I am trying to aggregate hourly data to daily data in R. The problem is missing values. I want to consider a threshold for the number of missing values before the aggregation. If the number of missing values is more than two in the given day, DO NOT compute the daily average and fill that day with NA.
My dummy data are daily data for the first day of 2005.
day hour amount amount2
1 2005-01-01 0 1 1
2 2005-01-01 1 2 NA
3 2005-01-01 2 4 4
4 2005-01-01 3 5 5
5 2005-01-01 4 11 NA
6 2005-01-01 5 4 NA
7 2005-01-01 6 NA NA
8 2005-01-01 7 2 2
9 2005-01-01 8 4 4
10 2005-01-01 9 2 2
11 2005-01-01 10 4 20
12 2005-01-01 11 12 12
13 2005-01-01 12 13 13
14 2005-01-01 13 7 7
15 2005-01-01 14 4 4
16 2005-01-01 15 12 12
17 2005-01-01 16 4 4
18 2005-01-01 17 12 12
19 2005-01-01 18 5 5
20 2005-01-01 19 11 11
21 2005-01-01 20 4 4
22 2005-01-01 21 12 12
23 2005-01-01 22 13 13
24 2005-01-01 23 7 7
What I already got
agg
day amount amount2
1 2005-01-01 6.9 7.7
what I want to have
agg
day amount amount2
1 2005-01-01 6.9 NA
Because the number of missing values in the column amount2 is more than two, I want its daily average filled by NA (not 7.7) while it is calculated for the column amount (6.9).
I have used the function "aggregate" from "stats" library.
library(stats)
amount=(c(1,2,4,5,11,4,NA,2,4,2,4,12,13,7,4,12,4,12,5,11,4,12,13,7))
amount2=(c(1,NA,4,5,NA,NA,NA,2,4,2,20,12,13,7,4,12,4,12,5,11,4,12,13,7))
day=c("2005-01-01","2005-01-01","2005-01-01","2005-01-01","2005-01-01",
"2005-01-01","2005-01-01","2005-01-01","2005-01-01","2005-01-01",
"2005-01-01","2005-01-01","2005-01-01","2005-01-01","2005-01-01",
"2005-01-01","2005-01-01","2005-01-01","2005-01-01","2005-01-01",
"2005-01-01","2005-01-01","2005-01-01","2005-01-01")
hour=seq(0,23)
date=data.frame(day,hour)
dummy=cbind(date,amount,amount2)
agg <- aggregate(cbind(amount,amount2) ~ day, dummy, mean)
Related
This is a representation of my dataset
ID<-c(rep(1,10),rep(2,8))
year<-c(2007,2007,2007,2008,2008,2009,2010,2009,2010,2011,
2008,2008,2009,2010,2009,2010,2011,2011)
month<-c(2,7,12,4,11,6,11,1,9,4,3,6,7,4,9,11,2,8)
mydata<-data.frame(ID,year,month)
I want to calculate for each individual the number of months from the initial date. I am using two variables: year and month.
I firstly order years and months:
mydata2<-mydata%>%group_by(ID,year)%>%arrange(year,month,.by_group=T)
Then I created the variable date considering that the day begin with 01:
mydata2$date<-paste("01",mydata2$month,mydata2$year,sep = "-")
then I used lubridate to change this variable in date format
mydata2$date<-dmy(mydata2$date)
But after this, I really don't know what to do, in order to have such a dataset (preferably using dplyr code) below:
ID year month date dif_from_init
1 1 2007 2 01-2-2007 0
2 1 2007 7 01-7-2007 5
3 1 2007 12 01-12-2007 10
4 1 2008 4 01-4-2008 14
5 1 2008 11 01-11-2008 21
6 1 2009 1 01-1-2009 23
7 1 2009 6 01-6-2009 28
8 1 2010 9 01-9-2010 43
9 1 2010 11 01-11-2010 45
10 1 2011 4 01-4-2011 50
11 2 2008 3 01-3-2008 0
12 2 2008 6 01-6-2008 3
13 2 2009 7 01-7-2009 16
14 2 2009 9 01-9-2009 18
15 2 2010 4 01-4-2010 25
16 2 2010 11 01-11-2010 32
17 2 2011 2 01-2-2011 35
18 2 2011 8 01-8-2011 41
One way could be:
mydata %>%
group_by(ID) %>%
