I've been having some trouble adding in titles to created data frames with for loops. This is the basic structure of the data:
> head(year2019)
date day_of_week away_team away_team_game_number home_team home_team_game_number away_score home_score day_night park game_length away_AB away_H away_2B
1 2019-03-20 Wed SEA 1 OAK 1 9 7 N OAK01 204 31 7 1
2 2019-03-21 Thu SEA 2 OAK 2 5 4 N OAK01 267 43 9 4
3 2019-03-28 Thu PIT 1 CIN 1 3 5 D CIN09 174 31 5 0
4 2019-03-28 Thu ARI 1 LAN 1 5 12 D LOS03 169 33 9 4
5 2019-03-28 Thu COL 1 MIA 1 6 3 D MIA02 175 36 9 5
6 2019-03-28 Thu SLN 1 MIL 1 4 5 D MIL06 156 32 5 0
With this code I've been able to create dataframes for each team:
for (i in teams) {
assign(i, year2019 %>% filter(away_team == i | home_team == i))
}
With teams <- c("ANA", "ARI", "ATL", ...)
However I want to run this with creating both home and away teams. I've tried some of the following but nothing has worked so far:
for(i in teams) {
i_home <- i %>% filter(home_team == i)
i_away <- i %>% filter(away_team == i)
}
Or
for (i in teams) {
i1 <- filter(year2019, home_team == i)
i2 <- filter(year2019, away_team == i)
}
Any advice on how to properly introduce added names for i in this?
Avoid flooding your global environment with many separate team data frames. Instead, use one list of many data frame elements which is easily index-able and searchable. For this, consider by or split.
home_team_dfs <- by(year_2019, year_2019$home_team, identity)
home_team_dfs <- split(year_2019, year_2019$home_team)
# RUN SELECT OPERATIONS ON DATA FRAMES
head(home_team_dfs$ANA)
tail(home_team_dfs$ARI)
summary(home_team_dfs$ATL)
away_team_dfs <- by(year_2019, year_2019$away_team, identity)
away_team_dfs <- split(year_2019, year_2019$away_team)
# RUN SELECT OPERATIONS ON DATA FRAMES
head(away_team_dfs$ANA)
tail(away_team_dfs$ARI)
summary(away_team_dfs$ATL)
Do note you lose no functionality of data frame if it is stored within a list. Therefore, any needed operation (e.g., head, tail, summary) should still be available. Also, you can easily run iterative, consistent, serial-able operations on list like with apply family functions to interact with single, multiple, or all underlying data frame elements.
Related
I know this is a classic question and there are also similar ones in the archive, but I feel like the answers did not really apply to this case. Basically I want to take one dataframe (covid cases in Berlin per district), calculate the sum of the columns and create a new dataframe with a column representing the name of the district and another one representing the total number. So I wrote
covid_bln <- read.csv('https://www.berlin.de/lageso/gesundheit/infektionsepidemiologie-infektionsschutz/corona/tabelle-bezirke-gesamtuebersicht/index.php/index/all.csv?q=', sep=';')
c_tot<-data.frame('district'=c(), 'number'=c())
for (n in colnames(covid_bln[3:14])){
x<-data.frame('district'=c(n), 'number'=c(sum(covid_bln$n)))
c_tot<-rbind(c_tot, x)
next
}
print(c_tot)
Which works properly with the names but returns only the number of cases for the 8th district, but for all the districts. If you have any suggestion, even involving the use of other functions, it would be great. Thank you
Here's a base R solution:
number <- colSums(covid_bln[3:14])
district <- names(covid_bln[3:14])
c_tot <- cbind.data.frame(district, number)
rownames(c_tot) <- NULL
# If you don't want rownames:
rownames(c_tot) <- NULL
This gives us:
district number
1 mitte 16030
2 friedrichshain_kreuzberg 10679
3 pankow 10849
4 charlottenburg_wilmersdorf 10664
5 spandau 9450
6 steglitz_zehlendorf 9218
7 tempelhof_schoeneberg 12624
8 neukoelln 14922
9 treptow_koepenick 6760
10 marzahn_hellersdorf 6960
11 lichtenberg 7601
12 reinickendorf 9752
I want to provide a solution using tidyverse.
