Put a title of plot inside a for loop

Put a title of plot inside a for loop - r

I have a code with a nested for loop that runs perfect and gives me 4X4 plots in a page. I need to insert the title in each plot. Below is my code.
What I wanted to do is create a vector and assign my titles inside it, as shown in code and then read it inside loop. For that, I need to convert the index i into number and use the position of second vector.
This is my approach which may not be that good so either you can use mine or give your own idea. You can play with any random datasets and use simple plot/histogram. The vectors represent the day of week and time of day respectively.
#set dimension
par(mfcol=c(4,4))
#vector definition
days<-c(1,2,3,4)
hours<-c(8,14,18,22)
#Title vector
D1<-c("Monday (7-8 am)","Monday (1-2 pm)","Monday (5-6 pm)",
"Monday (9-10 pm)")
D2<-c("Wednesday (7-8 am)","Wednesday (1-2 pm)","Wednesday (5-6 pm)",
"Wednesday (9-10 pm)")
D3<-c("Friday (7-8 am)","Friday (1-2 pm)","Friday (5-6 pm)",
"Friday (9-10 pm)")
D4<-c("Saturday (7-8 am)","Saturday (1-2 pm)","Saturday (5-6 pm)",
"Saturday (9-10 pm)")
#Loop
for (i in days)
{
for (j in hours)
{
# set positioning of the histogram
par("plt" = c(0.2,0.95,0.35,0.84))
# plot the histogram
hist(path$TT[path$days==i & path$hours==j], breaks = seq(0,60,by=3), xlab="Travel Time",
ylab="Number of paths",col="blue", **main=D??**, mgp=c(2.5,1,0))
}
}
here is data sample-> dput(path)
structure(list(days = c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L), hours = c(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, 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, 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, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L), TT = c(34.82720833,
34.13870083, 30.59218805, 35.1616205, 34.87982204, 30.74262596,
35.19981237, 35.14235172, 31.6716496, 29.84148401, 31.32268062,
30.58250275, 35.26514263, 33.55230269, 34.97001136, 31.09735713,
29.90509108, 33.78335499, 33.08419061, 33.9702478, 32.68267307,
32.88848951, 30.16693345, 32.85994732, 30.83277565, 34.62568305,
34.13923292, 33.50498645, 31.31095608, 34.31001321, 33.99902318,
33.7909643, 34.33340843, 32.30046602, 34.74999297, 29.87097318,
32.91255436, 30.37869556, 35.22453148, 33.91415576, 30.87027627,
34.32036758, 34.14405484, 32.52770687, 30.63412371, 30.69590367,
34.10350198, 33.51383263, 31.19792969, 35.26664132, 33.79975778,
30.9254123, 33.58382797, 32.47180323, 35.07275967, 30.97518331,
34.09754282, 31.30283331, 35.03617718, 35.0447385, 34.48088429,
34.93546837, 30.97837093, 31.14469741, 30.92743268, 34.10879646,
30.4886625, 35.00307314, 31.41065689, 31.82113768, 30.38511722,
30.39628127, 31.89778508, 31.5036342, 30.78847263, 30.63294595,
34.40494811, 32.57036077, 31.96399169, 33.90064885, 31.64029012,
34.1366935, 35.24047602, 30.50038163, 35.26178882, 30.67850437,
31.28041078, 31.13586861, 34.03564851, 30.45301463, 31.46075363,
32.79463877, 34.37256141, 31.14590299, 32.98806056, 34.61871373,
34.50000295, 33.64822723, 31.79305995, 32.95337037, 31.97535842,
33.01756184, 30.27499142, 31.52636985, 33.88390737, 29.86033691,
33.10717421, 31.13912362, 34.03308637, 29.82060846, 30.29160216,
30.68720702, 32.21043532, 32.38637581, 29.87286573, 31.91229798,
33.07799897, 30.41662694, 32.24261367, 35.3258724, 29.81198078,
29.87369792, 29.5469277, 31.07479327, 29.93749303, 31.32897414,
32.11042476, 31.74139691, 29.35309499, 31.91510643, 28.43111183,
30.64316778, 28.82045246, 31.2966231, 32.88217249, 28.85142648,
32.61772627, 28.89998879, 29.09439029, 31.17275104, 30.14374991,
32.54361297, 30.50674627, 32.01595442, 30.50549694, 30.92120556,
28.56600115, 32.6272292, 32.01189691, 32.48467475, 32.63696512,
30.92335971, 31.05045202, 30.7754939, 31.40027579, 29.12356583,
31.77973836, 28.78119827, 31.44082345, 30.73383322, 32.04126499,
30.09865077, 32.23577216, 29.08265343, 30.49423226, 31.46262176,
29.84828538, 30.18785884, 29.51834908, 29.37202672, 31.50806652,
32.40830835, 30.48030326, 31.25898945, 28.36670284, 31.28059981,
29.34232677, 30.09806882, 32.11127774, 29.59171523, 30.61713837,
29.76958526, 31.85824615, 32.16215903, 29.84655136, 31.07721122,
28.65494456, 30.9843114, 32.54863022, 31.46634971, 31.89779842,
32.82481805, 32.14782935, 32.08964421, 31.60785849, 32.91857557,
31.71183437, 31.81246841, 32.98599723, 28.95747656, 28.84662181,
31.71611474, 31.62086303, 32.53920721, 30.42499004, 28.99300588,
29.61203445, 32.4920689, 29.36255767, 32.6194317, 31.04202451,
28.75123245, 30.13704325, 30.92045914, 32.57753631, 30.83279548,
28.8546849, 30.74245368, 29.03716971, 28.37275181, 30.86814322,
30.61960665, 30.42719574, 30.27684903, 32.91275304, 29.80632759,
29.50108563, 32.6131215, 30.03530353, 30.24898855, 29.97890411,
29.91508311, 30.66431902, 29.44062756, 30.78040092, 30.42641885,
32.52252736, 32.02849124, 28.44168133, 28.77193919, 32.3661733,
32.50081923, 30.78754405, 29.31429942, 29.25319403, 29.41670938,
34.79250707, 28.45292865, 33.30658009, 36.95793072, 31.1241599,
29.47446652, 37.93368226, 29.99169743, 34.53286071, 33.30080173,
32.07298455, 34.59538339, 33.19895485, 32.39419483, 31.37985584,
33.10293436, 29.39098815, 29.6792889, 35.03296983, 37.90584009,
30.95003357, 33.20300797, 37.19244019, 35.17202829, 33.36301054,
35.45811104, 32.30603702, 35.90719466, 32.53788221, 32.98462237,
34.40384647, 34.60599035, 36.12782575, 34.22463048, 29.98624712,
35.806683, 36.85504472, 35.98104837, 35.97362738, 35.43026929,
29.52289309, 29.0544412, 28.38438112, 29.31043103, 34.55714132,
31.35110246, 35.45463173, 32.52063466, 29.64833452, 31.74827447,
31.19599864, 35.86874035, 31.36035725, 30.90048731, 36.67327499,
30.0504123, 37.41148645, 33.68205359, 29.2592527, 28.82514246,
30.62364715, 37.55578321, 32.25899523, 34.31735337, 37.1286007,
30.09667053, 37.77301539, 37.28325032, 33.82381014, 33.64911154,
32.23733708, 35.36476734, 31.19880018, 29.1404291, 30.72636631,
34.77003685, 37.31098961, 31.55246022, 28.51524079, 35.97250119,
35.08409392, 36.5458489, 37.35540297, 30.23406879, 29.17387163,
33.74088357, 29.40765925, 29.98726349, 29.58959745, 31.96605073,
31.94788415, 33.60347166, 28.43148601, 29.65454367, 36.06816061,
29.96597865, 31.90935292, 28.59771444, 32.44428733, 31.50734498,
30.23029062, 32.7213003, 33.17963215, 30.84546259, 35.61594726,
31.1375163, 33.58903731, 36.3755896, 30.15521544, 32.64832733,
29.75419547, 32.87727257, 32.86349263, 30.87051665, 34.99052692,
29.32459293, 29.75063939, 29.31336196, 30.26155711, 37.78471798,
29.29637466, 33.63983534, 29.0707227, 37.23740461, 30.46483145,
32.5191104, 32.38759822, 35.67256593, 31.96392716, 33.3250217,
35.46341363, 28.75439972, 33.2611733, 30.02014914, 35.78496489,
32.96781502, 31.43534921, 35.07596123, 34.52762462, 30.26655854,
35.32014083, 37.55183466, 34.14971103, 36.29105196, 32.40044715,
36.0587327, 31.83769864, 33.92873059, 34.70263617, 30.80816039,
30.68630199, 31.01802064, 30.80777532, 35.05333618, 27.06058834,
27.79241831, 27.33752079, 27.77903509, 26.947812, 27.8862964,
27.39365377, 27.9236377, 26.78983708, 27.98767273, 27.93024624,
27.84690108, 27.32830243, 26.81574528, 27.11055277, 27.39296015,
28.00610613, 27.71688355, 27.62271524, 27.69926561, 26.77071774,
26.75407601, 27.54772857, 26.85613667, 27.43762662, 27.45478206,
27.70204762, 27.66985159, 27.46593956, 28.00153523, 27.85391116,
26.78324156, 27.51476443, 27.54375831, 27.45536832, 27.25299275,
27.42563343, 27.35861323, 27.89703515, 27.94359525, 27.02701474,
28.01213784, 27.05632904, 27.219231, 28.00160216, 27.06621867,
26.83356071, 26.85138171, 26.9857268, 26.84488214, 27.04212578,
27.90226659, 26.88270484, 27.36445874, 27.98903653, 26.74879158,
27.91409337, 27.04442553, 27.76393403, 26.97261286, 26.82558533,
27.40286709, 26.90959192, 27.61358064, 27.67649126, 27.98923329,
27.27538051, 27.93429854, 27.24070111, 27.79609001, 27.51659686,
27.60029289, 26.85518925, 27.31821322, 27.1642527, 27.27570585,
27.67152235, 26.96014272, 27.89962397, 27.84824436)), .Names = c("days",
"hours", "TT"), class = "data.frame", row.names = c(NA, -480L
))

It seems like you have 4*4=16 plots, with 16 titles in 4 vectors.
Try this argument in your function,
main=get(paste0("D",i))[which(hours==j)]
get()function can get the object with the given object name.
I use some simulated data, just to check the titles. Looks good,
Sample codes:
x<-rnorm(50)#my simulated data
for (i in days)
{
for (j in hours)
{
hist(x,xlab="Travel Time",
ylab="Number of paths",col="blue",
main=get(paste0("D",i))[which(hours==j)], mgp=c(2.5,1,0))
}
}

This is revised to match the data that was provided.
The main idea here is to make a matrix of the titles and simply access the matrix each time you print. The code in the question looped through hours and days. Because I want to know the index, I have change this to looping through the indices, 1:4. That means that where the original code used the loop variable (hours or days) I use the index to select elements from hours or days.
I am assuming that we already have the data.frame and the OP's lists D1, D2, D3 and D4.
LabelMat = matrix(c(D1, D2, D3, D4), nrow=4)
for (i in 1:4) {
for (j in 1:4) {
# set positioning of the histogram
par("plt" = c(0.2,0.95,0.35,0.84))
# plot the histogram
hist(path$TT[path$days==days[i] & path$hours==hours[j]], breaks = seq(0,60,by=3),
xlab="Travel Time", ylab="Number of paths",
col="blue", main = LabelMat[i,j], mgp=c(2.5,1,0))
}
}

Related

Undirected network graph calculated by tidygraph shows more degree centrality than should be possible

