Project 2a - Flags of the World
Nate Jack
Image by Gordon Johnson from Pixabay
Data Import
Download flag.csv and flag.names to your working directory. Make sure to set your working directory appropriately!
Let’s look at some information about this file. Open flag.names in RStudio by double clicking it in the files pane in bottom left. Read through this file.
Who is the donor of this data? Richard S. Forsyth
Is there any missing data? No
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.1.3## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.1 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()getwd()## [1] "C:/Users/natha/OneDrive/Documents/Data 101"- Import the flag.csv data into R. Store it in a data.frame named flag_df.
setwd("~/Data 101")
flag_df <- read.csv("flag.csv")
library(tidyverse)- Check to make sure the class of flag_df is data.frame. Then find the dimensions of flag_df.
class(flag_df)## [1] "data.frame"dim(flag_df)## [1] 194 31- Print out the first 5 lines and the last 5 lines of flag_df.
head(flag_df, 5)## X name landmass zone area population language religion bars stripes
## 1 1 Afghanistan 5 1 648 16 10 2 0 3
## 2 2 Albania 3 1 29 3 6 6 0 0
## 3 3 Algeria 4 1 2388 20 8 2 2 0
## 4 4 American-Samoa 6 3 0 0 1 1 0 0
## 5 5 Andorra 3 1 0 0 6 0 3 0
## colours red green blue gold white black orange mainhue circles crosses
## 1 5 1 1 0 1 1 1 0 green 0 0
## 2 3 1 0 0 1 0 1 0 red 0 0
## 3 3 1 1 0 0 1 0 0 green 0 0
## 4 5 1 0 1 1 1 0 1 blue 0 0
## 5 3 1 0 1 1 0 0 0 gold 0 0
## saltires quarters sunstars crescent triangle icon animate text topleft
## 1 0 0 1 0 0 1 0 0 black
## 2 0 0 1 0 0 0 1 0 red
## 3 0 0 1 1 0 0 0 0 green
## 4 0 0 0 0 1 1 1 0 blue
## 5 0 0 0 0 0 0 0 0 blue
## botright
## 1 green
## 2 red
## 3 white
## 4 red
## 5 red- Print out the summary statistics of each variable of flag_df.
summary(flag_df)## X name landmass zone
## Min. : 1.00 Length:194 Min. :1.000 Min. :1.000
## 1st Qu.: 49.25 Class :character 1st Qu.:3.000 1st Qu.:1.000
## Median : 97.50 Mode :character Median :4.000 Median :2.000
## Mean : 97.50 Mean :3.572 Mean :2.211
## 3rd Qu.:145.75 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :194.00 Max. :6.000 Max. :4.000
## area population language religion
## Min. : 0.0 Min. : 0.00 Min. : 1.00 Min. :0.000
## 1st Qu.: 9.0 1st Qu.: 0.00 1st Qu.: 2.00 1st Qu.:1.000
## Median : 111.0 Median : 4.00 Median : 6.00 Median :1.000
## Mean : 700.0 Mean : 23.27 Mean : 5.34 Mean :2.191
## 3rd Qu.: 471.2 3rd Qu.: 14.00 3rd Qu.: 9.00 3rd Qu.:4.000
## Max. :22402.0 Max. :1008.00 Max. :10.00 Max. :7.000
## bars stripes colours red
## Min. :0.0000 Min. : 0.000 Min. :1.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.: 0.000 1st Qu.:3.000 1st Qu.:1.0000
## Median :0.0000 Median : 0.000 Median :3.000 Median :1.0000
## Mean :0.4536 Mean : 1.552 Mean :3.464 Mean :0.7887
## 3rd Qu.:0.0000 3rd Qu.: 3.000 3rd Qu.:4.000 3rd Qu.:1.0000
## Max. :5.0000 Max. :14.000 Max. :8.000 Max. :1.0000
## green blue gold white
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0000
