Calculating seasonal index from tbats components - r

I have aggregated retail weekly data with seasonal periods of 52.2 (a 53rd week every five years). I want to use this aggregated data to calculate a seasonal index that can be applied to each item within the category to derive its de-seasonalised demand.
Using stl, I would calculate the seasonal index as "seasonal" / "trend" + 1 (normalised to 52). I switched to tbats because my seasonality was not an integer and I have multiple seasonal periods (52.2 and 261)
I am using tbats with seasonal.periods = 52.2 and extract the components using tbats.components. The components are "observed", "level" and "season". Google has not revealed much in terms of what these components are and how to consume them. I also extracted the residuals
I noticed that "observed" is the log of my data. I also notice that season is changing over time (which is exactly what I want)
My questions are:
1.Is "season" a natural log too?
2.How can I extract the future "season" values? I can run a forecast on the data so I am assuming that there must be a projected "season"
3. What would be the best approach to calculating an "index" considering that it will be divided into the granular data. I am currently using: exp("season") / centered moving average(exp("season"))
My Data:
weeklyu <-structure(list(V1 = c(8L, 5L, 7L, 3L, 1L, 2L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 8L, 2L,
4L, 8L, 6L, 7L, 8L, 9L, 15L, 15L, 13L, 9L, 16L, 19L, 16L, 16L,
10L, 31L, 45L, 90L, 185L, 34L, 8L, 19L, 11L, 19L, 21L, 8L, 5L,
7L, 6L, 3L, 10L, 2L, 2L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 16L, 22L, 18L, 23L, 11L, 5L, 8L, 21L, 18L, 11L, 26L,
28L, 9L, 3L, 6L, 3L, 6L, 1L, 5L, 3L, 3L, 2L, 1L, 4L, 1L, 1L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L,
19L, 11L, 17L, 23L, 50L, 52L, 23L, 18L, 22L, 44L, 37L, 22L, 30L,
32L, 47L, 34L, 30L, 26L, 25L, 44L, 87L, 65L, 30L, 17L, 12L, 2L,
16L, 14L, 17L, 6L, 7L, 3L, 6L, 7L, 8L, 11L, 12L, 4L, 1L, 3L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L)), .Names = "V1", class = "data.frame", row.names = c(NA,
-188L))
My Code:
wklytbat <- tbats(msts(weeklyu, seasonal.periods = 52.2, ts.frequency=52.2), use.parallel=FALSE)
extract season:
seasu <-data.table(exp(as.numeric(tbats.components(wklytbat)[,'season'])))

