Cumulative percentages in R

Question

I have the following data frame

d2
# A tibble: 10 x 2
  ID Count
<int> <dbl>
  1     1
  2     1
  3     1
  4     1
  5     1
  6     2
  7     2
  8     2
  9     3
 10     3

Which states how many counts each person (ID) had.

I would like to calculate the cumulative percentage of each count: 1 - 50%, up to 2: 80%, up to 3: 100%. I tried

> d2 %>% mutate(cum = cumsum(Count)/sum(Count))
# A tibble: 10 x 3
  ID   Count     cum
 <int> <dbl>    <dbl>
   1     1   0.05882353
   2     1   0.11764706
   3     1   0.17647059
   4     1   0.23529412
   5     1   0.29411765
   6     2   0.41176471
   7     2   0.52941176
   8     2   0.64705882
   9     3   0.82352941
  10     3   1.00000000

but this result is obviously incorrect because I would expect that the count of 1 would correspond to 50% rather than 29.4%.

What is wrong here? How do I get the correct answer?

I'm not sure why question is down-voted while answers on question were getting up-voted. Probably appropriate comments should be added for down-votes. — MKR, Mar 29 '18 at 09:32
The question fails to make clear what the actual result is supposed to look like and does not provide example data in an easy-to-use form. — LAP, Mar 29 '18 at 09:57
I dont know what the result should look like, as I wrote above I know what to expect for 50%, 80% and 100% — Omry Atia, Mar 29 '18 at 10:20

akrun · Accepted Answer · 2018-03-29T08:25:56.610

7

We get the count of 'Count', create the 'Cum' by taking the cumulative sum of 'n' and divide it by the sum of 'n', then right_join with the original data

d2 %>% 
 count(Count) %>% 
 mutate(Cum = cumsum(n)/sum(n)) %>% 
 select(-n) %>% 
 right_join(d2) %>%
 select(names(d2), everything())
# A tibble: 10 x 3
#      ID Count   Cum
#   <int> <int> <dbl>
# 1     1     1 0.500
# 2     2     1 0.500
# 3     3     1 0.500
# 4     4     1 0.500
# 5     5     1 0.500
# 6     6     2 0.800
# 7     7     2 0.800
# 8     8     2 0.800
# 9     9     3 1.00 
#10    10     3 1.00

If we need the output as @LAP mentioned

d2 %>%
   mutate(Cum = row_number()/n())
#   ID Count Cum
#1   1     1 0.1
#2   2     1 0.2
#3   3     1 0.3
#4   4     1 0.4
#5   5     1 0.5
#6   6     2 0.6
#7   7     2 0.7
#8   8     2 0.8
#9   9     3 0.9
#10 10     3 1.0

edited Mar 29 '18 at 08:25

answered Mar 29 '18 at 08:07

akrun

874,273
37
540
662

The result of this is c(0.2000000 0.4000000 0.6000000 0.8000000 1.0000000 0.6666667 1.3333333 2.0000000 1.5000000 3.0000000), when it should end in 1, and be monotonically increasing – Omry Atia Mar 29 '18 at 08:09
Op wants to count 0.1 up for every row, as in treating every row as 10% of the data, regardless of the value of `Count`. – LAP Mar 29 '18 at 08:10
I'm pretty sure OP just wants something like `0.1, 0.2, 0.3, ..., 1.0` as the cumulative vector. – LAP Mar 29 '18 at 08:15

score 3 · Answer 2 · answered Mar 29 '18 at 08:17

3

This works:

d2 %>%
  mutate(cum = cumsum(rep(1/n(), n())))

   ID Count cum
1   1     1 0.1
2   2     1 0.2
3   3     1 0.3
4   4     1 0.4
5   5     1 0.5
6   6     2 0.6
7   7     2 0.7
8   8     2 0.8
9   9     3 0.9
10 10     3 1.0

answered Mar 29 '18 at 08:17

LAP

6,605
2
15
28

In the OP's post `I would expect that the count of 1 would correspond to 50% rather than 29.4%`. – akrun Mar 29 '18 at 08:18
Yes, because in his attempt all `1` summed up to `0.29` (line 5 of his output) instead of `0.5` (line 5 of my output). – LAP Mar 29 '18 at 08:20

score 1 · Answer 3 · answered Mar 29 '18 at 08:15

One option could be as:

library(dplyr)
d2 %>%
  group_by(Count) %>%
  summarise(proportion = n()) %>%
  mutate(Perc = cumsum(100*proportion/sum(proportion))) %>%
  select(-proportion)
# # A tibble: 3 x 2
# Count  Perc
# <int> <dbl>
# 1     1  50.0
# 2     2  80.0
# 3     3 100.0

Cumulative percentages in R

3 Answers3