[R] Need fresh eyes to see what I'm missing

[Rich Shepard]

The data file begins this way:
year,month,day,hour,min,fps
2016,03,03,12,00,1.74
2016,03,03,12,10,1.75
2016,03,03,12,20,1.76
2016,03,03,12,30,1.81
2016,03,03,12,40,1.79
2016,03,03,12,50,1.75
2016,03,03,13,00,1.78
2016,03,03,13,10,1.81

The script to process it:
library('tidyverse')
vel <- read.csv('../data/water/vel.dat', header = TRUE, sep = ',', stringsAsFactors = FALSE)
vel$year <- as.integer(vel$year)
vel$month <- as.integer(vel$month)
vel$day <- as.integer(vel$day)
vel$hour <- as.integer(vel$hour)
vel$min <- as.integer(vel$min)
vel$fps <- as.double(vel$fps, length = 6)

# use dplyr to filter() by year, month, day; summarize() to get monthly
# means
vel_by_month = vel %>%
     group_by(year, month) %>%
     summarize(flow = mean(fps, na.rm = TRUE))

R's display after running the script:
> source('vel.R')
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
Warning messages:
1: In eval(ei, envir) : NAs introduced by coercion
2: In eval(ei, envir) : NAs introduced by coercion
3: In eval(ei, envir) : NAs introduced by coercion

The dataframe created by the read.csv() command:
> head(vel)
   year month day hour min  fps
1 2016     3   3   12   0 1.74
2 2016     3   3   12  10 1.75
3 2016     3   3   12  20 1.76
4 2016     3   3   12  30 1.81
5 2016     3   3   12  40 1.79
6 2016     3   3   12  50 1.75

and the resulting grouping:
> vel_by_month
# A tibble: 67 × 3
# Groups:   year [8]
     year month   flow
    <int> <int>  <dbl>
  1     0    NA NaN
  2  2016     3   2.40
  3  2016     4   3.00
  4  2016     5   2.86
  5  2016     6   2.51
  6  2016     7   2.18
  7  2016     8   1.89
  8  2016     9   1.38
  9  2016    10   1.73
10  2016    11   2.01
# … with 57 more rows

I cannot find why line 1 is there. Other data sets don't produce this
result.

TIA,

Rich