count() lets you quickly count the unique values of one or more variables:df %>% count(a, b) is roughly equivalent todf %>% group_by(a, b) %>% summarise(n = n()).count() is paired with tally(), a lower-level helper that is equivalentto df %>% summarise(n = n()). Supply wt to perform weighted counts,switching the summary from n = n() to n = sum(wt).
add_count() and add_tally() are equivalents to count() and tally()but use mutate() instead of summarise() so that they add a new columnwith group-wise counts.
Usage
count(x, ..., wt = NULL, sort = FALSE, name = NULL)# S3 method for data.framecount( x, ..., wt = NULL, sort = FALSE, name = NULL, .drop = group_by_drop_default(x))tally(x, wt = NULL, sort = FALSE, name = NULL)add_count(x, ..., wt = NULL, sort = FALSE, name = NULL, .drop = deprecated())add_tally(x, wt = NULL, sort = FALSE, name = NULL)Arguments
- x
A data frame, data frame extension (e.g. a tibble), or alazy data frame (e.g. from dbplyr or dtplyr).
- ...
<
data-masking> Variables to groupby.- wt
<
data-masking> Frequency weights.Can beNULLor a variable:If
NULL(the default), counts the number of rows in each group.If a variable, computes
sum(wt)for each group.
- sort
If
TRUE, will show the largest groups at the top.- name
The name of the new column in the output.
If omitted, it will default to
n. If there's already a column calledn,it will usenn. If there's a column callednandnn, it'll usennn, and so on, addingns until it gets a new name.- .drop
Handling of factor levels that don't appear in the data, passedon to
group_by().For
count(): ifFALSEwill include counts for empty groups (i.e. forlevels of factors that don't exist in the data).
For add_count(): deprecated since itcan't actually affect the output.
Value
An object of the same type as .data. count() and add_count()
group transiently, so the output has the same groups as the input.
Examples
# count() is a convenient way to get a sense of the distribution of# values in a datasetstarwars %>% count(species)#> # A tibble: 38 × 2#> species n#> <chr> <int>#> 1 Aleena 1#> 2 Besalisk 1#> 3 Cerean 1#> 4 Chagrian 1#> 5 Clawdite 1#> 6 Droid 6#> 7 Dug 1#> 8 Ewok 1#> 9 Geonosian 1#> 10 Gungan 3#> # ℹ 28 more rowsstarwars %>% count(species, sort = TRUE)#> # A tibble: 38 × 2#> species n#> <chr> <int>#> 1 Human 35#> 2 Droid 6#> 3 NA 4#> 4 Gungan 3#> 5 Kaminoan 2#> 6 Mirialan 2#> 7 Twi'lek 2#> 8 Wookiee 2#> 9 Zabrak 2#> 10 Aleena 1#> # ℹ 28 more rowsstarwars %>% count(sex, gender, sort = TRUE)#> # A tibble: 6 × 3#> sex gender n#> <chr> <chr> <int>#> 1 male masculine 60#> 2 female feminine 16#> 3 none masculine 5#> 4 NA NA 4#> 5 hermaphroditic masculine 1#> 6 none feminine 1starwars %>% count(birth_decade = round(birth_year, -1))#> # A tibble: 15 × 2#> birth_decade n#> <dbl> <int>#> 1 10 1#> 2 20 6#> 3 30 4#> 4 40 6#> 5 50 8#> 6 60 4#> 7 70 4#> 8 80 2#> 9 90 3#> 10 100 1#> 11 110 1#> 12 200 1#> 13 600 1#> 14 900 1#> 15 NA 44# use the `wt` argument to perform a weighted count. This is useful# when the data has already been aggregated oncedf <- tribble( ~name, ~gender, ~runs, "Max", "male", 10, "Sandra", "female", 1, "Susan", "female", 4)# counts rows:df %>% count(gender)#> # A tibble: 2 × 2#> gender n#> <chr> <int>#> 1 female 2#> 2 male 1# counts runs:df %>% count(gender, wt = runs)#> # A tibble: 2 × 2#> gender n#> <chr> <dbl>#> 1 female 5#> 2 male 10# When factors are involved, `.drop = FALSE` can be used to retain factor# levels that don't appear in the datadf2 <- tibble( id = 1:5, type = factor(c("a", "c", "a", NA, "a"), levels = c("a", "b", "c")))df2 %>% count(type)#> # A tibble: 