mutate(date = as.Date(sprintf('%d-%d-01',year, month)),
diff = as.numeric(round((date - date[1])/365*12)))
# A tibble: 18 x 5
# Groups: ID [2]
ID year month date diff
<dbl> <dbl> <dbl> <date> <dbl>
1 1 2007 2 2007-02-01 0
2 1 2007 7 2007-07-01 5
3 1 2007 12 2007-12-01 10
4 1 2008 4 2008-04-01 14
5 1 2008 11 2008-11-01 21
6 1 2009 6 2009-06-01 28
7 1 2010 11 2010-11-01 45
8 1 2009 1 2009-01-01 23
9 1 2010 9 2010-09-01 43
10 1 2011 4 2011-04-01 50
11 2 2008 3 2008-03-01 0
12 2 2008 6 2008-06-01 3
13 2 2009 7 2009-07-01 16
14 2 2010 4 2010-04-01 25
15 2 2009 9 2009-09-01 18
16 2 2010 11 2010-11-01 32
17 2 2011 2 2011-02-01 35
18 2 2011 8 2011-08-01 41
I have a 2 column table (tibble), made up of a date object and a numeric variable. There is maximum one entry per day but not every day has an entry (ie date is a natural primary key). I am attempting to do a running sum of the numeric column along with dates but with the running sum resetting when the month turns (the data is sorted by ascending date). I have replicated what I want to get as a result below.
Date score monthly.running.sum
10/2/2019 7 7
10/9/2019 6 13
10/16/2019 12 25
10/23/2019 2 27
10/30/2019 13 40
11/6/2019 2 2
11/13/2019 4 6
11/20/2019 15 21
11/27/2019 16 37
12/4/2019 4 4
12/11/2019 24 28
12/18/2019 28 56
12/25/2019 8 64
1/1/2020 1 1
1/8/2020 15 16
1/15/2020 9 25
1/22/2020 8 33
It looks like the package "runner" is possibly suited to this but I don't really understand how to instruct it. I know I could use a join operation plus a group_by using dplyr to do this, but the data set is very very large and doing so would be wildly inefficient. i could also manually iterate through the list with a loop, but that also seems inelegant. last option i can think of is selecting out a unique vector of yearmon objects and then cutting the original list into many shorter lists and running a plain cumsum on it, but that also feels unoptimal. I am sure this is not the first time someone has to do this, and given how many tools there is in the tidyverse to do things, I think I just need help finding the right one. The reason I am looking for a tool instead of using one of the methods I described above (which would take less time than writing this post) is because this code needs to be very very readable by an audience that is less comfortable with code.
We can also use data.table
library(data.table)
setDT(df)[, Date := as.IDate(Date, "%m/%d/%Y")
][, monthly.running.sum := cumsum(score),format(Date, "%Y-%m")][]
# Date score monthly.running.sum
# 1: 2019-10-02 7 7
# 2: 2019-10-09 6 13
# 3: 2019-10-16 12 25
# 4: 2019-10-23 2 27
# 5: 2019-10-30 13 40
# 6: 2019-11-06 2 2
# 7: 2019-11-13 4 6
# 8: 2019-11-20 15 21
# 9: 2019-11-27 16 37
#10: 2019-12-04 4 4
#11: 2019-12-11 24 28
#12: 2019-12-18 28 56
#13: 2019-12-25 8 64
#14: 2020-01-01 1 1
#15: 2020-01-08 15 16
#16: 2020-01-15 9 25
#17: 2020-01-22 8 33
data
df <- structure(list(Date = c("10/2/2019", "10/9/2019", "10/16/2019",
"10/23/2019", "10/30/2019", "11/6/2019", "11/13/2019", "11/20/2019",
"11/27/2019", "12/4/2019", "12/11/2019", "12/18/2019", "12/25/2019",
"1/1/2020", "1/8/2020", "1/15/2020", "1/22/2020"), score = c(7L,
6L, 12L, 2L, 13L, 2L, 4L, 15L, 16L, 4L, 24L, 28L, 8L, 1L, 15L,
