Currently, I am trying to cut the dataset into three parts: developed, developing and under-developed. The cut criteria is quantiles. That is,
developed would be those above 75% quantiles, developing would be between 50%-75% and under-developed would be below 50%. However, quantiles are different by years.
data = data.frame("country" = c("U.S.A","U.S.A","Jamaica","Jamaica","Congo","Congo"),
"year" = c(2000,2001,2000,2001,2000,2001),
"gdp_per_capita" = c(30000,40000,100,200,50,60))
quantiles = do.call("data.frame",
tapply(data$gdp_per_capita, data$year, quantile))
What I did was to calculate the quantiles by year and I got a data frame with just that information. Now, I am trying to use this information to apply above criteria for each year.
Example
2000 = (50% = 3000, 75% = 15999)
2001 = (50% = 5000, 75% = 18000)
cut points changes
Possible results
year country gdp_per_capita status
2000 U.S. 1800000 "developed"
2000 France 200000 "developed"
....more than 500+ obs.
2000 Kenya 300 "under-developed"
2000 Malaysia 1500 "developing"
2001 Malaysia 3000 "developing"
2001 Kenya 500 "under-developed"
2001 Spain 30000 "developed"
2000 India 300 "under-developed"
2001 India 5100 "developing"
What will be the most efficient way to resolving this issue?
I tried using ifelse and doing that one by one. This seems like it is too much work and I felt like there was no reason to use computer if I am going to iterate them one by one.
Instead of data.frame, consider rbind in do.call to create quantile percents as columns, then merge to original dataset by year. Finally, calculate status with a nested ifelse conditional logic.
### QUANTILES
quantiles_matrix <- do.call("rbind", tapply(data$gdp_per_capita, data$year, quantile))
quantiles_df <- transform(data.frame(quantiles_matrix),
year = row.names(quantiles_matrix))
### MERGE
mdf <- merge(data, quantiles_df, by="year")
### STATUS COLUMN ASSIGNMENT
final_df <- transform(mdf,
status = ifelse(gdp_per_capita > X75., "developed",
ifelse(gdp_per_capita >= X50. & gdp_per_capita <= X75., "developing",
ifelse(gdp_per_capita < X50., "under-developed", NA)
)
)
)
Rextester demo
Related
I have a data frame and would like to calculate the growth rate of nominal GDP in R. I know how to do it in Excel with the formula ((gdp of this year)-gdp of last year)/( gdp of last year))*100. What kind of command could be used in R to calculate it?
year nominal gdp
2003 7696034.9
2004 8690254.3
2005 9424601.9
2006 10520792.8
2007 11399472.2
2008 12256863.6
2009 12072541.6
2010 13266857.9
2011 14527336.9
2012 15599270.7
2013 16078959.8
You can also use the lag() fuction from dplyr. It gives the previous values in a vector. Here is an example
data <- data.frame(year = c(2003:2013),
gdp = c(7696034.9, 8690254.3, 9424601.9, 10520792.8,
11399472.2, 12256863.6, 12072541.6, 13266857.9,
14527336.9, 15599270.7, 16078959.8))
library(dplyr)
growth_rate <- function(x)(x/lag(x)-1)*100
data$growth_rate <- growth_rate(data$gdp)
It's probably best for you to get familiar with data tables, and do something like this:
library(data.table)
dt_gdp <- data.table(df)
dt_gdp[, growth_rate_of_gdp := 100 * (Producto.interno.bruto..PIB. - shift(Producto.interno.bruto..PIB.)) / shift(Producto.interno.bruto..PIB.)]
A base-R solution:
with(data,
c(NA, ## augment results (growth rate unknown in year 1)
diff(gdp)/ ## this is gdp(t) - gdp(t-1)
head(gdp, -1)) ## gdp(t-1)
*100) ## scale to percentage growth
head(gdp, -1) is perhaps a little too clever. gdp[-length(gdp)] (i.e. "gdp, excluding the last value") would be slightly more idiomatic.
