I have a dataset that contains the residence period (start.date to end.date) of marked individuals (ID) at different sites. My goal is to generate a column that tells me the average number of other individuals per day that were also present at the same site (across the total residence period of each individual).
To do this, I need to determine the total number of individuals that were present per site on each date, summed across the total residence period of each individual. Ultimately, I will divide this sum by the total residence days of each individual to calculate the average. Can anyone help me accomplish this?
I calculated the total number of residence days (total.days) using lubridate and dplyr
mutate(total.days = end.date - start.date + 1)
site ID start.date end.date total.days
1 1 16 5/24/17 6/5/17 13
2 1 46 4/30/17 5/20/17 21
3 1 26 4/30/17 5/23/17 24
4 1 89 5/5/17 5/13/17 9
5 1 12 5/11/17 5/14/17 4
6 2 14 5/4/17 5/10/17 7
7 2 18 5/9/17 5/29/17 21
8 2 19 5/24/17 6/10/17 18
9 2 39 5/5/17 5/18/17 14
First of all, it is always advisable to give a sample of the data in a more friendly format using dput(yourData) so that other can easily regenerate your data. Here is the output of dput() you could better be sharing:
> dput(dat)
structure(list(site = c(1, 1, 1, 1, 1, 2, 2, 2, 2), ID = c(16,
46, 26, 89, 12, 14, 18, 19, 39), start.date = structure(c(17310,
17286, 17286, 17291, 17297, 17290, 17295, 17310, 17291), class = "Date"),
end.date = structure(c(17322, 17306, 17309, 17299, 17300,
17296, 17315, 17327, 17304), class = "Date")), class = "data.frame", row.names =
c(NA,
-9L))
To do this easily we first need to unpack the start.date and end.date to individual dates:
newDat <- data.frame()
for (i in 1:nrow(dat)){
expand <- data.frame(site = dat$site[i],
ID = dat$ID[i],
Dates = seq.Date(dat$start.date[i], dat$end.date[i], 1))
newDat <- rbind(newDat, expand)
}
newDat
site ID Dates
1 1 16 2017-05-24
2 1 16 2017-05-25
3 1 16 2017-05-26
4 1 16 2017-05-27
5 1 16 2017-05-28
6 1 16 2017-05-29
7 1 16 2017-05-30
. . .
. . .
Then we calculate the number of other individuals present in each site in each day:
individualCount = newDat %>%
group_by(site, Dates) %>%
summarise(individuals = n_distinct(ID) - 1)
individualCount
# A tibble: 75 x 3
# Groups: site [?]
site Dates individuals
<dbl> <date> <int>
1 1 2017-04-30 1
2 1 2017-05-01 1
3 1 2017-05-02 1
4 1 2017-05-03 1
5 1 2017-05-04 1
6 1 2017-05-05 2
7 1 2017-05-06 2
8 1 2017-05-07 2
9 1 2017-05-08 2
10 1 2017-05-09 2
# ... with 65 more rows
Then, we augment our data with the new information using left_join() and calculate the required average:
newDat <- left_join(newDat, individualCount, by = c("site", "Dates")) %>%
group_by(site, ID) %>%
summarise(duration = max(Dates) - min(Dates)+1,
av.individuals = mean(individuals))
newDat
# A tibble: 9 x 4
# Groups: site [?]
site ID duration av.individuals
<dbl> <dbl> <time> <dbl>
1 1 12 4 0.75
2 1 16 13 0
3 1 26 24 1.42
4 1 46 21 1.62
5 1 89 9 1.33
6 2 14 7 1.14
7 2 18 21 0.875
8 2 19 18 0.333
9 2 39 14 1.14
The final step is to add the required column to the original dataset (dat) again with left_join():
dat %>% left_join(newDat, by = c("site", "ID"))
dat
site ID start.date end.date duration av.individuals
1 1 16 2017-05-24 2017-06-05 13 days 0.000000
2 1 46 2017-04-30 2017-05-20 21 days 1.619048
3 1 26 2017-04-30 2017-05-23 24 days 1.416667
4 1 89 2017-05-05 2017-05-13 9 days 2.333333
5 1 12 2017-05-11 2017-05-14 4 days 2.750000
6 2 14 2017-05-04 2017-05-10 7 days 1.142857
7 2 18 2017-05-09 2017-05-29 21 days 0.857143
8 2 19 2017-05-24 2017-06-10 18 days 0.333333
9 2 39 2017-05-05 2017-05-18 14 days 1.142857
Related
I have a database of information pertaining to individuals observed over time. I would like to find a way to obtain the age of these individuals whenever a record was taken. Assuming the BIRTH assigns a value of 0, I would like to obtain the age either in days or months for the visits after. It would also be helpful to obtain a final age (either day or month) for each individual (*not included in the code). For example, for ID (A), the final age would be 10 months. I would like to use the lubridate function as it's in-built date feature makes it easier to work with dates. Any help with this is much appreciated.