9L, 8L)), row.names = c(NA, -17L), class = "data.frame")
Using lubridate, you can extract month and year values from the date, group_by those values and them perform the cumulative sum as follow:
library(lubridate)
library(dplyr)
df %>% mutate(Month = month(mdy(Date)),
Year = year(mdy(Date))) %>%
group_by(Month, Year) %>%
mutate(SUM = cumsum(score))
# A tibble: 17 x 6
# Groups: Month, Year [4]
Date score monthly.running.sum Month Year SUM
<chr> <int> <int> <int> <int> <int>
1 10/2/2019 7 7 10 2019 7
2 10/9/2019 6 13 10 2019 13
3 10/16/2019 12 25 10 2019 25
4 10/23/2019 2 27 10 2019 27
5 10/30/2019 13 40 10 2019 40
6 11/6/2019 2 2 11 2019 2
7 11/13/2019 4 6 11 2019 6
8 11/20/2019 15 21 11 2019 21
9 11/27/2019 16 37 11 2019 37
10 12/4/2019 4 4 12 2019 4
11 12/11/2019 24 28 12 2019 28
12 12/18/2019 28 56 12 2019 56
13 12/25/2019 8 64 12 2019 64
14 1/1/2020 1 1 1 2020 1
15 1/8/2020 15 16 1 2020 16
16 1/15/2020 9 25 1 2020 25
17 1/22/2020 8 33 1 2020 33
An alternative will be to use floor_date function in order ot convert each date as the first day of each month and the calculate the cumulative sum:
library(lubridate)
library(dplyr)
df %>% mutate(Floor = floor_date(mdy(Date), unit = "month")) %>%
group_by(Floor) %>%
mutate(SUM = cumsum(score))
# A tibble: 17 x 5
# Groups: Floor [4]
Date score monthly.running.sum Floor SUM
<chr> <int> <int> <date> <int>
1 10/2/2019 7 7 2019-10-01 7
2 10/9/2019 6 13 2019-10-01 13
3 10/16/2019 12 25 2019-10-01 25
4 10/23/2019 2 27 2019-10-01 27
5 10/30/2019 13 40 2019-10-01 40
6 11/6/2019 2 2 2019-11-01 2
7 11/13/2019 4 6 2019-11-01 6
8 11/20/2019 15 21 2019-11-01 21
9 11/27/2019 16 37 2019-11-01 37
10 12/4/2019 4 4 2019-12-01 4
11 12/11/2019 24 28 2019-12-01 28
12 12/18/2019 28 56 2019-12-01 56
13 12/25/2019 8 64 2019-12-01 64
14 1/1/2020 1 1 2020-01-01 1
15 1/8/2020 15 16 2020-01-01 16
16 1/15/2020 9 25 2020-01-01 25
17 1/22/2020 8 33 2020-01-01 33
A base R alternative :
df$Date <- as.Date(df$Date, "%m/%d/%Y")
df$monthly.running.sum <- with(df, ave(score, format(Date, "%Y-%m"),FUN = cumsum))
df
# Date score monthly.running.sum
#1 2019-10-02 7 7
#2 2019-10-09 6 13
#3 2019-10-16 12 25
#4 2019-10-23 2 27
#5 2019-10-30 13 40
#6 2019-11-06 2 2
#7 2019-11-13 4 6
#8 2019-11-20 15 21
#9 2019-11-27 16 37
#10 2019-12-04 4 4
#11 2019-12-11 24 28
#12 2019-12-18 28 56
#13 2019-12-25 8 64
#14 2020-01-01 1 1
#15 2020-01-08 15 16
#16 2020-01-15 9 25
#17 2020-01-22 8 33
The yearmon class represents year/month objects so just convert the dates to yearmon and accumulate by them using this one-liner:
library(zoo)
transform(DF, run.sum = ave(score, as.yearmon(Date, "%m/%d/%Y"), FUN = cumsum))
giving:
Date score run.sum
1 10/2/2019 7 7
2 10/9/2019 6 13
3 10/16/2019 12 25
4 10/23/2019 2 27
5 10/30/2019 13 40
6 11/6/2019 2 2
7 11/13/2019 4 6
8 11/20/2019 15 21
9 11/27/2019 16 37
10 12/4/2019 4 4
11 12/11/2019 24 28
12 12/18/2019 28 56
13 12/25/2019 8 64
14 1/1/2020 1 1
15 1/8/2020 15 16
16 1/15/2020 9 25
17 1/22/2020 8 33
I am trying to figure out how to create a cumulative or rolling sum in R based on a few conditions.
The data set in question is a few million observations of library loans, and the question is to determine how many copies of a given book/title would be necessary to meet demand.
