Create new dataset removing variables with high inflation factors - r

I have a dataset of environmental variables I would like to use for a GLMM. I am using the corvif function from the AED package (http://www.highstat.com/Book2/AED_1.0.zip) to identify and remove variables with high inflation factors.
Instead of removing one variable at a time manually from my dataset with a GVIF values > 3 (highest value removed first), I would like to know how to write a loop to accomplish this task automatically with the result being a new dataset with only the remaining variables (i.e. those with GVIF values < 3).
Any suggestions for how to approach this problem for a new R user?
Here is my sample data:
WW_Covs <- structure(list(Latitude = c(62.4419, 67.833333, 65.95, 63.72935,
60.966667, 60.266667, 55.660455, 62.216667, 61.3, 61.4, 62.084139,
55.662566, 64.48508, 63.208354, 62.87591, 62.70856, 62.64009,
63.79488, 59.55, 62.84206), BIO_02 = c(87, 82, 75, 70, 77, 70,
59, 84, 84, 79, 85, 60, 91, 87, 74, 74, 76, 70, 76, 74), BIO_03 = c(26,
23, 25, 26, 25, 24, 25, 25, 26, 25, 26, 26, 24, 25, 24, 25, 25,
25, 26, 24), BIO_04 = c(8443, 9219, 7594, 6939, 7928, 7593, 6160,
8317, 8167, 7972, 8323, 6170, 9489, 8578, 7814, 7680, 7904, 7149,
7445, 7803), BIO_05 = c(201, 169, 151, 166, 194, 210, 202, 205,
204, 186, 205, 200, 200, 195, 170, 154, 180, 166, 219, 170),
BIO_06 = c(-131, -183, -144, -102, -107, -75, -26, -119,
-113, -120, -120, -28, -169, -143, -131, -142, -124, -111,
-72, -129), BIO_08 = c(128, 109, 85, 78, 122, 145, 153, 134,
130, 126, 132, 152, 120, 119, 115, 98, 124, 104, 147, 115
), BIO_09 = c(-31, -81, -16, 13, -60, -6, 25, -25, -25, -70,
-25, 23, -56, -39, -47, -60, -39, 8, 0, -46), BIO_12 = c(667,
481, 760, 970, 645, 557, 645, 666, 652, 674, 670, 670, 568,
598, 650, 734, 620, 868, 571, 658), BIO_13 = c(78, 77, 96,
109, 85, 70, 67, 77, 84, 93, 78, 68, 72, 78, 93, 99, 90,
96, 72, 93), BIO_15 = c(23, 40, 25, 21, 36, 30, 21, 24, 28,
34, 24, 22, 28, 29, 34, 32, 36, 22, 30, 34), BIO_19 = c(147,
85, 180, 236, 108, 119, 154, 149, 135, 118, 148, 162, 117,
119, 120, 141, 111, 204, 111, 122)), .Names = c("Latitude",
"BIO_02", "BIO_03", "BIO_04", "BIO_05", "BIO_06", "BIO_08", "BIO_09",
"BIO_12", "BIO_13", "BIO_15", "BIO_19"), row.names = c(1:20), class = "data.frame")
Sample code:
library(AED)
WW_Final <- corvif(WW_Covs)
test <- corvif(WW_Covs])
test[order(-test$GVIF), ]
if(test$GVIF[1,] > 3, # this is where I get stuck...

Here is an algorithm for doing this. I illustrate with the built-in dataset longley, and I also use function vif in package car, rather than using package AED:
It's not pretty, and should be wrapped inside a function, but I leave that as an exercise for the interested reader.
The code:
library(car)
dat <- longley
cutoff <- 2
flag <- TRUE
while(flag){
fit <- lm(Employed ~ ., data=dat)
vfit <- vif(fit)
if(max(vfit) > cutoff){
dat <- dat[, -which.max(vfit)]
} else {
flag <- FALSE
}
}
print(fit)
print(vfit)
The output:
Call:
lm(formula = Employed ~ ., data = dat)
Coefficients:
(Intercept) Unemployed Armed.Forces
50.66281 0.02265 0.02847
Unemployed Armed.Forces
1.032501 1.032501

