Prototyping first may help keep you from investing far too much time for marginal gains.
The Art of UNIX Programming (Raymond 2003)
In this take-home exercise, you are required to select one of the module of your proposed Shiny application and complete the following tasks:
To evaluate and determine the necessary R packages needed for your Shiny application are supported in R CRAN,
To prepare and test the specific R codes can be run and returned the correct output as expected,
To determine the parameters and outputs that will be exposed on the Shiny applications, and
To select the appropriate Shiny UI components for exposing the parameters determine above.
All teams must consult the prototype page of AY2022-2023 January Term Group 3: Tanzania Tourism Analysis. There are three main modules in ths application the are: Exploratory Data Analysis & Confirmatory Data Analysis, Cluster Analysis and Decision Tree Analysis. Each of them were prepared by one of the member of the project team. After that they combined them into the Prototype page of their project website.
Take-home Exercise 4 will be similar to one of the prototype module prepared above in term of content but with the following differences:
You are required to prepare the prototype module report as Take-home Exercise 4 submission. This mean, it has to be published on your own coursework page.
You are required to include a section called UI design for the different components of the UIs for the proposed design.
For storyboarding the UI Design, please consult Storyboard link.
This is an individual assignment. You are required to work on the take-home exercises and prepare submission individually.
The specific submission instructions are as follows:
The analytical visualisation must be prepared by using R and appropriate R packages. The report must provide detail discussion and explanation of:
the data preparation process,
the selection of data visualisation techniques used,
and the data visualisation design and interactivity principles and best practices implemented.
The write-up of the take-home exercise must be in Quarto html document format and publish on Netlify.
The topic our group has chosen is about armed conflicts in Myanmar. The aim is to attempt to review through the open-sourced data obtained using various visual analytics techniques and possibly be able to weed out interesting conclusions. The shiny app would be create an analytical user interface whereby users are able to filter and explore by themselves if they can derive their own conclusion through their own assumptions and exploration.
Our main project website can be found here.
The dataset is obtained from Armed Conflict Location & Event Data Project website. The website provides datasets of conflict in various regions which users can obtain to conduct their own data analysis based on the region they are interested in. The datasets can also be used to conduct geospatial analysis which is something that is still developing and improving over time.
For our project, we would be mainly focusing on the Myanmar region due to the recent increase in conflict post military coup in 2021.
The data being used is titled 2010-01-01-2023-12-31-Southeast_Asia-Myanmar.csv.
pacman::p_load(shiny, tidyverse, shinydashboard,dplyr,
spatstat, spdep,
lubridate, leaflet,
plotly, DT, viridis,
ggplot2, sf, tmap, readr,
scales,ggthemes,gridExtra,knitr,data.table,
CGPfunctions, ggHoriPlot, patchwork)Myanmar <- read_csv("data/2010-01-01-2023-12-31-Southeast_Asia-Myanmar.csv")
glimpse(Myanmar)Rows: 55,574
Columns: 31
$ event_id_cnty <chr> "MMR56099", "MMR56222", "MMR56370", "MMR56376", "MM…
$ event_date <chr> "31 December 2023", "31 December 2023", "31 Decembe…
$ year <dbl> 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 202…
$ time_precision <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ disorder_type <chr> "Political violence", "Political violence", "Politi…
$ event_type <chr> "Explosions/Remote violence", "Explosions/Remote vi…
