Tracker

x %>% 
  dplyr::count(tracker)

## # A tibble: 2 x 2
##   tracker     n
##   <chr>   <int>
## 1 Defect   2868
## 2 Patch     958

x %>% 
  dplyr::count(tracker) %>% 
  ggplot2::ggplot(ggplot2::aes(x = "tracker", y = n, fill = tracker)) + 
    ggplot2::geom_bar(stat = "identity", width = 1, alpha = 0.75) +
    ggplot2::coord_polar(theta = "y", direction = 1) +
    ggplot2::guides(fill = guide_legend(reverse = TRUE))

Status

x %>% 
  dplyr::count(status)

## # A tibble: 6 x 2
##   status             n
##   <ord>          <int>
## 1 New              680
## 2 Needs feedback   160
## 3 Confirmed         29
## 4 Resolved          13
## 5 Closed          2934
## 6 Reopened          10

x %>% 
  dplyr::count(status) %>% 
  ggplot2::ggplot(ggplot2::aes(x = "status", y = n, fill = status)) + 
    ggplot2::geom_bar(stat = "identity", width = 1, alpha = 0.75) +
    ggplot2::coord_polar(theta = "y", direction = 1) +
    ggplot2::guides(fill = guide_legend(reverse = TRUE))

Priority

x %>% 
  dplyr::count(priority)

## # A tibble: 4 x 2
##   priority     n
##   <ord>    <int>
## 1 Low        108
## 2 Normal    3435
## 3 High       203
## 4 Urgent      80

x %>% 
  dplyr::count(priority) %>% 
  ggplot2::ggplot(ggplot2::aes(x = "priority", y = n, fill = priority)) + 
    ggplot2::geom_bar(stat = "identity", width = 1, alpha = 0.75) +
    ggplot2::coord_polar(theta = "y", direction = 1) +
    ggplot2::guides(fill = guide_legend(reverse = TRUE))

Category

カテゴリ数が多い場合、円グラフや積み上げ棒グラフによる可視化はあまり好ましくありません。
　

x %>% 
  dplyr::count(category)

## # A tibble: 56 x 2
##    category                      n
##    <chr>                     <int>
##  1 Accounts / authentication    58
##  2 Activity view                20
##  3 Administration               54
##  4 Attachments                  90
##  5 Calendar                      9
##  6 Code cleanup/refactoring    126
##  7 Core Plugins                  3
##  8 Custom fields               114
##  9 Database                     85
## 10 Documentation                60
## # ... with 46 more rows

x %>% 
  dplyr::count(category) %>% 
  ggplot2::ggplot(ggplot2::aes(x = "category", y = n, fill = category)) + 
    ggplot2::geom_bar(stat = "identity", width = 1, alpha = 0.75) +
    ggplot2::coord_polar(theta = "y", direction = 1) +
    ggplot2::guides(fill = guide_legend(reverse = TRUE))

Version

x %>% 
  dplyr::count(version)

## # A tibble: 72 x 2
##    version     n
##    <chr>   <int>
##  1 2.3.1       3
##  2 2.3.2      20
##  3 2.3.3      17
##  4 2.3.4      13
##  5 2.4.0      48
##  6 2.4.1       7
##  7 2.4.2      12
##  8 2.4.3       8
##  9 2.4.4       4
## 10 2.4.6       5
## # ... with 62 more rows

x %>% 
  dplyr::count(version) %>% 
  dplyr::filter(!is.na(version)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = "version", y = n, fill = version)) + 
    ggplot2::geom_bar(stat = "identity", width = 1, alpha = 0.75) +
    ggplot2::coord_polar(theta = "y", direction = 1) +
    ggplot2::guides(fill = guide_legend(reverse = TRUE))

Affected

x %>% 
  dplyr::count(affected)

