CODEWITHSUNDEEP
r
Natasha R.
Natasha R.

Reputation: 531

How does one evaluate changes in a categorical variable over time using R?

I have a dataset where two teams square off in an annual game. There are two divisions where these games take place, East and West. I want to determine who the reigning champion is for a given year based on results from a previous year's game. I'd like to do this for both divisions.

Here's my dataset:

data <- data.frame(
  Team = c("Hot Dogs", "Hamburgers", "Hot Dogs", "Hamburgers", "Hot Dogs",
           "Hamburgers", "Pho", "Ramen", "Pho", "Ramen", "Pho", "Ramen"),
  Division = c("West", "West", "West", "West", "West", "West", "East", "East",
               "East", "East", "East", "East"),
  Year = c("2017", "2017", "2018", "2018", "2019", "2019", "2017", "2017",
           "2018", "2018", "2019", "2019"),
  Score = c("37", "2", "26", "32", "37", "9", "22", "31", "25", "32", "24", "18"))

Ideally I would add a "Results" column to the original data to indicate whether or not the given team is the reigning champion going into that game. Something like this:

data$Result <- c("Initial Champion", "NA", "Champion", "NA", "NA", "Champion", "NA", 
"Initial Champion", "NA", "Champion", "NA", "Champion")

Is there a straightforward way to do this using R, specifically using the tidyverse library if possible?

I appreciate any advice. Thanks in advance.

Upvotes: 1

Views: 281

Answers (2)

StupidWolf
StupidWolf

Reputation: 46948

First we get a table that has all the champions and label them as "Initial Champion" if it is the first, and others as "Champion":

library(dplyr)
X = data %>% 
arrange(Year,desc(Score)) %>% 
group_by(Division) %>% 
filter(!duplicated(Year))%>% 
mutate(result=rep(c("Initial Champion","Champion"),times=c(1,n()-1)))

# A tibble: 6 x 5
# Groups:   Division [2]
  Team       Division Year  Score result          
  <fct>      <fct>    <fct> <fct> <chr>           
1 Hot Dogs   West     2017  37    Initial Champion
2 Ramen      East     2017  31    Initial Champion
3 Hamburgers West     2018  32    Champion        
4 Ramen      East     2018  32    Champion        
5 Hamburgers West     2019  9     Champion        
6 Pho        East     2019  24    Champion

To get your final table just do: 

left_join(data,X)

Upvotes: 3

Nareman Darwish
Nareman Darwish

Reputation: 1261

In the following answer I used dplyr in order to determine initial champion and champion where initial champion means that the team in first occurrence of data was the best in year in its division. For later years a team with highest score in its division it is considered the champion.

library(dplyr)

data <- data.frame(
  Team = c("Hot Dogs", "Hamburgers", "Hot Dogs", "Hamburgers", "Hot Dogs",
           "Hamburgers", "Pho", "Ramen", "Pho", "Ramen", "Pho", "Ramen"),
  Division = c("West", "West", "West", "West", "West", "West", "East", "East",
               "East", "East", "East", "East"),
  Year = c("2017", "2017", "2018", "2018", "2019", "2019", "2017", "2017",
           "2018", "2018", "2019", "2019"),
  Score = c("37", "2", "26", "32", "37", "9", "22", "31", "25", "32", "24", "18"),
  stringsAsFactors = FALSE)

result <-
  data %>%
  group_by(Year, Division) %>% # First we group by each year and division

  # For each division/year we get highest score then for the team with this score
  # we consider it champion
  mutate(high_score = as.character(max(as.numeric(Score), na.rm = TRUE)),
         result = ifelse(high_score == Score, "Champion", NA_character_)) %>%

  # Now to determine the initial champion we compare it with the first year
  # if the row contains data of the first year in data then it is initial
  mutate(result = 
           ifelse(min(data$Year) == Year & result == "Champion", "Initial Champion", result)) %>%

  # Here we drop high_score column because it is not needed in final output
  select(-high_score)

Upvotes: 0

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