I have a column in a dataframe which includes 30 different countries. I want to group these countries into 5 new values.
For example,
I have
China
Japan
US
Canada
....
Aggregate to new variables:
Asia
Asia
North America
North America
....
One solution I am thinking about is using nested ifelse. However it seems that I need 4 or 5 nested ifelse to get what I need. I don't think that's a good way. I want to know other efficient solutions.
One option would be to use a key/value dataset. The countrycode_data from the library(countrycode) can be used for this purpose. We match the 'country.name' column in 'countrycode_data' with the example data column ('Col1'). If there are no matches, it will return NA. Using the OP's example, 'US' returns NA as the 'country.name' is 'United States'. But, we can get the abbreviated form using the 'cowc' column. However, the abbreviated version is also USA, which we can find using grep. I would suggest to grep all NA elements in 'indx'. The 'indx' can be used for returning 'region' from the 'countrycode_data'.
library(countrycode)
indx <- match(df1$Col1, countrycode_data$country.name)
pat <- paste0('^',paste(df1$Col1[is.na(indx)], collapse='|'))
indx[is.na(indx)] <- grep(pat, countrycode_data$cowc)
countrycode_data$region[indx]
#[1] "Eastern Asia" "Eastern Asia" "Northern America" "Northern America"
NOTE: This will return a bit more specific than the general 'Asia'.
If we use the 'continent' column,
countrycode_data$continent[indx]
#[1] "Asia" "Asia" "Americas" "Americas"
data
df1 <- structure(list(Col1 = c("China", "Japan", "US", "Canada")),
.Names = "Col1", class = "data.frame", row.names = c(NA, -4L))
Another approach is to use the recode function from the car package:
library(car)
dat$Region <- recode(dat$Country, "c('China', 'Japan') = 'Asia'; c('US','Canada') = 'North America'")
Country Region
1 China Asia
2 Japan Asia
3 US North America
4 Canada North America
They are just 30 countries and so you can make few vectors like shown below, create a new column and replace according to the vectors.
asia <- c("India", "china")
NorthAmerica <- c("US", "canada")
df$continent <- df$countries
df$continent <- with(df, replace(continent, countries%in%asia,"Asia"))
df$continent <- with(df, replace(continent, countries%in%NorthAmerica,"North America"))
'continent' is a built-in destination code of the countrycode package. You can pass a vector of country names and get a vector of continent names back with...
library(countrycode)
countries <- c('China', 'Japan', 'US', 'Canada')
countrycode(countries, 'country.name', 'continent')
returns...
[1] "Asia" "Asia" "Americas" "Americas"
Make sure when using Veera's and Jay's approaches to define column as a vector in order to allow for the change of a column's levels:
df$continent <- as.factor(as.vector(df$countries))
Related
I have a data.frame that contains state names and I would like to create a new variable called "region" in which a value is assigned based on the state that is found under the "state" variable.
For example, if the state variable has "Alabama" or "Georgia", I would like to have "Region" assigned as "South". If state is "Washington" or "California", I would like it assigned to "West". I have to do this for each of the 48 contiguous U.S. states, and I'm having difficulty figuring out the best way to do this. Any help in this (I'm sure simple) procedure would be great. What I am looking for is something like this in the end:
State Region
Wyoming West
Michigan Midwest
Alabama South
Georgia South
California West
Texas Central
And to be clear, I don't have the regions in a separate file, i have to create this as a new variable and create the region names myself. I'm just looking for a way that the code can go through all 3000 lines that I have and can automatically assign the region name once I tell it how to do so.
Rather than type the region for every state, you can use the built-in "state.name" and "state.region" variables from the 'datasets' package (like Jon Spring suggests in his comment), e.g.
library(tidyverse)
library(datasets)
state_lookup_table <- data.frame(name = state.name,
region = state.region)
my_df <- data.frame(place = c("Washington", "California"),
value = c(1000, 2000))
my_df
#> place value
#> 1 Washington 1000
#> 2 California 2000
my_df %>%
left_join(state_lookup_table, by = c("place" = "name"))
#> place value region
#> 1 Washington 1000 West
#> 2 California 2000 West
Created on 2022-09-02 by the reprex package (v2.0.1)
I would go this way:
df <- data.frame(name = c("john", "will", "thomas", "Ali"),
state = c("California", "Alabama", "Washington", "Georgia"))
region_df <- data.frame(state= c("Alabama", "Georgia", "Washington"),
region = c("south", "south", "west"))
merged.df <- merge(df, region_df, all.x = TRUE, on= "state")
I think you need a reference to do so. For your specific question, a dict would be the best solution.
ref_ge <- {}
ref_ge["Georgia"]="South"
ref_ge["Alabama"]="South"
ref_ge["California"]="West"
ref1["Georgia"]
#Or, if you could read the state->region information from an excel to a dataframe
df=data.frame(state=c("Georgia","Alabama","California"),region=c("South","South","West"))
ref2 <- df$region
names(ref2) <- df$state
ref2["Georgia"]
In my data I have one column with country names. I want to make a new variable that lists which region each country is in based on an excel sheet I have where I have labelled each country by region.
