I have a very messy dataset created by a research device. This data shows a physiological measure ("Physio") for every few milliseconds ("Time"). The output lists several user messages, such as when a trial starts ("START_TRIAL n"), when a trial ends ("STOP_TRIAL"), and other random things that may be of interest to the researcher. Some times the "START_TRIAL n" message is repeated consecutively, and sometimes when there is no message, a simple "0" is left in what would otherwise be a blank cell.
I am hoping to create a new column that will signify which trial the current case belongs to. (See example data below).
Is there a way to do this with dplyr and mutate? I am wondering if I may need to do an if-then statement that changes the values of a new column for every case, but surely there's a more elegant solution? (Thank you in advance for helping out this newbie!)
Time Physio Cond
1 34 START_TRIAL 1
2 33 0
3 25 RANDOM_MSG
4 43 STOP_TRIAL
5 27 START_TRIAL 2
6 54 START_TRIAL 2
7 32 0
8 54 RANDOM_MSG
9 23 STOP_TRIAL
structure(list(Time = 1:9, Physio = c(34L, 33L, 25L, 43L, 27L,
54L, 32L, 54L, 23L), Cond = structure(c(4L, 2L, 3L, 6L, 5L, 5L,
2L, 3L, 6L), .Label = c("", "0", "RANDOM_MSG", "START_TRIAL 1",
"START_TRIAL 2", "STOP_TRIAL"), class = "factor")), .Names = c("Time",
"Physio", "Cond"), row.names = c(NA, 9L), class = "data.frame")
into
Time Physio Trial Cond
1 34 1 START_TRIAL 1
2 33 1 0
3 25 1 RANDOM_MSG
4 43 1 STOP_TRIAL
5 27 2 START_TRIAL 2
6 54 2 START_TRIAL 2
7 32 2 0
8 54 2 RANDOM_MSG
9 23 2 STOP_TRIAL
structure(list(Time = 1:9, Physio = c(34L, 33L, 25L, 43L, 27L,
54L, 32L, 54L, 23L), Trial = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L), Cond = structure(c(4L, 2L, 3L, 6L, 5L, 5L, 2L, 3L, 6L), .Label = c("",
"0", "RANDOM_MSG", "START_TRIAL 1", "START_TRIAL 2", "STOP_TRIAL"
), class = "factor")), .Names = c("Time", "Physio", "Trial",
"Cond"), row.names = c(NA, 9L), class = "data.frame")
One option would be to identify the 'START_TRIAL' with grep, do a match to get the index and fill the NA elements with the previous non-NA adjacent element
library(dplyr)
library(tidyr)
df1 %>%
mutate(Trial = match(PhysioCond, unique(grep("START_TRIAL",
PhysioCond, value = TRUE)))) %>%
fill(Trial)
# Time PhysioCond Trial
#1 34 START_TRIAL 1 1
#2 33 0 1
#3 25 RANDOM_MSG 1
#4 43 STOP_TRIAL 1
#5 27 START_TRIAL 2 2
#6 54 START_TRIAL 2 2
#7 32 0 2
#8 54 RANDOM_MSG 2
#9 23 STOP_TRIAL 2
NOTE: Not clear about the column name, but the logic should work well
data
df1 <- structure(list(Time = c(34L, 33L, 25L, 43L, 27L, 54L, 32L, 54L,
23L), PhysioCond = c("START_TRIAL 1", "0", "RANDOM_MSG", "STOP_TRIAL",
"START_TRIAL 2", "START_TRIAL 2", "0", "RANDOM_MSG", "STOP_TRIAL"
)), class = "data.frame", row.names = c("1", "2", "3", "4", "5",
"6", "7", "8", "9"))
Related
Toy data:
Say I have this df
df <- structure(list(x = structure(c(NA, 7L, NA, NA, 4L, 6L, 6L, 2L,
3L, 5L, 8L, 4L, 7L, 3L, 5L, 1L, 5L, 5L, 5L, NA), .Label = c("1",
"2", "3", "4", "5", "6", "7", "8"), class = "factor"), y = structure(c(NA,
2L, 3L, 2L, 2L, 2L, 2L, 1L, 3L, NA, 2L, 3L, 1L, 1L, 3L, 2L, 2L,
3L, 2L, 2L), .Label = c("1", "2", "3"), class = "factor"), z = structure(c(NA,