The final result is ordered alphabetically by districts
c_tot <- covid_bln %>%
select( mitte:reinickendorf) %>%
gather(district, number, mitte:reinickendorf) %>%
group_by(district) %>%
summarise(number = sum(number))
The rusult is
# A tibble: 12 x 2
district number
* <chr> <int>
1 charlottenburg_wilmersdorf 10736
2 friedrichshain_kreuzberg 10698
3 lichtenberg 7644
4 marzahn_hellersdorf 7000
5 mitte 16064
6 neukoelln 14982
7 pankow 10885
8 reinickendorf 9784
9 spandau 9486
10 steglitz_zehlendorf 9236
11 tempelhof_schoeneberg 12656
12 treptow_koepenick 6788
Anyone knows how to get all the random numbers different in the following code? E.g. with doRNG package? I don't care about reproducibility.
Edit: Duplicates by pure chance are accepted.
rm(list = ls())
set.seed(666)
cat("\014")
library(plyr)
library(dplyr)
library(doRNG)
# ====== Data Preparation ======
dt = data.frame(id = 1:10,
part = rep("dt",10),
HG = c(1,3,6,NA,NA,2,NA,NA,NA,NA),
random = NA)
# ====== Set Parallel Computing ======
library(foreach)
library(doParallel)
cl = makeCluster(3, outfile = "")
registerDoParallel(cl)
# ====== SIMULATION ======
nsim = 1000 # number of simulations
iterChunk = 100 # split nsim into this many chunks
out = data.frame() # prepare output DF
for(iter in 1:ceiling(nsim/iterChunk)){
strt = Sys.time()
out_iter =
foreach(i = 1:iterChunk, .combine = rbind, .multicombine = TRUE, .maxcombine = 100000, .inorder = FALSE, .verbose = FALSE,
.packages = c("plyr", "dplyr")) %dopar% {
# simulation number
id_sim = iterChunk * (iter - 1) + i
## Generate random numbers
tmp_sim = is.na(dt$HG) # no results yet
dt$random[tmp_sim] = runif(sum(tmp_sim))
dt$HG[tmp_sim] = 3
# Save Results
dt$id_sim = id_sim
dt$iter = iter
dt$i = i
print(Sys.time())
return(dt)
}#i;sim_forcycle
out = rbind.data.frame(out,subset(out_iter, !is.na(random)))
fnsh = Sys.time()
cat(" [",iter,"] ",fnsh - strt, sep = "")
}#iter
# ====== Stop Parallel Computing ======
stopCluster(cl)
# ====== Distinct Random Numbers ======
length(unique(out$random)) # expectation: 6000
I have been strugling with this for 2 days. I asked this question earlier with only general response about random numbers.
Here I would like to ask for a solution (if anybody knows) how to set doRNG package options (or similar package) in a way that all the random numbers are different. Across all the loops.
I have tried tons of doRNG settings and I still can't get it to work. Tried R versions 3.5.3 and 3.6.3 on two different computers.
UPDATE Following discussion with #Limey
Purpose of the code is to simulate football matches. As the simulation is large, I use iterChunk to "split" the simulation into managable parts and after each iter send the data into PostgreSQL database so the simulation doesn't overload RAM. Some matches already have real world results and have HG (home goals) filled in. I want to simulate the rest.
When setting iterChunk to 1 everything is fine. Increasing iterChunk leads to generation of same numbers within iter. For example when I set nsim to 100 and iterChunk to 10. (All matches simulated 100 times, 10 times in 10 loops). I expect 600 random numbers (each match independently simulated accross all the loops). However I only get 180 - following the logic: 3 cores * 6 matches * 10 iterChunks.) Using 2 workers I do get 120 distinct random numbers (2 * 6 * 10)
Furthermore: exluding dt$HG[tmp_sim] = 3 I do get all random numbers different with whatever setting.
