Making a map using IELTS Averages in Asia
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library(tidyverse)
## ── Attaching packages ──────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.1.0 ✔ purrr 0.3.0
## ✔ tibble 2.0.1 ✔ dplyr 0.8.0.1
## ✔ tidyr 0.8.2 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0
## ── Conflicts ─────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(spData)
library(spDataLarge)
library(rvest)
## Loading required package: xml2
##
## Attaching package: 'rvest'
## The following object is masked from 'package:purrr':
##
## pluck
## The following object is masked from 'package:readr':
##
## guess_encoding
library(tmap)
world %>%
filter(subregion %in% c("Eastern Asia", "South-Eastern Asia")) -> east_asia
tm_shape(east_asia, projection = 3857) +
tm_polygons(col = "lifeExp", n = 5) +
tm_layout(title = "EAAST",inner.margins = c(0.1, 0.02, 0.05, 0.02)) +
tm_scale_bar()
#loads the website
ielts.org <- read_html("https://www.ielts.org/teaching-and-research/test-taker-performance")
#print out all the tables on that page
ielts.org %>%
html_table()
## [[1]]
## X1 X2
## 1 2017
## 2 Academic 78.10%
## 3 General Training 21.9%
##
## [[2]]
## X1 X2 X3 X4 X5 X6
## 1 Gender Listening Reading Writing Speaking Overall
## 2 Female 6.26 6.18 5.66 5.97 6.08
## 3 Male 6.17 6.02 5.55 5.88 5.97
##
## [[3]]
## X1 X2 X3 X4 X5 X6
## 1 Gender Listening Reading Writing Speaking Overall
## 2 Female 6.66 6.27 6.15 6.62 6.49
## 3 Male 6.62 6.25 6.05 6.57 6.44
##
## [[4]]
## X1 X2 X3 X4 X5 X6
## 1 Listening Reading Writing Speaking Overall
## 2 Bangladesh 6.37 6.02 5.83 6.25 6.18
## 3 Brazil 6.74 6.91 5.98 6.72 6.65
## 4 Canada 7.09 6.78 6.16 7.15 6.86
## 5 China (People's Republic of) 5.90 6.11 5.37 5.39 5.76
## 6 Colombia 6.35 6.72 5.78 6.49 6.40
## 7 Egypt 6.74 6.43 5.87 6.46 6.44
## 8 France 6.95 7.04 6.02 6.56 6.71
## 9 Germany 7.76 7.52 6.60 7.36 7.37
## 10 Greece 7.43 7.16 6.27 6.76 6.97
## 11 Hong Kong 6.90 6.76 5.97 6.25 6.53
## 12 India 6.30 5.82 5.77 6.01 6.04
## 13 Indonesia 6.55 6.67 5.78 6.27 6.38
## 14 Iran, Islamic Republic of 6.24 5.98 5.58 6.43 6.12
## 15 Iraq 5.54 5.44 5.13 5.86 5.56
## 16 Italy 6.83 7.20 5.99 6.54 6.70
## 17 Japan 5.91 6.09 5.41 5.59 5.81
## 18 Jordan 6.27 5.89 5.47 6.35 6.06
## 19 Kazakhstan 6.17 6.16 5.57 5.91 6.01
## 20 Korea, Republic of 6.20 6.20 5.46 5.79 5.97
## 21 Kuwait 5.47 5.08 4.84 5.79 5.36
## 22 Malaysia 7.27 7.07 6.25 