Related

Editing a Row Value in R

I have a data frame that looks like this
Pick Name Team Round Player Position Position..
1 1 Javi Texans 1 Patrick Mahomes QB QB1
2 2 Justin Chiefs 1 Russell Wilson QB QB2
3 3 Blake Titans 1 Lamar Jackson QB QB3
4 4 Connor Dolphins 1 Deshaun Watson QB QB4
5 5 Isaac Jaguars 1 Carson Wentz QB QB5
6 6 Fitz Rams 1 Dak Prescott QB QB6
with more rows of course and some of the rows in the Player, Position and Position... Column are empty because they haven't been drafted yet. Is there a way to just manually insert the names, pos, pos... of the newly drafted players.
I tried
Redraft[112, "Player"] <- "Calvin Ridley"; Redraft
Since the empty cells start on row 112, but it just came up as N/A
When I do that I also get an error message:
Warning message:
In `[<-.factor`(`*tmp*`, iseq, value = "Calvin Ridley") :
invalid factor level, NA generated
and the data frame looks like
08 108 Jack Packers 4 TE3 Darren Waller TE
109 109 Justin Saints 4 LT6 Taylor Lewan LT
110 110 Sam Steelers 4 FS5 Kevin Byard FS
111 111 Jeremy Falcons 4 LB7 Isaiah Simmons LB
112 112 Will Bills 4 1 <NA>
113 113 Jeremy Colts 4 1
And heres the whole data frame:
structure(list(Pick = 1:384, Name = structure(c(12L, 14L, 1L,
2L, 7L, 5L, 8L, 6L, 9L, 12L, 9L, 2L, 10L, 16L, 11L, 13L, 20L,
13L, 17L, 14L, 8L, 3L, 3L, 19L, 5L, 19L, 7L, 1L, 6L, 4L, 18L,
15L, 15L, 18L, 4L, 6L, 1L, 7L, 19L, 5L, 19L, 3L, 3L, 8L, 14L,
17L, 13L, 20L, 13L, 11L, 16L, 10L, 2L, 9L, 12L, 9L, 6L, 8L, 5L,
7L, 2L, 1L, 14L, 12L, 12L, 14L, 1L, 2L, 7L, 5L, 8L, 6L, 9L, 12L,
9L, 2L, 10L, 16L, 11L, 13L, 20L, 13L, 17L, 14L, 8L, 3L, 3L, 19L,
5L, 19L, 7L, 1L, 6L, 4L, 18L, 15L, 15L, 18L, 4L, 6L, 1L, 7L,
19L, 5L, 19L, 3L, 3L, 8L, 14L, 17L, 13L, 20L, 13L, 11L, 16L,
10L, 2L, 9L, 12L, 9L, 6L, 8L, 5L, 7L, 2L, 1L, 14L, 12L, 12L,
14L, 1L, 2L, 7L, 5L, 8L, 6L, 9L, 12L, 9L, 2L, 10L, 16L, 11L,
13L, 20L, 13L, 17L, 14L, 8L, 3L, 3L, 19L, 5L, 19L, 7L, 1L, 6L,
4L, 18L, 15L, 15L, 18L, 4L, 6L, 1L, 7L, 19L, 5L, 19L, 3L, 3L,
8L, 14L, 17L, 13L, 20L, 13L, 11L, 16L, 10L, 2L, 9L, 12L, 9L,
6L, 8L, 5L, 7L, 2L, 1L, 14L, 12L, 12L, 14L, 1L, 2L, 7L, 5L, 8L,
6L, 9L, 12L, 9L, 2L, 10L, 16L, 11L, 13L, 20L, 13L, 17L, 14L,
8L, 3L, 3L, 19L, 5L, 19L, 7L, 1L, 6L, 4L, 18L, 15L, 15L, 18L,
4L, 6L, 1L, 7L, 19L, 5L, 19L, 3L, 3L, 8L, 14L, 17L, 13L, 20L,
13L, 11L, 16L, 10L, 2L, 9L, 12L, 9L, 6L, 8L, 5L, 7L, 2L, 1L,
14L, 12L, 12L, 14L, 1L, 2L, 7L, 5L, 8L, 6L, 9L, 12L, 9L, 2L,
10L, 16L, 11L, 13L, 20L, 13L, 17L, 14L, 8L, 3L, 3L, 19L, 5L,
19L, 7L, 1L, 6L, 4L, 18L, 15L, 15L, 18L, 4L, 6L, 1L, 7L, 19L,
5L, 19L, 3L, 3L, 8L, 14L, 17L, 13L, 20L, 13L, 11L, 16L, 10L,
2L, 9L, 12L, 9L, 6L, 8L, 5L, 7L, 2L, 1L, 14L, 12L, 12L, 14L,
1L, 2L, 7L, 5L, 8L, 6L, 9L, 12L, 9L, 2L, 10L, 16L, 11L, 13L,
20L, 13L, 17L, 14L, 8L, 3L, 3L, 19L, 5L, 19L, 7L, 1L, 6L, 4L,
18L, 15L, 15L, 18L, 4L, 6L, 1L, 7L, 19L, 5L, 19L, 3L, 3L, 8L,
14L, 17L, 13L, 20L, 13L, 11L, 16L, 10L, 2L, 9L, 12L, 9L, 6L,
8L, 5L, 7L, 2L, 1L, 14L, 12L), .Label = c("Blake", "Connor",
"Dakota", "FFB", "Fitz", "Haydon", "Isaac", "Jack", "Jackson",
"Jacob", "Jacob H", "Javi", "Jeremy", "Justin", "Nick", "Pete",
"Sam", "Simon", "Tucker", "Will"), class = "factor"), Team = structure(c(30L,
10L, 31L, 13L, 17L, 24L, 18L, 6L, 3L, 8L, 7L, 28L, 9L, 21L, 14L,
11L, 4L, 15L, 29L, 27L, 20L, 1L, 25L, 5L, 23L, 26L, 32L, 19L,
12L, 16L, 22L, 2L, 2L, 22L, 16L, 12L, 19L, 32L, 26L, 23L, 5L,
25L, 1L, 20L, 27L, 29L, 15L, 4L, 11L, 14L, 21L, 9L, 28L, 7L,
8L, 3L, 6L, 18L, 24L, 17L, 13L, 31L, 10L, 30L, 30L, 10L, 31L,
13L, 17L, 24L, 18L, 6L, 3L, 8L, 7L, 28L, 9L, 21L, 14L, 11L, 4L,
15L, 29L, 27L, 20L, 1L, 25L, 5L, 23L, 26L, 32L, 19L, 12L, 16L,
22L, 2L, 2L, 22L, 16L, 12L, 19L, 32L, 26L, 23L, 5L, 25L, 1L,
20L, 27L, 29L, 15L, 4L, 11L, 14L, 21L, 9L, 28L, 7L, 8L, 3L, 6L,
18L, 24L, 17L, 13L, 31L, 10L, 30L, 30L, 10L, 31L, 13L, 17L, 24L,
18L, 6L, 3L, 8L, 7L, 28L, 9L, 21L, 14L, 11L, 4L, 15L, 29L, 27L,
20L, 1L, 25L, 5L, 23L, 26L, 32L, 19L, 12L, 16L, 22L, 2L, 2L,
22L, 16L, 12L, 19L, 32L, 26L, 23L, 5L, 25L, 1L, 20L, 27L, 29L,
15L, 4L, 11L, 14L, 21L, 9L, 28L, 7L, 8L, 3L, 6L, 18L, 24L, 17L,
13L, 31L, 10L, 30L, 30L, 10L, 31L, 13L, 17L, 24L, 18L, 6L, 3L,
8L, 7L, 28L, 9L, 21L, 14L, 11L, 4L, 15L, 29L, 27L, 20L, 1L, 25L,
5L, 23L, 26L, 32L, 19L, 12L, 16L, 22L, 2L, 2L, 22L, 16L, 12L,
19L, 32L, 26L, 23L, 5L, 25L, 1L, 20L, 27L, 29L, 15L, 4L, 11L,
14L, 21L, 9L, 28L, 7L, 8L, 3L, 6L, 18L, 24L, 17L, 13L, 31L, 10L,
30L, 30L, 10L, 31L, 13L, 17L, 24L, 18L, 6L, 3L, 8L, 7L, 28L,
9L, 21L, 14L, 11L, 4L, 15L, 29L, 27L, 20L, 1L, 25L, 5L, 23L,
26L, 32L, 19L, 12L, 16L, 22L, 2L, 2L, 22L, 16L, 12L, 19L, 32L,
26L, 23L, 5L, 25L, 1L, 20L, 27L, 29L, 15L, 4L, 11L, 14L, 21L,
9L, 28L, 7L, 8L, 3L, 6L, 18L, 24L, 17L, 13L, 31L, 10L, 30L, 30L,
10L, 31L, 13L, 17L, 24L, 18L, 6L, 3L, 8L, 7L, 28L, 9L, 21L, 14L,
11L, 4L, 15L, 29L, 27L, 20L, 1L, 25L, 5L, 23L, 26L, 32L, 19L,
12L, 16L, 22L, 2L, 2L, 22L, 16L, 12L, 19L, 32L, 26L, 23L, 5L,
25L, 1L, 20L, 27L, 29L, 15L, 4L, 11L, 14L, 21L, 9L, 28L, 7L,
8L, 3L, 6L, 18L, 24L, 17L, 13L, 31L, 10L, 30L), .Label = c("49ers",
"Bears", "Bengals", "Bills", "Broncos", "Browns", "Buccaneers",