## # A tibble: 91 x 2
##    affected     n
##    <chr>    <int>
##  1 0.8.2        1
##  2 0.8.5        1
##  3 0.8.7        1
##  4 0.9.4        1
##  5 0.9.6        2
##  6 1.0.1        2
##  7 1.0.4        1
##  8 1.1.2        1
##  9 1.1.3        2
## 10 1.2.0        1
## # ... with 81 more rows

x %>% 
  dplyr::count(affected) %>% 
  dplyr::filter(!is.na(affected)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = "affected", y = n, fill = affected)) + 
    ggplot2::geom_bar(stat = "identity", width = 1, alpha = 0.75) +
    ggplot2::coord_polar(theta = "y", direction = 1) +
    ggplot2::guides(fill = guide_legend(reverse = TRUE))

Open

ggplot2パッケージでは日付は連続量として扱われるため円グラフで表現するのはおすゝめできません。
　

x %>% 
  dplyr::count(open)

## # A tibble: 1,466 x 2
##    open           n
##    <date>     <int>
##  1 2013-04-08     4
##  2 2013-04-09     6
##  3 2013-04-10     4
##  4 2013-04-11     5
##  5 2013-04-12     8
##  6 2013-04-14     3
##  7 2013-04-15     2
##  8 2013-04-16     5
##  9 2013-04-17     5
## 10 2013-04-18     3
## # ... with 1,456 more rows

x %>% 
  dplyr::count(open) %>% 
  ggplot2::ggplot(ggplot2::aes(x = "open", y = n, fill = open)) + 
    ggplot2::geom_bar(stat = "identity", width = 1, alpha = 0.75) +
    ggplot2::coord_polar(theta = "y", direction = 1) +
    ggplot2::guides(fill = guide_legend(reverse = TRUE))

Tracker and Status

x %>% 
  dplyr::count(tracker, status) %>% 
  tidyr::spread(key = tracker, value = n)

## # A tibble: 6 x 3
##   status         Defect Patch
##   <ord>           <int> <int>
## 1 New               504   176
## 2 Needs feedback    144    16
## 3 Confirmed          29    NA
## 4 Resolved           10     3
## 5 Closed           2174   760
## 6 Reopened            7     3

x %>% 
  dplyr::count(tracker, status) %>% 
  ggplot2::ggplot(ggplot2::aes(x = tracker, y = status, fill = n)) + 
    ggplot2::geom_tile() + 
    ggplot2::scale_fill_gradient(low = "lightyellow", high = "red") +
    ggplot2::geom_text(ggplot2::aes(label = n)) + 
    ggplot2::theme_bw()

x %>% 
  dplyr::count(tracker, status) %>% 
  ggplot2::ggplot(ggplot2::aes(x = tracker, y = n, fill = status)) + 
    ggplot2::geom_bar(stat = "identity", alpha = 0.75) + 
    ggplot2::coord_polar(theta = "y", direction = 1) +
    ggplot2::theme_bw() + 
    ggplot2::guides(fill = guide_legend(reverse = TRUE))

Tracker and Priority

大半のチケットがステータスがClosedな対応が完了しているチケットですので、Closedを除くチケットに対する優先度を見て見ます。
　

x %>% 
  dplyr::filter(status != "Closed") %>% 
  dplyr::count(tracker, priority) %>% 
  tidyr::spread(key = tracker, value = n)

## # A tibble: 4 x 3
##   priority Defect Patch
##   <ord>     <int> <int>
## 1 Low          28     2
## 2 Normal      606   193
## 3 High         50     3
## 4 Urgent       10    NA

x %>% 
  dplyr::filter(status != "Closed") %>% 
  dplyr::count(tracker, priority) %>% 
  ggplot2::ggplot(ggplot2::aes(x = tracker, y = priority, fill = n)) + 
    ggplot2::geom_tile() + 
    ggplot2::scale_fill_gradient(low = "lightyellow", high = "red") +
    ggplot2::geom_text(ggplot2::aes(label = n)) +
    ggplot2::theme_bw()

x %>% 
  dplyr::filter(status != "Closed") %>% 
  dplyr::count(tracker, priority) %>% 
  ggplot2::ggplot(ggplot2::aes(x = tracker, y = n, fill = priority)) + 
    ggplot2::geom_bar(stat = "identity", alpha = 0.75) + 
    ggplot2::coord_polar(theta = "y", direction = 1) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill = guide_legend(reverse = TRUE))

Priority and Status

x %>% 
  dplyr::filter(status != "Closed" & tracker == "Defect") %>% 
  dplyr::count(priority, status) %>% 
  tidyr::spread(key = priority, value = n)

