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This is my first time posting a question, so may not have the correct info to start, apologies in advance. Am new to R. Prefer to use dplyr or tidyverse because those are the packages we've used so far. I did search for a similar question, but most gender/sex related questions are around separating the data, or performing operations on each separately.

I have a table of population counts, with variables (factors) Age Range, Year and Sex, with Population as the dependent variable. I want to create a plot to show if the population is aging - that is, showing how the relative proportion of different ages groups changes over time. But gender is not relevant, so I want to add together the population counts for males and females, for each year and age range.

I don't know how to provide a copy of the raw data .csv file, so if you have any suggestions, please let me know.

This is a sample of the data(output table): output table

And here is the code so far:

 file_name <- "AusPopDemographics.csv"
AusDemo_df = read.table(file_name,",", header=TRUE)

(grp_AusDemo_df <- AusDemo_df %>% group_by(Year, Age))

I am guessing it may be something like pivot(wider) to bring male and female up as column headings, then transmute() to sum them and create a new population column.

Thanks for your help.

大陸北方網友
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stevo_q
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  • Please add data using `dput` and not as images. Also show the expected output for the same. Read the info about [how to ask a good question](http://stackoverflow.com/help/how-to-ask) and how to give a [reproducible example](http://stackoverflow.com/questions/5963269). – Ronak Shah Sep 07 '20 at 05:35
  • Thanks Kyle for the edit. Will check out dput Ronak. – stevo_q Sep 07 '20 at 06:54

1 Answers1

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With dplyr you could do something like this

library(dplyr)
grp_AusDemo_df <- AusDemo_df %>% 
  group_by(Year, Age) %>%
  summarise(Population = sum(Population, na.rm = TRUE))
Bertil Baron
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