tapply of two variables in a loop - r

My data :
TEST <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 3, 1, 0, 0,
0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 1, 1,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2,
0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0,
1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 3, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0,
3, 0, 0, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
0, 0, 2, 0, 1, 1, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0,
0, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 3, 0, 0, 1, 0, 0, 2, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0,
0, 0, 0, 1, 0, 0, 2, 0, 1, 2, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 1, 1, 0, 0, 1,
0, 0, 0), .Dim = c(22L, 20L), .Dimnames = list(NULL, c("month",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "")))
and :
month <- c(1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2)
I want to sum by row, so I used this code :
su_test <- list()
for (i in 1:ncol(TEST)){
su_test[[i]] <- tapply(TEST[,i], month, sum)
}
su_test <- do.call(cbind, su_test)
and to check the quantile :
su_test_obs <- apply(su_test,1,quantile,c(0.1,0.9))
This is an observation simulation per month. However I also have the detail by area. :
TEST2 <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 3, 1, 0,
0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 1,
1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0,
0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 3, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
0, 3, 0, 0, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3,
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2,
0, 0, 0, 2, 0, 1, 1, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 3, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 3, 0, 0, 1, 0, 0, 2, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1,
0, 0, 0, 0, 1, 0, 0, 2, 0, 1, 2, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
1, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
0, 1, 1), .Dim = c(22L, 20L), .Dimnames = list(NULL, c("month",
"area", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "")))
area <- c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
I would like to have the same result of su_test_obs
but with the details of the areas in addition like list(month, area) but I don't understand the logic.
Would you have a solution please? Maybe there is a solution simpler with dplyr?
Thanks


It would be much simpler if you convert your matrix to dataframe. We can then use aggregate which can be applied to multiple groups easily
df <- data.frame(TEST2)
apply(aggregate(.~month + area, df, sum)[-c(1, 2)], 1, quantile, c(0.1,0.9))
# [,1] [,2]
#10% 2 1.7
#90% 6 5.3

Related

Frequency bar graph in descending order

I created a frequency graph using ggplot2. I would like the bars to go in descending order based on frequency counts. So language measures from left to right, BNT, WAB_R, BDAE...etc. Of note, my dataframe is organized with the language measures are columns and the cases are rows. The values are 0 or 1 and 1 means that the participant endorsed the language measure. I have tried using reorder in various combinations but had no luck. I appreciate the help!
Here is sample data:
WAB-R BDAE BNT CAT
1 0 0 1 0
2 1 0 1 1
3 0 0 0 0
4 1 1 0 0
5 0 0 0 1
6 0 1 1 0
7 1 0 0 0
8 0 1 1 0
Portion of the Data Show in New WindowClear
OutputExpand/Collapse Output
structure(list(WAB_R = c(0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0), WAB_B = c(1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0), BDAE = c(0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), CAT = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0), BNT = c(0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1), PNT = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), PyramidPalms = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), QAB = c(0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0), PALPA = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0), BASA = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Compiled_lang = c(0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, -85L), class = c("tbl_df", "tbl", "data.frame"))
Code:
library(tidyverse) survey %>% select(c(WAB_R:other_lang_measure)) %>% pivot_longer(everything()) %>% filter(value==1) %>% ggplot(aes(x= value))+ geom_histogram(stat = 'count',aes(fill=name), position = position_dodge2(0.9,preserve = 'single'))+ labs(fill='Language Measures') + theme(axis.title.x=element_blank(), axis.text.x=element_blank(),axis.ticks.x=element_blank()) + scale_y_continuous(breaks=seq(0,50,5))+ ylab("Frequency Counts") + coord_cartesian(ylim=c(0, 45))+ ggtitle("\nLanguage Measures\n ")+ cleanup

