Tourism in Nepal

Friday, Nov 22, 2019 about 1207 words 6 min

Introduction

Nepal is celebrating year 2020 as “Tourism Year” targeting 2 million international tourist arrivals. You can learn more about the #VisitNepal2020. I want to see the trend/history of nepal tourism and extracted the data from the Wikipedia in Scraping Data with R blog post.

/images/tourism_in_nepal/vny2020.jpeg — Visit Nepal 2020

In this section, we will work on the scrapped data from the Scraping Data with R blog post and perform analysis, create some visualization and understand the trend of Tourism in Nepal.

Lets Start

I will load the required package for this blog.

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library(ggplot2)
library(dplyr)
library(tidyr)
library(purrr)
library(stringr)
library(lubridate)
library(gganimate)
library(plotly)

year	tourist_number	per_change

1993	293567	-12.2
1994	326531	11.2
1995	363395	11.3
1996	393613	8.3
1997	421857	7.2
1998	463684	9.9
1999	491504	6.0
2000	463646	-5.7
2001	361237	-22.1
2002	275468	-23.7

This is the data frame which I got after the extraction and cleaning process in the Scraping Data with R blog post. Now I will create a visualization from this data using ggplot2 and plotly.

Visuals

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gplot <- ggplot(tourist_df, aes(
  x = year,
  y = tourist_number, fill = tourist_number
)) +
  geom_col() +
  labs(
    y = "Number of Tourist",
    title = "Number of Tourist Arrival from 1993 to 2018"
  ) +
  scale_fill_gradient(low="#fedb81", high="red")+
  theme_minimal()
ggplotly(gplot)

This barplot shows the number of arrival of international tourist from 1993 to 2018. The trend of tourist number was good from 1993 to 2000. Then from 2001, the flow of the international tourist decreased as it was the period where civil war was at its height. The country was in the emergency period.

In 2015 there was an earthquake which destroyed most of the historic sites in Kathmandu valley and with large physical and human casualties. This decreased the number of tourist in Nepal. I can see that 2016 onward the number of tourists arrival increased every year. In 2018 the number of tourist arrival crossed first time 1 million.

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gplot <- ggplot(tourist_df, aes(
  x = year,
  y = per_change, fill = per_change
)) +
  geom_col() +
  labs(
    y = "Percent Change of Tourist",
    title = "Percentage Change  of Tourist Arrival from 1993 to 2018"
  ) +
  scale_fill_gradient(low="#fedb81", high="red")+
  theme_minimal()
ggplotly(gplot)

This plot shows the percentage change in the flow of the tourist arrival every year.

Top 10 Country

In this section, I will find the top 10 countries with must tourist to Nepal.

Rank	Country	2013	2014	2015	2016	2017
chr	chr	chr	chr	chr	chr	chr
1	India	160832	118249	75124	135343	180974
2	China	104664	104005	66984	123805	113173
3	United States	79146	53645	42687	49830	47355
4	United Kingdom	51058	46295	29730	36759	35688
5	Sri Lanka	45361	57521	44367	37546	32736
6	Thailand	39154	26722	32338	33422	40969
7	South Korea	34301	25171	18112	23205	19714
8	Australia	33371	25507	16619	24516	20469
9	Myanmar	30852	25769	21631	N/A	N/A
10	Germany	29918	23812	16405	18028	22263
11	Bangladesh	29060	23440	14831	21851	22410
12	Japan	27326	22979	17613	25892	26694
13	France	26140	20863	16405	24097	21842
14	Malaysia	18284	13669	9855	18915	18842
15	Spain	15953	12255	6741	13110	10412
16	Canada	15105	12491	8398	11610	12132
17	Netherlands	13393	11453	7515	12320	10516

This is the data I got from the Scraping Data with R blog post and now I need to extract the top 10 countries from this dataframe.

