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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