Fuel prices in KE

Fuel prices increase has been steadily increasing with the regulator EPRA. Annoucing new prices every mid month on the night of 14th.

Let us collect this fuel prices from their websites

NOTE: EPRA Changed website pages hence now file locations have changed and yet to be updated

library(rvest)
library(tibble)
library(purrr)
library(openxlsx)
library(readr)
library(stringr)
library(dplyr)

library(tidyr)


# URL <- "https://www.epra.go.ke/services/petroleum/petroleum-prices/"
# 
# epra <- read_html(URL)
# 
# epra_price_list <- epra %>% 
#   html_elements("p")
# 
# epra_links <- epra_price_list %>% 
#   html_elements("a") %>% 
#   html_attrs() %>% 
#   unlist() %>% 
#   unname()
# 
# epra_links <- epra_links[!str_detect(epra_links, fixed("FINAL-ACTUAL-PUMP-PRICES"))]
# 
# epra_data_Csv <- epra_links[str_detect(epra_links, ".csv")]
# 
# epra_data_Csv <- epra_data_Csv[!duplicated(epra_data_Csv)]
# 
# epra_data_Csv <- epra_data_Csv[!str_detect(epra_data_Csv, fixed("15th-April-2023-to-14th-may-2023"))]
# 
# epra_data_xlsx <- epra_links[str_detect(epra_links, ".xlsx")]

# epra_data_xlsx_merge <- epra_data_xlsx %>% 
#   set_names(epra_data_xlsx) %>% 
#   map_dfr(~openxlsx::read.xlsx(.),
#           .id = "file")

#https://www.epra.go.ke/wp-content/uploads/2020/07/15th-April-2023-to-14th-may-2023.csv

#Need to create A different read since the structure is slightly off.

# colnames(epra_data_xlsx_merge)
# 
# epra_data_xlsx_merge %>% 
#   select(contains(c("2022","2023"))) %>% 
#   view()
# 
# list_towns <- epra_data_xlsx_merge %>% 
#   select(contains(c("2022","2023"))) %>% 
#   colnames()
# 
# list_towns <- c(list_towns, "Town", "TOWN")
# 
# epra_data_xlsx_merge1 <- epra_data_xlsx_merge %>% 
#   unite("Town_comb", list_towns, na.rm = TRUE, remove = TRUE)
# 
# epra_data_xlsx_merge1 <- epra_data_xlsx_merge1 %>% 
#   mutate(across(c(Super, Super.Petrol, MAXIMUM.PUMP.PRICES), ~as.double(.)),
#          Super_comb = coalesce(Super, Super.Petrol, MAXIMUM.PUMP.PRICES))
#   
# epra_data_xlsx_merge1 <- epra_data_xlsx_merge1 %>% 
#   mutate(across(c(Diesel, X4), ~as.double(.)),
#          Diesel_comb = coalesce(Diesel, X4))
# 
# epra_data_xlsx_merge1 <- epra_data_xlsx_merge1 %>% 
#   mutate(across(c(Kerosene, X5), ~as.double(.)),
#          Kerosene_comb = coalesce(Kerosene, X5))
# 
# epra_data_xlsx_merge1 <- epra_data_xlsx_merge1 %>% 
#   mutate(Period_comb = coalesce(Price.Period, Period))
# 
# epra_data_xlsx_merge1 <- epra_data_xlsx_merge1 %>% 
#   select(-c(Super, Super.Petrol, MAXIMUM.PUMP.PRICES, Diesel, X4,Kerosene, X5))

# epra_data_Csv_merge <- epra_data_Csv %>% 
#   set_names(epra_data_Csv) %>% 
#   map_dfr(~read_csv(.),
#           .id = "file")
# 
# epra_data_Csv_merge1 <- "https://www.epra.go.ke/wp-content/uploads/2020/07/15th-April-2023-to-14th-may-2023.csv" %>% 
#   set_names("https://www.epra.go.ke/wp-content/uploads/2020/07/15th-April-2023-to-14th-may-2023.csv") %>% 
#   map_dfr(~read_csv(.),
#           .id = "file")

# csv_split1 <- epra_data_Csv_merge1 %>% 
#   select(file,"...1":"...5")
# 
# colnames(csv_split1) <- c("file","number","Town", "Super", "Diesel", "Kerosene")
# 
# csv_split2 <- epra_data_Csv_merge1 %>% 
#   select(file,"...7":"...11")
# 
# colnames(csv_split2) <- c("file","number","Town", "Super", "Diesel", "Kerosene")
# 
# csv_split3 <- epra_data_Csv_merge1 %>% 
#   select(file,"...13":"...17")
# 
# colnames(csv_split3) <- c("file","number","Town", "Super", "Diesel", "Kerosene")
# 
# epra_data_Csv_merge2 <- bind_rows(csv_split1, csv_split2) 


# colnames(epra_data_Csv_merge)
# 
# epra_data_Csv_merge %>% 
#   select(contains(c("2022","2023"))) %>% 
#   view()
# 
# list_towns <- epra_data_Csv_merge %>% 
#   select(contains(c("2022","2023"))) %>% 
#   colnames()
# 
# list_towns <- c(list_towns, "Town", "TOWN")
# 
# epra_data_xlsx_merge1 <- epra_data_xlsx_merge %>% 
#   unite("Town_comb", list_towns, na.rm = TRUE, remove = TRUE)
# 
# epra_data_xlsx_merge1 <- epra_data_xlsx_merge1 %>% 
#   mutate(across(c(Super, Super.Petrol, MAXIMUM.PUMP.PRICES), ~as.double(.)),
#          Super_comb = coalesce(Super, Super.Petrol, MAXIMUM.PUMP.PRICES))
#   
# epra_data_xlsx_merge1 <- epra_data_xlsx_merge1 %>% 
#   mutate(across(c(Diesel, X4), ~as.double(.)),
#          Diesel_comb = coalesce(Diesel, X4))
# 
# epra_data_xlsx_merge1 <- epra_data_xlsx_merge1 %>% 
#   mutate(across(c(Kerosene, X5), ~as.double(.)),
#          Kerosene_comb = coalesce(Kerosene, X5))
# 
# epra_data_xlsx_merge1 <- epra_data_xlsx_merge1 %>% 
#   mutate(Period_comb = coalesce(Price.Period, Period))
# 
# epra_data_xlsx_merge1 <- epra_data_xlsx_merge1 %>% 
#   select(-c(Super, Super.Petrol, MAXIMUM.PUMP.PRICES, Diesel, X4,Kerosene, X5))

The date continues next time

When we schedule the next date hope I wasn’t a boring partner.