6.71 6.89
## 23 Mexico 6.54 6.78 5.81 6.54 6.48
## 24 Nepal 6.27 5.75 5.56 5.81 5.91
## 25 Nigeria 6.82 6.46 6.51 7.11 6.79
## 26 Oman 5.11 4.98 4.90 5.62 5.22
## 27 Pakistan 6.57 6.20 5.95 6.43 6.35
## 28 Philippines 7.27 6.80 6.20 6.85 6.84
## 29 Romania 7.03 6.89 6.12 6.78 6.77
## 30 Russian Federation 6.93 6.91 5.99 6.67 6.69
## 31 Saudi Arabia 5.26 5.05 4.78 5.69 5.26
## 32 Spain 7.02 7.16 6.11 6.71 6.81
## 33 Sri Lanka 6.60 6.15 5.90 6.49 6.35
## 34 Sudan 6.43 6.10 5.68 6.41 6.22
## 35 Taiwan, China 6.16 6.21 5.60 6.08 6.08
## 36 Thailand 6.25 6.03 5.46 5.91 5.98
## 37 Turkey 6.40 6.42 5.69 6.21 6.24
## 38 Ukraine 6.65 6.58 5.94 6.49 6.48
## 39 United Arab Emirates 4.88 4.70 4.48 5.27 4.90
## 40 Uzbekistan 5.63 5.63 5.27 5.61 5.60
## 41 Vietnam 5.97 6.17 5.59 5.71 5.92
##
## [[5]]
## X1 X2 X3 X4 X5 X6
## 1 Listening Reading Writing Speaking Overall
## 2 Australia 7.32 6.92 6.44 7.55 7.12
## 3 Bangladesh 6.47 6.01 6.05 6.48 6.32
## 4 Brazil 6.68 6.76 6.11 6.72 6.63
## 5 Canada 7.10 6.79 6.28 7.15 6.89
## 6 China (People's Republic of) 6.06 6.03 5.61 5.74 5.93
## 7 Colombia 6.09 6.17 5.79 6.33 6.16
## 8 Egypt 6.67 6.35 5.96 6.52 6.44
## 9 France 6.91 6.83 6.15 6.83 6.74
## 10 Germany 7.22 6.91 6.40 7.38 7.04
## 11 Hong Kong 6.65 6.59 5.93 6.28 6.42
## 12 India 6.86 6.34 6.22 6.68 6.59
## 13 Indonesia 6.08 5.84 5.59 6.02 5.95
## 14 Iran, Islamic Republic of 6.38 6.06 5.81 6.53 6.26
## 15 Italy 6.28 6.09 5.68 6.40 6.17
## 16 Japan 5.86 5.53 5.39 5.71 5.68
## 17 Jordan 6.42 6.03 5.75 6.60 6.26
## 18 Korea, Republic of 5.89 5.53 5.39 5.63 5.67
## 19 Lebanon 6.80 6.41 6.05 6.73 6.56
## 20 Malaysia 7.11 6.89 6.31 6.87 6.86
## 21 Mexico 6.36 6.38 5.91 6.63 6.38
## 22 Nepal 6.32 5.67 5.79 6.23 6.07
## 23 Nigeria 6.77 6.38 6.80 7.22 6.85
## 24 Pakistan 6.75 6.28 6.21 6.75 6.56
## 25 Philippines 6.46 5.99 5.98 6.46 6.29
## 26 Poland 6.83 6.64 6.13 6.82 6.67
## 27 Russian Federation 6.90 6.88 6.13 6.68 6.71
## 28 Saudi Arabia 4.78 3.90 4.39 5.17 4.63
## 29 Singapore 7.71 7.49 6.78 7.48 7.43
## 30 South Africa 7.66 7.27 7.00 8.26 7.61
## 31 Spain 6.61 6.80 6.05 6.56 6.56
## 32 Sri Lanka 6.69 6.20 6.06 6.65 6.46
## 33 Sudan 6.31 5.84 5.80 6.55 6.19
## 34 Syrian Arab Republic 6.36 6.02 5.73 6.60 6.25
## 35 Taiwan, China 5.98 5.61 5.55 6.05 5.86
## 36 Thailand 5.37 4.83 5.06 5.45 5.24
## 37 Turkey 6.50 6.42 5.98 6.46 6.40
## 38 Ukraine 6.25 6.10 5.83 6.29 6.18
## 39 United States of America 