$ sub_event_type <chr> "Shelling/artillery/missile attack", "Shelling/arti…
$ actor1 <chr> "Military Forces of Myanmar (2021-)", "Military For…
$ assoc_actor_1 <chr> NA, NA, NA, NA, NA, NA, "Daw Na Column; YGF: Ye Gue…
$ inter1 <dbl> 1, 1, 3, 6, 1, 1, 3, 1, 2, 1, 1, 2, 2, 1, 1, 3, 3, …
$ actor2 <chr> NA, "Civilians (Myanmar)", "Military Forces of Myan…
$ assoc_actor_2 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ inter2 <dbl> 0, 7, 1, 0, 7, 0, 1, 0, 1, 7, 7, 1, 1, 0, 0, 1, 1, …
$ interaction <dbl> 10, 17, 13, 60, 17, 10, 13, 10, 12, 17, 17, 12, 12,…
$ civilian_targeting <chr> NA, "Civilian targeting", NA, NA, NA, NA, NA, NA, N…
$ iso <dbl> 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 1…
$ region <chr> "Southeast Asia", "Southeast Asia", "Southeast Asia…
$ country <chr> "Myanmar", "Myanmar", "Myanmar", "Myanmar", "Myanma…
$ admin1 <chr> "Mon", "Rakhine", "Bago-West", "Sagaing", "Bago-Wes…
$ admin2 <chr> "Mawlamyine", "Maungdaw", "Thayarwady", "Yinmarbin"…
$ admin3 <chr> "Ye", "Maungdaw", "Nattalin", "Salingyi", "Nattalin…
$ location <chr> "Aing Shey", "Kaing Gyi (NaTaLa)", "Kyauk Pyoke", "…
$ latitude <dbl> 15.2856, 20.7219, 18.6245, 22.0608, 18.6245, 21.199…
$ longitude <dbl> 98.0052, 92.4429, 95.8295, 95.1387, 95.8295, 92.200…
$ geo_precision <dbl> 1, 2, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, …
$ source <chr> "Democratic Voice of Burma", "Development Media Gro…
$ source_scale <chr> "National", "Subnational", "National", "National", …
$ notes <chr> "On 31 December 2023, in Aing Shey village (Ye town…
$ fatalities <dbl> 0, 0, 4, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 1, 2, …
$ tags <chr> NA, NA, NA, "crowd size=no report", NA, NA, NA, NA,…
$ timestamp <dbl> 1704831212, 1704831213, 1704831214, 1704831214, 170…kable(head(Myanmar))| event_id_cnty | event_date | year | time_precision | disorder_type | event_type | sub_event_type | actor1 | assoc_actor_1 | inter1 | actor2 | assoc_actor_2 | inter2 | interaction | civilian_targeting | iso | region | country | admin1 | admin2 | admin3 | location | latitude | longitude | geo_precision | source | source_scale | notes | fatalities | tags | timestamp |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MMR56099 | 31 December 2023 | 2023 | 1 | Political violence | Explosions/Remote violence | Shelling/artillery/missile attack | Military Forces of Myanmar (2021-) | NA | 1 | NA | NA | 0 | 10 | NA | 104 | Southeast Asia | Myanmar | Mon | Mawlamyine | Ye | Aing Shey | 15.2856 | 98.0052 | 1 | Democratic Voice of Burma | National | On 31 December 2023, in Aing Shey village (Ye township, Mawlamyine district, Mon state), following a clash between the military and resistance forces in Kyaung Ywar village (coded separately), the military (LIB-591 and LIB-583) fired artillery rounds into the village. Casualties unknown. | 0 | NA | 1704831212 |
| MMR56222 | 31 December 2023 | 2023 | 1 | Political violence | Explosions/Remote violence | Shelling/artillery/missile attack | Military Forces of Myanmar (2021-) | NA | 1 | Civilians (Myanmar) | NA | 7 | 17 | Civilian targeting | 104 | Southeast Asia | Myanmar | Rakhine | Maungdaw | Maungdaw | Kaing Gyi (NaTaLa) | 20.7219 | 92.4429 | 2 | Development Media Group; Narinjara News | Subnational | On 31 December 2023, in Kaing Gyi (Mro) village (coded as Kaing Gyi (NaTaLa)) (Maungdaw township, Maungdaw district, Rakhine state), Myanmar military troops from the Maw Ra Waddy strategic hill fired artillery rounds indiscriminately into the village at around 8:30 am. Some artillery shells landed on the village main road, injuring two women and a man. | 0 | NA | 1704831213 |
