Ggplot2 geom_line error

Ggplot2 geom_line error - r

I have a daaset which consists of data points over a time series for the proportion of people living in urban/rural areas for a number of countries. Sadly, not all countries have data for the same years. I have been trying to produce a simple line plot to show the different proportions of people living in different locations by year, but as each country has a different number of data points I am running into trouble.
I think this is because some of the countries only have data for a single year and using geom_line from ggplot2 throws the following error:
geom_path: Each group consist of only one observation. Do you need to
adjust the group aesthetic?
I was hoping that there would be some way to override this, or perhaps just plot a single point where a COUNTRY only has data for a single year. Does anyone know if this is possible, or indeed, if this is actually what this error means?!!?
Any help greatly appreciated!!!
Thanks
Here is my data:
structure(list(COUNTRY = structure(c(1L, 2L, 2L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L,
8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 1L,
2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L,
7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L,
14L, 14L, 14L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L,
9L, 9L, 10L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L,
13L, 13L, 13L, 14L, 14L, 14L, 14L, 1L, 2L, 2L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L,
8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 1L, 2L,
2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L,
7L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L,
14L, 14L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 6L,
6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L, 9L,
9L, 10L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L,
13L, 13L, 14L, 14L, 14L, 14L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L,
8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L), class = "factor", .Label = c("Comoros",
"Eritrea", "Ethiopia", "Kenya", "Lesotho", "Madagascar", "Malawi",
"Namibia", "Rwanda", "South Africa", "Swaziland", "Tanzania",
"Zambia", "Zimbabwe")), Year = structure(c(5L, 12L, 4L, 25L,
16L, 9L, 22L, 13L, 7L, 2L, 23L, 15L, 22L, 14L, 6L, 1L, 24L, 15L,
9L, 1L, 13L, 6L, 19L, 9L, 1L, 24L, 21L, 16L, 9L, 1L, 7L, 19L,
24L, 13L, 8L, 5L, 1L, 18L, 10L, 4L, 20L, 11L, 5L, 1L, 24L, 17L,
8L, 3L, 5L, 12L, 4L, 25L, 16L, 9L, 22L, 13L, 7L, 2L, 23L, 15L,
22L, 14L, 6L, 1L, 24L, 15L, 9L, 1L, 13L, 6L, 19L, 9L, 1L, 24L,
21L, 16L, 9L, 1L, 7L, 19L, 24L, 13L, 8L, 5L, 1L, 18L, 10L, 4L,
20L, 11L, 5L, 1L, 24L, 17L, 8L, 3L, 5L, 12L, 4L, 25L, 16L, 9L,
22L, 13L, 7L, 2L, 23L, 15L, 22L, 14L, 6L, 1L, 24L, 15L, 9L, 1L,
13L, 6L, 19L, 9L, 1L, 24L, 21L, 16L, 9L, 1L, 7L, 19L, 24L, 13L,
8L, 5L, 1L, 18L, 10L, 4L, 20L, 11L, 5L, 1L, 24L, 17L, 8L, 3L,
5L, 12L, 4L, 25L, 16L, 9L, 22L, 13L, 7L, 2L, 23L, 15L, 22L, 14L,
6L, 1L, 24L, 15L, 9L, 1L, 13L, 6L, 19L, 9L, 1L, 24L, 21L, 16L,
9L, 1L, 7L, 19L, 24L, 13L, 8L, 5L, 1L, 18L, 10L, 4L, 20L, 11L,
5L, 1L, 24L, 17L, 8L, 3L, 5L, 12L, 4L, 25L, 16L, 9L, 22L, 13L,