As mentioned by sage #r2evans you would need to format as factor the x-axis variable. Also, you can compute the counts directly using summarise() and then arrange in order to sketch the plot:
library(tidyverse)
#Code
survey %>%
select(c(WAB_R:Compiled_lang)) %>%
pivot_longer(everything()) %>% filter(value==1) %>%
group_by(name) %>%
summarise(value=sum(value)) %>%
arrange(desc(value)) %>%
mutate(name=factor(name,levels = unique(name),ordered = T)) %>%
ggplot(aes(x= name,y=value))+
geom_bar(stat = 'identity',aes(fill=name),
position = position_dodge2(0.9,preserve = 'single'))+
labs(fill='Language Measures') +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),axis.ticks.x=element_blank()) +
scale_y_continuous(breaks=seq(0,50,5))+
ylab("Frequency Counts") +
coord_cartesian(ylim=c(0, 45))+ ggtitle("\nLanguage Measures\n ")
Output:

Here is a way:
survey <- structure(list(WAB_R = c(0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0),
WAB_B = c(1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0),
BDAE = c(0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1),
CAT = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0),
BNT = c(0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1),
PNT = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
PyramidPalms = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
QAB = c(0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0),
PALPA = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0),
BASA = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
Compiled_lang = c(0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0)),
row.names = c(NA, -85L), class = c("tbl_df", "tbl", "data.frame"))
library(tidyverse,warn.conflicts = F)
survey %>%
pivot_longer(everything()) %>%
filter(value==1) %>%
count(name) %>%
ggplot(aes(x= name, y = n)) +
geom_col() +
labs(y = "Frequency Counts", title = "Language Measures", x = "")
survey %>%
pivot_longer(everything()) %>%
filter(value==1) %>%
count(name) %>%
ggplot(aes(x= name, y = n)) +
geom_col() +
labs(y = "Frequency Counts", title = "Language Measures", x = "") +
coord_flip()
Created on 2021-01-15 by the reprex package (v0.3.0)