"Cardinals", "Chargers", "Chiefs", "Colts", "Cowboys", "Dolphins",
"Eagles", "Falcons", "Giants", "Jaguars", "Jets", "Lions", "Packers",
"Panthers", "Patriots", "Raiders", "Rams", "Ravens", "Redskins",
"Saints", "Seahawks", "Steelers", "Texans", "Titans", "Vikings"
), class = "factor"), Round = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L),
Pos.. = structure(c(49L, 60L, 70L, 71L, 72L, 73L, 74L, 75L,
27L, 76L, 43L, 50L, 51L, 92L, 52L, 53L, 54L, 55L, 77L, 56L,
57L, 58L, 89L, 59L, 85L, 61L, 103L, 3L, 106L, 35L, 62L, 36L,
63L, 42L, 10L, 107L, 18L, 64L, 108L, 65L, 109L, 19L, 11L,
110L, 86L, 37L, 111L, 12L, 20L, 112L, 66L, 21L, 38L, 13L,
90L, 78L, 81L, 30L, 14L, 15L, 82L, 39L, 16L, 17L, 93L, 94L,
4L, 22L, 95L, 96L, 2L, 97L, 67L, 5L, 68L, 87L, 83L, 84L,
6L, 31L, 44L, 98L, 99L, 100L, 7L, 28L, 101L, 32L, 29L, 8L,
33L, 88L, 69L, 79L, 102L, 9L, 104L, 40L, 23L, 24L, 105L,
25L, 45L, 80L, 46L, 26L, 47L, 91L, 48L, 34L, 41L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("1", "C1", "CB1", "CB10", "CB11", "CB12",
"CB13", "CB14", "CB15", "CB2", "CB3", "CB4", "CB5", "CB6",
"CB7", "CB8", "CB9", "DE1", "DE2", "DE3", "DE4", "DE5", "DE6",
"DE7", "DE8", "DE9", "DT1", "DT2", "DT3", "FS1", "FS2", "FS3",
"FS4", "FS5", "LB1", "LB2", "LB3", "LB4", "LB5", "LB6", "LB7",
"LG1", "LT1", "LT2", "LT3", "LT4", "LT5", "LT6", "QB1", "QB10",
"QB11", "QB12", "QB13", "QB14", "QB15", "QB16", "QB17", "QB18",
"QB19", "QB2", "QB20", "QB21", "QB22", "QB23", "QB24", "QB25",
"QB26", "QB27", "QB28", "QB3", "QB4", "QB5", "QB6", "QB7",
"QB8", "QB9", "RB1", "RB2", "RB3", "RB4", "RG1", "RT1", "RT2",
"RT3", "SS1", "SS2", "SS3", "SS4", "TE1", "TE2", "TE3", "WR1",
"WR10", "WR11", "WR12", "WR13", "WR14", "WR15", "WR16", "WR17",
"WR18", "WR19", "WR2", "WR20", "WR21", "WR3", "WR4", "WR5",
"WR6", "WR7", "WR8", "WR9"), class = "factor"), Player = structure(c(87L,
91L, 72L, 38L, 14L, 24L, 79L, 78L, 3L, 57L, 90L, 70L, 107L,
31L, 39L, 10L, 56L, 68L, 20L, 4L, 94L, 93L, 45L, 52L, 51L,
44L, 80L, 97L, 62L, 67L, 40L, 98L, 101L, 89L, 50L, 9L, 85L,
104L, 19L, 41L, 109L, 58L, 106L, 81L, 37L, 26L, 29L, 27L,
18L, 86L, 60L, 84L, 17L, 74L, 105L, 95L, 111L, 63L, 76L,
55L, 92L, 110L, 12L, 15L, 64L, 96L, 34L, 25L, 61L, 103L,
54L, 21L, 53L, 49L, 59L, 108L, 71L, 83L, 77L, 82L, 102L,
7L, 65L, 2L, 69L, 32L, 22L, 75L, 43L, 5L, 8L, 46L, 35L, 42L,
23L, 88L, 6L, 11L, 60L, 48L, 16L, 13L, 30L, 36L, 73L, 33L,
99L, 28L, 100L, 66L, 47L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("", "A.J. Brown",
"Aaron Donald", "Aaron Rodgers", "Adoree' Jackson", "Allen Robinson",
"Amari Cooper", "Anthony Harris", "Antonio Brown", "Baker Mayfield",
"Bobby Wagner", "Byron Jones ", "Cameron Jordan", "Carson Wentz",
"Casey Hayward", "CeDee Lamb", "Chandler Jones", "Chase Young",
"Chris Godwin", "Christian McCaffrey", "Cooper Kupp", "Courtland Sutton",
"D.J. Moore", "Dak Prescott", "Danielle Hunter", "Darius Leonard",
"Darius Slay", "Darren Waller", "DaVante Adams", "David Bakhtiari",
"DeAndre Hopkins", "Deforest Buckner", "Demarcus Lawrence",
"Denzel Ward", "Derek Carr", "Derrick Henry", "Derwin James",
"Deshaun Watson", "Drew Brees", "Drew Lock", "Dwayne Haskins",
"Ezekiel Elliott", "Fletcher Cox", "Gardner Minshew", "George Kittle",
"Harrison Smith", "Isaiah Simmons", "J.J. Watt", "Jaire Alexander",
"Jalen Ramsey", "Jamal Adams", "Jared Goff", "Jarrett Stidham",
"Jason Kelce", "Jeffrey Okudah", "Jimmy Garappolo", "Joe Burrow",
"Joey Bosa", "Jordan Love", "Josh Allen", "Juju Smith-Schuster",
"Julio Jones", "Justin Simmons", "Keenan Allen", "Kenny Golladay",
"Kevin Byard", "Khalil Mack", "Kirk Cousins", "Kyle Fuller",
"Kyler Murray ", "La'el Collins", "Lamar Jackson ", "Laremy Tunsil",
"Marcus Peters", "Marcus Williams", "Marlon Humphrey", "Marshon Lattimore",
"Matt Ryan", "Matthew Stafford", "Michael Thomas", "Mike Evans",
"Minkah Fitzpatrick", "Mitchell Schwartz ", "Myles Garrett",
"Nick Bosa", "Odell Beckham Jr.", "Patrick Mahomes ", "Patrick Peterson",
"Quenton Nelson", "Ronnie Stanley", "Russell Wilson ", "Ryan Ramczyk",
"Ryan Tannehill", "Sam Darnold", "Saquon Barkley", "Stefon Diggs",
"Stephon Gilmore", "T.J. Watt", "Taylor Decker", "Taylor Lewan",
"Teddy Bridgewater", "Terron Armstead", "Terry McLaurin",
"Tom Brady", "Travis Kelce", "Tre White", "Tua Tagovailoa",
"Tyrann Mathieu", "Tyreek Hill", "Von Miller", "Zack Martin"
), class = "factor"), Position = structure(c(10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 5L, 10L, 9L, 10L, 10L, 16L, 10L,
10L, 10L, 10L, 11L, 10L, 10L, 10L, 15L, 10L, 14L, 10L, 16L,
3L, 16L, 7L, 10L, 7L, 10L, 8L, 3L, 16L, 4L, 10L, 16L, 10L,
16L, 4L, 3L, 16L, 14L, 7L, 16L, 3L, 4L, 16L, 10L, 4L, 7L,
3L, 15L, 11L, 12L, 6L, 3L, 3L, 13L, 7L, 3L, 3L, 16L, 16L,
3L, 4L, 16L, 16L, 2L, 16L, 10L, 3L, 10L, 14L, 13L, 13L, 3L,
6L, 9L, 16L, 16L, 16L, 3L, 5L, 16L, 6L, 5L, 3L, 6L, 14L,
10L, 11L, 16L, 3L, 16L, 7L, 4L, 4L, 16L, 4L, 9L, 11L, 9L,
4L, 9L, 15L, 9L, 6L, 7L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("", "C",
"CB", "DE", "DT", "FS", "LB", "LG", "LT", "QB", "RB", "RG",
"RT", "SS", "TE", "WR"), class = "factor")), row.names = c(NA,
384L), class = "data.frame")