7L, 2L, 23L, 15L, 22L, 14L, 6L, 1L, 24L, 15L, 9L, 1L, 13L, 6L,
19L, 9L, 1L, 24L, 21L, 16L, 9L, 1L, 7L, 19L, 24L, 13L, 8L, 5L,
1L, 18L, 10L, 4L, 20L, 11L, 5L, 1L, 24L, 17L, 8L, 3L, 5L, 12L,
4L, 25L, 16L, 9L, 22L, 13L, 7L, 2L, 23L, 15L, 22L, 14L, 6L, 1L,
24L, 15L, 9L, 1L, 13L, 6L, 19L, 9L, 1L, 24L, 21L, 16L, 9L, 1L,
7L, 19L, 24L, 13L, 8L, 5L, 1L, 18L, 10L, 4L, 20L, 11L, 5L, 1L,
24L, 17L, 8L, 3L, 5L, 12L, 4L, 25L, 16L, 9L, 22L, 13L, 7L, 2L,
23L, 15L, 22L, 14L, 6L, 1L, 24L, 15L, 9L, 1L, 13L, 6L, 19L, 9L,
1L, 24L, 21L, 16L, 9L, 1L, 7L, 19L, 24L, 13L, 8L, 5L, 1L, 18L,
10L, 4L, 20L, 11L, 5L, 1L, 24L, 17L, 8L, 3L), class = "factor", .Label = c("1992",
"1993", "1994", "1995", "1996", "1997", "1998", "1999", "2000",
"2000/1", "2001/2", "2002", "2003", "2003/4", "2004", "2005",
"2005/6", "2006", "2006/7", "2007", "2007/8", "2008/9", "2009",
"2010", "2011")), location = 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, 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, 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, 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), .Label = c("Urban",
"Rural", "Total", "Capital.City", "Other.Cities.towns", "Urban.Non.slum",
"Urban.Slum"), class = "factor"), percent = c(63.0434782608696,
93.8, 87, 79.5642604795185, 65.4240807416892, 63.0791092522326,
90.448386469558, 85.9419999774024, 92.7603614781794, 84.0437368780105,
89.9792286718626, 91.0916571421351, 87.1132950026762, 73.8624315865239,
60.8311005575454, 66.7, 96, 86.8, 90.6243926153181, 90.6911141749493,
90.7602286016099, 93.0377175475414, 86.073106379954, 84.253722056373,
77.8178199148702, 97.3, 91.8332260789258, 89.612164524266, 89.9070989918367,
94.9, 85.1351949905457, 94.8358752154967, 92.9, 89.656599879838,
90.2634019334124, 94.4, 91.6241263241579, 76.7337303943862, 68.4233513070184,
74.15601627144, 88.4802888646634, 85.4643913454376, 89.7457528950664,
81.3025210084024, 83.0579155525397, 71.5857386620092, 86.2324062094295,
87.687478493975, 63.5379061371841, 78.5, 40.7, 51.7763728811622,
32.2441768813334, 22.3138981723172, 83.3699691175754, 69.6742912391579,
76.0526239692028, 83.7290062290807, 77.4758329101792, 83.8081963934296,
67.5805226154664, 55.8951299980461, 41.9921451192584, 52.2, 92.5,
77.6, 82.0322170392223, 85.2850090044269, 70.8031150919282, 47.108593681531,
82.2215412952297, 78.3643348536815, 74.4253468485616, 94.8, 90.1711142192198,
85.0338348718722, 86.3134329333052, 90.4, 79.2813256726705, 90.7077549957666,
82.5, 77.7236217339155, 75.3278238729086, 77.7, 78.4592126267142,
67.1145693585691, 55.3459024734839, 57.8463881286199, 83.5604620304044,
83.9259722574938, 84.4589780509803, 73.3992444632325, 77.544833952707,
63.0503715222555, 75.6808008503601, 85.6943513045284, 63.4, 84.2,
51, 55.7151220012609, 34.9, 26.6, 85, 72.5, 79.2, 83.8, 80.3,
84.9, 69.6, 59, 46, 54, 93, 78.7, 83.2, 85.9, 76.7, 57.5, 83.8,
80.4, 75.6, 95, 90.4, 85.6, 86.9, 90.6, 82.2, 91.5, 84.5, 79.9,
78.1, 80.9, 81.2, 68.1, 56.8, 59.6, 84.9, 84.4, 86.5, 77, 79.1337842548663,
65.6, 79.1, 86.3, 68.421052631579, 96.1, 93.3, 93.461209969107,
82.2712525836501, 88.2708936990495, 87.6298001816506, 87.6386027991385,