| MMR56370 | 31 December 2023 | 2023 | 1 | Political violence | Battles | Armed clash | Phoenix DF: Phoenix Defense Force (Nattalin) | NA | 3 | Military Forces of Myanmar (2021-) | NA | 1 | 13 | NA | 104 | Southeast Asia | Myanmar | Bago-West | Thayarwady | Nattalin | Kyauk Pyoke | 18.6245 | 95.8295 | 2 | Khit Thit Media; Myanmar Pressphoto Agency | National | On 31 December 2023, near Kyauk Pyoke village (Nattalin township, Thayarwady district, Bago-West region), the Phoenix Defense Force (Nattalin) carried out mine attacks against a Myanmar military column of about 130 troops advancing towards Bago Yoma mountain range in the morning. The two sides then exchanged fire. Four military troops were killed. | 4 | NA | 1704831214 |
| MMR56376 | 31 December 2023 | 2023 | 1 | Demonstrations | Protests | Peaceful protest | Protesters (Myanmar) | NA | 6 | NA | NA | 0 | 60 | NA | 104 | Southeast Asia | Myanmar | Sagaing | Yinmarbin | Salingyi | Let Pa Taung | 22.0608 | 95.1387 | 2 | Myanmar Labour News | National | On 31 December 2023, in the Let Pa Taung area of Salingyi township (Yinmarbin district, Sagaing region), protesters and locals held a protest against the Myanmar military coup with the banner ‘China: Stand Together with the Oppressed People of Myanmar’. | 0 | crowd size=no report | 1704831214 |
| MMR56380 | 31 December 2023 | 2023 | 1 | Strategic developments | Strategic developments | Looting/property destruction | Military Forces of Myanmar (2021-) | NA | 1 | Civilians (Myanmar) | NA | 7 | 17 | NA | 104 | Southeast Asia | Myanmar | Bago-West | Thayarwady | Nattalin | Kyauk Pyoke | 18.6245 | 95.8295 | 1 | Myanmar Pressphoto Agency | National | Property destruction: On 31 December 2023, in Kyauk Pyoke village (Nattalin township, Thayarwady district, Bago-West region), the Myanmar military set fire to some houses in the village. | 0 | NA | 1704831214 |
| MMR56869 | 31 December 2023 | 2023 | 1 | Strategic developments | Strategic developments | Change to group/activity | Military Forces of Myanmar (2021-) | NA | 1 | NA | NA | 0 | 10 | NA | 104 | Southeast Asia | Myanmar | Rakhine | Maungdaw | Maungdaw | Taungpyoletwea | 21.1999 | 92.2007 | 1 | Democratic Voice of Burma | National | Movement of forces: On 31 December 2023, in Taungpyoletwea town (Maungdaw township, Maungdaw district, Rakhine state), the Myanmar military secretly transported weapons, ammunition, and rations to the town using motorboats at night. | 0 | NA | 1704831216 |
str(Myanmar)spc_tbl_ [55,574 × 31] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ event_id_cnty : chr [1:55574] "MMR56099" "MMR56222" "MMR56370" "MMR56376" ...
$ event_date : chr [1:55574] "31 December 2023" "31 December 2023" "31 December 2023" "31 December 2023" ...
$ year : num [1:55574] 2023 2023 2023 2023 2023 ...
$ time_precision : num [1:55574] 1 1 1 1 1 1 1 1 1 1 ...
$ disorder_type : chr [1:55574] "Political violence" "Political violence" "Political violence" "Demonstrations" ...
$ event_type : chr [1:55574] "Explosions/Remote violence" "Explosions/Remote violence" "Battles" "Protests" ...
$ sub_event_type : chr [1:55574] "Shelling/artillery/missile attack" "Shelling/artillery/missile attack" "Armed clash" "Peaceful protest" ...
$ actor1 : chr [1:55574] "Military Forces of Myanmar (2021-)" "Military Forces of Myanmar (2021-)" "Phoenix DF: Phoenix Defense Force (Nattalin)" "Protesters (Myanmar)" ...
$ assoc_actor_1 : chr [1:55574] NA NA NA NA ...
$ inter1 : num [1:55574] 1 1 3 6 1 1 3 1 2 1 ...
$ actor2 : chr [1:55574] NA "Civilians (Myanmar)" "Military Forces of Myanmar (2021-)" NA ...
$ assoc_actor_2 : chr [1:55574] NA NA NA NA ...
$ inter2 : num [1:55574] 0 7 1 0 7 0 1 0 1 7 ...
$ interaction : num [1:55574] 10 17 13 60 17 10 13 10 12 17 ...
$ civilian_targeting: chr [1:55574] NA "Civilian targeting" NA NA ...
$ iso : num [1:55574] 104 104 104 104 104 104 104 104 104 104 ...
$ region : chr [1:55574] "Southeast Asia" "Southeast Asia" "Southeast Asia" "Southeast Asia" ...
$ country : chr [1:55574] "Myanmar" "Myanmar" "Myanmar" "Myanmar" ...
$ admin1 : chr [1:55574] "Mon" "Rakhine" "Bago-West" "Sagaing" ...
$ admin2 : chr [1:55574] "Mawlamyine" "Maungdaw" "Thayarwady" "Yinmarbin" ...
$ admin3 : chr [1:55574] "Ye" "Maungdaw" "Nattalin" "Salingyi" ...
$ location : chr [1:55574] "Aing Shey" "Kaing Gyi (NaTaLa)" "Kyauk Pyoke" "Let Pa Taung" ...
