Reputation: 487
I have a dataset similar to sample dataset provided below:
| Name | Response_days | state |
|------|---------------|-------|
| John | 0 | NY |
| John | 6 | NY |
| John | 9 | NY |
| Mike | 3 | CA |
| Mike | 7 | CA |
The same is represented as:
Name = c("John","John", "John", "Mike", "Mike")
Response_days = c(0,6,9,3,7)
state= c("NY","NY","NY", "CA","CA")
df= data.frame(Name, Response_days, state, stringsAsFactors = TRUE)
df$Response_days= as.integer(df$Response_days)
I want to subset the data and look at only the Response_days>5. After that I want to group by on 'Name' and count the occurrences of 'Response_days'. I have tried the code mentioned below but it throws an error.
df1=subset(df, df$Response_days>5) %>% group_by(Name) %>%
summarise(count= count(Response_days))
The error that I get is Error:
Problem with `summarise()` input `count`.
x no applicable method for 'count' applied to an object of class "c('double', 'numeric')"
i Input `count` is `count(Response_days)`.
i The error occurred in group 1: Name = "John".
Could someone please explain me where am I going wrong? Also, my final output should look something like the one below:
| Name | Response_days |
|------|---------------|
| John | 2 |
| Mike | 1 |
Upvotes: 3
Views: 1645
Reputation: 886938
We can use data.table
library(data.table)
setDT(df)[Response_days > 5, .(count = .N), Name]
Or using base R
table(subset(df, Response_days > 5)$Name)
Upvotes: 0
Reputation: 388807
Here are couple ways to do this in dplyr
-
library(dplyr)
#1.
df %>% filter(Response_days>5) %>% count(Name, name = 'Count')
#2.
df %>% group_by(Name) %>% summarise(count = sum(Response_days > 5))
and in base R :
#1.
aggregate(Response_days~Name, subset(df, Response_days>5), length)
#2.
aggregate(Response_days~Name, df, function(x) sum(x > 5))
Upvotes: 3