7.92 7.67 7.48 8.44 7.95
## 40 Vietnam 6.16 6.12 5.83 6.00 6.09
## 41 Zimbabwe 7.15 6.78 6.84 7.55 7.14
##
## [[6]]
## X1 X2 X3 X4 X5 X6
## 1 Language Listening Reading Writing Speaking Overall
## 2 Arabic 5.63 5.37 5.06 5.88 5.55
## 3 Azeri 6.42 6.16 5.58 6.12 6.14
## 4 Bengali 6.45 6.11 5.88 6.31 6.25
## 5 Chinese 5.97 6.15 5.41 5.46 5.81
## 6 English 7.21 6.71 6.35 7.14 6.92
## 7 Farsi 6.29 6.03 5.61 6.47 6.16
## 8 Filipino 7.31 6.86 6.22 6.87 6.88
## 9 French 6.91 6.95 6.03 6.59 6.68
## 10 German 7.80 7.55 6.62 7.39 7.41
## 11 Greek 7.35 7.03 6.24 6.73 6.90
## 12 Gujarati 6.18 5.71 5.63 5.86 5.91
## 13 Hindi 6.67 6.13 5.94 6.37 6.34
## 14 Ibo/lgbo 6.60 6.27 6.47 7.09 6.67
## 15 Indonesian 6.54 6.67 5.78 6.26 6.37
## 16 Italian 6.83 7.22 5.99 6.53 6.70
## 17 Japanese 5.90 6.09 5.41 5.59 5.81
## 18 Kazakh 6.06 6.04 5.51 5.81 5.92
## 19 Khmer 5.92 5.73 5.48 5.92 5.82
## 20 Korean 6.20 6.21 5.46 5.79 5.98
## 21 Malay 7.03 6.86 6.07 6.54 6.69
## 22 Malayalam 6.73 6.34 6.10 6.39 6.45
## 23 Marathi 7.06 6.49 6.23 6.69 6.68
## 24 Nepali 6.28 5.75 5.56 5.82 5.92
## 25 Other 6.60 6.29 6.18 6.96 6.57
## 26 Polish 7.36 7.25 6.29 6.99 7.03
## 27 Portuguese 6.86 6.94 6.04 6.80 6.72
## 28 Punjabi 5.92 5.47 5.56 5.67 5.72
## 29 Romanian 7.01 6.89 6.12 6.79 6.77
## 30 Russian 6.74 6.71 5.89 6.52 6.53
## 31 Singhalese 6.58 6.14 5.89 6.46 6.33
## 32 Spanish 6.65 6.89 5.93 6.62 6.59
## 33 Tagalog 7.17 6.68 6.14 6.80 6.76
## 34 Tamil 6.86 6.41 6.05 6.54 6.53
## 35 Telugu 6.34 5.80 5.75 6.08 6.05
## 36 Thai 6.24 6.03 5.46 5.90 5.97
## 37 Turkish 6.42 6.42 5.70 6.22 6.25
## 38 Ukrainian 6.62 6.57 5.94 6.48 6.47
## 39 Urdu 6.61 6.21 5.97 6.47 6.38
## 40 Uzbek 5.57 5.56 5.23 5.55 5.54
## 41 Vietnamese 5.97 6.17 5.59 5.71 5.93
##
## [[7]]
## X1 X2 X3 X4 X5 X6
## 1 Language Listening Reading Writing Speaking Overall
## 2 Afrikaans 7.38 6.97 6.73 7.95 7.32
## 3 Albanian 5.92 5.57 5.93 6.47 6.03
## 4 Arabic 6.18 5.75 5.62 6.31 6.03
## 5 Bengali 6.62 6.19 6.16 6.60 6.46
## 6 Chinese 6.13 6.08 5.65 5.83 5.99
## 7 English 7.26 6.82 6.71 7.40 7.11
## 8 Farsi 6.39 6.07 5.82 6.54 6.27
## 9 Filipino 6.57 6.12 6.05 6.53 6.38
## 10 French 6.48 6.35 6.05 6.63 6.44
## 11 German 7.25 6.95 6.42 7.39 7.06
## 12 Gujurati 6.55 6.02 5.95 6.33 6.28
## 13 Hindi 7.07 6.57 6.37 6.90 6.79
## 14 Ibo/lgbo 6.60 6.18 6.73 7.19 6.74
## 15 Indonesian 6.07 5.84 5.59 6.01 5.95
## 16 Italian 6.33 6.17 5.71 6.44 6.23
## 17 Japanese 5.85 