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I have very little experience with R and am trying to make a stacked barplot using ggplot2.
I have 2 groups - control and experimental, and 2 choices - red and green. I'm not sure how to organise my data.
There were 80 animals in my trial (control n=40, experimental n=40) and they were given the choice of red and green substrate, I noted which substrate they chose, and that's the data I'm trying to plot.
I would essentially want 'Experimental' and 'Control on the x-axis, and the number of choices on the y-axis (e.g. Control, Red n=20, Control, Green = 12 etc).
Any help would be appreciated!
Edited to add:
This is the graph it's outputting
This is the code I'm using (including suggested adjustments):
df <- data.frame(group = rep(c("control", "experimental"), each = 40),
substrate = sample (c("red","green"), 80, TRUE))
ggplot(df, aes(x = group, y = substrate, fill = substrate)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c("red", "green"))
This is the output:
structure(list(group = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("control", "experimental"
), class = "factor"), substrate = structure(c(1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L), .Label = c("green",
"red"), class = "factor")), class = "data.frame", row.names = c(NA,
-80L))
output from df(behaviour) - original dataframe
structure(list(group = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Control", "Experimental"
), class = "factor"), substrate = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Green",
"Red"), class = "factor")), class = "data.frame", row.names = c(NA,
-80L))
Your data:
behaviour=structure(list(group = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Control", "Experimental"
), class = "factor"), substrate = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Green",
"Red"), class = "factor")), class = "data.frame", row.names = c(NA,
-80L))
We can tabulate your data:
table(behaviour$group,behaviour$substrate)
Green Red
Control 10 30
Experimental 27 13
So you can only specify fill or y with geom_bar. In your case, you specify the fill, the geom_bar() function will do the counting for you:
ggplot(behaviour,aes(x=group,fill=substrate))+
geom_bar() + scale_fill_manual(values=c("#29c7ac","#c02739"))
You could have your data like this, with one row for each observation (i.e. each animal), with the group and the substrate recorded for each:
df <- data.frame(group = rep(c("control", "experimental"), each = 40),
substrate = rep(c("green", "red", "green", "red"), c(10, 30, 27, 13)))
Now define your plot using ggplot, specifying group as your x axis, and ..count.. as your y axis. Use geom_bar to get the stacked bars you are looking for, and finally use scale_fill_manual to set the colours:
library(ggplot2)
ggplot(df, aes(x = group, y = ..count.., fill = substrate)) +
geom_bar(colour = "black") +
scale_fill_manual(values = c("green", "red"))
I am trying to run a poisson regression to predict a common binary outcome.
This is my first attempt at using dput - if I have used it inappropriately, please let me know so I can correct it.
Example data:
df <- structure(list(id = 1:30, sex = structure(c(1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L), .Label = c("Female", "Male"
), class = "factor"), migStat = structure(c(1L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L), .Label = c("Australian-born",
"Migrant"), class = "factor"), mhAreaBi = structure(c(1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L), .Label = c("Metropolitan",
"Regional"), class = "factor"), empStatBi = structure(c(2L, 2L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L,
2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Student / employed",
"Unemployed"), class = "factor"), pensBenBi = structure(c(1L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L), .Label = c("No benefit",
"In receipt of pension benefit"), class = "factor"), maritStatBi = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L), .Label = c("Married (including de facto)",
"Not married"), class = "factor"), cto = structure(c(1L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L), .Label = c("No",
"Yes"), class = "factor")), .Names = c("id", "sex", "migStat",
"mhAreaBi", "empStatBi", "pensBenBi", "maritStatBi", "cto"), row.names = c(NA,
-30L), class = "data.frame")
When running the regression using glm in R, I receive an error:
fit <- glm(cto ~ sex + migStat + mhAreaBi + empStatBi + pensBenBi + maritStatBi, df, family = poisson)
Error in if (any(y < 0)) stop("negative values not allowed for the 'Poisson' family") :
missing value where TRUE/FALSE needed
In addition: Warning message:
In Ops.factor(y, 0) : ‘<’ not meaningful for factors
The same error has been explained briefly in this thread:
Because the "<" operator is not defined for factors the result that is
passed to if is of length 0. Setting the factor variable on the RHS
and using the integer values on hte LHS succeeds.