93.1818181818183, 86.6666666666668, 88.1030398041979, 90.4761904761904,
83.4297434324662, 86.3744073211853, 83.6107223166148, 78.3, NA,
72.8, 80.952380952381, 87.5, 96.9073193030442, 99.1348508752745,
85.5297651573129, 86.4793919321843, 79.4520547945208, 98.2, 92.4613307718678,
85.4590408924955, 83.9378238341966, 92.1, 81.1594202898552, 96.0232554251852,
NA, 88.0377726639494, 83.690767555447, 93.4, 90.0349966633017,
71.2508707571865, 72, 79.4082828804656, 91.8032786885246, 84.5238095238095,
87.8787878787881, 75.6097560975609, 81.0643061692494, 68.4708412135189,
84.9056603773584, 89.5522388059702, 61.6438356164384, 91.7, 79.5,
77.0004220956012, 61.061381883032, 58.756042602018, 91.2594694272412,
85.20149612163, 92.4956062313464, 82.622382662868, 91.4036416540165,
91.6169313256523, 89.2957214499669, 67.6757501795213, 48.1479760952102,
NA, NA, 94.2, 94.3553068539161, 91.8799748693178, 89.3739230258784,
92.1418739343887, 86.4757947454868, 81.0102236379536, 77.0100025126874,
NA, 91.3720851411616, 92.2, 92.5003150086683, 97.8260869565219,
87.1461797069698, 93.5168077834096, NA, 90.1780793791367, 92.9758067301415,
94.9, 91.8829499602467, 81.749280834314, 65.1853441661798, 69.0503609949116,
87.2562445664681, 85.8298270239758, 90.6673511683335, 83.2861189801694,
84.9006282245266, 73.65452177457, 87.3075692692965, 85.5310215524833,
83.3333333333333, NA, NA, 98.5990187756088, 84.4640706359058,
NA, 93.9158337759274, 91.5744358611439, 100, NA, NA, NA, 88.7824144772468,
85.1972665683085, 89.54493171236, NA, NA, 89.8, NA, 100, 97.6261376125643,
96.3196943955923, 92.0952338262334, 87.9266080431752, 80.9429968520701,
NA, NA, 92.8, 95.2886158200472, 100, 86.4199793410402, NA, NA,
89.9001648604344, NA, NA, 91.5033109800214, 83.8918470610424,
73.9339911532972, 88.6921281548131, 94.309068022859, 85.3299585067346,
93.7362934447331, 86.5384615384618, 83.7424288707868, NA, 86.3836615391687,
88.1866796344726, 58.1081081081081, NA, NA, 75.7976468146464,
62.1289432084197, NA, 88.1488735873722, 84.2108238885019, 89.8335978405451,
NA, NA, NA, 86.9222656846515, 70.3584041024493, 70.9023609260137,
NA, NA, 85.9, NA, 89.8689917369566, 90.3864925686512, 92.628169473785,
80.9468895007753, 78.7885741638367, 75.4005791241575, NA, NA,
88.4, 87.7139456942162, 92.3809523809525, 83.7645232075473, NA,
NA, 89.567507133125, NA, NA, 91.6433898994358, 73.6225283043976,
65.9223049858496, 72.3148320483822, 86.2596215693035, 85.6224026570651,
87.4940330171337, 78.7499999999997, 81.9949404453665, NA, 84.5563115043796,
87.0190820047277)), .Names = c("COUNTRY", "Year", "location",
"percent"), row.names = c(NA, -336L), class = "data.frame")
I want to produce a simple plot with ggplot2 that is facetted by COUNTRY. I can do this fine using geom_point:
ggplot(meas_melt, aes(Year, percent, colour=location))+ geom_point() + facet_wrap(~COUNTRY)
However, if I try and produce a line plot with geom_line (ggplot(meas_melt, aes(Year, percent, colour=location))+ geom_line() + facet_wrap(~COUNTRY))
I get the following error:
geom_path: Each group consist of only one observation. Do you need to
adjust the group aesthetic?
