Can someone teach me how to delete tail word ,thanks.
from
1 North Africa
2 Algeria
3 Canary Islands (Spain)[153]
4 Ceuta (Spain)[154]
to
1 North Africa
2 Algeria
3 Canary Islands
4 Ceuta
I'm sad with my poor English.
It seems that you want to trim a trailing name in parentheses, along with anything which follows to the end of the string. We can use sub for this purpose:
df <- data.frame(id=c(1:4),
places=c("North Africa", "Algeria", "Canary Islands (Spain)[153]", "Ceuta (Spain)[154]"),
stringsAsFactors=FALSE)
df$places <- sub("\\s*\\(.*\\).*$", "", df$places)
df
id places
1 1 North Africa
2 2 Algeria
3 3 Canary Islands
4 4 Ceuta
Specifically, I'm trying to combine the two data frames UN_M.49_Countries and UN_M.49_Regions which contains the country codes in nested lists.
> UN_M.49_Countries
Code Name ISO_Alpha_3
1 004 Afghanistan AFG
2 248 Åland Islands ALA
3 008 Albania ALB
...
> UN_M.49_Regions
Code Name Parent Children Type
1 001 World 002, 019, 010, 142, 150, 009 Region
2 002 Africa 001 015, 202 Region
3 015 Northern Africa 002 012, 818, 434, 504, 729, 788, 732 Region
...
I would like to build a new table which adds two columns to UN_M.49_Countries.
> new_table
Code Name ISO_Alpha_3 Region Subregion
1 004 Afghanistan AFG Asia Southern Asia
2 248 Åland Islands ALA Europe Northern Europe
3 008 Albania ALB Europe Southern Europe
...
I am new to programming and R and, to be honest, I do not even know where to start. Any help would be much appreciated!
install.packages("ISOcodes")
library(ISOcodes)
UN_M.49_Countries
UN_M.49_Regions
if you need to get a specific version you can change Southern Europe to anything you would like, also if don't subset you can get the whole world.
Check out the package documentation.
https://cran.r-project.org/web/packages/ISOcodes/ISOcodes.pdf
data("UN_M.49_Regions")
data("UN_M.49_Countries")
region <- subset(UN_M.49_Regions, Name == "Southern Europe")
codes <- unlist(strsplit(region$Children, ", "))
subset(UN_M.49_Countries, Code %in% codes)
Using the tidyverse
library(ISOcodes)
library(tidyverse)
library(stringr)
countries <- UN_M.49_Countries
regions <- UN_M.49_Regions
countries <- UN_M.49_Countries
region_focused <- regions %>%
mutate(codes = str_split(Children,",")) %>%
unnest() %>%
left_join(countries, by = c("codes" = "Code"))
countr_focused <- regions %>%
mutate(codes = str_split(Children,",")) %>%
unnest() %>%
right_join(countries, by = c("codes" = "Code"))
I am having some problems with the following task
I have a data frame of this type with 99 different countries for thousands of IDs
ID Nationality var 1 var 2 ....
1 Italy //
2 Eritrea //
3 Italy //
4 USA
5 France
6 France
7 Eritrea
....
I want to add a variable corresponding to a given macroregion of Nationality
so I created a matrix of this kind with the rule to follow
Nationality Continent
Italy Europe
Eritrea Africa
Usa America
France Europe
Germany Europe
....
I d like to obtain this
ID Nationality var 1 var 2 Continent
1 Italy // Europe
2 Eritrea // Africa
3 Italy // Europe
4 USA America
5 France Europe
6 France Europe
7 Eritrea Africa
....