## # A tibble: 5 x 5
##   status           Low Normal  High Urgent
##   <ord>          <int>  <int> <int>  <int>
## 1 New               25    435    37      7
## 2 Needs feedback     3    131     8      2
## 3 Confirmed         NA     27     2     NA
## 4 Resolved          NA      8     1      1
## 5 Reopened          NA      5     2     NA

x %>% 
  dplyr::filter(status != "Closed" & tracker == "Defect") %>% 
  dplyr::count(priority, status) %>% 
  ggplot2::ggplot(ggplot2::aes(x = status, y = priority, fill = n)) + 
    ggplot2::geom_tile() + 
    ggplot2::scale_fill_gradient(low = "lightyellow", high = "red") +
    ggplot2::geom_text(ggplot2::aes(label = n)) +
    ggplot2::theme_bw()

x %>% 
  dplyr::filter(status != "Closed" & tracker == "Defect") %>% 
  dplyr::count(priority, status) %>% 
  ggplot2::ggplot(ggplot2::aes(x = priority, y = n, fill = status)) + 
    ggplot2::geom_bar(stat = "identity", alpha = 0.75) + 
    ggplot2::coord_polar(theta = "y", direction = 1) +
    ggplot2::theme_bw() + 
    ggplot2::guides(fill = guide_legend(reverse = TRUE))

x %>% 
  dplyr::filter(status != "Closed" & tracker == "Patch") %>% 
  dplyr::count(priority, status) %>% 
  tidyr::spread(key = priority, value = n)

## # A tibble: 4 x 4
##   status           Low Normal  High
##   <ord>          <int>  <int> <int>
## 1 New                2    171     3
## 2 Needs feedback    NA     16    NA
## 3 Resolved          NA      3    NA
## 4 Reopened          NA      3    NA

x %>% 
  dplyr::filter(status != "Closed" & tracker == "Patch") %>% 
  dplyr::count(priority, status) %>% 
  ggplot2::ggplot(ggplot2::aes(x = status, y = priority, fill = n)) + 
    ggplot2::geom_tile() + 
    ggplot2::scale_fill_gradient(low = "lightyellow", high = "red") +
    ggplot2::geom_text(ggplot2::aes(label = n)) +
    ggplot2::theme_bw()

x %>% 
  dplyr::filter(status != "Closed" & tracker == "Patch") %>% 
  dplyr::count(priority, status) %>% 
  ggplot2::ggplot(ggplot2::aes(x = priority, y = n, fill = status)) + 
    ggplot2::geom_bar(stat = "identity", alpha = 0.75) + 
    ggplot2::coord_polar(theta = "y", direction = 1) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill = guide_legend(reverse = TRUE))

x %>% 
  dplyr::filter(status != "Closed" & tracker == "Defect") %>% 
  dplyr::filter(priority == "Urgent") %>% 
  dplyr::select(no, tracker, status, subject, assignee)

## # A tibble: 10 x 5
##       no tracker status     subject                               assignee
##    <int> <chr>   <ord>      <chr>                                 <chr>   
##  1 28303 Defect  New        Documentation needs tutorial for ins… <NA>    
##  2 28188 Defect  New        No Access-Control-Allow-Origin        <NA>    
##  3 27863 Defect  New        If version is closed or locked  subt… <NA>    
##  4 18984 Defect  Resolved   migrate_from_mantis with NoMethodErr… <NA>    
##  5 15560 Defect  Needs fee… RJS leaking                           <NA>    
##  6 14979 Defect  Needs fee… Delete Issues Relation                <NA>    
##  7 14969 Defect  New        ActiceSupport::TimeWithZone failed i… <NA>    
##  8 14918 Defect  New        Name with quote displays in ASCII     <NA>    
##  9 14269 Defect  New        custom field not displayed but not i… <NA>    
## 10 14251 Defect  New        Redmine email reminders sending 4 em… <NA>

Priority and Category

x %>% 
  dplyr::filter(status != "Closed" & tracker == "Defect") %>% 
  dplyr::count(category, priority) %>% 
  tidyr::spread(key = priority, value = n)