Or
(gdp/c(NA,gdp[-length(gdp)])-1)*100
I have data on energy companies whose jurisdiction overlaps in places. I want to be able to compute an average of sales for the places where these companies overlap. These companies will always overlap - so how can I use this information to calculate the averages just for those pairs? There are about 20 pairs of companies.
data <- data.frame(Company = c("Energy USA","Good Energy",
"Hydropower 4 U",
"Coal Town",
"Energy USA/Good Energy",
"Good Energy/Coal Town"),
Sales = c(100, 2500, 550, 6000, "?", "?"))
Company Sales
1 Energy USA 100
2 Good Energy 2500
3 Hydropower 4 U 550
4 Coal Town 6000
5 Energy USA/Good Energy ? (Answer: 1300)
6 Good Energy/Coal Town ? (Answer: 4250)
We use 'grep' to get index of 'Company' elements that have more than one entries i.e. separated by '/'. Then, split those elements by the delimiter (output will be a list), loop through the list with sapply, match the elements with the 'Company' column to get the position, use that to get the corresponding 'Sales' elements. As the 'Sales' column was factor, we need to convert it to numeric to get the mean. When we convert factor to numeric class, all non-numeric elements i.e. ? will be converted to NA. Replace those NA elements with the mean values.
i1 <- grepl('/', data$Company)
v1 <- sapply(strsplit(as.character(data$Company[i1]), '/'),
function(x) mean(as.numeric(as.character(data$Sales[match(x,
data$Company)]))))
data$Sales <- as.numeric(as.character(data$Sales))
data$Sales[is.na(data$Sales)] <- v1
data
# Company Sales
#1 Energy USA 100
#2 Good Energy 2500
#3 Hydropower 4 U 550
#4 Coal Town 6000
#5 Energy USA/Good Energy 1300
#6 Good Energy/Coal Town 4250
Without knowing how your original data is, it is hard to give a working answer. However, assuming your data has Company and Sales columns with multiple rows for each company, you can do something like this:
mean(data$Sales[data$Company %in% c('Energy USA', 'Good Energy')]])
mean(data$Sales[data$Company %in% c('Good Energy', 'Coal Town')]])
you could create a new column "jurisdiction" in "data", if your dataset is rather small..
MeansByJurisdiction <- tapply(data$sales, data$jurisdiction, mean)
then you could convert the vector to dataframe
MeansByJurisdiction <- data.frame(MeansByJurisdiction)
the rownames in the MeansByJurisdiction dataframe will be populated with the jurisdictions and you can extract them with a simple line of code:
MeansByJurisdiction$jurisdictions <- row.names(MeansByJurisdiction)
I'm trying to do a zoo merge between stock prices from selected trading days and observations about those same stocks (we call these "Nx observations") made on the same days. Sometimes do not have Nx observations on stock trading days and sometimes we have Nx observations on non-trading days. We want to place an "NA" where we do not have any Nx observations on trading days but eliminate Nx observations where we have them on non-trading day since without trading data for the same day, Nx observations are useless.
The following SO question is close to mine, but I would characterize that question as REPLACING missing data, whereas my objective is to truly eliminate observations made on non-trading days (if necessary, we can change the process by which Nx observations are taken, but it would be a much less expensive solution to leave it alone).
