I have a large data frame regarding Covid patients. I have included a very simplified version of what this frame looks like.
CovidFake <- data.frame(DateReporting=sample(seq(as.Date("2020-10-1"), as.Date("2020-11-01"), by="day"), 50, replace=TRUE),
Industry=sample(c("Minor or Student", "Educational Services", "Medical Services", "Food Production"),50, replace =TRUE))
I want use ggplot to make a graph of the daily cases by industry of the patient. I have this function to structure the frame so ggplot can graph it.
library(zoo)
MainFunction <- function(MainFrame, CatVal){
Frame <- subset(MainFrame, Industry==CatVal)
Frame <- as.data.frame(table(Frame$DateReporting))
colnames(Frame) <- c("Var1", "Freq")
Frame$Var1 <- as.Date(Frame$Var1, "%Y-%m-%d")
Frame <- Frame %>% complete(Var1 = seq.Date(as.Date("2020-10-01", "%Y-%m-%d"),
as.Date("2020-11-01", "%Y-%m-%d"), by="day"))
Frame$Freq <- replace_na(Frame$Freq, 0)
Frame$CumSum <- cumsum(Frame$Freq)
Frame$Cat <- CatVal
Frame$SevenDayAverage <- rollmean(Frame$Freq, 7, fill=NA, align = "right")
colnames(Frame) <- c("Date", "DailyCases", "CumSum", "Industry", "SevenDayAve")
Frame <- subset(Frame, Date >= "2020-03-13")
return(Frame)
}
I need to create a frame that has all of these industries, so I've been doing something like this.
IndGraph <- rbind(MainFunction(CovidFake, "Minor or Student"),
MainFunction(CovidFake, "Educational Services"),
MainFunction(CovidFake, "Medical Services"),
MainFunction(CovidFake, "Food Production"))
The true frame has about 15 industries, so the code gets pretty long and seemingly unnecessarily repetitive. Is there anyway to loop in all the factors into the function and do this in one? Or is there a simpler way to structure the frame? I'm new to R so any and all help is much appreciated.
Thanks!
Using a for loop:
IndGraph <- vector()
for(i in CovidFake$Industry){
IndGraph <- rbind(IndGraph, MainFunction(CovidFake, i))}
Output:
> IndGraph
# A tibble: 1,600 x 5
Date DailyCases CumSum Industry SevenDayAve
<date> <dbl> <dbl> <chr> <dbl>
1 2020-10-01 0 0 Minor or Student NA
2 2020-10-02 0 0 Minor or Student NA
3 2020-10-03 1 1 Minor or Student NA
4 2020-10-04 0 1 Minor or Student NA
5 2020-10-05 0 1 Minor or Student NA
6 2020-10-06 0 1 Minor or Student NA
7 2020-10-07 1 2 Minor or Student 0.286
8 2020-10-08 1 3 Minor or Student 0.429
9 2020-10-09 2 5 Minor or Student 0.714
10 2020-10-10 0 5 Minor or Student 0.571
# ... with 1,590 more rows
One option would be:
do.call("rbind", lapply(unique(CovidFake$Industry), FUN = function(x, y = CovidFake) MainFunction(y, x)))
Related
I have a DF named JOB. In that DF i have 4 columns. Person_ID; JOB; FT (full time or part time with values of 1 for full time and 2 for part time) and YEAR. Every person can have only 1 full time job per year in this DF. This is the full time job they got most of their income during the year.
DF
PERSON_ID JOB FT YEAR
1 Analyst 1 2018
1 Analyst 1 2019
1 Analyst 1 2020
2 Coach 1 2018
2 Coach 1 2019
2 Analyst 1 2020
3 Gardener 1 2020
4 Coach 1 2018
4 Coach 1 2019
4 Analyst 1 2020
4 Coach 2 2019
4 Gardener 2 2019
I want to get different frequency in the lines of the following question:
What full time job changes occurred from 2019 and 2020?
I want to look only at changes where FT=1.