5.53 5.38 5.70 5.67
## 18 Kannada 6.98 6.50 6.42 6.91 6.76
## 19 Korean 5.89 5.53 5.39 5.63 5.67
## 20 Malayalam 6.87 6.47 6.25 6.62 6.62
## 21 Marathi 7.09 6.67 6.44 6.95 6.85
## 22 Nepali 6.34 5.69 5.80 6.24 6.08
## 23 Other 6.56 6.14 6.48 7.07 6.63
## 24 Pashto 6.37 5.85 5.93 6.54 6.24
## 25 Polish 6.84 6.64 6.13 6.82 6.67
## 26 Portuguese 6.70 6.75 6.11 6.74 6.64
## 27 Punjabi 6.46 5.83 5.88 6.22 6.16
## 28 Romanian 6.73 6.60 6.11 6.74 6.61
## 29 Russian 6.75 6.70 6.08 6.62 6.60
## 30 Shona 7.07 6.72 6.79 7.45 7.07
## 31 Singhalese 6.67 6.18 6.04 6.62 6.44
## 32 Spanish 6.37 6.43 5.95 6.55 6.39
## 33 Tagalog 6.31 5.82 5.89 6.35 6.15
## 34 Tamil 6.79 6.37 6.20 6.65 6.57
## 35 Telugu 6.75 6.24 6.18 6.64 6.52
## 36 Thai 5.36 4.83 5.06 5.45 5.24
## 37 Turkish 6.50 6.42 5.98 6.46 6.40
## 38 Ukrainian 6.15 5.93 5.76 6.23 6.08
## 39 Urdu 6.78 6.30 6.22 6.77 6.58
## 40 Vietnamese 6.16 6.13 5.84 6.00 6.10
## 41 Yoruba 6.62 6.31 6.76 7.09 6.76
#select the table of interest
ielts.org %>%
html_node(xpath = '//*[@id="main"]/div/div/div[2]/table[3]') %>%
html_table(header = TRUE) -> scores_by_country
#Change Korea, China, and Taiwan so that they merge correctly
scores_by_country[scores_by_country == "Korea, Republic of"] <- "Republic of Korea"
scores_by_country[scores_by_country == "China (People's Republic of)"] <- "China"
scores_by_country[scores_by_country == "Taiwan, China"] <- "Taiwan"
#name the first colunn
names(scores_by_country)[1] <- "name_long"
world_scores <- full_join(x = world, y = scores_by_country)
## Joining, by = "name_long"
world_scores %>%
filter(subregion %in% c("Eastern Asia", "South-Eastern Asia")) -> east_asia_scores
tm_shape(east_asia_scores, projection = 3857) +
tm_polygons(col = "Speaking", n = 4) +
tm_layout(title = "Average IELTS Speaking Score", inner.margins = c(0.1, 0.02, 0.1, 0.02))
tm_shape(east_asia_scores, projection = 3857) + tm_polygons(col = "Writing", n = 3) + tm_layout(title = "Average IELTS Writing Score", inner.margins = c(0.1, 0.02, 0.1, 0.02))
tm_shape(east_asia_scores, projection = 3857) + tm_polygons(col = "Listening", n = 3) + tm_layout(title = "Average IELTS Listening Score", inner.margins = c(0.1, 0.02, 0.1, 0.02))
tm_shape(east_asia_scores, projection = 3857) + tm_polygons(col = "Reading", n = 3) + tm_layout(title = "Average IELTS Reading Score", inner.margins = c(0.1, 0.02, 0.1, 0.02))
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