Related

How to identify parameters for SARIMA model in R

Part 2 Boston
plot(boston, ylab=" Boston crime data", xlab= "Time")
#Time series seem to have homogeneous variance upon visual inspection
#Q2
#Trend looks linear in the plot, so for trend differencing operator take d=1
newboston= as.numeric(unlist(boston))
xdiff = diff(newboston)
plot(xdiff)
#Q3
#ADF
library(tseries)
adf.test(xdiff)
#From the result, alternative hypothesis is stationary so null hypothesis is rejected
#KPSS test
install.packages('fpp3', dependencies = TRUE)
library ( fpp3 )
unitroot_kpss(xdiff)
#the p-value is >0.05, so fail to reject null hypothesis for KPSS
#Q4
library(astsa)
acf2(xdiff, max.lag = 50)
model1 = sarima(xdiff, p, 1, q)
So this is what I have tried so far. I am quite new to R and so do be kind if my workings make little sense. For context, Boston is the data I imported from an excel, that is simply a column of x axis data.
Firstly, I am trying to do Q4, but I am not sure how I would go about to find p and q.
Second, I am unsure whether what I did in Q2 to detrend my data is correct in the first place.
Here is the output of dput(boston)
dput(boston)
structure(list(x = c(41, 39, 50, 40, 43, 38, 44, 35, 39, 35,
29, 49, 50, 59, 63, 32, 39, 47, 53, 60, 57, 52, 70, 90, 74, 62,
55, 84, 94, 70, 108, 139, 120, 97, 126, 149, 158, 124, 140, 109,
114, 77, 120, 133, 110, 92, 97, 78, 99, 107, 112, 90, 98, 125,
155, 190, 236, 189, 174, 178, 136, 161, 171, 149, 184, 155, 276,
224, 213, 279, 268, 287, 238, 213, 257, 293, 212, 246, 353, 339,
308, 247, 257, 322, 298, 273, 312, 249, 286, 279, 309, 401, 309,
328, 353, 354, 327, 324, 285, 243, 241, 287, 355, 460, 364, 487,
452, 391, 500, 451, 375, 372, 302, 316, 398, 394, 431, 431),
y = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,
76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,
105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,
117, 118)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-118L))

FCT_Collapse using a range

Im trying to use a range (160:280) instead of '160', '161' and so on. How would i do that?
group_by(disp = fct_collapse(as.character(disp), Group1 = c(160:280), Group2 = c(281:400)) %>%
summarise(meanHP = mean(hp)))
Error: Problem adding computed columns in `group_by()`.
x Problem with `mutate()` column `disp`.
i `disp = `%>%`(...)`.
x Each input to fct_recode must be a single named string. Problems at positions: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 17```
For range of values it is better to use cut where you can define breaks and labels.
library(dplyr)
library(forcats)
mtcars %>%
group_by(disp = cut(disp, c(0, 160, 280, 400, Inf), paste0('Group', 1:4))) %>%
summarise(meanHP = mean(hp))
# disp meanHP
# <fct> <dbl>
#1 Group1 93.1
#2 Group2 143
#3 Group3 217.
#4 Group4 217.
So here 0-160 becomes 'Group1', 160-280 'Group2' and so on.
With fct_collapse you can do -
mtcars %>%
group_by(disp = fct_collapse(as.character(disp), Group1 = as.character(160:280), Group2 = as.character(281:400))) %>%
summarise(meanHP = mean(hp)) %>%
suppressWarnings()
However, this works only for exact values which are present so 160 would be in group1 but not 160.1.
We could also do
library(dplyr)
library(stringr)
mtcars %>%
group_by(disp = cut(disp, c(0, 160, 280, 400, Inf), strc('Group', 1:4))) %>%
summarise(meanHP = mean(hp))