## Median :0.0000 Median :1.0000 Median :0.0000 Median :1.0000
## Mean :0.4691 Mean :0.5103 Mean :0.4691 Mean :0.7526
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## black orange mainhue circles
## Min. :0.000 Min. :0.000 Length:194 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.000 Class :character 1st Qu.:0.0000
## Median :0.000 Median :0.000 Mode :character Median :0.0000
## Mean :0.268 Mean :0.134 Mean :0.1701
## 3rd Qu.:1.000 3rd Qu.:0.000 3rd Qu.:0.0000
## Max. :1.000 Max. :1.000 Max. :4.0000
## crosses saltires quarters sunstars
## Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. : 0.000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.: 0.000
## Median :0.0000 Median :0.00000 Median :0.0000 Median : 0.000
## Mean :0.1495 Mean :0.09278 Mean :0.1495 Mean : 1.387
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.: 1.000
## Max. :2.0000 Max. :1.00000 Max. :4.0000 Max. :50.000
## crescent triangle icon animate
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.000
## Mean :0.0567 Mean :0.1392 Mean :0.2526 Mean :0.201
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.7500 3rd Qu.:0.000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.000
## text topleft botright
## Min. :0.00000 Length:194 Length:194
## 1st Qu.:0.00000 Class :character Class :character
## Median :0.00000 Mode :character Mode :character
## Mean :0.08247
## 3rd Qu.:0.00000
## Max. :1.00000- Print out the structure of flag_df.
str(flag_df)## 'data.frame': 194 obs. of 31 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ name : chr "Afghanistan" "Albania" "Algeria" "American-Samoa" ...
## $ landmass : int 5 3 4 6 3 4 1 1 2 2 ...
## $ zone : int 1 1 1 3 1 2 4 4 3 3 ...
## $ area : int 648 29 2388 0 0 1247 0 0 2777 2777 ...
## $ population: int 16 3 20 0 0 7 0 0 28 28 ...
## $ language : int 10 6 8 1 6 10 1 1 2 2 ...
## $ religion : int 2 6 2 1 0 5 1 1 0 0 ...
## $ bars : int 0 0 2 0 3 0 0 0 0 0 ...
## $ stripes : int 3 0 0 0 0 2 1 1 3 3 ...
## $ colours : int 5 3 3 5 3 3 3 5 2 3 ...
## $ red : int 1 1 1 1 1 1 0 1 0 0 ...
## $ green : int 1 0 1 0 0 0 0 0 0 0 ...
## $ blue : int 0 0 0 1 1 0 1 1 1 1 ...
## $ gold : int 1 1 0 1 1 1 0 1 0 1 ...
## $ white : int 1 0 1 1 0 0 1 1 1 1 ...
## $ black : int 1 1 0 0 0 1 0 1 0 0 ...
## $ orange : int 0 0 0 1 0 0 1 0 0 0 ...
## $ mainhue : chr "green" "red" "green" "blue" ...
## $ circles : int 0 0 0 0 0 0 0 0 0 0 ...
## $ crosses : int 0 0 0 0 0 0 0 0 0 0 ...
## $ saltires : int 0 0 0 0 0 0 0 0 0 0 ...
## $ quarters : int 0 0 0 0 0 0 0 0 0 0 ...
## $ sunstars : int 1 1 1 0 0 1 0 1 0 1 ...
## $ crescent : int 0 0 1 0 0 0 0 0 0 0 ...
## $ triangle : int 0 0 0 1 0 0 0 1 0 0 ...
## $ icon : int 1 0 0 1 0 1 0 0 0 0 ...
## $ animate : int 0 1 0 1 0 0 1 0 0 0 ...
## $ text : int 0 0 0 0 0 0 0 0 0 0 ...
## $ topleft : chr "black" "red" "green" "blue" ...
## $ botright : chr "green" "red" "white" "red" ...Data Cleaning/Management
We are going to use the dplyr package.
