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
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
tidyr has some great tools for these sorts of problems. Take a look at complete. library(dplyr) library(tidyr) library(lubridate) want <- df.missing %>% ungroup() %>% complete(nesting(d1, d2), date = seq(min(date), max(date), by = “day”)) want %>% filter(d1 == “A” & d2 == 5) #> # A tibble: 10 x 5 #> d1 d2 date v1 v2 … Read more
I haven’t figured out how to put the coalesce_by_column function inside the dplyr pipeline, but this works: coalesce_by_column <- function(df) { return(coalesce(df[1], df[2])) } df %>% group_by(A) %>% summarise_all(coalesce_by_column) ## A B C D E ## <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 1 2 3 2 5 ## 2 2 4 5 3 4 … Read more
You can try : tidyr::pivot_longer(df, cols = -ID_IE, names_to = c(‘.value’, ‘grade’), names_pattern = ‘(.*)(\\d+)’) # A tibble: 8 x 4 # ID_IE grade BLS_tchrG ELS_tchrG # <dbl> <chr> <dbl> <dbl> #1 2135 2 1 1 #2 2135 7 1 1 #3 2101 2 0 0 #4 2101 7 2 0 #5 2103 2 0 … Read more
The following will convert all non-zero numeric values to 1: df.richness %>% mutate_if(is.numeric, ~1 * (. != 0)) while df.richness %>% mutate_if(is.numeric, ~1 * (. > 0)) will do that with those greater than zero.