## # A tibble: 50 x 5
##    category                    Low Normal  High Urgent
##    <chr>                     <int>  <int> <int>  <int>
##  1 Accounts / authentication    NA     11     1      1
##  2 Activity view                 1      3    NA     NA
##  3 Administration               NA      7     2     NA
##  4 Attachments                   2     14    NA     NA
##  5 Calendar                     NA      2    NA     NA
##  6 Code cleanup/refactoring     NA      4     1     NA
##  7 Custom fields                NA     33     1     NA
##  8 Database                      1     21     2     NA
##  9 Documentation                 1      9    NA      1
## 10 Documents                    NA      1    NA     NA
## # ... with 40 more rows

x %>% 
  dplyr::filter(status != "Closed" & tracker == "Defect") %>% 
  dplyr::count(category, priority) %>% 
  ggplot2::ggplot(ggplot2::aes(x = category, y = priority, fill = n)) + 
    ggplot2::geom_tile() + 
    ggplot2::scale_fill_gradient(low = "lightyellow", high = "red") +
    ggplot2::theme_bw() +
    ggplot2::coord_flip()

x %>% 
  dplyr::filter(status != "Closed" & tracker == "Defect") %>% 
  dplyr::count(category, priority) %>% 
  ggplot2::ggplot(ggplot2::aes(x = category, y = n, fill = priority)) + 
    ggplot2::geom_bar(stat = "identity", alpha = 0.75) + 
    ggplot2::coord_polar(theta = "x", direction = 1) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill = guide_legend(reverse = TRUE))

Priority and Assignee

x %>% 
  dplyr::filter(status != "Closed" & tracker == "Defect") %>% 
  dplyr::count(priority, assignee) %>% 
  tidyr::spread(key = priority, value = n)

## # A tibble: 7 x 5
##   assignee              Low Normal  High Urgent
##   <chr>               <int>  <int> <int>  <int>
## 1 Gregor Schmidt         NA      1    NA     NA
## 2 Jean-Baptiste Barth    NA      1    NA     NA
## 3 Jean-Philippe Lang     NA     22     3     NA
## 4 Marius BALTEANU        NA      3    NA     NA
## 5 Mischa The Evil        NA      3    NA     NA
## 6 Toshi MARUYAMA         NA      2    NA     NA
## 7 <NA>                   28    574    47     10

x %>% 
  dplyr::filter(status != "Closed" & tracker == "Defect") %>% 
  dplyr::count(priority, assignee) %>% 
  ggplot2::ggplot(ggplot2::aes(x = assignee, y = priority, fill = n)) + 
    ggplot2::geom_tile() + 
    ggplot2::scale_fill_gradient(low = "lightyellow", high = "red") +
    ggplot2::theme_bw() +
    ggplot2::coord_flip()

x %>% 
  dplyr::filter(status != "Closed" & tracker == "Defect") %>% 
  dplyr::count(priority, assignee) %>% 
  ggplot2::ggplot(ggplot2::aes(x = priority, y = n, fill = assignee)) + 
    ggplot2::geom_bar(stat = "identity", alpha = 0.75) + 
    ggplot2::coord_polar(theta = "y", direction = 1) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill = guide_legend(reverse = TRUE))

　
このデータではUrgentなチケットに担当者が割り当てられていないことが分かります。
　

日次集計

日次で集計する場合は日時の場合はlubridate::date関数で日付に変換しておきます。データがない日は集計対象外となります。稼働が発生していてデータがないのか、稼働が発生していないからデータがないのかで意味が変わってきますので集計の際には注意してください。
　

x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(open) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(open) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前日との差` = ticket - dplyr::lag(ticket))

