I have a big data frame that contains data about the outcomes of sports matches. I want to try and extract specific data from the data frame depending on certain criteria. Here's a quick example of what I mean...
Imagine I have a data frame df, which displays data about specific football matches of a tournament on each row, like so:
Winner_Teams Win_Capt_Nm Win_Country Loser_teams Lose_Capt_Nm Lose_Country
1 Man utd John England Barcalona Carlos Spain
2 Liverpool Steve England Juventus Mario Italy
3 Man utd John Scotland R Madrid Juan Spain
4 Paris SG Teirey France Chelsea Mark England
So, for example, in row [1] Man utd won against Barcalona, Man utd's captain's name was John and he is from England. Barcalona's (the losers of the match) captain's name was Carlos and he is from Spain.
I want to construct a vector with the names of all English players in the tournament, where the output should look something like this:
[1] "John" "Mark" "Steve"
Here's what I've tried so far...
My first step was to create a data frame that discards all the matches that don't have English captains
> England_player <- data.frame(filter(df, Win_Country=="England" ))
> England_player
Winner_Teams Win_Capt_Nm Win_Country Loser_teams Lose_Capt_Nm Lose_Country
1 Man utd John England Barcalona Carlos Spain
2 Liverpool Steve England Juventus Mario Italy
3 Paris SG Teirey France Chelsea MArk England
Then I used select() on England_player to isolate just the names:
> England_player_names <- select(England_player, Win_Capt_Nm, Lose_Capt_Nm)
> England_player_names
Win_Capt_Nm Lose_Capt_Nm
1 John Carlos
2 Steve Mario
3 Teirey Mark
And then I get stuck! As you can see, the output displays the English winner's name and the name of his opponent... which is not what I want!
It's easy to just read the names off this data frame.. but the data frame I'm working with is large, so just reading the values is no good!
Any suggestions as to how I'd do this?
english.players <- union(data$Win_Capt_Nm[data$Win_Country == 'England'], data$Lose_Capt_Nm[data$Lose_Country == 'England'])
[1] "John" "Steve" "Mark"
Related
I'm a bit new to R and tm so struggling with this exercise!
I have one description column with messy unstructured data containing words about the name, city and country of a customer. And another column with the amount of sold items.
**Description Sold Items**
Mrs White London UK 10
Mr Wolf London UK 20
Tania Maier Berlin Germany 10
Thomas Germany 30
Nick Forest Leeds UK 20
Silvio Verdi Italy Torino 10
Tom Cardiff UK 10
Mary House London 5
Using the tm package and documenttermmatrix, I'm able to break down each row into terms and get the frequency of each word (i.e. the number of customers with that word).
UK London Germany … Mary
Frequency 4 3 2 … 1
However, I would also like to sum the total amount of sold items.
The desired output should be:
UK London Germany … Mary
Frequency 4 3 2 … 1
Sum of Sold Items 60 35 40 … 5
How can I get to this result?
Assuming you can get to the stage where you have the Frequency table:
UK London Germany … Mary
Frequency 4 3 2 … 1
and you can extract the words you can use an apply function with a grep. Here I will create a vector which represents your dictionary you extract from your frequency table:
S_data<-read.csv("data.csv",stringsAsFactors = F)
Words<-c("UK","London","Germany","Mary")
Then use this in an apply as follows. This could be more efficiently done. But you will get the idea:
string_rows<-sapply(Words, function(x) grep(x,S_data$Description))
string_sum<-unlist(lapply(string_rows, function(x) sum(S_data$Items[x])))
> string_sum
UK London Germany Mary
60 35 40 5
Just bind this onto your frequency table
I know this question is very elementary, but I'm having a trouble adding an extra row to show summary of the row.
Let's say I'm creating a data.frame using the code below:
name <- c("James","Kyle","Chris","Mike")
nationality <- c("American","British","American","Japanese")
income <- c(5000,4000,4500,3000)
x <- data.frame(name,nationality,income)
The code above creates the data.frame below:
name nationality income
1 James American 5000
2 Kyle British 4000
3 Chris American 4500
4 Mike Japanese 3000
What I'm trying to do is to add a 5th row and contains: name = "total", nationality = "NA", age = total of all rows. My desired output looks like this:
name nationality income
1 James American 5000
2 Kyle British 4000
3 Chris American 4500
4 Mike Japanese 3000
5 Total NA 16500
In a real case, my data.frame has more than a thousand rows, and I need efficient way to add the total row.
Can some one please advice? Thank you very much!
