You can pass a vector of text which will call the API for each element. The return is a list of responses, each response being a list of tibbles holding the different types of analysis.
-library(googleLanguageR)
-
-# random text form wikipedia
-texts <- c("Norma is a small constellation in the Southern Celestial Hemisphere between Ara and Lupus, one of twelve drawn up in the 18th century by French astronomer Nicolas Louis de Lacaille and one of several depicting scientific instruments. Its name refers to a right angle in Latin, and is variously considered to represent a rule, a carpenter's square, a set square or a level. It remains one of the 88 modern constellations. Four of Norma's brighter stars make up a square in the field of faint stars. Gamma2 Normae is the brightest star with an apparent magnitude of 4.0. Mu Normae is one of the most luminous stars known, but is partially obscured by distance and cosmic dust. Four star systems are known to harbour planets. ",
- "Solomon Wariso (born 11 November 1966 in Portsmouth) is a retired English sprinter who competed primarily in the 200 and 400 metres.[1] He represented his country at two outdoor and three indoor World Championships and is the British record holder in the indoor 4 × 400 metres relay.")
-nlp_result <- gl_nlp(texts)library(googleLanguageR)
+
+# random text form wikipedia
+texts <- c("Norma is a small constellation in the Southern Celestial Hemisphere between Ara and Lupus, one of twelve drawn up in the 18th century by French astronomer Nicolas Louis de Lacaille and one of several depicting scientific instruments. Its name refers to a right angle in Latin, and is variously considered to represent a rule, a carpenter's square, a set square or a level. It remains one of the 88 modern constellations. Four of Norma's brighter stars make up a square in the field of faint stars. Gamma2 Normae is the brightest star with an apparent magnitude of 4.0. Mu Normae is one of the most luminous stars known, but is partially obscured by distance and cosmic dust. Four star systems are known to harbour planets. ",
+ "Solomon Wariso (born 11 November 1966 in Portsmouth) is a retired English sprinter who competed primarily in the 200 and 400 metres.[1] He represented his country at two outdoor and three indoor World Championships and is the British record holder in the indoor 4 × 400 metres relay.")
+nlp_result <- gl_nlp(texts)Each text has its own entry in returned tibbles
-str(nlp_result, max.level = 2)
-List of 7
- $ sentences :List of 2
- ..$ :'data.frame': 7 obs. of 4 variables:
- ..$ :'data.frame': 1 obs. of 4 variables:
- $ tokens :List of 2
- ..$ :'data.frame': 139 obs. of 17 variables:
- ..$ :'data.frame': 54 obs. of 17 variables:
- $ entities :List of 2
- ..$ :Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 52 obs. of 9 variables:
- ..$ :Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 8 obs. of 9 variables:
- $ language : chr [1:2] "en" "en"
- $ text : chr [1:2] "Norma is a small constellation in the Southern Celestial Hemisphere between Ara and Lupus, one of twelve drawn "| __truncated__ "Solomon Wariso (born 11 November 1966 in Portsmouth) is a retired English sprinter who competed primarily in th"| __truncated__
- $ documentSentiment:Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 2 obs. of 2 variables:
- ..$ magnitude: num [1:2] 2.4 0.1
- ..$ score : num [1:2] 0.3 0.1
- $ classifyText :Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1 obs. of 2 variables:
- ..$ name : chr "/Science/Astronomy"
- ..$ confidence: num 0.93str(nlp_result, max.level = 2)
+List of 7
+ $ sentences :List of 2
+ ..$ :'data.frame': 7 obs. of 4 variables:
+ ..$ :'data.frame': 1 obs. of 4 variables:
+ $ tokens :List of 2
+ ..$ :'data.frame': 139 obs. of 17 variables:
+ ..$ :'data.frame': 54 obs. of 17 variables:
+ $ entities :List of 2
+ ..$ :Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 52 obs. of 9 variables:
+ ..$ :Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 8 obs. of 9 variables:
+ $ language : chr [1:2] "en" "en"
+ $ text : chr [1:2] "Norma is a small constellation in the Southern Celestial Hemisphere between Ara and Lupus, one of twelve drawn "| __truncated__ "Solomon Wariso (born 11 November 1966 in Portsmouth) is a retired English sprinter who competed primarily in th"| __truncated__
+ $ documentSentiment:Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 2 obs. of 2 variables:
+ ..$ magnitude: num [1:2] 2.4 0.1
+ ..$ score : num [1:2] 0.3 0.1
+ $ classifyText :Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1 obs. of 2 variables:
+ ..$ name : chr "/Science/Astronomy"
+ ..$ confidence: num 0.93Sentence structure and sentiment:
-## sentences structure
-nlp_result$sentences[[2]]
-
-content
-1 Solomon Wariso (born 11 November 1966 in Portsmouth) is a retired English sprinter who competed primarily in the 200 and 400 metres.[1] He represented his country at two outdoor and three indoor World Championships and is the British record holder in the indoor 4 × 400 metres relay.
