Reading multiple excel files into R using the map function - r

Question

I know there are similar questions to this but I haven't came across ones using the map function from the purrr package. I am having a difficult time trying to read in some excel files(.xlsx) using purrr::map(). I would like each one to be it's own data frame. I tried the approach in this similar question: How can I reading multiple (excel) files into R?.
However, I keep getting this error:
Error: path does not exist: "tab3_DOfinal_HUClevel_assessment.xlsx"
I know for sure I have the right path. Not sure why I am getting this error. I have about 9 excel spreadsheets that I want to read in.
Code I tried:
# load necessary package
library(purrr)
file.list <- list.files(path="2016_Data_Tables",pattern='*.xlsx')
file.list <- setNames(file.list, file.list)
# store all .xlsx files as individual data frames inside of one list
df <- map(file.list, read_xlsx)
The file name pattern goes as follows:
tab3_DOfinal_HUClevel_assessment.xlsx
The only thing that changes is the DOfinal part.
Some sample data:
structure(list(ID = 1, WMA = 15, Number = "02040302020030-01",
HUC14 = "HUC02040302020030", Name = "Absecon Creek (AC Reserviors) (gage to SB)",
Region = "Atlantic Coast", NumofStations = "2", ListofStations = "01410455, R32",
ListofAssessment = "2, 2", HUCTier = "2", swqs = "PL, SE1",
TotalNumSamples5yrs = "NA", flgusgsprelim = "NA, 0", auassess = 2,
auassesstrout = -999, finalauassess = 2, finalauassesstrout = -999,
Changefrom2014 = "No Change-2", Changetroutfrom2014 = "No Change",
listHUC14assess5 = "NA", listHUC14assess3 = "NA", listHUC14assess2 = "01410455, R32",
His2014 = "Attaining", His2014trout = "-999", Notes = NA_character_,
OldStations2014 = "01410455", OldStationsAssess2014 = "2",
Error = NA_character_), .Names = c("ID", "WMA", "Number",
"HUC14", "Name", "Region", "NumofStations", "ListofStations",
"ListofAssessment", "HUCTier", "swqs", "TotalNumSamples5yrs",
"flgusgsprelim", "auassess", "auassesstrout", "finalauassess",
"finalauassesstrout", "Changefrom2014", "Changetroutfrom2014",
"listHUC14assess5", "listHUC14assess3", "listHUC14assess2", "His2014",
"His2014trout", "Notes", "OldStations2014", "OldStationsAssess2014",
"Error"), row.names = c(NA, -1L), class = c("tbl_df", "tbl",
"data.frame"))
structure(list(WMA = 15, Number = "02040302020030-01", HUC14 = "HUC02040302020030",
Name = "Absecon Creek (AC Reserviors) (gage to SB)", Region = "Atlantic Coast",
NumofStations = "1", ListofStations = "01410455", ListofAssessment = "2",
MaxStaAssessment = "2", MinStaAssessment = "2", TotalNumSamples5yrs = "NA",
auassess = "2", ChangeFrom2014 = "No Change-2", liststaassess2 = "01410455",
liststaassess3 = "NA", liststaassess5 = "NA", Assessment2014 = "Attaining",
Comments = NA_character_), .Names = c("WMA", "Number", "HUC14",
"Name", "Region", "NumofStations", "ListofStations", "ListofAssessment",
"MaxStaAssessment", "MinStaAssessment", "TotalNumSamples5yrs",
"auassess", "ChangeFrom2014", "liststaassess2", "liststaassess3",
"liststaassess5", "Assessment2014", "Comments"), row.names = c(NA,
-1L), class = c("tbl_df", "tbl", "data.frame"))
structure(list(WMA = 15, Number = "02040302020030-01", HUC14 = "HUC02040302020030",
Name = "Absecon Creek (AC Reserviors) (gage to SB)", Region = "Atlantic Coast",
NumofStations = "1", ListofStations = "R32", ListofAssessment = "3",
MaxStaAssessment = "3", MinStaAssessment = "3", TotalNumSamples5yrs = "9",
auassess = "3", ChangeFrom2014 = "No Change-3", liststaassess2 = "NA",
liststaassess3 = "R32", liststaassess5 = "NA", Assessment2014 = "N/A",
Comments = NA_character_), .Names = c("WMA", "Number", "HUC14",
"Name", "Region", "NumofStations", "ListofStations", "ListofAssessment",
"MaxStaAssessment", "MinStaAssessment", "TotalNumSamples5yrs",
"auassess", "ChangeFrom2014", "liststaassess2", "liststaassess3",
"liststaassess5", "Assessment2014", "Comments"), row.names = c(NA,
-1L), class = c("tbl_df", "tbl", "data.frame"))

Answer 1

Aurèle makes a really good point regarding your file paths.
I would like each one to be it's own data frame
If this is the goal, then a combination of purrr::iwalk and assign could easily get you there. The process goes as follows:
Get a list of all of the .xlsx files located in 2016_Data_Tables/.
Then use purrr::set_names to name each element in this list with its filename sans the .xlsx extension.
Then use purrr::iwalk to apply the assign function to each element in the list. Specifically, use read_xlsx to read each .xlsx file from disk into a data frame and then assign that data frame as a named object to R's global environment
list.files('data/mpg', pattern = '.xlsx', full.names = T) %>%
purrr::set_names(stringr::str_remove(basename(.), '.xlsx$')) %>%
purrr::iwalk(function(x, i) assign(i, readxl::read_xlsx(x), .GlobalEnv))