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
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