$ latitude : num [1:55574] 15.3 20.7 18.6 22.1 18.6 ...
$ longitude : num [1:55574] 98 92.4 95.8 95.1 95.8 ...
$ geo_precision : num [1:55574] 1 2 2 2 1 1 1 2 2 1 ...
$ source : chr [1:55574] "Democratic Voice of Burma" "Development Media Group; Narinjara News" "Khit Thit Media; Myanmar Pressphoto Agency" "Myanmar Labour News" ...
$ source_scale : chr [1:55574] "National" "Subnational" "National" "National" ...
$ notes : chr [1:55574] "On 31 December 2023, in Aing Shey village (Ye township, Mawlamyine district, Mon state), following a clash betw"| __truncated__ "On 31 December 2023, in Kaing Gyi (Mro) village (coded as Kaing Gyi (NaTaLa)) (Maungdaw township, Maungdaw dist"| __truncated__ "On 31 December 2023, near Kyauk Pyoke village (Nattalin township, Thayarwady district, Bago-West region), the P"| __truncated__ "On 31 December 2023, in the Let Pa Taung area of Salingyi township (Yinmarbin district, Sagaing region), protes"| __truncated__ ...
$ fatalities : num [1:55574] 0 0 4 0 0 0 3 0 0 0 ...
$ tags : chr [1:55574] NA NA NA "crowd size=no report" ...
$ timestamp : num [1:55574] 1.7e+09 1.7e+09 1.7e+09 1.7e+09 1.7e+09 ...
- attr(*, "spec")=
.. cols(
.. event_id_cnty = col_character(),
.. event_date = col_character(),
.. year = col_double(),
.. time_precision = col_double(),
.. disorder_type = col_character(),
.. event_type = col_character(),
.. sub_event_type = col_character(),
.. actor1 = col_character(),
.. assoc_actor_1 = col_character(),
.. inter1 = col_double(),
.. actor2 = col_character(),
.. assoc_actor_2 = col_character(),
.. inter2 = col_double(),
.. interaction = col_double(),
.. civilian_targeting = col_character(),
.. iso = col_double(),
.. region = col_character(),
.. country = col_character(),
.. admin1 = col_character(),
.. admin2 = col_character(),
.. admin3 = col_character(),
.. location = col_character(),
.. latitude = col_double(),
.. longitude = col_double(),
.. geo_precision = col_double(),
.. source = col_character(),
.. source_scale = col_character(),
.. notes = col_character(),
.. fatalities = col_double(),
.. tags = col_character(),
.. timestamp = col_double()
.. )
- attr(*, "problems")=<externalptr> Based on the information above, our dataset consists of 31 variables and 55,574 observations. The date range used for our study would be between 1st Jan 2010 to 31st Dec 2023. The detailed description and explanation of each variable can be found in the codebook provided by ACLED.
The flowchart above shows some of the additional variables that will be created and used in this project.
The code below changes the attributes of some of the variables shown above so that we can properly use them for analysis
Myanmar <- Myanmar %>%
mutate(year =factor(year))
Myanmar$event_date <- dmy(Myanmar$event_date)
str(Myanmar)tibble [55,574 × 31] (S3: tbl_df/tbl/data.frame)
$ event_id_cnty : chr [1:55574] "MMR56099" "MMR56222" "MMR56370" "MMR56376" ...
$ event_date : Date[1:55574], format: "2023-12-31" "2023-12-31" ...
$ year : Factor w/ 14 levels "2010","2011",..: 14 14 14 14 14 14 14 14 14 14 ...
$ time_precision : num [1:55574] 1 1 1 1 1 1 1 1 1 1 ...
$ disorder_type : chr [1:55574] "Political violence" "Political violence" "Political violence" "Demonstrations" ...
$ event_type : chr [1:55574] "Explosions/Remote violence" "Explosions/Remote violence" "Battles" "Protests" ...
$ sub_event_type : chr [1:55574] "Shelling/artillery/missile attack" "Shelling/artillery/missile attack" "Armed clash" "Peaceful protest" ...
$ actor1 : chr [1:55574] "Military Forces of Myanmar (2021-)" "Military Forces of Myanmar (2021-)" "Phoenix DF: Phoenix Defense Force (Nattalin)" "Protesters (Myanmar)" ...
$ assoc_actor_1 : chr [1:55574] NA NA NA NA ...
$ inter1 : num [1:55574] 1 1 3 6 1 1 3 1 2 1 ...