merge data frames to eliminate missing observations
The script I have prepared to illustrate follows (I'm new to R and SO; all suggestions welcome):
# create Stk_data data.frame for use in the Stack Overflow question
Date_Stk <- c("1/2/13", "1/3/13", "1/4/13", "1/7/13", "1/8/13") # dates for stock prices used in the example
ABC_Stk <- c(65.73, 66.85, 66.92, 66.60, 66.07) # stock prices for tkr ABC for Jan 1 2013 through Jan 8 2013
DEF_Stk <- c(42.98, 42.92, 43.47, 43.16, 43.71) # stock prices for tkr DEF for Jan 1 2013 through Jan 8 2013
GHI_Stk <- c(32.18, 31.73, 32.43, 32.13, 32.18) # stock prices for tkr GHI for Jan 1 2013 through Jan 8 2013
Stk_data <- data.frame(Date_Stk, ABC_Stk, DEF_Stk, GHI_Stk) # create the stock price data.frame
# create Nx_data data.frame for use in the Stack Overflow question
Date_Nx <- c("1/2/13", "1/4/13", "1/5/13", "1/6/13", "1/7/13", "1/8/13") # dates for Nx Observations used in the example
ABC_Nx <- c(51.42857, 51.67565, 57.61905, 57.78349, 58.57143, 58.99564) # Nx scores for stock ABC for Jan 1 2013 through Jan 8 2013
DEF_Nx <- c(35.23809, 36.66667, 28.57142, 28.51778, 27.23150, 26.94331) # Nx scores for stock DEF for Jan 1 2013 through Jan 8 2013
GHI_Nx <- c(7.14256, 8.44573, 6.25344, 6.00423, 5.99239, 6.10034) # Nx scores for stock GHI for Jan 1 2013 through Jan 8 2013
Nx_data <- data.frame(Date_Nx, ABC_Nx, DEF_Nx, GHI_Nx) # create the Nx scores data.frame
# create zoo objects & merge
z.Stk_data <- zoo(Stk_data, as.Date(as.character(Stk_data[, 1]), format = "%m/%d/%Y"))
z.Nx_data <- zoo(Nx_data, as.Date(as.character(Nx_data[, 1]), format = "%m/%d/%Y"))
z.data.outer <- merge(z.Stk_data, z.Nx_data)
The NAs on Jan 3 2013 for the Nx observations are fine (we'll use the na.locf) but we need to eliminate the Nx observations that appear on Jan 5 and 6 as well as the associated NAs in the Stock price section of the zoo objects.
I've read the R Documentation for merge.zoo regarding the use of "all": that its use "allows
intersection, union and left and right joins to be expressed". But trying all combinations of the
following use of "all" yielded the same results (as to why would be a secondary question).
z.data.outer <- zoo(merge(x = Stk_data, y = Nx_data, all.x = FALSE)) # try using "all"
While I would appreciate comments on the secondary question, I'm primarily interested in learning how to eliminate the extraneous Nx observations on days when there is no trading of stocks. Thanks. (And thanks in general to the community for all the great explanations of R!)
The all argument of merge.zoo must be (quoting from the help file):
logical vector having the same length as the number of "zoo" objects to be merged
(otherwise expanded)
and you want to keep all rows from the first argument but not the second so its value should be c(TRUE, FALSE).
merge(z.Stk_data, z.Nx_data, all = c(TRUE, FALSE))
The reason for the change in all syntax for merge.zoo relative to merge.data.frame is that merge.zoo can merge any number of arguments whereas merge.data.frame only handles two so the syntax had to be extended to handle that.
Also note that %Y should have been %y in the question's code.
I hope I have understood your desired output correctly ("NAs on Jan 3 2013 for the Nx observations are fine"; "eliminate [...] observations that appear on Jan 5 and 6"). I don't quite see the need for zoo in the merging step.
merge(Stk_data, Nx_data, by.x = "Date_Stk", by.y = "Date_Nx", all.x = TRUE)
# Date_Stk ABC_Stk DEF_Stk GHI_Stk ABC_Nx DEF_Nx GHI_Nx
# 1 1/2/13 65.73 42.98 32.18 51.42857 35.23809 7.14256
# 2 1/3/13 66.85 42.92 31.73 NA NA NA
# 3 1/4/13 66.92 43.47 32.43 51.67565 36.66667 8.44573
# 4 1/7/13 66.60 43.16 32.13 58.57143 27.23150 5.99239
# 5 1/8/13 66.07 43.71 32.18 58.99564 26.94331 6.10034
I have a data set which contains the following identifiers, an rscore, gvkey, sic2, year, and cdom. What I am looking to do is calculate percentile ranks based on summed rscores for all temporal spans (~1500) for a given gvkey, and then calculate percentile ranks in a given temporal time span and sic2 based on gvkey.
