Get continent name from country name in R - r

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.

Related

How to group a column with character values in a new column in r

I have a data set with countries column, I want to create a new column and classify the countries into the following categories (first world, second world, third world) countries.
I'm relatively new to R and I'm finding it difficult to find a proper function that deals with characters!
My dataset contains the countries like this, and I have three vectors with a list of countries as shown below:
nt_final_table$`Country name`
#[1] "Finland" "Denmark" "Switzerland"
#[4] "Iceland" "Netherlands" "Norway"
#[7] "Sweden" "Luxembourg" "New Zealand"
#[10] "Austria" "Australia" "Israel"
first_world_countries <- c("Australia","Austria","Belgium","Canada","Denmark","France","Germany","Greece","Iceland","Ireland","Israel","Italy","Japan","Luxembourg","Netherlands","New Zealand","Norway","Portugal","South Korea",
"Spain","Sweden","Switzerland","Turkey","United Kingdom","USA")
Second_world_countries <- c("Albania","Armenia","Azerbaijan","Belarus","Bosnia and Herzegovina","Bulgaria","China","Croatia","Cuba","Czech Republic","EastGermany","Estonia","Georgia","Hungary","Kazakhstan","Kyrgyzstan","Laos","Poland","Romania","Russia","Serbia","Slovakia","Slovenia","Tajikistan","Turkmenistan","Ukraine","Uzbekistan","Vietnam")
Third_world_countries <- ("Somalia","Niger","South Sudan")
I would want a new column that contains the following values :
First World, Second World, Third World based on the Country name column
Any help would be appreciated!
Thanks!
Here are 2 ways you could do this.
Using dplyr package
You could use case_when from the dplyr package to do this.
library(dplyr)
country_name <-c("Finland", "Denmark", "Switzerland","Iceland", "Netherlands", "Norway", "Sweden", "Luxembourg", "New Zealand",
"Austria", "Australia", "Israel")
nt_final_table <- data.frame(country_name)
first_world_countries <- c("Australia","Austria","Belgium","Canada","Denmark","France","Germany","Greece","Iceland","Ireland","Israel","Italy","Japan","Luxembourg","Netherlands","New Zealand","Norway","Portugal","South Korea", "Spain","Sweden","Switzerland","Turkey","United Kingdom","USA")
second_world_countries <- c("Albania","Armenia","Azerbaijan","Belarus","Bosnia and Herzegovina","Bulgaria","China","Croatia","Cuba","Czech Republic","EastGermany","Estonia","Georgia","Hungary","Kazakhstan","Kyrgyzstan","Laos","Poland","Romania","Russia","Serbia","Slovakia","Slovenia","Tajikistan","Turkmenistan","Ukraine","Uzbekistan","Vietnam")
third_world_countries <- c("Somalia","Niger","South Sudan")
nt_final_table_categorized <- nt_final_table %>% mutate(category = case_when(country_name %in% first_world_countries ~ "First",
country_name %in% second_world_countries ~ "Second",
country_name %in% third_world_countries ~ "Third",
TRUE ~"Not listed"))
nt_final_table_categorized
Sample output
country_name category
1 Finland Not listed
2 Denmark First
3 Switzerland First
4 Iceland First
5 Netherlands First
6 Norway First
7 Sweden First
8 Luxembourg First
9 New Zealand First
10 Austria First
11 Australia First
12 Israel First
Using base R
In base R we could create a data frame that lists the countries and their category then use merge to perform a left-join on the 2 dataframes.
country_name <-c("Finland", "Denmark", "Switzerland","Iceland", "Netherlands", "Norway", "Sweden", "Luxembourg", "New Zealand",
"Austria", "Australia", "Israel")
nt_final_table <- data.frame(country_name)
first_world_countries <- c("Australia","Austria","Belgium","Canada","Denmark","France","Germany","Greece","Iceland","Ireland","Israel","Italy","Japan","Luxembourg","Netherlands","New Zealand","Norway","Portugal","South Korea", "Spain","Sweden","Switzerland","Turkey","United Kingdom","USA")
second_world_countries <- c("Albania","Armenia","Azerbaijan","Belarus","Bosnia and Herzegovina","Bulgaria","China","Croatia","Cuba","Czech Republic","EastGermany","Estonia","Georgia","Hungary","Kazakhstan","Kyrgyzstan","Laos","Poland","Romania","Russia","Serbia","Slovakia","Slovenia","Tajikistan","Turkmenistan","Ukraine","Uzbekistan","Vietnam")
third_world_countries <- c("Somalia","Niger","South Sudan")
country_name <- c(first_world_countries,second_world_countries,third_world_countries)
categories <- c(rep("First", length(first_world_countries)),
rep("Second",length(second_world_countries)),
rep("Third",length(third_world_countries)))
all_countries_categorised <- data.frame(country_name, categories)
nt_final_table_categorized <-merge(nt_final_table, all_countries_categorised, by ="country_name", all.x=TRUE)
nt_final_table_categorized
Sample output
country_name categories
1 Australia First
2 Austria First
3 Denmark First
4 Finland <NA>
5 Iceland First
6 Israel First
7 Luxembourg First
8 Netherlands First
9 New Zealand First
10 Norway First
11 Sweden First
12 Switzerland First

