I can seem to find a proper code for my problem. I want to create groups and summarize (sum, count or length) other columns based on different conditions.
I've tried group_by and summarize with different conditions but haven't found anything that works yet.
I have a table similar to this:
data <- data.frame(Name= c('Anna', 'Anna', 'Anna', 'Anna', 'Anna',
'Bella', 'Bella', 'Bella', 'Camilla', 'Camilla'),
Date= c('1.1.2021', '1.1.2021', '2.1.2021', '3.1.2021', '3.1.2021',
'1.1.2021', '5.1.2021', '5.1.2021', '7.1.2021', '8.1.2021'),
Item= c('Apple','Pear', 'Zucini','Apple', 'Broccoli',
'Apple','Pear','Apple','Apple', 'Tomato'),
Category= c('Fruit', 'Fruit', 'Vegetable', 'Fruit', 'Vegetable',
'Fruit', 'Fruit', 'Fruit', 'Fruit', 'Vegetable'),
Weight_kg= c(0.2,0.3,0.5,0.4,1.1,
1,0.5,0.8,1.2,0.5)
)
This would be my desired output:
desired_table <- data.frame(Name=c('Anna', 'Bella', 'Camilla'),
Shopping_days=c(3,2,2),
days_fruit=c(2,2,1),
days_vegetables=c(2,0,1),
Total_kg=c(2.5,2.3,1.7),
Fruit_kg=c(0.9,2.3,1.2),
Vegetables=c(1.6,0,0.5))
Ive tried many variations of a code similar to this one that obviously doesn't work:
data1 <- data %>%
group_by(Name) %>%
summarize(Shopping_days = length(unique(Date)),
days_fruit = length(unique(Date, Category='Fruit')),
days_vegetables = length(unique(Date, Category='Vegetables')),
Total_kg = sum(Weight_kg),
Fruit_kg = sum(Weight_kg, if Category=Fruit),
Vegetables_kg = sum(Weight_kg, if Category=Vegetables))
Any help would be much appreciated.
Using group_by
and summarise
:
library(dplyr)
data %>%
group_by(Name) %>%
summarise(Shopping_days = n_distinct(Date),
days_fruit = n_distinct(Item[Category == 'Fruit']),
days_vegetables = n_distinct(Item[Category == 'Vegetable']),
Total_kg = sum(Weight_kg),
Fruit_kg = sum(Weight_kg[Category == 'Fruit']),
Vegetables_kg = sum(Weight_kg[Category == 'Vegetable']))
# Name Shopping_days days_fruit days_vegetables Total_kg Fruit_kg Vegetables_kg
# <chr> <int> <int> <int> <dbl> <dbl> <dbl>
#1 Anna 3 2 2 2.5 0.9 1.6
#2 Bella 2 2 0 2.3 2.3 0
#3 Camilla 2 1 1 1.7 1.2 0.5