The error does not appear when I convert the outcome to an integer; however, this:
seems to defeat the purpose of predicting a binary outcome (unless a numeric variable with range 0-1 is treated the same as a factor variable with two levels); and
does not seem necessary (at least according to this post, which uses geeglm from geepack to predict a binary outcome [unfortunately, I receive the same error when I adapt the code to my own dataset])
Questions:
Could I receive further explanation of the error?
If I convert my outcome to an integer with range 0-1, will glm treat it the same as a binary variable? If not, is there an approach better suited to running a regression for a common binary outcome?
I think the best option here is:
df$cto_binary <- as.numeric(df$cto == "Yes")
fit <- glm(cto_binary ~ sex + migStat + mhAreaBi + empStatBi + pensBenBi + maritStatBi,
df, family = poisson)
As this way you explicitly show in your code what will be a 1/success in your binary outcome and don't get tripped up by things like the ordering of factor levels. Note that in R as.numeric(c(FALSE, TRUE)) gives c(0, 1), so you always know what you're going to get from a logical comparison.
I am trying to use standard evaluation with dplyr to calculate percents as a function of two grouping variables. The problem is in my mutate_ statement.
Here is a dataset:
structure(list(
var1 = structure(c(2L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L
),
.Label = c("No", "Yes"), class = "factor"),
var2 = structure(c(2L, 2L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L
),
.Label = c("Female", "Male"), class = "factor")),
.Names = c("var1", "var2"), row.names = c(NA, -100L), class = "data.frame")
Here is the code I am working with:
for_plots = function(data, var1, var2){
grouped_data = data %>% group_by_(var1, var2) %>%
summarise_(n_in_group = ~n()) %>%
mutate_(.dots = setNames(list(
interp(quote(n_in_group / sum(n_in_group, na.rm = TRUE) * 100),
n_in_group = as.name(n_in_group)))
))
return(grouped_data)
}
When I run the code, I receive an error:
Error in setNames(list(interp(quote(n_in_group/sum(n_in_group, na.rm = TRUE) * :
argument "nm" is missing, with no default
Any thoughts?
Here is some code based on #Frank's response:
for_plots = function(data, var1, var2) {
grouped_data = data %>% group_by_(var1, var2) %>%
summarise_(n_in_group = ~n()) %>%
mutate(percent = (n_in_group / sum(n_in_group, na.rm = TRUE)) * 100)
return(grouped_data)
}
I have 4 data.frames with two factor levels in each data.frame. df1 is reproduced below. Please duplicate df1 to produce df2...df4.
How can I produce boxplots with ggplot2 such that my final figure looks very similar to the figure below? The seasons in the figure represent the dataframe names while present and future represent level names and the legend represents heavy, heavy, heaviest in the data reproduced here.
Ignore the dotted horizontal red line.
df1= structure(list(id = 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, 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), .Label = c("NN", "SS"), class = "factor"),
heavy = c(0.136230125, 0.136281211, 0.136038018, 0.135392862,
0.137088902, 0.136028293, 0.13640057, 0.135317058, 0.13688615,
0.136448994, 0.137089424, 0.136810847, 0.135865471, 0.136130096,
0.136361327, 0.137796714, 0.136052839, 0.135892646, 0.13544437,
0.136452363, 0.135367421, 0.135617509, 0.138202559, 0.135396942,
0.135930092, 0.135661805, 0.135666, 0.135860128, 0.137648687,
0.136057353, 0.136057731, 0.135162399, 0.136080113, 0.135285036,
0.136204839, 0.138058091, 0.137215664, 0.135696637, 0.135863902,
0.135733243, 0.138274445, 0.136632122, 0.137787919, 0.135033093,
0.136926798, 0.136766413, 0.13690947, 0.135203152, 0.138370968,
0.136862356, 0.136083112, 0.138212845, 0.135964773, 0.13583601,
0.134923731, 0.135828965, 0.136272539, 0.138127602, 0.137028323,
0.136526836, 0.136407397, 0.137025373, 0.138358757, 0.137858521,
0.135464076, 0.136302506, 0.135528362, 0.137540677, 0.136455865,
0.138470144, 0.137227895, 0.136296955, 0.136792631, 0.135875782,