4L, 4L, 4L, 5L, 4L, 5L, 5L, 2L, NA, 4L, 1L, 1L, 3L, 2L, 5L, 2L,
2L, 4L, NA), .Label = c("1", "2", "3", "4", "5"), class = "factor"),
a = c(-32L, -51L, -22L, 44L, 55L, -24L, -50L, 67L, 1L, -47L,
66L, -98L, -91L, -42L, -89L, -31L, -8L, -33L, 38L, 61L),
b = c(46L, -19L, -37L, 47L, -28L, -48L, 14L, -10L, -13L,
-31L, 32L, 21L, -21L, 25L, -8L, 42L, -26L, -24L, 36L, -39L
)), row.names = c(NA, -20L), class = c("tbl_df", "tbl", "data.frame"
))
df
# A tibble: 20 × 5
x y z a b
<fct> <fct> <fct> <int> <int>
1 NA NA NA -32 46
2 7 2 4 -51 -19
3 NA 3 4 -22 -37
4 NA 2 4 44 47
5 4 2 5 55 -28
6 6 2 4 -24 -48
7 6 2 5 -50 14
8 2 1 5 67 -10
9 3 3 2 1 -13
10 5 NA NA -47 -31
11 8 2 4 66 32
12 4 3 1 -98 21
13 7 1 1 -91 -21
14 3 1 3 -42 25
15 5 3 2 -89 -8
16 1 2 5 -31 42
17 5 2 2 -8 -26
18 5 3 2 -33 -24
19 5 2 4 38 36
20 NA 2 NA 61 -39
I want to normalize variables x, y, and z on a 0-1 scale, and then produce some summary stats on them. I can produce the summary stats just fine using the code below
Code that works:
library(tidyverse)
vars <- c('x', 'y', 'z')
names(vars) <- vars
summary_stats <- function(data){
tibble(
n = sum(!is.na(data)),
mean = round(mean(as.numeric(data), na.rm = T), digits = 3),
sd = round(sd(as.numeric(data), na.rm = T), digits = 3),
se = round(sd/sqrt(n), digits = 3)
)
}
table <- map_df(
df %>%
dplyr::select(vars),
summary_stats,
.id = "covariate")
table
# A tibble: 3 × 5
covariate n mean sd se
<chr> <int> <dbl> <dbl> <dbl>
1 x 16 4.75 1.88 0.47
2 y 18 2.11 0.676 0.159
3 z 17 3.35 1.41 0.342
Code that doesn't work:
But i'm struggling to figure out how to normalize the variables. My latest attempt is to try this
summary_stats <- function(data){
data_norm <- drop_na(data) %>% dplyr::summarize(
(as.numeric(data) - min(as.numeric(data))) /
(max(as.numeric(data)) - min(as.numeric(data)))
)
tibble(
n = sum(!is.na(data_norm)),
mean = round(mean(as.numeric(data_norm), na.rm = T), digits = 3),
sd = round(sd(as.numeric(data_norm), na.rm = T), digits = 3),
se = round(sd/sqrt(n), digits = 3)
)
}
table <- map_df(
df %>%
dplyr::select(vars),
summary_stats,
.id = "covariate")
Errors:
But this returns the error
Error in UseMethod("drop_na_") : no applicable method for 'drop_na_' applied to an object of class "factor"
If I convert it to a numeric on the fly, so I have data_norm <- drop_na(as.numeric(data)) etc., I then get a very similar error saying
Error in UseMethod("drop_na_") : no applicable method for 'drop_na_' applied to an object of class "c('double', 'numeric')"
However, if I do this outside of the function it works fine
df %>% drop_na(x) %>% summarise(std_mean = (as.numeric(x) - min(as.numeric(x))) / (max(as.numeric(x)) - min(as.numeric(x))))
# A tibble: 16 × 1
std_mean
<dbl>
1 0.857
2 0.429
3 0.714
4 0.714
5 0.143
6 0.286
7 0.571
....
I need to remove the NA values or when I try and normalize the returned variable will have all NAs if there is at least 1 NA in that column. And if I apply drop_na() outside the function (to the master tibble i feed in to the map_dfr function), it will drop any row that has at least 1 NA value in any variable from the df, rather than just the NA values from that column.
