I have a dataset divided into passenger names and their status (suppose, 10 cateogories) like this.
Passenger
Status
Peter
Captain
Mary
Mrs.
Claudette
Mrs.
Marius
Doc.
Holmes
Mr.
...
...
ecc.
In R, how can I display how many passengers are characterised by a specific Status and who?
I had in mind a table that represented a situation like "n passengers into the "Mrs." category and their names are "Claudette, Mary ecc."
(I don't need the whole string message, only the number and their names)
How can I do it?
Simply using dplyr
dummy <- read.table(text = "Passenger Status
Peter Captain
Mary Mrs.
Claudette Mrs.
Marius Doc.
Holmes Mr.", header = T)
dummy %>%
group_by(Status) %>%
summarise(n = n(),
names = paste0(Passenger, collapse = ", ")) %>%
mutate(res = paste0(n, ' passengers into the ', Status, "category and their names are ", names))
Status n names res
<chr> <int> <chr> <chr>
1 Captain 1 Peter 1 passengers into the Captaincategory and their names are Peter
2 Doc. 1 Marius 1 passengers into the Doc.category and their names are Marius
3 Mr. 1 Holmes 1 passengers into the Mr.category and their names are Holmes
4 Mrs. 2 Mary, Claudette 2 passengers into the Mrs.category and their names are Mary, Claudette
Related
To set the scene, I have a set of data where two columns of the data have been mixed up. To give a simple example:
df1 <- data.frame(Name = c("Bob", "John", "Mark", "Will"), City=c("Apple", "Paris", "Orange", "Berlin"), Fruit=c("London", "Pear", "Madrid", "Orange"))
df2 <- data.frame(Cities = c("Paris", "London", "Berlin", "Madrid", "Moscow", "Warsaw"))
As a result, we have two small data sets:
> df1
Name City Fruit
1 Bob Apple London
2 John Paris Pear
3 Mark Orange Madrid
4 Will Berlin Orange
> df2
Cities
1 Paris
2 London
3 Berlin
4 Madrid
5 Moscow
6 Warsaw
My aim is to create a new column where the cities are in the correct place using df2. I am a bit new to R so I don't know how this would work.
I don't really know where to even start with this sort of a problem. My full dataset is much larger and it would be good to have an efficient method of unpicking this issue!
If the 'City' values are only different. We may loop over the rows, create a logical vector based on the matching values with 'Cities' from 'df2', and concatenate with the rest of the values by getting the matched values second in the order
df1[] <- t(apply(df1, 1, function(x)
{
i1 <- x %in% df2$Cities
i2 <- !i1
x1 <- x[i2]
c(x1[1], x[i1], x1[2])}))
-output
> df1
Name City Fruit
1 Bob London Apple
2 John Paris Pear
3 Mark Madrid Orange
4 Will Berlin Orange
using dplyr package this is a solution, where it looks up the two City and Fruit values in df1, and takes the one that exists in the df2 cities list.
if none of the two are a city name, an empty string is returned, you can replace that with anything you prefer.
library(dplyr)
df1$corrected_City <- case_when(df1$City %in% df2$Cities ~ df1$City,
df1$Fruit%in% df2$Cities ~ df1$Fruit,
TRUE ~ "")
output, a new column created as you wanted with the city name on that row.
> df1
Name City Fruit corrected_City
1 Bob Apple London London
2 John Paris Pear Paris
3 Mark Orange Madrid Madrid
4 Will Berlin Orange Berlin
Another way is:
library(dplyr)
library(tidyr)
df1 %>%
mutate(across(1:3, ~case_when(. %in% df2$Cities ~ .), .names = 'new_{col}')) %>%
unite(New_Col, starts_with('new'), na.rm = TRUE, sep = ' ')
Name City Fruit New_Col
1 Bob Apple London London
2 John Paris Pear Paris
3 Mark Orange Madrid Madrid
4 Will Berlin Orange Berlin
I'm working on a banking project where I'm trying to find a yearly sum of money spent, while the dataset has these listed as monthly transactions.
Month Name Money Spent
2 John Smith 10
3 John Smith 25
4 John Smith 20
2 Joe Nais 10
3 Joe Nais 25
4 Joe Nais 20
Right now, this is the code I have:
OTData <- OTData %>%
mutate(
OTData,
Full Year = [CODE NEEDED TO SUM UP]
)
Thanks!
As #Pawel said, there's no question here. I assume you want:
df <- data.frame(Month = c(2,3,4,2,3,4),
Name = c("John Smith", "John Smith", "John Smith",
"Joe Nais", "Joe Nais", "Joe Nais"),
Money_Spent = c(10,25,20,10,25,20))
df %>%
group_by(Name) %>%
summarize(Full_year = sum(Money_Spent))
Name Full_year
<fct> <dbl>
1 Joe Nais 55
2 John Smith 55
NOTE: You're going to run into trouble if you include spaces in your variable names. You really should replace them with ., _, or camelCase as in the above example.
