R -> Sum part of Columns + agreggating observations [duplicate] - r

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I am very new to coding and just started doing some R graphics and now I am kinda lost with my data analyse and need some light! I am training some analyses and I got a very long dataset with 19 Countries x 12 months x 22 Products and for every month a Profit. Kinda like this:
Country Month Product Profit
Brazil Jan A 50
Brazil fev A 80
Brazil mar A 15
Austria Jan A 35
Austria fev A 80
Austria mar A 47
France Jan A 21
France fev A 66
France mar A 15
[...]
France Dez C 40 etc...
I am was thinking to do one graph showing the profits through the year and another for every country, so I could see the top and bottom 2 countries. I wanted to have something like:
All Countries Jan 106 or Brazil 2021 145
All Countries Fev 146 Austria 2021 162
All Countries Mar 77 France 2021 112
but the sum function can't help with characters type and as I have a long List, idk how to sum only part of the column.
sorry if it got confusing.

Answer 1

The package dplyr has quite a natural syntax for this:
require(dplyr)
#> Loading required package: dplyr
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
df <- data.frame(
Country = rep(c(rep("Brazil", 3L), rep("Austria", 3L), rep("France", 3L)), 3L),
Profit = rep(c(50, 80, 15, 35, 80, 47, 21, 66, 15), 3L),
Month = rep(c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep"), 3L),
Year = sort(rep(c(2019, 2020, 2021), 9L))
)
df %>%
group_by(Country, Month) %>%
summarize(sum = sum(Profit))
#> `summarise()` has grouped output by 'Country'. You can override using the `.groups` argument.
#> # A tibble: 9 × 3
#> # Groups: Country [3]
#> Country Month sum
#> <chr> <chr> <dbl>
#> 1 Austria Apr 105
#> 2 Austria Jun 141
#> 3 Austria May 240
#> 4 Brazil Feb 240
#> 5 Brazil Jan 150
#> 6 Brazil Mar 45
#> 7 France Aug 198
#> 8 France Jul 63
#> 9 France Sep 45

Answer 2

Using base R, you can try something along these lines.
# sum of profit per month
out1 <- tapply(df$Profit, df$Month, sum)
# sum of profit per year per country
out2 <- data.frame(
profit = sapply(split(df, f = ~ df$Country + df$Year), function(x) sum(x$Profit))
)
out2$Country <- gsub('\\.[0-9]*', '', rownames(out2))
out2$Year <- gsub('[a-zA-z]*\\.', '', rownames(out2))
rownames(out2) <- NULL
Output
> out1
Apr Aug Feb Jan Jul Jun Mar May Sep
105 198 240 150 63 141 45 240 45
> head(out2)
profit Country Year
1 162 Austria 2019
2 145 Brazil 2019
3 102 France 2019
4 162 Austria 2020
5 145 Brazil 2020
6 102 France 2020
Data
# sample data
df <- data.frame(
Country = rep(c(rep('Brazil',3L),rep('Austria',3L),rep('France',3L)), 3L),
Profit = rep(c(50,80,15,35,80,47,21,66,15), 3L),
Month = rep(c('Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep'),3L),
Year = sort(rep(c(2019,2020,2021), 9L))
)