Batch column aggregation and reordering dataframe in R

I have census tract data divided my age variables by sex, into a value for males (varname_m) and females (varname_f):
Rows: 146,112
Columns: 13
$ tractid <chr> "01001020100", "01001020100", "01001020200", "01001020200", "01001020300", "01001020300", "01001020400", "01001020400", "01001020500", "01001020500", "0100102060…
$ ag18to19_m <dbl> 37, 57, 24, 15, 49, 27, 87, 33, 293, 159, 57, 40, 19, 41, 18, 56, 143, 86, 25, 155, 41, 7, 40, 0, 35, 0, 99, 25, 190, 420, 61, 157, 63, 110, 37, 127, 67, 45, 198…
$ ag20_m <dbl> 6, 14, 64, 0, 11, 18, 16, 8, 115, 21, 42, 15, 53, 71, 16, 0, 63, 77, 43, 96, 32, 15, 21, 0, 12, 44, 8, 0, 105, 80, 34, 20, 8, 0, 13, 46, 88, 0, 83, 241, 10, 96, …
$ ag21_m <dbl> 18, 0, 15, 7, 0, 16, 117, 18, 14, 40, 23, 26, 45, 47, 32, 0, 41, 50, 0, 76, 14, 45, 20, 1, 48, 11, 11, 30, 18, 30, 60, 55, 20, 0, 28, 43, 31, 21, 9, 0, 11, 8, 0,…
$ ag22to24_m <dbl> 48, 64, 109, 45, 25, 62, 65, 41, 224, 531, 28, 51, 31, 60, 0, 24, 132, 96, 59, 98, 27, 45, 111, 30, 113, 58, 71, 61, 46, 114, 11, 86, 116, 99, 28, 158, 72, 135, …
$ ag25to29_m <dbl> 49, 31, 83, 99, 87, 144, 153, 142, 428, 327, 69, 35, 36, 22, 61, 113, 202, 420, 184, 255, 94, 84, 118, 82, 71, 30, 47, 195, 44, 135, 118, 150, 215, 157, 118, 180…
$ ag30to34_m <dbl> 52, 72, 59, 97, 84, 157, 124, 85, 415, 227, 95, 13, 105, 202, 37, 86, 274, 334, 161, 182, 91, 173, 84, 84, 81, 106, 79, 67, 263, 77, 40, 115, 199, 411, 81, 115, …
$ ag18to19_f <dbl> 33, 8, 51, 7, 31, 19, 107, 15, 33, 25, 47, 37, 35, 81, 98, 92, 127, 147, 72, 0, 109, 57, 7, 74, 78, 0, 36, 24, 109, 268, 88, 62, 10, 0, 47, 33, 79, 191, 63, 134,…
$ ag20_f <dbl> 13, 40, 23, 18, 27, 18, 12, 11, 37, 0, 58, 83, 19, 45, 20, 77, 16, 103, 0, 36, 15, 0, 8, 37, 29, 34, 36, 0, 23, 30, 37, 0, 10, 48, 51, 67, 17, 15, 125, 55, 27, 1…
$ ag21_f <dbl> 40, 6, 13, 24, 36, 0, 16, 19, 17, 0, 11, 0, 0, 89, 28, 31, 39, 20, 15, 0, 7, 13, 0, 17, 9, 13, 17, 47, 106, 36, 42, 94, 0, 13, 19, 50, 67, 0, 122, 48, 21, 9, 145…
$ ag22to24_f <dbl> 21, 67, 71, 21, 69, 35, 28, 165, 346, 350, 15, 0, 53, 50, 25, 42, 207, 165, 158, 114, 20, 0, 73, 66, 29, 29, 59, 39, 83, 94, 22, 24, 79, 69, 37, 21, 73, 201, 282…
$ ag25to29_f <dbl> 36, 24, 86, 51, 88, 160, 130, 73, 318, 539, 157, 127, 128, 111, 86, 29, 334, 365, 87, 217, 57, 60, 177, 92, 17, 90, 86, 113, 67, 204, 136, 120, 130, 108, 211, 51…
$ ag30to34_f <dbl> 36, 73, 38, 42, 87, 154, 63, 84, 440, 414, 51, 95, 151, 73, 27, 70, 429, 458, 231, 173, 54, 82, 104, 24, 61, 159, 69, 30, 218, 82, 88, 214, 222, 158, 76, 125, 24…
I want to aggregate each of the variables divided by sex to a single combined variable. For example, I want to add ag18to19_m and ag18to19_f to create ag18to19. I can easily do this using mutate and the following code and order them to the front of the data frame:
aggregated <- merged %>%
mutate(ag18to19 = ag18to19_m + ag18to19_f) %>%
relocate(ag18to19, .before = ag18to19_m) %>%
mutate(ag20 = ag20_m + ag20_f) %>%
relocate(ag20, .before = ag20_m) %>%
mutate(ag21 = ag21_m + ag21_f) %>%
relocate(ag21, .before = ag21_m) %>%
mutate(ag22to24 = ag22to24_m + ag22to24_f) %>%
relocate(ag22to24, .before = ag22to24_m) %>%
mutate(ag25to29 = ag25to29_m + ag25to29_f) %>%
relocate(ag25to29, .before = ag25to29_m) %>%
mutate(ag30to34 = ag30to34_m + ag30to34_f) %>%
relocate(ag30to34, .before = ag30to34_m)
I know there's a more efficient way to do this using a loop or map_df function that will also give me a data frame as an output. I've been trying for the last hour to write a function and use map_df but haven't had any success. Does anyone have a suggestion?
More efficient code here is best practice and will help me apply this same data cleaning step to several other variables that are grouped in the same way (e.g., income grouped by sex or education grouped by age).
Any help would be greatly appreciated. Thanks.
Here is an option in tidyverse
library(dplyr)
library(stringr)
merged1 <- merged %>%
mutate(across(ends_with('_m'), ~
. + get(str_replace(cur_column(), '_m', '_f')),
.names = '{.col}_new')) %>%
rename_at(vars(ends_with('_new')),
~ str_remove(., '_[m]_new$')) %>%
select(tract_id, order(names(.)[-1]) + 1)

