Project 2a - Flags of the World
Chat Halambe
library(tidyverse)## -- Attaching packages --------------------------------------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.2.1 v purrr 0.3.2
## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 1.0.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0## -- Conflicts ------------------------------------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()getwd()## [1] "C:/Users/chat5/OneDrive/Desktop/Data 101/project2a files"
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? The donor for this data is Collins Gem Guide to Flags.
Is there any missing data? According the the flag names NAMES file, there are no missing values as its stated on line 70.
- Import the flag.csv data into R. Store it in a data.frame named flag_df.
flag_df <- read_csv('flag.csv')## Warning: Missing column names filled in: 'X1' [1]## Parsed with column specification:
## cols(
## .default = col_double(),
## name = col_character(),
## mainhue = col_character(),
## topleft = col_character(),
## botright = col_character()
## )## See spec(...) for full column specifications.- Check to make sure the class of flag_df is data.frame. Then find the dimensions of flag_df.
class(flag_df)## [1] "spec_tbl_df" "tbl_df" "tbl" "data.frame"dim(flag_df) # Dimensions are 194 rows by 31 columns.## [1] 194 31- Print out the first 5 lines and the last 5 lines of flag_df.
head(flag_df, 5)## # A tibble: 5 x 31
## X1 name landmass zone area population language religion bars
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Afgh~ 5 1 648 16 10 2 0
## 2 2 Alba~ 3 1 29 3 6 6 0
## 3 3 Alge~ 4 1 2388 20 8 2 2
## 4 4 Amer~ 6 3 0 0 1 1 0
## 5 5 Ando~ 3 1 0 0 6 0 3
## # ... with 22 more variables: stripes <dbl>, colours <dbl>, red <dbl>,
## # green <dbl>, blue <dbl>, gold <dbl>, white <dbl>, black <dbl>,
## # orange <dbl>, mainhue <chr>, circles <dbl>, crosses <dbl>,
## # saltires <dbl>, quarters <dbl>, sunstars <dbl>, crescent <dbl>,
## # triangle <dbl>, icon <dbl>, animate <dbl>, text <dbl>, topleft <chr>,
## # botright <chr>- Print out the summary statistics of each variable of flag_df.
summary(flag_df)## X1 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)## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 194 obs. of 31 variables:
## $ X1 : num 1 2 3 4 5 6 7 8 9 10 ...
## $ name : chr "Afghanistan" "Albania" "Algeria" "American-Samoa" ...
## $ landmass : num 5 3 4 6 3 4 1 1 2 2 ...
## $ zone : num 1 1 1 3 1 2 4 4 3 3 ...
## $ area : num 648 29 2388 0 0 ...
## $ population: num 16 3 20 0 0 7 0 0 28 28 ...
## $ language : num 10 6 8 1 6 10 1 1 2 2 ...
## $ religion : num 2 6 2 1 0 5 1 1 0 0 ...
## $ bars : num 0 0 2 0 3 0 0 0 0 0 ...
## $ stripes : num 3 0 0 0 0 2 1 1 3 3 ...
## $ colours : num 5 3 3 5 3 3 3 5 2 3 ...
## $ red : num 1 1 1 1 1 1 0 1 0 0 ...
## $ green : num 1 0 1 0 0 0 0 0 0 0 ...
## $ blue : num 0 0 0 1 1 0 1 1 1 1 ...
## $ gold : num 1 1 0 1 1 1 0 1 0 1 ...
## $ white : num 1 0 1 1 0 0 1 1 1 1 ...
## $ black : num 1 1 0 0 0 1 0 1 0 0 ...
## $ orange : num 0 0 0 1 0 0 1 0 0 0 ...
## $ mainhue : chr "green" "red" "green" "blue" ...
## $ circles : num 0 0 0 0 0 0 0 0 0 0 ...
## $ crosses : num 0 0 0 0 0 0 0 0 0 0 ...
## $ saltires : num 0 0 0 0 0 0 0 0 0 0 ...
## $ quarters : num 0 0 0 0 0 0 0 0 0 0 ...
## $ sunstars : num 1 1 1 0 0 1 0 1 0 1 ...
## $ crescent : num 0 0 1 0 0 0 0 0 0 0 ...
## $ triangle : num 0 0 0 1 0 0 0 1 0 0 ...
## $ icon : num 1 0 0 1 0 1 0 0 0 0 ...
## $ animate : num 0 1 0 1 0 0 1 0 0 0 ...
## $ text : num 0 0 0 0 0 0 0 0 0 0 ...
## $ topleft : chr "black" "red" "green" "blue" ...
## $ botright : chr "green" "red" "white" "red" ...
## - attr(*, "spec")=
## .. cols(
## .. X1 = col_double(),
## .. name = col_character(),
## .. landmass = col_double(),
## .. zone = col_double(),
## .. area = col_double(),
## .. population = col_double(),
## .. language = col_double(),
## .. religion = col_double(),
## .. bars = col_double(),
## .. stripes = col_double(),
## .. colours = col_double(),
## .. red = col_double(),
## .. green = col_double(),
## .. blue = col_double(),
## .. gold = col_double(),
## .. white = col_double(),
## .. black = col_double(),
## .. orange = col_double(),
## .. mainhue = col_character(),
## .. circles = col_double(),
## .. crosses = col_double(),
## .. saltires = col_double(),
## .. quarters = col_double(),
## .. sunstars = col_double(),
## .. crescent = col_double(),
## .. triangle = col_double(),
## .. icon = col_double(),
## .. animate = col_double(),
## .. text = col_double(),
## .. topleft = col_character(),
## .. botright = col_character()
## .. )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_tibble <- as_tibble(flag_df)
class(flag_tibble)## [1] "tbl_df" "tbl" "data.frame"- Find the variable (column) names of flag_df.
flag_tibble %>% 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_tibble %>% select(1)## # A tibble: 194 x 1
## X1
## <dbl>
## 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? The first column contains numbers, which count the number of rows in the tibble.
Do we really need it? No, its a filler column that takes up space.
- Delete the first column of flag_df.
flag_tibble = subset(flag_tibble, select = -c(X1) )- Verify that there are no missing values in flag_df.
flag_tibble## # A tibble: 194 x 30
## name landmass zone area population language religion bars stripes
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Afgh~ 5 1 648 16 10 2 0 3
## 2 Alba~ 3 1 29 3 6 6 0 0
## 3 Alge~ 4 1 2388 20 8 2 2 0
## 4 Amer~ 6 3 0 0 1 1 0 0
## 5 Ando~ 3 1 0 0 6 0 3 0
## 6 Ango~ 4 2 1247 7 10 5 0 2
## 7 Angu~ 1 4 0 0 1 1 0 1
## 8 Anti~ 1 4 0 0 1 1 0 1
## 9 Arge~ 2 3 2777 28 2 0 0 3
## 10 Arge~ 2 3 2777 28 2 0 0 3
## # ... with 184 more rows, and 21 more variables: colours <dbl>, red <dbl>,
## # green <dbl>, blue <dbl>, gold <dbl>, white <dbl>, black <dbl>,
## # orange <dbl>, mainhue <chr>, circles <dbl>, crosses <dbl>,
## # saltires <dbl>, quarters <dbl>, sunstars <dbl>, crescent <dbl>,
## # triangle <dbl>, icon <dbl>, animate <dbl>, text <dbl>, topleft <chr>,
## # botright <chr>At 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_tibble <- flag_tibble %>% mutate(landmass = replace(landmass, landmass == '1', "N.America")) %>% mutate(landmass = replace(landmass, landmass == '2', "S.America")) %>%
mutate(landmass = replace(landmass, landmass == '3', "Europe")) %>%
mutate(landmass = replace(landmass, landmass == '4', "Africa")) %>%
mutate(landmass = replace(landmass, landmass == '5', "Asia")) %>%
mutate(landmass = replace(landmass, landmass == '6', "Oceania"))
flag_tibble <- flag_tibble %>% mutate(zone = replace(zone, zone == '1', "NE")) %>% mutate(zone = replace(zone, zone == '2', "SE")) %>%
mutate(zone = replace(zone, zone == '3', "SW")) %>%
mutate(zone = replace(zone, zone == '4', "NW"))
flag_tibble <- flag_tibble %>% mutate(language = replace(language, language == '1', "English")) %>% mutate(language = replace(language, language == '2', "Spanish")) %>%
mutate(language = replace(language, language == '3', "French")) %>%
mutate(language = replace(language, language == '4', "German")) %>%
mutate(language = replace(language, language == '5', "Slavic")) %>%
mutate(language = replace(language, language == '6', "Other, Indo-European")) %>%
mutate(language = replace(language, language == '7', "Chinese")) %>%
mutate(language = replace(language, language == '8', "Arabic")) %>%
mutate(language = replace(language, language == '9', "Japanese/Turkish/Finnish/Magyar")) %>%
mutate(language = replace(language, language == '10', "Others"))
flag_tibble <- flag_tibble %>% mutate(religion = replace(religion, religion == '1', "English")) %>% mutate(religion = replace(religion, religion == '2', "Spanish")) %>%
mutate(religion = replace(religion, religion == '3', "French")) %>%
mutate(religion = replace(religion, religion == '4', "German")) %>%
mutate(religion = replace(religion, religion == '5', "Slavic")) %>%
mutate(religion = replace(religion, religion == '6', "Other, Indo-European")) %>%
mutate(religion = replace(religion, religion == '7', "Chinese"))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_tibble$red <- as.logical(flag_tibble$red)
flag_tibble$green <- as.logical(flag_tibble$green)
flag_tibble$blue <- as.logical(flag_tibble$blue)
flag_tibble$gold <- as.logical(flag_tibble$gold)
flag_tibble$white <- as.logical(flag_tibble$white)
flag_tibble$black <- as.logical(flag_tibble$black)
flag_tibble$orange <- as.logical(flag_tibble$orange)
flag_tibble$crescent <- as.logical(flag_tibble$crescent)
flag_tibble$triangle <- as.logical(flag_tibble$triangle)
flag_tibble$icon <- as.logical(flag_tibble$icon)
flag_tibble$animate <- as.logical(flag_tibble$animate)
flag_tibble$text <- as.logical(flag_tibble$text)
str(flag_tibble)## Classes 'tbl_df', 'tbl' and 'data.frame': 194 obs. of 30 variables:
## $ name : chr "Afghanistan" "Albania" "Algeria" "American-Samoa" ...
## $ landmass : chr "Asia" "Europe" "Africa" "Oceania" ...
## $ zone : chr "NE" "NE" "NE" "SW" ...
## $ area : num 648 29 2388 0 0 ...
## $ population: num 16 3 20 0 0 7 0 0 28 28 ...
## $ language : chr "Others" "Other, Indo-European" "Arabic" "English" ...
## $ religion : chr "Spanish" "Other, Indo-European" "Spanish" "English" ...
## $ bars : num 0 0 2 0 3 0 0 0 0 0 ...
## $ stripes : num 3 0 0 0 0 2 1 1 3 3 ...
## $ colours : num 5 3 3 5 3 3 3 5 2 3 ...
## $ red : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ green : logi TRUE FALSE TRUE FALSE FALSE FALSE ...
## $ blue : logi FALSE FALSE FALSE TRUE TRUE FALSE ...
## $ gold : logi TRUE TRUE FALSE TRUE TRUE TRUE ...
## $ white : logi TRUE FALSE TRUE TRUE FALSE FALSE ...
## $ black : logi TRUE TRUE FALSE FALSE FALSE TRUE ...
## $ orange : logi FALSE FALSE FALSE TRUE FALSE FALSE ...
## $ mainhue : chr "green" "red" "green" "blue" ...
## $ circles : num 0 0 0 0 0 0 0 0 0 0 ...
## $ crosses : num 0 0 0 0 0 0 0 0 0 0 ...
## $ saltires : num 0 0 0 0 0 0 0 0 0 0 ...
## $ quarters : num 0 0 0 0 0 0 0 0 0 0 ...
## $ sunstars : num 1 1 1 0 0 1 0 1 0 1 ...
## $ crescent : logi FALSE FALSE TRUE FALSE FALSE FALSE ...
## $ triangle : logi FALSE FALSE FALSE TRUE FALSE FALSE ...
## $ icon : logi TRUE FALSE FALSE TRUE FALSE TRUE ...
## $ animate : logi FALSE TRUE FALSE TRUE FALSE FALSE ...
## $ text : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ topleft : chr "black" "red" "green" "blue" ...
## $ botright : chr "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_tibble$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?