## # A tibble: 98 x 4
##    open       ticket  累計 前日との差
##    <date>      <dbl> <dbl>      <dbl>
##  1 2018-01-02      1     1         NA
##  2 2018-01-03      5     6          4
##  3 2018-01-04      1     7         -4
##  4 2018-01-05      3    10          2
##  5 2018-01-08      1    11         -2
##  6 2018-01-09      2    13          1
##  7 2018-01-11      1    14         -1
##  8 2018-01-12      2    16          1
##  9 2018-01-13      2    18          0
## 10 2018-01-15      1    19         -1
## # ... with 88 more rows

gg_bar <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(open) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(open) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前日との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = open)) + 
    ggplot2::geom_bar(ggplot2::aes(y = ticket), stat = "identity")

gg_line <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(open) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(open) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前日との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = open)) + 
    ggplot2::geom_line(ggplot2::aes(y = `累計`))

gg_line_2 <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(open) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(open) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前日との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = open)) + 
    ggplot2::geom_line(ggplot2::aes(y = `前日との差`))

layout <- rbind(c(1, 1), c(2, 3))
gridExtra::grid.arrange(gg_bar, gg_line, gg_line_2, layout_matrix = layout)

週次集計

“週”を求めるにはlubridate::week関数を用います。ただし、lubridate::week関数は1から53までの値しか返しませんので、年をまたぐ際はlubridate::year関数などを用いて年の識別ができるようにしてください。
なお、lubridate::week関数が返す週番号は１月１日を基準に単純に７日単位で計算した週番号です。ISOの週番号とは異なりますので注意してください。
　

x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(week = lubridate::week(open)) %>% 
  dplyr::filter(!is.na(week)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(week) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(week) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前週との差` = ticket - dplyr::lag(ticket))

## # A tibble: 23 x 4
##     week ticket  累計 前週との差
##    <dbl>  <dbl> <dbl>      <dbl>
##  1     1     10    10         NA
##  2     2      8    18         -2
##  3     3      9    27          1
##  4     4     11    38          2
##  5     5     14    52          3
##  6     6      5    57         -9
##  7     7     10    67          5
##  8     8      9    76         -1
##  9     9      9    85          0
## 10    10      4    89         -5
## # ... with 13 more rows

gg_bar <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(week = lubridate::week(open)) %>% 
  dplyr::filter(!is.na(week)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(week) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(week) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前週との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = week)) + 
    ggplot2::geom_bar(ggplot2::aes(y = ticket), stat = "identity")

gg_line <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(week = lubridate::week(open)) %>% 
  dplyr::filter(!is.na(week)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(week) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(week) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前週との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = week)) + 
    ggplot2::geom_line(ggplot2::aes(y = `累計`))

gg_line_2 <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(week = lubridate::week(open)) %>% 
  dplyr::filter(!is.na(week)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(week) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(week) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前週との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = week)) + 
    ggplot2::geom_line(ggplot2::aes(y = `前週との差`))

layout <- rbind(c(1, 1), c(2, 3))
gridExtra::grid.arrange(gg_bar, gg_line, gg_line_2, layout_matrix = layout)

x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(week = lubridate::week(close)) %>% 
  dplyr::filter(!is.na(week)) %>% 
  dplyr::mutate(flag = ifelse(is.na(close), 0, 1)) %>% 
  dplyr::group_by(week) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(week) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前週との差` = ticket - dplyr::lag(ticket))

## # A tibble: 23 x 4
##     week ticket  累計 前週との差
##    <dbl>  <dbl> <dbl>      <dbl>
##  1     1      5     5         NA
##  2     2      3     8         -2
##  3     3      4    12          1
##  4     4      7    19          3
##  5     5      6    25         -1
##  6     6      2    27         -4
##  7     7      3    30          1
##  8     8      8    38          5
##  9     9      4    42         -4
## 10    10      2    44         -2
## # ... with 13 more rows

gg_bar <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(week = lubridate::week(close)) %>% 
  dplyr::filter(!is.na(week)) %>% 
  dplyr::mutate(flag = ifelse(is.na(close), 0, 1)) %>% 
  dplyr::group_by(week) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(week) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前週との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = week)) + 
    ggplot2::geom_bar(ggplot2::aes(y = ticket), stat = "identity")

gg_line <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(week = lubridate::week(close)) %>% 
  dplyr::filter(!is.na(week)) %>% 
  dplyr::mutate(flag = ifelse(is.na(close), 0, 1)) %>% 
  dplyr::group_by(week) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(week) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前週との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = week)) + 
    ggplot2::geom_line(ggplot2::aes(y = `累計`))

gg_line_2 <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(week = lubridate::week(close)) %>% 
  dplyr::filter(!is.na(week)) %>% 
  dplyr::mutate(flag = ifelse(is.na(close), 0, 1)) %>% 
  dplyr::group_by(week) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(week) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前週との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = week)) + 
    ggplot2::geom_line(ggplot2::aes(y = `前週との差`))

layout <- rbind(c(1, 1), c(2, 3))
gridExtra::grid.arrange(gg_bar, gg_line, gg_line_2, layout_matrix = layout)

