library(tidyverse)
library(ggplot2)
library(readxl)
library(treemap)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 8
challenge_8
faostat
Joining Data
Reading and Describing Data
chicken = read_csv("_data/FAOSTAT_egg_chicken.csv")
chickencountry = read_csv("_data/FAOSTAT_country_groups.csv")
countryTidy Data (as needed)
Data is already tidy; all columns have their respective values–none of the columns need to be mutated to further understand the data. We could possibly remove the column “year” or “year code” as they both represent the same values.
Joining and Describing Data
First, I downsized both data frames in order to choose what specific columns I wanted to look at. This would help minimize time looking through all columns in each data frame. To join the columns, I used a left_join with the country groups as the x-value and the chicken products as the y-value. I wanted to join them using their countries as the equal values, so I had to equate the columns “Country Code” and “Area Code” in the “by” parameter because they represent the same things in both data frames. I also placed the “keep” parameter as TRUE in order to see if all the column data matched once the data frame printed. Afterwards, I removed the similar columns, the area code and the area, to reduce the size of the data frame–both columns were already represented by other similar columns. As you can see in the produce table, the M49 codes from the country groups are now matched with their respective domains, items, and units from the chickens table. If we wanted to add additional columns from the original country groups data, we could simply backtrack to the select portion, and select any additional columns to add before joining the two data sets.
Joining Data
chicken_new = chicken %>%
select(Domain, `Area Code`, Area, Item, Unit, Value ) #downsides data frame to necessary columns
chicken_newcountry_new = country %>%
select(`Country Group`, `Country Code`, Country, `M49 Code`) #downsized data frame to necessary columns
country_newjoined = country_new %>%
left_join(chicken_new, by = c("Country Code"="Area Code"), keep = TRUE) %>% #joins common values from chicken_new to country_new
subset(select = -c(`Area Code`, Area)) #removed area code and area to clean data/duplicate columns used to join
joinedAnalyzing Joined Data
Now that the data is joined, I chose to use a treemap to analyze the value column. This treemap represents the mean values in each country–specifically in the country group of Africa.
joined_plot = joined %>%
filter(`Country Group` == "Africa") %>%
group_by(Country) %>%
summarize(mean_v = mean(Value)) %>%
treemap(index = "Country" , vSize = "mean_v", type = "index")
joined_plot$tm
Country vSize vColor stdErr vColorValue
1 Algeria 63990.247 1 63990.247 NA
2 Angola 14301.667 1 14301.667 NA
3 Benin 11870.305 1 11870.305 NA
4 Botswana 12250.580 1 12250.580 NA
5 Burkina Faso 21668.655 1 21668.655 NA
6 Burundi 11025.339 1 11025.339 NA
7 Cabo Verde 17509.414 1 17509.414 NA
8 Cameroon 15424.621 1 15424.621 NA
9 Central African Republic 11928.253 1 11928.253 NA
10 Chad 16817.621 1 16817.621 NA
11 Comoros 15974.368 1 15974.368 NA
12 Congo 9286.431 1 9286.431 NA
13 Côte d'Ivoire 20653.466 1 20653.466 NA
14 Democratic Republic of the Congo 12800.989 1 12800.989 NA
15 Egypt 95552.241 1 95552.241 NA
16 Equatorial Guinea 13302.759 1 13302.759 NA
17 Eritrea 14407.385 1 14407.385 NA
18 Eswatini 13549.609 1 13549.609 NA
19 Ethiopia PDR 41150.615 1 41150.615 NA
20 Ethiopia 30446.436 1 30446.436 NA
21 Gabon 11760.276 1 11760.276 NA
22 Gambia 11474.218 1 11474.218 NA
23 Ghana 16696.897 1 16696.897 NA
24 Guinea 17089.701 1 17089.701 NA
25 Guinea-Bissau 16230.069 1 16230.069 NA
26 Kenya 29968.264 1 29968.264 NA
27 Lesotho 13319.672 1 13319.672 NA
28 Liberia 18347.385 1 18347.385 NA
29 Libya 34736.603 1 34736.603 NA
30 Madagascar 15595.385 1 15595.385 NA
31 Malawi 17974.563 1 17974.563 NA
32 Mali 14829.937 1 14829.937 NA
33 Mauritania 10831.672 1 10831.672 NA
34 Mauritius 30681.885 1 30681.885 NA
35 Morocco 78656.632 1 78656.632 NA