- Load the tidyverse and convert the type of flag_df to tibble.
library(tidyverse)
flag_df<-as_tibble(flag_df)- Find the variable (column) names of flag_df.
flag_df %>% select(name)## # A tibble: 194 x 1
## name
## <chr>
## 1 Afghanistan
## 2 Albania
## 3 Algeria
## 4 American-Samoa
## 5 Andorra
## 6 Angola
## 7 Anguilla
## 8 Antigua-Barbuda
## 9 Argentina
## 10 Argentine
## # ... with 184 more rowsSomething should look strange about the first column name. Let’s investigate this.
- Print out the first column.
flag_df %>% select(1)## # A tibble: 194 x 1
## X
## <int>
## 1 1
## 2 2
## 3 3
## 4 4
## 5 5
## 6 6
## 7 7
## 8 8
## 9 9
## 10 10
## # ... with 184 more rowsWhat is in this first column? FILL IN YOUR ANSWER HERE
Do we really need it? FILL IN YOUR ANSWER HERE
- Delete the first column of flag_df.
select(flag_df , -X) ## # A tibble: 194 x 30
## name landmass zone area population language religion bars stripes colours
## <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 Afgh~ 5 1 648 16 10 2 0 3 5
## 2 Alba~ 3 1 29 3 6 6 0 0 3
## 3 Alge~ 4 1 2388 20 8 2 2 0 3
## 4 Amer~ 6 3 0 0 1 1 0 0 5
## 5 Ando~ 3 1 0 0 6 0 3 0 3
## 6 Ango~ 4 2 1247 7 10 5 0 2 3
## 7 Angu~ 1 4 0 0 1 1 0 1 3
## 8 Anti~ 1 4 0 0 1 1 0 1 5
## 9 Arge~ 2 3 2777 28 2 0 0 3 2
## 10 Arge~ 2 3 2777 28 2 0 0 3 3
## # ... with 184 more rows, and 20 more variables: red <int>, green <int>,
## # blue <int>, gold <int>, white <int>, black <int>, orange <int>,
## # mainhue <chr>, circles <int>, crosses <int>, saltires <int>,
## # quarters <int>, sunstars <int>, crescent <int>, triangle <int>, icon <int>,
## # animate <int>, text <int>, topleft <chr>, botright <chr>- Verify that there are no missing values in flag_df.
flag_df## # A tibble: 194 x 31
## X name landmass zone area population language religion bars stripes
## <int> <chr> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 1 Afghan~ 5 1 648 16 10 2 0 3
## 2 2 Albania 3 1 29 3 6 6 0 0
## 3 3 Algeria 4 1 2388 20 8 2 2 0
## 4 4 Americ~ 6 3 0 0 1 1 0 0
## 5 5 Andorra 3 1 0 0 6 0 3 0
## 6 6 Angola 4 2 1247 7 10 5 0 2
## 7 7 Anguil~ 1 4 0 0 1 1 0 1
## 8 8 Antigu~ 1 4 0 0 1 1 0 1
## 9 9 Argent~ 2 3 2777 28 2 0 0 3
## 10 10 Argent~ 2 3 2777 28 2 0 0 3
## # ... with 184 more rows, and 21 more variables: colours <int>, red <int>,
## # green <int>, blue <int>, gold <int>, white <int>, black <int>,
## # orange <int>, mainhue <chr>, circles <int>, crosses <int>, saltires <int>,
## # quarters <int>, sunstars <int>, crescent <int>, triangle <int>, icon <int>,
## # animate <int>, text <int>, topleft <chr>, botright <chr>sum(is.na(flag_df))## [1] 0At this point, we know there are no missing values in the dataset so we will use dplyr to make the dataset a bit more readable to us. Look at the flag.names file again. Under “Attribute Information” look at the variables landmass, zone, language, religion.
Instead of encoding these categories using numbers, we would like to just use the categories in the variables. For example, in the zone column, we want our data to be “NE”, “SE”, “SW”, “NW”, instead of 1, 2, 3, 4.