To understand the problem, I suggest:
Run the code as is. (possibly setting nsim to 100 and iterChunk to 10) You will get 180 different random numbers. With lower number of nsim & iterChunk things may work as expected.
Comment out dt$HG[tmp_sim] = 3.
You will get 6000 different random numbers (600 if you change nsim and iterChunk)
The code in 2nd step assigns goals scored by home team. It looks like some kind of bug I can't get over. Even information that someone gets the same result and doesn't know why will be helpful - it will lift the weight of my own stupidity out of me.
Thank you, I highly appreciate any effort.
I realised what the problem with OP's code was whilst I was in the shower. It's simple, and obvious in retrospect: all the loops and parallel processes are working on the same object - the dt data frame. So they're constantly overwriting the changes that each makes, and at the end of the outer loop, you just have multiple copies of the changes made by the last loop to complete. The solution is equally simple: work on a copy of the dt data frame.
To minimise the changes, I renamed dt to baseDT
# ====== Data Preparation ======
baseDT = data.frame(id = 1:10,
part = rep("dt",10),
HG = c(1,3,6,NA,NA,2,NA,NA,NA,NA),
random = NA)
and then took a copy of it at the top of the foreach loop
out_iter = foreach(i = 1:iterChunk,
.combine = rbind, .multicombine = TRUE, .maxcombine = 100000,
.inorder = FALSE, .verbose = FALSE,
.packages = c("plyr", "dplyr")) %dopar% {
dt <- baseDT
This gives
> length(unique(out$random)) # expectation: 6000
[1] 6000
as expected.
Modifying the "Hello World" example in the "getting started with doParallel" vignette to generate random numbers, I came up with:
library(doParallel)
cl <- makeCluster(2)
registerDoParallel(cl)
myFunc <- function(n) {runif(n)}
foreach(i=1:3) %dopar% myFunc(10)
[[1]]
[1] 0.18492375 0.13388278 0.65455450 0.93093066 0.41157625 0.89479764 0.14736529 0.47935995 0.03062963 0.16110714
[[2]]
[1] 0.89245145 0.20980791 0.83828019 0.04411547 0.38184303 0.48110619 0.51509058 0.93732055 0.40159834 0.81414140
[[3]]
[1] 0.74393129 0.66999730 0.44411989 0.85040773 0.80224527 0.72483644 0.64566262 0.22546420 0.14526819 0.05931329
Suggesting that getting random numbers across threads is straightforward. Indeed, the examples on pages 2 and 3 of the doRNG reference manual say the same thing.
In fact, if I understand you correctly, the purpose of doRNG is to do precisely the opposite of what you want: to make random processes reproducible across threads.
Of course, this doesn't guarantee that all numbers are different across all threads. But it makes duplication very unlikely. A guarantee of no duplicates would mean some degree of determinism in the process: a completely random process might produce duplicates by chance.
Update
Following on from our conversation in the comments...
We've established that the problem is in your program logic, not the parallelisation per se. So we need to refocus the question: what are you trying to do. I'm afraid it's not at all clear to me. So that means we need to simplify.
I set nsim to 5 and iterChunk to 1. I get 5 data frames which look like
id part HG random id_sim iter i
1 1 dt 1 NA 1 1 1
2 2 dt 3 NA 1 1 1
3 3 dt 6 NA 1 1 1
4 4 dt 3 0.6919744 1 1 1
5 5 dt 3 0.5413398 1 1 1
6 6 dt 2 NA 1 1 1
7 7 dt 3 0.3983175 1 1 1
8 8 dt 3 0.3342174 1 1 1
9 9 dt 3 0.6126020 1 1 1
10 10 dt 3 0.4185468 1 1 1
In each, the values of id_sim and iter are always the same, and run from 1 in the first data frame to 5 in the fifth. i is 1 for all rows in all data frames. Values in random do appear to be random, and different between data frames. But the NAs are all in the same positions in every data frame: the 1st, 2nd, 3rd and 6th rows. The values of HG are as shown above for all five data frames.
