We can use tidyverse. After grouping by ‘start_end’, ‘id’, create a sequence column ‘ind’ , then spread from ‘long’ to ‘wide’ format library(dplyr) library(tidyr) df %>% group_by(start_end, id) %>% mutate(ind = row_number()) %>% spread(start_end, date) %>% select(start, end) # id start end #* <int> <fctr> <fctr> #1 2 1994-05-01 1996-11-04 #2 4 1979-07-18 NA #3 … Read more
You can use : tidyr::pivot_longer(dat, cols = -id, names_to = c(‘.value’, ‘brand’), names_sep = “_”) # id brand Q1r1 Q1r2 # <chr> <chr> <dbl> <dbl> #1 A pepsi 1 1 #2 A cola 0 0 #3 B pepsi 0 1 #4 B cola 0 1 #5 C pepsi 1 1 #6 C cola 1 1
An option would be to replace the duplicated elements by ‘Letter’ to NA and then in the reshaped data, remove the columns that are all NA library(data.table) out <- dcast(setDT(sample_df)[, lapply(.SD, function(x) replace(x, duplicated(x), NA)), Letter], Letter ~ rowid(Letter), value.var = c(“Number”, “Fruit”)) nm1 <- out[, names(which(!colSums(!is.na(.SD))))] out[, (nm1) := NULL][] # Letter Number_1 Number_2 … Read more
These are a few ways of many to go about it: 1) With library dplyr, you can simply group things and count into the format needed: library(dplyr) other %>% group_by(name) %>% summarise(N = sum(result == ‘N’), Y = sum(result == ‘Y’)) Source: local data frame [4 x 3] name N Y <fctr> <int> <int> 1 … Read more
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.frame(one=1:10) dfcomb <- merge(df,df1) dim(dfcomb) [1] 100 2 #gives … Read more
The complete takes the first argument as ‘data’, followed by the columns to expand. By default, the fill is NA, but we can change it to 0 by specifying it in a list. complete(df, PERSON, FRUIT, fill = list(A=0, B = 0))
This is a good question – my usual repsonse is to use strsplit, then unnest and spread, which is also not super efficient: library(dplyr) library(tidyr) dat %>% mutate(to = strsplit(to, “,”)) %>% unnest(to) %>% group_by(from) %>% mutate(row = row_number()) %>% spread(row, to) Source: local data frame [4 x 5] date from 1 2 3 (time) … Read more
There is a new function complete in the development version of tidyr that does this. df1 %>% complete(itemid, groupid, fill = list(value = 0)) ## itemid groupid value ## 1 1 one 3 ## 2 1 two 0 ## 3 2 one 2 ## 4 2 two 0 ## 5 3 one 1 ## 6 … Read more
Using reshape2 we can dcast from long to wide. As you only want a binary outcome we can unique the data first library(reshape2) si <- unique(studentInfo) dcast(si, formula = StudentID ~ Subject, fun.aggregate = length) # StudentID English History Maths Science #1 1 1 0 1 1 #2 2 0 0 1 0 #3 3 … Read more
You could use lapply(lolz, `length<-`, max(lengths(lolz))) # $a # [1] 2 4 5 2 3 NA NA # # $b # [1] 3 3 2 NA NA NA NA # # $c # [1] 1 1 2 4 5 3 3 # # $d # [1] 1 2 3 1 NA NA NA # # … Read more