You're dealing with a factor column. "Calvin Ridley" isn't yet a level of the factor. After adding it you can rename the cell.
class(Redraft$Player)
# [1] "factor"
levels(Redraft$Player) <- c(levels(Redraft$Player), "Calvin Ridley")
Redraft[112, "Player"] <- "Calvin Ridley"
Redraft[112, "Player"]
# [1] Calvin Ridley
# 112 Levels: A.J. Brown Aaron Donald Aaron Rodgers Adoree' Jackson Allen Robinson ... Calvin Ridley

jay.sf's answer is correct, of course, but I'd add my 2ยข since I think it's missing the point.
The reason you have factors instead of plain strings here in the first place, is kind of a historical accident with R being a statistical language. In practice, you rarely want to be dealing with factors in a dataframe of this kind. You probably want your player names to be plain-old strings.
Typically when you read a dataframe from a file, e.g. via read.csv, you have the option to pass the argument stringsAsFactors = TRUE, to ensure that strings are kept as strings rather than converted to factors. Some people (e.g. this guy) feel so strongly against this bizzare default behaviour, that they include a line in their .Rprofile to make importing data with stringsAsFactors=T as their default. (but this is dangerous for writing code that works the same across users with different .Rprofile initializations!)
If you already have the dataset, you can convert your factors to strings instead:
df[ , 'Player'] <- as.character( df[ , 'Player' ] )
You can now continue with your analysis without worrying about factors and their annoyances.
E.g. setting a new name is as simple as you'd expect:
df[112,'Player'] <- 'Calvin Ridley'

