I'm trying to calculate the best goal differentials in the group stage of the 2014 world cup.
football <- read.csv(
file="http://pastebin.com/raw.php?i=iTXdPvGf",
header = TRUE,
strip.white = TRUE
)
football <- head(football,n=48L)
football[which(max(abs(football$home_score - football$away_score)) == abs(football$home_score - football$away_score)),]
Results in
home home_continent home_score away away_continent away_score result
4 Cameroon Africa 0 Croatia Europe 4 l
7 Spain Europe 1 Netherlands Europe 5 l
37 Germany
So those are the games with the highest goal differntial, but now I need to make a new data frame that has a team name, and abs(football$home_score-football$away_score)
football$score_diff <- abs(football$home_score - football$away_score)
football$winner <- ifelse(football$home_score > football$away_score, as.character(football$home),
ifelse(football$result == "d", NA, as.character(football$away)))
You could save some typing in this way. You first get score differences and winners. When the result indicates w, home is the winner. So you do not have to look into scores at all. Once you add the score difference and winner, you can subset your data by subsetting data with max().
mydf <- read.csv(file="http://pastebin.com/raw.php?i=iTXdPvGf",
header = TRUE, strip.white = TRUE)
mydf <- head(mydf,n = 48L)
library(dplyr)
mutate(mydf, scorediff = abs(home_score - away_score),
winner = ifelse(result == "w", as.character(home),
ifelse(result == "l", as.character(away), "draw"))) %>%
filter(scorediff == max(scorediff))
# home home_continent home_score away away_continent away_score result scorediff winner
#1 Cameroon Africa 0 Croatia Europe 4 l 4 Croatia
#2 Spain Europe 1 Netherlands Europe 5 l 4 Netherlands
#3 Germany Europe 4 Portugal Europe 0 w 4 Germany
Here is another option without using ifelse for creating the "winner" column. This is based on row/column indexes. The numeric column index is created by matching the result column with its unique elements (match(football$result,..), and the row index is just 1:nrow(football). Subset the "football" dataset with columns 'home', 'away' and cbind it with an additional column 'draw' with NAs so that the 'd' elements in "result" change to NA.
football$score_diff <- abs(football$home_score - football$away_score)
football$winner <- cbind(football[c('home', 'away')],draw=NA)[
cbind(1:nrow(football), match(football$result, c('w', 'l', 'd')))]
football[with(football, score_diff==max(score_diff)),]
# home home_continent home_score away away_continent away_score result
#60 Brazil South America 1 Germany Europe 7 l
# score_diff winner
#60 6 Germany
If the dataset is very big, you could speed up the match by using chmatch from library(data.table)
library(data.table)
chmatch(as.character(football$result), c('w', 'l', 'd'))
NOTE: I used the full dataset in the link
Related
This question already has answers here:
dplyr filter with condition on multiple columns
(6 answers)
Closed 2 years ago.
I have a data set like such.
df = data.frame(Business = c('HR','HR','Finance','Finance','Legal','Legal','Research'), Country = c('Iceland','Iceland','Norway','Norway','US','US','France'), Gender=c('Female','Male','Female','Male','Female','Male','Male'), Value =c(10,5,20,40,10,20,50))
I need to be filter out all rows where both male value and female value are >= 10. For example, Iceland HR should be removed as well as Research France.
I've tried df %>% group_by(Business,Country) %>% filter((Value>=10)) but this filters out any value less than 10. any ideas?