xgb.cv with no folds and return the results based on a split of the data

I have some data which looks like:
# A tibble: 50 x 28
sanchinarro date holiday weekday weekend workday_on_holi… weekend_on_holi… protocol_active
<dbl> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 -1.01 2010-01-01 1 1 0 1 0 0
2 0.832 2010-01-02 0 0 1 0 0 0
3 1.29 2010-01-03 0 0 1 0 0 0
4 1.04 2010-01-04 0 1 0 0 0 0
5 0.526 2010-01-05 0 1 0 0 0 0
6 -0.292 2010-01-06 1 1 0 1 0 0
7 -0.394 2010-01-07 0 1 0 0 0 0
8 -0.547 2010-01-08 0 1 0 0 0 0
9 -0.139 2010-01-09 0 0 1 0 0 0
10 0.628 2010-01-10 0 0 1 0 0 0
I want to run xgb.cv on the first 40 rows and validate it on the final 10 rows.
I try the following:
library(xgboost)
library(dplyr)
X_Val <- ddd %>% select(-c(1:2))
Y_Val <- ddd %>% select(c(1)) %>% pull()
dVal <- xgb.DMatrix(data = as.matrix(X_Val), label = as.numeric(Y_Val))
xgb.cv(data = dVal, nround = 30, folds = NA, params = list(eta = 0.1, max_depth = 5))
which gives me this error:
Error in xgb.cv(data = dVal, nround = 30, folds = NA, eta = 0.1,
max_depth = 5) : 'folds' must be a list with 2 or more elements
that are vectors of indices for each CV-fold
How can I run a simple xgb.cv on the first 40 rows and test it on the last 10 rows.
I eventually want to apply a gird search with a list of parameters and save the results in a list. Since I am dealing with time series data I do not want to mix the folds up, I just want a simple train and in-sample test of 40:10.
Data:
ddd <- structure(list(sanchinarro = c(-1.00742964973274, 0.832453587904369,
1.29242439731365, 1.03688505875294, 0.525806381631517, -0.291919501762755,
-0.394135237187039, -0.547458840323464, -0.138595898626329, 0.628022117055801,
1.19020866188936, 1.5990716035865, 1.5990716035865, -0.70078244345989,
2.11015028070792, 1.95682667757149, 0.985777191040795, 0.883561455616511,
0.985777191040795, 0.270267043070807, 2.51901322240505, 2.41679748698077,
0.372482778495091, -0.291919501762755, -0.905213914308458, -0.905213914308458,
-0.649674575747748, 1.2413165296015, 1.54796373587436, -0.70078244345989,
-0.905213914308458, -0.0363801632020448, 1.54796373587436, 2.00793454528363,
1.54796373587436, -0.445243104899181, -0.445243104899181, 1.03688505875294,
0.628022117055801, -0.496350972611323, 0.168051307646523, -0.649674575747748,
0.0658355722222391, -1.00742964973274, -0.291919501762755, 0.0147277045100972,
0.168051307646523, -0.189703766338471, 0.219159175358665, 0.679129984767943
), date = structure(c(14610, 14611, 14612, 14613, 14614, 14615,
14616, 14617, 14618, 14619, 14620, 14621, 14622, 14623, 14624,
14625, 14626, 14627, 14628, 14629, 14630, 14631, 14632, 14633,
14634, 14635, 14636, 14637, 14638, 14639, 14640, 14641, 14642,
14643, 14644, 14645, 14646, 14647, 14648, 14649, 14650, 14651,
14652, 14653, 14654, 14655, 14656, 14657, 14658, 14659), class = "Date"),
holiday = c(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), weekday = c(1,
0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,
1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1,
1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1), weekend = c(0, 1, 1, 0,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 1, 1, 0, 0, 0, 0, 0), workday_on_holiday = c(1, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), weekend_on_holiday = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), protocol_active = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), text_broken_clouds = c(0,
1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,
1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1), text_clear = c(0, 0, 0,
0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 1), text_fog = c(0, 1, 0, 1, 1, 0,
0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0), text_partly_cloudy = c(0, 1, 0, 0, 0,
1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), text_partly_sunny = c(1, 1, 1, 1, 1,
0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0,
0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1), text_passing_clouds = c(1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 0, 0, 1, 1, 1), text_scattered_clouds = c(1, 1,
0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,
0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1), text_sunny = c(0, 0, 0, 0,
0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1), month_1 = c(1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), month_2 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1), month_3 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_4 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_5 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), month_6 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), month_7 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), month_8 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_9 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_10 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), month_11 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), month_12 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-50L))
EDIT: List data:
The final data comes in the form of lists.
datalst <- list(structure(list(sanchinarro = c(-1.00742964973274, 0.832453587904369,
1.29242439731365, 1.03688505875294, 0.525806381631517, -0.291919501762755,
-0.394135237187039, -0.547458840323464, -0.138595898626329, 0.628022117055801,
1.19020866188936, 1.5990716035865, 1.5990716035865, -0.70078244345989
), date = structure(c(14610, 14611, 14612, 14613, 14614, 14615,
14616, 14617, 14618, 14619, 14620, 14621, 14622, 14623), class = "Date"),
holiday = c(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0), weekday = c(1,
0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1), weekend = c(0, 1,
1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0), workday_on_holiday = c(1,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0), weekend_on_holiday = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), protocol_active = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), text_broken_clouds = c(0,
1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0), text_clear = c(0,
0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0), text_fog = c(0, 1,
0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0), text_partly_cloudy = c(0,