I had thought that this could be because a couple of the countries have only one year's worth of data so I subsetted the date to remove these three countries like so:
ggplot(meas_melt, aes(Year, percent, colour=location))+ geom_line(data=meas_melt[!meas_melt$COUNTRY %in% c('Comoros','South Africa','Swaziland'),]) + facet_wrap(~COUNTRY)
However, I get the same error!

#Sven's answer is correct but fixes only part of the problem. Note how there's no plot for Comoros, South Africe, or Swaziland. This is because in your data, sometimes year is, e.g., 2006 or 2007, and sometimes it is "2006/7".
data[meas_melt$COUNTRY=="Swaziland",]
COUNTRY Year location percent
32 Swaziland 2006/7 Urban 94.83588
80 Swaziland 2006/7 Rural 90.70775
128 Swaziland 2006/7 Total 91.50000
176 Swaziland 2006/7 Capital.City 96.02326
224 Swaziland 2006/7 Other.Cities.towns 93.51681
272 Swaziland 2006/7 Urban.Non.slum NA
320 Swaziland 2006/7 Urban.Slum NA
Those countries really have only one "year" (hence, no line). More importantly, these odd year designations distort your x-axis. You can see that using the scales="free" argument to facet_wrap(...):
ggplot(meas_melt, aes(x=Year,y=percent, color=location)) +
geom_line(aes(group=location)) +facet_wrap(~COUNTRY, scales="free") +
theme(axis.text.x=element_text(angle=90, vjust=0.5, size=8),
legend.position="bottom")
Which produces this:

You have to specify aes(group = location) inside geom_line:
library(ggplot2)
ggplot(meas_melt, aes(Year, percent, colour=location)) +
geom_line(aes(group = location)) +
facet_wrap(~COUNTRY)

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I am estimating a panel regression model, and I need to add the cross sectional average of the dependent variable and regressors to the model.
I am struggling to implement the cross sectional averages in R. Can anyone help me out.
So I have a panel regression code below - using plm package.
I need to add cross sectional average of variable A, B, C and D to the right hand side of the regression
library(plm)
panel_fe <- plm(A ~ B+ C + D, model = "fd", effect="individual", data = PanelS)
So my final regression model would be like this A = B+ C+D + A_bar + B_bar + C_bar + D_bar, where A_bar, B_bar , C_bar and D_bar are the cross sectional averages of A, B,C and D respectively.
My panel datasets is below, PanelS.
structure(list(Country = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
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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, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L,
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8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L,
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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, 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,
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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("2000", "2001",
"2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009",
"2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017",
"2018", "2019"), class = "factor")), class = c("pindex", "data.frame"
), row.names = c(NA, 200L)))

You can use function Between from package plm to calculate the cross sectional averages and add them to your data:
library(plm)
# PanelS is a pdata.frame (otherwise use pdata.frame(your_data, index))
PanelS$A_bar <- Between(PanelS$A)
PanelS$B_bar <- Between(PanelS$B)
PanelS$C_bar <- Between(PanelS$C)
PanelS$D_bar <- Between(PanelS$D)
mod <- plm(A ~ B + C + D + A_bar + B_bar + C_bar + D_bar, model = "pooling", effect="individual", data = PanelS)
summary(mod)
