Cannot group data by a column in R

Question

I want to get a count of how many times each Player's name appears in my data frame ph1. My code is showing how many times each name is in the dataset, but I cannot group it by nso I can see how many times each Player's name is in the dataset.

I would prefer a dplyr solution but am open to others.

For example:

n Number_Players 1 21 2 2

Code

ph1 %>% filter(!is.na(Player)) %>% group_by(Player) %>% mutate(n= n()) %>% relocate(n, .after=Player)

Sample of how my output is appearing

Player n Year PA AB Peavy, Jake 1 2008 58 49 Gallardo, Yovani 1 2014 59 53 Stratton, Chris 1 2018 50 43 Cashner, Andrew 1 2015 61 60 Anderson, Chase 1 2016 52 45 Burnes, Corbin 1 2021 59 52 Wolf, Randy 1 2009 80 67 Davies, Zach 2 2016 61 53 Senzatela, Antonio 1 2021 51 39 Syndergaard, Noah 2 2015 50 43

Sample data

structure(list(Player = c("Peavy, Jake", "Gallardo, Yovani", "Stratton, Chris", "Cashner, Andrew", "Anderson, Chase", "Burnes, Corbin", "Wolf, Randy", "Davies, Zach", "Senzatela, Antonio", "Syndergaard, Noah", "Syndergaard, Noah", "Davies, Zach", "Sánchez, Aníbal", "Hudson, Dakota", "Leake, Mike", "De La Rosa, Jorge", "Cueto, Johnny", "González, Gio", "Peralta, Wily", "Vólquez, Edinson", "Ryu, Hyun Jin", "Dempster, Ryan", "Holland, Derek", "Wheeler, Zack", "Sabathia, CC"), Year = c(2008, 2014, 2018, 2015, 2016, 2021, 2009, 2016, 2021, 2015, 2016, 2019, 2019, 2019, 2016, 2015, 2008, 2015, 2014, 2014, 2014, 2009, 2018, 2014, 2008), PA = c(58, 59, 50, 61, 52, 59, 80, 61, 51, 50, 67, 54, 56, 59, 55, 50, 56, 56, 68, 64, 56, 75, 56, 62, 50), AB = c(49, 53, 43, 60, 45, 52, 67, 53, 39, 43, 58, 51, 52, 51, 49, 48, 45, 43, 57, 53, 47, 64, 53, 50, 48)), row.names = c(NA, -25L), class = c("tbl_df", "tbl", "data.frame"))

I checked the existing stackoverflow solutions, but did not see one that matches my need. If one exists, I would appreciate it if you could provide its link.

Do you need count i.e. ph1 %>% filter(!is.na(Player)) %>% count(Player, name = "Number_Players") or group_by/summarise instead of mutate — akrun
– akrun, Commented Feb 13, 2022 at 17:02
Or may be ph1 %>% filter(!is.na(Player)) %>% count(Player) %>% group_by(n) %>% summarise(Number_Players = n_distinct(Player)) or > ph1 %>% filter(!is.na(Player)) %>% count(Player) %>% group_by(n) %>% summarise(Number_Players = n()) I get 21 for n = 1 compared to your 8 — akrun
– akrun, Commented Feb 13, 2022 at 17:04
@akrun: You are correct when you wrote "I get 21 for n = 1 compared to your 8." Thanks for the feedback. — Metsfan
– Metsfan, Commented Feb 13, 2022 at 17:14
Do you need count i.e. ph1 %>% filter(!is.na(Player)) %>% count(Player, name = "Number_Players") or group_by/summarise instead of mutate @akrun--Good question. I am a bit more focused on the count part, but please feel free to share whatever you think can be helpful. — Metsfan
– Metsfan, Commented Feb 13, 2022 at 17:32
count is a compact way to the frequency count instead of group_by/summarise with n() — akrun
– akrun, Commented Feb 13, 2022 at 17:33

akrun · Accepted Answer · 2022-02-13 17:30:48Z

We may use count to return the frequency count and then do a grouping on the count to do the count or n() again

library(dplyr) ph1 %>% filter(!is.na(Player)) %>% count(Player) %>% group_by(n) %>% summarise(Number_Players = n())

-output

# A tibble: 2 × 2 n Number_Players <int> <int> 1 1 21 2 2 2

or in base R, we can use table twice

stack(table(table(ph1$Player)))[2:1]

langtang · Accepted Answer · 2022-02-13 17:12:08Z

ph %>% filter(!is.na(Player)) %>% group_by(Player) %>% count() %>% group_by(n) %>% count(name="Number_Players") # A tibble: 2 x 2 # Groups: n [2] n Number_Players <int> <int> 1 1 21 2 2 2

TarJae · Accepted Answer · 2022-02-13 17:53:34Z

Here is another dplyr way:

First we use add_count to add the count as a new column, then we have to consider the duplets with distinct and finally we can apply count on n (that was generated by add_count)

ph1 %>% add_count(Player) %>% distinct(Player, .keep_all = TRUE) %>% count(n, name= "Number_Players")

 n Number_Players <int> <int> 1 1 21 2 2 2

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