I was trying with this command
datasubset <- merge(dataset , continent.matrix )
but it doesn't work, it reports the following error
Error: cannot allocate vector of size 56.6 Mb
that seems very strange to me, also trying to apply this code to a subset it doesn't work. do you have any suggestion on how to proceed?
thank you very much in advance for your help, I hope my question doesn't sound too trivial, but I am quite new to R
You can do this with the left_join function (dplyr's library):
library(dplyr)
df <- tibble(ID=c(1,2,3),
Nationality=c("Italy", "Usa", "France"),
var1=c("a", "b", "c"),
var2=c(4,5,6))
nat_cont <- tibble(Nationality=c("Italy", "Eritrea", "Usa", "Germany", "France"),
Continent=c("Europe", "Africa", "America", "Europe", "Europe"))
df_2 <- left_join(df, nat_cont, by=c("Nationality"))
The output:
> df_2
# A tibble: 3 x 5
ID Nationality var1 var2 Continent
<dbl> <chr> <chr> <dbl> <chr>
1 1 Italy a 4 Europe
2 2 Usa b 5 America
3 3 France c 6 Europe
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I've been given a set of country groups and I'm trying to get a set of mutually exclusive regions so that I can compare them. The problem is that my data contains several groups, many of which overlap. How can I get a set of groups which contain all countries, but do not overlap with each other?
For example, assume that this is the list of countries in the world:
World <- c("Angola", "France", "Germany", "Australia", "New Zealand")
Assume that this is my set of groups:
df <- data.frame(group = c("Africa", "Western Europe", "Europe", "Europe", "Oceania", "Oceania", "Commonwealth Countries"),
element = c("Angola", "France", "Germany", "France", "Australia", "New Zealand", "Australia"))
group element
1 Africa Angola
2 Western Europe France
3 Europe Germany
4 Europe France
5 Oceania Australia
6 Oceania New Zealand
7 Commonwealth Countries Australia
How could I remove overlapping groups (in this case Western Europe) to get a set of groups that contains all countries like the following:
df_solved <- data.frame(group = c("Africa", "Europe", "Europe", "Oceania", "Oceania"),
element = c("Angola", "France", "Germany", "Australia", "New Zealand"))
group element
1 Africa Angola
2 Europe France
3 Europe Germany
4 Oceania Australia
5 Oceania New Zealand
One possible rule could be to minimize the number of groups, e.g. to associate an element with that group which includes the most elements.
library(data.table)
setDT(df)[, n.elements := .N, by = group][
order(-n.elements), .(group = group[1L]), by = element]
element group
1: Germany Europe
2: France Europe
3: Australia Oceania
4: New Zealand Oceania
5: Angola Africa
Explanation
setDT(df)[, n.elements := .N, by = group][]
returns
group element n.elements
1: Africa Angola 1
2: Western Europe France 1
3: Europe Germany 2
4: Europe France 2
5: Oceania Australia 2
6: Oceania New Zealand 2
7: Commonwealth Countries Australia 1
Now, the rows are ordered by decreasing number of elements and for each country the first, i.e., the "largest", group is picked. This should return a group for each country as requested.
In case of ties, i.e., one group contains equally many elements, you can add additional citeria when ordering, e.g., length of the group name, or just alphabetical order.
1) If you want to simply eliminate duplicate elements then use !duplicated(...) as shown. No packages are used.
subset(df, !duplicated(element))
giving:
group element
1 Africa Angola
2 Europe France
3 Europe Germany
5 Oceania Australia
6 Oceania New Zealand
2) set partitioning If each group must be wholly in or wholly out and each element may only appear once then this is a set partitioning problem:
library(lpSolve)
const.mat <- with(df, table(element, group))
obj <- rep(1L, ncol(const.mat))
res <- lp("min", obj, const.mat, "=", 1L, all.bin = TRUE)
subset(df, group %in% colnames(const.mat[, res$solution == 1]))
giving:
group element
1 Africa Angola
2 Europe France
3 Europe Germany
5 Oceania Australia
6 Oceania New Zealand
3) set covering Of course there may be no exact set partition so we could consider the set covering problem (same code exceept "=" is replaced by ">=" in the lp line.
library(lpSolve)
const.mat <- with(df, table(element, group))
obj <- rep(1L, ncol(const.mat))
res <- lp("min", obj, const.mat, ">=", 1L, all.bin = TRUE)
subset(df, group %in% colnames(const.mat[, res$solution == 1]))
giving:
group element
1 Africa Angola
2 Europe France
3 Europe Germany
5 Oceania Australia
6 Oceania New Zealand
and we could optionally then apply (1) to remove any duplicates in the cover.