So for each Title.ID, begin with 1 copy for the first instance (ID.Index). Then for each instance after, determine whether another copy is needed based on whether the REQUEST.DATE is within 16 weeks (112 days) of the previous request.
# A tibble: 15 x 3
# Groups: Title.ID [2]
REQUEST.DATE Title.ID ID.Index
<date> <int> <int>
1 2013-07-09 2 1
2 2013-08-07 2 2
3 2013-08-20 2 3
4 2013-09-08 2 4
5 2013-09-28 2 5
6 2013-12-27 2 6
7 2014-02-10 2 7
8 2014-03-12 2 8
9 2014-03-14 2 9
10 2014-08-27 2 10
11 2014-04-27 6 1
12 2014-08-01 6 2
13 2014-11-13 6 3
14 2015-02-14 6 4
15 2015-05-14 6 5
The tricky part is that determining whether a new copy is needed is based not only on the number of request (ID.Index) and the REQUEST.DATE of some previous loan, but also on the preceding accumulating sum.
For instance, for the third request for title 2 (Title.ID 2, ID.Index 3), there are now two copies, so to determine whether a new copy is needed, you have to see whether the REQUEST.DATE is within 112 days of the first (not second) request (ID.Index 1). By contrast, for the third request for title 6 (Title.ID 6, ID.Index 3), there is only one copy available (since request 2 was not within 112 days), so determining whether a new copy is needed is based on looking back to the REQUEST.DATE of ID.Index 2.
The desired output ("Copies") would take each new request (ID.Index), then look back to the relevant REQUEST.DATE based on the number of available copies, and doing that would mean looking at the accumulating sum for the preceding calculation. (Note: The max number of copies would be 10.)
I've provided the desired output for the sample below ("Copies").
# A tibble: 15 x 4
# Groups: Title.ID [2]
REQUEST.DATE Title.ID ID.Index Copies
<date> <int> <int> <dbl>
1 2013-07-09 2 1 1
2 2013-08-07 2 2 2
3 2013-08-20 2 3 3
4 2013-09-08 2 4 4
5 2013-09-28 2 5 5
6 2013-12-27 2 6 5
7 2014-02-10 2 7 5
8 2014-03-12 2 8 5
9 2014-03-14 2 9 5
10 2014-08-27 2 10 5
11 2014-04-27 6 1 1
12 2014-08-01 6 2 2
13 2014-11-13 6 3 2
14 2015-02-14 6 4 2
15 2015-05-14 6 5 2
>
I recognize that the solution will be way beyond my abilities, so I will be extremely grateful for any solution or advice about how to solve this type of problem in the future.
Thanks a million!
*4/19 update: new examples where new copy may be added after delay, i.e., not in sequence. I've also added columns showing days since a given previous request, which helps checking whether a new copy should be added, based on how many copies there are.
Sample 2: new copy should be added with third request, since it has only been 96 days since last request (and there is only one copy)
REQUEST.NUMBER REQUEST.DATE Title.ID ID.Index Days.Since Days.Since2 Days.Since3 Days.Since4 Days.Since5 Copies
<fct> <date> <int> <int> <drtn> <drtn> <drtn> <drtn> <drtn> <int>
1 BRO-10680332 2013-10-17 6 1 NA days NA days NA days NA days NA days 1
2 PEN-10835735 2014-04-27 6 2 192 days NA days NA days NA days NA days 1
3 PEN-10873506 2014-08-01 6 3 96 days 288 days NA days NA days NA days 1
4 PEN-10951264 2014-11-13 6 4 104 days 200 days 392 days NA days NA days 1
5 PEN-11029526 2015-02-14 6 5 93 days 197 days 293 days 485 days NA days 1
6 PEN-11106581 2015-05-14 6 6 89 days 182 days 286 days 382 days 574 days 1
Sample 3: new copy should be added with last request, since there are two copies, and the oldest request is 45 days.