I don't want to use the package countrycode because it doesn't have specific enough regions (i.e. it labels the Netherlands as Europe, and not Northern Europe). Is there a way to get R to inspect a cell and match the contents of that cell to another dataset?
Import your spreadsheet into R. (Use RExcel, or export as CSV and import that using base functions.) Suppose your spreadsheet has two columns, named Country and Region, something like this:
regions <- data.frame(Country = c("Greece", "Netherlands"),
Region = c("Southern Europe", "Northern Europe"),
stringsAsFactors = FALSE)
regions
#> Country Region
#> 1 Greece Southern Europe
#> 2 Netherlands Northern Europe
Now create a named vector from the dataframe:
named <- regions$Region
names(named) <- regions$Country
named
#> Greece Netherlands
#> "Southern Europe" "Northern Europe"
Now you can index the named vector to convert country names to regions in any other vector.
other <- c("Netherlands", "Greece", "Greece")
named[other]
#> Netherlands Greece Greece
#> "Northern Europe" "Southern Europe" "Southern Europe"
If you have any missing countries (or variant spellings), you'll get NA for the region, e.g.
other2 <- c("Greece", "France")
named[other2]
#> Greece <NA>
#> "Southern Europe" NA
The rnaturalearth library has country shapefiles with region and subregion.
library(rnaturalearth)
world <- rnaturalearth::ne_countries(returnclass = "sf")
world$region
world$subregion
I have a data frame with one column representing country names. My goal is to add one more column which gives the continent information. Please check the following use case:
my.df <- data.frame(country = c("Afghanistan","Algeria"))
Is there a package that I can use to append a column of data containing the continent names without having the original data?
You can use the countrycode package for this task.
library(countrycode)
df <- data.frame(country = c("Afghanistan",
"Algeria",
"USA",
"France",
"New Zealand",
"Fantasyland"))
df$continent <- countrycode(sourcevar = df[, "country"],
origin = "country.name",
destination = "continent")
#warning
#In countrycode(sourcevar = df[, "country"], origin = "country.name", :
# Some values were not matched unambiguously: Fantasyland
Result
df
# country continent
#1 Afghanistan Asia
#2 Algeria Africa
#3 USA Americas
#4 France Europe
#5 New Zealand Oceania
#6 Fantasyland <NA>
Expanding on Markus' answer, countrycode draws on codelists 'continent' declaration.
?codelist
Definition of continent:
continent: Continent as defined in the World Bank Development Indicators
The question asked for continents but sometimes continents don't provide enough groups for you to delineate the data. For example, continents groups North and South America into Americas.
What you might want is region:
region: Regions as defined in the World Bank Development Indicators
It is unclear how the World Bank groups regions but the below code shows how this destination is more granular.
library(countrycode)
egnations <- c("Afghanistan","Algeria","USA","France","New Zealand","Fantasyland")
countrycode(sourcevar = egnations, origin = "country.name",destination = "region")
Output:
[1] "Southern Asia"
[2] "Northern Africa"
[3] "Northern America"
[4] "Western Europe"
[5] "Australia and New Zealand"
[6] NA
You can try
my.df <- data.frame(country = c("Afghanistan","Algeria"),
continent= as.factor(c("Asia","Africa")))
merge(my.df, raster::ccodes()[,c("NAME", "CONTINENT")], by.x="country", by.y="NAME", all.x=T)
# country continent CONTINENT
# 1 Afghanistan Asia Asia
# 2 Algeria Africa Africa
Some country values might need an adjustment; I dunno since you did not provide all values.
I am trying to do a kind of 'if' statement in R where I want to find if two values (string) are the same in two different columns. For example, if my Origin and my Destination country are the same, I want to create a new column with Domestic as a result. If false, then eventually I would code the NA as International.
I try several functions in R but still can't have it properly!
I think the recode function from car library could fit. Here is an example of data and two examples of lines of code I have tried.
Thanks for the help.