Can anyone help here?
Update:
If I remove the drop_na() call from the function i get the following error
Error in UseMethod("summarise") :
no applicable method for 'summarise' applied to an object of class "c('double', 'numeric')"
This makes zero sense to me (i'm probably not understanding it) as summarise definitely works with numeric variables...
Looks like what's happening is that you're trying to write a function to take an entire data frame as an argument, but when you go to map it, you're actually only passing a single vector (e.g. df$x) as the argument to the function. This works fine for the first version of your function, but in the second version drop_na fails to work because it takes an entire data frame for an argument. Same goes for summarize, which is why you were getting a similar error. It also works outside of your function because you're able to specify a single vector.
So, what I did was swap out drop_na for na_omit, and also reorganized your code a bit.
First, let's just define a separate std_mean function so we don't have to deal with summarize:
std_mean <- function(x){
x <- na.omit(x)
(as.numeric(x) - min(as.numeric(x)))/(max(as.numeric(x)) - min(as.numeric(x)))
}
Now we can go back and fix your original function:
summary_stats <- function(vec){
data_norm <- std_mean(vec)
n = length(data_norm)
sd = round(sd(as.numeric(data_norm), na.rm = T), digits = 3)
data.frame(
n = n,
mean = round(mean(as.numeric(data_norm), na.rm = T), digits = 3),
sd = sd,
se = round(sd/sqrt(n), digits = 3)
)
}
We have to define n and sd beforehand because they were being used as arguments in other columns of the data frame. While it would be cool for data.frame to calculate the first column to then allow you to feed into later columns, that isn't the case.
And now we're ready to map:
map(df[vars],summary_stats)
$x
n mean sd se
1 16 0.536 0.269 0.067
$y
n mean sd se
1 18 0.556 0.338 0.08
$z
n mean sd se
1 17 0.588 0.353 0.086
I am trying to solve is how to calculate the weighted score for each class each month.
Each class has multiple students and the weight (contribution) of a student's score varies through time.
To be included in the calculation a student must have both score and weight.
I am a bit lost and none of the approaches I have used have worked.
Student Class Jan_18_score Feb_18_score Jan_18_Weight Feb_18_Weight
Adam 1 3 2 150 153
Char 1 5 7 30 60
Fred 1 -7 8 NA 80
Greg 1 2 NA 80 40
Ed 2 1 2 60 80
Mick 2 NA 6 80 30
Dave 3 5 NA 40 25
Nick 3 8 8 12 45
Tim 3 -2 7 23 40
George 3 5 3 65 NA
Tom 3 NA 8 78 50
The overall goal is to calculate the weighted score for each class each month.
Taking Class 1 (first 4 rows) as an example and looking at Jan_18.
-The observations of Adam, Char and Greg are valid since they have both scores and weights. Their scores and weights should be included
- Fred does not have a Jan_18_weight, therefore both his Jan_18_score and Jan_18_weight are excluded from the calculation.
The following calculation should then occur:
= [(3*150)+(5*30)+(2*80)]/ [150+30+80]
= 2.92307
This calculation would be repeated for each class and each month.
A new dataframe something like the following should be the output
Class Jan_18_Weight_Score Feb_18_Weight_Score
1 2.92307 etc
2 etc etc
3 etc etc
There are many columns and many rows.
Any help is appreciated.
Here's a way with tidyverse. The main trick is to replace NA with 0 in "weights" columns and then use weighted.mean() with na.rm = T to ignore NA scores. To do so, you can gather the scores and weights into a single column and then group by Class and month_abb (a calculated field for grouping) and then use weighted.mean().