- beginOffset magnitude score
-1 0 0.1 0.1## sentences structure
+nlp_result$sentences[[2]]
+
+content
+1 Solomon Wariso (born 11 November 1966 in Portsmouth) is a retired English sprinter who competed primarily in the 200 and 400 metres.[1] He represented his country at two outdoor and three indoor World Championships and is the British record holder in the indoor 4 × 400 metres relay.
+ beginOffset magnitude score
+1 0 0.1 0.1Information on what words (tokens) are within each text:
-# word tokens data
-str(nlp_result$tokens[[1]])
-'data.frame': 139 obs. of 17 variables:
- $ content : chr "Norma" "is" "a" "small" ...
- $ beginOffset : int 0 6 9 11 17 31 34 38 47 57 ...
- $ tag : chr "NOUN" "VERB" "DET" "ADJ" ...
- $ aspect : chr "ASPECT_UNKNOWN" "ASPECT_UNKNOWN" "ASPECT_UNKNOWN" "ASPECT_UNKNOWN" ...
- $ case : chr "CASE_UNKNOWN" "CASE_UNKNOWN" "CASE_UNKNOWN" "CASE_UNKNOWN" ...
- $ form : chr "FORM_UNKNOWN" "FORM_UNKNOWN" "FORM_UNKNOWN" "FORM_UNKNOWN" ...
- $ gender : chr "GENDER_UNKNOWN" "GENDER_UNKNOWN" "GENDER_UNKNOWN" "GENDER_UNKNOWN" ...
- $ mood : chr "MOOD_UNKNOWN" "INDICATIVE" "MOOD_UNKNOWN" "MOOD_UNKNOWN" ...
- $ number : chr "SINGULAR" "SINGULAR" "NUMBER_UNKNOWN" "NUMBER_UNKNOWN" ...
- $ person : chr "PERSON_UNKNOWN" "THIRD" "PERSON_UNKNOWN" "PERSON_UNKNOWN" ...
- $ proper : chr "PROPER" "PROPER_UNKNOWN" "PROPER_UNKNOWN" "PROPER_UNKNOWN" ...
- $ reciprocity : chr "RECIPROCITY_UNKNOWN" "RECIPROCITY_UNKNOWN" "RECIPROCITY_UNKNOWN" "RECIPROCITY_UNKNOWN" ...
- $ tense : chr "TENSE_UNKNOWN" "PRESENT" "TENSE_UNKNOWN" "TENSE_UNKNOWN" ...
- $ voice : chr "VOICE_UNKNOWN" "VOICE_UNKNOWN" "VOICE_UNKNOWN" "VOICE_UNKNOWN" ...
- $ headTokenIndex: int 1 1 4 4 1 4 9 9 9 5 ...
- $ label : chr "NSUBJ" "ROOT" "DET" "AMOD" ...
- $ value : chr "Norma" "be" "a" "small" ...# word tokens data
+str(nlp_result$tokens[[1]])
+'data.frame': 139 obs. of 17 variables:
+ $ content : chr "Norma" "is" "a" "small" ...
+ $ beginOffset : int 0 6 9 11 17 31 34 38 47 57 ...
+ $ tag : chr "NOUN" "VERB" "DET" "ADJ" ...
+ $ aspect : chr "ASPECT_UNKNOWN" "ASPECT_UNKNOWN" "ASPECT_UNKNOWN" "ASPECT_UNKNOWN" ...
