I am attempting to fill in a new column in my dataset. I have a dataset containing information on football matches. There is a column called "Stadium", which has various stadium names. I wish to add a new column which contains the country of which the stadium is located within. My set looks something like this
Match ID Stadium
1 Anfield
2 Camp Nou
3 Stadio Olimpico
4 Anfield
5 Emirates
I am attempting to create a new column looking like this:
Match ID Stadium Country
1 Anfield England
2 Camp Nou Spain
3 Stadio Olimpico Italy
4 Anfield England
5 Emirates England
There is only a handful of stadiums but many rows, meaning I am trying to find a way to avoid inserting the values manually. Any tips?
You want to get the unique stadium names from your data, manually create a vector with the country for each of those stadiums, then join them using Stadium as a key.
library(dplyr)
# Example data
df <- data.frame(`Match ID` = 1:12,
Stadium = rep(c("Stadio Olympico", "Anfield",
"Emirates"), 4))
# Get the unique stadium names in a vector
unique_stadiums <- df %>% pull(Stadium) %>% unique()
unique_stadiums
#> [1] "Stadio Olympico" "Anfield" "Emirates"
# Manually create a vector of country names corresponding to each element of
# the unique stadum name vector. Ordering matters here!
countries <- c("Italy", "England", "England")
# Place them both into a data.frame
lookup <- data.frame(Stadium = unique_stadiums, Country = countries)
# Join the country names to the original data on the stadium key
left_join(x = df, y = lookup, by = "Stadium")
#> Match.ID Stadium Country
#> 1 1 Stadio Olympico Italy
#> 2 2 Anfield England
#> 3 3 Emirates England
#> 4 4 Stadio Olympico Italy
#> 5 5 Anfield England
#> 6 6 Emirates England
#> 7 7 Stadio Olympico Italy
#> 8 8 Anfield England
#> 9 9 Emirates England
#> 10 10 Stadio Olympico Italy
#> 11 11 Anfield England
#> 12 12 Emirates England
Related
This question already has answers here:
extracting country name from city name in R
(3 answers)
Closed 7 months ago.
I have a dataset containing information on a range of cities, but there is no column which says what country the city is located in. In order to perform the analysis, I need to add an extra column which has the name of the country.
population city
500,000 Oslo
750,000 Bristol
500,000 Liverpool
1,000,000 Dublin
I expect the output to look like this:
population city country
500,000 Oslo Norway
750,000 Bristol England
500,000 Liverpool England
1,000,000 Dublin Ireland
How can I add a column of country names based on the city and population to a large dataset in R?
I am adapting Tom Hoel's answer, as suggested by Ian Campbell. If this is selected I am happy to mark it as community wiki.
library(maps)
library(dplyr)
data("world.cities")
df <- readr::read_table("population city
500,000 Oslo
750,000 Bristol
500,000 Liverpool
1,000,000 Dublin")
df |>
inner_join(
select(world.cities, name, country.etc, pop),
by = c("city" = "name")
) |> group_by(city) |>
filter(
abs(pop - population) == min(abs(pop - population))
)
# A tibble: 4 x 4
# Groups: city [4]
# population city country.etc pop
# <dbl> <chr> <chr> <int>
# 1 500000 Oslo Norway 821445
# 2 750000 Bristol UK 432967
# 3 500000 Liverpool UK 468584
# 4 1000000 Dublin Ireland 1030431
As stated by others, the cities exists in other countries too as well.
library(tidyverse)
library(maps)
data("world.cities")
df <- read_table("population city
500,000 Oslo
750,000 Bristol
500,000 Liverpool
1,000,000 Dublin")
df %>%
merge(., world.cities %>%
select(name, country.etc),
by.x = "city",
by.y = "name")
# A tibble: 7 × 3
city population country.etc
<chr> <dbl> <chr>
1 Bristol 750000 UK
2 Bristol 750000 USA
3 Dublin 1000000 USA
4 Dublin 1000000 Ireland
5 Liverpool 500000 UK
6 Liverpool 500000 Canada
7 Oslo 500000 Norway
I think your best bet would be to add a new column in your dataset called country and fill it out, this is part of the CRSIP-DM process data preparation so this is not uncommon. If that does not answer your question please let me know and i will do my best to help.
nubie here with a dataframe/mutate question... I want to update a dataframe (df1) based on data in another dataframe (df2). For one offs I've used MUTATE so I figure this is the way to go. Additionally I would like a check function added (TRUE/FALSE ?) to indicate if the the field in df1 was updated.
For Example..
df1-
State
<chr>
1 N.Y.
2 FL
3 AL
4 MS
5 IL
6 WS
7 WA
8 N.J.
9 N.D.
10 S.D.
11 CALL
df2
State New_State
<chr> <chr>
1 N.Y. New York
2 FL Florida
3 AL Alabama
4 MS Mississippi
5 IL Illinois
6 WS Wisconsin
7 WA Washington
8 N.J. New Jersey
9 N.D. North Dakota
10 S.D. South Dakota
11 CAL California
I want the output to look like this
df3
New_State Test
<chr>
1 New York TRUE
2 Florida TRUE
3 Alabama TRUE
4 Mississippi TRUE
5 Illinois TRUE
6 Wisconsin TRUE
7 Washington TRUE
8 New Jersey TRUE
9 North Dakota TRUE
10 South Dakota TRUE
11 CALL FALSE
In essence I want R to read the data in df1 and change df1 based on the match in df2 chaining out to the full state name and replace. Lastly if the data in df1 was update mark as "TRUE" (N.Y. to NEW YORK) and "FALSE" if not updated (CALL vs CAL)
Thanks in advance for any and all help.