df %>%
mutate_at(vars(ends_with("Weight")), ~replace_na(., 0)) %>%
gather(month, value, -Student, -Class) %>%
group_by(Class, month_abb = paste0(substr(month, 1, 3), "_Weight_Score")) %>%
summarize(
weight_score = weighted.mean(value[grepl("score", month)], value[grepl("Weight", month)], na.rm = T)
) %>%
ungroup() %>%
spread(month_abb, weight_score)
# A tibble: 3 x 3
Class Feb_Weight_Score Jan_Weight_Score
<int> <dbl> <dbl>
1 1 4.66 2.92
2 2 3.09 1
3 3 7.70 4.11
Data -
df <- structure(list(Student = c("Adam", "Char", "Fred", "Greg", "Ed",
"Mick", "Dave", "Nick", "Tim", "George", "Tom"), Class = c(1L,
1L, 1L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L), Jan_18_score = c(3L,
5L, -7L, 2L, 1L, NA, 5L, 8L, -2L, 5L, NA), Feb_18_score = c(2L,
7L, 8L, NA, 2L, 6L, NA, 8L, 7L, 3L, 8L), Jan_18_Weight = c(150L,
30L, NA, 80L, 60L, 80L, 40L, 12L, 23L, 65L, 78L), Feb_18_Weight = c(153L,
60L, 80L, 40L, 80L, 30L, 25L, 45L, 40L, NA, 50L)), class = "data.frame", row.names = c(NA,
-11L))
Maybe this could be solved in a much better way but here is one Base R option where we perform aggregation twice and then combine the results.
#Separate score and weight columns
score_cols <- grep("score$", names(df))
weight_cols <- grep("Weight$", names(df))
#Replace NA's in corresponding score and weight columns to 0
inds <- is.na(df[score_cols]) | is.na(df[weight_cols])
df[score_cols][inds] <- 0
df[weight_cols][inds] <- 0
#Find sum of weight columns for each class
df1 <- aggregate(.~Class, cbind(df["Class"], df[weight_cols]), sum)
#find sum of multiplication of score and weight columns for each class
df2 <- aggregate(.~Class, cbind(df["Class"], df[score_cols] * df[weight_cols]), sum)
#Get the ratio between two dataframes.
cbind(df1[1], df2[-1]/df1[-1])
# Class Jan_18_score Feb_18_score
#1 1 2.92 4.66
#2 2 1.00 3.09
#3 3 4.11 7.70
I am trying to solve is how to calculate the weighted score for each class each month.
Each class has multiple students and the weight (contribution) of a student's score varies through time.
To be included in the calculation a student must have both score and weight.
I am a bit lost and none of the approaches I have used have worked.
Student Class Jan_18_score Feb_18_score Jan_18_Weight Feb_18_Weight
Adam 1 3 2 150 153
Char 1 5 7 30 60
Fred 1 -7 8 NA 80
Greg 1 2 NA 80 40
Ed 2 1 2 60 80
Mick 2 NA 6 80 30
Dave 3 5 NA 40 25
Nick 3 8 8 12 45
Tim 3 -2 7 23 40
George 3 5 3 65 NA
Tom 3 NA 8 78 50
The overall goal is to calculate the weighted score for each class each month.
Taking Class 1 (first 4 rows) as an example and looking at Jan_18.
-The observations of Adam, Char and Greg are valid since they have both scores and weights. Their scores and weights should be included
- Fred does not have a Jan_18_weight, therefore both his Jan_18_score and Jan_18_weight are excluded from the calculation.
The following calculation should then occur:
= [(3*150)+(5*30)+(2*80)]/ [150+30+80]
= 2.92307
This calculation would be repeated for each class and each month.
A new dataframe something like the following should be the output
Class Jan_18_Weight_Score Feb_18_Weight_Score
1 2.92307 etc
2 etc etc
3 etc etc
There are many columns and many rows.
Any help is appreciated.
Here's a way with tidyverse. The main trick is to replace NA with 0 in "weights" columns and then use weighted.mean() with na.rm = T to ignore NA scores. To do so, you can gather the scores and weights into a single column and then group by Class and month_abb (a calculated field for grouping) and then use weighted.mean().