date<-c("2000-01-01","2000-01-14","2000-01-25","2000-02-12","2000-02-27","2000-06-05","2000-10-30",
"2001-02-04","2001-06-15","2001-12-26","2002-05-22","2002-06-04",
"2000-01-08","2000-07-11","2000-08-18","2000-11-27")
ID<-c("A","A","A","A","A","A","A",
"B","B","B","B","B",
"C","C","C","C")
status<-c("BIRTH","ETC","ETC","ETC","ETC","ETC","ETC",
"BIRTH","ETC","ETC","ETC","ETC",
"BIRTH","ETC","ETC","ETC")
df1<-data.frame(date,ID,status)
print(df1)
date ID status
1 2000-01-01 A BIRTH
2 2000-01-14 A ETC
3 2000-01-25 A ETC
4 2000-02-12 A ETC
5 2000-02-27 A ETC
6 2000-06-05 A ETC
7 2000-10-30 A ETC
8 2001-02-04 B BIRTH
9 2001-06-15 B ETC
10 2001-12-26 B ETC
11 2002-05-22 B ETC
12 2002-06-04 B ETC
13 2000-01-08 C BIRTH
14 2000-07-11 C ETC
15 2000-08-18 C ETC
16 2000-11-27 C ETC
date.new<-c("2000-01-01","2000-01-14","2000-01-25","2000-02-12","2000-02-27","2000-06-05","2000-10-30",
"2001-02-04","2001-06-15","2001-12-26","2002-05-22","2001-02-04",
"2000-01-08","2000-07-11","2000-08-18","2000-11-27")
ID.new<-c("A","A","A","A","A","A","A",
"B","B","B","B","B",
"C","C","C","C")
status.new<-c("BIRTH","ETC","ETC","ETC","ETC","ETC","ETC",
"BIRTH","ETC","ETC","ETC","ETC",
"BIRTH","ETC","ETC","ETC")
age<-c(0,1,1,2,2,6,10,
0,4,10,15,16,
0,6,7,10)
df2<-data.frame(date.new,ID.new,status.new,age)
print(df2)
date.new ID.new status.new age
1 2000-01-01 A BIRTH 0
2 2000-01-14 A ETC 1
3 2000-01-25 A ETC 1
4 2000-02-12 A ETC 2
5 2000-02-27 A ETC 2
6 2000-06-05 A ETC 6
7 2000-10-30 A ETC 10
8 2001-02-04 B BIRTH 0
9 2001-06-15 B ETC 4
10 2001-12-26 B ETC 10
11 2002-05-22 B ETC 15
12 2001-02-04 B ETC 16
13 2000-01-08 C BIRTH 0
14 2000-07-11 C ETC 6
15 2000-08-18 C ETC 7
16 2000-11-27 C ETC 10
For calculations related to age in years or months, I'd like to encourage you to try the clock package rather than lubridate. lubridate is a great package, but produces some unexpected results with these kinds of calculations if you aren't 100% sure of what you are doing. In clock, the function to do this is date_count_between(). Notice that one of the results is different between clock and lubridate here:
library(clock)
library(lubridate, warn.conflicts = FALSE)
library(dplyr, warn.conflicts = FALSE)
df <- tibble(
date = c("2000-01-01","2000-01-14",
"2000-01-25","2000-02-12","2000-02-27","2000-06-05",
"2000-10-30","2001-02-04","2001-06-15","2001-12-26",
"2002-05-22","2002-06-04","2000-01-08","2000-07-11",
"2000-08-18","2000-11-27"),
ID = c("A","A","A","A","A","A",
"A","B","B","B","B","B","C","C","C","C"),
status = c("BIRTH","ETC","ETC","ETC",
"ETC","ETC","ETC","BIRTH","ETC","ETC","ETC","ETC",
"BIRTH","ETC","ETC","ETC")
)
df %>%
mutate(date = date_parse(date)) %>%
group_by(ID) %>%
mutate(birth_date = date[status == "BIRTH"]) %>%
ungroup() %>%
mutate(
age_clock = date_count_between(birth_date, date, "month"),
age_lubridate = as.period(date - birth_date) %/% months(1))
#> # A tibble: 16 × 6
#> date ID status birth_date age_clock age_lubridate
#> <date> <chr> <chr> <date> <int> <dbl>
#> 1 2000-01-01 A BIRTH 2000-01-01 0 0
#> 2 2000-01-14 A ETC 2000-01-01 0 0
#> 3 2000-01-25 A ETC 2000-01-01 0 0
#> 4 2000-02-12 A ETC 2000-01-01 1 1