Is that what you would expect? If not, what do you expect? Given we know the problem is not the paraellisation, you need to give us more information.
Update 2
Do you know Arduan? They posted a related question over the weekend...
I'm not going to tell you what's wrong with your code. I'll show you how I would apprach your problem. I hope you'll agree it's more readable, if nothing else.
So, we're simulating some football matches. I'll assume its a league format and use the english Premier League as an example. Start by generating the fixture list for a single season.
library(tidyverse)
teams <- c("Arsenal", "Aston Villa", "Bournemouth", "Brighton & Hove Albion",
"Burnley", "Chelsea", "Crystal Palace", "Everton", "Leicester City",
"Liverpool", "Manchester City", "Manchester United", "Newcastle United",
"Norwich City", "Sheffield United", "Southampton", "Tottenham Hotspur",
"Watford", "West Ham United", "Wolverhampton Wanderers")
fixtures <- tibble(HomeTeam=teams, AwayTeam=teams) %>%
complete(HomeTeam, AwayTeam) %>%
filter(HomeTeam != AwayTeam) # A team can't play itself
fixtures %>% head(5)
# A tibble: 5 x 2
HomeTeam AwayTeam
<chr> <chr>
1 Arsenal Aston Villa
2 Arsenal Bournemouth
3 Arsenal Brighton & Hove Albion
4 Arsenal Burnley
5 Arsenal Chelsea
Suppose we know some results. I'll use yesterday's matches as an illustration.
knownResults <- tribble(~HomeTeam, ~AwayTeam, ~HomeGoals, ~AwayGoals,
"Burnley", "Sheffield United", 1, 1,
"Newcastle United", "West Ham United", 2, 2,
"Liverpool", "Aston Villa", 2, 0,
"Southampton", "Manchester City", 1, 0)
resultsSoFar <- fixtures %>%
left_join(knownResults, by=c("HomeTeam", "AwayTeam"))
resultsSoFar %>% filter(!is.na(HomeGoals))
# A tibble: 4 x 4
HomeTeam AwayTeam HomeGoals AwayGoals
<chr> <chr> <dbl> <dbl>
1 Burnley Sheffield United 1 1
2 Liverpool Aston Villa 2 0
3 Newcastle United West Ham United 2 2
4 Southampton Manchester City 1 0
Now some utility functions. You could certainly combine them, but I think it's clearer to keep them separate so you can see exactly what each one is doing.
First, a function to simulate the results of all matches whose results are unknown. The details of how you simulate the scores are entirely arbitrary. I've assumed that home teams score an average of 1.5 goals a game, away teams score 1.2 goals per game. Later on, I'm going to use this to simulate many seasons in one go, so I'll add a variable (Iteration) to index the season.
simulateResults <- function(i=NA, data) {
n <- nrow(data)
data %>%
add_column(Iteration=i, .before=1) %>%
mutate(
# Give the home team a slight advantage
HomeGoals=ifelse(is.na(HomeGoals), floor(rexp(n, rate=1/1.5)), HomeGoals),
AwayGoals=ifelse(is.na(AwayGoals), floor(rexp(n, rate=1/1.2)), AwayGoals)
)
}
Use it, and check that we haven't overwritten known results:
simulateResults(1, resultsSoFar) %>% filter(HomeTeam=="Burnley", AwayTeam=="Sheffield United")
# A tibble: 1 x 5
Iteration HomeTeam AwayTeam HomeGoals AwayGoals
<dbl> <chr> <chr> <dbl> <dbl>
1 1 Burnley Sheffield United 1 1
I'm going to parallelise the overall simulation, so now let's have a function to simulate a chunk of simulations. Again, create an index column to identify the chunk.