0.13815733, 0.136383864, 0.136696618, 0.13857652, 0.136700903,
0.136743873, 0.136033619, 0.135970522, 0.135816385, 0.136003984,
0.136583925, 0.136768202, 0.136292002, 0.136316737, 0.136540075,
0.136051218, 0.135924119, 0.136736303, 0.136946894, 0.136266073,
0.136263692, 0.136399301, 0.13611577, 0.135857095, 0.136769488,
0.136072466, 0.135564224, 0.136496131, 0.137659507, 0.136704681,
0.136542173, 0.136777403, 0.135771538, 0.13665463, 0.136984748,
0.137717859, 0.138195237, 0.136232227, 0.135956814), heavier = c(0.227332679,
0.227200132, 0.227299118, 0.227289816, 0.22724478, 0.227082442,
0.227861315, 0.227055561, 0.227112284, 0.228651438, 0.228158412,
0.228789678, 0.227188949, 0.228850198, 0.227246991, 0.227359368,
0.227359531, 0.227310607, 0.229490445, 0.227295226, 0.227958185,
0.228104958, 0.227254823, 0.22715392, 0.228062515, 0.227509559,
0.227143662, 0.230048719, 0.227860836, 0.228467792, 0.227263728,
0.227222794, 0.227165592, 0.227140611, 0.228424335, 0.227356425,
0.227243374, 0.228936267, 0.227320467, 0.22738371, 0.227694891,
0.227270428, 0.227751798, 0.228803279, 0.227330453, 0.229679261,
0.228999206, 0.227227604, 0.227247085, 0.227198567, 0.229234921,
0.227211613, 0.23007234, 0.226793036, 0.226474338, 0.226654333,
0.229964991, 0.22880328, 0.22700099, 0.226640822, 0.227522393,
0.227463578, 0.227832692, 0.227293936, 0.230154101, 0.229813709,
0.22761097, 0.227445308, 0.228669159, 0.22660539, 0.229017398,
0.230421347, 0.227041103, 0.227583471, 0.229547568, 0.22676335,
0.226737661, 0.229922588, 0.226907188, 0.227102239, 0.226469073,
0.230680908, 0.227763879, 0.226882448, 0.226741993, 0.226693024,
0.22671415, 0.226773662, 0.227795194, 0.226983096, 0.226647946,
0.226799552, 0.226759218, 0.22692942, 0.226601519, 0.227098192,
0.226886889, 0.226959012, 0.226552119, 0.226809761, 0.226786285,
0.226709252, 0.226834015, 0.228033943, 0.226693494, 0.22748613,
0.227608804, 0.22685023, 0.226586619, 0.227718907, 0.228890098,
0.226701909, 0.230919944), heaviest = c(0.316870607, 0.316772978,
0.316851707, 0.317017543, 0.316673994, 0.317224709, 0.319234458,
0.31861305, 0.319804304, 0.318605816, 0.316930034, 0.31688398,
0.316789552, 0.320783976, 0.317094325, 0.31809319, 0.317134565,
0.318173976, 0.317213167, 0.317084404, 0.321712205, 0.317128056,
0.316866913, 0.3170489, 0.31712423, 0.31684494, 0.319497635,
0.316932301, 0.316864646, 0.317279005, 0.316887692, 0.317134437,
0.316792589, 0.320894499, 0.319883014, 0.316924639, 0.316575642,
0.31686389, 0.316985994, 0.321566256, 0.316683995, 0.320299883,
0.317308965, 0.318151948, 0.316479828, 0.319857732, 0.317171909,
0.322137849, 0.316526917, 0.316870364, 0.322205784, 0.317055758,
0.320329144, 0.318015397, 0.318719989, 0.317910658, 0.317292016,
0.321348723, 0.319915048, 0.317160762, 0.318773245, 0.319627925,
0.31869767, 0.322422407, 0.32082693, 0.318034899, 0.318760783,
0.318325502, 0.320739086, 0.317216142, 0.32284544, 0.319466593,
0.318740499, 0.317489944, 0.319064923, 0.322014928, 0.317353897,
0.318904583, 0.317931141, 0.323295254, 0.318924712, 0.318965677,
0.317700019, 0.31793468, 0.317699508, 0.317168657, 0.318903983,
0.317493401, 0.317511406, 0.317483897, 0.31748495, 0.317776804,
0.318893431, 0.317663608, 0.316978585, 0.317473467, 0.317500429,
0.317144259, 0.317330826, 0.317610353, 0.317881476, 0.31707787,
0.317728374, 0.317452137, 0.31938939, 0.317199373, 0.31898747,
0.318878952, 0.317987024, 0.318951952, 0.318419561, 0.319568088,
0.321165413)), .Names = c("id", "heavy", "heavier", "heaviest"
), class = "data.frame", row.names = c(NA, -113L))
## create some data.frames: this results in a list of four dfs
createDF <- quote(data.frame(id=sample(c("NN", "SS"), 100, rep=T),
heavy=runif(100),
heavier=runif(100),
heaviest=runif(100)))
dfs <- lapply(1:4, function(i) eval(createDF))
## join and shape them
library(reshape2)
dat <- do.call(rbind, dfs)
dat$dfid <- paste("df", rep(1:4, times=sapply(dfs, nrow)))
dat <- melt(dat, id.vars=c("id", "dfid"))
ggplot(dat, aes(id, value, group=interaction(variable, id), fill=variable)) +
geom_boxplot() +
facet_grid(~dfid)
Something like this?