Sample data frame:
name <- c("Smith John Michael","Smith, John Michael","Smith John, Michael","Smith-John Michael","Smith-John, Michael")
df <- data.frame(name)
df
name
1 Smith John Michael
2 Smith, John Michael
3 Smith John, Michael
4 Smith-John Michael
5 Smith-John, Michael
I need to achieve the following desired output:
name first.name last.name
1 Smith John Michael John Smith
2 Smith, John Michael John Smith
3 Smith John, Michael Michael Smith John
4 Smith-John Michael Michael Smith-John
5 Smith-John, Michael Michael Smith-John
The rules are: if there is a comma in the string, then anything before is the last name. the first word following the comma is first name. If no comma in string, first word is last name, second word is last name. hyphenated words are one word. I would rather acheive this with dplyr and regex but I'll take any solution. Thanks for the help
You can achieve your desired result using strsplit switching between splitting by "," or " " based on whether there is a comma or not in name. Here, we define two functions to make the presentation clearer. You can just as well inline the code within the functions.
get.last.name <- function(name) {
lapply(ifelse(grepl(",",name),strsplit(name,","),strsplit(name," ")),`[[`,1)
}
The result of strsplit is a list. The lapply(...,'[[',1) loops through this list and extracts the first element from each list element, which is the last name.
get.first.name <- function(name) {
d <- lapply(ifelse(grepl(",",name),strsplit(name,","),strsplit(name," ")),`[[`,2)
lapply(strsplit(gsub("^ ","",d), " "),`[[`,1)
}
This function is similar except we extract the second element from each list element returned by strsplit, which contains the first name. We then remove any starting spaces using gsub, and we split again with " " to extract the first element from each list element returned by that strsplit as the first name.
Putting it all together with dplyr:
library(dplyr)
res <- df %>% mutate(first.name=get.first.name(name),
last.name=get.last.name(name))
The result is as expected:
print(res)
## name first.name last.name
## 1 Smith John Michael John Smith
## 2 Smith, John Michael John Smith
## 3 Smith John, Michael Michael Smith John
## 4 Smith-John Michael Michael Smith-John
## 5 Smith-John, Michael Michael Smith-John
Data:
df <- structure(list(name = c("Smith John Michael", "Smith, John Michael",
"Smith John, Michael", "Smith-John Michael", "Smith-John, Michael"
)), .Names = "name", row.names = c(NA, -5L), class = "data.frame")
## name
##1 Smith John Michael
##2 Smith, John Michael
##3 Smith John, Michael
##4 Smith-John Michael
##5 Smith-John, Michael
I am not sure if this is any better than aichao's answer but I gave it a shot anyway. I gives the right output.
df1 <- df %>%
filter(grepl(",",name)) %>%
separate(name, c("last.name","first.middle.name"), sep = "\\,", remove=F) %>%
mutate(first.middle.name = trimws(first.middle.name)) %>%
separate(first.middle.name, c("first.name","middle.name"), sep="\\ ",remove=T) %>%
select(-middle.name)
df2 <- df %>%
filter(!grepl(",",name)) %>%
separate(name, c("last.name","first.name"), sep = "\\ ", remove=F)
df<-rbind(df1,df2)
I know nothing of R, and I have a data.frame with 2 columns, both of them are about the sex of the animals, but one of them have some corrections and the other doesn't.
My desired data.frame would be like this:
id sex father mother birth.date farm
0 1 john ray 05/06/94 1
1 1 doug ana 18/02/93 NA
2 2 bryan kim 21/03/00 3
But i got to this data.frame by using merge on 2 others data.frames
id sex.x father mother birth.date sex.y farm
0 2 john ray 05/06/94 1 1
1 1 doug ana 18/02/93 NA NA
2 2 bryan kim 21/03/00 2 3
data.frame 1 or Animals (Has the wrong sex for some animals)
id sex father mother birth.date
0 2 john ray 05/06/94
1 1 doug ana 18/02/93
2 2 bryan kim 21/03/00
data.frame 2 or Farm (Has the correct sex):
id farm sex
0 1 1
2 3 2
The code i used was: Animals_Farm <- merge(Animals , Farm, by="id", all.x=TRUE)
I need to combine the 2 sex columns into one, prioritizing sex.y. How do I do that?
If I correctly understand you example you have a situation similar to what I show below based on the example from the merge function.