REQUEST.NUMBER REQUEST.DATE Title.ID ID.Index Days.Since Days.Since2 Days.Since3 Days.Since4 Days.Since5 Copies
<fct> <date> <int> <int> <drtn> <drtn> <drtn> <drtn> <drtn> <int>
1 BRO-10999392 2015-01-20 76 1 NA days NA days NA days NA days NA days 1
2 YAL-11004302 2015-01-22 76 2 2 days NA days NA days NA days NA days 2
3 COR-11108471 2015-05-18 76 3 116 days 118 days NA days NA days NA days 2
4 HVD-11136632 2015-07-27 76 4 70 days 186 days 188 days NA days NA days 2
5 MIT-11164843 2015-09-09 76 5 44 days 114 days 230 days 232 days NA days 2
6 HVD-11166239 2015-09-10 76 6 1 days 45 days 115 days 231 days 233 days 2
You can use runner package to apply any R function on cumulative window.
This time we execute function f using x = REQUEST.DATE. We just count number of observations which are within min(x) + 112.
library(dplyr)
library(runner)
data %>%
group_by(Title.ID) %>%
mutate(
Copies = runner(
x = REQUEST.DATE,
f = function(x) {
length(x[x <= (min(x + 112))])
}
)
)
# # A tibble: 15 x 4
# # Groups: Title.ID [2]
# REQUEST.DATE Title.ID ID.Index Copies
# <date> <int> <int> <int>
# 1 2013-07-09 2 1 1
# 2 2013-08-07 2 2 2
# 3 2013-08-20 2 3 3
# 4 2013-09-08 2 4 4
# 5 2013-09-28 2 5 5
# 6 2013-12-27 2 6 5
# 7 2014-02-10 2 7 5
# 8 2014-03-12 2 8 5
# 9 2014-03-14 2 9 5
# 10 2014-08-27 2 10 5
# 11 2014-04-27 6 1 1
# 12 2014-08-01 6 2 2
# 13 2014-11-13 6 3 2
# 14 2015-02-14 6 4 2
# 15 2015-05-14 6 5 2
data
data <- read.table(
text = " REQUEST.DATE Title.ID ID.Index
1 2013-07-09 2 1
2 2013-08-07 2 2
3 2013-08-20 2 3
4 2013-09-08 2 4
5 2013-09-28 2 5
6 2013-12-27 2 6
7 2014-02-10 2 7
8 2014-03-12 2 8
9 2014-03-14 2 9
10 2014-08-27 2 10
11 2014-04-27 6 1
12 2014-08-01 6 2
13 2014-11-13 6 3
14 2015-02-14 6 4
15 2015-05-14 6 5",
header = TRUE)
data$REQUEST.DATE <- as.Date(as.character(data$REQUEST.DATE))
I was able to find a workable solution based on finding the max number of other requests within 112 days of a request (after creating return date), for each title.
data$RETURN.DATE <- as.Date(data$REQUEST.DATE + 112)
data <- data %>%
group_by(Title.ID) %>%
mutate(
Copies = sapply(REQUEST.DATE, function(x)
sum(as.Date(REQUEST.DATE) <= as.Date(x) &
as.Date(RETURN.DATE) >= as.Date(x)
))
)
Then I de-duplicated the list of titles, using the max number for each title, and added it back to the original data.
I still think there's a solution to the original problem, where I could go back and see at which point new copies needed to be added (for analysis based on when a title is published), but this works for now.
So here's a sample of some of the rows from my dataframe:
> data[1:25, c("TR_DATE", "TR_TYPE...")]
TR_DATE TR_TYPE...
1 2016-03-01 4
2 2016-03-01 4
3 2016-03-01 5
4 2016-03-01 4
5 2016-03-01 1
6 2016-03-01 7
7 2016-03-01 4
8 2016-03-01 4
9 2016-03-01 24
10 2016-03-01 23
11 2016-03-01 4
12 2016-03-02 4
13 2016-03-02 1
14 2016-03-02 1
15 2016-03-02 4
16 2016-03-02 4
17 2016-03-02 14
18 2016-03-02 4
19 2016-03-02 4
20 2016-03-03 4
21 2016-03-03 1
22 2016-03-03 4
23 2016-03-03 23
24 2016-03-03 1
25 2016-03-03 4
What I'd like to do exactly is rearrange in such a way that for every unique day, I get the number of unique transaction types and the frequency of each transaction type
Here's the code that I tried:
data %>%
group_by(TR_DATE) %>%
summarise(trancount = n(), trantype = n_distinct(TR_TYPE...))
which gave me part of the result that I wanted:
# A tibble: 68 x 3
TR_DATE trancount trantype
<date> <int> <int>
1 2016-03-01 5816 6
2 2016-03-02 5637 3
3 2016-03-03 4818 3
4 2016-03-04 5070 8
5 2016-03-05 4 2
6 2016-03-08 6707 5
7 2016-03-09 5228 5
8 2016-03-10 4722 6
9 2016-03-11 4469 8
10 2016-03-12 1 1
# ... with 58 more rows
so trantype tells me the number of unique transaction types that happened on a particular day, but I'd like to know the frequency of each of these unique transaction types. What would be the best way to go around doing this?