df %>%
mutate_at(vars(ends_with("Weight")), ~replace_na(., 0)) %>%
gather(month, value, -Student, -Class) %>%
group_by(Class, month_abb = paste0(substr(month, 1, 3), "_Weight_Score")) %>%
summarize(
weight_score = weighted.mean(value[grepl("score", month)], value[grepl("Weight", month)], na.rm = T)
) %>%
ungroup() %>%
spread(month_abb, weight_score)
# A tibble: 3 x 3
Class Feb_Weight_Score Jan_Weight_Score
<int> <dbl> <dbl>
1 1 4.66 2.92
2 2 3.09 1
3 3 7.70 4.11
Data -
df <- structure(list(Student = c("Adam", "Char", "Fred", "Greg", "Ed",
"Mick", "Dave", "Nick", "Tim", "George", "Tom"), Class = c(1L,
1L, 1L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L), Jan_18_score = c(3L,
5L, -7L, 2L, 1L, NA, 5L, 8L, -2L, 5L, NA), Feb_18_score = c(2L,
7L, 8L, NA, 2L, 6L, NA, 8L, 7L, 3L, 8L), Jan_18_Weight = c(150L,
30L, NA, 80L, 60L, 80L, 40L, 12L, 23L, 65L, 78L), Feb_18_Weight = c(153L,
60L, 80L, 40L, 80L, 30L, 25L, 45L, 40L, NA, 50L)), class = "data.frame", row.names = c(NA,
-11L))
Maybe this could be solved in a much better way but here is one Base R option where we perform aggregation twice and then combine the results.
#Separate score and weight columns
score_cols <- grep("score$", names(df))
weight_cols <- grep("Weight$", names(df))
#Replace NA's in corresponding score and weight columns to 0
inds <- is.na(df[score_cols]) | is.na(df[weight_cols])
df[score_cols][inds] <- 0
df[weight_cols][inds] <- 0
#Find sum of weight columns for each class
df1 <- aggregate(.~Class, cbind(df["Class"], df[weight_cols]), sum)
#find sum of multiplication of score and weight columns for each class
df2 <- aggregate(.~Class, cbind(df["Class"], df[score_cols] * df[weight_cols]), sum)
#Get the ratio between two dataframes.
cbind(df1[1], df2[-1]/df1[-1])
# Class Jan_18_score Feb_18_score
#1 1 2.92 4.66
#2 2 1.00 3.09
#3 3 4.11 7.70
For this dataset, I would like to order the Var1 by the corresponding frequency in order from largest to smallest and take the top 5 largest by row. I've been using the functions rank(), sort(), and order() with no avail.
Var1 Freq
2 Moderate 33
3 Luxury 31
4 Couples 31
5 Families with Children 33
6 Nightlife 23
7 Europe 60
8 Architecture 23
9 Drink 58
10 Northern Europe 27
11 Skiing 29
Ideally, I would like the final output to be:
Var1 Freq
7 Europe 60
9 Drink 58
5 Families with Children 33
2 Moderate 33
3 Luxury 31
When I use the functions stated above, R returns a series of numbers such that are either jibberish or it will only return the Freq column in a ranked order.
Here's a dplyr solution.
df %>% top_n(5, Freq) %>% arrange(-Freq)
This gives you the top 5 scores in order.
# Var1 Freq
# 1 Europe 60
# 2 Drink 58
# 3 Moderate 33
# 4 Families with Children 33
# 5 Luxury 31
# 6 Couples 31
Note that 6 entries are included due to a tie.
If you just want the top 5 regardless of ties, then you can use this:
df %>% arrange(-Freq) %>% filter(row_number() <= 5)
# Var1 Freq
# 1 Europe 60
# 2 Drink 58
# 3 Moderate 33
# 4 Families with Children 33
# 5 Luxury 31
Here is a one-liner. It uses order and head.
head(dat[order(dat$Freq, decreasing = TRUE), ], 5)
# Var1 Freq
#7 Europe 60
#9 Drink 58
#2 Moderate 33
#5 Families with Children 33
#3 Luxury 31
DATA.
dat <-
structure(list(Var1 = structure(c(7L, 6L, 2L, 5L, 8L, 4L, 1L,
3L, 9L, 10L), .Label = c("Architecture", "Couples", "Drink",
"Europe", "Families with Children", "Luxury", "Moderate", "Nightlife",
"Northern Europe", "Skiing"), class = "factor"), Freq = c(33L,
31L, 31L, 33L, 23L, 60L, 23L, 58L, 27L, 29L)), .Names = c("Var1",
"Freq"), class = "data.frame", row.names = c("2", "3", "4", "5",
"6", "7", "8", "9", "10", "11"))
dat <- structure(list(Var1 = structure(c(7L, 6L, 2L, 5L, 8L, 4L, 1L, 3L, 9L, 10L), .Label = c("Architecture", "Couples", "Drink",
"Europe", "Families with Children", "Luxury", "Moderate", "Nightlife",
"Northern Europe", "Skiing"), class = "factor"), Freq = c(33L,
31L, 31L, 33L, 23L, 60L, 23L, 58L, 27L, 29L)), .Names = c("Var1",
"Freq"), class = "data.frame", row.names = c("2", "3", "4", "5",
"6", "7", "8", "9", "10", "11"))
Using data.table.