simulateChunk <- function(chunkID=NA, n) {
bind_rows(lapply(1:n, simulateResults, data=resultsSoFar)) %>%
add_column(Chunk=chunkID, .before=1)
}
simulateChunk(chunkID=1, n=3)
# A tibble: 1,140 x 6
Chunk Iteration HomeTeam AwayTeam HomeGoals AwayGoals
<dbl> <int> <chr> <chr> <dbl> <dbl>
1 1 1 Arsenal Aston Villa 2 0
2 1 1 Arsenal Bournemouth 0 0
3 1 1 Arsenal Brighton & Hove Albion 2 0
4 1 1 Arsenal Burnley 2 0
5 1 1 Arsenal Chelsea 1 0
6 1 1 Arsenal Crystal Palace 0 0
7 1 1 Arsenal Everton 2 3
8 1 1 Arsenal Leicester City 2 0
9 1 1 Arsenal Liverpool 0 1
10 1 1 Arsenal Manchester City 4 0
OK. Now I'm ready to do the main simulation work. I'll run 10 chunks of 100 simulations eash, to give 1000 simulated seasons in total, the same as you had.
library(doParallel)
cl <- makeCluster(3)
registerDoParallel(cl)
chunkSize <- 100
nChunks <- 10
startedAt <- Sys.time()
x <- bind_rows(foreach(i=1:nChunks, .packages=c("tidyverse")) %dopar% simulateChunk(i, n=chunkSize))
finishedAt <- Sys.time()
print(finishedAt - startedAt)
Time difference of 6.772928 secs
stopCluster(cl)
> x
# A tibble: 380,000 x 6
Chunk Iteration HomeTeam AwayTeam HomeGoals AwayGoals
<int> <int> <chr> <chr> <dbl> <dbl>
1 1 1 Arsenal Aston Villa 2 0
2 1 1 Arsenal Bournemouth 3 1
3 1 1 Arsenal Brighton & Hove Albion 0 1
4 1 1 Arsenal Burnley 3 0
5 1 1 Arsenal Chelsea 1 0
6 1 1 Arsenal Crystal Palace 0 0
7 1 1 Arsenal Everton 1 2
8 1 1 Arsenal Leicester City 0 0
9 1 1 Arsenal Liverpool 0 0
10 1 1 Arsenal Manchester City 0 0
Let's check I've got sensible results. As a basic check, I'll look at the results of Arsenal vs Aston Villa:
x %>%
filter(HomeTeam == "Arsenal", AwayTeam=="Aston Villa") %>%
group_by(HomeGoals, AwayGoals) %>%
summarise(N=n(), .groups="drop") %>%
pivot_wider(
values_from="N", names_prefix="AwayGoals",
names_sep="", names_from=AwayGoals
)
# A tibble: 8 x 10
HomeGoals AwayGoals0 AwayGoals1 AwayGoals2 AwayGoals3 AwayGoals4 AwayGoals5 AwayGoals6 AwayGoals8 AwayGoals7
<dbl> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 0 299 129 57 19 12 7 NA NA NA
2 1 135 63 25 6 4 4 1 2 NA
3 2 75 21 12 9 4 1 NA NA 1
4 3 30 13 10 1 NA NA NA NA NA
5 4 21 7 1 1 NA NA NA NA NA
6 5 11 2 1 NA 2 NA NA NA NA
7 6 4 2 2 NA NA NA NA NA NA
8 7 4 1 1 NA NA NA NA NA NA
That looks reasonable. Now confirm that the matches with known results don't vary. For example:
x %>%
filter(HomeTeam == "Liverpool", AwayTeam=="Aston Villa") %>%
group_by(HomeGoals, AwayGoals) %>%
summarise(N=n(), .groups="drop") %>%
pivot_wider(values_from="N", names_prefix="AwayGoals", names_sep="", names_from=AwayGoals)
HomeGoals AwayGoals0
<dbl> <int>
1 2 1000
All good.