X1 <- filter(flag_tibble, red, white, blue)
X1 # 11 rows present, so 11 countries have red, white and blue in their flag.## # A tibble: 63 x 30
## name landmass zone area population language religion bars stripes
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
## 1 Amer~ Oceania SW 0 0 English English 0 0
## 2 Anti~ N.Ameri~ NW 0 0 English English 0 1
## 3 Aust~ Oceania SE 7690 15 English English 0 0
## 4 Beli~ N.Ameri~ NW 23 0 English English 0 2
## 5 Berm~ N.Ameri~ NW 0 0 English English 0 0
## 6 Brit~ N.Ameri~ NW 0 0 English English 0 0
## 7 Bulg~ Europe NE 111 9 Slavic Other, ~ 0 3
## 8 Burma Asia NE 678 35 Others French 0 0
## 9 Caym~ N.Ameri~ NW 0 0 English English 0 0
## 10 Cent~ Africa NE 623 2 Others Slavic 1 0
## # ... with 53 more rows, and 21 more variables: colours <dbl>, red <lgl>,
## # green <lgl>, blue <lgl>, gold <lgl>, white <lgl>, black <lgl>,
## # orange <lgl>, mainhue <chr>, circles <dbl>, crosses <dbl>,
## # saltires <dbl>, quarters <dbl>, sunstars <dbl>, crescent <lgl>,
## # triangle <lgl>, icon <lgl>, animate <lgl>, text <lgl>, topleft <chr>,
## # botright <chr>X2 <- filter(X1, green == FALSE)
X2 # There are no flags that have only red, white and blue in their flags. ## # A tibble: 39 x 30
## name landmass zone area population language religion bars stripes
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
## 1 Amer~ Oceania SW 0 0 English English 0 0
## 2 Anti~ N.Ameri~ NW 0 0 English English 0 1
## 3 Aust~ Oceania SE 7690 15 English English 0 0
## 4 Burma Asia NE 678 35 Others French 0 0
## 5 Chile S.Ameri~ SW 757 11 Spanish 0 0 2
## 6 Cook~ Oceania SW 0 0 English English 0 0
## 7 Cost~ N.Ameri~ NW 51 2 Spanish 0 0 5
## 8 Cuba N.Ameri~ NW 115 10 Spanish Other, ~ 0 5
## 9 Czec~ Europe NE 128 15 Slavic Other, ~ 0 0
## 10 Domi~ N.Ameri~ NW 49 6 Spanish 0 0 0
## # ... with 29 more rows, and 21 more variables: colours <dbl>, red <lgl>,
## # green <lgl>, blue <lgl>, gold <lgl>, white <lgl>, black <lgl>,
## # orange <lgl>, mainhue <chr>, circles <dbl>, crosses <dbl>,
## # saltires <dbl>, quarters <dbl>, sunstars <dbl>, crescent <lgl>,
## # triangle <lgl>, icon <lgl>, animate <lgl>, text <lgl>, topleft <chr>,
## # botright <chr>- 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.
flag_tibble## # A tibble: 194 x 30
## name landmass zone area population language religion bars stripes
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
## 1 Afgh~ Asia NE 648 16 Others Spanish 0 3
## 2 Alba~ Europe NE 29 3 Other, ~ Other, ~ 0 0
## 3 Alge~ Africa NE 2388 20 Arabic Spanish 2 0
## 4 Amer~ Oceania SW 0 0 English English 0 0
## 5 Ando~ Europe NE 0 0 Other, ~ 0 3 0
## 6 Ango~ Africa SE 1247 7 Others Slavic 0 2
## 7 Angu~ N.Ameri~ NW 0 0 English English 0 1
## 8 Anti~ N.Ameri~ NW 0 0 English English 0 1
## 9 Arge~ S.Ameri~ SW 2777 28 Spanish 0 0 3
## 10 Arge~ S.Ameri~ SW 2777 28 Spanish 0 0 3
## # ... with 184 more rows, and 21 more variables: colours <dbl>, red <lgl>,
## # green <lgl>, blue <lgl>, gold <lgl>, white <lgl>, black <lgl>,
## # orange <lgl>, mainhue <chr>, circles <dbl>, crosses <dbl>,
## # saltires <dbl>, quarters <dbl>, sunstars <dbl>, crescent <lgl>,
## # triangle <lgl>, icon <lgl>, animate <lgl>, text <lgl>, topleft <chr>,
## # botright <chr>flag_tibblepop <-arrange(flag_tibble, desc(population))
head(flag_tibblepop, 10)## # A tibble: 10 x 30
## name landmass zone area population language religion bars stripes
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
## 1 China Asia NE 9561 1008 Chinese Other, ~ 0 0
## 2 India Asia NE 3268 684 Other, ~ German 0 3
## 3 USSR Asia NE 22402 274 Slavic Other, ~ 0 0
## 4 USA N.Ameri~ NW 9363 231 English English 0 13
## 5 Indo~ Oceania SE 1904 157 Others Spanish 0 2
## 6 Braz~ S.Ameri~ SW 8512 119 Other, ~ 0 0 0
## 7 Japan Asia NE 372 118 Japanes~ Chinese 0 0
## 8 Bang~ Asia NE 143 90 Other, ~ Spanish 0 0
## 9 Paki~ Asia NE 804 84 Other, ~ Spanish 1 0
## 10 Mexi~ N.Ameri~ NW 1973 77 Spanish 0 3 0
## # ... with 21 more variables: colours <dbl>, red <lgl>, green <lgl>,
## # blue <lgl>, gold <lgl>, white <lgl>, black <lgl>, orange <lgl>,
## # mainhue <chr>, circles <dbl>, crosses <dbl>, saltires <dbl>,
## # quarters <dbl>, sunstars <dbl>, 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]
}
library(tidyverse)
flagzone <- flag_tibble %>% group_by(zone)
# median(flagzone)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.
FILL IN YOUR ANSWER HERE