This should give you the result you're looking for:
match_vec <- match(df1$State, table = df2$State)
This vector should match all the abbreviated state names in df1 with those in df2. Where there's no match, you end up with a missing value:
Then the following code using dplyr should produce the df3 you requested.
library(dplyr)
df3 <- df1 %>%
mutate(New_State = df2$New_State[match_vec]) %>%
mutate(Test = !is.na(match_vec)) %>%
mutate(New_State = ifelse(is.na(New_State),
State, New_State)) %>%
select(New_State, Test)
I have two data sets. One with a numeric value assigned to individual categorical variables (country name) and a second with survey responses including a person's nationality. How do I assign the numeric value to a new column in the survey dataset with matching nationality/country name?
Here is the head of data set 1 (my.data1):
EN HCI
1 South Korea 0.845
2 UK 0.781
3 USA 0.762
Here is the head of data set 2 (my.data2):
Nationality OIS IR
1 South Korea 2 2
2 South Korea 3 3
3 USA 3 4
4 UK 3 3
I would like to make it look like this:
Nationality OIS IR HCI
1 South Korea 2 2 0.845
2 South Korea 3 3 0.845
3 USA 3 4 0.762
4 UK 3 3 0.781
I have tried this but unsuccessfully:
my.data2$HCI <- NA
for (i in i:nrow(my.data2)) {
my.data2$HCI[i] <- my.data1$HCI[my.data1$EN == my.data2$Nationality[i]]
}
We can use a left_join
library(dplyr)
left_join(my.data2, my.data1, by = c("Nationality" = "EN"))
Or with merge from base R
merge(my.data2, my.data1, by.x = c("Nationality", by.y = "EN", all.x = TRUE)
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
I have a origin-destination table like this.
library(dplyr)
set.seed(1983)
namevec <- c('Portugal', 'Romania', 'Nigeria', 'Peru', 'Texas', 'New Jersey', 'Colorado', 'Minnesota')
## Create OD pairs
df <- data_frame(origins = sample(namevec, size = 100, replace = TRUE),
destinations = sample(namevec, size = 100, replace = TRUE))
Question
I got stucked in counting the relationships for each origin-destination (with no directionality).
How can I get output that Colorado-Minnesota and Minnesota-Colorado are seen as one group?
What I have tried so far:
## Counts for each OD-pairs
df %>%
group_by(origins, destinations) %>%
summarize(counts = n()) %>%
ungroup() %>%
arrange(desc(counts))
Source: local data frame [48 x 3]
origins destinations counts
(chr) (chr) (int)
1 Nigeria Colorado 5
2 Colorado Portugal 4
3 New Jersey Minnesota 4
4 New Jersey New Jersey 4
5 Peru Nigeria 4
6 Peru Peru 4
7 Romania Texas 4
8 Texas Nigeria 4
9 Minnesota Minnesota 3
10 Nigeria Portugal 3
.. ... ... ...
One way is to combine the sorted combination of the two locations into a single field. Summarizing on that will remove your two original columns, so you'll need to join them back in.
paired <- df %>%
mutate(
orderedpair = paste(pmin(origins, destinations), pmax(origins, destinations), sep = "::")
)
paired
# # A tibble: 100 × 3
# origins destinations orderedpair
# <chr> <chr> <chr>
# 1 Peru Colorado Colorado::Peru
# 2 Romania Portugal Portugal::Romania
# 3 Romania Colorado Colorado::Romania
# 4 New Jersey Minnesota Minnesota::New Jersey
# 5 Minnesota Texas Minnesota::Texas
# 6 Romania Texas Romania::Texas
# 7 Peru Peru Peru::Peru
# 8 Romania Nigeria Nigeria::Romania
# 9 Portugal Minnesota Minnesota::Portugal
# 10 Nigeria Colorado Colorado::Nigeria
# # ... with 90 more rows
left_join(
paired,
group_by(paired, orderedpair) %>% count(),
by = "orderedpair"
) %>%
select(-orderedpair) %>%
distinct() %>%
arrange(desc(n))
# # A tibble: 48 × 3
# origins destinations n
# <chr> <chr> <int>
# 1 Romania Portugal 6
# 2 New Jersey Minnesota 6
# 3 Portugal Romania 6
# 4 Minnesota New Jersey 6
# 5 Romania Texas 5
# 6 Nigeria Colorado 5
# 7 Texas Nigeria 5
# 8 Texas Romania 5
# 9 Nigeria Texas 5
# 10 Peru Peru 4
# # ... with 38 more rows
(The only reason I used "::" as the separator is in the unlikely event you need to parse orderedpair; using the default " " (space) won't work with (e.g.) "New Jersey" in the mix.)