Undesired error in length() when running a glmer() model in R

My dataset looks like this:
> head(GLM_df)
hour Feeding Foraging Standing ID Area Feeding_Foraging
1 0 0.119 0.789 0.0339 41361 Seronera 0.908
2 1 0.0920 0.819 0.0339 41361 Seronera 0.911
3 2 0.0847 0.824 0.0678 41361 Seronera 0.909
4 3 0.233 0.632 0.132 41361 Seronera 0.866
5 4 0.254 0.597 0.124 41361 Seronera 0.852
6 5 0.245 0.664 0.0832 41361 Seronera 0.909
And I'm trying to run a glmer() model as such to verify an interaction, the error associated is found below:
> m <- glmer(cbind(Feeding_Foraging,Standing) ~ poly(hour,2)*Area+(1|ID) , data=GLM_df , family=binomial)
Error in length(value <- as.numeric(value)) == 1L :
(maxstephalfit) PIRLS step-halvings failed to reduce deviance in pwrssUpdate
In addition: Warning message:
In eval(family$initialize, rho) : non-integer counts in a binomial glm!
I apologize if I'm not asking on the right forum, but does somebody know what is the cause of this error? I've been using this dataset to run other glmer() models not having such issue, so I hope somebody can help me.
I can provide a dput() sample of the data below:
> dput(GLM_df)
structure(list(hour = c(0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L,
23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L), Feeding = c(0.118579234700529,
0.0919594065024507, 0.0846994533575204, 0.233092895639896, 0.254098360072561,
0.244523639258233, 0.238513660654777, 0.245289616923379, 0.211748633393801,
0.253514225911475, 0.275555554923133, 0.222477230819087, 0.232641165221989,
0.238368461591879, 0.30265937999754, 0.433661201190504, 0.178745053292422,
0.12125395428024, 0.10605844594333, 0.163238946470857, 0.174611180767811,
0.22483854891269, 0.177868852050793, 0.183918813004901, 0.241998438164344,
0.161698956409812, 0.158105646267371, 0.36138433432542, 0.468670308578279,
0.333151183206247, 0.32072859671381, 0.301413227120555, 0.295571885509692,
0.313952640445209, 0.343315117609149, 0.309435336266141, 0.345573769698683,
0.307176684176607, 0.322987248803344, 0.303788706042306, 0.266520946564997,
0.179710144515087, 0.151781420416677, 0.272293057460473, 0.384777516681307,
0.358157688483229, 0.370418942683556, 0.295571885509692, 0.194038747691774,
0.0980730512560762, 0.104719324151116, 0.287394007254483, 0.360255008280653,
0.356867030146353, 0.303788706042306, 0.297908422154037, 0.295883423728938,
0.309435336266141, 0.335409835295781, 0.294754097684171, 0.329763205071946,
0.311693988355675, 0.252969034027794, 0.320554854245385, 0.269908924699298,
0.114670029160951, 0.145400728263743, 0.208925318281884, 0.252065573191981,
0.343637782193368, 0.234552332374672, 0.25071038193826, 0.139938227286338,
0.127049180036281, 0.0779234970889187, 0.271038250744065, 0.37923497180722,
0.365027321566604, 0.313661201465914, 0.342076501947147, 0.292896174191167,
0.283060108639971, 0.271038250744065, 0.238251365573412, 0.196721311023918,
0.191256830162143, 0.16601092858074, 0.0626775954845651, 0.134426229199678,
0.105704917790185, 0.11195058182907, 0.140192198660723, 0.14806719253611,
0.21262483463543, 0.226733921295516, 0.21891551021636, 0.120612021581109,
0.140939890386914, 0.0931693986932724, 0.2142076497816, 0.228415300022216,
0.194244079699913, 0.181821493207477, 0.186922931547631, 0.153588342088304,
0.15187488188245, 0.135519125372033, 0.171657558804575, 0.144302772386887,
0.113322027250751, 0.0931693986932724, 0.0657666343717217, 0.126775955993192,
0.0912147959234835, 0.0966201171633936, 0.143219075677262, 0.127049180036281,
0.145683059774935, 0.171657558804575, 0.140731399424803, 0.238570126957016,
0.109339294334254, 0.14013909555517, 0.190856101565613, 0.175240248325904,
0.217486338298665, 0.251366119641673, 0.295081966535877, 0.278688523950551,
0.268852458399355, 0.349726775153633, 0.328961747878886, 0.351912567498343,
0.284153004812326, 0.220218578729553, 0.179437360446302, 0.283460837236502,
0.156693988711413, 0.114187411193102, 0.207187893597627, 0.198761383878981,
0.22134790477432, 0.199890709923748, 0.218466176246294), Foraging = c(0.78939890529209,
0.81876138245603, 0.824408012679865, 0.632422585069486, 0.59741347768171,
0.66404371432296, 0.599672129771244, 0.632422585069486, 0.629034606935185,
0.575956282831139, 0.525136610816626, 0.588378869323575, 0.577085608875906,
0.574826956786372, 0.482222221115483, 0.336377829048438, 0.677595626860163,
0.811985426187429, 0.797304187605459, 0.744225863501412, 0.727285972829908,
0.702440799845036, 0.721639342606074, 0.744225863501412, 0.593480307663729,
0.692276865442133, 0.705828777979336, 0.29136611954987, 0.178520386307389,
0.320647930567756, 0.343470886718772, 0.422913132626516, 0.393706424572198,
0.350480496651808, 0.350091073877751, 0.339966081752254, 0.289107467460336,
0.294403617187519, 0.226644054501503, 0.185602280400827, 0.465282330443979,
0.671948996636328, 0.677595626860163, 0.525136610816626, 0.359125682235886,
0.398652093802729, 0.407725644438271, 0.496903459697453, 0.519489980592792,
0.647103823651456, 0.618870672532282, 0.247583017506598, 0.159987856341983,
0.170810564270999, 0.290898812221001, 0.315807961804469, 0.2952380945605,
0.274543055710583, 0.21405861848537, 0.274947456283643, 0.241067674940635,
0.254098360072561, 0.192437158028286, 0.1589743586095, 0.334732239668921,
0.591766847457876, 0.587638966052866, 0.500018841889913, 0.436807180886641,
0.401884302827407, 0.44922080447396, 0.438017173077463, 0.748633878063245,
0.820765025438681, 0.896174861331183, 0.336612021085371, 0.116546447819948,
0.204633879311769, 0.282720933965792, 0.313952640445209, 0.293235348865346,
0.217959926640019, 0.244687309699503, 0.267759562227, 0.256357012162095,
0.20666666619235, 0.110109289364776, 0.0532396563961557, 0.284590163281268,
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0.47682655223493, 0.493009231956877, 0.637874503906291, 0.632422585069486,
0.726775954616143, 0.817486336921616, 0.340983605774792, 0.142779078516963,
0.193598750531475, 0.256357012162095, 0.254682494233647, 0.206783493024567,
0.19198542761038, 0.221428570920375, 0.213793102957603, 0.203278688058049,
0.194157208465701, 0.112932604476694, 0.0948633877604228, 0.380582877086458,
0.787978140268028, 0.810928959887485, 0.719125681409657, 0.625136610587118,
0.562404370293935, 0.366120217738959, 0.535519124454, 0.655009105964824,
0.782513659406253, 0.757377047442085, 0.18996877395901, 0.158105646267371,
0.182574377237322, 0.24367381196702, 0.248087431124608, 0.269869982421893,
0.283586317908142, 0.23846153791425, 0.29272131080359, 0.220218578729553,