- Change each of the columns landmass, zone, language, and religion to hold their actual categorical data (not their encoded numbers). The type of each of these columns should be Factor.
flag_df$landmass<-as.character(flag_df$landmass)
flag_df$landmass[flag_df$landmass == "1"]<- "NE"
flag_df$landmass[flag_df$landmass == "2"]<- "SE"
flag_df$landmass[flag_df$landmass == "3"]<- "SW"
flag_df$landmass[flag_df$landmass == "4"]<- "NW"
flag_df$zone<-as.character(flag_df$zone)
flag_df$zone[flag_df$zone == "1"]<- "NE"
flag_df$zone[flag_df$zone == "2"]<- "SE"
flag_df$zone[flag_df$zone == "3"]<- "SW"
flag_df$zone[flag_df$zone == "4"]<- "NW"
flag_df$language<-as.character(flag_df$language)
flag_df$language[flag_df$language == "1"]<- "NE"
flag_df$language[flag_df$language== "2"]<- "SE"
flag_df$language[flag_df$language== "3"]<- "SW"
flag_df$language[flag_df$language== "4"]<- "NW"
flag_df$religion<-as.character(flag_df$religion)
flag_df$religion[flag_df$religion== "1"]<- "NE"
flag_df$religion[flag_df$religion== "2"]<- "SE"
flag_df$religion[flag_df$religion== "3"]<- "SW"
flag_df$religion[flag_df$religion== "4"]<- "NW"
flag_df$landmass<-as.factor(flag_df$landmass)
flag_df$zone<-as.factor(flag_df$zone)
flag_df$language<-as.factor(flag_df$language)
flag_df$religion<-as.factor(flag_df$religion)Notice from our earlier structure command that the data types for columns red, green, blue, gold, white, black, orange, crescent, triangle, icon, animate, text are all integer. Looking at flag.names these integer variables are really just an encoding for true (1) or false (0). We don’t want to compute with these 1s and 0s (for example find a mean). So we should change these to logicals.
- Change the column type to logical for the following columns: red, green, blue, gold, white, black, orange, crescent, triangle, icon, animate, and text.
flag_df$red<-as.logical(flag_df$red)
flag_df$green<-as.logical(flag_df$green)
flag_df$blue<-as.logical(flag_df$blue)
flag_df$gold<-as.logical(flag_df$gold)
flag_df$white<-as.logical(flag_df$white)
flag_df$black<-as.logical(flag_df$black)
flag_df$orange<-as.logical(flag_df$orange)
flag_df$crescent<-as.logical(flag_df$crescent)
flag_df$triangle<-as.logical(flag_df$triangle)
flag_df$icon<-as.logical(flag_df$icon)
flag_df$animate<-as.logical(flag_df$animate)
flag_df$text<-as.logical(flag_df$text)
str(flag_df)## tibble [194 x 31] (S3: tbl_df/tbl/data.frame)
## $ X : int [1:194] 1 2 3 4 5 6 7 8 9 10 ...
## $ name : chr [1:194] "Afghanistan" "Albania" "Algeria" "American-Samoa" ...
## $ landmass : Factor w/ 6 levels "5","6","NE","NW",..: 1 6 4 2 6 4 3 3 5 5 ...
## $ zone : Factor w/ 4 levels "NE","NW","SE",..: 1 1 1 4 1 3 2 2 4 4 ...
## $ area : int [1:194] 648 29 2388 0 0 1247 0 0 2777 2777 ...
## $ population: int [1:194] 16 3 20 0 0 7 0 0 28 28 ...
## $ language : Factor w/ 10 levels "10","5","6","7",..: 1 3 5 7 3 1 7 7 9 9 ...
## $ religion : Factor w/ 8 levels "0","5","6","7",..: 7 3 7 5 1 2 5 5 1 1 ...
## $ bars : int [1:194] 0 0 2 0 3 0 0 0 0 0 ...
## $ stripes : int [1:194] 3 0 0 0 0 2 1 1 3 3 ...
## $ colours : int [1:194] 5 3 3 5 3 3 3 5 2 3 ...
## $ red : logi [1:194] TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ green : logi [1:194] TRUE FALSE TRUE FALSE FALSE FALSE ...
