from the file. Pattern for the file is where there is one blank row and a header title is there , it is a new df. I Have tried this Splitting dataframes in R based on empty rows but this does not take care of any blank row within the new df (V1 column 9th row). I want the data to be divided on empty row and a header title my data and code i have tried is given below . Also how can

问题 I get an error message when I want to convert multiple columns from wide to long with pivot_longer I have code which converts from wide to long with gather but I have to do this column by column. I want to use pivot_longer to gather multiple columns rather than column by column. This is some input data: structure(list(id = c("81", "83", "85", "88", "1", "2"), look_work = c("yes", "yes", "yes", "yes", "yes", "yes"), current_work = c("no", "yes", "no", "no", "no", "no"), before_work = c("no",

问题 This is a follow up question to Subset by group with data.table using the same data.table: library(data.table) bdt <- as.data.table(baseball) # Aggregating and loosing information on other columns dt1 <- bdt[ , .(max_g = max(g)), by = id] # Aggregating and keeping information on other columns dt2 <- bdt[bdt[, .I[g == max(g)], by = id]$V1] Why do dt1 and dt2 differ in number of rows? Isn't dt2 supposed to have the same result just without loosing the respective information in the other columns

问题 I need to insert Column Name, Department, into its value. i have code like here: Department <- c("Store1","Store2","Store3","Store4","Store5") Department2 <- c("IT1","IT2","IT3","IT4","IT5") x <- c(100,200,300,400,500) Result <- data.frame(Department,Department2,x) Result The expected result is like: Department <- c("Department_Store1","Departmentz_Store2","Department_Store3","Department_Store4","Department_Store5") Department2 <- c("Department2_IT1","Department2_IT2","Department2_IT3",