CODEWITHSUNDEEP
rdplyr
hgz
hgz

Reputation: 349

How to count the 'number of values of a particular variable of a tibble' in an interval in R?

packages required

'dplyr'

'nycflights13'

the tibble I am using is

 q4<-flights%>%group_by(year,month,day)%>%summarise(cancelled=sum(is.na(dep_time)),avg_delay=mean(arr_delay,na.rm = T),totalflights=n())

 q4<-q4%>%mutate(prop=cancelled/totalflights)

using 

q4%>%ungroup()%>%count(prop)

gives me

     # A tibble: 342 x 2
       prop     n
       <dbl> <int>
    1 0           7
    2 0.00101     1
    3 0.00102     2
    4 0.00102     1
    5 0.00102     1
    6 0.00102     1
    7 0.00103     1
    8 0.00103     1
    9 0.00104     1
    10 0.00104     1
    # ... with 332 more rows

Is there a way that I can ( without using brute force logic like for loops etc) get output in the desired form, I am looking for a one-line or two-line solution. Is there a function in dplyr that does it??

Desired Output:

     # A tibble: X x Y
       prop     n
       <dbl> <int>
    1 0-0.1       45          #random numbers
    2 0.1-0.2     54
    3 0.2-0.3     23

Upvotes: 2

Views: 1590

Answers (3)

hgz
hgz

Reputation: 349

I figured one out myself, which I also feel is the best.

           q4%>%ungroup()%>%count(cut_width(prop,0.025))

Output:

                   # A tibble: 11 x 2
                  `cut_width(prop, 0.025)`     n
                    <fct>                    <int>
                 1 [-0.0125,0.0125]           233
                 2 (0.0125,0.0375]             66
                 3 (0.0375,0.0625]             26
                 4 (0.0625,0.0875]             13
                 5 (0.0875,0.112]              14
                 6 (0.112,0.138]                4

Upvotes: 0

Tarssio Barreto
Tarssio Barreto

Reputation: 40

You can use after q4<-q4%>%mutate(prop=cancelled/totalflights) :

q4 %>% ungroup() %>%
mutate(category = cut(prop, breaks = c(-Inf,0.1,0.2,Inf), labels = c("0-0.1","0.1-0.2", "0.2 - 0.3") %>%
count(category)

I believe it will work

Upvotes: 0

Dan
Dan

Reputation: 12084

Below, I use cut to bin the data and then table to count instances of each bin.

data.frame(cut(q4$prop, breaks = c(0, 0.1, 0.2, 0.3)) %>% table)

produces

#           . Freq
# 1   (0,0.1]  341
# 2 (0.1,0.2]   13
# 3 (0.2,0.3]    2

Upvotes: 7

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