Calculating the percentiles for all temporal time spans is a fairly quick process, however once I add in calculating the sic2 percentile ranks it's fairly slow, but we are likely looking at about ~65,000 subsets in total. I'm wondering if there is a possibility of speeding up this process.
The data for one temporal time span looks like the following
gvkey sic2 cdom rscoreSum pct
1187 10 USA 8.00E-02 0.942268617
1265 10 USA -1.98E-01 0.142334654
1266 10 USA 4.97E-02 0.88565478
1464 10 USA -1.56E-02 0.445748247
1484 10 USA 1.40E-01 0.979807985
1856 10 USA -2.23E-02 0.398252565
1867 10 USA 4.69E-02 0.8791019
2047 10 USA -5.00E-02 0.286701209
2099 10 USA -1.78E-02 0.430915371
2127 10 USA -4.24E-02 0.309255308
2187 10 USA 5.07E-02 0.893020421
The code to calculate the industry ranks is below, and fairly straightforward.
#generate 2 digit industry SICs percentile ranks
dout <- ddply(dfSum, .(sic2), function(x){
indPct <- rank(x$rscoreSum)/nrow(x)
gvkey <- x$gvkey
x <- data.frame(gvkey, indPct)
})
#merge 2 digit industry SIC percentile ranks with market percentile ranks
dfSum <- merge(dfSum, dout, by = "gvkey")
names(dfSum)[2] <- 'sic2'
Any suggestions to speed the process would be appreciated!
You might try the data.table package for fast operations across relatively large datasets like yours. For example, my machine has no problem working through this:
library(data.table)
# Create a dataset like yours, but bigger
n.rows <- 2e6
n.sic2 <- 1e4
dfSum <- data.frame(gvkey=seq_len(n.rows),
sic2=sample.int(n.sic2, n.rows, replace=TRUE),
cdom="USA",
rscoreSum=rnorm(n.rows))
# Now make your dataset into a data.table
dfSum <- data.table(dfSum)
# Calculate the percentiles
# Note that there is no need to re-assign the result
dfSum[, indPct:=rank(rscoreSum)/length(rscoreSum), by="sic2"]
whereas the plyr equivalent takes a while.
If you like the plyr syntax (I do), you may also be interested in the dplyr package, which is billed as "the next generation of plyr", with support for faster data stores in the backend.
First I will have to apologize for my ignorance as I'm sure this is a very simple question but I am very new to R. My question is that I have a data frame that looks like this:
countrydes Insured
USA 4500
CANADA 4500
USA 7500
CANADA 7600
All I want to do is aggregate the sum of the insured value by country and produce a bar graph e.g.
countrydes Insured
USA 12000
Canada 12100
Many thanks.
This will do the trick:
# Define your data
dfr <- data.frame(
countrydes=rep(c("USA", "CANADA"), 2),
Insured=c(4500, 4500, 7500, 7600))
# Sum by country
totals <- with(dfr, by(Insured, countrydes, sum))
# Plot the answer
barplot(totals)
(As Etiennebr mentioned, you could use aggregate instead of by, in which case you need to coerce countrydes to be a list.)
You could simply sum each one separatly. Let's call your data frame df :
USA <- sum(df[df$countrydes=="USA",]$Insured)
CANADA <- sum(df[df$countrydes=="CANADA",]$Insured)
But with aggregate() you can handle all the countries in one line.
aggsumcount <- aggregate(x=df$Insured,by=list(df$countrydes),FUN=sum)