## $ blue : logi [1:194] FALSE FALSE FALSE TRUE TRUE FALSE ...
## $ gold : logi [1:194] TRUE TRUE FALSE TRUE TRUE TRUE ...
## $ white : logi [1:194] TRUE FALSE TRUE TRUE FALSE FALSE ...
## $ black : logi [1:194] TRUE TRUE FALSE FALSE FALSE TRUE ...
## $ orange : logi [1:194] FALSE FALSE FALSE TRUE FALSE FALSE ...
## $ mainhue : chr [1:194] "green" "red" "green" "blue" ...
## $ circles : int [1:194] 0 0 0 0 0 0 0 0 0 0 ...
## $ crosses : int [1:194] 0 0 0 0 0 0 0 0 0 0 ...
## $ saltires : int [1:194] 0 0 0 0 0 0 0 0 0 0 ...
## $ quarters : int [1:194] 0 0 0 0 0 0 0 0 0 0 ...
## $ sunstars : int [1:194] 1 1 1 0 0 1 0 1 0 1 ...
## $ crescent : logi [1:194] FALSE FALSE TRUE FALSE FALSE FALSE ...
## $ triangle : logi [1:194] FALSE FALSE FALSE TRUE FALSE FALSE ...
## $ icon : logi [1:194] TRUE FALSE FALSE TRUE FALSE TRUE ...
## $ animate : logi [1:194] FALSE TRUE FALSE TRUE FALSE FALSE ...
## $ text : logi [1:194] FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ topleft : chr [1:194] "black" "red" "green" "blue" ...
## $ botright : chr [1:194] "green" "red" "white" "red" ...Now that our data is clean, let’s answer some questions about it!
Data Investigation
- Print out how many countries have each “mainhue” category.
table(flag_df$mainhue)##
## black blue brown gold green orange red white
## 5 40 2 19 31 4 71 22- How many countries have the three colors red, white, and blue in their flags? How many countries have ONLY the three colors red, white, and blue in their flags?
library(dplyr)
RWB_ <- flag_df %>%
filter(red==TRUE|blue==TRUE|white==TRUE&black==FALSE&gold==FALSE&green==FALSE&orange==FALSE)
dim(RWB_)## [1] 182 31182 countries have three colors red, white, and blue in their flags
RWB <- flag_df %>%
filter(red==TRUE&blue==TRUE&white==TRUE&black==FALSE&gold==FALSE&green==FALSE&orange==FALSE)
dim(RWB)## [1] 27 3127 countries have ONLY three colors red, white, and blue in their flags
library(dplyr)
RWB_ <- flag_df %>%
filter(red==TRUE&blue==TRUE&white==TRUE&black==FALSE&gold==FALSE&green==FALSE&orange==FALSE)
dim(RWB_)## [1] 27 31- Print out the data observations for the 10 countries with the largest populations. The 10 data observations should be printed out in descending order according to population.
arrange(flag_df,desc(population)) %>%
head(10)## # A tibble: 10 x 31
## X name landmass zone area population language religion bars stripes
## <int> <chr> <fct> <fct> <int> <int> <fct> <fct> <int> <int>
## 1 38 China 5 NE 9561 1008 7 6 0 0
## 2 82 India 5 NE 3268 684 6 NW 0 3
## 3 185 USSR 5 NE 22402 274 5 6 0 0
## 4 184 USA NE NW 9363 231 NE NE 0 13
## 5 83 Indone~ 6 SE 1904 157 10 SE 0 2
## 6 24 Brazil SE SW 8512 119 6 0 0 0
## 7 91 Japan 5 NE 372 118 9 7 0 0
## 8 15 Bangla~ 5 NE 143 90 6 SE 0 0
## 9 133 Pakist~ 5 NE 804 84 6 SE 1 0
## 10 113 Mexico NE NW 1973 77 SE 0 3 0
## # ... with 21 more variables: colours <int>, red <lgl>, green <lgl>,
## # blue <lgl>, gold <lgl>, white <lgl>, black <lgl>, orange <lgl>,
## # mainhue <chr>, circles <int>, crosses <int>, saltires <int>,
## # quarters <int>, sunstars <int>, crescent <lgl>, triangle <lgl>, icon <lgl>,
## # animate <lgl>, text <lgl>, topleft <chr>, botright <chr>Let’s see if we can find any patterns in the data.
- Group the flags by landmass and find the following for each group:
- the mode mainhue
- the median number of sun or star symbols
- the number of flags with animate images
- the percentage of flags with animate images
Your output should be a data frame with each row corresponding to a group. There will be five columns.
