r - how can I find percentage of similar string within and across various columns -

i have data below

df<-structure(list(v1 = structure(c(5l, 1l, 7l, 3l, 2l, 4l, 6l, 6l ), .label = c("cpsiaaaiaavnalhgr", "dlnycfsgmsdhr", "fpehelivdpqr",  "iadpdavkpddwdedapsk", "lwadhgvqacfgr", "wgeagaeyvvestgvfttmek",  "yyvtiidapghr"), class = "factor"), v2 = structure(c(5l, 2l,  7l, 3l, 4l, 6l, 1l, 1l), .label = c("", "cpsiaaaiaavnalhgr",  "gcitiigggdtatccak", "hvgpgvlsmanagpntngsqffictik", "llelgpkpevaqqtr",  "mvccsawsedhpicnlftcgfdr", "yyvtiidapghr"), class = "factor"),      v3 = structure(c(4l, 3l, 2l, 4l, 3l, 1l, 1l, 1l), .label = c("",      "avcmlsnttaiaeawar", "dlnycfsgmsdhr", "fpehelivdpqr"), class = "factor")), .names = c("v1",  "v2", "v3"), class = "data.frame", row.names = c(na, -8l)) 

i want know , how many of strings shared in each column

for example

cpsiaaaiaavnalhgr          1,2        yyvtiidapghr               1,2 wgeagaeyvvestgvfttmek      1,1 fpehelivdpqr               1,3 dlnycfsgmsdhr              1,3 dlnycfsgmsdhr              1,3 

it means first string cpsiaaaiaavnalhgr repeated in first column , second column. yyvtiidapghr repetaed in first column , second column . etc etc

and give me percentage first column has 8 rows, among these 8 rows, shares 2 rows column 2 has 2/8*100 = 25% share first third share 3/8*100 = 37% column 2 column 3 shares 0% etc etc

i feel number of common stings after removing duplicates each row (i.e considering unique strings match) more useful. so, i'm updating solution , i'm adding more code venn diagram.

library(dplyr) library(tidyr) library(gplots)  # reshape dataset df_reshaped = df %>%    gather(column, string) %>%    filter(string != '') %>%   distinct()  # dataset shows strings , in columns appear df_result1 = df_reshaped %>%    group_by(string) %>%    summarise(columns = paste(unique(column), collapse=","))  df_result1   # # tibble: 12 x 2 #                        string columns #                         <chr>   <chr> # 1           avcmlsnttaiaeawar      v3 # 2           cpsiaaaiaavnalhgr   v1,v2 # 3               dlnycfsgmsdhr   v1,v3 # 4                fpehelivdpqr   v1,v3 # 5           gcitiigggdtatccak      v2 # 6 hvgpgvlsmanagpntngsqffictik      v2 # 7         iadpdavkpddwdedapsk      v1 # 8             llelgpkpevaqqtr      v2 # 9               lwadhgvqacfgr      v1 # 10     mvccsawsedhpicnlftcgfdr     v2 # 11       wgeagaeyvvestgvfttmek     v1 # 12                yyvtiidapghr  v1,v2   # function number of common rows f1 = function(v1, v2) {   x1 = (df_reshaped %>% filter(column == v1))$string   x2 = (df_reshaped %>% filter(column == v2))$string   length(x2[x2 %in% x1]) } f1 = vectorize(f1)   # function number of rows of each column f2 = function(v) {df_reshaped %>% filter(column == v) %>% nrow} f2 = vectorize(f2)  # dataset shows overlap of columns (number of common strings) expand.grid(unique(df_reshaped$column), unique(df_reshaped$column)) %>%   filter(var1 != var2) %>%   mutate(numshared = f1(var1, var2),          numrows = f2(var1),          prc = numshared/numrows) %>%   arrange(var1, var2)  #   var1 var2 numshared numrows       prc # 1   v1   v2         2       7 0.2857143 # 2   v1   v3         2       7 0.2857143 # 3   v2   v1         2       6 0.3333333 # 4   v2   v3         0       6 0.0000000 # 5   v3   v1         2       3 0.6666667 # 6   v3   v2         0       3 0.0000000   # reshape dataset , create venn diagram df_reshaped %>%   mutate(exist = true) %>%   spread(column, exist, fill=false) %>%   select(-string) %>%   venn() 

the venn diagram looks like:

obviously, sum of numbers shown in diagram should equal number of unique strings in table df_result1. 12 in our case.