In R, how can I properly subset a data frame based on a list of values inside of a function?

I have a function that is attempting to select rows from a dataframe based on a list of values.
For instance, some values might be:
> subset_ids
[1] "JUL_0003_rep1" "JUL_0003_rep2"
[3] "JUL_0003_rep3" "JUL_0007_rep1"
[5] "JUL_0007_rep2" "JUL_0007_rep3"
I have a data frame called "targets" with a column called "LongName". It has many other columns but no big deal. I want to select the rows from targets when LongName is in subset ids.
I can do this fine with either:
targets[is.element(targets$LongName, subset_ids),]
or
targets[targets$LongName %in% subset_ids,]
The problem is that I want to do this in a function, and I don't know what the column will be called in advance.
So I tried using the eval/parse method, which upon recent reading may not be the best way to do it. When I do the following:
sub1 <- paste("targets[is.element(targets$", column_name, ", subset_ids),]", sep="")
targets_subset <- as.character(eval(parse(text = sub1)))
It returns some strange concatenation of row numbers. It looks like this:
[1] "c(5, 6, 7, 17, 18, 19, 26, 27, 28, 35, 36, 46, 47, 48, 54, 55, 61, 62, 63, 64, 73, 74, 75, 76, 77, 78, 91, 92, 93, 102, 103, 104, 114, 117, 118, 129, 136, 137, 140, 141, 151, 152, 153, 157, 158, 159, 169, 172, 173, 183, 187, 188, 199, 200, 201, 208, 209, 210, 232, 233, 241, 242, 243, 252, 253, 254, 264, 265, 270, 271, 285, 286, 296, 297, 298)"
[2] "c(5, 6, 7, 17, 18, 19, 26, 27, 28, 35, 36, 46, 47, 48, 54, 55, 61, 62, 63, 64, 73, 74, 75, 76, 77, 78, 91, 92, 93, 102, 103, 104, 114, 117, 118, 129, 136, 137, 140, 141, 151, 152, 153, 157, 158, 159, 169, 172, 173, 183, 187, 188, 199, 200, 201, 208, 209, 210, 232, 233, 241, 242, 243, 252, 253, 254, 264, 265, 270, 271, 285, 286, 296, 297, 298)"
[3] "c(3, 3, 3, 7, 7, 7, 11, 11, 11, 15, 15, 19, 19, 19, 22, 22, 26, 26, 27, 27, 31, 31, 31, 32, 32, 32, 39, 39, 39, 43, 43, 43, 47, 49, 49, 53, 57, 57, 59, 59, 63, 63, 63, 65, 65, 65, 70, 72, 72, 76, 78, 78, 83, 83, 83, 86, 86, 86, 97, 97, 100, 100, 100, 104, 104, 104, 108, 108, 111, 111, 117, 117, 121, 121, 121)"
So 5, 6, 7, 17 ... appear to be the right rows for the target i'm trying to pick, but I don't understand why it sent this back in the first place, or what item [3] is at all.
If I manually execute the line generated by the above "sub1 <- ...", then it returns the proper data. If I ask the function to do it, it returns this garbage.
My question is two-fold. 1: Why is the data being returned this way? 2: Is there a better way than eval/parse to do what I'm trying to do?
I suspect some strange scope or environment level issue, but it is unclear to me at this point. I appreciate any advice anyone has.
The data are returned that way because you are coercing the dataframe to a character object. Try
as.character(head(targets))
to see a short example.
So, your method works if you eliminate the as.character(). Here it is as a MWE:
targets <- data.frame(LongName = sample(letters, 1000, replace = TRUE),
SeqNum= 1:1000,
X = rnorm(1000))
subset_ids <- c("a","f")
targets[is.element(targets$LongName, subset_ids),]
targets[targets$LongName %in% subset_ids,]
testfun <- function(targets, column_name, subset_ids){
sub1 <- paste("targets[is.element(targets$", column_name, ", subset_ids),]", sep="")
targets_subset <- eval(parse(text = sub1))
return(targets_subset)
}
testfun(targets, column_name = "LongName", subset_ids)