0.13834244048395, 0.101639344029024, 0.0846994533575204, 0.23846153791425,
0.745355189546179, 0.686338796239004, 0.605318759995079, 0.500936767000192,
0.414375787195254, 0.393442622047837, 0.509364988467295), Standing = c(0.0338797813430082,
0.0338797813430082, 0.0677595626860163, 0.131754705222809, 0.124225864924363,
0.0831594632964746, 0.162622950446439, 0.101639344029024, 0.112932604476694,
0.0931693986932724, 0.0975737702678635, 0.101639344029024, 0.12046144477514,
0.128743169103431, 0.137059115433078, 0.14761904728025, 0.0677595626860163,
0.0338797813430082, 0.0338797813430082, 0.0639951425367932, 0.0423497266787602,
0.0677595626860163, 0.107285974252859, 0.054207650148813, 0.0790528231336857,
0.0609836064174147, 0.0451730417906775, 0.195749847759603, 0.229629629102611,
0.225865208953388, 0.198259461192418, 0.160928961379289, 0.183201780595526,
0.203278688058049, 0.149321999252517, 0.198605614769358, 0.212958625584623,
0.281462798849606, 0.306128024277895, 0.398379497860889, 0.111677797760286,
0.0677595626860163, 0.0547288775540901, 0.0931693986932724, 0.145830363172079,
0.153350589236774, 0.105403764178248, 0.149071037909236, 0.152459016043537,
0.135519125372033, 0.119882303213721, 0.254098360072561, 0.296740153831865,
0.255227686117328, 0.178182553729895, 0.206102003169966, 0.186338797386545,
0.175045536938875, 0.264028640811029, 0.235903662684649, 0.235855400887864,
0.189259468191977, 0.333151183206247, 0.403169397981797, 0.203278688058049,
0.0884638735067435, 0.116461748366591, 0.127819175066803, 0.183918813004901,
0.155538996165628, 0.179710144515087, 0.15951730382333, 0.190573770054421,
0.167140254625507, 0.11067395238716, 0.392349725875482, 0.526775955075159,
0.469945354112694, 0.421857922529069, 0.365901638504488, 0.43278688425262,
0.506010927800412, 0.515846993351608, 0.493989069904506, 0.555191255556392,
0.608743168001792, 0.768306009165636, 0.947540981431873, 0.590163933071755,
0.169398906715041, 0.163752276491206, 0.297658078942143, 0.42228727459678,
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0.637158468483024, 0.575956282831139, 0.58688524455469, 0.657923495757771,
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0.468475597191251, 0.426885244921903, 0.380496005852245), ID = structure(c(1L,
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5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), .Label = c("41361",
"41365", "41366", "41366bis", "41367", "41368"), class = "factor"),
Area = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Loliondo",
"Seronera"), class = "factor"), Feeding_Foraging = c(0.907978139992619,
0.910720788958481, 0.909107466037385, 0.865515480709382,
0.851511837754272, 0.908567353581193, 0.838185790426022,
0.877712201992865, 0.840783240328986, 0.829470508742613,
0.800692165739759, 0.810856100142662, 0.809726774097895,
0.81319541837825, 0.784881601113022, 0.770039030238942, 0.856340680152585,
0.933239380467668, 0.903362633548788, 0.90746480997227, 0.901897153597719,
0.927279348757726, 0.899508194656866, 0.928144676506314,
0.835478745828073, 0.853975821851945, 0.863934424246708,
0.65275045387529, 0.647190694885669, 0.653799113774003, 0.664199483432583,
0.724326359747071, 0.68927831008189, 0.664433137097017, 0.6934061914869,
0.649401418018395, 0.634681237159019, 0.601580301364126,
0.549631303304847, 0.489390986443134, 0.731803277008976,
0.851659141151415, 0.82937704727684, 0.797429668277099, 0.743903198917193,
0.756809782285958, 0.778144587121826, 0.792475345207145,
0.713528728284566, 0.745176874907532, 0.723589996683398,
0.534977024761081, 0.520242864622636, 0.527677594417352,
0.594687518263307, 0.613716383958506, 0.591121518289437,
0.583978391976724, 0.54946845378115, 0.569701553967814, 0.570830880012581,
0.565792348428236, 0.44540619205608, 0.479529212854885, 0.604641164368219,
0.706436876618826, 0.733039694316609, 0.708944160171797,
0.688872754078621, 0.745522085020775, 0.683773136848632,
0.688727555015723, 0.888572105349583, 0.947814205474962,
0.974098358420102, 0.607650271829437, 0.495781419627168,
0.569661200878373, 0.596382135431706, 0.656029142392356,
0.586131523056514, 0.501020035279991, 0.515725560443569,
0.506010927800412, 0.453078323186013, 0.397923496354493,
0.276120217945516, 0.115917251880721, 0.419016392480946,
0.916633877677671, 0.902114514441809, 0.760192197237773,
0.671451400869477, 0.68945138687036, 0.719743153252393, 0.856790014122652,
0.753034606650595, 0.867715845003057, 0.910655735614888,
0.555191255556392, 0.371194378539179, 0.387842830231389,
0.438178505369572, 0.441605425781279, 0.360371835112871,
0.34386030949283, 0.356947696292407, 0.385450661762178, 0.347581460444935,
0.307479235716452, 0.206102003169966, 0.160630022132145,
0.50735883307965, 0.879192936191512, 0.907549077050879, 0.862344757086919,
0.752185790623399, 0.70808743006887, 0.537777776543534, 0.676250523878803,
0.89357923292184, 0.891852953740506, 0.897516142997256, 0.380824875524623,
0.333345894593276, 0.400060715535987, 0.495039931608694,
0.543169397660485, 0.548558506372443, 0.552438776307497,
0.588188313067882, 0.621683058682476, 0.572131146227896,
0.422495445296276, 0.321857922758577, 0.264136813803823,
0.521922375150751, 0.902049178257592, 0.800526207432105,
0.812506653592706, 0.699698150879173, 0.635723691969573,
0.593333331971585, 0.727831164713589)), row.names = c(NA,
-144L), vars = "hour", indices = list(c(0L, 24L, 48L, 72L, 96L,
120L), c(1L, 25L, 49L, 73L, 97L, 121L), c(2L, 26L, 50L, 74L,
98L, 122L), c(3L, 27L, 51L, 75L, 99L, 123L), c(4L, 28L, 52L,
76L, 100L, 124L), c(5L, 29L, 53L, 77L, 101L, 125L), c(6L, 30L,
54L, 78L, 102L, 126L), c(7L, 31L, 55L, 79L, 103L, 127L), c(8L,
32L, 56L, 80L, 104L, 128L), c(9L, 33L, 57L, 81L, 105L, 129L),
c(10L, 34L, 58L, 82L, 106L, 130L), c(11L, 35L, 59L, 83L,
107L, 131L), c(12L, 36L, 60L, 84L, 108L, 132L), c(13L, 37L,
61L, 85L, 109L, 133L), c(14L, 38L, 62L, 86L, 110L, 134L),
c(15L, 39L, 63L, 87L, 111L, 135L), c(16L, 40L, 64L, 88L,
112L, 136L), c(17L, 41L, 65L, 89L, 113L, 137L), c(18L, 42L,
66L, 90L, 114L, 138L), c(19L, 43L, 67L, 91L, 115L, 139L),
c(20L, 44L, 68L, 92L, 116L, 140L), c(21L, 45L, 69L, 93L,
117L, 141L), c(22L, 46L, 70L, 94L, 118L, 142L), c(23L, 47L,
71L, 95L, 119L, 143L)), group_sizes = c(6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L), biggest_group_size = 6L, labels = structure(list(
hour = 0:23), row.names = c(NA, -24L), class = "data.frame", vars = "hour"), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"))
Any input is appreciated!