1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0), text_partly_sunny = c(1,
1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1), text_passing_clouds = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), text_scattered_clouds = c(1,
1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1), text_sunny = c(0,
0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0), month_1 = c(1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), month_2 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_3 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_4 = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), month_5 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), month_6 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), month_7 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), month_8 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), month_9 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0), month_10 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), month_11 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), month_12 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-14L)), structure(list(sanchinarro = c(0.832179838392013, 1.29225734336885,
1.03665872949283, 0.525461501740789, -0.292454062662475, -0.394693508212883,
-0.548052676538495, -0.139094894336863, 0.627700947291197, 1.19001789781844,
1.59897568002007, 1.59897568002007, -0.701411844864107, 2.11017290777211
), date = structure(c(14611, 14612, 14613, 14614, 14615, 14616,
14617, 14618, 14619, 14620, 14621, 14622, 14623, 14624), class = "Date"),
holiday = c(0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), weekday = c(0,
0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1), weekend = c(1, 1,
0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0), workday_on_holiday = c(0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), weekend_on_holiday = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), protocol_active = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), text_broken_clouds = c(1,
0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), text_clear = c(0,
0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1), text_fog = c(1, 0,
1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0), text_partly_cloudy = c(1,
0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0), text_partly_sunny = c(1,
1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0), text_passing_clouds = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), text_scattered_clouds = c(1,
0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0), text_sunny = c(0,
0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1), month_1 = c(1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), month_2 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_3 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_4 = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), month_5 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), month_6 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), month_7 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), month_8 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), month_9 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0), month_10 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), month_11 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), month_12 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-14L)), structure(list(sanchinarro = c(1.29293502084952, 1.03729933727253,
0.526027970118536, -0.292006217327851, -0.394260490758649, -0.547641900904846,
-0.138624807181653, 0.628282243549334, 1.19068074741873, 1.59969784114192,
1.59969784114192, -0.701023311051044, 2.11096920829591, 1.95758779814971
), date = structure(c(14612, 14613, 14614, 14615, 14616, 14617,
14618, 14619, 14620, 14621, 14622, 14623, 14624, 14625), class = "Date"),
holiday = c(0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), weekday = c(0,
1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0), weekend = c(1, 0,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1), workday_on_holiday = c(0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), weekend_on_holiday = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), protocol_active = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), text_broken_clouds = c(0,
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), text_clear = c(0,
0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0), text_fog = c(0, 1,
1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0), text_partly_cloudy = c(0,
0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0), text_partly_sunny = c(1,
1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1), text_passing_clouds = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), text_scattered_clouds = c(0,
0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0), text_sunny = c(0,
0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0), month_1 = c(1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), month_2 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_3 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_4 = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), month_5 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), month_6 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), month_7 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), month_8 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), month_9 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0), month_10 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), month_11 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), month_12 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-14L)))
EDIT:
I think this gives me what I am after - I need to double/tripple check it. (if you see any errors please let me know)
splt <- 0.80 * nrow(ddd)
ddd[c(1:splt), "id"] = 1
ddd$id[is.na(ddd$id)] = 2
fold.ids <- unique(ddd$id)
custom.folds <- vector("list", length(fold.ids))
i <- 1
for( id in fold.ids){
custom.folds[[i]] <- which( ddd$id %in% id )
i <- i+1
}
custom.folds
cv <- xgb.cv(params = list(eta = 0.1, max_depth = 5), dVal, nround = 10, folds = custom.folds, prediction = TRUE)
cv$evaluation_log
I now need to find a way to apply this to all 3 lists in the "new" added data.