ggvis barchart using dates as x axis

I have been switching over from ggplot to ggvis when working with shiny apps. I have figured out a lot but am really stumped when it comes to bar graphs. I have a timeseries with dates and values. I simply want bars instead of points for each value (I would ideally like to be able to plot multiple semi-transparent bars if anyone has had success there please share) but here I wanted to get one bar working.
Works with layer_points()
df %>% ggvis(~date, ~x) %>% layer_points() %>% scale_datetime("x")
Doesnt work with layer_bars()
df %>% ggvis(~date, ~x) %>% layer_bars() %>% scale_datetime("x")
Data I am using...
structure(list(date = structure(c(7680, 7687, 7694, 7701, 7708,
7715, 7722, 7729, 7736, 7743, 7750, 7757, 7764, 7771, 7778, 7785,
7792, 7799, 7806, 7813, 7820, 7827, 7834, 7841, 7848, 7855, 7862,
7869, 7876, 7883, 7890, 7897, 7904, 7911, 7918, 7925, 7932, 7939,
7946, 7953, 7960, 7967, 7974, 7981, 7988, 7995, 8002, 8009, 8016,
8023, 8030, 8037, 8044, 8051, 8058, 8065, 8072, 8079, 8086, 8093,
8100, 8107, 8114, 8121, 8128, 8135, 8142, 8149, 8156, 8163, 8170,
8177, 8184, 8191, 8198, 8205, 8212, 8219, 8226, 8233, 8240, 8247,
8254, 8261, 8268, 8275, 8282, 8289, 8296, 8303, 8310, 8317, 8324,
8331, 8338, 8345, 8352, 8359, 8366, 8373, 8380, 8387, 8394, 8401,
8408, 8415, 8422, 8429, 8436, 8443, 8450, 8457, 8464, 8471, 8478,
8485, 8492, 8499, 8506, 8513, 8520, 8527, 8534, 8541, 8548, 8555,
8562, 8569, 8576, 8583, 8590, 8597, 8604, 8611, 8618, 8625, 8632,
8639, 8646, 8653, 8660, 8667, 8674, 8681, 8688, 8695, 8702, 8709,
8716, 8723, 8730, 8737, 8744, 8751, 8758, 8765, 8772, 8779, 8786,
8793, 8800, 8807, 8814, 8821, 8828, 8835, 8842, 8849, 8856, 8863,
8870, 8877, 8884, 8891, 8898, 8905, 8912, 8919, 8926, 8933, 8940,
8947, 8954, 8961, 8968, 8975, 8982, 8989, 8996, 9003, 9010, 9017,
9024, 9031, 9038, 9045, 9052, 9059, 9066, 9073), class = "Date"),
x = c(-0.034038302, 0.122310949, -0.002797319, 0.026515253,
0.039961798, 0.034473263, 0.00549937, -0.024125944, 0.000132490000000001,
0.011038357, -0.02135072, 0.030663311, -0.008915551, 0.004855042,
0.01563688, -0.007397493, 0.013569146, -0.004968811, -0.00250391,
0.014624532, 0.036937453, -0.023685917, 0.018921356, -0.003066779,
-0.009217771, 0.005317513, 0.010378968, 0.001580798, -0.015085972,
-0.000121644000000001, 0.020468644, 0.007925229, 0.007721276,
-0.003123545, -0.018317891, -0.014900591, 0.003260844, -0.001565358,
-0.014833886, 0.00366766, 0.014297139, -0.00725552, 0.012207931,
0.024035152, -0.024195095, -0.0043564, 0.000847468, 0.033031596,
0.023685033, 0.025143071, 0.046264348, 0.038285177, -0.009180356,
-0.01630399, -0.010131294, -0.009939386, -0.007620427, 0.013062259,