I tried looking around and found similar questions but was unable to modify the solutions to my requirement.
I'm fairly new to R and would really appreciate some help. Thanks.
You should group by both variables:
data %>%
group_by(TR_DATE, TR_TYPE...) %>%
summarise(trancount = n(), trantype = n_distinct(TR_TYPE...))
Suppose I have the following data
set.seed(123)
Company <- c(rep("Company 1",5),rep("Company 2",10))
Dates <- c(seq(as.Date("2014-09-01"), as.Date("2015-01-01"), by="months"),
seq(as.Date("2011-09-01"), as.Date("2012-06-01"), by="months"))
X.1 <- sample(c(0,0,5,10,15,20,25,30,40,50),size=15,replace=TRUE)
X.2 <- sample(c(0,0,5,10,15,20,25,30,40,50),size=15,replace=TRUE)
df <- data.frame(Dates,Company,X.1,X.2)
Dates Company X.1 X.2
1 2014-09-01 Company 1 50 0
2 2014-10-01 Company 1 50 5
3 2014-11-01 Company 1 25 15
4 2014-12-01 Company 1 30 5
5 2015-01-01 Company 1 0 40
6 2011-09-01 Company 2 15 0
7 2011-10-01 Company 2 30 15
8 2011-11-01 Company 2 5 30
9 2011-12-01 Company 2 10 0
10 2012-01-01 Company 2 5 20
11 2012-02-01 Company 2 0 5
12 2012-03-01 Company 2 15 0
13 2012-04-01 Company 2 15 30
14 2012-05-01 Company 2 10 40
15 2012-06-01 Company 2 0 10
What I am trying to do is find monthly growth rates for variables X.1 and X.2
within each company and bind those columns to the right side of the dataframe. The problem here is that the date ranges for each Company are different, which is why I am having trouble with this. Also, since I have 0s in the data Inf and NAs are okay.
Thanks for your help.
#I computed the growth using log: growth=log(X(t)/X(t-1)). If you want to compute using (X(t)-X(t-1))/X(t-1), you can just use that. Also, for the first period, it will be NA.
#Assumption: the data are equally spaced for each company. You get Inf, if your last period value is 0 and -Inf if your current period value is 0 (because we used log). The growth will be 0 (when current period is zero) if you don't use log (see second method)
library(dplyr)
df%>%
group_by(Company)%>%
mutate(gx_1=log(X.1/lag(X.1,1)),gx_2=log(X.1/lag(X.2,1))
)
Source: local data frame [15 x 6]
Groups: Company
Dates Company X.1 X.2 gx_1 gx_2
1 2014-09-01 Company 1 5 40 NA NA
2 2014-10-01 Company 1 30 5 1.7917595 -0.2876821
3 2014-11-01 Company 1 15 0 -0.6931472 1.0986123
4 2014-12-01 Company 1 40 10 0.9808293 Inf
5 2015-01-01 Company 1 50 50 0.2231436 1.6094379
6 2011-09-01 Company 2 0 40 NA NA
7 2011-10-01 Company 2 20 25 Inf -0.6931472
8 2011-11-01 Company 2 40 25 0.6931472 0.4700036
9 2011-12-01 Company 2 20 50 -0.6931472 -0.2231436
10 2012-01-01 Company 2 15 25 -0.2876821 -1.2039728
11 2012-02-01 Company 2 50 30 1.2039728 0.6931472
12 2012-03-01 Company 2 15 20 -1.2039728 -0.6931472
13 2012-04-01 Company 2 25 20 0.5108256 0.2231436
14 2012-05-01 Company 2 20 5 -0.2231436 0.0000000
15 2012-06-01 Company 2 0 0 -Inf -Inf
#without using log , i.e.
df%>%
group_by(Company)%>%
mutate(gx_1=((X.1-lag(X.1,1))/lag(X.1,1)),gx_2=((X.2-lag(X.2,1))/lag(X.2,1)))