> (authors <- data.frame(
surname = I(c("Tukey", "Venables", "Tierney", "Ripley", "McNeil")),
nationality = c("US", "Australia", "US", "UK", "Australia"),
deceased = c("yes", rep("no", 3), "yes")))
surname nationality deceased
1 Tukey US yes
2 Venables Australia no
3 Tierney US no
4 Ripley UK no
5 McNeil Australia yes
> (books <- data.frame(
name = I(c("Tukey", "Venables", "Tierney",
"Ripley", "Ripley", "McNeil", "R Core")),
title = c("Exploratory Data Analysis",
"Modern Applied Statistics ...", "LISP-STAT",
"Spatial Statistics", "Stochastic Simulation",
"Interactive Data Analysis",
"An Introduction to R"),
deceased = c("yes", rep("no", 6))))
name title deceased
1 Tukey Exploratory Data Analysis yes
2 Venables Modern Applied Statistics ... no
3 Tierney LISP-STAT no
4 Ripley Spatial Statistics no
5 Ripley Stochastic Simulation no
6 McNeil Interactive Data Analysis no
7 R Core An Introduction to R no
> (m1 <- merge(authors, books, by.x = "surname", by.y = "name"))
surname nationality deceased.x title deceased.y
1 McNeil Australia yes Interactive Data Analysis no
2 Ripley UK no Spatial Statistics no
3 Ripley UK no Stochastic Simulation no
4 Tierney US no LISP-STAT no
5 Tukey US yes Exploratory Data Analysis yes
6 Venables Australia no Modern Applied Statistics ... no
Where authors might represent your first dataframe and books your second and deceased might be the value that is in both dataframe but only up to date in one of them (authors).
The easiest way to only include the correct value of deceased would be to simply exclude the incorrect one from the merge.
> (m2 <- merge(authors, books[names(books) != "deceased"],
by.x = "surname", by.y = "name"))
surname nationality deceased title
1 McNeil Australia yes Interactive Data Analysis
2 Ripley UK no Spatial Statistics
3 Ripley UK no Stochastic Simulation
4 Tierney US no LISP-STAT
5 Tukey US yes Exploratory Data Analysis
6 Venables Australia no Modern Applied Statistics ...
The line of code books[names(books) != "deceased"] simply subsets the dataframe books to remove the deceased column leaving only the correct deceased column from authors in the final merge.
I have a df where each row represents an individual and each column a characteristic of these individuals. One of the columns is TeamName, which is the name of the Team that individual belongs to. Multiple individuals belong to a Team.
I'd like a function in R that creates a new column with the number of team members for each Team.
So, for example I have:
df
Name Surname TeamName
John Smith Champions
Mary Osborne Socceroos
Mark Johnson Champions
Rory Bradon Champions
Jane Bryant Socceroos
Bruce Harper
I'd like to have
df1
Name Surname TeamName TeamNo
John Smith Champions 3
Mary Osborne Socceroos 2
Mark Johnson Champions 3
Rory Bradon Champions 3
Jane Bryant Socceroos 2
Bruce Harper 0
So as you can see the counting includes that individual too, and if someone (e.g. Bruce Harper) has no Team name, then he gets a 0.
How can I do that? Thanks!
This is a solution based on using data.table which perhaps is too much for what you need, but here it goes:
library(data.table)
dt=data.table(df)
# First, let's convert the factors of TeamName, to characters
dt[,TeamName:=as.character(TeamName)]
# Now, let find all the team numbers
dt[,TeamNo:=.N, by='TeamName']
# Let's exclude the special cases
dt[is.na(TeamName),TeamNo:=NA]
dt[TeamName=="",TeamNo:=NA]
It is clearly not the best solution, but I hope this helps
If you need to know the number of unique members in the first two columns based on the 'TeamName' column, one option is n_distinct from dplyr
library(dplyr)
library(tidyr)
df %>%
unite(Var, Name, Surname) %>% #paste the columns together
group_by(TeamName) %>% #group by TeamName
mutate(TeamNo= n_distinct(Var)) %>% #create the TeamNo column
separate(Var, into=c('Name', 'Surname')) #split the 'Var' column
Or if it just the number of rows per 'TeamName', we can group by 'TeamName', get the number of rows per group with n(), create the 'TeamNo' column with mutate based on that n(), and if needed an ifelse condition can be used to give NA for 'TeamName' that are '' or NA.
df %>%
group_by(TeamName) %>%
mutate(TeamNo = ifelse(is.na(TeamName)|TeamName=='', NA_integer_, n()))
# Name Surname TeamName TeamNo
#1 John Smith Champions 3
#2 Mary Osborne Socceroos 2
#3 Mark Johnson Champions 3
#4 Rory Bradon Champions 3
#5 Jane Bryant Socceroos 2
#6 Bruce Harper NA
Or you can use ave from base R. Suppose if there are '' and NA, I would first convert the '' to NA and then use ave to get the length of 'TeamNo' grouped by that column. It will give NA for `NA' values. For example.
v1 <- c(df$TeamName, NA)# appending an NA with the example to show the case
is.na(v1) <- v1=='' #convert the `'' to `NA`
as.numeric(ave(v1, v1, FUN=length))
#[1] 3 2 3 3 2 NA NA
Using sqldf:
library(sqldf)
sqldf("SELECT Name, Surname, TeamName, n
FROM df
LEFT JOIN
(SELECT TeamName, COUNT(Name) AS n
FROM df
WHERE NOT TeamName IS '' GROUP BY TeamName)
USING (TeamName)")
Output:
Name Surname TeamName n
1 John Smith Champions 3
2 Mary Osborne Socceroos 2
3 Mark Johnson Champions 3
4 Rory Bradon Champions 3
5 Jane Bryant Socceroos 2
6 Bruce Harper NA