$ actor2 : chr [1:55574] NA "Civilians (Myanmar)" "Military Forces of Myanmar (2021-)" NA ...
$ assoc_actor_2 : chr [1:55574] NA NA NA NA ...
$ inter2 : num [1:55574] 0 7 1 0 7 0 1 0 1 7 ...
$ interaction : num [1:55574] 10 17 13 60 17 10 13 10 12 17 ...
$ civilian_targeting: chr [1:55574] NA "Civilian targeting" NA NA ...
$ iso : num [1:55574] 104 104 104 104 104 104 104 104 104 104 ...
$ region : chr [1:55574] "Southeast Asia" "Southeast Asia" "Southeast Asia" "Southeast Asia" ...
$ country : chr [1:55574] "Myanmar" "Myanmar" "Myanmar" "Myanmar" ...
$ admin1 : chr [1:55574] "Mon" "Rakhine" "Bago-West" "Sagaing" ...
$ admin2 : chr [1:55574] "Mawlamyine" "Maungdaw" "Thayarwady" "Yinmarbin" ...
$ admin3 : chr [1:55574] "Ye" "Maungdaw" "Nattalin" "Salingyi" ...
$ location : chr [1:55574] "Aing Shey" "Kaing Gyi (NaTaLa)" "Kyauk Pyoke" "Let Pa Taung" ...
$ latitude : num [1:55574] 15.3 20.7 18.6 22.1 18.6 ...
$ longitude : num [1:55574] 98 92.4 95.8 95.1 95.8 ...
$ geo_precision : num [1:55574] 1 2 2 2 1 1 1 2 2 1 ...
$ source : chr [1:55574] "Democratic Voice of Burma" "Development Media Group; Narinjara News" "Khit Thit Media; Myanmar Pressphoto Agency" "Myanmar Labour News" ...
$ source_scale : chr [1:55574] "National" "Subnational" "National" "National" ...
$ notes : chr [1:55574] "On 31 December 2023, in Aing Shey village (Ye township, Mawlamyine district, Mon state), following a clash betw"| __truncated__ "On 31 December 2023, in Kaing Gyi (Mro) village (coded as Kaing Gyi (NaTaLa)) (Maungdaw township, Maungdaw dist"| __truncated__ "On 31 December 2023, near Kyauk Pyoke village (Nattalin township, Thayarwady district, Bago-West region), the P"| __truncated__ "On 31 December 2023, in the Let Pa Taung area of Salingyi township (Yinmarbin district, Sagaing region), protes"| __truncated__ ...
$ fatalities : num [1:55574] 0 0 4 0 0 0 3 0 0 0 ...
$ tags : chr [1:55574] NA NA NA "crowd size=no report" ...
$ timestamp : num [1:55574] 1.7e+09 1.7e+09 1.7e+09 1.7e+09 1.7e+09 ...The code chunk below creates a new dataset whilst removing those not being used in the study
Myanmar_final <- Myanmar %>%
select(-time_precision, -geo_precision, -source_scale, -timestamp, -tags)Due to the changes of some of the township names through the years, the following code chunk below corrects them such that they can be recognized.
ACLED_MMR_1 <- Myanmar_final %>%
mutate(admin1 = case_when(
admin1 == "Bago-East" ~ "Bago (East)",
admin1 == "Bago-West" ~ "Bago (West)",
admin1 == "Shan-North" ~ "Shan (North)",
admin1 == "Shan-South" ~ "Shan (South)",
admin1 == "Shan-East" ~ "Shan (East)",
TRUE ~ as.character(admin1)
))ACLED_MMR_1 <- Myanmar_final %>%
mutate(admin2 = case_when(
admin2 == "Yangon-East" ~ "Yangon (East)",
admin2 == "Yangon-West" ~ "Yangon (West)",
admin2 == "Yangon-North" ~ "Yangon (North)",
admin2 == "Yangon-South" ~ "Yangon (South)",
admin2 == "Mong Pawk (Wa SAD)" ~ "Tachileik",
admin2 == "Nay Pyi Taw" ~ "Det Khi Na",
admin2 == "Yangon" ~ "Yangon (West)",
TRUE ~ as.character(admin2)
))Summary_Data <- ACLED_MMR_1 %>%
group_by(year,admin1, event_type) %>%
summarize(
Total_incidents = n(),
Total_Fatalities = sum(fatalities, na.rm=TRUE)
)The code above groups the year, admin1, and event type together so that we can create a data table summarizing the total number of incidents for the year based on the township(admin1) and event type as well as a column totaling the amount of fatalities based on the same grouping selection
ggplot(Summary_Data,
aes(x = year,
y = Total_incidents,
color = event_type)) +
geom_point() +
facet_wrap(~event_type) +
theme_minimal() +
labs(y = "Total Number of Incidents", x = "Years") +
theme(
panel.grid.major.y = element_line(color = "pink", linetype = 2),
strip.background = element_rect(fill = "black"),
strip.text = element_text(colour = "white"),
legend.position = "none"
)
Based on the chart above, it can be seen that there was an increase incidents for all incident types except for riot. The spikes in the increased incident can be attributed to the start of the Civil War within the country itself.