So, That's 23 statements to generate the fixtures, take account of known results, simulate the remainder of the matches and do some basic sanity checking. I could easily get that down to under 20 statements if I had to. That's about a third less than you were using just to try to simulate the unknown results. [The actual simulation takes fewer than 10 statements.] I think my approach is easier to understand: by using tidy verbs the code is almost self-documenting.
I have a dataset of a hypothetical exam.
id <- c(1,1,3,4,5,6,7,7,8,9,9)
test_date <- c("2012-06-27","2012-07-10","2013-07-04","2012-03-24","2012-07-22", "2013-09-16","2012-06-21","2013-10-18", "2013-04-21", "2012-02-16", "2012-03-15")
result_date <- c("2012-07-29","2012-09-02","2013-08-01","2012-04-25","2012-09-01","2013-10-20","2012-07-01","2013-10-31", "2013-05-17", "2012-03-17", "2012-04-20")
data1 <- as_data_frame(id)
data1$test_date <- test_date
data1$result_date <- result_date
colnames(data1)[1] <- "id"
"id" indicates the ID of the students who have taken a particular exam. "test_date" is the date the students took the test and "result_date" is the date when the students' results are posted. I'm interested in finding out which students retook the exam BEFORE the result of that exam session was released, e.g. students who knew that they have underperformed and retook the exam without bothering to find out their scores. For example, student with "id" 1 took the exam for the second time on "2012-07-10" which was before the result date for his first exam - "2012-07-29".
I tried to:
data1%>%
group_by(id) %>%
arrange(id, test_date) %>%
filter(n() >= 2) %>% #To only get info on students who have taken the exam more than once and then merge it back in with the original data set using a join function
So essentially, I want to create a new column called "re_test" where it would equal 1 if a student retook the exam BEFORE receiving the result of a previous exam and 0 otherwise (those who retook after seeing their marks or those who did not retake).
I have tried to mutate in order to find cases where dates are either positive or negative by subtracting the 2nd test_date from the 1st result_date:
mutate(data1, re_test = result_date - lead(test_date, default = first(test_date)))
However, this leads to mixing up students with different id's. I tried to split but mutate won't work on a list of dataframes so now I'm stuck:
split(data1, data1$id)
Just to add on, this is a part of the desired result:
data2 <- as_data_frame(id <- c(1,1,3,4))
data2$test_date_result <- c("2012-06-27","2012-07-10", "2013-07-04","2012-03-24")
data2$result_date_result <- c("2012-07-29","2012-09-02","2013-08-01","2012-04-25")
data2$re_test <- c(1, 0, 0, 0)
Apologies for the verbosity and hope I was clear enough.
Thanks a lot in advance!
library(reshape2)
library(dplyr)
# first melt so that we can sequence by date
data1m <- data1 %>%
melt(id.vars = "id", measure.vars = c("test_date", "result_date"), value.name = "event_date")
# any two tests in a row is a flag - use dplyr::lag to comapre the previous
data1mc <- data1m %>%
arrange(id, event_date) %>%
group_by(id) %>%
mutate (multi_test = (variable == "test_date" & lag(variable == "test_date"))) %>%
filter(multi_test)
# id variable event_date multi_test
# 1 1 test_date 2012-07-10 TRUE
# 2 9 test_date 2012-03-15 TRUE
## join back to the original
data1 %>%
left_join (data1mc %>% select(id, event_date, multi_test),
by=c("id" = "id", "test_date" = "event_date"))
I have a piecewise answer that may work for you. I first create a data.frame called student that contains the re-test information, and then join it with the data1 object. If students re-took the test multiple times, it will compare the last test to the first, which is a flaw, but I'm unsure if students have the ability to re-test multiple times?
student <- data1 %>%
group_by(id) %>%
summarise(retest=(test_date[length(test_date)] < result_date[1]) == TRUE)
Some re-test values were NA. These were individuals that only took the test once. I set these to FALSE here, but you can retain the NA, as they do contain information.
student$retest[is.na(student$retest)] <- FALSE
Join the two data.frames to a single object called data2.
data2 <- left_join(data1, student, by='id')
I am sure there are more elegant ways to approach this. I did this by taking advantage of the structure of your data (sorted by id) and the lag function that can refer to the previous records while dealing with a current record.