I have a cleaned data set with 26 nodes. I am placing these 26 nodes in an undirected network graph using tidygraph, where I use the centrality_degree() function to calculate the centrality degree. However, when I graph the resulting network, my highest possible centrality degree is 40, which should not be possible. When I change the graph to directed, this is corrected.
I somewhat confused, as other methods I have used in the past, where I manually calculated the centrality degree, I have never once come across this issue.
Is this regular behaviour, or am I doing something wrong?
Reproducible example:
library(tidygraph)
library(ggraph)
library(tidyverse)
nodes <- structure(list(id = 1:26, label = c("a", "b", "c", "d", "e",
"f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r",
"s", "t", "u", "v", "w", "x", "y", "z")), row.names = c(NA, -26L
), class = "data.frame")
edges <- structure(list(from = c(21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 17L, 17L, 17L, 17L, 17L),
to = c(1L, 12L, 3L, 16L, 24L, 4L, 10L, 6L, 22L, 2L, 8L, 1L,
12L, 13L, 3L, 18L, 16L, 24L, 5L, 7L, 14L, 4L, 10L, 6L, 9L,
22L, 15L, 2L, 20L, 8L, 21L, 12L, 13L, 3L, 16L, 24L, 5L, 7L,
14L, 4L, 10L, 6L, 22L, 15L, 2L, 8L, 17L, 21L, 1L, 13L, 3L,
16L, 5L, 7L, 14L, 10L, 6L, 9L, 22L, 15L, 2L, 20L, 8L, 17L,
21L, 1L, 3L, 18L, 16L, 5L, 7L, 14L, 4L, 10L, 6L, 25L, 9L,
22L, 15L, 20L, 8L, 17L, 21L, 11L, 1L, 12L, 13L, 18L, 16L,
24L, 5L, 7L, 14L, 4L, 10L, 6L, 25L, 9L, 22L, 15L, 20L, 8L,
17L, 1L, 3L, 10L, 6L, 22L, 20L, 8L, 21L, 11L, 1L, 13L, 3L,
18L, 24L, 7L, 4L, 10L, 6L, 25L, 9L, 22L, 15L, 2L, 20L, 8L,
17L, 21L, 11L, 1L, 12L, 13L, 18L, 16L, 5L, 7L, 14L, 10L,
6L, 25L, 9L, 22L, 15L, 20L, 8L, 17L, 1L, 3L, 18L, 16L, 7L,
14L, 4L, 10L, 6L, 9L, 22L, 15L, 2L, 20L, 8L, 17L, 21L, 11L,
1L, 12L, 13L, 3L, 18L, 16L, 24L, 14L, 4L, 10L, 6L, 25L, 9L,
22L, 15L, 2L, 20L, 8L, 11L, 1L, 3L, 18L, 16L, 7L, 10L, 6L,
9L, 22L, 15L, 2L, 20L, 8L, 17L, 21L, 11L, 1L, 12L, 13L, 3L,
18L, 16L, 24L, 5L, 7L, 14L, 10L, 6L, 25L, 9L, 22L, 15L, 2L,
20L, 8L, 17L, 21L, 11L, 1L, 12L, 13L, 3L, 18L, 16L, 24L,
5L, 7L, 14L, 4L, 6L, 25L, 9L, 22L, 15L, 2L, 20L, 8L, 17L,
21L, 11L, 1L, 12L, 13L, 3L, 18L, 24L, 5L, 7L, 14L, 4L, 10L,
25L, 9L, 22L, 15L, 2L, 20L, 8L, 21L, 1L, 13L, 3L, 18L, 5L,
10L, 6L, 22L, 2L, 20L, 8L, 21L, 1L, 13L, 3L, 18L, 16L, 24L,
4L, 10L, 6L, 22L, 15L, 2L, 20L, 8L, 11L, 1L, 12L, 13L, 3L,
16L, 24L, 5L, 7L, 14L, 4L, 10L, 6L, 25L, 9L, 15L, 2L, 20L,
8L, 17L, 21L, 1L, 12L, 3L, 18L, 16L, 24L, 7L, 10L, 6L, 25L,
9L, 22L, 2L, 20L, 8L, 17L, 21L, 11L, 1L, 12L, 13L, 3L, 18L,
16L, 24L, 5L, 7L, 14L, 4L, 6L, 25L, 9L, 22L, 15L, 20L, 8L,
17L, 21L, 11L, 1L, 3L, 16L, 24L, 7L, 10L, 6L, 22L, 2L, 8L,
21L, 11L, 1L, 12L, 13L, 3L, 18L, 16L, 24L, 14L, 4L, 10L,
6L, 25L, 9L, 22L, 2L, 20L, 7L, 6L, 25L, 22L, 8L), weight = c(3L,
1L, 3L, 2L, 1L, 1L, 5L, 1L, 8L, 2L, 1L, 2L, 3L, 2L, 5L, 1L,
4L, 1L, 4L, 4L, 4L, 1L, 5L, 13L, 3L, 7L, 3L, 2L, 3L, 8L,
1L, 1L, 1L, 15L, 10L, 7L, 2L, 4L, 2L, 5L, 19L, 23L, 6L, 2L,
11L, 7L, 1L, 1L, 2L, 3L, 3L, 5L, 4L, 5L, 4L, 4L, 21L, 2L,
9L, 8L, 1L, 1L, 12L, 1L, 2L, 1L, 3L, 1L, 6L, 6L, 5L, 6L,
1L, 6L, 22L, 2L, 2L, 9L, 8L, 3L, 13L, 1L, 5L, 6L, 4L, 10L,
13L, 3L, 41L, 46L, 11L, 39L, 9L, 55L, 2L, 108L, 2L, 8L, 31L,
30L, 13L, 39L, 2L, 2L, 1L, 3L, 4L, 8L, 5L, 1L, 8L, 1L, 6L,
1L, 8L, 2L, 3L, 23L, 2L, 12L, 96L, 1L, 3L, 21L, 1L, 6L, 12L,
38L, 4L, 5L, 4L, 4L, 8L, 8L, 3L, 29L, 3L, 11L, 3L, 3L, 63L,
2L, 5L, 18L, 19L, 4L, 25L, 1L, 2L, 3L, 1L, 7L, 6L, 7L, 1L,
3L, 17L, 1L, 3L, 6L, 1L, 4L, 11L, 1L, 5L, 1L, 5L, 1L, 1L,
15L, 4L, 7L, 3L, 1L, 4L, 12L, 8L, 1L, 9L, 32L, 3L, 7L, 5L,
35L, 1L, 1L, 3L, 1L, 6L, 4L, 4L, 12L, 2L, 5L, 4L, 2L, 2L,
9L, 1L, 2L, 3L, 4L, 9L, 13L, 2L, 1L, 25L, 25L, 10L, 14L,
10L, 4L, 59L, 4L, 5L, 21L, 19L, 1L, 8L, 27L, 3L, 5L, 8L,
8L, 11L, 12L, 111L, 5L, 50L, 45L, 15L, 32L, 10L, 49L, 109L,
1L, 8L, 28L, 39L, 53L, 13L, 48L, 5L, 13L, 2L, 20L, 3L, 3L,
27L, 10L, 8L, 1L, 58L, 1L, 7L, 32L, 13L, 21L, 110L, 1L, 17L,
27L, 124L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 2L, 7L, 1L, 1L, 1L,
2L, 2L, 1L, 5L, 2L, 2L, 2L, 1L, 3L, 3L, 14L, 2L, 2L, 4L,
1L, 3L, 14L, 5L, 8L, 44L, 16L, 14L, 4L, 12L, 4L, 19L, 41L,
47L, 2L, 1L, 11L, 24L, 2L, 18L, 1L, 7L, 5L, 1L, 7L, 3L, 27L,
3L, 15L, 7L, 54L, 1L, 4L, 17L, 5L, 6L, 27L, 1L, 1L, 2L, 3L,
4L, 10L, 56L, 3L, 25L, 25L, 7L, 16L, 5L, 29L, 59L, 3L, 3L,
20L, 17L, 5L, 31L, 3L, 6L, 1L, 4L, 7L, 1L, 3L, 1L, 6L, 5L,
13L, 1L, 2L, 9L, 1L, 15L, 2L, 1L, 16L, 4L, 4L, 3L, 1L, 6L,
17L, 10L, 1L, 13L, 63L, 11L, 12L, 1L, 5L, 1L, 2L, 3L)), row.names = c(NA,
-383L), class = c("tbl_df", "tbl", "data.frame"))
routes_tidy <- tbl_graph(nodes=nodes, edges=edges, directed=FALSE) %>% mutate(neighbors = centrality_degree())
# Filtering out 3 nodes out of the graph as they have no connections and zoom the figure way out
ggraph(routes_tidy, layout="graphopt") +
geom_node_point(aes(size=neighbors, filter=(label!="z" & label!="s" & label!="w"))) +
geom_edge_link(aes(width=weight, alpha=weight)) +
scale_edge_width(range=c(0.2, 2)) +
geom_node_text(aes(label=label, fontface="bold", size=neighbors, filter=(label!="z" & label!="s" & label!="w")), repel=TRUE) +
labs(edge_width="N") +
theme_graph()

I'm new to the whole tidygraph thing, stumbled over this question, got confused, and figured it'd be a nice way to get to know stuff. So, I don't know if it's a bug or a feature, but the behaviour is triggered because you have doubled edges:
# Given your edges
edges %>%
filter((from == 1 & to == 2) | from == 2 & to == 1)
# A tibble: 2 x 3
from to weight
<int> <int> <int>
1 1 2 11
2 2 1 3
And those count as 2 connections in the calculation of the degree centrality. One way to remove those double edges is to convert the network to a simple network:
routes_simple <-
routes_tidy %>%
morph(to_simple) %>%
crystallise() %>%
pull(graph) %>%
getElement(1) %>%
activate(nodes) %>%
mutate(neighbors = centrality_degree())
Now the maximum degree is 22 (and the heighest possible, presumably, 25).

Calculating cumulative return for each quarter by investor

I m looking to calculate cumulative returns based on column values for each quarter grouped by investors. I tried using Return.cumulative but didn't get any success.
I appreciate if someone can help me with some easy way to calculate cumulative return in R?
structure(list(Quarter = structure(c(1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L), .Label = c("2012Q1", "2012Q2", "2012Q3",
"2012Q4", "2013Q1", "2013Q2", "2013Q3", "2013Q4", "2014Q1", "2014Q2",
"2014Q3", "2014Q4", "2015Q1", "2015Q2", "2015Q3", "2015Q4", "2016Q1",
"2016Q2", "2016Q3", "2016Q4"), class = "factor"), Total_Return = c(0.040561972,
0.012692509, 0.053079761, 0.048656856, 0.037110412, 0.041422455,
0.052373109, 0.049826591, 0.053255331, 0.050956964, 0.038683073,
0.018446161, 0.039546641, 0.057108385, 0.020790648, 0.020743042,
0.015486459, 0.001202289, 0.066082963, 0.036178889, 0.037096464,
0.003068485, 0.026307213, 0.052918456, 0.019292362, 0.058390755,
0.040255949, 0.020420614, 0.024955646, 0.051180526, 0.04598829,
0.012425778, 0.036190369, 0.079480322, 0.00574259, 0.026401296,
0.018309495, 0.004887553, 0.05935355, 0.051702238, 0.080892981,
0.07076032, 0.088251171, 0.045903253, 0.029692483, 0.058297815,
0.065338687, 0.071947108, 0.074878083, 0.03989637, -0.031255434,
0.029883299, 0.008148657, 0.078836907, 0.030064965, 0.048887451,
0.034827005, -0.065304898, 0.136766281, 0.019039148, 0.075818622,
0.037509338, 0.060238115, 0.03877549, 0.027433037, 0.033627931,
0.053488836, 0.024999278, 0.016037836, 0.011863841, -0.02610323,
0.046568702, 0.021033516, 0.052322078, 0.038724408, 0.023703685,
0.013482776, 0.018159864, 0.01098064, 0.014761168, 0.010590211,
0.001237805, 0.097323777, 0.088712748, 0.034759189, 0.022507656,
0.036512294, 0.048105471, 0.030822456, 0.07172102, 0.029038233,
0.032163273, 0.015176988, 0.041039802, -0.006245358, 0.049354849,
0.00318641, 0.012988646, 0.053365281, 0.03352103, 0.030454118,
-0.011862117, 0.015271336, 0.036371973, 0.045939313, 0.047864175,
0.053764664, 0.055199293, 0.072631781, 0.063949369, 0.09113885,
0.012533175, 0.049910727, 0.055676551, 0.008841404, 0.01962578,
0.015040302, 0.020496695, 0.054345313, 0.052533934), Investor = structure(c(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, 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, 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), .Label = c("Active", "Total", "America",
"Africa", "China", "Europe"), class = "factor"), Date = structure(c(6L,
11L, 16L, 1L, 7L, 12L, 17L, 2L, 8L, 13L, 18L, 3L, 9L, 14L, 19L,
4L, 10L, 15L, 20L, 5L, 6L, 11L, 16L, 1L, 7L, 12L, 17L, 2L, 8L,
13L, 18L, 3L, 9L, 14L, 19L, 4L, 10L, 15L, 20L, 5L, 6L, 11L, 16L,
1L, 7L, 12L, 17L, 2L, 8L, 13L, 18L, 3L, 9L, 14L, 19L, 4L, 10L,
15L, 20L, 5L, 6L, 11L, 16L, 1L, 7L, 12L, 17L, 2L, 8L, 13L, 18L,
3L, 9L, 14L, 19L, 4L, 10L, 15L, 20L, 5L, 6L, 11L, 16L, 1L, 7L,
12L, 17L, 2L, 8L, 13L, 18L, 3L, 9L, 14L, 19L, 4L, 10L, 15L, 20L,
5L, 6L, 11L, 16L, 1L, 7L, 12L, 17L, 2L, 8L, 13L, 18L, 3L, 9L,
14L, 19L, 4L, 10L, 15L, 20L, 5L), .Label = c("12/1/2012", "12/1/2013",
"12/1/2014", "12/1/2015", "12/1/2016", "3/1/2012", "3/1/2013",
"3/1/2014", "3/1/2015", "3/1/2016", "6/1/2012", "6/1/2013", "6/1/2014",
"6/1/2015", "6/1/2016", "9/1/2012", "9/1/2013", "9/1/2014", "9/1/2015",
"9/1/2016"), class = "factor")), class = "data.frame", row.names = c(NA,
-120L))

library(tidyverse)
df %>%
arrange(Investor, Date) %>%
group_by(Investor) %>%
mutate(return_coef = 1 + Total_Return,
return_coef_cuml = cumprod(return_coef),
return_cuml = return_coef_cuml - 1) %>%
ungroup()
# A tibble: 120 x 7
# Groups: Investor [6]
Quarter Total_Return Investor Date return_coef return_coef_cuml return_cuml
<fct> <dbl> <fct> <fct> <dbl> <dbl> <dbl>
1 2012Q4 0.0487 Active 12/1/2012 1.05 1.05 0.0487
2 2013Q4 0.0498 Active 12/1/2013 1.05 1.10 0.101
3 2014Q4 0.0184 Active 12/1/2014 1.02 1.12 0.121
4 2015Q4 0.0207 Active 12/1/2015 1.02 1.14 0.144
5 2016Q4 0.0362 Active 12/1/2016 1.04 1.19 0.186
6 2012Q1 0.0406 Active 3/1/2012 1.04 1.23 0.234
7 2013Q1 0.0371 Active 3/1/2013 1.04 1.28 0.280
8 2014Q1 0.0533 Active 3/1/2014 1.05 1.35 0.348
9 2015Q1 0.0395 Active 3/1/2015 1.04 1.40 0.401
10 2016Q1 0.0155 Active 3/1/2016 1.02 1.42 0.423

