I have data organized by two ID variables, Year and Country, like so:
Year Country VarA VarB
2015 USA 1 3
2016 USA 2 2
2014 Canada 0 10
2015 Canada 6 5
2016 Canada 7 8
I'd like to keep Year as an ID variable, but create multiple columns for VarA and VarB, one for each value of Country (I'm not picky about column order), to make the following table:
Year VarA.Canada VarA.USA VarB.Canada VarB.USA
2014 0 NA 10 NA
2015 6 1 5 3
2016 7 2 8 2
I managed to do this with the following code:
require(data.table)
require(reshape2)
data <- as.data.table(read.table(header=TRUE, text='Year Country VarA VarB
2015 USA 1 3
2016 USA 2 2
2014 Canada 0 10
2015 Canada 6 5
2016 Canada 7 8'))
molten <- melt(data, id.vars=c('Year', 'Country'))
molten[,variable:=paste(variable, Country, sep='.')]
recast <- dcast(molten, Year ~ variable)
But this seems a bit hacky (especially editing the default-named variable field). Can I do it with fewer function calls? Ideally I could just call one function, specifying the columns to drop as IDs and the formula for creating new variable names.
Using dcast you can cast multiple value.vars at once (from data.table v1.9.6 on). Try:
dcast(data, Year ~ Country, value.var = c("VarA","VarB"), sep = ".")
# Year VarA.Canada VarA.USA VarB.Canada VarB.USA
#1: 2014 0 NA 10 NA
#2: 2015 6 1 5 3
#3: 2016 7 2 8 2
Related
I have a data frame as following:
df1
ID closingdate
1 31/12/2005
2 01/12/2009
3 02/01/2002
4 09/10/2000
5 15/11/2007
I want to add a third column (let's call it infoyear) in my df that shows the year before for each data element that I have in my second column.
In other words I want to get the following result:
df1
ID closingdate infoyear
1 31/12/2005 2004
2 01/12/2009 2008
3 02/01/2002 2001
4 09/10/2000 1999
5 15/11/2007 2006
Normally, to add a column presenting only years I would use:
library(data.table)
setDT(df1)[, infoyear := year(as.IDate(closingdate, '%d/%m/%Y'))]
In my case it would produce me the following:
df1
ID closingdate infoyear
1 31/12/2005 2005
2 01/12/2009 2009
3 02/01/2002 2002
4 09/10/2000 2000
5 15/11/2007 2007
But instead of this result for column infoyear, I would like 1 year before closingdate (as the result presented before).
How can I solve a problem like this in R? Thank you!
You can try
df1 <- read.table(header=TRUE, text="ID closingdate
1 31/12/2005
2 01/12/2009
3 02/01/2002
4 09/10/2000
5 15/11/2007")
setDT(df1)
df1[, closingdate:= as.IDate(closingdate,"%d/%m/%Y")]
df1[, infoyear:= year(closingdate)-1]
df1
#Output
ID closingdate infoyear
1: 1 2005-12-31 2004
2: 2 2009-12-01 2008
3: 3 2002-01-02 2001
4: 4 2000-10-09 1999
5: 5 2007-11-15 2006
I have tried to find a solution via similar topics, but haven't found anything suitable. This may be due to the search terms I have used. If I have missed something, please accept my apologies.
Here is a excerpt of my data UN_ (the provided sample should be sufficient):
country year sector UN
AT 1990 1 1.407555
AT 1990 2 1.037137
AT 1990 3 4.769618
AT 1990 4 2.455139
AT 1990 5 2.238618
AT 1990 Total 7.869005
AT 1991 1 1.484667
AT 1991 2 1.001578
AT 1991 3 4.625927
AT 1991 4 2.515453
AT 1991 5 2.702081
AT 1991 Total 8.249567
....
BE 1994 1 3.008115
BE 1994 2 1.550344
BE 1994 3 1.080667
BE 1994 4 1.768645
BE 1994 5 7.208295
BE 1994 Total 1.526016
BE 1995 1 2.958820
BE 1995 2 1.571759
BE 1995 3 1.116049
BE 1995 4 1.888952
BE 1995 5 7.654881
BE 1995 Total 1.547446
....
What I want to do is, to add another row with UN_$sector = Residual. The value of residual will be (UN_$sector = Total) - (the sum of column UN for the sectors c("1", "2", "3", "4", "5")) for a given year AND country.
This is how it should look like:
country year sector UN
AT 1990 1 1.407555
AT 1990 2 1.037137
AT 1990 3 4.769618
AT 1990 4 2.455139
AT 1990 5 2.238618
----> AT 1990 Residual TO BE CALCULATED
AT 1990 Total 7.869005
As I don't want to write many, many lines of code I'm looking for a way to automate this. I was told about loops, but can't really follow the concept at the moment.
Thank you very much for any type of help!!