#Data
Origin.Country <- c("Canada","Vietnam","Maldives", "Indonesia", "Spain", "Canada","Vietnam")
Passengers <- c(100, 5000, 200, 10000, 200, 20, 4000)
Destination.Country <- c("France","Vietnam","Portugal", "Thailand", "Spain", "Canada","Thailand")
data2<-data.frame(Origin.Country, Destination.Country, Passengers)
#Creating new column
data2$Domestic<-NA
#If Origin and Destination is the same = Domestic
data2$Domestic[data2$Origin.Country==data2$Destination.Country <- Domestic
data2$Domestic <- recode(data2$Origin.Country, c(data2$Destination.Country)='Domestic', else='International')
You can use ifelse:
data2$Domestic <- ifelse(as.character(data2$Origin.Country) ==
as.character(data2$Destination.Country),
'Domestic', 'International')
I used as.character to coerce the country name variables to be characters for comparison. ifelse takes a logical as the first argument, and returns the second argument if TRUE, and the third argument if FALSE. In this instance, it performs a comparison of the variables by row.
This might be a bit slow because it's not vectorised, but it worked based on your example:
data2$domestic <- apply(data2, 1, function(x) {
( x["Origin.Country"] == x["Destination.Country"] )
} )
You can use recode in this way:
library(dplyr); library(car)
data2 %>% mutate(Domestic = recode(as.character(Origin.Country) == as.character(Destination.Country),
"TRUE='domestic'; else='international'"))
Origin.Country Destination.Country Passengers Domestic
1 Canada France 100 international
2 Vietnam Vietnam 5000 domestic
3 Maldives Portugal 200 international
4 Indonesia Thailand 10000 international
5 Spain Spain 200 domestic
6 Canada Canada 20 domestic
7 Vietnam Thailand 4000 international
This question is related to: Searching a data.frame in R
I want to search for multiple patterns , e.g. 'america' and 'united', in
all fields
in a given field
How can this be done? The case needs to be ignored.
Data:
ddf
id country area
1 1 United States of America North America
2 2 United Kingdom Europe
3 3 United Arab Emirates Arab
4 4 Saudi Arabia Arab
5 5 Brazil South America
ddf = structure(list(id = 1:5, country = c("United States of America",
"United Kingdom", "United Arab Emirates", "Saudi Arabia", "Brazil"
), area = c("North America", "Europe", "Arab", "Arab", "South America"
)), .Names = c("id", "country", "area"), class = "data.frame", row.names = c(NA,
-5L))
EDIT: To clarify, I have to search with AND and not OR. In this example, only 'United States of America' (row number 1) should come. If I search for 'brazil' and 'america', row number 5 should come (i.e. different search strings can be in different columns).
This actually fails for the "brazil" & "america" case but it was a useful test-bed for diagnosisng the logical problems;
hasAm <- sapply( ddf, grepl, patt="america", ignore.case=TRUE)
ddf[ rowSums(hasAm) > 0 , ]
#----------
id country area
1 1 United States of America North America
5 5 Brazil South America
#---------
hasUn <- sapply( ddf, grepl, patt="united", ignore.case=TRUE)
#---------
ddf[ rowSums( hasAm & hasUn) > 0 , ]
#-----------
id country area
1 1 United States of America North America
This edited version generalizes that strategy although it requires entering the selection criteria as a formula. I needed to first collapse each matrix so that summing across the cbind()-ed values didn't pick up multiple hits on a single term. So I have two rowSums, the outer one being done on m-column matrices where m is the number of terms in the formula, and the inner one being done on n-column matrices where n is the number of columns in the data-argument:
dfsel <- function(form, data) {
vars = all.vars(form)
selmatx <- lapply( vars, function(v)
sapply (data, grepl, patt=v, ignore.case=TRUE))
data[ rowSums( do.call(cbind,
lapply(selmatx,
function(L) {rowSums(L) > 0}) ) ) == length(vars)
, ] }
Demonstration:
> res <- dfsel( ~ united + america , ddf)
> res
id country area
1 1 United States of America North America
> res <- dfsel( ~ brazil + america , ddf)
> res
id country area
5 5 Brazil South America
Dumb way of solving it. Interested in other answers.
pattern<-c('America','United')
ddf1<-NULL
for (i in 1:length(pattern)){
new<-ddf[grep(paste0(pattern[i]),ddf$country),]
ddf1<-rbind(ddf1,new)
}
Going on the logic that no country in the world has "America" before "United" in its name, you could do
> f <- lapply(ddf, grep, pattern = "(united)(.*)(america)", ignore.case = TRUE)
> ddf[unique(unlist(f)), ]
# id country area
# 1 1 United States of America North America