Maybe this can help:
library(reshape2)
df2 <- reshape(df,idvar = c('Business','Country'),timevar = 'Gender',direction = 'wide')
df2 %>% mutate(Index=ifelse(Value.Female>=10 & Value.Male>=10,1,0)) %>%
filter(Index==1) -> df3
df4 <- reshape2::melt(df3[,-5],idvar=c('Business','Country'))
Business Country variable value
1 Finance Norway Value.Female 20
2 Legal US Value.Female 10
3 Finance Norway Value.Male 40
4 Legal US Value.Male 20
You could just use two ave steps, one with length, one with min.
df <- df[with(df, ave(Value, Country, FUN=length)) == 2, ]
df[with(df, ave(Value, Country, FUN=min)) >= 10, ]
# Business Country Gender Value
# 3 Finance Norway Female 20
# 4 Finance Norway Male 40
# 5 Legal US Female 10
# 6 Legal US Male 20
Notice that this also works if we disturb the data frame.
set.seed(42)
df2 <- df[sample(1:nrow(df)), ]
df2 <- df2[with(df2, ave(Value, Country, FUN=length)) == 2, ]
df2[with(df2, ave(Value, Country, FUN=min)) >= 10, ]
# Business Country Gender Value
# 5 Legal US Female 10
# 6 Legal US Male 20
# 3 Finance Norway Female 20
# 4 Finance Norway Male 40
I want to code the values in a column into fewer values in another column.
For example,
if the value in zipcode column is one of the following c(90272,90049,90077,90210,90046,90069,90024,90025,90048,90036,90038,90028),
code it as "west" in district column.
How can I do it in R?
You can use the ifelse() function.
Set up the data in a dataframe:
df <- data.frame(zipcode = c(90272,90049,90077,90210,90046,90069,90024,90025,90048,90036,90038,90028))
Then use ifelse() to code a new value based on the values of zipcode.
df$district <- ifelse(df$zipcode %in% c(90272,90049,90077,90210,90046,90069,90024,90025,90048,90036,90038,90028),
"west",
NA)
> df
zipcode region
1 90272 west
2 90049 west
3 90077 west
4 90210 west
5 90046 west
6 90069 west
7 90024 west
8 90025 west
9 90048 west
10 90036 west
11 90038 west
12 90028 west
I have two dataframes in R:
city price bedroom
San Jose 2000 1
Barstow 1000 1
NA 1500 1
Code to recreate:
data = data.frame(city = c('San Jose', 'Barstow'), price = c(2000,1000, 1500), bedroom = c(1,1,1))
and:
Name Density
San Jose 5358
Barstow 547
Code to recreate:
population_density = data.frame(Name=c('San Jose', 'Barstow'), Density=c(5358, 547));
I want to create an additional column named city_type in the data dataset based on condition, so if the city population density is above 1000, it's an urban, lower than 1000 is a suburb, and NA is NA.
city price bedroom city_type
San Jose 2000 1 Urban
Barstow 1000 1 Suburb
NA 1500 1 NA
I am using a for loop for conditional flow:
for (row in 1:length(data)) {
if (is.na(data[row,'city'])) {
data[row, 'city_type'] = NA
} else if (population[population$Name == data[row,'city'],]$Density>=1000) {
data[row, 'city_type'] = 'Urban'
} else {
data[row, 'city_type'] = 'Suburb'
}
}
The for loop runs with no error in my original dataset with over 20000 observations; however, it yields a lot of wrong results (it yields NA for the most part).
What has gone wrong here and how can I do better to achieve my desired result?
I have become quite a fan of dplyr pipelines for this type of join/filter/mutate workflow. So here is my suggestion:
library(dplyr)
# I had to add that extra "NA" there, did you not? Hm...
data <- data.frame(city = c('San Jose', 'Barstow', NA), price = c(2000,1000, 500), bedroom = c(1,1,1))
population <- data.frame(Name=c('San Jose', 'Barstow'), Density=c(5358, 547));
data %>%
# join the two dataframes by matching up the city name columns
left_join(population, by = c("city" = "Name")) %>%
# add your new column based on the desired condition
mutate(
city_type = ifelse(Density >= 1000, "Urban", "Suburb")
)
Output:
city price bedroom Density city_type
1 San Jose 2000 1 5358 Urban
2 Barstow 1000 1 547 Suburb
3 <NA> 500 1 NA <NA>
Using ifelse create the city_type in population_density, then we using match
population_density$city_type=ifelse(population_density$Density>1000,'Urban','Suburb')
data$city_type=population_density$city_type[match(data$city,population_density$Name)]
data
city price bedroom city_type
1 San Jose 2000 1 Urban
2 Barstow 1000 1 Suburb
3 <NA> 1500 1 <NA>
How to map column of one CSV file to column of another CSV file in R. If both are in same data type.
