CODEWITHSUNDEEP
rdplyr
Jul
Jul

Reputation: 15

data frame transformation: add/remove categories and make calculation

I am trying to modify a dataframe, similar to "cars" dataframe (mine is called countries). Its type is a list.

First column is the name of countries (type chr). Second column is data (type dbl).

   Country          Number
 1 Portugal         100000       
 2 Poland           200000       
 3 Israel           300000
 4 South Africa     400000
 5 Austria          500000

I want to rename/group countries in the first column by "Others" when number is over 250000 (for example) and then plot a graph with ggplot.

I got a good bar chart for the moment but I want to have one with "others" now. "Others" bar will just be the sum of the number by country.

Which method is the most efficient to manipulate the data? To create a function with "if" and apply it to the dataframe? Or to create a new column with two categories and then sum all "others"?

I already tried to manipulate the code by using the pipe %>% and mutate.

Upvotes: 1

Views: 47

Answers (1)

rt.l
rt.l

Reputation: 306

a solution is to use dplyr to change the country's name according to a rule and then aggregating results with group_by / summarise functions. Bellow you will find a small example.

countries <- data.frame(Country= c("Portugal","Poland","Israel","South Africa","Austria"), 
                        Number = c(100000,200000,300000,400000,500000), stringsAsFactors = F)

# using dplyr
countries_dp <- countries  %>%
  mutate(Country = ifelse(Number > 250000, "Other", Country)) %>%
  group_by(Country) %>%
  summarise(Number = sum(Number))

Upvotes: 0

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