Repeat this process except group by zone, language, and religion.
# You may find this function useful (ie. you should call this function in your code)! It calculates the mode of a factor.
cat_mode <- function(cat_var){
mode_idx <- which.max(table(cat_var))
levels(cat_var)[mode_idx]
}
# fill in your code here
flag_df %>%
group_by(landmass) %>%
summarise(ModeMainhue=cat_mode(mainhue),MedianLandmass=median(sunstars),animateLandmass=sum(animate), animateLandmassPercent=animateLandmass/(length(animate))*100)## # A tibble: 6 x 4
## landmass MedianLandmass animateLandmass animateLandmassPercent
## <fct> <dbl> <int> <dbl>
## 1 5 1 6 15.4
## 2 6 2.5 6 30
## 3 NE 0 13 41.9
## 4 NW 0 7 13.5
## 5 SE 0 3 17.6
## 6 SW 0 4 11.4flag_df %>%
group_by(zone) %>%
summarise(ModeZone=cat_mode(mainhue),MedianZone=median(sunstars),animateZone=sum(animate), animateZonePercent=animateZone/(length(animate))*100)## # A tibble: 4 x 4
## zone MedianZone animateZone animateZonePercent
## <fct> <dbl> <int> <dbl>
## 1 NE 0 14 15.4
## 2 NW 0 15 25.9
## 3 SE 0 7 24.1
## 4 SW 1 3 18.8flag_df %>%
group_by(language) %>%
summarise(ModeLang=cat_mode(mainhue),MedianLang=median(sunstars),animateLang=sum(animate), animateLangPercent=animateLang/(length(animate))*100)## # A tibble: 10 x 4
## language MedianLang animateLang animateLangPercent
## <fct> <dbl> <int> <dbl>
## 1 10 0 9 19.6
## 2 5 0.5 1 25
## 3 6 0 5 16.7
## 4 7 3 1 25
## 5 8 0 2 10.5
## 6 9 0.5 0 0
## 7 NE 0 18 41.9
## 8 NW 0 0 0
## 9 SE 0 3 14.3
## 10 SW 0 0 0flag_df %>%
group_by(religion) %>%
summarise(ModeReligion=cat_mode(mainhue),MedianReligion=median(sunstars),animateReligion=sum(animate), animateReligionPercent=animateReligion/(length(animate))*100)## # A tibble: 8 x 4
## religion MedianReligion animateReligion animateReligionPercent
## <fct> <dbl> <int> <dbl>
## 1 0 0 4 10
## 2 5 0 6 22.2
## 3 6 1 3 20
## 4 7 1 0 0
## 5 NE 0 19 31.7
## 6 NW 0 0 0
## 7 SE 0 3 8.33
## 8 SW 0 4 50Do you see any patterns in flag mainhue, sun or star symbols, and animate images? If so, describe these patterns. (Hint: you should see patterns! Look at the trends when grouping by landmass, zone, language, and religion.) Write a paragraph to answer this question.
FILL IN YOUR ANSWER HERE
When grouping by landmass, zone, language and religion, most of the NE zones have the highest number of animate objects and the highest percentage. Another pattern is that when there are sun or star symbols present, the number of animate objects go down.
flag_df %>%
group_by(zone) %>%
summarise(Modesunstar=cat_mode(mainhue),Mediansunstar=median(sunstars),animatesunstar=sum(animate), animatesunstarPercent=animatesunstar/(length(animate))*100)## # A tibble: 4 x 4
## zone Mediansunstar animatesunstar animatesunstarPercent
## <fct> <dbl> <int> <dbl>
## 1 NE 0 14 15.4
## 2 NW 0 15 25.9
## 3 SE 0 7 24.1
## 4 SW 1 3 18.8