I want my end table to look like this
2019 2020 frequency
Analyst Analyst 1
Coach Analyst 2
NA Gardener 1
I want to look at the data so that i can say 2 people moved from they coaching job to analyst job. 1 analyst did not change their job and one person entered the labour market as a gardener.
I tried to fiddle around with the table function but did not even get close to what i wanted. I could not get the YEAR's to go to separate variables.
10 Bonus points if i can do it in base R :)
Thank you for your help
Not pretty but worked:
# split df by year
df_2019 <- df[df$YEAR %in% c(2019) & df$FT == 1, ]
df_2020 <- df[df$YEAR %in% c(2020) & df$FT == 1, ]
# rename Job columns
df_2019$JOB_2019 <- df_2019$JOB
df_2020$JOB_2020 <- df_2020$JOB
# select needed columns
df_2019 <- df_2019[, c("PERSON_ID", "JOB_2019")]
df_2020 <- df_2020[, c("PERSON_ID", "JOB_2020")]
# merge dfs
df2 <- merge(df_2019, df_2020, by = "PERSON_ID", all = TRUE)
df2$frequency <- 1
df2$JOB_2019 <- addNA(df2$JOB_2019)
df2$JOB_2020 <- addNA(df2$JOB_2020)
# aggregate frequency
aggregate(frequency ~ JOB_2019 + JOB_2020, data = df2, FUN = sum, na.action=na.pass)
JOB_2019 JOB_2020 frequency
1 Analyst Analyst 1
2 Coach Analyst 2
3 <NA> Gardener 1
Not R base but worked:
library(dplyr)
library(tidyr)
data %>%
filter(FT==1, YEAR %in% c(2019, 2020)) %>%
group_by(YEAR, JOB, PERSON_ID) %>%
tally() %>%
pivot_wider(names_from = YEAR, values_from = JOB) %>%
select(-PERSON_ID) %>%
group_by(`2019`, `2020`) %>%
summarise(n = n())
`2019` `2020` n
<chr> <chr> <int>
1 Analyst Analyst 1
2 Coach Analyst 2
3 NA Gardener 1
I found that dplyr is speedy and simple for aggregate and summarise data. But I can't find out how to solve the following problem with dplyr.
Given these data frames:
df_2017 <- data.frame(
expand.grid(1:195,1:65,1:39),
value = sample(1:1000000,(195*65*39)),
period = rep("2017",(195*65*39)),
stringsAsFactors = F
)
df_2017 <- df_2017[sample(1:(195*65*39),450000),]
names(df_2017) <- c("company", "product", "acc_concept", "value", "period")
df_2017$company <- as.character(df_2017$company)
df_2017$product <- as.character(df_2017$product)
df_2017$acc_concept <- as.character(df_2017$acc_concept)
df_2017$value <- as.numeric(df_2017$value)
ratio_df <- data.frame(concept=c("numerator","numerator","numerator","denom", "denom", "denom","name"),
ratio1=c("1","","","4","","","Sales over Assets"),
ratio2=c("1","","","5","6","","Sales over Expenses A + B"), stringsAsFactors = F)
where the columns in df_2017 are:
company = This is a categorical variable with companies from 1 to 195
product = This is a categorical, with home apliance products from 1 to 65. For example, 1 could be equal to irons, 2 to television, etc
acc_concept = This is a categorical variable with accounting concepts from 1 to 39. For example, 1 would be equal to "Sales", 2 to "Total Expenses", 3 to Returns", 4 to "Assets, etc
value = This is a numeric variable, with USD from 1 to 100.000.000
period = Categorical variable. Always 2017
As the expand.grid implies, the combinations of company - product - acc_concept are never duplicated, but, It could happen that certains subjects have not every company - product - acc_concept combinations. That's why the code line "df_2017 <- df_2017[sample(1:195*65*39),450000),]", and that's why the output could turn out into NA (see below).