# Pooling Model
#
# Call:
# plm(formula = A ~ B + C + D + A_bar + B_bar + C_bar + D_bar,
# data = PanelS, effect = "individual", model = "pooling")
#
# Balanced Panel: n = 10, T = 20, N = 200
#
# Residuals:
# Min. 1st Qu. Median 3rd Qu. Max.
# -0.06143690 -0.01311792 0.00070253 0.01186605 0.05107105
#
# Coefficients:
# Estimate Std. Error t-value Pr(>|t|)
# (Intercept) -0.00000000000001042 0.03313743211380626 0.0000 1.000000
# B -0.00076930351859426 0.00020566635571130 -3.7405 0.000242 ***
# C 0.10827039012266901 0.00949296134830719 11.4053 < 0.00000000000000022 ***
# D -0.04222788490989914 0.01136058813979121 -3.7171 0.000264 ***
# A_bar 0.99999999999911215 0.09632471140222754 10.3816 < 0.00000000000000022 ***
# C_bar -0.10827039012256123 0.01033406661607372 -10.4770 < 0.00000000000000022 ***
# D_bar 0.04222788490990802 0.03874710199411169 1.0898 0.277145
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Total Sum of Squares: 0.17549
# Residual Sum of Squares: 0.07128
# R-Squared: 0.59382
# Adj. R-Squared: 0.58119
# F-statistic: 47.0268 on 6 and 193 DF, p-value: < 0.000000000000000222
Note that it seems like you want to estimate a fixed effects model but your estimation has model = "fd" to estimate a first-differenced model. Also note that the cross sectional averages will drop out of the estimation of a fixed effects model.

R - Easy significant test on 2 dataframes

I am stucking a simple statistical comparism of 2 dataframes. Both dataframes consist of different kind of observations (columns) and the observation days (rows). I counted the number of occurrences for each day and each case. I dont have the same number of observation days, the observations took place under different conditions and I want to find out if there is a significant difference between those two dataframes. So basically I want to compare Case1 of df1 with Case1 of df2. For that I calculated the number of occurrences per day of each dataframe and compared them (in%).
In reality I have thousands of these dataframes and all have different number of rows.
My problem is now, how can I get an idea of which of the results are significant? How can I see if only 9 day of observation is too less to be significant?
I tried to perform a Chi-Square test, is that the right thing to do?
Here is Dataframe 1:
structure(list(Case1 = c(17L, 9L, 4L, 3L, 5L, 4L, 5L, 4L, 6L, 13L,
7L, 17L, 9L, 11L, 10L, 8L, 7L, 22L, 7L, 14L, 15L, 13L, 17L, 7L,
13L, 12L, 10L, 16L, 7L, 6L, 13L, 10L, 12L, 12L, 11L, 13L, 12L,
9L, 11L, 12L, 14L, 10L, 11L, 14L, 15L, 9L, 12L, 13L, 19L, 14L,
10L, 10L, 4L, 10L, 9L, 11L, 10L, 4L, 6L, 3L, 11L, 10L, 7L, 8L,
12L, 8L, 7L, 3L, 5L, 5L, 6L, 5L, 8L, 10L, 9L, 3L, 5L, 9L, 9L,
4L, 9L, 7L, 8L, 6L, 4L, 7L, 6L, 9L, 4L, 17L, 16L, 9L, 16L, 12L,
9L, 10L, 14L, 6L, 17L, 14L, 14L, 11L, 10L, 11L, 15L, 12L, 11L,
