How to Read this Text Files(when they are in thousands) & how to convert RowNames into Variables, not able to do with Readr

[Shail_Prithvi]

Could anyone help in, How to read below .txt file( such files are in thousands) in tabular form and convert the row names into Variables names along with DataType

Delivery_person_Age 22.000000
Delivery_person_Ratings 4.700000
Restaurant_latitude 18.530963
Restaurant_longitude 73.828972
Delivery_location_latitude 18.560963
Delivery_location_longitude 73.858972
Order_Date 01-03-2022
Time_Orderd 23:35
Time_Order_picked 23:40
Weather conditions Sunny
Road_traffic_density Low
Vehicle_condition 2
Type_of_order Drinks
Type_of_vehicle scooter
multiple_deliveries 0.000000
Festival No
City NaN
Time_taken (min) 22.000000
Name: 13, dtype: object

[FactOREO]

Is this file complete and in the way, your actual *.txt files are saved?
Your last two lines will cause problems, since the rowname Time_taken (min) is poorly chosen. Especially the last line with two ":" will break the reading.

However, if you always have 18 rows, you could just do this and skip the last line:

library(tidyverse)
data <- read_delim('given_file.txt', col_names = FALSE, n_max = 18) |>
  pivot_wider(names_from = X1, values_from = X2) |>
  mutate(Time_taken = str_extract(string = Time_taken, "[0-9\\.,]+"))
#> Warning: One or more parsing issues, see `problems()` for details
#> Rows: 18 Columns: 2
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: " "
#> chr (2): X1, X2
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
data
#> # A tibble: 1 × 18
#>   Delivery_per…¹ Deliv…² Resta…³ Resta…⁴ Deliv…⁵ Deliv…⁶ Order…⁷ Time_…⁸ Time_…⁹
#>   <chr>          <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>  
#> 1 22.000000      4.7000… 18.530… 73.828… 18.560… 73.858… 01-03-… 23:35   23:40  
#> # … with 9 more variables: Weather <chr>, Road_traffic_density <chr>,
#> #   Vehicle_condition <chr>, Type_of_order <chr>, Type_of_vehicle <chr>,
#> #   multiple_deliveries <chr>, Festival <chr>, City <chr>, Time_taken <chr>,
#> #   and abbreviated variable names ¹​Delivery_person_Age,
#> #   ²​Delivery_person_Ratings, ³​Restaurant_latitude, ⁴​Restaurant_longitude,
#> #   ⁵​Delivery_location_latitude, ⁶​Delivery_location_longitude, ⁷​Order_Date,
#> #   ⁸​Time_Orderd, ⁹​Time_Order_picked
#> # ℹ Use `colnames()` to see all variable names

^{Created on 2022-08-31 by the reprex package (v2.0.1)}

You can now take care of the columns by yourself and clean them up (e.g. convert to correct types instead of chr).

Kind regards