0.009912238, 0.000192973, -0.01683559, -0.002627549, 0.019836063,
-0.019946159, -0.020124331, 0.012921737, 0.034604405, -0.020774015,
0.00334805, 0.002271156, -0.018676732, 0.019160923, -0.01945997,
-0.014342636, -0.004867796, -0.010002446, -0.004372991, 0.023164369,
0.019824112, -0.00321832, -0.015785746, 0.040836652, 0.00148831,
0.012084485, -0.009603897, -0.004642148, -0.008399234, 0.010463218,
0.000256571000000001, -0.01978405, -0.003439498, -0.015669975,
0.026180724, 0.020373255, 0.019160773, 0.00692683, 0.010215506,
0.010861939, 0.012041143, 0.025734568, -0.004828156, 0.006914552,
-0.00720089, -0.000538489999999999, -0.008479448, 0.022926604,
0.002131842, -0.003688597, 0.025325639, -0.009562293, -0.024336741,
0.012907537, 0.004339383, 0.010744364, -0.013058765, -0.003672014,
-0.023887493, 0.01062259, 0.02088054, -0.035249878, -0.001462821,
0.01904368, -0.001308787, 0.009203217, 0.019856479, 0.011296979,
0.010039545, -0.01559142, 0.006083419, -0.017958978, -0.007488063,
0.01236649, -0.004459064, -0.004375386, 0.025500722, 0.005557851,
0.008444321, 0.002827649, 0.020320308, 0.031611803, -0.010199803,
-0.009425874, 0.007942729, -2.59379999999999e-05, 0.016669077,
-0.011666062, 0.022835386, -0.025599107, 0.013562535, -0.018365192,
0.018148786, 0.016649144, -0.009530455, 0.012996597, 0.002034778,
-0.005926478, -0.004897238, -0.004419719, 0.010848926, -0.006039757,
-0.030287605, 0.019221837, 0.001808161, -0.009566133, 0.005009292,
0.005365023, -0.004879922, -0.024637933, -0.0186584, 0.004786059,
-0.008245254, -0.000106243, -0.001714888, -0.017804006, -0.021200061,
0.003812757, 0.021940886, 0.002270448, -0.015417493, -0.045754612,
-0.003468442, -0.006242659, 0.022383824, -0.018753927, 0.008577571,
0.008655048, 0.02374636, 0.029522811, 0.009946946, 0.015419714,
-0.016714623, -0.014616188, 0.019670855, -0.038979063, 0.020491563,
-0.009640674, 0.046051144, -0.021434575, 0.000190443999999998,
-0.029013969), id = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,
57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,
87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124,
125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136,
137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148,
149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160,
161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,
185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196,
197, 198, 199, 200)), .Names = c("date", "x", "id"), row.names = 53:252, class = "data.frame")
Set format df$date as character:
df$date <- as.character(df$date)
and then:
df %>% ggvis(~date, ~x) %>% layer_bars()

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