From August to September 2016, I conducted a field survey of Guesthouses in the Maldives to obtain information about the education and work experience of Guesthouse owners/managers as well as the quality of the environment where the hotels are located. The sample Guesthouses were randomly (stratified) chosen from the population of all Guesthouses that post their prices on booking.com. Geographically, I restricted the survey to islands where more than two Guesthouses are in operation and the atolls that are close to the international airport because this reduced our travel time and costs. This yielded a sample of 166 variables for 147 Guesthouses and 24 islands across 5 atolls. Here I will continue to present a descriptive analysis of the key variables.
* If you are interested to collaborate analysing the data send me a mail: phd14403@grips.ac.jp
Figure 1: How old are guesthouse managers
Figure 2: Average days spent by the managers on IN-HOUSE training in the past 2 years.
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# File: Analysis gh_survey2016
# Author: Dr. Ibarhaim Zuhuree, phd14403@grips.ac.jp
# Date: 03 September 2021
# Updated 03 September 2021
# relevant packages
library(tidyverse)
library(readr)
library(dplyr)
library(stringr)
gh_survey_2016_a1 <- gh_survey_2016_c1 %>%
rename(“age”=”B. 2a. What is your AGE? (SHOW CARD M1)”)
gh_survey_2016_a2 <- gh_survey_2016_c2 %>%
rename(“md_tdays”=”D.4 days spent on IN-HOUSE training of Managers/Directors”) %>%
rename(“st_tdays”=”D.4 days spent on IN-HOUSE training of Supervisory/Technical staff”)
# gh_survey_2016_a1$age = as.factor(gh_survey_2016_a1$age)
gh_survey_2016_a1$age[gh_survey_2016_a1$age==”1″] <-“18-25 years”
gh_survey_2016_a1$age[gh_survey_2016_a1$age==”2″] <-“26-34 years”
gh_survey_2016_a1$age[gh_survey_2016_a1$age==”3″] <-“35-49 years”
gh_survey_2016_a1$age[gh_survey_2016_a1$age==”4″] <-“50-64 years”
gh_survey_2016_a1$age[gh_survey_2016_a1$age==”5″] <-“65+ years”
gh_survey_2016_a2$st_tdays[gh_survey_2016_a2$st_tdays==”1″] <-“No in-house training”
gh_survey_2016_a2$st_tdays[gh_survey_2016_a2$st_tdays==”2″] <-“1 to 7 days”
gh_survey_2016_a2$st_tdays[gh_survey_2016_a2$st_tdays==”3″] <-“8 to 14 days”
gh_survey_2016_a2$st_tdays[gh_survey_2016_a2$st_tdays==”4″] <-“15 to 29 days”
gh_survey_2016_a2$st_tdays[gh_survey_2016_a2$st_tdays==”5″] <-“more than 30 days”
## Figure 1: How old are guesthouse managers
ggplot(data = gh_survey_2016_a1) +
geom_bar(mapping = aes(x = age, fill = age)) # y = stat(prop), group = 1 to show probability
bar <- ggplot(data = gh_survey_2016_a1) +
geom_bar(
mapping = aes(x = age,fill = age),
show.legend = FALSE,
width = 1
) +
theme(aspect.ratio = 1) +
labs(x = NULL, y = NULL)
## Figure 2: Average days spent by the managers on IN-HOUSE training in the past 2 years.
bar + coord_flip()
bar + coord_polar(
bar <- ggplot(data = gh_survey_2016_a2) +
geom_bar(
mapping = aes(x = st_tdays,fill = st_tdays),
show.legend = FALSE,
width = 1
) +
theme(aspect.ratio = 1) +
labs(x = NULL, y = NULL)
bar + coord_flip()
bar + coord_polar()