Project Flags of the World
Chandler Brown
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? There is no missing data.
- Import the flag.csv data into R. Store it in a data.frame named flag_df.
flag_df <- read.csv("flag.csv")- 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)## 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
## 6 6 Angola 4 2 1247 7 10 5 0 2
## 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
## 6 3 1 0 0 1 0 1 0 red 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
## 6 0 0 1 0 0 1 0 0 red
## botright
## 1 green
## 2 red
## 3 white
## 4 red
## 5 red
## 6 blacktail(flag_df)## X name landmass zone area population language religion bars
## 189 189 Vietnam 5 1 333 60 10 6 0
## 190 190 Western-Samoa 6 3 3 0 1 1 0
## 191 191 Yugoslavia 3 1 256 22 6 6 0
## 192 192 Zaire 4 2 905 28 10 5 0
## 193 193 Zambia 4 2 753 6 10 5 3
## 194 194 Zimbabwe 4 2 391 8 10 5 0
## stripes colours red green blue gold white black orange mainhue circles
## 189 0 2 1 0 0 1 0 0 0 red 0
## 190 0 3 1 0 1 0 1 0 0 red 0
## 191 3 4 1 0 1 1 1 0 0 red 0
## 192 0 4 1 1 0 1 0 0 1 green 1
## 193 0 4 1 1 0 0 0 1 1 green 0
## 194 7 5 1 1 0 1 1 1 0 green 0
## crosses saltires quarters sunstars crescent triangle icon animate text
## 189 0 0 0 1 0 0 0 0 0
## 190 0 0 1 5 0 0 0 0 0
## 191 0 0 0 1 0 0 0 0 0
## 192 0 0 0 0 0 0 1 1 0
## 193 0 0 0 0 0 0 0 1 0
## 194 0 0 0 1 0 1 1 1 0
## topleft botright
## 189 red red
## 190 blue red
## 191 blue red
## 192 green green
## 193 green brown
## 194 green green- 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)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 1.0.1
## ✔ tibble 3.1.8 ✔ dplyr 1.1.0
## ✔ tidyr 1.3.0 ✔ stringr 1.5.0
## ✔ readr 2.1.3 ✔ forcats 1.0.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()flag_df <- as_tibble(flag_df)- Find the variable (column) names of flag_df.
colnames(flag_df)## [1] "X" "name" "landmass" "zone" "area"
## [6] "population" "language" "religion" "bars" "stripes"
## [11] "colours" "red" "green" "blue" "gold"
## [16] "white" "black" "orange" "mainhue" "circles"
## [21] "crosses" "saltires" "quarters" "sunstars" "crescent"
## [26] "triangle" "icon" "animate" "text" "topleft"
## [31] "botright"Something should look strange about the first column name. Let’s investigate this.
- Print out the first column.
flag_df$X## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194What is in this first column? The number of the observations.
Do we really need it? This column is unnecessary
- Delete the first column of flag_df.
flag_df <- flag_df[, -1]- Verify that there are no missing values in flag_df.
sum(is.na(flag_df))## [1] 0At this point, we know there are no missing values in the dataset so we will use dplyr to make the dataset a bit more readable to us. Look at the flag.names file again. Under “Attribute Information” look at the variables landmass, zone, language, religion.
Instead of encoding these categories using numbers, we would like to just use the categories in the variables. For example, in the zone column, we want our data to be “NE”, “SE”, “SW”, “NW”, instead of 1, 2, 3, 4.
- Change each of the columns landmass, zone, language, and religion to hold their actual categorical data (not their encoded numbers). The type of each of these columns should be Factor.
flag_df$landmass <- factor(flag_df$landmass, levels = 1:6, labels = c("N.Am", "S.Am", "Europe", "Africa", "Asia", "Oceania"))
flag_df$zone <- factor(flag_df$zone, levels = 1:4, labels = c("NE", "SE", "SW", "NW"))
flag_df$language <- factor(flag_df$language, levels = 1:10, labels = c("English", "Spanish", "French", "German", "Slavic", "Other Indo-European", "Chinese", "Arabic", "Japanese/Turkish/Finnish/Magyar", "Others"))
flag_df$religion <- factor(flag_df$religion, levels = 0:7, labels = c("Catholic", "Other Christian", "Muslim", "Buddhist", "Hindu", "Ethnic", "Marxist", "Others"))Notice from our earlier structure command that the data types for columns red, green, blue, gold, white, black, orange, crescent, triangle, icon, animate, text are all integer. Looking at flag.names these integer variables are really just an encoding for true (1) or false (0). We don’t want to compute with these 1s and 0s (for example find a mean). So we should change these to logicals.