We can use rbind
rbind(x, data.frame(name='Total', nationality=NA, income = sum(x$income)))
# name nationality income
#1 James American 5000
#2 Kyle British 4000
#3 Chris American 4500
#4 Mike Japanese 3000
#5 Total <NA> 16500
using index.
name <- c("James","Kyle","Chris","Mike")
nationality <- c("American","British","American","Japanese")
income <- c(5000,4000,4500,3000)
x <- data.frame(name,nationality,income, stringsAsFactors=FALSE)
x[nrow(x)+1, ] <- c('Total', NA, sum(x$income))
UPDATE: using list
x[nrow(x)+1, ] <- list('Total', NA, sum(x$income))
x
# name nationality income
# 1 James American 5000
# 2 Kyle British 4000
# 3 Chris American 4500
# 4 Mike Japanese 3000
# 5 Total <NA> 16500
sapply(x, class)
# name nationality income
# "character" "character" "numeric"
If you want the exact row as you put in your post, then the following should work:
newdata = rbind(x, data.frame(name='Total', nationality='NA', income = sum(x$income)))
I though agree with Jaap that you may not want this row to add to the end. In case you need to load the data and use it for other analysis, this will add to unnecessary trouble. However, you may also use the following code to remove the added row before other analysis:
newdata = newdata[-newdata$name=='Total',]
I have a data frame that I'm working with in R, and am trying to check how many times a value occurs within its larger, associated group. Specifically, I'm trying to count the number of cities that are listed for each particular country.
My data look something like this:
City Country
=========================
New York US
San Francisco US
Los Angeles US
Paris France
Nantes France
Berlin Germany
It seems that table() is the way to go, but I can't quite figure it out — how can I find out how many cities are listed for each country? That is to say, how can I find out how many fields in one column are associated with a particular value in another column?
EDIT:
I'm hoping for something along the lines of
3 US
2 France
1 Germany
I guess you can try table.
table(df$Country)
# France Germany US
# 2 1 3
Or using data.table
library(data.table)
setDT(df)[, .N, by=Country]
# Country N
#1: US 3
#2: France 2
#3: Germany 1
Or
library(plyr)
count(df$Country)
# x freq
#1 France 2
#2 Germany 1
#3 US 3
I have a R data frame that looks like this:
Country Property Value
Canada Capital Ottawa
Canada Population 38
Canada Language1 French
Canada Language2 English
United States Capital Washington
United States Population 280
United States Language1 English
United States Language2 NA
I want to re-arrange this so that it looks like this:
Country Capital Population Language1 Language2
Canada Ottawa 38 French English
United States Washington 280 English NA
Is there any way to do this transformation ?
Thanks.
As per Paul Hiemstra's comment:
the reshape2 package's dcast will do this nicely:
dcast(data=yourdataframe, Country~Property, value.var='Value')
If you've got duplicated values in there though it will try to aggregate them using length as a default, which isn't what you want!
I keep reading about the importance of vectorized functionality so hopefully someone can help me out here.
Say I have a data frame with two columns: name and ID. Now I also have another data frame with name and birthplace, but this data frame is much larger than the first, and contains some but not all of the names from the first data frame. How can I add a third column to the the first table that is populated with birthplaces looked up using the second table.
What I have is now is:
corresponding.birthplaces <- sapply(table1$Name,
function(name){return(table2$Birthplace[table2$Name==name])})
This seems inefficient. Thoughts? Does anyone know of a good book/resource for using R 'properly'..I get the feeling that I generally do think in the least computationally effective manner conceivable.
Thanks :)
See ?merge which will perform a database link merge or join.
Here is an example:
set.seed(2)
d1 <- data.frame(ID = 1:5, Name = c("Bill","Bob","Jessica","Jennifer","Robyn"))
d2 <- data.frame(Name = c("Bill", "Gavin", "Bob", "Joris", "Jessica", "Andrie",
"Jennifer","Joshua","Robyn","Iterator"),
Birthplace = sample(c("London","New York",
"San Francisco", "Berlin",
"Tokyo", "Paris"), 10, rep = TRUE))
which gives:
> d1
ID Name
1 1 Bill
2 2 Bob
3 3 Jessica
4 4 Jennifer
5 5 Robyn
> d2
Name Birthplace
1 Bill New York
2 Gavin Tokyo
3 Bob Berlin
4 Joris New York
5 Jessica Paris
6 Andrie Paris
7 Jennifer London
8 Joshua Paris
9 Robyn San Francisco
10 Iterator Berlin
Then we use merge() to do the join:
> merge(d1, d2)
Name ID Birthplace
1 Bill 1 New York
2 Bob 2 Berlin
3 Jennifer 4 London
4 Jessica 3 Paris
5 Robyn 5 San Francisco