df1$season<- 'winter'
df2$season<- 'spring'
df3$season<- 'summer'
df4$season<- 'fall'
df1.m <- melt(df1, id.vars=c('id', 'season'), variable.name='weight', value.name='weight')
df2.m <- melt(df2, id.vars=c('id', 'season'), variable.name='weight', value.name='weight')
df3.m <- melt(df3, id.vars=c('id', 'season'), variable.name='weight', value.name='weight')
df4.m <- melt(df4, id.vars=c('id', 'season'), variable.name='weight', value.name='weight')
df.all <- rbind(df1.m, df2.m, df3.m, df4.m)
ggplot(df.all, aes(x=id, y=weight, fill=weightCat)) + geom_boxplot() + facet_grid(. ~ season)
I'm new to R and have the following challenge;
I want to create a visualization that basically combines 2 kind of 'heatmaps' in order to visualize at what times there are truly dark skies (for astronomy). For this I want to have a heatmap that visualizes the brightness of the moon based on the moonrise and moonset times and the phase of the moon. On this then we can plot a 'band'like heatmap for the time the sun is up with some transparency.
I'm not sure if this is going to work visualy or if I need to find some other solution, however this seems like a good challenge to get into R some more.
But I could use some pointers as I'm stuck already loading the matrix of size 24(hours) x 31(days) with all the 720 values. When trying to create a basic data.frame from the vectors I get the error that the number of rows are inconsistent.
Furthermore I have some heatmap examples working already, but I'm not sure how to combine 2 of them in the same plot like I described.
As an illustration the current 'heatmap' as it is in excel
And some data:
MOON
moon <- structure(list(X1.9.12 = structure(c(2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L), .Label = c("0%", "100%"), class = "factor"), X2.9.12 = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L), .Label = c("0%", "98%"), class = "factor"),
X3.9.12 = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L
), .Label = c("0%", "94%"), class = "factor"), X4.9.12 = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L), .Label = c("0%", "89%"), class = "factor"),
X5.9.12 = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L
), .Label = c("0%", "82%"), class = "factor"), X6.9.12 = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L), .Label = c("0%", "74%"), class = "factor"),
X7.9.12 = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), .Label = c("0%", "65%"), class = "factor"), X8.9.12 = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("0%", "56%"), class = "factor"),
X9.9.12 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), .Label = c("0%", "47%"), class = "factor"), X10.9.12 = structure(c(2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("0%", "37%"), class = "factor"),
X11.9.12 = structure(c(2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L
), .Label = c("0%", "28%"), class = "factor"), X12.9.12 = structure(c(2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("0%", "20%"), class = "factor"),
X13.9.12 = structure(c(2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L
), .Label = c("0%", "12%"), class = "factor"), X14.9.12 = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L), .Label = c("0%", "6%"), class = "factor"),
X15.9.12 = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L
), .Label = c("0%", "2%"), class = "factor"), X16.9.12 = 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), .Label = "0%", class = "factor"),