+ $ case : chr "CASE_UNKNOWN" "CASE_UNKNOWN" "CASE_UNKNOWN" "CASE_UNKNOWN" ...
+ $ form : chr "FORM_UNKNOWN" "FORM_UNKNOWN" "FORM_UNKNOWN" "FORM_UNKNOWN" ...
+ $ gender : chr "GENDER_UNKNOWN" "GENDER_UNKNOWN" "GENDER_UNKNOWN" "GENDER_UNKNOWN" ...
+ $ mood : chr "MOOD_UNKNOWN" "INDICATIVE" "MOOD_UNKNOWN" "MOOD_UNKNOWN" ...
+ $ number : chr "SINGULAR" "SINGULAR" "NUMBER_UNKNOWN" "NUMBER_UNKNOWN" ...
+ $ person : chr "PERSON_UNKNOWN" "THIRD" "PERSON_UNKNOWN" "PERSON_UNKNOWN" ...
+ $ proper : chr "PROPER" "PROPER_UNKNOWN" "PROPER_UNKNOWN" "PROPER_UNKNOWN" ...
+ $ reciprocity : chr "RECIPROCITY_UNKNOWN" "RECIPROCITY_UNKNOWN" "RECIPROCITY_UNKNOWN" "RECIPROCITY_UNKNOWN" ...
+ $ tense : chr "TENSE_UNKNOWN" "PRESENT" "TENSE_UNKNOWN" "TENSE_UNKNOWN" ...
+ $ voice : chr "VOICE_UNKNOWN" "VOICE_UNKNOWN" "VOICE_UNKNOWN" "VOICE_UNKNOWN" ...
+ $ headTokenIndex: int 1 1 4 4 1 4 9 9 9 5 ...
+ $ label : chr "NSUBJ" "ROOT" "DET" "AMOD" ...
+ $ value : chr "Norma" "be" "a" "small" ...What entities within text have been identified, with optional wikipedia URL if its available.
-nlp_result$entities
-[[1]]
-# A tibble: 52 x 9
- name type salience mid wikipedia_url magnitude score beginOffset mention_type
- <chr> <chr> <dbl> <chr> <chr> <dbl> <dbl> <int> <chr>
- 1 angle OTHER 0.0133 NA NA 0 0 261 COMMON
- 2 Ara ORGANIZATION 0.0631 NA NA 0 0 76 PROPER
- 3 astronomer NA NA NA NA NA NA 144 COMMON
- 4 carpenter PERSON 0.0135 NA NA 0 0 328 COMMON
- 5 constellation OTHER 0.150 NA NA 0 0 17 COMMON
- 6 constellations OTHER 0.0140 NA NA 0.9 0.9 405 COMMON
- 7 distance OTHER 0.00645 NA NA 0 0 649 COMMON
- 8 dust OTHER 0.00645 NA NA 0.3 -0.3 669 COMMON
- 9 field LOCATION 0.00407 NA NA 0.6 -0.6 476 COMMON
-10 French LOCATION 0.0242 NA NA 0 0 137 PROPER
-# ... with 42 more rows
-
-[[2]]
-# A tibble: 8 x 9
- name type salience mid wikipedia_url magnitude score beginOffset mention_type
- <chr> <chr> <dbl> <chr> <chr> <dbl> <dbl> <int> <chr>
-1 British LOCATION 0.0255 NA NA 0 0 226 PROPER
-2 country LOCATION 0.0475 NA NA 0 0 155 COMMON
-3 English OTHER 0.0530 NA NA 0 0 66 PROPER
-4 Portsmouth LOCATION 0.0530 /m/0619_ https://en.wiki… 0 0 41 PROPER
-5 record holder PERSON 0.0541 NA NA 0 0 234 COMMON
-6 Solomon Wariso ORGANIZATION 0.156 /g/120x5nf6 https://en.wiki… 0 0 0 PROPER
-7 sprinter PERSON 0.600 NA NA 0 0 74 COMMON
-8 World Championships EVENT 0.0113 NA NA 0.1 0.1 195 PROPER nlp_result$entities
+[[1]]
+# A tibble: 52 x 9
+ name type salience mid wikipedia_url magnitude score beginOffset mention_type
+ <chr> <chr> <dbl> <chr> <chr> <dbl> <dbl> <int> <chr>
+ 1 angle OTHER 0.0133 NA NA 0 0 261 COMMON
+ 2 Ara ORGANIZATION 0.0631 NA NA 0 0 76 PROPER
+ 3 astronomer NA NA NA NA NA NA 144 COMMON
+ 4 carpenter PERSON 0.0135 NA NA 0 0 328 COMMON
+ 5 constellation OTHER 0.150 NA NA 0 0 17 COMMON
+ 6 constellations OTHER 0.0140 NA NA 0.9 0.9 405 COMMON
+ 7 distance OTHER 0.00645 NA NA 0 0 649 COMMON
+ 8 dust OTHER 0.00645 NA NA 0.3 -0.3 669 COMMON
+ 9 field LOCATION 0.00407 NA NA 0.6 -0.6 476 COMMON
+10 French LOCATION 0.0242 NA NA 0 0 137 PROPER
