Inflation rate with the CPI multiples country, with R - r

Question

I have to calculate the inflation rate from 2015 to 2019. I have to do this with the CPI, which I have for each month during the 4 years. This means that I have to calculate the percentage growth rate for the same month last year.
They ask me for the calculation of several countries and then calculate or show the average for the period 2015-2019.
This is my database:
data <- read.table("https://pastebin.com/raw/6cetukKb")
I have tried the quantmod, dplyr, lubridate packages, but I can't do the CPI conversion.
I tried this but I know it is not correct:
data$year <- year(data$date)
anual_cpi <- data %>% group_by(year) %>% summarize(cpi = mean(Argentina))
anual_cpi$adj_factor <- anual_cpi$cpi/anual_cpi$cpi[anual_cpi$year == 2014]
**
UPDATE
**
my teacher gave us a hint on how to get the result, but when I try to add it to the code, I get an error.
data %>%
tidyr::pivot_longer(cols = Antigua_Barbuda:Barbados) %>%
group_by(name, year) %>%
summarise(value = mean(value)) %>%
mutate((change=(x-lag(x,1))/lag(x,1)*100))
| Antigua_Barbuda | -1.55 |
|----------------- |------- |
| Argentina | 1.03 |
| Aruba | -1.52 |
| Bahamas | -1.56 |
| Barbados | -1.38 |
where "value" corresponds to the average inflation for each country during the entire period 2015-2019

Answer 1

We can use data.table methods
library(data.table)
melt(fread("https://pastebin.com/raw/6cetukKb"),
id.var = c('date', 'year', 'period', 'periodName'))[,
.(value = mean(value)), .(variable, year)][,
adj_factor := value/value[year == 2014]][]
# variable year value adj_factor
# 1: Antigua_Barbuda 2014 96.40000 1.0000000
# 2: Antigua_Barbuda 2015 96.55833 1.7059776
# 3: Antigua_Barbuda 2016 96.08333 1.0146075
# 4: Antigua_Barbuda 2017 98.40833 0.9900235
# 5: Antigua_Barbuda 2018 99.62500 0.5822618
# 6: Antigua_Barbuda 2019 101.07500 1.0484959
# 7: Argentina 2014 56.60000 1.0000000
# ..

Answer 2

You should read your data with header = TRUE since the first row are the names of the columns. Then get your data in long format which makes it easy to do the calculation.
After this you can perform whichever calculation you want. For example, to perform the same steps as your attempt i.e divide all the values with the value in the year 2014 for each country you can do.
library(dplyr)
data <- read.table("https://pastebin.com/raw/6cetukKb", header = TRUE)
data %>%
tidyr::pivot_longer(cols = Antigua_Barbuda:Barbados) %>%
group_by(name, year) %>%
summarise(value = mean(value)) %>%
mutate(adj_factor = value/value[year == 2014])
# name year value adj_factor
# <chr> <int> <dbl> <dbl>
# 1 Antigua_Barbuda 2014 96.4 1
# 2 Antigua_Barbuda 2015 96.6 1.00
# 3 Antigua_Barbuda 2016 96.1 0.997
# 4 Antigua_Barbuda 2017 98.4 1.02
# 5 Antigua_Barbuda 2018 99.6 1.03
# 6 Antigua_Barbuda 2019 101. 1.05
# 7 Argentina 2014 56.6 1
# 8 Argentina 2015 64.0 1.13
# 9 Argentina 2016 89.9 1.59
#10 Argentina 2017 113. 2.00
# … with 20 more rows