X17.9.12 = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L
), .Label = c("0%", "1%"), class = "factor")), .Names = c("X1.9.12",
"X2.9.12", "X3.9.12", "X4.9.12", "X5.9.12", "X6.9.12", "X7.9.12",
"X8.9.12", "X9.9.12", "X10.9.12", "X11.9.12", "X12.9.12", "X13.9.12",
"X14.9.12", "X15.9.12", "X16.9.12", "X17.9.12"), class = "data.frame", row.names = c("0:00:00",
"1:00:00", "2:00:00", "3:00:00", "4:00:00", "5:00:00", "6:00:00",
"7:00:00", "8:00:00", "9:00:00", "10:00:00", "11:00:00", "12:00:00",
"13:00:00", "14:00:00", "15:00:00", "16:00:00", "17:00:00", "18:00:00",
"19:00:00", "20:00:00", "21:00:00", "22:00:00", "23:00:00"))
SUN
September
Day Sunrise Sunset
1 6:52 20:26
2 6:54 20:24
3 6:56 20:22
4 6:57 20:20
5 6:59 20:17
6 7:00 20:15
7 7:02 20:13
8 7:04 20:10
9 7:05 20:08
10 7:07 20:06
11 7:08 20:05
12 7:09 20:02
13 7:11 20:00
14 7:13 19:58
15 7:14 19:55
16 7:16 19:53
17 7:17 19:51
18 7:19 19:48
19 7:21 19:46
20 7:22 19:44
21 7:25 19:40
22 7:26 19:38
23 7:28 19:35
24 7:30 19:33
25 7:31 19:31
26 7:33 19:28
27 7:35 19:26
28 7:36 19:24
29 7:38 19:21
30 7:40 19:19
So from what I understood, there are basically two questions:
Data organization
The easiest would be, if you'd have all data in one data.frame in long format. I.e. for each combination of time and date you have one row, with additional columns for the moon and sun intensity.
So we start with melting and fixing the moon data:
library(reshape2)
moon$time <- row.names(moon)
moon <- melt(moon, id.vars="time", variable.name="date", value.name="moon" )
moon$date <- sub("X(.*)", "\\1", moon$date)
moon$moon <- 1 - as.numeric(sub("%", "", moon$moon)) /100
Now we bring the sun data to an comparable form, by at least give them the same identifier for the date:
sun$Day <- paste( sun$Day, "9.12", sep ="." )
Next step is to merge the data by the date resp. Day and to set a comparable column for the sun intensity as is given already for the moon intensity. This can be done by casting the times to a time format and compare Sunrise and Sunset with the actual time:
mdf <- merge( moon, sun, by.x = "date", by.y = "Day" )
mdf$time.tmp <- strptime(mdf$time, format="%H:%M")
mdf$Sunrise <- round(strptime(mdf$Sunrise, format="%H:%M"), units = "hours")
mdf$Sunset <- round(strptime(mdf$Sunset, format="%H:%M"), units = "hours")
mdf$sun <- ifelse( mdf$Sunrise <= mdf$time.tmp & mdf$Sunset >= mdf$time.tmp, 1, 0 )
mdf <- mdf[c("date", "time", "moon", "sun")]
mdf[ 5:10, ]
date time moon sun
1.9.12 4:00:00 0 0
1.9.12 5:00:00 0 0
1.9.12 6:00:00 0 0
1.9.12 7:00:00 0 1
1.9.12 8:00:00 1 1
1.9.12 9:00:00 1 1
Plotting
Adding multiple layers with different transparencies begs literally for ggplot2. In order to use this in a proper way, there is one more data manipulation necessary, which ensures the proper order on the axes: date and time have to be converted to factors with factor levels ordered not lexically, but by time:
mdf <- within( mdf, {
date <- factor( date, levels=unique(date)[ order(as.Date( unique(date), "%d.%m.%y" ) ) ] )
time <- factor( time, levels=unique(time)[ order(strptime( time, format="%H:%M:%S"), decreasing=TRUE ) ] )
} )
This can be plot now:
library( ggplot2 )
ggplot( data = mdf, aes(x = date, y = time ) ) +
geom_tile( aes( alpha = sun ), fill = "goldenrod1" ) +
geom_tile( aes( alpha = moon ), fill = "dodgerblue3" ) +
scale_alpha_continuous( "moon", range=c(0,0.5) ) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Which gives you the following result