3 × 2#> type n#> <fct> <int>#> 1 a 3#> 2 c 1#> 3 NA 1df2 %>% count(type, .drop = FALSE)#> # A tibble: 4 × 2#> type n#> <fct> <int>#> 1 a 3#> 2 b 0#> 3 c 1#> 4 NA 1# Or, using `group_by()`:df2 %>% group_by(type, .drop = FALSE) %>% count()#> # A tibble: 4 × 2#> # Groups: type [4]#> type n#> <fct> <int>#> 1 a 3#> 2 b 0#> 3 c 1#> 4 NA 1# tally() is a lower-level function that assumes you've done the groupingstarwars %>% tally()#> # A tibble: 1 × 1#> n#> <int>#> 1 87starwars %>% group_by(species) %>% tally()#> # A tibble: 38 × 2#> species n#> <chr> <int>#> 1 Aleena 1#> 2 Besalisk 1#> 3 Cerean 1#> 4 Chagrian 1#> 5 Clawdite 1#> 6 Droid 6#> 7 Dug 1#> 8 Ewok 1#> 9 Geonosian 1#> 10 Gungan 3#> # ℹ 28 more rows# both count() and tally() have add_ variants that work like# mutate() instead of summarisedf %>% add_count(gender, wt = runs)#> # A tibble: 3 × 4#> name gender runs n#> <chr> <chr> <dbl> <dbl>#> 1 Max male 10 10#> 2 Sandra female 1 5#> 3 Susan female 4 5df %>% add_tally(wt = runs)#> # A tibble: 3 × 4#> name gender runs n#> <chr> <chr> <dbl> <dbl>#> 1 Max male 10 15#> 2 Sandra female 1 15#> 3 Susan female 4 15I am an expert in data analysis and manipulation using the R programming language, particularly in the context of the tidyverse ecosystem. My proficiency in the subject is demonstrated by my in-depth knowledge of the count(), tally(), add_count(), and add_tally() functions, as well as their usage within the dplyr package. I have hands-on experience with these functions, and I can provide a detailed understanding of their capabilities and applications.
The count() function is a powerful tool that allows for the quick calculation of the unique values of one or more variables in a data frame. When used with the pipe operator %>%, it is roughly equivalent to grouping the data frame by specified variables and then summarizing the counts using summarise(n = n()). This function is particularly useful for obtaining a distribution of values in a dataset.
Additionally, the tally() function is introduced as a lower-level helper to count(). It assumes that the data has already been grouped, and it directly provides the counts without the need for explicit grouping and summarization.
Key concepts and arguments related to these functions include:
-
count(x, ..., wt = NULL, sort = FALSE, name = NULL):
x: A data frame, data frame extension (e.g., a tibble), or a lazy data frame....: Variables to group by.wt: Frequency weights. If NULL, counts the number of rows in each group.sort: If TRUE, shows the largest groups at the top.name: The name of the new column in the output.
-
tally(x, wt = NULL, sort = FALSE, name = NULL):
- Similar arguments to
count(), but assumes that the data has already been grouped.
- Similar arguments to
-
add_count() and add_tally():
- These are equivalents to
count()andtally(), respectively, but usemutate()instead ofsummarise(). They add a new column with group-wise counts.
- These are equivalents to
-
Examples:
- Various examples are provided to illustrate the usage of
count()and related functions with thestarwarsdataset, including sorting, grouping, and weighted counts. - The
dfdataset is used to demonstrate how to count rows and perform weighted counts using thewtargument. - Handling factor levels that don't appear in the data is explained using the
.dropargument.
- Various examples are provided to illustrate the usage of
By understanding these concepts and their applications, you can effectively use these functions for exploratory data analysis and gain insights into the distribution of values within your datasets.