Firstly, you should split the data onto dtrain (40 first rows) and dval (10 last rows). Secondly, you need rather xgb.train, not xgb.cv.
So, your code should be modified to something like that:
library(xgboost)
library(dplyr)
# you code regarding ddd
X <- ddd %>% select(-c(1:2))
Y <- ddd %>% select(c(1)) %>% pull()
dtrain <- xgb.DMatrix(data = as.matrix(X[1:40,]), label = as.numeric(Y[1:40,]))
dval <- xgb.DMatrix(data = as.matrix(X[41:50,]), label = as.numeric(Y[41:50,]))
watchlist <- list(train=dtrain, val=dval)
model <- xgb.train(data=dtrain, watchlist=watchlist, nround = 30, eta = 0.1, max_depth = 5)
IMHO, 40+10 rows only and so sparse features give no hope to obtain good results using XGBoost.

How to use ids from one dataframe to sum rows in another dataframe

I feel like this answer has been asked before, but I can't seem to find an answer to this question. Maybe my title is too vague, so feel free to change it.
So I have one data frame, a, with ids the correspond to column name in data frame b. Both data frames are simplified versions of a much larger data frame.
here is data frame a
a <- structure(list(V1 = structure(c(4L, 5L, 1L, 2L, 3L), .Label = c("GEN[D00105].GT",
"GEN[D00151].GT", "GEN[D00188].GT", "GEN[D86396].GT", "GEN[D86397].GT"
), class = "factor")), row.names = c(NA, -5L), class = "data.frame")
here is data frame b
b <- structure(list(`GEN[D01104].GT` = c(0, 0, 0, 0, 1, 0, 0, 2, 0,
1, 1, 1, 1, 0, 0, 0, 2, 0, 0, 0), `GEN[D01312].GT` = c(1, 0,
2, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 2, 0, 0, 2, 0, 0, 0), `GEN[D01878].GT` = c(0,
0, 0, 2, 0, 0, 2, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 2, 0, 0), `GEN[D01882].GT` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 2, 0, 0, 0, 0), `GEN[D01952].GT` = c(0,
0, 1, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0), `GEN[D01953].GT` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 2, 0, 0, 0, 2, 0), `GEN[D02053].GT` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0), `GEN[D00316].GT` = c(0,
0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2, 0, 0), `GEN[D01827].GT` = c(0,
0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0), `GEN[D01881].GT` = c(0,
0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 0, 2, 0, 2, 0), `GEN[D02044].GT` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0), `GEN[D02085].GT` = c(0,
0, 0, 2, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0), `GEN[D02204].GT` = c(0,
0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0), `GEN[D02276].GT` = c(0,
0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0), `GEN[D02297].GT` = c(0,
0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0), `GEN[D02335].GT` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 2, 0, 0), `GEN[D02397].GT` = c(0,
0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0), `GEN[D00856].GT` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0), `GEN[D00426].GT` = c(0,
0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0), `GEN[D02139].GT` = c(0,
0, 1, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0), `GEN[D02168].GT` = c(0,
0, 2, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0)), row.names = c(NA,
-20L), class = "data.frame")
I want to be able to use the ids from data frame a to sum the row in data frame b that have a matching id if that makes sense.
So in the past, I just did something like
b$affected.samples <- (b$`GEN[D86396].GT` + b$`GEN[D86397].GT` + b$`GEN[D00105].GT` + b$`GEN[D00151].GT` + b$`GEN[D00188].GT`)
which got annoying and took to much time, so I moved over to
b$affected.samples <- rowSums(b[,c(1:5)])
Which isn't too bad for this example but with my large data set, my sample can be all over the place, and it's starting to take too much time to finds where everything is. I was hoping there is a way just to use my data frame a to sum the correct rows in data frame b.
Hopefully, I gave this is all the information you need! Let me know if you have any questions.
Thanks in advance!!

Extract the 'V1' column as a character string, use that to select the columns of 'b' (assuming these column names are found in 'b') and get the rowSums
rowSums( b[as.character(a$V1)], na.rm = TRUE)

Why is auto.arima() giving me the Error: "'by' argument is much too small"