For example first column of data frame A consist some text with country name in it. While column of second data frame B contains a standard list of all country .Now I have to map all rows of first data frame with standard country column.
For example column (location) of data frame A consist 10000 rows of data like this
Sydney, Australia
Aarhus C, Central Region, Denmark
Auckland, New Zealand
Mumbai Area, India
Singapore
df1 <- data.frame(col1 = 1:5, col2=c("Sydney, Australia", "Aarhus C, Central Region, Denmark", "Auckland, New Zealand", "Mumbai Area, India", "Singapore"))
Now I have another column (country) of data frame B as
India
USA
New Zealand
UK
Singapore
Denmark
China
df2 <- data.frame(col1=1:7, col2=c("India", "USA", "New Zealand", "UK", "Singapore", "Denmark", "China"))
If location column matches with Country column then, I want to replace that location with country name otherwise it will remain as it is. Sample output is as
Sydney, Australia
Denmark
New Zealand
India
Singapore
Initially, it looked like a trivial question but it's not. This approach works like this:
1. We convert the location string into vector using unlist, strsplit.
2. Then we check if any string in the vector is available in country column. If it is available, we store the country name in res and if not we store notfound.
2. Finally, we check if res contains a country name or not.
df1 <- data.frame(location = c('Sydney, Australia',
'Aarhus C, Central Region, Denmark',
'Auckland, New Zealand',
'Mumbai Area, India',
'Singapore'),stringsAsFactors = F)
df2 <- data.frame(country = c('India',
'USA',
'New Zealand',
'UK',
'Singapore',
'Denmark',
'China'),stringsAsFactors = F)
get_values <- function(i)
{
val <- unlist(strsplit(i, split = ','))
val <- sapply(val, str_trim)
res <- c()
for(j in val)
{
if(j %in% df2$country) res <- append(res, j)
else res <- append(res, 'notfound')
}
if(all(res == 'notfound')) return (i)
else return (res[res!='notfound'])
}
df1$location2 <- sapply(df1$location, get_values)
location location2
1 Sydney, Australia Sydney, Australia
2 Aarhus C, Central Region, Denmark Denmark
3 Auckland, New Zealand New Zealand
4 Mumbai Area, India India
5 Singapore Singapore
A solution using tidyverse. First, please convert your col2 to character by setting stringsAsFactors = FALSE because that is easier to work with.
We can use str_extract to extract the matched country name, and then create a new col2 with mutate and ifelse.
df3 <- df1 %>%
mutate(Country = str_extract(col2, paste0(df2$col2, collapse = "|")),
col2 = ifelse(is.na(Country), col2, Country)) %>%
select(-Country)
df3
# col1 col2
# 1 1 Sydney, Australia
# 2 2 Denmark
# 3 3 New Zealand
# 4 4 India
# 5 5 Singapore
We can also start with df1, use separate_rows to separate the country name. After that, use semi_join to check if the country names are in df2. Finally, we can combine the data frame with the original df1 by rows, and then filter the first one for each id in col1. df3 is the final output.
library(tidyverse)
df3 <- df1 %>%
separate_rows(col2, sep = ", ") %>%
semi_join(df2, by = "col2") %>%
bind_rows(df1) %>%
group_by(col1) %>%
slice(1) %>%
ungroup() %>%
arrange(col1)
df3
# # A tibble: 5 x 2
# col1 col2
# <int> <chr>
# 1 1 Sydney, Australia
# 2 2 Denmark
# 3 3 New Zealand
# 4 4 India
# 5 5 Singapore
DATA
df1 <- data.frame(col1 = 1:5,
col2=c("Sydney, Australia", "Aarhus C, Central Region, Denmark", "Auckland, New Zealand", "Mumbai Area, India", "Singapore"),
stringsAsFactors = FALSE)
df2 <- data.frame(col1=1:7,
col2=c("India", "USA", "New Zealand", "UK", "Singapore", "Denmark", "China"),
stringsAsFactors = FALSE)
If you are looking for the countries, and they come after the cities then you can do something like this.