Best,
Constantin
PS: (for Parfait)
country year sector UN ETS
UK 2012 1 190336512 NA
UK 2012 2 18107910 NA
UK 2012 3 8333564 NA
UK 2012 4 11269017 NA
UK 2012 5 2504751 NA
UK 2012 Total 580957306 NA
UK 2013 1 177882200 NA
UK 2013 2 20353347 NA
UK 2013 3 8838575 NA
UK 2013 4 11051398 NA
UK 2013 5 2684909 NA
UK 2013 Total 566322778 NA
Consider calculating residual first and then stack it with other pieces of data:
# CALCULATE RESIDUALS BY MERGED COLUMNS
agg <- within(merge(aggregate(UN ~ country + year, data = subset(df, sector!='Total'), sum),
aggregate(UN ~ country + year, data = subset(df, sector=='Total'), sum),
by=c("country", "year")),
{UN <- UN.y - UN.x
sector = 'Residual'})
# ROW BIND DIFFERENT PIECES
final_df <- rbind(subset(df, sector!='Total'),
agg[c("country", "year", "sector", "UN")],
subset(df, sector=='Total'))
# ORDER ROWS AND RESET ROWNAMES
final_df <- with(final_df, final_df[order(country, year, as.character(sector)),])
row.names(final_df) <- NULL
Rextester demo
final_df
# country year sector UN
# 1 AT 1990 1 1.407555
# 2 AT 1990 2 1.037137
# 3 AT 1990 3 4.769618
# 4 AT 1990 4 2.455139
# 5 AT 1990 5 2.238618
# 6 AT 1990 Residual -4.039062
# 7 AT 1990 Total 7.869005
# 8 AT 1991 1 1.484667
# 9 AT 1991 2 1.001578
# 10 AT 1991 3 4.625927
# 11 AT 1991 4 2.515453
# 12 AT 1991 5 2.702081
# 13 AT 1991 Residual -4.080139
# 14 AT 1991 Total 8.249567
# 15 BE 1994 1 3.008115
# 16 BE 1994 2 1.550344
# 17 BE 1994 3 1.080667
# 18 BE 1994 4 1.768645
# 19 BE 1994 5 7.208295
# 20 BE 1994 Residual -13.090050
# 21 BE 1994 Total 1.526016
# 22 BE 1995 1 2.958820
# 23 BE 1995 2 1.571759
# 24 BE 1995 3 1.116049
# 25 BE 1995 4 1.888952
# 26 BE 1995 5 7.654881
# 27 BE 1995 Residual -13.643015
# 28 BE 1995 Total 1.547446
I think there are multiple ways you can do this. What I may recommend is to take advantage of the tidyverse suite of packages which includes dplyr.
Without getting too far into what dplyr and tidyverse can achieve, we can talk about the power of dplyr's inline commands group_by(...), summarise(...), arrange(...) and bind_rows(...) functions. Also, there are tons of great tutorials, cheat sheets, and documentation on all tidyverse packages.
Although it is less and less relevant these days, we generally want to avoid for loops in R. Therefore, we will create a new data frame which contains all of the Residual values then bring it back into your original data frame.
Step 1: Calculating all residual values
We want to calculate the sum of UN values, grouped by country and year. We can achieve this by this value
res_UN = UN_ %>% group_by(country, year) %>% summarise(UN = sum(UN, na.rm = T))
Step 2: Add sector column to res_UN with value 'residual'
This should yield a data frame which contains country, year, and UN, we now need to add a column sector which the value 'Residual' to satisfy your specifications.
res_UN$sector = 'Residual'
Step 3 : Add res_UN back to UN_ and order accordingly
res_UN and UN_ now have the same columns and they can now be added back together.
UN_ = bind_rows(UN_, res_UN) %>% arrange(country, year, sector)
Piecing this all together, should answer your question and can be achieved in a couple lines!
TLDR:
res_UN = UN_ %>% group_by(country, year) %>% summarise(UN = sum(UN, na.rm = T))`
res_UN$sector = 'Residual'
UN_ = bind_rows(UN_, res_UN) %>% arrange(country, year, sector)
I have a data frame as shown below:
Country Year X1 X2 convex1 convex2
UK 2011 100 5
UK 2012 110 5.5
UK 2013 NA 10
UK 2014 115 2
US 2011 NA 10
US 2012 120 11
US 2013 118 9.2
US 2014 NA NA
And I would like to create two new variables to capture convexity. These two variables will apply the following logic:
Convex1 if X1>average (X1 by country) then 1 else 0
I have pinned down Convex1 using
dataa <- data.table(dataa , key = "Country")
dataa[ , convex1 := (X1 ) /mean(X1, na.rm = TRUE), "Country" ]
dataa$convex1var<- ifelse(dataa$convex1>1.1 ,1 ,0)
But I would like to do it in one step as I have to perform this for a long list of vars.
Convex2 if X2>= 10 for two consecutive years (by country) then 1 else 0
For Convex2, I am not sure how to do this.
dataa$Convex2<-ifelse( dataa$X2 > lag(dataa$X2, by=Country) &
lag(dataa$X2, by=Country) > lag(dataa$X2,2, by=Country),1,0)
I have a dataset and I want to perform something like Group By Rollup like we have in SQL for aggregate values.