Removing greater than and less than characters and number of decimals in data frame in r

I am having a dataframe of 2 variables
structure(list(X1 = structure(c(17L, 27L, 6L, 1L, 28L, 1L, 1L,4L, 17L, 28L, 28L, 12L, 21L, 28L, 28L, 8L, 28L, 1L, 1L, 10L, 4L, 21L, 30L, 1L, 8L, 28L, 1L, 1L, 1L, 1L, 8L, 1L, 17L, 1L, 1L, 28L, 8L, 23L, 15L, 23L, 25L, 13L, 8L, 4L, 28L, 10L, 1L, 30L, 13L, 4L, 1L, 1L, 17L, 13L, 13L, 8L, 4L, 4L, 4L, 28L, 28L, 13L,1L, 4L, 28L, 1L, 1L, 1L, 1L, 1L, 12L, 2L, 6L, 1L, 8L, 1L, 21L, 1L, 21L, 1L, 30L,13L, 25L, 17L, 1L, 28L, 13L, 1L, 1L, 1L, 1L,8L, 30L, 25L, 28L, 4L, 1L, 13L, 17L, 4L,1L, 1L, 28L, 1L, 1L, 8L, 1L, 8L, 1L, 13L, 1L, 1L, 1L, 4L, 6L, 1L, 1L, 30L,1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 6L, 6L, 1L, 15L, 21L, 10L, 21L, 1L, 10L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 28L, 28L, 1L, 30L, 15L, 25L, 6L, 17L, 25L, 15L, 8L, 18L, 22L, 14L, 22L, 28L, 30L, 3L, 30L, 14L, 18L, 22L, 24L, 10L, 26L, 26L, 18L, 26L, 30L, 29L, 18L, 14L, 9L, 9L, 16L, 16L, 29L, 18L, 16L, 27L, 24L, 14L, 26L, 5L, 22L, 28L, 22L, 11L, 9L, 26L, 30L, 18L, 28L, 16L, 26L, 7L, 30L, 7L, 28L, 5L, 18L, 9L, 26L, 24L, 27L, 16L, 16L, 14L, 26L, 29L, 5L, 22L, 24L, 26L, 18L, 27L, 9L, 18L, 11L, 14L, 18L, 22L, 29L, 26L, 22L, 26L, 20L, 24L, 14L, 7L, 16L, 24L, 26L, 29L, 24L, 24L, 24L, 20L, 20L, 24L, 11L, 20L, 29L, 16L, 18L, 24L, 24L, 7L, 24L, 18L, 11L, 11L, 24L, 24L, 7L, 11L, 18L, 24L, 24L, 16L, 29L, 7L, 30L, 24L, 22L, 24L, 18L, 26L, 9L, 9L, 24L, 29L, 9L, 24L, 30L, 11L, 24L, 16L, 26L, 26L, 26L, 30L, 26L, 16L, 26L, 24L, 29L, 20L, 24L, 14L, 9L, 7L, 29L, 29L, 15L, 6L, 15L, 2L, 6L, 6L, 3L, 2L, 17L, 30L, 27L, 23L, 2L, 15L, 8L, 13L, 21L, 28L, 23L, 25L, 1L, 25L, 19L, 27L, 23L, 15L, 19L, 19L, 23L, 2L, 27L, 27L, 15L, 2L, 2L, 3L, 23L, 2L, 23L, 6L, 2L, 15L, 13L,1L, 1L, 13L, 28L, 1L, 1L, 28L, 21L, 1L, 28L, 4L, 1L, 17L, 17L, 13L, 21L, 1L, 1L, 1L, 17L, 1L, 1L, 17L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 13L, 1L, 1L, 1L, 1L, 8L,25L, 1L, 28L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 8L, 4L, 1L, 25L, 28L, 13L, 1L, 1L, 28L, 1L, 4L, 1L, 1L, 8L, 1L, 8L, 13L, 4L, 28L, 21L, 28L, 28L, 28L, 28L, 28L, 8L, 1L, 1L, 1L, 1L, 13L, 21L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 28L, 4L, 1L, 17L, 17L, 28L, 1L, 13L, 8L, 17L, 1L, 13L, 13L, 8L, 4L, 1L, 17L, 25L, 1L, 1L, 8L, 8L, 1L, 4L, 17L, 21L),
.Label = c("<8", ">1024", "1024", "11", "11.000000000000007", "128", "128.00000000000009", "16", "16.000000000000007", "181", "181.00000000000006", "22", "23", "23.000000000000011", "256", "256.00000000000017", "32", "32.000000000000014", "362", "362.00000000000017", "45", "45.000000000000014", "512", "512.00000000000045", "64", "64.000000000000028", "724", "8", "8.0000000000000018", "90"),
class = "factor"),
X2 = structure(c(7L, 2L, 2L, 8L, 18L, 4L, 13L, 18L, 8L, 13L, 8L, 18L, 12L, 13L, 18L, 16L, 7L, 5L, 1L, 16L, 18L, 18L, 18L, 12L, 7L, 1L, 4L, 4L, 2L,16L, 12L, 12L, 2L, 2L, 13L, 13L, 18L, 2L, 16L, 2L, 16L, 16L, 2L, 12L, 16L, 2L, 12L,2L, 2L, 16L, 16L, 2L, 2L, 2L, 2L, 2L, 7L, 18L, 18L, 18L, 13L, 18L, 13L, 18L, 9L, 13L, 8L, 4L, 1L, 13L, 8L, 2L, 16L, 12L, 7L, 7L, 18L, 18L, 18L, 12L, 16L, 7L, 16L, 7L, 12L, 12L, 16L, 12L, 13L, 13L, 12L, 16L, 12L, 12L, 7L, 7L, 13L,16L, 7L, 18L, 16L, 13L, 18L, 4L, 12L, 7L, 4L, 18L, 18L, 18L, 9L, 17L, 13L, 7L, 12L, 7L, 18L, 12L, 18L, 13L, 9L, 1L, 18L, 1L, 13L, 13L, 13L, 1L, 1L, 13L, 12L, 4L, 1L,1L, 4L, 12L, 9L, 1L, 1L, 1L, 2L, 12L, 9L, 2L, 18L, 2L, 18L, 7L, 12L, 1L, 9L, 9L, 7L, 18L, 9L, 18L, 1L, 12L, 13L, 12L, 16L, 7L, 12L, 7L, 16L, 2L, 12L,7L, 16L, 12L, 16L, 2L, 12L, 2L, 15L, 7L, 7L, 2L, 7L, 3L, 12L, 16L, 1L, 17L, 2L, 18L, 5L, 7L, 1L, 16L, 7L, 10L, 1L, 12L, 18L, 16L, 16L, 13L, 12L, 7L, 2L, 1L, 9L, 18L, 12L, 13L, 2L, 2L, 12L, 2L, 2L, 2L, 16L, 2L, 1L, 18L, 12L, 7L, 2L, 2L, 12L, 7L, 12L, 4L, 2L, 18L, 13L, 2L, 16L, 7L, 2L, 2L, 12L, 2L, 14L, 12L, 12L, 16L, 1L, 2L, 4L, 2L, 2L, 2L, 17L, 2L, 2L, 2L, 18L, 16L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 12L, 2L, 2L, 1L, 2L, 12L, 18L, 2L, 15L, 16L, 16L, 2L, 2L, 2L, 2L, 11L, 12L, 14L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 16L, 16L, 12L, 2L, 12L, 2L, 2L, 2L, 12L, 2L,16L, 2L, 12L, 14L, 7L, 2L, 4L, 14L, 2L, 16L, 15L, 7L, 16L, 18L, 2L, 16L, 2L, 2L, 12L, 12L, 2L, 2L, 4L, 2L, 2L, 2L, 16L, 2L, 12L,18L, 3L, 16L, 2L, 2L, 13L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 16L, 16L, 2L, 2L, 4L, 4L, 11L, 13L, 4L, 4L, 8L, 4L, 13L, 1L, 4L, 1L, 1L, 2L, 2L, 11L, 18L, 8L, 8L, 4L, 7L, 8L, 4L, 8L, 4L, 4L, 8L, 8L, 1L, 4L, 8L, 4L, 13L, 1L, 6L, 1L, 17L, 2L, 2L, 8L, 18L, 8L, 8L, 4L, 7L, 8L, 17L, 8L, 4L, 1L, 4L, 13L, 1L, 2L, 4L, 16L, 13L, 4L, 4L, 17L, 4L, 7L, 4L, 4L, 1L, 1L, 4L, 1L, 17L, 8L, 1L, 8L, 1L, 4L, 1L, 8L, 8L, 8L, 1L, 13L, 16L, 16L, 17L, 8L, 13L, 1L, 4L, 7L, 1L, 1L, 4L, 4L, 8L, 6L, 4L, 1L, 12L, 13L, 8L, 4L, 4L, 18L, 2L, 4L, 8L, 13L, 17L,13L, 18L, 7L, 16L, 7L, 1L, 13L, 8L, 13L, 4L, 1L, 7L),
.Label = c("<8", ">1024", "1024", "11", "128", "16", "181", "22", "23", "256", "32", "362", "45", "512", "64", "724", "8", "90"), class = "factor")),
.Names = c("X1", "X2"),
row.names = c(NA, -471L),
class = "data.frame")
I have 2 questions
1) Each one is having some greater than values and some with less than values. i want to remove the > and < characters from data frame and retain only the number in the dataframe. I can do it in excel but i want to learn the code for learning it in R.
2) I want to reduce the number of decimals to integer/whole number as some are having more number of decimals.
It may be a small question, but i am struggling to do this. i highly appreciate for this help.