#> 5 2000-02-27 A ETC 2000-01-01 1 1
#> 6 2000-06-05 A ETC 2000-01-01 5 5
#> 7 2000-10-30 A ETC 2000-01-01 9 9
#> 8 2001-02-04 B BIRTH 2001-02-04 0 0
#> 9 2001-06-15 B ETC 2001-02-04 4 4
#> 10 2001-12-26 B ETC 2001-02-04 10 10
#> 11 2002-05-22 B ETC 2001-02-04 15 15
#> 12 2002-06-04 B ETC 2001-02-04 16 15
#> 13 2000-01-08 C BIRTH 2000-01-08 0 0
#> 14 2000-07-11 C ETC 2000-01-08 6 6
#> 15 2000-08-18 C ETC 2000-01-08 7 7
#> 16 2000-11-27 C ETC 2000-01-08 10 10
clock says that 2001-02-04 to 2002-06-04 is 16 months, while the lubridate method here only says it is 15 months. This has to do with the fact that the lubridate calculation uses the length of an average month, which doesn't always accurately reflect how we think about months.
Consider this simple example, I think most people would agree that a child born on this date in February is considered "1 month and 1 day" old. But lubridate shows 0 months!
library(clock)
library(lubridate, warn.conflicts = FALSE)
# "1 month and 1 day apart"
feb <- as.Date("2020-02-28")
mar <- as.Date("2020-03-29")
# As expected when thinking about age in months
date_count_between(feb, mar, "month")
#> [1] 1
# Not expected
as.period(mar - feb) %/% months(1)
#> [1] 0
secs_in_day <- 86400
secs_in_month <- as.numeric(months(1))
secs_in_month / secs_in_day
#> [1] 30.4375
# Less than 30.4375 days, so not 1 month
mar - feb
#> Time difference of 30 days
The issue is that lubridate uses the length of an average month in the computation, which is 30.4375 days. But there are only 30 days between these two dates, so it isn't considered a full month.
clock, on the other hand, uses the day component of the starting date to determine if a "full month" has passed or not. In other words, because we have passed the 28th of March, clock decides that 1 month has passed, which is consistent with how we generally think about age.
Using dplyr and lubridate, we can do the following. We first turn the date column into a date. Then we group by ID, find the birth date and calculate the number of months since that date via some lubridate magic (see How do I use the lubridate package to calculate the number of months between two date vectors where one of the vectors has NA values?).
library(dplyr)
library(lubridate)
df1 %>%
mutate(date = as_date(date)) %>%
group_by(ID) %>%
mutate(birth_date = date[status == "BIRTH"],
age = as.period(date - birth_date) %/% months(1)) %>%
ungroup()
Which gives:
date ID status birth_date age
<date> <fct> <fct> <date> <dbl>
1 2000-01-01 A BIRTH 2000-01-01 0
2 2000-01-14 A ETC 2000-01-01 0
3 2000-01-25 A ETC 2000-01-01 0
4 2000-02-12 A ETC 2000-01-01 1
5 2000-02-27 A ETC 2000-01-01 1
6 2000-06-05 A ETC 2000-01-01 5
7 2000-10-30 A ETC 2000-01-01 9
8 2001-02-04 B BIRTH 2001-02-04 0
9 2001-06-15 B ETC 2001-02-04 4
10 2001-12-26 B ETC 2001-02-04 10
11 2002-05-22 B ETC 2001-02-04 15
12 2002-06-04 B ETC 2001-02-04 15
13 2000-01-08 C BIRTH 2000-01-08 0
14 2000-07-11 C ETC 2000-01-08 6
15 2000-08-18 C ETC 2000-01-08 7
16 2000-11-27 C ETC 2000-01-08 10
Which is your expected output except for some rounding differences. See my comment on your question.