Below is a reproducible example. I know aggregate works really well as explained here but not a satisfactory fit for my case.
year<- c('2016','2016','2016','2016','2017','2017','2017','2017')
month<- c('1','1','1','1','2','2','2','2')
region<- c('east','west','east','west','east','west','east','west')
sales<- c(100,200,300,400,200,400,600,800)
df<- data.frame(year,month,region,sales)
df
year month region sales
1 2016 1 east 100
2 2016 1 west 200
3 2016 1 east 300
4 2016 1 west 400
5 2017 2 east 200
6 2017 2 west 400
7 2017 2 east 600
8 2017 2 west 800
now what I want to do is aggregation (sum- by year-month-region) and add the new aggregate row in the existing dataframe
e.g. there should be two additional rows like below with a new name for region as 'USA' for the aggreagted rows
year month region sales
1 2016 1 east 400
2 2016 1 west 600
3 2016 1 USA 1000
4 2017 2 east 800
5 2017 2 west 1200
6 2017 2 USA 2000
I have figured out a way (below) but I am very sure that there exists an optimum solution for this OR a better workaround than mine
df1<- setNames(aggregate(df$sales, by=list(df$year,df$month, df$region), FUN=sum),
c('year','month','region', 'sales'))
df2<- setNames(aggregate(df$sales, by=list(df$year,df$month), FUN=sum),
c('year','month', 'sales'))
df2$region<- 'USA' ## added a new column- region- for total USA
df2<- df2[, c('year','month','region', 'sales')] ## reordering the columns of df2
df3<- rbind(df1,df2)
df3<- df3[order(df3$year,df3$month,df3$region),] ## order by
rownames(df3)<- NULL ## renumbered the rows after order by
df3
Thanks for the support!
melt/dcast in the reshape2 package can do subtotalling. After running dcast we replace "(all)" in the month column with the month using na.locf from the zoo package:
library(reshape2)
library(zoo)
m <- melt(df, measure.vars = "sales")
dout <- dcast(m, year + month + region ~ variable, fun.aggregate = sum, margins = "month")
dout$month <- na.locf(replace(dout$month, dout$month == "(all)", NA))
giving:
> dout
year month region sales
1 2016 1 east 400
2 2016 1 west 600
3 2016 1 (all) 1000
4 2017 2 east 800
5 2017 2 west 1200
6 2017 2 (all) 2000
In recent devel data.table 1.10.5 you can use new feature called "grouping sets" to produce sub totals:
library(data.table)
setDT(df)
res = groupingsets(df, .(sales=sum(sales)), sets=list(c("year","month"), c("year","month","region")), by=c("year","month","region"))
setorder(res, na.last=TRUE)
res
# year month region sales
#1: 2016 1 east 400
#2: 2016 1 west 600
#3: 2016 1 NA 1000
#4: 2017 2 east 800
#5: 2017 2 west 1200
#6: 2017 2 NA 2000
You can substitute NA to USA using res[is.na(region), region := "USA"].
plyr::ddply(df, c("year", "month", "region"), plyr::summarise, sales = sum(sales))
I have a table that looks like the following:
Year Country Variable 1 Variable 2
1970 UK 1 3
1970 USA 1 3
1971 UK 2 5
1971 UK 2 3
1971 UK 1 5
1971 USA 2 2
1972 USA 1 1
1972 USA 2 5
I'd be grateful if someone could tell me how I can aggregate the data to group it first by year, then country with the sum of variable 1 and variable 2 coming afterwards so the output would be:
Year Country Sum Variable 1 Sum Variable 2
1970 UK 1 3
1970 USA 1 3
1971 UK 5 13
1971 USA 2 2
1972 USA 3 6
This is the code I've tried to no avail (the real dataframe is 125,000 rows by 30+ columns hence the subset. Please be kind, I'm new to R!)
#making subset from data
GT2 <- subset(GT1, select = c("iyear", "country_txt", "V1", "V2"))
#making sure data types are correct
GT2[,2]=as.character(GT2[,2])
GT2[,3] <- as.numeric(as.character( GT2[,3] ))
GT2[,4] <- as.numeric(as.character( GT2[,4] ))
#removing NA values
GT2Omit <- na.omit(GT2)
#trying to aggregate - i.e. group by year, then country with the sum of Variable 1 and Variable 2 being shown
aggGT2 <-aggregate(GT2Omit, by=list(GT2Omit$iyear, GT2Omit$country_txt), FUN=sum, na.rm=TRUE)
Your aggregate is almost correct:
> aggGT2 <-aggregate(GT2Omit[3:4], by=GT2Omit[c("country_txt", "iyear")], FUN=sum, na.rm=TRUE)
> aggGT2
country_txt iyear V1 V2
1 UK 1970 1 3
2 USA 1970 1 3
3 UK 1971 5 13
4 USA 1971 2 2
5 USA 1972 3 6
dplyr is almost always the answer nowadays.
library(dplyr)
aggGT1 <- GT1 %>% group_by(iyear, country_txt) %>% summarize(sv1=sum(V1), sv2=sum(V2))
Having said that, it is good to learn basic R functions like aggregate and by.