Stack Overflow Asked by Armel Tedjou on January 5, 2022
I want to compute the frequency of the modalities according to the species found.
Here is the data frame, and I want to count the number of each type _gite
and count those where only aegypti
was found, only those where albo
where found and mixed where both were found together.
type_gite aegypti albopictus total
recipient_abandonne 19 0 19
recipient_stockage 0 2 2
recipient_stockage 8 0 8
recipient_stockage 36 0 36
recipient_stockage 13 0 13
recipient_stockage 1 3 4
autres 0 1 1
autres 0 9 9
recipient_abandonne 3 0 3
Here is how it should look like:
type gite aegypti albopictus mixed total
recipient_abandonne 2 0 0 2
recipient stockage 3 1 1 5
autres 0 2 0 2
total 5 3 1 9
Which code or aggregation formula is suited the most?
You can use dplyr
and janitor
(to get the Total
row) to achieve what you need:
#install.packages("janitor")
#install.packages("dplyr")
library(dplyr)
df1 %>% select(-total_collected) %>% group_by(type_gite) %>%
mutate(mixed = +(aegyti_collected * albopictus_collected > 0)) %>%
mutate_at(vars(aegyti_collected:albopictus_collected), list(~+(. > 0)*!(mixed))) %>%
summarise_all(sum) %>% janitor::adorn_totals(c("row", "col"))
#> type_gite aegyti_collected albopictus_collected mixed Total
#> autres 0 2 0 2
#> recipient_abandonne 2 0 0 2
#> recipient_stockage 3 1 1 5
#> Total 5 3 1 9
Data:
df1 <- structure(list(type_gite = structure(c(2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 2L),
.Label = c("autres", "recipient_abandonne", "recipient_stockage"),
class = "factor"),
aegyti_collected = c(19, 0, 8, 36, 13, 1, 0, 0, 3),
albopictus_collected = c(0, 2, 0, 0, 0, 3, 1, 9, 0),
total_collected = c(19, 2, 8, 36, 13, 4, 1, 9, 3)),
class = "data.frame", row.names = c(NA, -9L))
Created on 2019-04-30 by the reprex package (v0.2.1)
Answered by M-- on January 5, 2022
Here's what I came up with:
#create data
df = data.frame(type_gite = c('recipient_abandonne', 'recipient_stockage', 'recipient_stockage',
'recipient_stockage', 'recipient_stockage', 'recipient_stockage', 'autres', 'autres',
'recipient_abandonne'),
aegyti_collected = c(19, 0, 8, 36,13,1,0,0,3),
albopictus_collected = c(0,2,0,0,0,3,1,9,0),
total_collected = c(19,2,8,36,13,4,1,9,3))
#Classify as Mixed or only one of species using case when
df$label = case_when(df$albopictus_collected == 0 ~ 'Aegyti Only',
df$aegyti_collected == 0 ~ 'Albopictus Only',
TRUE ~'Mixed')
#frequency table
df = data.frame(rbind(table(df$type_gite, df$label)))
#add column title back in
df = df %>% tibble::rownames_to_column(var = 'type_gite')
#create total column
library(janitor)
df = df %>% adorn_totals("col")
Answered by Kirsty Weitzel on January 5, 2022
I think you are looking for something like this. I took some random dummy data as an example.
library(dplyr)
# Create dummy data
df <- data.frame(matrix(rnorm(10), ncol = 2))
df <- cbind(c("blah", "blah", "meh", "meh", "meh"), df)
colnames(df) <- c("grouping_variable", "some_var", "some_other_var")
# Group by 1 variable & summarise on rest
df %>% group_by(grouping_variable) %>% summarise_all(sum)
Answered by blah_crusader on January 5, 2022
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