Use aggregate function to calculate output in data frame - r

Question

I've been trying myself and searching for a while now over the net and stackoverflow to no success. I've got a dataframe which I subset from applying conditions and select for projection but fail to retrieve aggregated output.
Dataframe mydf:
mydf = list()
mydf = cbind(mydf,
c("New York", "New York", "San Francisco"),
c(4000, 7600, 2500),
c("Bartosz", "Damian", "Maciej"))
mydf = as.data.frame(mydf)
colnames(mydf) = c("city","salary","name")
Let's assume given part of dataframe returned with:
subset(mydf, city == "New York", select = c(salary, name))
which return a data frame such as:
salary name
9 4000 Bartosz
10 7600 Damian
Now I need to calculate from the given salary a sum, avg and choose an employee with least salary from above data frame, preferably using one-liner by modifying the above code (I'm guessing it's possible), so that it returns:
for sum: 11600
for avg: 5800
for least: 4000 Bartosz
I've tried things as (1)
subset(mydf, city == "New York", select = sum(salary))
or (2)
x = subset(mydf, city == "New York", select = salary)
min(x)
and many more combination which only yields errors saying that summary function is only defined on a data frame with all variables being numbers (2) or the same output as the first code without sum (1)

Answer 1

The problem might be that your dataframe object actually contains a bunch of lists. So if you take
ny.df = subset(mydf, city == "New York", select = c(salary, name))
then any of the subsequent work needs to be peppered with as.numeric calls to translate your lists into vectors. These will give you your answers:
sum(as.numeric(ny.df$salary)) # sum
mean(as.numeric(ny.df$salary)) # avg
ny.df[which(as.numeric(ny.df$salary) == min(as.numeric(ny.df$salary))),] # row with min salary
Alternatively, you can define mydf as a dataframe of vectors instead of a dataframe of lists:
mydf = data.frame(c("New York", "New York", "San Francisco"),
c(4000, 7600, 2500),
c("Bartosz", "Damian", "Maciej"))
colnames(mydf) = c("city","salary","name")
ny.df = subset(mydf, city == "New York", select = c(salary, name))
sum(ny.df$salary)
mean(ny.df$salary)
ny.df[which(ny.df$salary == min(ny.df$salary)),]

Answer 2

Your mydf was weird so I made my own. I split mydf by city and then obtained the necessary data from running necessary operations (mean, sum, etc.) on each subgroup.
#DATA
mydf = structure(list(city = structure(c(1L, 1L, 2L), .Label = c("New York",
"San Francisco"), class = "factor"), salary = c(4000, 7600, 2500
), name = structure(1:3, .Label = c("Bartosz", "Damian", "Maciej"
), class = "factor")), .Names = c("city", "salary", "name"), row.names = c(NA,
-3L), class = "data.frame")
do.call(rbind, lapply(split(mydf, mydf$city), function(a)
data.frame(employee = a$name[which.min(a$salary)], #employee with least salary
mean = mean(a$salary), #mean salary
sum = sum(a$salary)))) #sum of salary
# employee mean sum
#New York Bartosz 5800 11600
#San Francisco Maciej 2500 2500

Answer 3

There is a simple and fast solution using data.table
library(data.table)
setDT(mydf)[, .( salary_sum = sum(salary),
salary_avg = mean(salary),
name = name[which.min(salary)]), by= city]
> city salary_sum salary_avg name
> 1: New York 11600 5800 Bartosz
> 2: San Francisco 2500 2500 Maciej
your dataset:
mydf = data.frame(city=c("New York", "New York", "San Francisco"),
salary=c(4000, 7600, 2500),
name=c("Bartosz", "Damian", "Maciej"))

Answer 4

Your data frame is structured unusally as lists within the dataframe, which may be casuign you issues. Here is a dplyr solution (now edited to find th elowest salary)
library(dplyr)
mydf <- data.frame(
city = c("New York", "New York", "San Francisco"),
salary = c(4000, 7600, 2500),
name = c("Bartosz", "Damian", "Maciej"))
mydf %>%
group_by(city) %>%
mutate(avg = mean(salary),
sum = sum(salary)) %>%
top_n(-1, wt = salary)
# city salary name avg sum
# <fctr> <dbl> <fctr> <dbl> <dbl>
# 1 New York 4000 Bartosz 5800 11600
# 2 San Francisco 2500 Maciej 2500 2500

Answer 5

I think the dplyr is what you might be looking for:
library(dplyr)
mydf %>%
group_by(city) %>%
filter (city =="New York") %>%
summarise(mean(salary), sum(salary))
# A tibble: 1 x 3
# city mean(salary) sum(salary)
# <fctr> <dbl> <dbl>
#1 New York 5800 11600
There is a good tutorial at this link link[https://rpubs.com/justmarkham/dplyr-tutorial]