### Ensure Data are sorted by ID ###
data1 <- arrange(data1,id)
### Create Flag for those that repeated ###
data1$repeater <- ifelse(lag(data1$id) == data1$id,1,0)
### I chose to do this on all data, you could filter on repeater flag first ###
data1$timegap <- as.Date(data1$result_date) - as.Date(data1$test_date)
data1$lagdate <- as.Date(data1$test_date) - lag(as.Date(data1$result_date))
### Display results where your repeater flag is 1 and there is negative time lag ###
data1[data1$repeater==1 & !is.na(data1$repeater) & as.numeric(data1$lagdate) < 0,]
# A tibble: 2 × 6
id test_date result_date repeater timegap lagdate
<dbl> <chr> <chr> <dbl> <time> <time>
1 1 2012-07-10 2012-09-02 1 54 days -19 days
2 9 2012-03-15 2012-04-20 1 36 days -2 days
I went with a simple shift comparison. 1 line of code.
data1 <- data.frame(id = c(1,1,3,4,5,6,7,7,8,9,9), test_date = c("2012-06-27","2012-07-10","2013-07-04","2012-03-24","2012-07-22", "2013-09-16","2012-06-21","2013-10-18", "2013-04-21", "2012-02-16", "2012-03-15"), result_date = c("2012-07-29","2012-09-02","2013-08-01","2012-04-25","2012-09-01","2013-10-20","2012-07-01","2013-10-31", "2013-05-17", "2012-03-17", "2012-04-20"))
data1$re_test <- unlist(lapply(split(data1,data1$id), function(x)
ifelse(as.Date(x$test_date) > c(NA, as.Date(x$result_date[-nrow(x)])), 0, 1)))
data1
id test_date result_date re_test
1 1 2012-06-27 2012-07-29 NA
2 1 2012-07-10 2012-09-02 1
3 3 2013-07-04 2013-08-01 NA
4 4 2012-03-24 2012-04-25 NA
5 5 2012-07-22 2012-09-01 NA
6 6 2013-09-16 2013-10-20 NA
7 7 2012-06-21 2012-07-01 NA
8 7 2013-10-18 2013-10-31 0
9 8 2013-04-21 2013-05-17 NA
10 9 2012-02-16 2012-03-17 NA
11 9 2012-03-15 2012-04-20 1
I think there is benefit in leaving NAs but if you really want all others as zero, simply:
data1$re_test <- ifelse(is.na(data1$re_test), 0, data1$re_test)
data1
id test_date result_date re_test
1 1 2012-06-27 2012-07-29 0
2 1 2012-07-10 2012-09-02 1
3 3 2013-07-04 2013-08-01 0
4 4 2012-03-24 2012-04-25 0
5 5 2012-07-22 2012-09-01 0
6 6 2013-09-16 2013-10-20 0
7 7 2012-06-21 2012-07-01 0
8 7 2013-10-18 2013-10-31 0
9 8 2013-04-21 2013-05-17 0
10 9 2012-02-16 2012-03-17 0
11 9 2012-03-15 2012-04-20 1
Let me know if you have any questions, cheers.
For an assignment for my graduate program, I have been asked to extract data from datasets of English Premier League results (located here). I am very close to being done but need help on the last two outputs.
We must create a function that can receive two arguments, a date and a season. The function must return a data frame with the table of the respective season on that date. It must include wins, losses, home record, away record, etc. The only ones I have not managed to figure out are W/L streak and the results of the last 10 matches.