+# ... with 42 more rows
+
+[[2]]
+# A tibble: 8 x 9
+ name type salience mid wikipedia_url magnitude score beginOffset mention_type
+ <chr> <chr> <dbl> <chr> <chr> <dbl> <dbl> <int> <chr>
+1 British LOCATION 0.0255 NA NA 0 0 226 PROPER
+2 country LOCATION 0.0475 NA NA 0 0 155 COMMON
+3 English OTHER 0.0530 NA NA 0 0 66 PROPER
+4 Portsmouth LOCATION 0.0530 /m/0619_ https://en.wiki… 0 0 41 PROPER
+5 record holder PERSON 0.0541 NA NA 0 0 234 COMMON
+6 Solomon Wariso ORGANIZATION 0.156 /g/120x5nf6 https://en.wiki… 0 0 0 PROPER
+7 sprinter PERSON 0.600 NA NA 0 0 74 COMMON
+8 World Championships EVENT 0.0113 NA NA 0.1 0.1 195 PROPER Sentiment of the entire text:
-nlp_result$documentSentiment
-# A tibble: 2 x 2
- magnitude score
- <dbl> <dbl>
-1 2.4 0.3
-2 0.1 0.1nlp_result$documentSentiment
+# A tibble: 2 x 2
+ magnitude score
+ <dbl> <dbl>
+1 2.4 0.3
+2 0.1 0.1The category for the text as defined by the list here.
-nlp_result$classifyText
-# A tibble: 1 x 2
- name confidence
- <chr> <dbl>
-1 /Science/Astronomy 0.93nlp_result$classifyText
+# A tibble: 1 x 2
+ name confidence
+ <chr> <dbl>
+1 /Science/Astronomy 0.93The language for the text:
-nlp_result$language
-# [1] "en" "en"The original passed in text, to aid with working with the output:
-nlp_result$text
-[1] "Norma is a small constellation in the Southern Celestial Hemisphere between Ara and Lupus, one of twelve drawn up in the 18th century by French astronomer Nicolas Louis de Lacaille and one of several depicting scientific instruments. Its name refers to a right angle in Latin, and is variously considered to represent a rule, a carpenter's square, a set square or a level. It remains one of the 88 modern constellations. Four of Norma's brighter stars make up a square in the field of faint stars. Gamma2 Normae is the brightest star with an apparent magnitude of 4.0. Mu Normae is one of the most luminous stars known, but is partially obscured by distance and cosmic dust. Four star systems are known to harbour planets."
-[2] "Solomon Wariso (born 11 November 1966 in Portsmouth) is a retired English sprinter who competed primarily in the 200 and 400 metres.[1] He represented his country at two outdoor and three indoor World Championships and is the British record holder in the indoor 4 × 400 metres relay."nlp_result$text
+[1] "Norma is a small constellation in the Southern Celestial Hemisphere between Ara and Lupus, one of twelve drawn up in the 18th century by French astronomer Nicolas Louis de Lacaille and one of several depicting scientific instruments. Its name refers to a right angle in Latin, and is variously considered to represent a rule, a carpenter's square, a set square or a level. It remains one of the 88 modern constellations. Four of Norma's brighter stars make up a square in the field of faint stars. Gamma2 Normae is the brightest star with an apparent magnitude of 4.0. Mu Normae is one of the most luminous stars known, but is partially obscured by distance and cosmic dust. Four star systems are known to harbour planets."