I am working on predicting intra-day sales for a retailer. We want to know if we can predict sales through the rest of the day, based off sales within that day. I'm working with roughly 3 years of data in a time series, which has given me roughly 26,000 rows of data.
I've never worked with a time series this large so my approach might be off. Or auto.arima() may not have been made to handle data this large.
I've tried limiting my data down to even 300 rows and had marginal success, but have not found anything that works with my larger data set. auto.arima() doesn't even have a by = argument from what I can find.
my_ts <- structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 3,
3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 4, 1,
1, 0, 3, 1, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4,
1, 9, 1, 6, 5, 1, 0, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 3, 1, 0, 3, 5, 2, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 3, 1, 0, 0, 6, 0, 6, 0, 1, 2, 3, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 2, 4, 6, 5, 0, 1, 0, 2, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 2, 0, 2, 0, 0, 0, 1, 1,
3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 3, 0, 1, 0,
3, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 0,
8, 2, 7, 4, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
0, 0, 2, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 6, 2, 0, 1, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, -1, 2, 3, 1, 0, 0, 2, 5, 7, 0, -1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, -1, 6, 1, 2, 2, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 3, 0, 2, 0,
4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 4, 0, 0,
2, 2, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2,
2, 0, 2, 3, 6, 5, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 2, 2, 2, 1, 4, 3, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 3, 4, 0, 4, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 3, 2, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 3, 0, 3, 4, 3, 0, 0, 2, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 1, 1, 1, 0, 3,
0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 2,
0, 1, 1, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1,
1, 4, 1, 2, 9, 1, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 4, 1, 0, 1, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 3, 1, 2, 1, 1, 4, 0, 3, 3, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 2, 2, 1, 6, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 0, 1, 2, 1, 4, 0, 0, 5,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 5, 1, 1, 3, 3,
4, 4, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 3, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
3, 0, 2, 8, 0, 2, 3, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 1, 2, 0, 1, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 3, 1, 2, 2, 3, 1, 4, 4, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 4, 1, 2, 0, 0, 5, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 1, 1, 4, 0, 4, 3,
2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 6, 1, 0, 0, 0,
3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 8,
1, 2, 0, 4, 2, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,
1, 6, 7, 0, 1, 0, 1, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 5, 0, 2, 1, 4, 1, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 2, 1, 4, 7, 5, 0, 7, 4, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 1, 6, 0, 4, 6, 2, 1, 3, 5, 3, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 3, 0, 1, 1, 3,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 6, 0, 0), index = structure(c(1435190400,
1435194000, 1435197600, 1435201200, 1435204800, 1435208400, 1435212000,
1435215600, 1435219200, 1435222800, 1435226400, 1435230000, 1435233600,
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1438412400, 1438416000, 1438419600, 1438423200, 1438426800, 1438430400,
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1438671600, 1438675200, 1438678800, 1438682400, 1438686000, 1438689600,
1438693200, 1438696800, 1438700400, 1438704000, 1438707600, 1438711200,
1438714800, 1438718400, 1438722000, 1438725600, 1438729200, 1438732800,
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1438758000, 1438761600, 1438765200, 1438768800, 1438772400, 1438776000,
1438779600, 1438783200, 1438786800), class = c("POSIXct", "POSIXt"
), tzone = "UTC"), class = c("zooreg", "zoo"), frequency = 24)
fit1 <-auto.arima(my_ts,seasonal = TRUE)
I was hoping to get a model through arima, but I'm only getting the error:
"Error in seq.default(head(tt, 1), tail(tt, 1), deltat) :
'by' argument is much too small"

