Making a map using IELTS Averages in Asia

(This article was first published on Home on educatorsRlearners: A blog about education and R, and kindly contributed to R-bloggers)

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))

To leave a comment for the author, please follow the link and comment on their blog: Home on educatorsRlearners: A blog about education and R.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)