library(data.table)
DFDT <- as.data.table(dat)
DFDT[order(-Freq)][1:5]
Var1 Freq
1: Europe 60
2: Drink 58
3: Moderate 33
4: Families with Children 33
5: Luxury 31
I am trying to filter some data that I have in R. It is formatted like this:
id config_id alpha begin end day
1 1 1 5 138 139 6
2 1 2 5 137 138 6
3 1 3 5 47 48 2
4 1 3 3 46 47 2
5 1 4 3 45 46 2
6 1 4 3 43 44 2
...
id config_id alpha begin end day
1 2 1 5 138 139 6
2 2 2 5 137 138 6
3 2 2 5 136 137 6
4 2 3 3 45 46 2
5 2 3 3 44 45 2
6 2 4 3 43 44 2
My goal is to remove any configuration which results in having beginnings and endings on the same day. For example, in the top example config_id 3 is not acceptable because both instances of config_id occur on day 2. Same story for config_id 4. In the bottom example config_id 2 and config_id 3 are unacceptable for the same reason.
Basically, if I have a repeated config_id AND any day (from the day) column shows up more than once for that config_id, then I want to remove that config_id from the list.
Right now I'm using something of a fairly complex lapply algorithm but there must be an easier way.
Thanks!
You can do this several ways, assuming your data is stored in a data frame called my_data.
base R
same_day <- aggregate(my_data$day, my_data["config_id"], function(x) any(table(x) > 1))
names(same_day)[2] <- "same_day"
my_data <- merge(my_data, same_day, by = "config_id")
my_data <- same_day[!same_day$repeated_id, ]
dplyr
library(dplyr)
my_data %<>% group_by(config_id) %>%
mutate(same_day = any(table(day) > 1)) %>%
filter(!same_day)
data.table
library(data.table)
my_data <- data.table(my_data, key = "config_id")
same_day <- my_data[, .(same_day = any(table(day) > 1)), by = "config_id"]
my_data[!my_data[same_day]$same_day, ]
We can also use n_distinct from dplyr. Here, I am grouping by 'id' and 'config_id', then remove the rows using filter. If the number of elements within the group is greater than 1 (n()>1) and (&) the number of distinct elements in 'day' is equal to 1 (n_distinct==1), we remove it.
library(dplyr)
df1 %>%
group_by(id, config_id) %>%
filter(!(n()>1 & n_distinct(day)==1))
#Source: local data frame [4 x 6]
#Groups: id, config_id [4]
# id config_id alpha begin end day
# (int) (int) (int) (int) (int) (int)
#1 1 1 5 138 139 6
#2 1 2 5 137 138 6
#3 2 1 5 138 139 6
#4 2 4 3 43 44 2
This should also work if we have different 'day' for the same 'config_id'.
df1$day[4] <- 3
A similar option using data.table is uniqueN. We convert the 'data.frame' to 'data.table' (setDT(df1)), grouped by 'id' and 'config_id', we subset the dataset (.SD) using the logical condition.
library(data.table)#v1.9.6+
setDT(df1)[, if(!(.N>1 & uniqueN(day) == 1L)) .SD, by = .(id, config_id)]
data
df1 <- structure(list(id = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L), config_id = c(1L, 2L, 3L, 3L, 4L, 4L, 1L, 2L, 2L, 3L,
3L, 4L), alpha = c(5L, 5L, 5L, 3L, 3L, 3L, 5L, 5L, 5L, 3L, 3L,
3L), begin = c(138L, 137L, 47L, 46L, 45L, 43L, 138L, 137L, 136L,
45L, 44L, 43L), end = c(139L, 138L, 48L, 47L, 46L, 44L, 139L,
138L, 137L, 46L, 45L, 44L), day = c(6L, 6L, 2L, 2L, 2L, 2L, 6L,
6L, 6L, 2L, 2L, 2L)), .Names = c("id", "config_id", "alpha",
"begin", "end", "day"), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"))