I have yearly observations of income for a series of geographies, like this:
library(dplyr)
library(lubridate)
date <- c("2004-01-01", "2005-01-01", "2006-01-01",
"2004-01-01", "2005-01-01", "2006-01-01")
geo <- c(1, 1, 1, 2, 2, 2)
inc <- c(10, 12, 14, 32, 34, 50)
data <- tibble(date = ymd(date), geo, inc)
date geo inc
<date> <dbl> <dbl>
1 2004-01-01 1 10
2 2005-01-01 1 12
3 2006-01-01 1 14
4 2004-01-01 2 32
5 2005-01-01 2 34
6 2006-01-01 2 50
I need to insert mid-year values, as averages of the start-of-year and end-of-year observations, so that the data is every 6 months. The outcome would like this:
2004-01-01 1 10
2004-06-01 1 11
2005-01-01 1 12
2004-06-01 1 13
2006-01-01 1 14
2004-01-01 2 32
2004-06-01 2 33
2005-01-01 2 34
2004-06-01 2 42
2006-01-01 2 50
Would appreciate any ideas.
Grouped by 'geoo', add (+) the 'inc' with the next value (lead) and get the average (/2), as well as add 5 months to the 'date', then filter out the NA elements in 'inc', bind the rows with the original data
library(dplyr)
library(lubridate)
data %>%
group_by(geo) %>%
summarise(date = date %m+% months(5),
inc = (inc + lead(inc))/2, .groups = 'drop') %>%
filter(!is.na(inc)) %>%
bind_rows(data, .) %>%
arrange(geo, date)
-output
# A tibble: 10 x 3
# date geo inc
# <date> <dbl> <dbl>
# 1 2004-01-01 1 10
# 2 2004-06-01 1 11
# 3 2005-01-01 1 12
# 4 2005-06-01 1 13
# 5 2006-01-01 1 14
# 6 2004-01-01 2 32
# 7 2004-06-01 2 33
# 8 2005-01-01 2 34
# 9 2005-06-01 2 42
#10 2006-01-01 2 50
You can use complete to create a sequence of dates for 6 months and then use na.approx to fill the NA values with interpolated values.
library(dplyr)
library(lubridate)
data %>%
group_by(geo) %>%
tidyr::complete(date = seq(min(date), max(date), by = '6 months')) %>%
mutate(date = if_else(is.na(inc), date %m-% months(1), date),
inc = zoo::na.approx(inc))
# geo date inc
# <dbl> <date> <dbl>
# 1 1 2004-01-01 10
# 2 1 2004-06-01 11
# 3 1 2005-01-01 12
# 4 1 2005-06-01 13
# 5 1 2006-01-01 14
# 6 2 2004-01-01 32
# 7 2 2004-06-01 33
# 8 2 2005-01-01 34
# 9 2 2005-06-01 42
#10 2 2006-01-01 50
I have a dataset on a stock exchange's daily closing price and their respective dates for several years. I have further created a counter, counting which trading day in the month each day is (because the dataset is excluding weekends and holidays). It looks like this:
df$date <- as.Date(c("2017-03-25","2017-03-26","2017-03-27","2017-03-29","2017-03-30",
"2017-03-31","2017-04-03","2017-04-04","2017-04-05","2017-04-06",
"2017-04-07","2017-04-08","2017-04-09"))
df$DayofMonth <- c(18,19,20,21,22,23,1,2,3,4,5,6,7)
df$price <- (100, 100.53, 101.3 ,100.94, 101.42, 101.40, 101.85, 102, 101.9, 102, 102.31, 102.1, 102.23)
I would now like to create a dummyvariable taking the value 1 for the last 3 trading days and the first 5 trading days of the following month, for every month. So it would in this case look something like this:
df$ToM_dummy <- c(0,0,0,1,1,1,1,1,1,1,1,0,0)
Thanks for helping out!
Here's a dplyr solution. It's probably a little more complex than it needs to be for your real data because your sample stops on the 7th day of a month, and the algorithm needs to know that 7 isn't really the end of the month - the data is just incomplete for that month.
