How to use case_when when doing quartiles in R? - r

Question

If i have this tibble:
tibble(
period = c("2010END", "2011END",
"2010Q1","2010Q2","2011END"),
date = c('31-12-2010','31-12-2011', '30-04-2010','31-07-2010','30-09-2010'),
website = c(
"google",
"google",
"facebook",
"facebook",
"youtube"
),
method = c("website",
"phone",
"website",
"laptop",
"phone"),
values = c(1, NA, 1, 2, 3))
And then i have this dataframe which tells you which quantiles to create along with the rankings to be made from the ranks:
tibble(
method = c(
"phone",
"phone",
"phone",
"website",
"website",
"website",
"laptop",
"laptop",
"laptop"
),
rank = c(3,2,1,3,2,1,3,2,1),
tile_condition = c("lowest 25%", "25 to 50%", "more than 50%",
"highest 25%", "25 to 50%", "less than 25%",
"lowest 25%", "25 to 50%", "more than 50%")
)
How can i use a case_when statement to correctly allow myself to create a ranking column which is based on the quartile calculation from the values column in the first dataframe?
I'm trying to apply the quantiles from the other dataframe to create a ranking column in the original dataframe - stuck on how to use case_when for it.

Answer 1

I would do something like this:
set.seed(124)
left_join(
df1[sample(1:5,1000, replace=T),] %>%
mutate(values=sample(c(df1$values,1:30),1000, replace=T)) %>%
group_by(method) %>%
mutate(q=as.double(cut(values,quantile(values,probs=seq(0,1,0.25), na.rm=T), labels=c(1:4), include.lowest=T))) %>%
ungroup(),
df2 %>% mutate(q = list(1,2,c(3,4),4,c(2,3),1,1,2,c(3,4))) %>% unnest(q),
by=c("method", "q")
) %>% select(-q)
Output:
# A tibble: 1,000 × 7
period date website method values rank tile_condition
<chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
1 2010END 31-12-2010 google website 7 2 25 to 75%
2 2011END 31-12-2011 google phone 18 1 more than 50%
3 2010Q1 30-04-2010 facebook website 21 2 25 to 75%
4 2011END 30-09-2010 youtube phone 15 1 more than 50%
5 2011END 30-09-2010 youtube phone 26 1 more than 50%
6 2011END 31-12-2011 google phone 3 3 lowest 25%
7 2010END 31-12-2010 google website 1 1 less than 25%
8 2010Q1 30-04-2010 facebook website 2 1 less than 25%
9 2010Q2 31-07-2010 facebook laptop 14 2 25 to 50%
10 2010Q2 31-07-2010 facebook laptop 16 1 more than 50%
# … with 990 more rows
Notice that I updated your input to 1000 rows, and random new values for the purposes of illustration. Also, notice that I fixed df2, so that method website covers the full range of values. In your example the 50% to 75% quartile is missing.
Adjusted df2 input:
structure(list(method = c("phone", "phone", "phone", "website",
"website", "website", "laptop", "laptop", "laptop"), rank = c(3,
2, 1, 3, 2, 1, 3, 2, 1), tile_condition = c("lowest 25%", "25 to 50%",
"more than 50%", "highest 25%", "25 to 75%", "less than 25%",
"lowest 25%", "25 to 50%", "more than 50%")), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -9L))

Answer 2

If I've understood your question correctly, you first have to create a table to compare against, as:
df_quants <-
df1 %>%
drop_na(values) %>%
group_by(method) %>%
summarize(quant25 = quantile(values, probs = 0.25),
quant50 = quantile(values, probs = 0.5),
quant75 = quantile(values, probs = 0.75),
quant100 = quantile(values, probs = 1))
Then, using a join and a case_when statement, you would arrive at:
df2 %>%
left_join(df_quants, by = 'method') %>%
mutate(tiles =
case_when(rank < quant25 ~ 'lowest 25%',
rank < quant50 ~ '25 to 50%',
rank < quant75 ~ 'more than 50%',
rank >= quant75 ~ 'highest 25%'))

Answer 3

Here's a quick version. It doesn't get the exact labels you want. To do that you'd have to parse your tile_condition column, which is a little tricky.
library(santoku)
df |>
group_by(method) |>
mutate(
quantile = chop_quantiles(values, c(0.25, 0.50),
labels = c("Lowest 25%", "25 to 50%", "Above 50%"), extend = TRUE)
)