You can use dplyr::mutate_all and stringr::str_replace_all.
Decimals are directly approximated by as.numeric since it is ~10^(-13)magnitude.
your_df <- structure(list(X1 = structure(c(17L, 27L, 6L, 1L, 28L, 1L, 1L,4L, 17L, 28L, 28L, 12L, 21L, 28L, 28L, 8L, 28L, 1L, 1L, 10L, 4L, 21L, 30L, 1L, 8L, 28L, 1L, 1L, 1L, 1L, 8L, 1L, 17L, 1L, 1L, 28L, 8L, 23L, 15L, 23L, 25L, 13L, 8L, 4L, 28L, 10L, 1L, 30L, 13L, 4L, 1L, 1L, 17L, 13L, 13L, 8L, 4L, 4L, 4L, 28L, 28L, 13L,1L, 4L, 28L, 1L, 1L, 1L, 1L, 1L, 12L, 2L, 6L, 1L, 8L, 1L, 21L, 1L, 21L, 1L, 30L,13L, 25L, 17L, 1L, 28L, 13L, 1L, 1L, 1L, 1L,8L, 30L, 25L, 28L, 4L, 1L, 13L, 17L, 4L,1L, 1L, 28L, 1L, 1L, 8L, 1L, 8L, 1L, 13L, 1L, 1L, 1L, 4L, 6L, 1L, 1L, 30L,1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 6L, 6L, 1L, 15L, 21L, 10L, 21L, 1L, 10L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 28L, 28L, 1L, 30L, 15L, 25L, 6L, 17L, 25L, 15L, 8L, 18L, 22L, 14L, 22L, 28L, 30L, 3L, 30L, 14L, 18L, 22L, 24L, 10L, 26L, 26L, 18L, 26L, 30L, 29L, 18L, 14L, 9L, 9L, 16L, 16L, 29L, 18L, 16L, 27L, 24L, 14L, 26L, 5L, 22L, 28L, 22L, 11L, 9L, 26L, 30L, 18L, 28L, 16L, 26L, 7L, 30L, 7L, 28L, 5L, 18L, 9L, 26L, 24L, 27L, 16L, 16L, 14L, 26L, 29L, 5L, 22L, 24L, 26L, 18L, 27L, 9L, 18L, 11L, 14L, 18L, 22L, 29L, 26L, 22L, 26L, 20L, 24L, 14L, 7L, 16L, 24L, 26L, 29L, 24L, 24L, 24L, 20L, 20L, 24L, 11L, 20L, 29L, 16L, 18L, 24L, 24L, 7L, 24L, 18L, 11L, 11L, 24L, 24L, 7L, 11L, 18L, 24L, 24L, 16L, 29L, 7L, 30L, 24L, 22L, 24L, 18L, 26L, 9L, 9L, 24L, 29L, 9L, 24L, 30L, 11L, 24L, 16L, 26L, 26L, 26L, 30L, 26L, 16L, 26L, 24L, 29L, 20L, 24L, 14L, 9L, 7L, 29L, 29L, 15L, 6L, 15L, 2L, 6L, 6L, 3L, 2L, 17L, 30L, 27L, 23L, 2L, 15L, 8L, 13L, 21L, 28L, 23L, 25L, 1L, 25L, 19L, 27L, 23L, 15L, 19L, 19L, 23L, 2L, 27L, 27L, 15L, 2L, 2L, 3L, 23L, 2L, 23L, 6L, 2L, 15L, 13L,1L, 1L, 13L, 28L, 1L, 1L, 28L, 21L, 1L, 28L, 4L, 1L, 17L, 17L, 13L, 21L, 1L, 1L, 1L, 17L, 1L, 1L, 17L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 13L, 1L, 1L, 1L, 1L, 8L,25L, 1L, 28L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 8L, 4L, 1L, 25L, 28L, 13L, 1L, 1L, 28L, 1L, 4L, 1L, 1L, 8L, 1L, 8L, 13L, 4L, 28L, 21L, 28L, 28L, 28L, 28L, 28L, 8L, 1L, 1L, 1L, 1L, 13L, 21L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 28L, 4L, 1L, 17L, 17L, 28L, 1L, 13L, 8L, 17L, 1L, 13L, 13L, 8L, 4L, 1L, 17L, 25L, 1L, 1L, 8L, 8L, 1L, 4L, 17L, 21L), .Label = c("<8", ">1024", "1024", "11", "11.000000000000007", "128", "128.00000000000009", "16", "16.000000000000007", "181", "181.00000000000006", "22", "23", "23.000000000000011", "256", "256.00000000000017", "32", "32.000000000000014", "362", "362.00000000000017", "45", "45.000000000000014", "512", "512.00000000000045", "64", "64.000000000000028", "724", "8", "8.0000000000000018", "90"), class = "factor"), X2 = structure(c(7L, 2L, 2L, 8L, 18L, 4L, 13L, 18L, 8L, 13L, 8L, 18L, 12L, 13L, 18L, 16L, 7L, 5L, 1L, 16L, 18L, 18L, 18L, 12L, 7L, 1L, 4L, 4L, 2L,16L, 12L, 12L, 2L, 2L, 13L, 13L, 18L, 2L, 16L, 2L, 16L, 16L, 2L, 12L, 16L, 2L, 12L,2L, 2L, 16L, 16L, 2L, 2L, 2L, 2L, 2L, 7L, 18L, 18L, 18L, 13L, 18L, 13L, 18L, 9L, 13L, 8L, 4L, 1L, 13L, 8L, 2L, 16L, 12L, 7L, 7L, 18L, 18L, 18L, 12L, 16L, 7L, 16L, 7L, 12L, 12L, 16L, 12L, 13L, 13L, 12L, 16L, 12L, 12L, 7L, 7L, 13L,16L, 7L, 18L, 16L, 13L, 18L, 4L, 12L, 7L, 4L, 18L, 18L, 18L, 9L, 17L, 13L, 7L, 12L, 7L, 18L, 12L, 18L, 13L, 9L, 1L, 18L, 1L, 13L, 13L, 13L, 1L, 1L, 13L, 12L, 4L, 1L,1L, 4L, 12L, 9L, 1L, 1L, 1L, 2L, 12L, 9L, 2L, 18L, 2L, 18L, 7L, 12L, 1L, 9L, 9L, 7L, 18L, 9L, 18L, 1L, 12L, 13L,
12L, 16L, 7L, 12L, 7L, 16L, 2L, 12L,7L, 16L, 12L, 16L, 2L, 12L, 2L, 15L, 7L, 7L, 2L, 7L, 3L, 12L, 16L, 1L, 17L, 2L, 18L, 5L, 7L, 1L, 16L, 7L, 10L, 1L, 12L, 18L, 16L, 16L, 13L, 12L, 7L, 2L, 1L, 9L, 18L, 12L, 13L, 2L, 2L, 12L, 2L, 2L, 2L, 16L, 2L, 1L, 18L, 12L, 7L, 2L, 2L, 12L, 7L, 12L, 4L, 2L, 18L, 13L, 2L, 16L, 7L, 2L, 2L, 12L, 2L, 14L, 12L, 12L, 16L, 1L, 2L, 4L, 2L, 2L, 2L, 17L, 2L, 2L, 2L, 18L, 16L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 12L, 2L, 2L, 1L, 2L, 12L, 18L, 2L, 15L, 16L, 16L, 2L, 2L, 2L, 2L, 11L, 12L, 14L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 16L, 16L, 12L, 2L, 12L, 2L, 2L, 2L, 12L, 2L,16L, 2L, 12L, 14L, 7L, 2L, 4L, 14L, 2L, 16L, 15L, 7L, 16L, 18L, 2L, 16L, 2L, 2L, 12L, 12L, 2L, 2L, 4L, 2L, 2L, 2L, 16L, 2L, 12L,18L, 3L, 16L, 2L, 2L, 13L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 16L, 16L, 2L, 2L, 4L, 4L, 11L, 13L, 4L, 4L, 8L, 4L, 13L, 1L, 4L, 1L, 1L, 2L, 2L, 11L, 18L, 8L, 8L, 4L, 7L, 8L, 4L, 8L, 4L, 4L, 8L, 8L, 1L, 4L, 8L, 4L, 13L, 1L, 6L, 1L, 17L, 2L, 2L, 8L, 18L, 8L, 8L, 4L, 7L, 8L, 17L, 8L, 4L, 1L, 4L, 13L, 1L, 2L, 4L, 16L, 13L, 4L, 4L, 17L, 4L, 7L, 4L, 4L, 1L, 1L, 4L, 1L, 17L, 8L, 1L, 8L, 1L, 4L, 1L, 8L, 8L, 8L, 1L, 13L, 16L, 16L, 17L, 8L, 13L, 1L, 4L, 7L, 1L, 1L, 4L, 4L, 8L, 6L, 4L, 1L, 12L, 13L, 8L, 4L, 4L, 18L, 2L, 4L, 8L, 13L, 17L,13L, 18L, 7L, 16L, 7L, 1L, 13L, 8L, 13L, 4L, 1L, 7L),
.Label = c("<8", ">1024", "1024", "11", "128", "16", "181", "22", "23", "256", "32", "362", "45", "512", "64", "724", "8", "90"), class = "factor")), .Names = c("X1", "X2"), row.names = c(NA, -471L), class = "data.frame")
library(dplyr)
library(stringr)
mutate_all(your_df, function(x) as.numeric(str_replace_all(x, pattern = "<|>", replacement = "")))
#> X1 X2
#> 1 32 181
#> 2 724 1024
#> 3 128 1024
#> 4 8 22
#> 5 8 90
#> 6 8 11
#> 7 8 45
#> 8 11 90
#> 9 32 22
#> 10 8 45
#> 11 8 22
#> 12 22 90
#> 13 45 362

You can do this with base R:
my_df <- as.data.frame(sapply(my_df, gsub, pattern = "<|>", replacement = ""))
my_df <- as.data.frame(sapply(my_df, as.numeric))
my_df
# X1 X2
# 1 8 23
# 2 8 90
# 3 8 8
# 4 8 362
# 5 8 45
# 6 90 362
# 7 256 724
# 8 64 181
# 9 128 362
# 10 32 181
# 11 64 724
# 12 256 1024
# 13 16 362
# 14 32.000000000000014 181
# 15 45.000000000000014 724
# 16 23.000000000000011 362
# 17 45.000000000000014 724
# 18 8 1024
# 19 90 362
# 20 1024 1024
# 21 90 64
# 22 23.000000000000011 181
# 23 32.000000000000014 181
# 24 45.000000000000014 1024
# 25 512.00000000000045 181
If you only want to round the decimals, but keep the < and > signs you can do the following (without perfoming the steps above):
sapply(my_df,
function(x) paste0(gsub(x, pattern = "\\d|\\.", replacement = ""),
round(as.numeric(gsub(x, pattern = "<|>", replacement = "")))))
# X1 X2
# [1,] "<8" "23"
# [2,] "<8" "90"
# [3,] "8" "<8"
# [4,] "8" "362"
# [5,] "<8" "45"
# [6,] "90" "362"
# [7,] "256" "724"
# [8,] "64" "181"
# [9,] "128" "362"
# [10,] "32" "181"
# [11,] "64" "724"
# [12,] "256" ">1024"
# [13,] "16" "362"
# [14,] "32" "181"
# [15,] "45" "724"
# [16,] "23" "362"
# [17,] "45" "724"
# [18,] "8" ">1024"
# [19,] "90" "362"
# [20,] "1024" ">1024"
# [21,] "90" "64"
# [22,] "23" "181"
# [23,] "32" "181"
# [24,] "45" ">1024"
# [25,] "512" "181"
How it works
sapply takes the data.frame and applys the function specified after the comma to each column of the data.frame. gsub substitute the pattern with the replacement in x (a column of the data.frame). There I used regular expression, so \\d means all digits (0-9) and \\. the dot and | combines them with a OR logic.
stringr-solution
There's a shorter solution with stringr:
library(stringr)
sapply(my_df,
function(x) str_c(str_extract(x, "[<>]?"),
round(as.numeric(str_extract(x, "\\d+")))))
Here the pattern we want to have are extracted and then combined again after rounding the decimals.
Data
my_df <-
structure(list(X1 = structure(c(1L, 1L, 28L, 28L, 1L, 30L, 15L,
25L, 6L, 17L, 25L, 15L, 8L, 18L,
22L, 14L, 22L, 28L, 30L, 3L, 30L,
14L, 18L, 22L, 24L),
.Label = c("<8", ">1024", "1024", "11",
"11.000000000000007", "128",
"128.00000000000009", "16",
"16.000000000000007", "181",
"181.00000000000006", "22",
"23", "23.000000000000011",
"256", "256.00000000000017",
"32", "32.000000000000014",
"362", "362.00000000000017",
"45", "45.000000000000014",
"512", "512.00000000000045",
"64", "64.000000000000028",
"724", "8",
"8.0000000000000018", "90"),
class = "factor"),
X2 = structure(c(9L, 18L, 1L, 12L, 13L, 12L, 16L, 7L,
12L, 7L, 16L, 2L, 12L, 7L, 16L, 12L,
16L, 2L, 12L, 2L, 15L, 7L, 7L, 2L, 7L),
.Label = c("<8", ">1024", "1024", "11",
"128", "16", "181", "22", "23",
"256", "32", "362", "45", "512",
"64", "724", "8", "90"),
class = "factor")),
.Names = c("X1", "X2"),
row.names = c(NA, -25L),
class = "data.frame")
# X1 X2
# 1 <8 23
# 2 <8 90
# 3 8 <8
# 4 8 362
# 5 <8 45
# 6 90 362
# 7 256 724
# 8 64 181
# 9 128 362
# 10 32 181
# 11 64 724
# 12 256 >1024
# 13 16 362
# 14 32.000000000000014 181
# 15 45.000000000000014 724
# 16 23.000000000000011 362
# 17 45.000000000000014 724
# 18 8 >1024
# 19 90 362
# 20 1024 >1024
# 21 90 64
# 22 23.000000000000011 181
# 23 32.000000000000014 181
# 24 45.000000000000014 >1024
# 25 512.00000000000045 181