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con_tour_df<- head(con_tour_df,10)

Rank	Country	2013	2014	2015	2016	2017
chr	chr	chr	chr	chr	chr	chr
1	India	160832	118249	75124	135343	180974
2	China	104664	104005	66984	123805	113173
3	United States	79146	53645	42687	49830	47355
4	United Kingdom	51058	46295	29730	36759	35688
5	Sri Lanka	45361	57521	44367	37546	32736
6	Thailand	39154	26722	32338	33422	40969
7	South Korea	34301	25171	18112	23205	19714
8	Australia	33371	25507	16619	24516	20469
9	Myanmar	30852	25769	21631	N/A	N/A
10	Germany	29918	23812	16405	18028	22263

This dataframe has the years in a different column. I keep all the years in year column along with there tourist arrival value.

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con_tour_df <- pivot_longer(con_tour_df,-c(Country,Rank),names_to = "year")
con_tour_df$year <- as.numeric(con_tour_df$year)
con_tour_df$value <- as.numeric(con_tour_df$value)

Now, I need to rank the country based on the number of tourist arrival from that country. I use mutate() to create a new column “rank”.

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con_tour_df <- con_tour_df%>%
  group_by(year) %>%
  mutate(rank = rank(-value),Value_lbl = paste0(" ",value))%>% filter(rank <= 10)

con_tour_df$value <- as.integer(con_tour_df$value)

Rank	Country	year	value	rank	Value_lbl

1	India	2013	160832	1	160832
1	India	2014	118249	1	118249
1	India	2015	75124	1	75124
1	India	2016	135343	1	135343
1	India	2017	180974	1	180974
2	China	2013	104664	2	104664
2	China	2014	104005	2	104005
2	China	2015	66984	2	66984
2	China	2016	123805	2	123805
2	China	2017	113173	2	113173

This is the final data frame that I got after cleaning. Now, it is time to create some visualization/animation.

I am using the ggplot2 package to create static visualization and gganimate to create beautiful animations.

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colors <- c("#a6cee3", "#e31a1c", "#b2df8a", "#33a02c",
            "#fb9a99", "orange", "#fdbf6f", "#ff7f00", 
            "#cab2d6", "#6a3d9a", "#b15928", "#fb8072",
            "#80b1d3", "#fdb462", "#b3de69")

In this visualization, I need 10 different colours to show a different country in the animation. I created a colour palette and named colors which will be used while creating visualization below.

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ggplot(con_tour_df, aes(x = rank, y = value, group = Country)) +
  geom_bar(stat = "identity", aes(fill = Country)) +
  geom_text(aes(y = 0, label = paste(Country, " ")), vjust = 0.2, hjust = 1) +
  geom_text(aes(y = value, label = Value_lbl, hjust = 0)) +
  scale_y_continuous(labels = scales::comma) +
  scale_x_reverse() +
  coord_flip(clip = "off", expand = FALSE) +
  scale_fill_manual(values = colors) +
  theme_minimal() +
  theme(
    axis.line = element_blank(),
    axis.text.x = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    legend.position = "none",
    panel.background = element_blank(),
    panel.border = element_blank(),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_line(size = .1, color = "grey"),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    plot.title = element_text(size = 25, hjust = 0.5, face = "bold", vjust = 1),
    plot.subtitle = element_text(size = 18, hjust = 0.5, face = "italic"),
    plot.caption = element_text(size = 8, hjust = 0.5, face = "italic", color = "grey"),
    plot.background = element_blank(),
    plot.margin = margin(2, 2, 2, 4, "cm")) +
  transition_states(year, transition_length = 3, state_length = 1) +
  enter_drift(x_mod = -2) +
  exit_drift(x_mod = 2) +
  ease_aes("cubic-in") +
  view_follow(fixed_x = TRUE) +
  labs(title = "Tourist Arrival by country : {closest_state}", 
       subtitle = "Top 10 Countries")

Conclusion

In this blog I showed the interactive visualization made with ggplot2 and plotly using the scraped data. I also made the animation showing the number of tourist arrival based on country from 2013 to 2017.

Feel free to send to me your feedback and suggestions regarding this post!

Reference

AbdulMajedRaja Rs. gganimate ggplot2