transform(df1,col3= sub(paste0(".*,\\s*(",paste0(df2$col2,collapse="|"),")"),"\\1",col2))
col1 col2 col3
1 1 Sydney, Australia Sydney, Australia
2 2 Aarhus C, Central Region, Denmark Denmark
3 3 Auckland, New Zealand New Zealand
4 4 Mumbai Area, India India
5 5 Singapore Singapore
Breakdown:
> A=sub(".*,\\s(.*)","\\1",df1$col2)
> B=sapply(A,grep,df2$col2,value=T)
> transform(df1,col3=replace(A,!lengths(B),col2[!lengths(B)]))
col1 col2 col3
1 1 Sydney, Australia Sydney, Australia
2 2 Aarhus C, Central Region, Denmark Denmark
3 3 Auckland, New Zealand New Zealand
4 4 Mumbai Area, India India
5 5 Singapore Singapore
I wonder if there is an simpler way than writing if...else... for the following case. I have a dataframe and I only want the rows with number in column "percentage" >=95. Moreover, for one object, if there is multiple rows fitting this criteria, I only want the largest one(s). If there are more than one largest ones, I would like to keep all of them.
For example:
object city street percentage
A NY Sun 100
A NY Malino 97
A NY Waterfall 100
B CA Washington 98
B WA Lieber 95
C NA Moon 75
Then I'd like the result shows:
object city street percentage
A NY Sun 100
A NY Waterfall 100
B CA Washington 98
I am able to do it using if else statement, but I feel there should be some smarter ways to say: 1. >=95 2. if more than one, choose the largest 3. if more than one largest, choose them all.
You can do this by creating an variable that indicates the rows that have the maximum percentage for each of the objects. We can then use this indicator to subset the data.
# your data
dat <- read.table(text = "object city street percentage
A NY Sun 100
A NY Malino 97
A NY Waterfall 100
B CA Washington 98
B WA Lieber 95
C NA Moon 75", header=TRUE, na.strings="", stringsAsFactors=FALSE)
# create an indicator to identify the rows that have the maximum
# percentage by object
id <- with(dat, ave(percentage, object, FUN=function(i) i==max(i)) )
# subset your data - keep rows that are greater than 95 and have the
# maximum group percentage (given by id equal to one)
dat[dat$percentage >= 95 & id , ]
This works by the addition statement creating a logical, which can then be used to subset the rows of dat.
dat$percentage >= 95 & id
#[1] TRUE FALSE TRUE TRUE FALSE FALSE
Or putting these together
with(dat, dat[percentage >= 95 & ave(percentage, object,
FUN=function(i) i==max(i)) , ])
# object city street percentage
# 1 A NY Sun 100
# 3 A NY Waterfall 100
# 4 B CA Washington 98
You could do this also in data.table using the same approach by #user20650
library(data.table)
setDT(dat)[dat[,percentage==max(percentage) & percentage >=95, by=object]$V1,]
# object city street percentage
#1: A NY Sun 100
#2: A NY Waterfall 100
#3: B CA Washington 98
Or using dplyr
dat %>%
group_by(object) %>%
filter(percentage==max(percentage) & percentage >=95)
Following works:
ddf2 = ddf[ddf$percentage>95,]
ddf3 = ddf2[-c(1:nrow(ddf2)),]
for(oo in unique(ddf2$object)){
tempdf = ddf2[ddf2$object == oo, ]
maxval = max(tempdf$percentage)
tempdf = tempdf[tempdf$percentage==maxval,]
for(i in 1:nrow(tempdf)) ddf3[nrow(ddf3)+1,] = tempdf[i,]
}
ddf3
object city street percentage
1 A NY Sun 100
3 A NY Waterfall 100
4 B CA Washington 98