R: Shiny - How to subset and then make a bargraph based on daterangeInput

i've this data frame:
date sessions Fuentes
1 2014-12-01 197 Directo
2 2014-12-01 1 Referencias
3 2014-12-01 7 Social Media
4 2014-12-01 13 SEO
5 2014-12-01 1 Email
6 2014-12-01 1 Referencias
This is the data after using dput():
structure(list(date = structure(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, 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, 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,
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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 12L, 12L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L,
27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L,
27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L,
27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L,
27L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 29L, 29L,
29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L,
29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L,
29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L,
29L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, 31L, 31L,
31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L,
31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L), .Label = c("2014-12-01",
"2014-12-02", "2014-12-03", "2014-12-04", "2014-12-05", "2014-12-06",
"2014-12-07", "2014-12-08", "2014-12-09", "2014-12-10", "2014-12-11",
"2014-12-12", "2014-12-13", "2014-12-14", "2014-12-15", "2014-12-16",
"2014-12-17", "2014-12-18", "2014-12-19", "2014-12-20", "2014-12-21",
"2014-12-22", "2014-12-23", "2014-12-24", "2014-12-25", "2014-12-26",
"2014-12-27", "2014-12-28", "2014-12-29", "2014-12-30", "2014-12-31"
), class = "factor"), sessions = c(197L, 1L, 7L, 13L, 1L, 1L,
10L, 1L, 3L, 3L, 5L, 3L, 566L, 1L, 27L, 159L, 7L, 1L, 6L, 1L,
1L, 4L, 1L, 6L, 10L, 129L, 1L, 7L, 2L, 1L, 10L, 1L, 5L, 6L, 9L,
1L, 28L, 1L, 7L, 386L, 1L, 146L, 1L, 89L, 41L, 9L, 1L, 1L, 1L,
6L, 3L, 4L, 182L, 1L, 5L, 8L, 2L, 1L, 1L, 4L, 1L, 1L, 2L, 3L,
2L, 524L, 4L, 26L, 1L, 152L, 4L, 2L, 3L, 1L, 2L, 2L, 1L, 5L,
10L, 142L, 1L, 1L, 8L, 1L, 3L, 1L, 1L, 1L, 1L, 7L, 4L, 13L, 3L,
375L, 3L, 2L, 147L, 1L, 101L, 29L, 4L, 1L, 1L, 2L, 3L, 1L, 1L,
2L, 1L, 7L, 5L, 5L, 224L, 3L, 12L, 1L, 7L, 2L, 1L, 4L, 141L,
4L, 632L, 2L, 2L, 32L, 1L, 138L, 1L, 1L, 9L, 5L, 1L, 1L, 1L,
2L, 1L, 6L, 3L, 139L, 4L, 1L, 9L, 1L, 1L, 5L, 9L, 8L, 36L, 1L,
537L, 1L, 2L, 5L, 3L, 174L, 1L, 106L, 39L, 9L, 2L, 2L, 2L, 3L,
1L, 6L, 3L, 2L, 689L, 1L, 14L, 2L, 2L, 35L, 1L, 15L, 1L, 1L,
1L, 3L, 20L, 465L, 1L, 3269L, 1L, 2L, 1L, 9L, 1L, 32L, 6L, 2L,
293L, 1L, 3L, 1L, 11L, 2L, 1L, 9L, 10L, 1L, 1L, 1L, 1L, 1L, 2L,
7L, 2L, 433L, 1L, 4L, 1L, 1L, 3L, 19L, 1L, 2L, 1L, 1L, 12L, 1L,
4L, 1L, 1L, 3L, 37L, 10L, 88L, 6L, 1808L, 5L, 4L, 451L, 5L, 219L,
112L, 4L, 3L, 1L, 6L, 1L, 2L, 3L, 5L, 10L, 2L, 264L, 8L, 1L,
1L, 1L, 17L, 1L, 1L, 7L, 1L, 1L, 4L, 6L, 516L, 1L, 948L, 2L,
1L, 2L, 1L, 33L, 1L, 1L, 133L, 1L, 2L, 1L, 5L, 11L, 1L, 4L, 1L,
1L, 1L, 6L, 10L, 5L, 168L, 1L, 1L, 5L, 1L, 10L, 1L, 1L, 3L, 9L,
1L, 2L, 1L, 8L, 3L, 98L, 1L, 548L, 1L, 1L, 177L, 97L, 17L, 4L,
1L, 6L, 2L, 1L, 2L, 1L, 1L, 5L, 4L, 5L, 235L, 1L, 2L, 9L, 2L,
19L, 1L, 2L, 2L, 1L, 1L, 3L, 6L, 5L, 396L, 1209L, 1L, 2L, 1L,
41L, 1L, 125L, 3L, 5L, 1L, 4L, 1L, 1L, 4L, 1L, 3L, 1L, 1L, 5L,
2L, 121L, 2L, 1L, 1L, 10L, 1L, 1L, 4L, 1L, 2L, 10L, 3L, 75L,
5L, 632L, 1L, 2L, 2L, 178L, 1L, 67L, 33L, 6L, 1L, 1L, 1L, 2L,
1L, 12L, 3L, 194L, 1L, 1L, 1L, 1L, 1L, 20L, 1L, 1L, 6L, 1L, 1L,
1L, 1L, 1L, 3L, 2L, 296L, 1L, 1L, 979L, 6L, 4L, 1L, 33L, 1L,
109L, 5L, 2L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 6L, 3L, 118L,
1L, 1L, 15L, 1L, 1L, 1L, 1L, 1L, 4L, 2L, 1L, 18L, 6L, 53L, 3L,
584L, 2L, 1L, 2L, 172L, 2L, 100L, 27L, 9L, 2L, 1L, 2L, 1L, 1L,
1L, 11L, 3L, 202L, 6L, 20L, 2L, 1L, 1L, 4L, 1L, 8L, 2L, 292L,
719L, 2L, 1L, 2L, 29L, 106L, 7L, 3L, 8L, 2L, 2L, 1L, 1L, 1L,
7L, 3L, 139L, 4L, 1L, 2L, 17L, 1L, 2L, 3L, 2L, 20L, 53L, 3L,
530L, 2L, 1L, 1L, 172L, 113L, 23L, 2L, 1L, 4L, 2L, 2L, 1L, 7L,
891L, 10L, 1L, 1L, 12L, 1L, 1L, 1L, 1L, 1L, 4L, 5L, 6L, 1312L,
1L, 1L, 1168L, 1L, 4L, 2L, 39L, 133L, 3L, 13L, 5L, 2L, 6L, 1L,
1L, 1L, 13L, 3L, 297L, 4L, 1L, 1L, 9L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 25L, 182L, 1L, 776L, 2L, 1L, 1L, 260L, 2L, 115L, 52L,
14L, 2L, 4L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 14L,
2L, 731L, 7L, 2L, 1L, 16L, 1L, 1L, 3L, 2L, 1L, 1L, 11L, 6L, 294L,
1L, 1135L, 1L, 3L, 1L, 6L, 1L, 36L, 1L, 1L, 126L, 4L, 1L, 1L,
4L, 11L, 1L, 2L, 1L, 2L, 2L, 1L, 6L, 355L, 3L, 9L, 1L, 4L, 1L,
13L, 2L, 1L, 1L, 7L, 1L, 1L, 22L, 5L, 67L, 1L, 2L, 926L, 1L,
1L, 1L, 1L, 2L, 1L, 208L, 1L, 1L, 136L, 44L, 12L, 1L, 1L, 2L,
2L, 4L, 2L, 1L, 1L, 1L, 1L, 8L, 9L, 1L, 198L, 1L, 8L, 13L, 2L,
4L, 1L, 4L, 2L, 205L, 568L, 1L, 1L, 19L, 94L, 2L, 3L, 8L, 1L,
1L, 1L, 1L, 1L, 1L, 8L, 157L, 4L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 12L, 28L, 3L, 444L, 3L, 1L, 2L, 118L, 2L, 75L, 27L,
1L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 6L, 7L, 166L, 1L, 1L, 11L, 1L,
1L, 3L, 1L, 1L, 1L, 3L, 203L, 644L, 2L, 1L, 1L, 2L, 26L, 1L,
4L, 75L, 1L, 4L, 2L, 5L, 1L, 1L, 1L, 1L, 1L, 4L, 155L, 1L, 1L,
1L, 3L, 4L, 1L, 2L, 6L, 1L, 36L, 1L, 2L, 446L, 3L, 1L, 99L, 86L,
27L, 1L, 2L, 1L, 1L, 3L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 7L,
1L, 7L, 159L, 1L, 3L, 12L, 1L, 3L, 1L, 1L, 8L, 174L, 733L, 1L,
1L, 1L, 1L, 22L, 2L, 84L, 1L, 1L, 6L, 3L, 1L, 1L, 1L, 3L, 1L,
100L, 6L, 2L, 3L, 1L, 8L, 3L, 38L, 7L, 502L, 2L, 1L, 86L, 6L,
83L, 24L, 6L, 1L, 1L, 1L, 2L, 2L, 321L, 8L, 11L, 1L, 4L, 1L,
2L, 2L, 13L, 191L, 1L, 5L, 1417L, 1L, 6L, 1L, 1L, 28L, 2L, 1L,
150L, 1L, 1L, 7L, 1L, 3L, 2L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 1L,
4L, 1L, 218L, 3L, 1L, 1L, 8L, 1L, 2L, 1L, 1L, 16L, 4L, 45L, 1L,
3L, 879L, 3L, 1L, 1L, 2L, 207L, 2L, 115L, 44L, 1L, 3L, 1L, 1L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 171L, 4L, 1L, 1L, 7L, 1L, 5L,
4L, 178L, 614L, 3L, 1L, 3L, 1L, 5L, 20L, 1L, 94L, 3L, 4L, 8L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 121L, 1L, 1L, 6L, 1L, 1L, 3L,
2L, 1L, 7L, 3L, 31L, 1L, 1L, 433L, 1L, 3L, 23L, 94L, 79L, 25L,
1L, 2L, 2L, 6L, 2L, 160L, 3L, 6L, 1L, 3L, 2L, 2L, 3L, 1L, 568L,
1L, 2L, 5L, 15L, 5L, 86L, 1L, 2L, 4L, 8L, 3L, 4L, 1L, 1L, 2L,
1L, 118L, 9L, 7L, 1L, 2L, 2L, 11L, 3L, 10L, 1L, 530L, 2L, 3L,
2L, 121L, 1L, 1L, 72L, 34L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 7L, 4L,
326L, 13L, 1L, 1L, 18L, 1L, 2L, 8L, 4L, 2L, 2L, 1L, 1271L, 1L,
1L, 1L, 2L, 3L, 17L, 2L, 161L, 3L, 1L, 14L, 1L, 1L, 2L, 1L, 1L,
4L, 1L, 1L, 10L, 1L, 195L, 1L, 6L, 1L, 1L, 1L, 1L, 23L, 1L, 1L,
2L, 1L, 1L, 2L, 20L, 4L, 10L, 1L, 1050L, 1L, 1L, 3L, 1L, 1L,
1L, 19L, 1L, 196L, 134L, 52L, 4L, 1L, 1L, 1L, 1L, 2L, 3L, 3L,
1L, 1L, 5L, 6L, 1L, 120L, 1L, 3L, 6L, 1L, 1L, 2L, 1L, 2L, 371L,
1L, 1L, 7L, 74L, 2L, 11L, 1L, 3L, 84L, 1L, 1L, 3L, 4L, 14L, 2L,
1L, 5L, 1L, 6L, 1L, 382L, 3L, 1L, 2L, 6L, 2L, 69L, 1L, 54L, 17L,
2L, 1L, 1L, 3L, 7L, 1L, 168L, 2L, 1L, 7L, 1L, 1L, 1L, 1L, 2L,
1L, 5L, 374L, 2L, 5L, 7L, 2L, 69L, 1L, 10L, 6L, 85L, 1L, 1L,
16L, 1L, 1L, 1L, 5L, 2L, 2L, 393L, 3L, 17L, 53L, 75L, 22L, 2L,
2L, 1L, 1L, 1L, 7L, 3L, 1L, 136L, 1L, 7L, 3L, 3L, 2L, 1L, 2L,
488L, 1L, 4L, 25L, 1L, 71L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 126L,
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2L, 197L, 47L, 39L, 19L, 1L), Fuentes = structure(c(3L, 5L, 6L,
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5L, 4L, 5L, 2L, 5L, 5L, 5L, 5L, 6L, 3L, 5L, 4L, 4L, 6L, 4L, 5L,
5L, 5L, 7L, 6L, 5L, 5L, 4L, 5L, 5L, 4L, 4L, 5L, 5L, 5L, 3L, 6L,
6L, 5L, 5L, 7L, 6L, 5L, 5L, 5L, 5L, 7L, 5L, 4L, 2L, 5L, 5L, 5L,
5L, 5L, 6L, 7L, 3L, 4L, 6L, 5L, 5L, 4L, 5L, 5L, 7L, 1L, 6L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 6L, 3L, 6L, 6L, 6L, 5L, 5L,
5L, 5L, 7L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 3L, 6L, 4L, 6L, 5L,
5L, 7L, 7L, 5L, 6L, 5L, 5L, 5L, 5L), .Label = c("Adwords", "Campañas",
"Directo", "Email", "Referencias", "SEO", "Social Media"), class = "factor")), .Names = c("date",
"sessions", "Fuentes"), class = "data.frame", row.names = c(NA,
-1724L))
In a Shiny App, want to plot bars for Fuentes, acording to a data range specified by the user. I use daterangeInput in my ui.R, but cannot get it to plot what I need.
My ui.R
library(shiny)
# Define the overall UI
shinyUI(
# Use a fluid Bootstrap layout
fluidPage(
# Give the page a title
br(),
br(),
titlePanel("Visitas por fuente"),
# Generate a row with a sidebar
sidebarLayout(
# Define the sidebar with one input
sidebarPanel(
dateRangeInput("dates", label = h3("Date range"),
start = "2014-12-01", end = "2014-12-31")
),
# Create a spot for the barplot
mainPanel(
plotOutput("VisitasFuente")
)
)
)
)
My server.R ### Edited - Now can plot, but labels appeare as a blur from botton to top.
library(ggplot2)
library(dplyr)
require(scales)
Visitas_Por_Fuente <- read.csv("D:\\RCoursera\\Movistar- App-2\\Visitas_Por_Fuente_Dic.csv")
labels = c("Directo", "Email", "Referencias", "SEO", "Social Media")
Visitas_Por_Fuente$date <- as.Date(Visitas_Por_Fuente$date)
shinyServer(
function(input, output) {
output$VisitasFuente <- renderPlot({
# Filter the data based on user selection month
date_seq <- seq(input$dates[1], input$dates[2], by = "day")
#VisitasData <- filter(Visitas_Por_Fuente, date >= input$dates[1],
# date <= input$dates[2])
VisitasData <- filter(Visitas_Por_Fuente, date %in% date_seq & Fuentes %in% labels)
# Bar graph using ggplot2 library
ggplot(VisitasData, aes(factor(Fuentes), sessions, fill = Fuentes)) +
geom_bar(stat="identity", position = "dodge") +
geom_text(aes(label = comma(sessions)), position=position_dodge(width=0.9), vjust=-0.25) +
scale_fill_manual(breaks = c("0", "1", "3", "6", "9"),
labels = c("Directo", "Email", "References",
"SEO", "Social Media"),
values = c("#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2"))
})
})
This was fixied by loading the corresponded packages
Thanks to #goodtimeslim, i've made the recomendations you gave me. But now i get:
Error in match(x, table, nomatch = 0L) :
'match' requires vector arguments
What could it be? Thanks again.
#