With the civil unrest, the highest incidents occurs with protests and strategic developments.
Next, we will look at the total amount of fatalities by event_type.
ggplot(Summary_Data,
aes(x = year, y = Total_Fatalities, color = event_type)) +
geom_point() +
facet_wrap(~event_type) +
theme_minimal() +
labs(y = "Total Number of Fatalities", x = "Years") +
theme(
panel.grid.major.y = element_line(color = "pink", linetype = 2),
strip.background = element_rect(fill = "black"),
strip.text = element_text(colour = "white"),
legend.position = "none"
)
gg1 <- ggplot(Summary_Data,
aes(x = admin1, y=Total_incidents,size = Total_Fatalities, color = event_type)) +
geom_point(aes(frame = year)) +
theme_minimal() +
labs(y = "Total Number of Incidents", x = "Regions") +
theme(axis.text.x = element_blank())
ggplotly(gg1)The chart above builds on the earlier summary of incidents and fatalities where it further splits the event types according to region. As you move the slider through the years, it seems that some regions have increased incidents as well as fatalities depending on the event type.
What is interesting to see is that at the start of 2021, there was an increased number of protests where in most regions but as time progressed the number dropped. This drop in number of protest eventually led to other types of events being increased such as battles which also has the higher amount of fatalities as compared to other event types.
Next, we would do some Confirmatory Data Analysis to see if what we see above is true.
This next section would delve more into confirmatory data analysis where we would be testing for statistical significance based on a few null hypothesis that would be described according to the EDA portion in the earlier section. For the analysis and comparisons, we would mainly be using ggstatsplot package for analysis.
pacman::p_load(ggstatsplot, corrplot)Null Hypothesis: Are there specific event types more correlated with number of fatalities?
In order to test the above hypothesis, there would be some data wrangling that needs to be done first.
Incident_hypo1 <- ACLED_MMR_1 %>%
group_by(event_type,year) %>%
summarize(
Total_incidents = n(),
Total_Fatalities = sum(fatalities, na.rm=TRUE)
)gg2 <- ggplot(Incident_hypo1,
aes(x = year, y=Total_Fatalities)) +
geom_boxplot() +
stat_summary(geom = "point",
fun.y="mean",
colour ="red",
size=2)
ggplotly(gg2)ACLED_Filtered <- ACLED_MMR_1 %>%
filter(year == c(2020,2021,2022,2023))
ggstatsplot::grouped_ggbetweenstats(ACLED_Filtered,
x = year,
y = fatalities,
grouping.var = event_type,
type = "np",
pairwise.display = "s",
pairwise_comparisons = TRUE,
output = "plot")
set.seed(123)
ggbetweenstats(ACLED_Filtered,
x = year,
y = fatalities)
ggbetweenstats(Incident_hypo1,
x= event_type,
y= Total_incidents)
ggbetweenstats(ACLED_Filtered,
x= event_type,
y= fatalities)
Battles_Summary <- ACLED_MMR_1 %>%
filter(event_type == "Battles")
Explosion_Summary <- ACLED_MMR_1 %>%
filter(event_type == "Explosions/Remote violence")
Protests_Summary <- ACLED_MMR_1 %>%
filter(event_type == "Protests")
StratD_Summary <- ACLED_MMR_1 %>%
filter(event_type == "Strategic developments")
VioCiv_Summary <- ACLED_MMR_1 %>%
filter(event_type == "Violence against civilians")set.seed(123)
Battles_Summary_Grouped <- Battles_Summary %>%
group_by(admin1) %>%
summarize(
Total_Incidents = n(),
Total_Fatalities = sum(fatalities, na.rm=TRUE),
Mean_Fatalities = mean(fatalities)
)
gg3 <- ggbarstats(ACLED_Filtered,
x = event_type,
y = admin1)
ggplotly(gg3)