R diff lead difference between two columns?

Here is my dataframe:
structure(list(replicate = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 10L,
10L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L,
14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L), press_id = c(1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L), start_time = c(164429106370979,
164429411618825, 164429837271940, 164430399454285, 164429106370980,
164429411618826, 164429837271941, 164430399454286, 164429106370981,
164429411618827, 164429837271942, 164430399454287, 164429106370982,
164429411618828, 164429837271943, 164430399454288, 164429106370983,
164429411618829, 164429837271944, 164430399454289, 164429106370984,
164429411618830, 164429837271945, 164430399454290, 164429106370985,
164429411618831, 164429837271946, 164430399454291, 164429106370986,
164429411618832, 164429837271947, 164430399454292, 164429106370987,
164429411618833, 164429837271948, 164430399454293, 164429106370988,
164429411618834, 164429837271949, 164430399454294, 164429106370989,
164429411618835, 164429837271950, 164430399454295, 164429106370990,
164429411618836, 164429837271951, 164430399454296, 164429106370991,
164429411618837, 164429837271952, 164430399454297, 164429106370992,
164429411618838, 164429837271953, 164430399454298, 164429106370993,
164429411618839, 164429837271954, 164430399454299), end_time = c(164429182443825,
164429512525748, 164429903243170, 164430465927555, 164429182443826,
164429512525749, 164429903243171, 164430465927556, 164429182443827,
164429512525750, 164429903243172, 164430465927557, 164429182443828,
164429512525751, 164429903243173, 164430465927558, 164429182443829,
164429512525752, 164429903243174, 164430465927559, 164429182443830,
164429512525753, 164429903243175, 164430465927560, 164429182443831,
164429512525754, 164429903243176, 164430465927561, 164429182443832,
164429512525755, 164429903243177, 164430465927562, 164429182443833,
164429512525756, 164429903243178, 164430465927563, 164429182443834,
164429512525757, 164429903243179, 164430465927564, 164429182443835,
164429512525758, 164429903243180, 164430465927565, 164429182443836,
164429512525759, 164429903243181, 164430465927566, 164429182443837,
164429512525760, 164429903243182, 164430465927567, 164429182443838,
164429512525761, 164429903243183, 164430465927568, 164429182443839,
164429512525762, 164429903243184, 164430465927569)), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -60L), vars = c("replicate",
"press_id"), drop = TRUE, indices = list(0L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L,
54L, 55L, 56L, 57L, 58L, 59L), group_sizes = c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), biggest_group_size = 1L, labels = structure(list(
replicate = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L,
7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L,
11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L,
14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L), press_id = c(1L,
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L,
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L,
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L)), class = "data.frame", row.names = c(NA,
-60L), vars = c("replicate", "press_id"), drop = TRUE, indices = list(
0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L,
26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L,
38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L,
50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L), group_sizes = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), biggest_group_size = 1L, labels = structure(list(
replicate = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L,
7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L,
11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L,
14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L), press_id = c(1L,
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L,
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L,
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L)), class = "data.frame", row.names = c(NA,
-60L), vars = c("replicate", "press_id"), drop = TRUE, .Names = c("replicate",
"press_id")), .Names = c("replicate", "press_id")), .Names = c("replicate",
"press_id", "start_time", "end_time"))
I want to get the inter press_id time diff for example:
replicate press_id start_time end_time time_diff
1 1 1.644291e+14 1.644292e+14 0 (it's a first row)
1 2 1.644294e+14 1.644295e+14 1.644294e+14 - 1.644292e+14
1 3 1.644298e+14 1.644299e+14 1.644298e+14 - 1.644295e+14
1 4 1.644304e+14 1.644305e+14 .....
2 1 1.644291e+14 1.644292e+14
2 2 1.644294e+14 1.644295e+14
2 3 1.644298e+14 1.644299e+14
2 4 1.644304e+14 1.644305e+14
I am trying to do this using mutate, lag, lead and diff but without any luck. I have grouped, and ungrouped the dataset, nothing helped me.
df %>%
group_by(replicate) %>%
mutate(d = ifelse(row_number() == 1, 0, lead(start_time) - end_time))

