R trends in 2015 (based on cranlogs)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220

library(rvest)
library(dplyr)
# devtools::install_github("hadley/multidplyr")
library(multidplyr)
library(magrittr)
library(lubridate)
 
getCranberriesElmnt <- function(txt, elmnt_name){
  desc <- grep(sprintf("^%s:", elmnt_name), txt)
  if (length(desc) == 1){
    txt <- txt[desc:length(txt)]
    end <- grep("^[A-Za-z/@]{2,}:", txt[-1])
    if (length(end) == 0)
      end <- length(txt)
    else
      end <- end[1]
 
    desc <-
      txt[1:end] %>% 
      gsub(sprintf("^%s: (.+)", elmnt_name),
           "\1", .) %>% 
      paste(collapse = " ") %>% 
      gsub("[ ]{2,}", " ", .) %>% 
      gsub(" , ", ", ", .)
  }else if (length(desc) == 0){
    desc <- paste("No", tolower(elmnt_name))
  }else{
    stop("Could not find ", elmnt_name, " in text: n",
         paste(txt, collapse = "n"))
  }
  return(desc)
}
 
convertCharset <- function(txt){
  if (grepl("Windows", Sys.info()["sysname"]))
    txt <- iconv(txt, from = "UTF-8", to = "cp1252")
  return(txt)
}
 
getAuthor <- function(txt, package){
  author <- getCranberriesElmnt(txt, "Author")
  if (grepl("No author|See AUTHORS file", author)){
    author <- getCranberriesElmnt(txt, "Maintainer")
  }
 
  if (grepl("(No m|M)aintainer|(No a|A)uthor|^See AUTHORS file", author) || 
      is.null(author) ||
      nchar(author)  <= 2){
    cran_txt <- read_html(sprintf("http://cran.r-project.org/web/packages/%s/index.html",
                                  package))
    author <- cran_txt %>% 
      html_nodes("tr") %>% 
      html_text %>% 
      convertCharset %>% 
      gsub("(^[ tn]+|[ tn]+$)", "", .) %>% 
      .[grep("^Author", .)] %>% 
      gsub(".*n", "", .)
 
    # If not found then the package has probably been
    # removed from the repository
    if (length(author) == 1)
      author <- author
    else
      author <- "No author"
  }
 
  # Remove stuff such as:
  # [cre, auth]
  # (worked on the...)
  # <my@email.com>
  # "John Doe"
  author %<>% 
    gsub("^Author: (.+)", 
         "\1", .) %>% 
    gsub("[ ]*\[[^]]{3,}\][ ]*", " ", .) %>% 
    gsub("\([^)]+\)", " ", .) %>% 
    gsub("([ ]*<[^>]+>)", " ", .) %>% 
    gsub("[ ]*\[[^]]{3,}\][ ]*", " ", .) %>% 
    gsub("[ ]{2,}", " ", .) %>% 
    gsub("(^[ '"]+|[ '"]+$)", "", .) %>% 
    gsub(" , ", ", ", .)
  return(author)
}
 
getDate <- function(txt, package){
  date <- 
    grep("^Date/Publication", txt)
  if (length(date) == 1){
    date <- txt[date] %>% 
      gsub("Date/Publication: ([0-9]{4,4}-[0-9]{2,2}-[0-9]{2,2}).*",
           "\1", .)
  }else{
    cran_txt <- read_html(sprintf("http://cran.r-project.org/web/packages/%s/index.html",
                                  package))
    date <- 
      cran_txt %>% 
      html_nodes("tr") %>% 
      html_text %>% 
      convertCharset %>% 
      gsub("(^[ tn]+|[ tn]+$)", "", .) %>% 
      .[grep("^Published", .)] %>% 
      gsub(".*n", "", .)
 
 
    # The main page doesn't contain the original date if 
    # new packages have been submitted, we therefore need
    # to check first entry in the archives
    if(cran_txt %>% 
       html_nodes("tr") %>% 
       html_text %>% 
       gsub("(^[ tn]+|[ tn]+$)", "", .) %>% 
       grepl("^Old.{1,4}sources", .) %>% 
       any){
      archive_txt <- read_html(sprintf("http://cran.r-project.org/src/contrib/Archive/%s/",
                                       package))
      pkg_date <- 
        archive_txt %>% 
        html_nodes("tr") %>% 
        lapply(function(x) {
          nodes <- html_nodes(x, "td")
          if (length(nodes) == 5){
            return(nodes[3] %>% 
                     html_text %>% 
                     as.Date(format = "%d-%b-%Y"))
          }
        }) %>% 
        .[sapply(., length) > 0] %>% 
        .[!sapply(., is.na)] %>% 
        head(1)
 
      if (length(pkg_date) == 1)
        date <- pkg_date[[1]]
    }
  }
  date <- tryCatch({
    as.Date(date)
  }, error = function(e){
    "Date missing"
  })
  return(date)
}
 
getNewPkgStats <- function(published_in){
  # The parallel is only for making cranlogs requests
  # we can therefore have more cores than actual cores
  # as this isn't processor intensive while there is
  # considerable wait for each http-request
  cl <- create_cluster(parallel::detectCores() * 4)
  parallel::clusterEvalQ(cl, {
    library(cranlogs)
  })
  set_default_cluster(cl)
  on.exit(stop_cluster())
 
  berries <- read_html(paste0("http://dirk.eddelbuettel.com/cranberries/", published_in, "/"))
  pkgs <- 
    # Select the divs of the package class
    html_nodes(berries, ".package") %>% 
    # Extract the text
    html_text %>% 
    # Split the lines
    strsplit("[n]+") %>% 
    # Now clean the lines
    lapply(.,
           function(pkg_txt) {
             pkg_txt[sapply(pkg_txt, function(x) { nchar(gsub("^[ t]+", "", x)) > 0}, 
                            USE.NAMES = FALSE)] %>% 
               gsub("^[ t]+", "", .) 
           })
 