36 Mozambique 19003.787 1 19003.787 NA
37 Namibia 11312.356 1 11312.356 NA
38 Niger 12382.247 1 12382.247 NA
39 Nigeria 144759.540 1 144759.540 NA
40 Réunion 36912.253 1 36912.253 NA
41 Rwanda 11052.063 1 11052.063 NA
42 Sao Tome and Principe 13279.500 1 13279.500 NA
43 Senegal 14839.494 1 14839.494 NA
44 Seychelles 24407.753 1 24407.753 NA
45 Sierra Leone 10783.500 1 10783.500 NA
46 Somalia 11587.592 1 11587.592 NA
47 South Africa 127497.575 1 127497.575 NA
48 Sudan (former) 30982.052 1 30982.052 NA
49 Sudan 40646.952 1 40646.952 NA
50 Togo 16786.960 1 16786.960 NA
51 Tunisia 34973.230 1 34973.230 NA
52 Uganda 18488.810 1 18488.810 NA
53 United Republic of Tanzania 36559.040 1 36559.040 NA
54 Zambia 26045.144 1 26045.144 NA
55 Zimbabwe 19340.874 1 19340.874 NA
level x0 y0 w h color
1 1 0.2512797 0.45420615 0.17855345 0.24483875 #E19B79
2 1 0.7089374 0.40753152 0.07641633 0.12786011 #C6A856
3 1 0.8630713 0.28963136 0.06878315 0.11790016 #9FB454
4 1 0.7089374 0.00000000 0.08501497 0.09844550 #6ABD74
5 1 0.4298331 0.00000000 0.11335518 0.13059459 #05C19E
6 1 0.9375925 0.16893623 0.06240750 0.12069513 #00BFC5
7 1 0.5431883 0.38557228 0.08982305 0.13317369 #55B6E1
8 1 0.6330114 0.39660149 0.07592599 0.13879014 #9DA7EC
9 1 0.7939523 0.28963136 0.06911894 0.11790016 #CE96E4
10 1 0.5431883 0.12767883 0.08982305 0.12791203 #E88ECA
11 1 0.8203670 0.53539163 0.09089472 0.12006588 #ED90A4
12 1 0.9624456 0.00000000 0.03755439 0.16893623 #D2A361
13 1 0.5431883 0.65545751 0.09844849 0.14332386 #B0B050
14 1 0.7089374 0.19794907 0.08501497 0.10286857 #82BA65
15 1 0.0000000 0.00000000 0.25127969 0.25978741 #3EC08D
16 1 0.9289210 0.40753152 0.07107899 0.12786011 #00C1B6
17 1 0.6330114 0.00000000 0.07592599 0.12963709 #28BBD7
18 1 0.7853537 0.40753152 0.07239796 0.12786011 #82ADEA
19 1 0.2512797 0.29675614 0.17855345 0.15745001 #BE9CEA
20 1 0.4298331 0.61528406 0.11335518 0.18349731 #E190D6
21 1 0.9318544 0.28963136 0.06814558 0.11790016 #ED8EB4
22 1 0.7939523 0.09531615 0.08108139 0.09667997 #E7968A
23 1 0.6330114 0.53539163 0.09500594 0.12006588 #DC9E70
24 1 0.5431883 0.25559085 0.08982305 0.12998143 #C0AB52
25 1 0.7280173 0.53539163 0.09234967 0.12006588 #97B658
26 1 0.4298331 0.43466863 0.11335518 0.18061542 #5DBE7D
27 1 0.8577516 0.40753152 0.07116936 0.12786011 #00C1A7
28 1 0.9125439 0.65545751 0.08745614 0.14332386 #00BECB
29 1 0.6727001 0.79878137 0.11793800 0.20121863 #64B3E4
30 1 0.9112617 0.53539163 0.08873830 0.12006588 #A9A3EC
31 1 0.5431883 0.51874597 0.08982305 0.13671154 #D694E0
32 1 0.6330114 0.12963709 0.07592599 0.13343920 #EB8DC3
33 1 0.8750337 0.08427987 0.08741189 0.08465636 #EC929B
34 1 0.8958286 0.79878137 0.10417139 0.20121863 #CDA55B
35 1 0.2512797 0.69904490 0.17855345 0.30095510 #A9B251
36 1 0.7338286 0.65545751 0.09058500 0.14332386 #76BC6D
37 1 0.7939523 0.00000000 0.08108139 0.09531615 #28C196
38 1 0.7089374 0.09844550 0.08501497 0.09950357 #00C0BE
39 1 0.0000000 0.60642779 0.25127969 0.39357221 #40B9DC
40 1 0.2512797 0.00000000 0.17855345 0.14123324 #91AAEC
41 1 0.8750337 0.16893623 0.06255877 0.12069513 #C699E7
42 1 0.7089374 0.30081764 0.08501497 0.10671388 #E58FD1
43 1 0.6330114 0.26307629 0.07592599 0.13352520 #EE8FAC
44 1 0.4298331 0.13059459 0.11335518 0.14710283 #E49981
45 1 0.8750337 0.00000000 0.08741189 0.08427987 #D8A069
46 1 0.7939523 0.19199612 0.08108139 0.09763524 #B9AD50
47 1 0.0000000 0.25978741 0.25127969 0.34664038 #8CB85F
48 1 0.7906381 0.79878137 0.10519052 0.20121863 #4FBF85
49 1 0.2512797 0.14123324 0.17855345 0.15552290 #00C1AE
50 1 0.5431883 0.00000000 0.08982305 0.12767883 #00BDD1
51 1 0.5539587 0.79878137 0.11874140 0.20121863 #75B0E8
52 1 0.8244136 0.65545751 0.08813027 0.14332386 #B4A0EC
53 1 0.4298331 0.79878137 0.12412555 0.20121863 #DB92DC
54 1 0.4298331 0.27769743 0.11335518 0.15697121 #ED8EBC
55 1 0.6416368 0.65545751 0.09219178 0.14332386 #EA9492
$type
[1] "index"
$vSize
[1] "mean_v"
$vColor
[1] NA
$stdErr
[1] "mean_v"
$algorithm
[1] "pivotSize"
$vpCoorX
[1] 0.02812148 0.97187852
$vpCoorY
[1] 0.01968504 0.91031496
$aspRatio
[1] 1.483512
$range
[1] NA
$mapping
[1] NA NA NA
$draw
[1] TRUE