And where the columns in ratio_df are:
Concept = which acc_concept corresponds to numerator, which one to
denominator, and which is name of the ratio
ratio1 = acc_concept and name for ratio1
ratio2 = acc_concept and name for ratio2
I want to calculate 2 ratios (ratio_df) between acc_concept, for each product within each company.
For example:
I take the first ratio "acc_concepts" and "name" from ratio_df:
num_acc_concept <- ratio_df[ratio_df$concept == "numerator", 2]
denom_acc_concept <- ratio_df[ratio_df$concept == "denom", 2]
ratio_name <- ratio_df[ratio_df$concept == "name", 2]
Then I calculate the ratio for one product of one company, just to show you want i want to do:
ratio1_value <- sum(df_2017[df_2017$company == 1 & df_2017$product == 1 & df_2017$acc_concept %in% num_acc_concept, 4]) / sum(df_2017[df_2017$company == 1 & df_2017$product == 1 & df_2017$acc_concept %in% denom_acc_concept, 4])
Output:
output <- data.frame(Company="1", Product="1", desc_ratio=ratio_name, ratio_value = ratio1_value, stringsAsFactors = F)
As i said before i want to do this for each product within each company
The output data.frame could be something like this (ratios aren't the true ones because i haven't done the calculations yet):
company product desc_ratio ratio_value
1 1 Sales over Assets 0.9303675
1 2 Sales over Assets 1.30
1 3 Sales over Assets Nan
1 4 Sales over Assets Inf
1 5 Sales over Assets 2.32
1 6 Sales over Assets NA
.
.
.
1 1 Sales over Expenses A + B 3.25
.
.
.
2 1 Sales over Assets 0.256
and so on...
NaN when ratio is 0 / 0
Inf when ratio is number / 0
NA when there is no data for certain company and product.
I hope i have made myself clear this time :)
Is there any way to solve this row problem with dplyr? Should I cast the df_2017 for mutating? In this case, which is the best way for casting?
Any help would be welcome!
This is one way of doing it. At the end I timed the code on all of your records.
First create a function to create all the ratios. Do note, this function is only useful inside the dplyr code.
ratio <- function(data){
result <- data.frame(desc_ratio = rep(NA, ncol(ratio_df) -1), ratio_value = rep(NA, ncol(ratio_df) -1))
for(i in 2:ncol(ratio_df)){
num <- ratio_df[ratio_df$concept == "numerator", i]
denom <- ratio_df[ratio_df$concept == "denom", i]
result$desc_ratio[i-1] <- ratio_df[ratio_df$concept == "name", i]
result$ratio_value[i-1] <- sum(ifelse(data$acc_concept %in% num, data$value, 0)) / sum(ifelse(data$acc_concept %in% denom, data$value, 0))
}
return(result)
}
Using dplyr, tidyr and purrr to put everything together. First group by the data, nest the data needed for the function, run the function with a mutate on the nested data. Drop the not needed nested data and unnest to get your wanted output. I leave the sorting up to you.
library(dplyr)
library(purrr)
library(tidyr)
output <- df_2017 %>%
group_by(company, product, period) %>%
nest() %>%
mutate(ratios = map(data, ratio)) %>%
select(-data) %>%
unnest
output
# A tibble: 25,350 x 5
company product period desc_ratio ratio_value
<chr> <chr> <chr> <chr> <dbl>
1 103 2 2017 Sales over Assets 0.733
2 103 2 2017 Sales over Expenses A + B 0.219
3 26 26 2017 Sales over Assets 0.954
4 26 26 2017 Sales over Expenses A + B 1.01
5 85 59 2017 Sales over Assets 4.14
6 85 59 2017 Sales over Expenses A + B 1.83
7 186 38 2017 Sales over Assets 7.85
8 186 38 2017 Sales over Expenses A + B 0.722
9 51 25 2017 Sales over Assets 2.34
10 51 25 2017 Sales over Expenses A + B 0.627
# ... with 25,340 more rows
Time it took to run this code on my machine measured with system.time:
user system elapsed
6.75 0.00 6.81
I have a problem when specifying a loop with a data frame.