Okay, first thing, you need to tell R that Visitas_Por_Fuente$date is a date, with Visitas_Por_Fuente$date <- as.Date(Visitas_Por_Fuente$date) .
You can do this right after you import your data at the beginning.
Now you want to create a range of dates, in your server file, using the date inputs, like so:
date_seq <- seq(input$dates[1], input$dates[2], by = "day")
Now you just need to change your filter, so that the date is in that sequence, like so:
VisitasData <- filter(Visitas_Por_Fuente, date %in% date_seq)
Now I admit that doesn't solve everything, I was getting some weird errors with your ggplot code, but this will solve the subsetting issue.
This issue with your ggplot is that your data has 7 variables, but you're only giving it information for 5. If you just want those 5 variables, then at the top (right after you import your data), write this:
labels = c("Directo", "Email", "References", "SEO", "Social Media")
and then, for your plot, get rid of the scale_manual line and replace it with:
scale_x_discrete(limit = labels)
That'll force those 5 on there, and at the moment, it'll do it in whatever color R wants. I'll let you figure out the rest if you want to change it.
Let me know if this is clear enough or if you just want the whole server.r code.
edit: Okay, I fixed it. You had an error in your code, you have "References", but in your data, it's "Referencias". So now, assuming you still want those five variables only, and not all 7, do this: change labels (at the top) like so:
labels = c("Directo", "Email", "Referencias", "SEO", "Social Media")
Change your filter like so:
VisitasData <- filter(Visitas_Por_Fuente, date %in% date_seq & Fuentes %in% labels)
Then you can get rid of that scale_x_discrete line I had, and put your line back in. It should all work now. (Except edit your labels in the manual_scale part to reflect the proper "Referencias".
edit 2: Here's the full server.r that runs just fine on my computer. I've made some slight changes for consistency/clarity, but otherwise it's mostly the same.
library(ggplot2)
Visitas_Por_Fuente <- read.csv("visitas.csv") ## put your path here
labelsF = c("Directo", "Email", "Referencias", "SEO", "Social Media")
Visitas_Por_Fuente$date <- as.Date(Visitas_Por_Fuente$date)
shinyServer(
function(input, output) {
output$VisitasFuente <- renderPlot({
# Filter the data based on user selection month
date_seq <- seq(input$dates[1], input$dates[2], by = "day")
#VisitasData <- filter(Visitas_Por_Fuente, date >= input$dates[1],
# date <= input$dates[2])
VisitasData <- filter(Visitas_Por_Fuente, date %in% date_seq & Fuentes %in% labelsF)
# Bar graph using ggplot2 library
ggplot(VisitasData, aes(factor(Fuentes), sessions, fill = Fuentes)) +
geom_bar(stat="identity", position = "dodge") +
scale_fill_manual(breaks = c("0", "1", "3", "6", "9"),
labels = labelsF,
values = c("#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2"))
})
})