Here is an example of what the initial dataset looks like:
e.Date e.HomeTeam e.AwayTeam e.FTHG e.FTAG e.FTR
1 2015-08-08 Bournemouth Aston Villa 0 1 A
2 2015-08-08 Chelsea Swansea 2 2 D
3 2015-08-08 Everton Watford 2 2 D
4 2015-08-08 Leicester Sunderland 4 2 H
5 2015-08-08 Man United Tottenham 1 0 H
My plan was to get Home and Away data sorted out for each club then merge them together before doing the analysis to find streak and last 10 results.
I manipulated the data to look like this:
HomeTeam FTR Date freq
1 Arsenal L 2015-08-09 1
2 Arsenal D 2015-08-24 1
3 Arsenal W 2015-09-12 1
4 Aston Villa L 2015-08-14 1
5 Aston Villa L 2015-09-19 1
6 Aston Villa D 2015-08-29 1
And now I'm kinda lost. My idea was to run some kind of loop (for? ddply? data.table?) to create a data frame for each club with their results in it and then loop again to do whatever calculations to get the desired variables (streak and last 10) and somehow push those back into the main data frame where I am housing all of the other outputs.
I don't want to be told the answer outright since it's important I learn this on my own. However, if someone could point me in the right direction that would be great. Thanks so much.
I created some dummy data just to demonstrate a few commands and maybe give you some ideas.
set.seed(321)
dat <- data.frame(team = sample(letters[1:3], 20, replace=TRUE),
season = rep("season1", 20),
time = rnorm(20),
win_loss = sample(c("win", "loss"), 20, replace=TRUE))
Problem 1. Find win/loss streak
Take a look at the rle function example below
# 1. find wl streak of team 'a'
tmp <- dat[dat$team == "a", ]
tmp <- tmp[order(tmp$time), ]
> tmp
team season time win_loss
19 a season1 -1.12032742 loss
14 a season1 -1.07223880 loss
16 a season1 0.09500072 loss
3 a season1 0.18832552 loss
8 a season1 0.42033257 loss
4 a season1 2.44325982 win
# shows runs of 5 consecutive losses, then 1 consecutive win
rle(tmp$win_loss == "win")
Run Length Encoding
lengths: int [1:2] 5 1
values : logi [1:2] FALSE TRUE
Here's a very helpful post on rle How can I count runs in a sequence?
Problem 2. Last 3 results
I reversed the order of time and then picked the top 3 results.
# 2. find last 3 matches for team 'b'
tmp <- dat[dat$team == "b", ]
tmp <- tmp[rev(order(tmp$time)), ]
> tmp[1:3, ]
team season time win_loss
11 b season1 0.9172555 loss
9 b season1 0.5775845 win
7 b season1 0.4560691 loss
I have the following data read into R as a data frame named "data_old":
yes year month
1 15 2004 5
2 9 2005 6
3 15 2006 3
4 12 2004 5
5 14 2005 1
6 15 2006 7
. . ... .
. . ... .
I have written a small loop which goes through the data and sums up the yes variable for each month/year combination:
year_f <- c(2004:2006)
month_f <- c(1:12)
for (i in year_f){
for (j in month_f){
x <- subset(data_old, month == j & year == i, select="yes")
if (nrow(x) > 0){
print(sum(x))
}
else{print("Nothing")}
}
}
My question is this: I can print the sum for each month/year combination in the terminal, but how do i store it in a vector? (the nested loop is giving me headaches trying to figure this out).
Thomas
Another way,
library(plyr)
ddply(data_old,.(year,month),function(x) sum(x[1]))
year month V1
1 2004 5 27
2 2005 1 14
3 2005 6 9
4 2006 3 15
5 2006 7 15
Forget the loops, you want to use an aggregation function. There's a recent discussion of them in this SO question.
with(data_old, tapply(yes, list(year, month), sum))
is one of many solutions.
Also, you don't need to use c() when you aren't concatenating anything. Plain 1:12 is fine.
Just to add a third option:
aggregate(yes ~ year + month, FUN=sum, data=data_old)