- Change the column type to logical for the following columns: red, green, blue, gold, white, black, orange, crescent, triangle, icon, animate, and text.
flag_df$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$text)Now that our data is clean, let’s answer some questions about it!
Data Investigation
- Print out how many countries have each “mainhue” category.
table(flag_df$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?
rwb_flags <- flag_df[flag_df$red == 1 & flag_df$white == 1 & flag_df$blue == 1, ]
nrow(rwb_flags)## [1] 63only_red_white_blue_flags <- rwb_flags %>%
filter(green != 1 & gold != 1 & black != 1 & orange != 1)
nrow(only_red_white_blue_flags)## [1] 27- Print out the data observations for the 10 countries with the largest populations. The 10 data observations should be printed out in descending order according to population.
top_10_pop <- head(flag_df[order(-flag_df$population), ], 10)
top_10_pop## # A tibble: 10 × 30
## name landm…¹ zone area popul…² langu…³ relig…⁴ bars stripes colours red
## <chr> <fct> <fct> <int> <int> <fct> <fct> <int> <int> <int> <lgl>
## 1 China Asia NE 9561 1008 Chinese Marxist 0 0 2 TRUE
## 2 India Asia NE 3268 684 Other … Hindu 0 3 4 FALSE
## 3 USSR Asia NE 22402 274 Slavic Marxist 0 0 2 TRUE
## 4 USA N.Am NW 9363 231 English Other … 0 13 3 TRUE
## 5 Indo… Oceania SE 1904 157 Others Muslim 0 2 2 TRUE
## 6 Braz… S.Am SW 8512 119 Other … Cathol… 0 0 4 FALSE
## 7 Japan Asia NE 372 118 Japane… Others 0 0 2 TRUE
## 8 Bang… Asia NE 143 90 Other … Muslim 0 0 2 TRUE
## 9 Paki… Asia NE 804 84 Other … Muslim 1 0 2 FALSE
## 10 Mexi… N.Am NW 1973 77 Spanish Cathol… 3 0 4 TRUE
## # … with 19 more variables: 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>, and abbreviated variable names ¹landmass, ²population,
## # ³language, ⁴religionLet’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]
}
landmass_summary <- flag_df %>%
group_by(landmass) %>%
summarise(
mode_mainhue = cat_mode(mainhue),
median_sunstars = median(sunstars),
num_animate = sum(animate),
perc_animate = mean(animate) * 100
)
zone_summary <- flag_df %>%
group_by(zone) %>%
summarise(
mode_mainhue = cat_mode(mainhue),
median_sunstars = median(sunstars),
num_animate = sum(animate),
perc_animate = mean(animate) * 100
)
lang_summary <- flag_df %>%
group_by(language) %>%
summarise(
mode_mainhue = cat_mode(mainhue),
median_sunstars = median(sunstars),
num_animate = sum(animate),
perc_animate = mean(animate) * 100
)
religion_summary <-flag_df %>%
group_by(religion) %>%
summarise(
mode_mainhue = cat_mode(mainhue),
median_sunstars = median(sunstars),
num_animate = sum(animate),
perc_animate = mean(animate) * 100
)Do 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.
It appears that the number of sun stars are are centralized within countries in Asia and Oceania, which primarily Marxist or a religion under the other category, and speak either Slavic, Chinese, or Japanese/Turkish/Finnish/Magyar. This is an interesting trend because the median of the rest of the regions, language, and religions are all zero, meaning these are the only parts of the world that use sun stars in there flags. I also notice that northern countries, especially in North America, tend to use way more animate images, which all heavily speak English and are either Catholic or other Christian in their religious beliefs. I find this trend intriguing because it is almost the exact opposite of the sun star trend, as seemingly polar opposite parts of the world that speak different languages and practice different religions have completely opposing design choices for their flags, with what appears to be little to no overlap at all.