Get world region name from a country name in R

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

aggregates variables into new variable

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))

Searching for multiple text patterns in R

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

RScript to create World Map with own values

I would like to be able to plot my own values for a hand full of countries. For Example: China, United States, United Kingdom, Canada and Russia.
I have my own txt file that has 3 columns - ISO3V10, Country and No of Documents.
ISO3V10 Country No of Documents
CAN Canada 30
CHN China 20
RUS Russia 10
GBR United Kingdom 38
USA United States 50
The idea would be to have a world map coloured in for the Country and the data being plotted is No of Documents.
So far I have done this:
myData2 <- read.delim("noofdocuments.txt",header=T, sep='\t')
names(myData2)
myData2[]
jessdata <- data.frame(myData2=c("China", "United States", "United Kingdom",
"Russia", "Canada"))
sPDF <- joinCountryData2Map(jessdata,
joinCode = "NAME",
nameJoinColumn = "myData2")
par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")
mapCountryData(sPDF, nameColumnToPlot="REGION")
Ideally I would like sPDF to be:
sPDF <- joinCountryData2Map(countryExData,
joinCode = "ISO3", nameJoinColumn = "ISO3V10")
Also for REGION to be:
mapCountryData(sPDF, nameColumnToPlot="No.of.Documents")
I have tried all the ways possible to do this, which is why I have REGION as nameColumnToPlot as this is the only way I can get it too work.
Would someone be able to tell me where I have gone wrong in the code?
If the following code works for you, then there may be a problem with the format of your text file or the way that it is read into R.
library(rworldmap)
countryExData<-read.table(text="
ISO3V10\tCountry\tNo of Documents
CAN\tCanada\t30
CHN\tChina\t20
RUS\tRussia\t10
GBR\tUnited Kingdom\t38
USA\tUnited States\t50"
,sep="\t",header=TRUE)
# > countryExData
# ISO3V10 Country No.of.Documents
# 1 CAN Canada 30
# 2 CHN China 20
# 3 RUS Russia 10
# 4 GBR United Kingdom 38
# 5 USA United States 50
sPDF <- joinCountryData2Map(countryExData,
joinCode = "ISO3", nameJoinColumn = "ISO3V10")
# 5 codes from your data successfully matched countries in the map
# 0 codes from your data failed to match with a country code in the map
# 241 codes from the map weren't represented in your data
par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")
mapCountryData(sPDF, nameColumnToPlot="No.of.Documents")
If that worked, you should examine your countryExData object (or myData2? it's unclear from your post) for differences between it and the above object. If you do not discover anything amiss, please post the result of dput(head(countryExData)) in your original post.

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