The general idea I have is the following:
I have an area which contains a certain number of raster quadrants. These raster quadrants have been visited irregularily over several years (e.g. from 1950 -2015).
I have two data frames:
1) a data frame containing the IDs of the rasterquadrants (and one column for the year of first visit of this quadrant):
df1<- as.data.frame(cbind(c("12345","12346","12347","12348"),rep(NA,4)))
df1[,1]<- as.character(df1[,1])
df1[,2]<- as.numeric(df1[,2])
names(df1)<-c("Raster_Q","First_visit")
2) a data frame that contains the infos on the visits; this one is ordered with by 1st rasterquadrants and then 2nd years. This dataframe has the info when the rasterquadrant was visited and when.
df2<- as.data.frame(cbind(c(rep("12345",5),rep("12346",7),rep("12347",3),rep(12348,9)),
c(1950,1952,1955,1967,1951,1968,1970,
1998,2001,2014,2015,2017,1965,1986,2000,1952,1955,1957,1965,2003,2014,2015,2016,2017)))
df2[,1]<- as.character(df2[,1])
df2[,2]<- as.numeric(as.character(df2[,2]))
names(df2)<-c("Raster_Q","Year")
I want to know when and how often the full area was 'sampled'.
Scheme of what I want to do; different colors indicate different areas/regions
My rationale:
I sorted the complete data in df2 according to Quadrant and Year. I then match the rasterquadrant in df1 with the name of the rasterquadrant in df2 and the first value of year from df2 is added.
For this I wrote a loop (see below)
In order not to replicate a quadrant I created a vector "visited"
visited<-c()
Every entry of df2 that matches df1 will be written into this vector, so that the second entry of e.g. rasterquadrant "12345" in df2 is ignored in the loop.
Here comes the loop:
visited<- c()
for (i in 1:nrow(df2)){
index<- which(df1$"Raster_Q"==df2$"Raster_Q"[i])
if(length(index)==0) {next()} else{
if(df1$"Raster_Q"[index] %in% visited){next()} else{
df1$"First_visit"[index]<- df2$"Year"[i]
visited[index]<- df1$"Raster_Q"[index]
}
}
}
This gives me the first full sampling period.
Raster_Q First_visit
1 12345 1950
2 12346 1968
3 12347 1965
4 12348 1952
However, I want to have all full sampling periods.
So I do:
df1$"Second_visit"<-NA
I reset the visited vector and specify the following loop:
visited <- c()
for (i in 1:nrow(df2)){
if(df2$Year[i]<=max(df1$"First_visit")){next()} else{
index<- which(df1$"Raster_Q"==df2$"Raster_Q"[i])
if(length(index)==0) {next()} else{
if(df1$"Raster_Q"[index] %in% visited){next()} else{
df1$"Second_visit"[index]<- df2$"Year"[i]
visited[index]<- df1$"Raster_Q"[index]
}
}
}
}
Which is basically the same loop as before, however, only making sure that, if df2$"Year" in a certain raster quadrant has already been included in the first visit, then it is skipped.
That gives me the second full sampling period:
Raster_Q First_visit Second_visit
1 12345 1950 NA
2 12346 1968 1970
3 12347 1965 1986
4 12348 1952 2003
Okay, so far so good. I could do that all by hand. But I have loads and loads of rasterquadrants and several areas that can and should be screened in this way.
