Rename various rows - r

Question

I have a dataset that looks like this:
Starting Dataset
Code used to create the Starting dataset:
dataset<-data.frame(Attorney=c("John Doe", "Client #1","274", "296",
"297", "Client #2", "633", "Jane Doe",
"Client #1", "309", "323"),
Date=c(NA, NA, "2019/4/4", "2019/4/4", "2019/4/12",
NA, " 2019/2/3", NA, NA, "2019/12/1", "2019/12/4"),
Code=c(NA, NA, "7NP/7NP", "1UE/1UE", "2C1/2C1",NA,
"7NP/7NP", NA, NA, "7NP/7NP", "7FU/7FU"),
Billed_Amount=c(NA, NA, 1200.00, 4000.00, 2775.00,
NA, 1200.00, NA, NA, 1200.00, 385),
Amount= c(NA, NA, "1200", "4000", "2775", NA, "1200",
NA, NA, "1200", "385"),
Current =c(NA, NA, 0, 0, 0, NA, 0, NA, NA, 0, 0),
X.120=c(NA, NA, "1200", "4000", "2775", NA, "1200",
NA, NA, "1200", "385"))
My goal is to end up with a dataset that looks like:
Goal Dataset
Code used to create Goal dataset:
dataset<-data.frame(Attorney=c("John Doe", "John Doe", "John Doe",
"John Doe", "Jane Jane", "Jane Jane"),
Date=c("2019/4/4", "2019/4/4", "2019/12/4", " 2019/2/3",
"2019/12/1","2019/12/4" ),
Code=c("7NP/7NP", "1UE/1UE","2C1/2C1", "7NP/7NP",
"7NP/7NP", "7FU/7FU"),
Billed_Amount=c(1200.00, 4000.00,2775.00, 1200.00,
1200.00, 385),
Amount= c(1200, 4000, 2775, 1200,1200, 385),
Current= c(0, 0, 0, 0, 0, 0),
X.120=c(1200, 4000, 2775,1200, 1200, 385))
I want to rename the rows underneath each attorney with the attorney's name while not worrying about preserving the client's name. My original dataset has a number of attorneys and they have a varying number of clients and those clients have a various number of codes, dates, and amounts associated with them.
I tried to use if else statement but encountered an error message.
I appreciate any help you can give me. Thanks!
Edit: I have edited my question to include hypothetical attorney names.

Answer 1

An option is to create a grouping variable based on the presence of 'Attorney substring in 'Attorney' column, then mutate the 'Attorney' column with the first element of 'Attorney' after grouping by 'grp', filter out the NA elements
library(dplyr)
library(stringr)
dataset %>%
group_by(grp = cumsum(str_detect(Attorney, "^Attorney"))) %>%
mutate(Attorney = first(Attorney)) %>%
filter_at(vars(Date:X.120), all_vars(!is.na(.))) %>%
ungroup %>%
select(-grp)
We can also use na.omit here
dataset %>%
group_by(grp = cumsum(str_detect(Attorney, "^Attorney"))) %>%
mutate(Attorney = first(Attorney)) %>%
ungroup %>%
select(-grp) %>%
na.omit
# A tibble: 6 x 7
# Attorney Date Code Billed_Amount Amount Current X.120
# <fct> <fct> <fct> <dbl> <fct> <dbl> <fct>
#1 Attorney #1 "2019/4/4" 7NP/7NP 1200 1200 0 1200
#2 Attorney #1 "2019/4/4" 1UE/1UE 4000 4000 0 4000
#3 Attorney #1 "2019/4/12" 2C1/2C1 2775 2775 0 2775
#4 Attorney #1 " 2019/2/3" 7NP/7NP 1200 1200 0 1200
#5 Attorney #2 "2019/12/1" 7NP/7NP 1200 1200 0 1200
#6 Attorney #2 "2019/12/4" 7FU/7FU 385 385 0 385
Or another option is to fill the 'Attorney' column after replaceing the non 'Attorney' substring elements with NA so that it gets filled with the previous non-NA element, then do na.omit
library(tidyr)
dataset %>%
mutate(Attorney = replace(Attorney, !str_detect(Attorney, "Attorney"), NA)) %>%
fill(Attorney) %>%
na.omit

Answer 2

Base R solution (using #akrun's logic):
data.frame(do.call("rbind",
lapply(split(dataset, cumsum(!(grepl("\\d+", dataset$Attorney)))),
function(x){
non_att_cols <- names(x)[names(x) != "Attorney"]
y <- data.frame(na.omit(x[,non_att_cols]))
y$Attorney <- x$Attorney[1]
return(y[,c("Attorney", non_att_cols)])
}
)
),
row.names = NULL
)