Life expectancy: Malaysia-clean file

Load library

library(tidyverse)

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library(readxl)
library(janitor)

Attaching package: 'janitor'

The following objects are masked from 'package:stats':

    chisq.test, fisher.test

Load data

state_group_all <- read_excel("input/Summary Tables, 2021-2023.xlsx", 
                           sheet = "Jadual 1.1",
                           range = "A5:J27",
                           col_names = T) %>% 
  clean_names()

New names:
• `` -> `...2`
• `` -> `...3`

state_group_male <- read_excel("input/Summary Tables, 2021-2023.xlsx", 
                           sheet = "Jadual 1.1",
                           range = "A33:J48",
                           col_names = F)

New names:
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`
• `` -> `...4`
• `` -> `...5`
• `` -> `...6`
• `` -> `...7`
• `` -> `...8`
• `` -> `...9`
• `` -> `...10`

state_group_female <- read_excel("input/Summary Tables, 2021-2023.xlsx", 
                           sheet = "Jadual 1.1",
                           range = "A69:J84",
                           col_names = F)

New names:
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`
• `` -> `...4`
• `` -> `...5`
• `` -> `...6`
• `` -> `...7`
• `` -> `...8`
• `` -> `...9`
• `` -> `...10`

Cleaning

State

• Remove unnecessary columns & rows

## Remove unnecessary columns
state_group_all <- state_group_all %>% 
  select(negeri_state, x2023e)

state_group_all <- state_group_all %>% 
  slice(7:22)

State with gender

• Rename columns

• Remove unnecessary columns & rows

state_group_male <- state_group_male %>% 
  select(...1,...10)

state_group_female <- state_group_female %>% 
  select(...1,...10)

Export

write_csv(state_group_all,"source/state_group_all.csv")
write_csv(state_group_male,"source/state_group_male.csv")
write_csv(state_group_female,"source/state_group_female.csv")