R: Shiny and Ggplot2 show different plot with same code

Have data for everyday of dicember 2014. want to plot a barchart according to the selection of dates:
Original data:
structure(list(date = structure(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, 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, 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,
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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 12L, 12L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L,
27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L,
27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L,
27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L,
27L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 29L, 29L,
29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L,
29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L,
29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L,
29L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, 31L, 31L,
31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L,
31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L), .Label = c("2014-12-01",
"2014-12-02", "2014-12-03", "2014-12-04", "2014-12-05", "2014-12-06",
"2014-12-07", "2014-12-08", "2014-12-09", "2014-12-10", "2014-12-11",
"2014-12-12", "2014-12-13", "2014-12-14", "2014-12-15", "2014-12-16",
"2014-12-17", "2014-12-18", "2014-12-19", "2014-12-20", "2014-12-21",
"2014-12-22", "2014-12-23", "2014-12-24", "2014-12-25", "2014-12-26",
"2014-12-27", "2014-12-28", "2014-12-29", "2014-12-30", "2014-12-31"
), class = "factor"), sessions = c(197L, 1L, 7L, 13L, 1L, 1L,
10L, 1L, 3L, 3L, 5L, 3L, 566L, 1L, 27L, 159L, 7L, 1L, 6L, 1L,
1L, 4L, 1L, 6L, 10L, 129L, 1L, 7L, 2L, 1L, 10L, 1L, 5L, 6L, 9L,
1L, 28L, 1L, 7L, 386L, 1L, 146L, 1L, 89L, 41L, 9L, 1L, 1L, 1L,
6L, 3L, 4L, 182L, 1L, 5L, 8L, 2L, 1L, 1L, 4L, 1L, 1L, 2L, 3L,
2L, 524L, 4L, 26L, 1L, 152L, 4L, 2L, 3L, 1L, 2L, 2L, 1L, 5L,
10L, 142L, 1L, 1L, 8L, 1L, 3L, 1L, 1L, 1L, 1L, 7L, 4L, 13L, 3L,
375L, 3L, 2L, 147L, 1L, 101L, 29L, 4L, 1L, 1L, 2L, 3L, 1L, 1L,
2L, 1L, 7L, 5L, 5L, 224L, 3L, 12L, 1L, 7L, 2L, 1L, 4L, 141L,
4L, 632L, 2L, 2L, 32L, 1L, 138L, 1L, 1L, 9L, 5L, 1L, 1L, 1L,
2L, 1L, 6L, 3L, 139L, 4L, 1L, 9L, 1L, 1L, 5L, 9L, 8L, 36L, 1L,
537L, 1L, 2L, 5L, 3L, 174L, 1L, 106L, 39L, 9L, 2L, 2L, 2L, 3L,
1L, 6L, 3L, 2L, 689L, 1L, 14L, 2L, 2L, 35L, 1L, 15L, 1L, 1L,
1L, 3L, 20L, 465L, 1L, 3269L, 1L, 2L, 1L, 9L, 1L, 32L, 6L, 2L,
293L, 1L, 3L, 1L, 11L, 2L, 1L, 9L, 10L, 1L, 1L, 1L, 1L, 1L, 2L,
7L, 2L, 433L, 1L, 4L, 1L, 1L, 3L, 19L, 1L, 2L, 1L, 1L, 12L, 1L,
4L, 1L, 1L, 3L, 37L, 10L, 88L, 6L, 1808L, 5L, 4L, 451L, 5L, 219L,
112L, 4L, 3L, 1L, 6L, 1L, 2L, 3L, 5L, 10L, 2L, 264L, 8L, 1L,
1L, 1L, 17L, 1L, 1L, 7L, 1L, 1L, 4L, 6L, 516L, 1L, 948L, 2L,
1L, 2L, 1L, 33L, 1L, 1L, 133L, 1L, 2L, 1L, 5L, 11L, 1L, 4L, 1L,
1L, 1L, 6L, 10L, 5L, 168L, 1L, 1L, 5L, 1L, 10L, 1L, 1L, 3L, 9L,
1L, 2L, 1L, 8L, 3L, 98L, 1L, 548L, 1L, 1L, 177L, 97L, 17L, 4L,
1L, 6L, 2L, 1L, 2L, 1L, 1L, 5L, 4L, 5L, 235L, 1L, 2L, 9L, 2L,
19L, 1L, 2L, 2L, 1L, 1L, 3L, 6L, 5L, 396L, 1209L, 1L, 2L, 1L,
41L, 1L, 125L, 3L, 5L, 1L, 4L, 1L, 1L, 4L, 1L, 3L, 1L, 1L, 5L,
2L, 121L, 2L, 1L, 1L, 10L, 1L, 1L, 4L, 1L, 2L, 10L, 3L, 75L,
5L, 632L, 1L, 2L, 2L, 178L, 1L, 67L, 33L, 6L, 1L, 1L, 1L, 2L,
1L, 12L, 3L, 194L, 1L, 1L, 1L, 1L, 1L, 20L, 1L, 1L, 6L, 1L, 1L,
1L, 1L, 1L, 3L, 2L, 296L, 1L, 1L, 979L, 6L, 4L, 1L, 33L, 1L,
109L, 5L, 2L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 6L, 3L, 118L,
1L, 1L, 15L, 1L, 1L, 1L, 1L, 1L, 4L, 2L, 1L, 18L, 6L, 53L, 3L,
584L, 2L, 1L, 2L, 172L, 2L, 100L, 27L, 9L, 2L, 1L, 2L, 1L, 1L,
1L, 11L, 3L, 202L, 6L, 20L, 2L, 1L, 1L, 4L, 1L, 8L, 2L, 292L,
719L, 2L, 1L, 2L, 29L, 106L, 7L, 3L, 8L, 2L, 2L, 1L, 1L, 1L,
7L, 3L, 139L, 4L, 1L, 2L, 17L, 1L, 2L, 3L, 2L, 20L, 53L, 3L,
530L, 2L, 1L, 1L, 172L, 113L, 23L, 2L, 1L, 4L, 2L, 2L, 1L, 7L,
891L, 10L, 1L, 1L, 12L, 1L, 1L, 1L, 1L, 1L, 4L, 5L, 6L, 1312L,
1L, 1L, 1168L, 1L, 4L, 2L, 39L, 133L, 3L, 13L, 5L, 2L, 6L, 1L,
1L, 1L, 13L, 3L, 297L, 4L, 1L, 1L, 9L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 25L, 182L, 1L, 776L, 2L, 1L, 1L, 260L, 2L, 115L, 52L,
14L, 2L, 4L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 14L,
2L, 731L, 7L, 2L, 1L, 16L, 1L, 1L, 3L, 2L, 1L, 1L, 11L, 6L, 294L,
1L, 1135L, 1L, 3L, 1L, 6L, 1L, 36L, 1L, 1L, 126L, 4L, 1L, 1L,
4L, 11L, 1L, 2L, 1L, 2L, 2L, 1L, 6L, 355L, 3L, 9L, 1L, 4L, 1L,
13L, 2L, 1L, 1L, 7L, 1L, 1L, 22L, 5L, 67L, 1L, 2L, 926L, 1L,
1L, 1L, 1L, 2L, 1L, 208L, 1L, 1L, 136L, 44L, 12L, 1L, 1L, 2L,
2L, 4L, 2L, 1L, 1L, 1L, 1L, 8L, 9L, 1L, 198L, 1L, 8L, 13L, 2L,
4L, 1L, 4L, 2L, 205L, 568L, 1L, 1L, 19L, 94L, 2L, 3L, 8L, 1L,
1L, 1L, 1L, 1L, 1L, 8L, 157L, 4L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 12L, 28L, 3L, 444L, 3L, 1L, 2L, 118L, 2L, 75L, 27L,
1L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 6L, 7L, 166L, 1L, 1L, 11L, 1L,
1L, 3L, 1L, 1L, 1L, 3L, 203L, 644L, 2L, 1L, 1L, 2L, 26L, 1L,
4L, 75L, 1L, 4L, 2L, 5L, 1L, 1L, 1L, 1L, 1L, 4L, 155L, 1L, 1L,
1L, 3L, 4L, 1L, 2L, 6L, 1L, 36L, 1L, 2L, 446L, 3L, 1L, 99L, 86L,
27L, 1L, 2L, 1L, 1L, 3L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 7L,
1L, 7L, 159L, 1L, 3L, 12L, 1L, 3L, 1L, 1L, 8L, 174L, 733L, 1L,
1L, 1L, 1L, 22L, 2L, 84L, 1L, 1L, 6L, 3L, 1L, 1L, 1L, 3L, 1L,
100L, 6L, 2L, 3L, 1L, 8L, 3L, 38L, 7L, 502L, 2L, 1L, 86L, 6L,
83L, 24L, 6L, 1L, 1L, 1L, 2L, 2L, 321L, 8L, 11L, 1L, 4L, 1L,
2L, 2L, 13L, 191L, 1L, 5L, 1417L, 1L, 6L, 1L, 1L, 28L, 2L, 1L,
150L, 1L, 1L, 7L, 1L, 3L, 2L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 1L,
4L, 1L, 218L, 3L, 1L, 1L, 8L, 1L, 2L, 1L, 1L, 16L, 4L, 45L, 1L,
3L, 879L, 3L, 1L, 1L, 2L, 207L, 2L, 115L, 44L, 1L, 3L, 1L, 1L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 171L, 4L, 1L, 1L, 7L, 1L, 5L,
4L, 178L, 614L, 3L, 1L, 3L, 1L, 5L, 20L, 1L, 94L, 3L, 4L, 8L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 121L, 1L, 1L, 6L, 1L, 1L, 3L,
2L, 1L, 7L, 3L, 31L, 1L, 1L, 433L, 1L, 3L, 23L, 94L, 79L, 25L,
1L, 2L, 2L, 6L, 2L, 160L, 3L, 6L, 1L, 3L, 2L, 2L, 3L, 1L, 568L,
1L, 2L, 5L, 15L, 5L, 86L, 1L, 2L, 4L, 8L, 3L, 4L, 1L, 1L, 2L,
1L, 118L, 9L, 7L, 1L, 2L, 2L, 11L, 3L, 10L, 1L, 530L, 2L, 3L,
2L, 121L, 1L, 1L, 72L, 34L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 7L, 4L,
326L, 13L, 1L, 1L, 18L, 1L, 2L, 8L, 4L, 2L, 2L, 1L, 1271L, 1L,
1L, 1L, 2L, 3L, 17L, 2L, 161L, 3L, 1L, 14L, 1L, 1L, 2L, 1L, 1L,
4L, 1L, 1L, 10L, 1L, 195L, 1L, 6L, 1L, 1L, 1L, 1L, 23L, 1L, 1L,
2L, 1L, 1L, 2L, 20L, 4L, 10L, 1L, 1050L, 1L, 1L, 3L, 1L, 1L,
1L, 19L, 1L, 196L, 134L, 52L, 4L, 1L, 1L, 1L, 1L, 2L, 3L, 3L,
1L, 1L, 5L, 6L, 1L, 120L, 1L, 3L, 6L, 1L, 1L, 2L, 1L, 2L, 371L,
1L, 1L, 7L, 74L, 2L, 11L, 1L, 3L, 84L, 1L, 1L, 3L, 4L, 14L, 2L,
1L, 5L, 1L, 6L, 1L, 382L, 3L, 1L, 2L, 6L, 2L, 69L, 1L, 54L, 17L,
2L, 1L, 1L, 3L, 7L, 1L, 168L, 2L, 1L, 7L, 1L, 1L, 1L, 1L, 2L,
1L, 5L, 374L, 2L, 5L, 7L, 2L, 69L, 1L, 10L, 6L, 85L, 1L, 1L,
16L, 1L, 1L, 1L, 5L, 2L, 2L, 393L, 3L, 17L, 53L, 75L, 22L, 2L,
2L, 1L, 1L, 1L, 7L, 3L, 1L, 136L, 1L, 7L, 3L, 3L, 2L, 1L, 2L,
488L, 1L, 4L, 25L, 1L, 71L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 126L,
5L, 1L, 8L, 2L, 1L, 1L, 1L, 1L, 1L, 10L, 1L, 4L, 1L, 1L, 445L,
1L, 1L, 90L, 1L, 77L, 20L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
248L, 8L, 1L, 1L, 19L, 1L, 2L, 1L, 1L, 1L, 4L, 1L, 3L, 981L,
2L, 2L, 1L, 3L, 1L, 14L, 1L, 2L, 134L, 3L, 2L, 1L, 1L, 3L, 1L,
1L, 2L, 5L, 194L, 5L, 1L, 16L, 1L, 1L, 2L, 2L, 1L, 9L, 3L, 8L,
850L, 1L, 1L, 155L, 1L, 117L, 43L, 4L, 4L, 4L, 3L, 5L, 124L,
1L, 1L, 4L, 6L, 1L, 1L, 2L, 3L, 1L, 2L, 373L, 4L, 1L, 2L, 8L,
1L, 63L, 1L, 2L, 12L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 125L, 7L, 2L,
1L, 1L, 7L, 2L, 5L, 1L, 2L, 287L, 2L, 3L, 1L, 54L, 1L, 49L, 19L,
2L, 2L, 3L, 5L, 8L, 1L, 91L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 2L,
289L, 1L, 1L, 1L, 12L, 61L, 1L, 1L, 14L, 2L, 1L, 91L, 1L, 1L,
1L, 7L, 2L, 1L, 4L, 1L, 241L, 1L, 5L, 42L, 1L, 51L, 9L, 4L, 1L,
1L, 4L, 98L, 2L, 4L, 2L, 2L, 251L, 1L, 12L, 1L, 47L, 3L, 1L,
2L, 1L, 1L, 1L, 3L, 2L, 73L, 2L, 3L, 1L, 1L, 11L, 2L, 3L, 1L,
214L, 2L, 1L, 40L, 41L, 17L, 3L, 2L, 103L, 1L, 8L, 5L, 1L, 2L,
1L, 270L, 1L, 1L, 3L, 21L, 60L, 2L, 1L, 2L, 2L, 73L, 4L, 2L,
2L, 1L, 1L, 4L, 1L, 2L, 1L, 219L, 1L, 55L, 60L, 13L, 1L, 2L,
1L, 1L, 168L, 3L, 7L, 1L, 7L, 1L, 1L, 1L, 404L, 8L, 8L, 1L, 99L,
3L, 3L, 11L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 115L,
1L, 2L, 3L, 2L, 2L, 1L, 1L, 1L, 1L, 5L, 3L, 6L, 362L, 1L, 2L,
64L, 2L, 88L, 15L, 1L, 4L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 104L, 2L, 1L, 9L, 1L, 5L, 1L, 2L, 1L, 1L, 343L, 1L, 1L, 1L,
3L, 10L, 64L, 2L, 10L, 1L, 1L, 1L, 1L, 1L, 4L, 106L, 3L, 1L,
1L, 1L, 2L, 6L, 286L, 1L, 2L, 43L, 2L, 56L, 24L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 140L, 1L, 4L, 2L, 1L, 2L, 2L, 479L, 1L,
1L, 4L, 20L, 87L, 1L, 2L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 118L, 5L,
1L, 9L, 4L, 1L, 14L, 4L, 1L, 1L, 389L, 1L, 1L, 66L, 1L, 75L,
13L, 1L, 1L, 2L, 1L, 1L, 1L, 98L, 3L, 1L, 8L, 2L, 2L, 1L, 1L,
341L, 3L, 1L, 21L, 101L, 2L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 85L,
1L, 1L, 1L, 2L, 2L, 4L, 1L, 1L, 4L, 278L, 10L, 67L, 2L, 54L,
15L, 1L, 1L, 1L, 1L, 1L, 98L, 1L, 6L, 3L, 2L, 1L, 315L, 1L, 1L,
6L, 13L, 1L, 59L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 4L, 2L, 90L, 1L,
4L, 1L, 1L, 1L, 1L, 2L, 7L, 1L, 235L, 1L, 1L, 1L, 2L, 53L, 72L,
18L, 3L, 2L, 1L, 1L, 68L, 1L, 1L, 4L, 2L, 1L, 2L, 1L, 1L, 241L,
1L, 1L, 4L, 9L, 37L, 1L, 1L, 66L, 1L, 1L, 7L, 5L, 4L, 2L, 1L,
2L, 197L, 47L, 39L, 19L, 1L), Fuentes = structure(c(3L, 5L, 6L,
6L, 4L, 5L, 5L, 5L, 5L, 7L, 7L, 1L, 6L, 7L, 5L, 5L, 4L, 5L, 5L,
5L, 5L, 5L, 5L, 6L, 7L, 3L, 5L, 6L, 6L, 5L, 6L, 5L, 5L, 5L, 5L,
7L, 7L, 6L, 1L, 6L, 5L, 5L, 4L, 5L, 5L, 4L, 6L, 5L, 5L, 5L, 5L,
7L, 3L, 5L, 6L, 6L, 4L, 6L, 5L, 5L, 4L, 4L, 5L, 7L, 7L, 6L, 7L,
5L, 4L, 5L, 4L, 2L, 2L, 6L, 5L, 5L, 5L, 6L, 7L, 3L, 6L, 4L, 6L,
4L, 5L, 5L, 5L, 5L, 4L, 5L, 7L, 7L, 1L, 6L, 7L, 5L, 5L, 4L, 5L,
5L, 4L, 2L, 2L, 5L, 5L, 5L, 4L, 5L, 4L, 5L, 6L, 7L, 3L, 6L, 6L,
5L, 5L, 5L, 5L, 7L, 7L, 1L, 6L, 7L, 5L, 5L, 7L, 5L, 4L, 2L, 2L,
5L, 5L, 5L, 5L, 5L, 5L, 6L, 7L, 3L, 6L, 4L, 6L, 4L, 4L, 5L, 5L,
7L, 7L, 1L, 6L, 5L, 5L, 7L, 5L, 5L, 4L, 5L, 5L, 4L, 2L, 5L, 5L,
5L, 4L, 5L, 6L, 7L, 3L, 5L, 6L, 6L, 5L, 6L, 5L, 5L, 5L, 5L, 5L,
5L, 7L, 7L, 1L, 6L, 5L, 5L, 5L, 7L, 5L, 5L, 7L, 4L, 5L, 5L, 4L,
2L, 2L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 7L, 3L, 5L,
6L, 4L, 4L, 4L, 6L, 4L, 4L, 4L, 5L, 5L, 4L, 5L, 5L, 5L, 4L, 5L,
7L, 7L, 1L, 6L, 7L, 5L, 5L, 4L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 6L, 7L, 3L, 6L, 6L, 4L, 4L, 6L, 4L, 4L, 5L, 4L, 5L, 5L,
7L, 7L, 5L, 6L, 5L, 5L, 7L, 7L, 5L, 5L, 5L, 5L, 6L, 4L, 5L, 2L,
2L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 7L, 3L, 5L, 5L, 6L, 4L, 6L, 4L,
4L, 4L, 5L, 4L, 5L, 4L, 5L, 7L, 7L, 6L, 6L, 7L, 5L, 5L, 5L, 5L,
4L, 2L, 5L, 5L, 5L, 4L, 4L, 5L, 5L, 6L, 7L, 3L, 5L, 5L, 6L, 6L,
6L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 7L, 7L, 6L, 5L, 5L, 7L, 5L, 4L,
5L, 4L, 2L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 7L, 3L, 5L,
6L, 6L, 6L, 4L, 4L, 5L, 5L, 4L, 5L, 7L, 7L, 1L, 6L, 5L, 7L, 5L,
5L, 4L, 5L, 5L, 4L, 6L, 5L, 5L, 5L, 5L, 6L, 7L, 3L, 5L, 6L, 6L,
6L, 4L, 6L, 5L, 4L, 5L, 4L, 5L, 7L, 7L, 4L, 5L, 7L, 7L, 5L, 1L,
6L, 5L, 7L, 5L, 5L, 4L, 5L, 4L, 2L, 2L, 6L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 6L, 7L, 3L, 6L, 4L, 6L, 4L, 4L, 5L, 5L, 4L, 4L, 5L, 4L,
5L, 7L, 7L, 1L, 6L, 5L, 7L, 5L, 5L, 4L, 5L, 5L, 4L, 6L, 5L, 5L,
5L, 5L, 5L, 6L, 7L, 3L, 6L, 6L, 5L, 5L, 4L, 5L, 4L, 5L, 7L, 7L,
6L, 5L, 5L, 7L, 5L, 5L, 4L, 2L, 2L, 5L, 5L, 5L, 5L, 5L, 6L, 7L,
3L, 6L, 6L, 4L, 6L, 4L, 4L, 5L, 5L, 5L, 7L, 1L, 6L, 5L, 5L, 5L,
5L, 5L, 5L, 4L, 6L, 5L, 5L, 4L, 5L, 6L, 3L, 6L, 6L, 4L, 6L, 5L,
4L, 4L, 5L, 4L, 5L, 5L, 7L, 7L, 6L, 1L, 6L, 5L, 5L, 7L, 5L, 5L,
4L, 2L, 5L, 5L, 5L, 5L, 5L, 4L, 6L, 7L, 3L, 6L, 4L, 4L, 6L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 4L, 5L, 7L, 1L, 6L, 5L, 7L, 5L, 5L, 4L,
5L, 5L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 5L, 5L, 5L,
6L, 7L, 3L, 6L, 6L, 4L, 6L, 4L, 4L, 5L, 5L, 7L, 4L, 5L, 7L, 7L,
1L, 6L, 5L, 5L, 5L, 7L, 5L, 5L, 4L, 5L, 5L, 4L, 5L, 5L, 2L, 2L,
6L, 5L, 5L, 5L, 5L, 5L, 6L, 3L, 5L, 6L, 4L, 4L, 4L, 6L, 4L, 4L,
4L, 5L, 5L, 4L, 5L, 7L, 7L, 5L, 1L, 6L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 4L, 5L, 5L, 5L, 4L, 5L, 4L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 5L,
5L, 6L, 7L, 3L, 5L, 6L, 6L, 4L, 5L, 4L, 5L, 7L, 7L, 6L, 5L, 7L,
5L, 5L, 4L, 2L, 2L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 3L, 6L, 6L, 6L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 7L, 1L, 6L, 5L, 5L, 5L, 5L,
4L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 6L, 3L, 5L, 5L,
6L, 5L, 5L, 5L, 4L, 5L, 5L, 7L, 7L, 6L, 5L, 5L, 7L, 5L, 5L, 7L,
5L, 5L, 6L, 4L, 2L, 2L, 5L, 5L, 5L, 5L, 5L, 6L, 3L, 6L, 6L, 4L,
6L, 5L, 4L, 5L, 5L, 7L, 7L, 5L, 1L, 6L, 5L, 5L, 5L, 5L, 5L, 4L,
4L, 5L, 2L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 2L, 4L, 5L, 4L, 6L, 3L,
5L, 6L, 6L, 5L, 5L, 5L, 5L, 7L, 7L, 6L, 5L, 7L, 5L, 7L, 5L, 5L,
5L, 2L, 5L, 2L, 5L, 5L, 5L, 5L, 6L, 7L, 3L, 6L, 5L, 5L, 4L, 5L,
7L, 7L, 1L, 6L, 7L, 5L, 5L, 4L, 5L, 5L, 4L, 2L, 2L, 5L, 6L, 7L,
3L, 6L, 6L, 5L, 5L, 5L, 7L, 5L, 7L, 7L, 5L, 5L, 6L, 5L, 5L, 5L,
5L, 5L, 4L, 5L, 5L, 4L, 5L, 2L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 6L, 5L, 3L, 6L, 6L, 4L, 6L, 5L, 5L, 5L, 5L, 5L, 7L,
7L, 5L, 1L, 6L, 5L, 5L, 7L, 5L, 5L, 4L, 5L, 5L, 5L, 4L, 5L, 6L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 3L, 6L, 6L, 4L, 6L, 5L, 5L, 7L,
7L, 6L, 5L, 5L, 7L, 5L, 5L, 5L, 5L, 5L, 4L, 2L, 2L, 5L, 5L, 5L,
5L, 5L, 5L, 6L, 7L, 3L, 6L, 4L, 6L, 5L, 4L, 5L, 5L, 4L, 5L, 7L,
7L, 5L, 1L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 6L, 3L,
6L, 6L, 4L, 5L, 5L, 7L, 7L, 1L, 6L, 7L, 5L, 5L, 5L, 5L, 5L, 5L,
4L, 2L, 2L, 5L, 5L, 5L, 5L, 6L, 7L, 3L, 6L, 6L, 5L, 5L, 5L, 5L,
7L, 7L, 5L, 6L, 7L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 4L, 4L, 5L,
5L, 5L, 4L, 5L, 6L, 3L, 6L, 6L, 4L, 6L, 4L, 5L, 5L, 5L, 5L, 7L,
6L, 6L, 5L, 5L, 7L, 5L, 5L, 5L, 5L, 5L, 4L, 2L, 2L, 6L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 7L, 3L, 5L, 6L, 4L, 4L, 4L, 5L, 6L, 4L,
4L, 5L, 4L, 5L, 4L, 5L, 7L, 7L, 1L, 6L, 5L, 5L, 5L, 5L, 7L, 5L,
5L, 5L, 5L, 5L, 5L, 4L, 4L, 2L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
6L, 7L, 3L, 5L, 6L, 6L, 5L, 4L, 5L, 5L, 7L, 6L, 5L, 5L, 5L, 5L,
2L, 2L, 5L, 6L, 3L, 5L, 5L, 6L, 4L, 6L, 5L, 4L, 5L, 7L, 7L, 1L,
6L, 5L, 7L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 7L,
3L, 6L, 6L, 6L, 4L, 5L, 5L, 5L, 5L, 7L, 7L, 6L, 5L, 5L, 5L, 5L,
5L, 2L, 2L, 6L, 3L, 6L, 4L, 6L, 4L, 5L, 4L, 5L, 7L, 1L, 6L, 5L,
5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 2L, 5L, 6L, 7L, 3L, 6L, 6L, 5L,
5L, 5L, 7L, 7L, 6L, 5L, 5L, 5L, 5L, 5L, 4L, 5L, 2L, 2L, 5L, 5L,
5L, 6L, 3L, 6L, 4L, 6L, 4L, 5L, 5L, 4L, 4L, 5L, 5L, 7L, 7L, 5L,
1L, 6L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 4L, 5L, 6L, 5L, 5L, 6L,
7L, 3L, 6L, 4L, 5L, 6L, 5L, 5L, 5L, 5L, 4L, 5L, 7L, 7L, 6L, 5L,
5L, 7L, 5L, 5L, 5L, 4L, 5L, 5L, 4L, 2L, 2L, 5L, 5L, 5L, 5L, 4L,
6L, 3L, 6L, 6L, 6L, 4L, 5L, 5L, 5L, 4L, 5L, 7L, 7L, 6L, 5L, 5L,
5L, 4L, 5L, 5L, 4L, 5L, 5L, 5L, 6L, 3L, 5L, 5L, 6L, 6L, 5L, 4L,
5L, 5L, 7L, 7L, 6L, 5L, 5L, 5L, 5L, 4L, 5L, 4L, 2L, 2L, 5L, 5L,
5L, 5L, 5L, 6L, 5L, 3L, 6L, 4L, 5L, 5L, 5L, 7L, 7L, 5L, 1L, 6L,
5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 4L, 5L, 5L, 6L, 7L, 3L, 5L, 6L,
6L, 5L, 5L, 5L, 5L, 7L, 6L, 7L, 5L, 5L, 5L, 5L, 4L, 2L, 2L, 5L,
7L, 3L, 6L, 4L, 5L, 6L, 5L, 5L, 5L, 7L, 6L, 5L, 5L, 5L, 4L, 5L,
5L, 4L, 5L, 5L, 6L, 3L, 6L, 6L, 5L, 1L, 6L, 5L, 5L, 4L, 5L, 4L,
2L, 2L, 5L, 5L, 5L, 5L, 6L, 3L, 6L, 6L, 5L, 5L, 5L, 7L, 7L, 5L,
6L, 5L, 5L, 5L, 5L, 5L, 4L, 6L, 3L, 6L, 6L, 5L, 5L, 7L, 7L, 6L,
5L, 5L, 5L, 5L, 5L, 4L, 2L, 2L, 6L, 3L, 6L, 5L, 6L, 4L, 4L, 5L,
7L, 7L, 1L, 6L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 3L, 6L, 6L, 5L,
5L, 5L, 5L, 7L, 6L, 5L, 5L, 4L, 5L, 4L, 2L, 2L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 4L, 6L, 7L, 3L, 5L, 6L, 6L, 4L, 5L, 5L, 5L, 4L,
4L, 5L, 7L, 7L, 6L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 4L, 2L, 5L, 5L,
5L, 4L, 4L, 5L, 5L, 4L, 6L, 3L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 7L,
5L, 6L, 5L, 5L, 7L, 5L, 5L, 5L, 2L, 2L, 5L, 5L, 5L, 5L, 5L, 6L,
3L, 6L, 4L, 4L, 5L, 4L, 5L, 6L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 5L,
4L, 2L, 5L, 5L, 4L, 4L, 6L, 3L, 6L, 6L, 5L, 5L, 5L, 7L, 6L, 5L,
5L, 5L, 5L, 5L, 4L, 2L, 5L, 5L, 5L, 5L, 5L, 6L, 7L, 3L, 6L, 4L,
6L, 5L, 5L, 5L, 7L, 5L, 1L, 6L, 7L, 5L, 5L, 4L, 5L, 5L, 4L, 5L,
5L, 5L, 6L, 7L, 3L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L,
5L, 4L, 5L, 2L, 5L, 5L, 5L, 5L, 6L, 3L, 5L, 4L, 4L, 6L, 4L, 5L,
5L, 5L, 7L, 6L, 5L, 5L, 4L, 5L, 5L, 4L, 4L, 5L, 5L, 5L, 3L, 6L,
6L, 5L, 5L, 7L, 6L, 5L, 5L, 5L, 5L, 7L, 5L, 4L, 2L, 5L, 5L, 5L,
5L, 5L, 6L, 7L, 3L, 4L, 6L, 5L, 5L, 4L, 5L, 5L, 7L, 1L, 6L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 6L, 3L, 6L, 6L, 6L, 5L, 5L,
5L, 5L, 7L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 3L, 6L, 4L, 6L, 5L,
5L, 7L, 7L, 5L, 6L, 5L, 5L, 5L, 5L), .Label = c("Adwords", "Campañas",
"Directo", "Email", "Referencias", "SEO", "Social Media"), class = "factor")), .Names = c("date",
"sessions", "Fuentes"), class = "data.frame", row.names = c(NA,
-1724L))
My data after the summarise function:
Fuentes sessions
1 Adwords 71
2 Campa�as 280
3 Directo 11610
4 Email 437
5 Referencias 13143
6 SEO 39837
7 Social Media 5981
Howcome my shiny code does not print right my plot?
1) when used within Shiny, the label appears like blured:
do a summarise before plotting (see code below):
server.R file:
library(ggplot2)
library(dplyr)
require(scales)
Visitas_Por_Fuente <- read.csv("D:\\RCoursera\\Movistar-App-2\\Visitas_Por_Fuente_Dic.csv")
labelsF = c("Directo", "Email", "Referencias", "SEO", "Social Media", "Campañas", "Adwords")
Visitas_Por_Fuente$date <- as.Date(Visitas_Por_Fuente$date)
shinyServer(
function(input, output) {
output$VisitasFuente <- renderPlot({
# Filter the data based on user selection month
date_seq <- seq(input$dates[1], input$dates[2], by = "day")
VisitasData <- filter(Visitas_Por_Fuente, date %in% date_seq & Fuentes %in% labelsF)
VisitasData %>% group_by(Fuentes) %>%
summarise(sessions = sum(sessions))
ggplot(VisitasData, aes(factor(Fuentes), sessions, fill = Fuentes)) +
geom_bar(stat="identity", position = "dodge") +
geom_text(aes(label = comma(sessions)), position=position_dodge(width=0.9), vjust=-0.25) +
scale_fill_manual(breaks = c("0", "1", "3", "6", "9", "12", "15"),
labels = labelsF,
values = c("#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#A082F8", "#F072A2"))
})
})
2) Then i use the same code, but not within shiny, just ggplot2 code:
ggplot(VisitasData, aes(factor(Fuentes), sessions, fill = Fuentes)) +
geom_bar(stat="identity", position = "dodge") +
geom_text(aes(label = comma(sessions)), position=position_dodge(width=0.9), vjust=-0.25) +
scale_fill_manual(breaks = c("0", "1", "3", "6", "9", "12", "15"),
labels = labelsF,
values = c("#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#A082F8", "#F072A2"))
And get what i need:
I also tried, using a reactive function (as recommended in comments), but got:
Error : ggplot2 doesn't know how to deal with data of class reactive
Googled that and found:
http://stackoverflow.com/questions/27771691/many-error-signs-when-running-ggplot-in-render-plot-shiny-in-general
But now,prints a blank sheet:
This is my code with the reactive function:
function(input, output) {
dataSeq <- reactive({
date_seq <- seq(input$dates[1], input$dates[2], by = "day")
})
VisitasData <- reactive({
VisitasData <- filter(Visitas_Por_Fuente, date %in% dataSeq & Fuentes %in% labelsF)
VisitasData %>% group_by(Fuentes) %>%
summarise(sessions = sum(sessions))
})
output$VisitasFuente <- renderPlot({
# Bar graph using ggplot2 library
ggplot(ggplot(selectedData(VisitasData), aes(factor(VisitasData$Fuentes), VisitasData$sessions,
fill = Fuentes)) +
geom_bar(stat="identity", position = "dodge") +
geom_text(aes(label = comma(sessions)), position=position_dodge(width=0.9), vjust=-0.25) +
scale_fill_manual(breaks = c("0", "1", "3", "6", "9", "12", "15"),
labels = labelsF,
values = c("#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#A082F8", "#F072A2"))
})
})