So doing all of this in a single loop for this would be really great! However, I realized that this will create a problem because the loop then gets recursive:
The added column will not be included in the subsequent iteration of the loop, because the df1 itself is not re-read for each loop, and in consequence, the new coulmn for the new sampling period will not be included in the following iterations:
visited<- c()
for (i in 1:nrow(df2)){
m<-ncol(df1)
index<- which(df1$"Raster_Q"==df2$"Raster_Q"[i])
if(length(index)==0) {next()} else{
if(df1$"Raster_Q"[index] %in% visited){next()} else{
df1[index,m]<- df2$"Year"[i]
visited[index]<- df1$"Raster_Q"[index]
#finish "first_visit"
df1[,m+1]<-NA
# add column for "second visit"
if(df2$Year[i]<=max(df1$"First_visit")){next()} else{
# make sure that the first visit year are not included
index<- which(df1$"Raster_Q"==df2$"Raster_Q"[i])
if(length(index)==0) {next()} else{
if(df1$"Raster_Q"[index] %in% visited){next()} else{
df1[index,m+1]<- df2$"Year"[i]
visited[index]<- df1$"Raster_Q"[index]
}
}
}
This won't work. Another issue is that the vector visited() is not emptied during this loop, so that basically every Raster_Q has already been visited in the second sampling period.
I am stuck.... any ideas?
You can do this without a for loop by using the dplyr and tidyr packages. First, you take your df2 and use dplyr::arrange to order by raster and year. Then you can rank the years visited using the rank function inside of the dplyr::mutate function. Then using tidyr::spread you can put them all in their own columns. Here is the code:
df <- df2 %>%
arrange(Raster_Q, Year) %>%
group_by(Raster_Q) %>%
mutate(visit = rank(Year),
visit = paste0("visit_", as.character(visit))) %>%
tidyr::spread(key = visit, value = Year)
Here is the output:
> df
# A tibble: 4 x 10
# Groups: Raster_Q [4]
Raster_Q visit_1 visit_2 visit_3 visit_4 visit_5 visit_6 visit_7 visit_8 visit_9
* <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 12345 1950 1951 1952 1955 1967 NA NA NA NA
2 12346 1968 1970 1998 2001 2014 2015 2017 NA NA
3 12347 1965 1986 2000 NA NA NA NA NA NA
4 12348 1952 1955 1957 1965 2003 2014 2015 2016 2017
EDIT: So I think I understand your problem a little better now. You are looking to remove all duplicate visits to each quadrant that happened before the maximum Year of each respective "round" of visits. So to accomplish this, I wrote a short function that in essence does what the code above does, but with a slight change. Here is the function:
filter_by_round <- function(data, round) {
output <- data %>%
arrange(Raster_Q, Year) %>%
group_by(Raster_Q) %>%
mutate(visit = rank(Year, ties.method = "first")) %>%
ungroup() %>%
mutate(in_round = ifelse(Year <= max(.$Year[.$visit == round]) & visit > round,
TRUE, FALSE)) %>%
filter(!in_round) %>%
select(-c(in_round, visit))
return(output)
}
What this function does, is look through the data and if a given year is less than the max year for the specified "visit round" then it is removed. To apply this only to the first round, you would do this:
df2 %>%
filter_by_round(1) %>%
group_by(Raster_Q) %>%
mutate(visit = rank(Year, ties.method = "first")) %>%
ungroup() %>%
mutate(visit = paste0("visit_", as.character(visit))) %>%
tidyr::spread(key = visit, value = Year)
which would give you this:
# A tibble: 4 x 8
Raster_Q visit_1 visit_2 visit_3 visit_4 visit_5 visit_6 visit_7
* <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 12345 1950 NA NA NA NA NA NA
2 12346 1968 1970 1998 2001 2014 2015 2017
3 12347 1965 1986 2000 NA NA NA NA
4 12348 1952 2003 2014 2015 2016 2017 NA
However, while it does accomplish what your for loop would have, you now have other occurrences of the same problem. I have come up with a way to do this successfully but it requires you to know how many "visit rounds" you had or some trial and error. To accomplish this, you can use map and assign the change to a global variable.