15L, 10L, 12L, 12L, 5L, 7L, 7L, 15L, 9L, 8L, 14L, 15L, 20L, 8L,
12L, 12L, 19L, 10L, 18L, 6L, 14L, 17L, 17L, 17L, 13L, 12L, 10L,
15L, 11L, 17L, 12L, 8L, 15L, 9L, 9L, 13L, 14L, 9L, 6L, 18L, 5L,
8L, 8L, 5L, 7L, 4L, 6L, 4L, 6L, 4L, 7L, 7L, 8L, 4L, 6L, 9L, 4L,
4L, 5L, 9L, 2L, 4L, 4L, 7L, 10L, 7L, 8L, 4L), Case2 = c(17L, 9L,
4L, 3L, 5L, 4L, 4L, 3L, 6L, 11L, 6L, 10L, 9L, 7L, 9L, 6L, 7L,
20L, 7L, 11L, 12L, 12L, 15L, 6L, 10L, 10L, 9L, 14L, 6L, 6L, 12L,
9L, 10L, 10L, 9L, 10L, 11L, 7L, 10L, 12L, 14L, 8L, 9L, 10L, 15L,
9L, 11L, 10L, 14L, 13L, 10L, 8L, 4L, 9L, 8L, 11L, 6L, 4L, 6L,
2L, 8L, 6L, 7L, 8L, 12L, 6L, 7L, 2L, 4L, 4L, 5L, 4L, 8L, 8L,
8L, 3L, 4L, 8L, 8L, 4L, 9L, 5L, 7L, 6L, 3L, 6L, 6L, 9L, 4L, 15L,
12L, 8L, 15L, 11L, 7L, 9L, 13L, 6L, 12L, 12L, 14L, 10L, 10L,
9L, 14L, 11L, 10L, 11L, 9L, 11L, 9L, 4L, 7L, 7L, 14L, 8L, 8L,
13L, 13L, 16L, 7L, 10L, 10L, 13L, 10L, 16L, 6L, 14L, 16L, 16L,
17L, 10L, 10L, 7L, 15L, 10L, 17L, 12L, 8L, 12L, 8L, 9L, 13L,
12L, 9L, 6L, 13L, 5L, 7L, 8L, 5L, 3L, 2L, 6L, 4L, 5L, 4L, 7L,
6L, 6L, 4L, 6L, 7L, 3L, 3L, 4L, 5L, 1L, 4L, 3L, 6L, 8L, 7L, 7L,
3L), Case3 = c(0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 2L, 1L, 7L, 0L,
4L, 1L, 2L, 0L, 2L, 0L, 3L, 3L, 1L, 2L, 1L, 3L, 2L, 1L, 2L, 1L,
0L, 1L, 1L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 0L, 0L, 2L, 2L, 4L, 0L,
0L, 1L, 3L, 5L, 1L, 0L, 2L, 0L, 1L, 1L, 0L, 4L, 0L, 0L, 1L, 3L,
4L, 0L, 0L, 0L, 2L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 2L, 1L, 0L, 1L,
1L, 1L, 0L, 0L, 2L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 2L, 4L, 1L, 1L,
1L, 2L, 1L, 1L, 0L, 5L, 2L, 0L, 1L, 0L, 2L, 1L, 1L, 1L, 4L, 1L,
1L, 3L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 2L, 4L, 1L, 2L, 2L, 6L, 0L,
2L, 0L, 0L, 1L, 1L, 0L, 3L, 2L, 3L, 0L, 1L, 0L, 0L, 0L, 3L, 1L,
0L, 0L, 2L, 0L, 0L, 5L, 0L, 1L, 0L, 0L, 4L, 2L, 0L, 0L, 1L, 0L,
0L, 1L, 2L, 0L, 0L, 2L, 1L, 1L, 1L, 4L, 1L, 0L, 1L, 1L, 2L, 0L,
1L, 1L)), .Names = c("Case1", "Case2", "Case3"), class = "data.frame", row.names = c(NA,
-175L))
Here is Dataframe 2:
structure(list(Case1 = c(9L, 11L, 10L, 4L, 9L, 6L, 4L, 7L, 13L),
Case2 = c(7L, 10L, 8L, 4L, 8L, 4L, 3L, 6L, 8L), Case3 = c(2L, 1L,
2L, 0L, 1L, 2L, 1L, 1L, 5L)), .Names = c("Case1", "Case2", "Case3"), class = "data.frame", row.names = c(NA,
-9L))

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

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,
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,
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2L, 197L, 47L, 39L, 19L, 1L), Fuentes = structure(c(3L, 5L, 6L,
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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))
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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