counting leading & trailing zeros for every row in a dataframe in R

user_13149216 0
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I am trying to analyse a dataframe where every row represents a timeseries. My df is structured as follows:

df <- data.frame(key = c("10A", "11xy", "445pe"), 
                 Obs1 = c(0, 22, 0),
                 Obs2 = c(10, 0, 0),
                 Obs3 = c(0,  3, 5),
                 Obs4 = c(0, 10, 0)
)

I would now like to create a new dataframe, where every row represents again the key, and the columns consist of the following results:

  1. "TotalZeros": counts the total number of zeros for each row (=key)
  2. "LeadingZeros": counts the number of zeros before the first nonzero obs for each row

This means I would like to receive the following dataframe in the end:

key   TotalZeros   LeadingZeros
10A            3              1
11xy           1              0
445pe          3              2

I managed to count the total number of zeros for each row:

zeroCountDf <- data.frame(key = df$key, TotalNonZeros = rowSums(df ! = 0))

But I am struggling with counting the LeadingZeros. I found how to count the first non-zero position in a vector, but I don't understand how to apply this approach to my dataframe:

vec <- c(0,1,1)
min(which(vec != 0)) # returns 2, meaning the second position is first nonzero value

Can anyone explain how to count leading zeros for every row in a dataframe? I am new to R and thankful for any insight and tips. Thanks in advance.

zero dataframe r
3 Answers

Edit Added Miff's comment to the solution.

Here is a tidyverse solution:

library(dplyr)
library(tidyr)

df %>% 
  pivot_longer(starts_with("Obs"),
               names_pattern = "Obs(\\d+)") %>% 
  arrange(key, as.integer(name)) %>% 
  group_by(key) %>% 
  summarize(
    leading_zeros = sum(cumsum(abs(value)) == 0),
    total_zeros   = sum(value == 0),
    trailing_zeros = sum(cumsum(abs(value)) == last(cumsum(abs(value)))) - 1)

This returns

# A tibble: 3 x 4
  key   leading_zeros total_zeros trailing_zeros
  <chr>         <int>       <int>          <dbl>
1 10A               1           3              2
2 11xy              0           1              0
3 445pe             2           3              1
user_125052510

We could use rowCumsums from matrixStats along with rowSums

library(matrixStats)
cbind(df[1], total_zeros = rowSums(df[-1] == 0), 
     Leading_zeros = rowSums(!rowCumsums(df[-1] != 0)))

-output

     key total_zeros Leading_zeros
1   10A           3              1
2  11xy           1              0
3 445pe           3              2

or in tidyverse, we may also use rowwise

library(dplyr)
df %>% 
   mutate(total_zeros = rowSums(select(., starts_with("Obs")) == 0)) %>%
   rowwise %>%
   transmute(key, total_zeros,
       Leading_zeros = sum(!cumsum(c_across(starts_with('Obs')) != 0))) %>%
      ungroup

-output

# A tibble: 3 x 3
  key   total_zeros Leading_zeros
  <chr>       <dbl>         <int>
1 10A             3             1
2 11xy            1             0
3 445pe           3             2
user_37322710

A data.table option

setDT(df)[
  , .(
    total_zeros = rowSums(.SD == 0),
    Leading_zeros = which.max(.SD != 0) - 1,
    Trailing_zeros = length(.SD)-max(which(.SD!=0)) 
  ),
  key
]

gives

     key total_zeros Leading_zeros Trailing_zeros
1:   10A           3             1              2
2:  11xy           1             0              0
3: 445pe           3             2              1
user_121587570
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