# I do this so we do not lose the original dataset
df <- df2
# I chose 1:5 after some trial and error showed there are 5 unique
# "visit rounds" in your toy dataset
# However, if you overshoot your number, it should still work,
# you will just get warnings about `max` not working correctly
# however, this may casue issues, so figuring out your exact number is
# recommended
purrr::map(1:5, function(x){
# this assigns the output of each iteration to the global variable df
df <<- df %>%
filter_by_round(x)
})
# now applying the original transformation to get the spread dataset
df %>%
group_by(Raster_Q) %>%
mutate(visit = rank(Year, ties.method = "first")) %>%
ungroup() %>%
mutate(visit = paste0("visit_", as.character(visit))) %>%
tidyr::spread(key = visit, value = Year)
This will give you the following output:
# A tibble: 4 x 6
Raster_Q visit_1 visit_2 visit_3 visit_4 visit_5
* <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 12345 1950 NA NA NA NA
2 12346 1968 1970 2014 2015 2017
3 12347 1965 1986 NA NA NA
4 12348 1952 2003 2014 2015 2016
granted, this is probably not the most elegant solution, but it works. Hopefully this solves the problem for you
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.
I'm having a trouble answering a homework question.
Create a larger dataset by stacking gm2
n=100 times over. That is, if nrg is the number
of rows of gm2 and ncg is the number of columns,
the larger dataset should have 100*nrg rows and
ncg columns.
Call your stacked dataset biggm2.
To create the stacked dataset,
initialize with biggm2 <- NULL and use
a for loop to build up biggm2 one layer
at a time. Time this code using the system.time() function.
An example use of system.time() to time an R
command, e.g., x <- rnorm(100000) is:
Code given:
system.time(
{
x <- rnorm(100000) # Could put multiple lines of R code here
})
I am new to R and this is taking too much time to find the answer. Hence, I'm asking this question here.
I'm using library(gapminder)
To add, 'gm2' is a dataset:
The columns year, lifeExp, pop and
gdpPercap and save this dataset as gm2.
Also coerce gm2 to a matrix and save as gm3.
This is what I did to read the dataset. Is this correct?
gm2 <- c(CanUS2$year, CanUS2$lifeExp, CanUS2$pop, CanUS2$gbpPercap)
gm2
gm3 <- matrix(gm2, nrow=24, ncol=4)
gm3
Or is this correct?
gm2 <- subset(CanUS2, select = c("year","lifeExp","pop","gdpPercap"))
But here, there is no point of coercing to a matrix since gm2 is already a matrix.
gm3 <- matrix(gm2v, nrow=24, ncol=4)
gm3
I am really confused.
Any advice will be really appreciated.
library(gapminder)
Tidyverse:
library(dplyr)
filter(gapminder, country %in% c("United States", "Canada")) %>%
select(year, lifeExp, pop, gdpPercap) -> tidyverse_can_us
head(tidyverse_can_us)
## # A tibble: 6 × 4
## year lifeExp pop gdpPercap
## <int> <dbl> <int> <dbl>
## 1 1952 68.75 14785584 11367.16
## 2 1957 69.96 17010154 12489.95
## 3 1962 71.30 18985849 13462.49
## 4 1967 72.13 20819767 16076.59
## 5 1972 72.88 22284500 18970.57
## 6 1977 74.21 23796400 22090.88
Base R:
gapminder[with(gapminder, country %in% c("United States", "Canada")),
c("year", "lifeExp", "pop", "gdpPercap")] -> base_r_can_us
head(base_r_can_us)
## # A tibble: 6 × 4
## year lifeExp pop gdpPercap
## <int> <dbl> <int> <dbl>
## 1 1952 68.75 14785584 11367.16
## 2 1957 69.96 17010154 12489.95
## 3 1962 71.30 18985849 13462.49
## 4 1967 72.13 20819767 16076.59
## 5 1972 72.88 22284500 18970.57
## 6 1977 74.21 23796400 22090.88
For either of those you've lost the factor variable that ties the values to the country. We also have no idea why you want/need a matrix. I made the assumption you were extracting Canada & U.S. data from your variable name but your entire question was mostly incoherent so it's hard to know what data you have and what outcome you want/need.