I have therefore arbitrarily added a cutoff of 18 days to indicate that if there are less trading days than that in a month we can assume the data for that month is incomplete. You may wish to change this if needed (I have no idea whether there are always more than 18 trading days in December or February, for example)
library(dplyr)
df %>%
mutate(month = lubridate::month(date)) %>%
group_by(month) %>%
mutate(ToM_dummy = +(DayofMonth < 6 |
(DayofMonth > (max(DayofMonth) - 3) &
max(DayofMonth) > 18))) # Change to appropriate number
#> # A tibble: 13 x 5
#> # Groups: month [2]
#> date DayofMonth price month ToM_dummy
#> <date> <dbl> <dbl> <dbl> <int>
#> 1 2017-03-25 18 100 3 0
#> 2 2017-03-26 19 101. 3 0
#> 3 2017-03-27 20 101. 3 0
#> 4 2017-03-29 21 101. 3 1
#> 5 2017-03-30 22 101. 3 1
#> 6 2017-03-31 23 101. 3 1
#> 7 2017-04-03 1 102. 4 1
#> 8 2017-04-04 2 102 4 1
#> 9 2017-04-05 3 102. 4 1
#> 10 2017-04-06 4 102 4 1
#> 11 2017-04-07 5 102. 4 1
#> 12 2017-04-08 6 102. 4 0
#> 13 2017-04-09 7 102. 4 0
Data
df <- structure(list(date = structure(c(17250, 17251, 17252, 17254,
17255, 17256, 17259, 17260, 17261, 17262, 17263, 17264, 17265
), class = "Date"), DayofMonth = c(18, 19, 20, 21, 22, 23, 1,
2, 3, 4, 5, 6, 7), price = c(100, 100.53, 101.3, 100.94, 101.42,
101.4, 101.85, 102, 101.9, 102, 102.31, 102.1, 102.23)), row.names = c(NA,
-13L), class = "data.frame")
df
#> date DayofMonth price
#> 1 2017-03-25 18 100.00
#> 2 2017-03-26 19 100.53
#> 3 2017-03-27 20 101.30
#> 4 2017-03-29 21 100.94
#> 5 2017-03-30 22 101.42
#> 6 2017-03-31 23 101.40
#> 7 2017-04-03 1 101.85
#> 8 2017-04-04 2 102.00
#> 9 2017-04-05 3 101.90
#> 10 2017-04-06 4 102.00
#> 11 2017-04-07 5 102.31
#> 12 2017-04-08 6 102.10
#> 13 2017-04-09 7 102.23
I want to select distinct entries for my dataset based on two specific variables. I may, in fact, like to create a subset and do analysis using each subset.
The data set looks like this
id <- c(3,3,6,6,4,4,3,3)
date <- c("2017-1-1", "2017-3-3", "2017-4-3", "2017-4-7", "2017-10-1", "2017-11-1", "2018-3-1", "2018-4-3")
date_cat <- c(1,1,1,1,2,2,3,3)
measurement <- c(10, 13, 14,13, 12, 11, 14, 17)
myData <- data.frame(id, date, date_cat, measurement)
myData
myData$date1 <- as.Date(myData$date)
myData
id date date_cat measurement date1
1 3 2017-1-1 1 10 2017-01-01
2 3 2017-3-3 1 13 2017-03-03
3 6 2017-4-3 1 14 2017-04-03
4 6 2017-4-7 1 13 2017-04-07
5 4 2017-10-1 2 12 2017-10-01
6 4 2017-11-1 2 11 2017-11-01
7 3 2018-3-1 3 14 2018-03-01
8 3 2018-4-3 3 17 2018-04-03
#select the last date for the ID in each date category.
Here date_cat is the date category and date1 is date formatted as date. How can I get the last date for each ID in each date_category?
I want my data to show up as
id date date_cat measurement date1
1 3 2017-3-3 1 13 2017-03-03
2 6 2017-4-7 1 13 2017-04-07
3 4 2017-11-1 2 11 2017-11-01
4 3 2018-4-3 3 17 2018-04-03
Thanks!