+[2] "Solomon Wariso (born 11 November 1966 in Portsmouth) is a retired English sprinter who competed primarily in the 200 and 400 metres.[1] He represented his country at two outdoor and three indoor World Championships and is the British record holder in the indoor 4 × 400 metres relay."#.Renviron
GL_AUTH=location_of_json_file.jsonThen, when you load the library you should auto-authenticate:
-library(googleLanguageR)
-# Setting scopes to https://www.googleapis.com/auth/cloud-platform
-# Set any additional scopes via options(googleAuthR.scopes.selected = c('scope1', 'scope2')) before loading library.
-# Successfully authenticated via location_of_json_file.jsonlibrary(googleLanguageR)
+# Setting scopes to https://www.googleapis.com/auth/cloud-platform
+# Set any additional scopes via options(googleAuthR.scopes.selected = c('scope1', 'scope2')) before loading library.
+# Successfully authenticated via location_of_json_file.jsonYou can also authenticate directly using the gl_auth function pointing at your JSON auth file:
library(googleLanguageR)
-gl_auth("location_of_json_file.json")You can then call the APIs via the functions:
gl_nlp() - Natural Langage APITranslate text via gl_translate. Note this is a lot more refined than the free version on Google’s translation website.
library(googleLanguageR)
-
-text <- "to administer medicince to animals is frequently a very difficult matter, and yet sometimes it's necessary to do so"
-## translate British into Danish
-gl_translate(text, target = "da")$translatedTextlibrary(googleLanguageR)
+
+text <- "to administer medicince to animals is frequently a very difficult matter, and yet sometimes it's necessary to do so"
+## translate British into Danish
+gl_translate(text, target = "da")$translatedTextYou can choose the target language via the argument target. The function will automatically detect the language if you do not define an argument source. This function which will also detect the langauge. As it costs the same as gl_translate_detect, its usually cheaper to detect and translate in one step.
You can pass a vector of text which will first be attempted to translate in one API call - if that fails due to being greater than the API limits, it will attempt again but vectorising the API calls. This will result in more calls and be slower, but cost the same as you are charged per character translated, not per API call.
You can also supply web HTML and select the format='html' which will handle HTML tags to give you a cleaner translation.
Consider removing anything not needed to be translated first, such as JavaScript and CSS scripts using the tools of rvest - an example is shown below:
# translate webpages
-library(rvest)
-library(googleLanguageR)
-
-my_url <- "http://www.dr.dk/nyheder/indland/greenpeace-facebook-og-google-boer-foelge-apples-groenne-planer"
-
-## in this case the content to translate is in css select .wcms-article-content
-read_html(my_url) %>% # read html
- html_node(css = ".wcms-article-content") %>% # select article content
- html_text %>% # extract text
- gl_translate(format = "html") %>% # translate with html flag
- dplyr::select(translatedText) # show translatedText column of output tibble# translate webpages
+library(rvest)
+library(googleLanguageR)
+
+my_url <- "http://www.dr.dk/nyheder/indland/greenpeace-facebook-og-google-boer-foelge-apples-groenne-planer"
+
+## in this case the content to translate is in css select .wcms-article-content
+read_html(my_url) %>% # read html
+ html_node(css = ".wcms-article-content") %>% # select article content
+ html_text %>% # extract text
+ gl_translate(format = "html") %>% # translate with html flag
+ dplyr::select(translatedText) # show translatedText column of output tibbleThis function only detects the language:
-## which language is this?
-gl_translate_detect("katten sidder på måtten")The more text it has, the better. And it helps if its not Danish…
It may be better to use cld2 to translate offline first, to avoid charges if the translation is unnecessary (e.g. already in English). You could then verify online for more uncertain cases.
cld2::detect_language("katten sidder på måtten")