Assuming you want those numbers that showed up in your first call
Fuentes sessions
1 Adwords 71
2 Campa�as 280
3 Directo 11610
4 Email 437
5 Referencias 13143
6 SEO 39837
7 Social Media 5981
You just made a little mistake here:
VisitasData %>% group_by(Fuentes) %>%
summarise(sessions = sum(sessions))
You made a new dataframe, but you didn't assign it to anything. What you want is:
VisitasData <- VisitasData %>% group_by(Fuentes) %>%
summarise(sessions = sum(sessions))
Then you don't need to do a reactive thing, just used the code you did when you first posted it above.
library(ggplot2)
library(dplyr)
require(scales)
Visitas_Por_Fuente <- read.csv("D:\\RCoursera\\Movistar-App-2\\Visitas_Por_Fuente_Dic.csv")
labelsF = c("Directo", "Email", "Referencias", "SEO", "Social Media", "Campañas", "Adwords")
Visitas_Por_Fuente$date <- as.Date(Visitas_Por_Fuente$date)
shinyServer(
function(input, output) {
output$VisitasFuente <- renderPlot({
# Filter the data based on user selection month
date_seq <- seq(input$dates[1], input$dates[2], by = "day")
VisitasData <- filter(Visitas_Por_Fuente, date %in% date_seq & Fuentes %in% labelsF)
VisitasData <- VisitasData %>% group_by(Fuentes) %>%
summarise(sessions = sum(sessions))
# Bar graph using ggplot2 library
ggplot(VisitasData, aes(factor(Fuentes), sessions, fill = Fuentes)) +
geom_bar(stat="identity", position = "dodge") +
geom_text(aes(label = comma(sessions)), position=position_dodge(width=0.9), vjust=-0.25) +
scale_fill_manual(breaks = c("0", "1", "3", "6", "9", "12", "15"),
labels = labelsF,
values = c("#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#A082F8", "#F072A2"))
})
})
Is this what you intended?

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