I am not sure if you want something like below
subset(myData,ave(date1,id,date_cat,FUN = function(x) tail(sort(x),1))==date1)
which gives
> subset(myData,ave(date1,id,date_cat,FUN = function(x) tail(sort(x),1))==date1)
id date date_cat measurement date1
2 3 2017-3-3 1 13 2017-03-03
4 6 2017-4-7 1 13 2017-04-07
6 4 2017-11-1 2 11 2017-11-01
8 3 2018-4-3 3 17 2018-04-03
Using data.table:
library(data.table)
myData_DT <- as.data.table(myData)
myData_DT[, .SD[.N] , by = .(date_cat, id)]
We could create a group with rleid on the 'id' column, slice the last row, remove the temporary grouping column
library(dplyr)
library(data.table)
myData %>%
group_by(grp = rleid(id)) %>%
slice(n()) %>%
ungroup %>%
select(-grp)
# A tibble: 4 x 5
# id date date_cat measurement date1
# <dbl> <chr> <dbl> <dbl> <date>
#1 3 2017-3-3 1 13 2017-03-03
#2 6 2017-4-7 1 13 2017-04-07
#3 4 2017-11-1 2 11 2017-11-01
#4 3 2018-4-3 3 17 2018-04-03
Or this can be done on the fly without creating a temporary column
myData %>%
filter(!duplicated(rleid(id), fromLast = TRUE))
Or using base R with subset and rle
subset(myData, !duplicated(with(rle(id),
rep(seq_along(values), lengths)), fromLast = TRUE))
# id date date_cat measurement date1
#2 3 2017-3-3 1 13 2017-03-03
#4 6 2017-4-7 1 13 2017-04-07
#6 4 2017-11-1 2 11 2017-11-01
#8 3 2018-4-3 3 17 2018-04-03
Using dplyr:
myData %>%
group_by(id,date_cat) %>%
top_n(1,date)
I have a data frame (with N=16) contains ID (character), w_from (date), and w_to (date). Each record represent a task.
Here’s the data in R.
ID <- c(1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2)
w_from <- c("2010-01-01","2010-01-05","2010-01-29","2010-01-29",
"2010-03-01","2010-03-15","2010-07-15","2010-09-10",
"2010-11-01","2010-11-30","2010-12-15","2010-12-31",
"2011-02-01","2012-04-01","2011-07-01","2011-07-01")
w_to <- c("2010-01-31","2010-01-15", "2010-02-13","2010-02-28",
"2010-03-16","2010-03-16","2010-08-14","2010-10-10",
"2010-12-01","2010-12-30","2010-12-20","2011-02-19",
"2011-03-23","2012-06-30","2011-07-31","2011-07-06")
df <- data.frame(ID, w_from, w_to)
df$w_from <- as.Date(df$w_from)
df$w_to <- as.Date(df$w_to)
I need to generate a group number by ID for the records that their time intervals overlap. As an example, and in general terms, if record#1 overlaps with record#2, and record#2 overlaps with record#3, then record#1, record#2, and record#3 overlap.
Also, if record#1 overlaps with record#2 and record#3, but record#2 doesn't overlap with record#3, then record#1, record#2, record#3 are all overlap.
In the example above and for ID=1, the first four records overlap.
Here is the final output:
Also, if this can be done using dplyr, that would be great!
Try this:
library(dplyr)
df %>%
group_by(ID) %>%
arrange(w_from) %>%
mutate(group = 1+cumsum(
cummax(lag(as.numeric(w_to), default = first(as.numeric(w_to)))) < as.numeric(w_from)))
# A tibble: 16 x 4
# Groups: ID [2]
ID w_from w_to group
<dbl> <date> <date> <dbl>
1 1 2010-01-01 2010-01-31 1
2 1 2010-01-05 2010-01-15 1
3 1 2010-01-29 2010-02-13 1
4 1 2010-01-29 2010-02-28 1
5 1 2010-03-01 2010-03-16 2
6 1 2010-03-15 2010-03-16 2
7 1 2010-07-15 2010-08-14 3
8 1 2010-09-10 2010-10-10 4
9 1 2010-11-01 2010-12-01 5
10 1 2010-11-30 2010-12-30 5
11 1 2010-12-15 2010-12-20 5
12 1 2010-12-31 2011-02-19 6
13 1 2011-02-01 2011-03-23 6
14 2 2011-07-01 2011-07-31 1
15 2 2011-07-01 2011-07-06 1
16 2 2012-04-01 2012-06-30 2