4) Non-dominated groups Another approach is to remove any group whose elements form a strict subset of the elements of some other group. For example, every element in Western Europe is in Europe and Europe has more elements than Western Europe so the elements of Western Europe are a strict subset of the elements of Europe and we remove Western Europe. Using const.mat from above:
# returns TRUE if jth column of const.mat is dominated by some other column
is_dom_fun <- function(j) any(apply(const.mat[, j] <= const.mat[, -j], 2, all) &
sum(const.mat[, j]) < colSums(const.mat[, -j]))
is_dom <- sapply(seq_len(ncol(const.mat)), is_dom_fun)
subset(df, group %in% colnames(const.mat)[!is_dom])
giving:
group element
1 Africa Angola
3 Europe Germany
4 Europe France
5 Oceania Australia
6 Oceania New Zealand
If there are any duplicates left we can use (1) to remove them.
library(dplyr)
df %>% distinct(element, .keep_all=TRUE)
group element
1 Africa Angola
2 Europe France
3 Europe Germany
4 Oceania Australia
5 Oceania New Zealand
Shoutout to Axeman for beating me with this answer.
Update
Your question is ill-defined. Why is 'Europe' preferred over 'Western Europe'? Put another way, each country is assigned several groups. You want to reduce it to one group per country. How do you decide which group?
Here's one way, we always prefer the biggest:
groups <- df %>% count(group)
df %>% inner_join(groups, by='group') %>%
arrange(desc(n)) %>% distinct(elemenet, .keep_all=TRUE)
group element n
1 Europe France 2
2 Europe Germany 2
3 Oceania Australia 2
4 Oceania New Zealand 2
5 Africa Angola 1
Here is one option with data.table
library(data.table)
setDT(df)[, head(.SD, 1), element]
Or with unique
unique(setDT(df), by = 'element')
# group element
#1: Africa Angola
#2: Europe France
#3: Europe Germany
#4: Oceania Australia
#5: Oceania New Zealand
Packages are used and it is data.table
A completely different approach would be to ignore the given groups but to look up just the country names in the catalogue of UN regions which are available in the countrycodes or ISOcodes packages.
The countrycodes package seems to offer the simpler interface and it also warns about country names which can not be found in its database:
# given country names - note the deliberately misspelled last entry
World <- c("Angola", "France", "Germany", "Australia", "New Zealand", "New Sealand")
# regions
countrycode::countrycode(World, "country.name.en", "region")
[1] "Middle Africa" "Western Europe" "Western Europe" "Australia and New Zealand"
[5] "Australia and New Zealand" NA
Warning message:
In countrycode::countrycode(World, "country.name.en", "region") :
Some values were not matched unambiguously: New Sealand
# continents
countrycode::countrycode(World, "country.name.en", "continent")
[1] "Africa" "Europe" "Europe" "Oceania" "Oceania" NA
Warning message:
In countrycode::countrycode(World, "country.name.en", "continent") :
Some values were not matched unambiguously: New Sealand
I want to use R for text classification. I use DocumentTermMatrix to return the matrix of word:
library(tm)
crude <- "japan korea usa uk albania azerbaijan"
corps <- Corpus(VectorSource(crude))
dtm <- DocumentTermMatrix(corps)
inspect(dtm)
words <- c("australia", "korea", "uganda", "japan", "argentina", "turkey")
test <- DocumentTermMatrix(corps, control=list(dictionary = words))
inspect(test)
The first inspect(dtm) work as expected with result:
Terms
Docs albania azerbaijan japan korea usa
1 1 1 1 1 1
But the second inspect(test) show this result:
Terms
Docs argentina australia japan korea turkey uganda
1 0 1 0 1 0 0
While the expected result is:
Terms
Docs argentina australia japan korea turkey uganda
1 0 0 1 1 0 0
Is it a bug or I use it the wrong way ?
Corpus() seems to have a bug when indexing word frequency.
Use VCorpus() instead, this will give you the expected result.