"non-finite value supplied by optim" error when using betareg

I'm working with betareg package for beta regression but receive the below error:
Error in optim(par = start, fn = loglikfun, gr = gradfun, method = method, :
non-finite value supplied by optim
I can trace this error to creating the initial values for optim. Specifically, these lines of betareg.fit, which uses lm.wfit to generate starting values.
It turns out that one of the starting values is returned as NA for my dataset. I'm unsure why this is the case, since there are no missing values in the data / input to lm.wfit.
Reproducible example to see NA
## data -- a sample of 100 obs from my actual data
nobs <- 100L
w <- rep(1, nobs)
offset <- rep(0, nobs)
y <- stats::rbeta(nobs, 0.75, 1.658)
x <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0165928242550604,
0.0984749494334759, 0.05517578125, 0.0185352577155742, 0.168701442841287,
0.0514759697487192, 0.026507054296708, 0.0188496858385694, 0.108620689655172,
0.0722387772757858, 0.0272373540856031, 0.0538907902524382, 0.0295235311312482,
0.0318257956448911, 0.231788079470199, 0.0674772036474164, 0.14846108458939,
0.0969908238068386, 0.0441553321506012, 0.154121863799283, 0,
0.110460389247421, 0.0292207792207792, 0.0522853185595568, 0.205288796102992,
0.00961124552835874, 0.0546908714289824, 0.0268199233716475,
0.0253164556962025, 0.181780542384243, 0.0551724137931034, 0.128842504743833,
0.0751429349305745, 0.217853751187085, 0.0510314875135722, 0.108407709439207,
0.04, 0.0638009815535624, 0.128329297820823, 0.0398115958281933,
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0.105830475257227, 0.0482315112540193, 0.0394736842105263, 0,
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0.03601108033241, 0.160055671537926, 0.02201309207499, 0.0668891510112489,
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0, 0.00778642936596218, 0.0745676859521289, 0.0191304347826087,
0.0443828016643551, 0.0975012248897599, 0.00964630225080386,
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0.197810150080232, 0.0363158937772493, 0.0415070411371503, 0.109979633401222,
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0.381862921002717, 0.310497634940381, 0.266713526509752, 0.221418234442836,
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1, 1, 0), .Dim = c(100L, 35L), .Dimnames = list(c("2801", "2316",
"382", "8062", "2687", "2731", "8019", "5652", "8429", "3479",
"7753", "9001", "2188", "8121", "8478", "5817", "1528", "2460",
"3946", "3531", "3421", "2802", "1975", "3639", "2894", "5897",
"9331", "9490", "7135", "5858", "7724", "9414", "9095", "6601",
"5064", "7111", "3593", "7322", "9522", "7116", "6922", "5172",
"2458", "5199", "1387", "3878", "6119", "8722", "6378", "4661",
"6109", "3682", "5751", "9390", "7915", "5268", "1029", "5953",
"242", "2912", "8798", "9607", "9768", "2222", "8260", "851",
"4205", "1823", "5063", "4189", "7541", "608", "6849", "7220",
"2889", "6770", "7064", "646", "4919", "1404", "120", "9716",
"7722", "7700", "6638", "8176", "5745", "6", "9481", "2233",
"341", "228", "1543", "553", "9709", "9493", "881", "7647", "6039",
"2925"), c("(Intercept)", "x 1", "x 2", "x 3", "x 4", "x 5",
"x 6", "x 7", "x 8", "x 9", "x 10", "x 11", "x 12", "x 13", "x 14",
"x 15", "x 16", "x 17", "x 18", "x 19", "x 20", "x 21", "x 22",
"x 23", "x 24", "x 25", "x 26", "x 27", "x 28", "x 29", "x 30",
"x 31", "x 32", "x 33", "x 34")))
Inside betareg: the NA that causes the problem
linkfun <- function(mu) {.Call(stats:::C_logit_link, mu)}
auxreg_test <- lm.wfit(x, linkfun(y), w, offset)
# problem:
(beta <- auxreg_test$coefficients)
is.na(beta['x 8'])
> beta['x 8']
x 8
NA
I originally thought this might be related to using the CRAN version of betareg (3.1-0). But I updated to the rforge version (3.2-0) via devtools::install_github("rforge/betareg/pkg") and still have the same problem.
If I remove the offending predictor from my formula, betareg runs fine; however, the predictor is a necessary one.

NA coefficients from glm / lm / lm.fit / .lm.fit / lm.wfit imply the model matrix to be rank-deficient. They are just 0 with 0 standard error (i.e., fixed at 0).
I appreciated that you have done much debugging work and located the source of the error, but giving us a model matrix x directly is less informative for us to investigate. It would be good if you show us the model formula and the data frame.
Anyway, I have (with some pain) found the collinearity problem from your model matrix.
rowSums(, x[, 2:9])
#2801 2316 382 8062 2687 2731 8019 5652 8429 3479 7753 9001 2188 8121 8478 5817
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#1528 2460 3946 3531 3421 2802 1975 3639 2894 5897 9331 9490 7135 5858 7724 9414
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#9095 6601 5064 7111 3593 7322 9522 7116 6922 5172 2458 5199 1387 3878 6119 8722
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#6378 4661 6109 3682 5751 9390 7915 5268 1029 5953 242 2912 8798 9607 9768 2222
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#8260 851 4205 1823 5063 4189 7541 608 6849 7220 2889 6770 7064 646 4919 1404
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
# 120 9716 7722 7700 6638 8176 5745 6 9481 2233 341 228 1543 553 9709 9493
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
# 881 7647 6039 2925
# 1 1 1 1
Columns x1 to x8, if all included, has collinearity problem with the intercept (strange; those columns are not dummy ones so they are not from factor variables). If you don't want to drop any of them, drop intercept instead.

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