  # Now we select the new packages
  new_packages <- 
    pkgs %>% 
    # The first line is key as it contains the text "New package"
    sapply(., function(x) x[1], USE.NAMES = FALSE) %>% 
    grep("^New package", .) %>% 
    pkgs[.] %>% 
    # Now we extract the package name and the date that it was published
    # and merge everything into one table
    lapply(function(txt){
      txt <- convertCharset(txt)
      ret <- data.frame(
        name = gsub("^New package ([^ ]+) with initial .*", 
                     "\1", txt[1]),
        stringsAsFactors = FALSE
      )
 
      ret$desc <- getCranberriesElmnt(txt, "Description")
      ret$author <- getAuthor(txt, ret$name)
      ret$date <- getDate(txt, ret$name)
 
      return(ret)
    }) %>% 
    rbind_all %>% 
    # Get the download data in parallel
    partition(name) %>% 
    do({
      down <- cran_downloads(.$name[1], 
                             from = max(as.Date("2015-01-01"), .$date[1]), 
                             to = "2015-12-31")$count 
      cbind(.[1,],
            data.frame(sum = sum(down), 
                       avg = mean(down))
      )
    }) %>% 
    collect %>% 
    ungroup %>% 
    arrange(desc(avg))
 
  return(new_packages)
}
 
pkg_list <- 
  lapply(2010:2015,
         getNewPkgStats)
 
pkgs <- 
  rbind_all(pkg_list) %>% 
  mutate(time = as.numeric(as.Date("2016-01-01") - date),
         year = format(date, "%Y"))

1
2
3
4
5
6
7
8

pkgs %<>% 
  mutate(time_yrs = time/365.25)
fit <- lm(avg ~ time_yrs, data = pkgs)
 
# Test for non-linearity
library(splines)
anova(fit,
      update(fit, .~.-time_yrs+ns(time_yrs, 2)))

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

library(quantreg)
library(htmlTable)
lapply(c(.5, .75, .95, .99),
       function(tau){
         rq_fit <- rq(avg ~ time_yrs, data = pkgs, tau = tau)
         rq_sum <- summary(rq_fit)
         c(Estimate = txtRound(rq_sum$coefficients[2, 1], 1), 
           `95 % CI` = txtRound(rq_sum$coefficients[2, 1] + 
                                        c(1,-1) * rq_sum$coefficients[2, 2], 1) %>% 
             paste(collapse = " to "))
       }) %>% 
  do.call(rbind, .) %>% 
  htmlTable(rnames = c("Median",
                       "Upper quartile",
                       "Top 5%",
                       "Top 1%"))

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82

top_coders <- list(
  "2015" = 
    pkgs %>% 
    filter(format(date, "%Y") == 2015) %>% 
    partition(author) %>% 
    do({
      authors <- strsplit(.$author, "[ ]*([,;]| and )[ ]*")[[1]]
      authors <- authors[!grepl("^[ ]*(Inc|PhD|Dr|Lab).*[ ]*$", authors)]
      if (length(authors) >= 1){
        # If multiple authors the statistic is split among
        # them but with an added 20% for the extra collaboration
        # effort that a multi-author envorionment calls for
        .$sum <- round(.$sum/length(authors)*1.2)
        .$avg <- .$avg/length(authors)*1.2
        ret <- .
        ret$author <- authors[1]
        for (m in authors[-1]){
          tmp <- .
          tmp$author <- m
          ret <- rbind(ret, tmp)
        }
        return(ret)
      }else{
        return(.)
      }
    }) %>% 
    collect() %>% 
    group_by(author) %>% 
    summarise(download_ave = round(sum(avg)),
              no_packages = n(),
              packages = paste(name, collapse = ", ")) %>% 
    select(author, download_ave, no_packages, packages) %>% 
    collect() %>% 
    arrange(desc(download_ave)) %>% 
    head(10),
  "all" =
    pkgs %>% 
    partition(author) %>% 
    do({
      if (grepl("Jeroen Ooms", .$author))
        browser()
      authors <- strsplit(.$author, "[ ]*([,;]| and )[ ]*")[[1]]
      authors <- authors[!grepl("^[ ]*(Inc|PhD|Dr|Lab).*[ ]*$", authors)]
      if (length(authors) >= 1){
        # If multiple authors the statistic is split among
        # them but with an added 20% for the extra collaboration
        # effort that a multi-author envorionment calls for
        .$sum <- round(.$sum/length(authors)*1.2)
        .$avg <- .$avg/length(authors)*1.2
        ret <- .
        ret$author <- authors[1]
        for (m in authors[-1]){
          tmp <- .
          tmp$author <- m
          ret <- rbind(ret, tmp)
        }
        return(ret)
      }else{
        return(.)
      }
    }) %>% 
    collect() %>% 
    group_by(author) %>% 
    summarise(download_ave = round(sum(avg)),
              no_packages = n(),
              packages = paste(name, collapse = ", ")) %>% 
    select(author, download_ave, no_packages, packages) %>% 
    collect() %>% 
    arrange(desc(download_ave)) %>% 
    head(30))
 
interactiveTable(
  do.call(rbind, top_coders) %>% 
    mutate(download_ave = txtInt(download_ave)),
  align = "lrr",
  header = c("Coder", "Total ave. downloads per day", "No. of packages", "Packages"),
  tspanner = c("Top coders 2015",
               "Top coders 2010-2015"),
  n.tspanner = sapply(top_coders, nrow),
  minimized.columns = 4, 
  rnames = FALSE, 
  col.rgroup = c("white", "#F0F0FF"))