df %>%
group_by(replicate) %>%
mutate(d = start_time - lag(end_time))
And if you want zeroes except NAs for the first row of each unique value in the replicate column, you could do:
df %>%
group_by(replicate) %>%
mutate(d = start_time - lag(end_time),
d = ifelse(is.na(d), 0, d))
Or just:
df %>%
group_by(replicate) %>%
mutate(d = ifelse(row_number() == 1, 0, start_time - lag(end_time)))

Linear function for condition1 and cubic function for condition2 in one plot

I have data of participants that had numerous trials, where certain trials had one condition, and other trials were another.
My analyses show that for condition 1, there is a linear null effect (flat line), while for condition 2 there is a cubic effect. I want to plot them together.
The code below creates a plot that gives the cubic function for both groups:
ggplot(dat, aes(x=trial, y=y, group=condition, colour=condition)) +
geom_point() + geom_jitter(height=0.2) +
geom_smooth(alpha=0.1, method="lm", formula = y ~ poly(x,3, raw=TRUE)) +
labs(x="Trial", y="y") +
scale_x_discrete(breaks=c(1,9,18,27,36,45,54,63))
What I want is to not have the cubic function for condition 2, but have a linear function. I tried to force this through aes() calls within geom_smooth(), but this seems to give me a much flatter cubic function for condition 1:
ggplot(dat, aes(x=trial, y=y)) +
geom_point(aes(group=condition, colour=condition)) + geom_jitter(height=0.2, aes(group=condition, colour=condition)) +
geom_smooth(alpha=0.1, method="lm", formula = y ~ poly(x,3, raw=TRUE), aes(group=(condition="1"), colour=(condition="1"))) +
geom_smooth(alpha=0.1, method="lm", aes(group=(condition="2"), colour=(condition="2"))) +
labs(x="Trial", y="y") +
scale_x_discrete(breaks=c(1,9,18,27,36,45,54,63))
Obviously this is not the way to go. How would I accomplish this? Script for reproducible example (first 250 lines of the total dataset, so your figures will be different) below:
structure(list(id = c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L
), trial = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L,
26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L,
52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L,
55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L,
32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L,
58L, 59L, 60L, 61L, 62L, 63L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L,
22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L,
35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L,
61L), condition = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L),
y = c(NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L,
0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 1L, 1L, 1L, NA, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 0L, 1L, 1L, NA, NA, NA, 0L, NA, 0L, NA, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, NA, 0L, 0L, 1L, 0L, 0L, 1L,
1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, NA, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, NA,
0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, NA, NA, 1L, 1L,
1L, 1L, NA, 1L, 1L, 1L, 1L, NA, 1L, 0L, 1L, 1L, 1L, 0L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L)), .Names = c("id",
"trial", "condition", "y"), row.names = c(NA, 250L), class = "data.frame")
Edit: The reason I'm not using geom_smooth() using gam or loess, is because there are multiple polynomials in condition 1, so it will show more than just the cubic function if I use that solution. I wish to show the cubic function, not the composite of multiple polynomials.

You could filter your data inside geom_smooth.
library(tidyverse)
ggplot(dat, aes(x=trial, y=y, colour=as.factor(condition))) +
geom_point() + geom_jitter(height=0.2) +
geom_smooth(data = filter(dat, condition == 2), alpha=0.1, method="lm", formula = y ~ poly(x,3, raw=TRUE)) +
geom_smooth(data = filter(dat, condition == 1), alpha=0.1, method="lm", formula = y ~ 1) +
labs(x="Trial", y="y") +
scale_x_continuous(breaks=c(1,9,18,27,36,45,54,63))
Which gives you this plot

Re-assemble dataframe by index

What I'm trying to do is getting a dataframe where the repeated rows in the first column act as an index to copy the corresponding rows of other columns. I know this sound messy, and my inability to accurately state the issue is one of the reasons I'm having so many problems with this.
I'll provide a reproducible example below.
structure(list(Var1 = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L), .Label = c("2016-01", "2016-02", "2016-03", "2016-04",
"2016-05", "2016-06", "2016-07", "2016-08", "2016-09", "2016-10",
"2016-11", "2016-12", "2017-01", "2017-02", "2017-03", "2017-04",
"2017-05"), class = "factor"), Var2 = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L
), .Label = c("B2B", "B2C", "B2K"), class = "factor"), Freq = c(5L,
13L, 8L, 13L, 36L, 5L, 18L, 1L, 12L, 24L, 22L, 6L, 24L, 15L,
11L, 26L, 1L, 338L, 285L, 291L, 232L, 142L, 42L, 92L, 9L, 46L,
34L, 45L, 35L, 30L, 31L, 36L, 56L, 9L, 0L, 1L, 0L, 0L, 0L, 0L,
7L, 0L, 13L, 0L, 1L, 0L, 0L, 0L, 0L, 2L, 0L)), .Names = c("Var1",
"Var2", "Freq"), class = "data.frame", row.names = c(NA, -51L
))
basically what I want is:
On Var1 no repeated dates
On the row where the date is repeated, take the value of Var2 and Freq and copy them in two new columns to the index of the unique date
This must be done for every distinct level of Var2
Thank you in advance!

I think what your trying to explain is a dcast. Does this end up how you want it?
library(reshape2)
dcast(x,Var1~Var2,value.var="Freq")

A base R option would be
xtabs(Freq~Var1 + Var2, df1)

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