月次集計

“月”を求めるにはlubridate::month関数を用います。ただし、lubridate::month関数は1から12までの値しか返しませんので、年をまたぐ際はlubridate::year関数などを用いて年の識別ができるようにしてください。
　

x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(month = lubridate::month(open)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(month) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(month) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前月との差` = ticket - dplyr::lag(ticket))

## # A tibble: 6 x 4
##   month ticket  累計 前月との差
##   <dbl>  <dbl> <dbl>      <dbl>
## 1     1     45    45         NA
## 2     2     39    84         -6
## 3     3     27   111        -12
## 4     4     22   133         -5
## 5     5     35   168         13
## 6     6      5   173        -30

gg_bar <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(month = lubridate::month(open)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(month) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(month) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前月との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = month)) + 
    ggplot2::geom_bar(ggplot2::aes(y = ticket), stat = "identity")

gg_line <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(month = lubridate::month(open)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(month) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(month) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前月との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = month)) + 
    ggplot2::geom_line(ggplot2::aes(y = `累計`))

gg_line_2 <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(month = lubridate::month(open)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(month) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(month) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前月との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = month)) + 
    ggplot2::geom_line(ggplot2::aes(y = `前月との差`))

layout <- rbind(c(1, 1), c(2, 3))
gridExtra::grid.arrange(gg_bar, gg_line, gg_line_2, layout_matrix = layout)

四半期次集計

“四半期”を求めるにはlubridate::quarter関数を用います。年をまたぐ際はwith_yearオプションを使用すると年の識別ができるようになります。また、第一四半期が1月以外から始まる場合はfiscal_startオプションを使用してください。
　

x %>% 
  dplyr::filter(tracker == "Defect") %>% 
  dplyr::mutate(quarter = lubridate::quarter(open, with_year = TRUE,
                                             fiscal_start = 1)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(quarter) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(quarter) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前四半期との差` = ticket - dplyr::lag(ticket))

## # A tibble: 21 x 4
##    quarter ticket  累計 前四半期との差
##      <dbl>  <dbl> <dbl>          <dbl>
##  1   2013.    188   188             NA
##  2   2013.    207   395             19
##  3   2013.    161   556            -46
##  4   2014.    174   730             13
##  5   2014.    136   866            -38
##  6   2014.    136  1002              0
##  7   2014.    131  1133             -5
##  8   2015.    236  1369            105
##  9   2015.    143  1512            -93
## 10   2015.    146  1658              3
## # ... with 11 more rows

x %>% 
  dplyr::filter(tracker == "Defect") %>% 
  dplyr::mutate(quarter = lubridate::quarter(open, with_year = TRUE,
                                             fiscal_start = 1)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(quarter) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(quarter) %>% 
  dplyr::mutate(quarter = as.character(quarter),
                `累計` = cumsum(ticket),
                `前四半期との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = quarter)) + 
    ggplot2::geom_bar(ggplot2::aes(y = ticket), stat = "identity")

# x_name <- x %>% 
#   dplyr::filter(tracker == "Defect") %>% 
#   dplyr::mutate(quarter = lubridate::quarter(open, with_year = TRUE,
#                                              fiscal_start = 1)) %>% 
#   dplyr::mutate(quarter = as.character(quarter)) %>% 
#   dplyr::distinct(quarter) %>% 
#   dplyr::arrange(quarter)

x %>% 
  dplyr::filter(tracker == "Defect") %>% 
  dplyr::mutate(quarter = lubridate::quarter(open, with_year = TRUE,
                                             fiscal_start = 1)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(quarter) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(quarter) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前四半期との差` = ticket - dplyr::lag(ticket))

## # A tibble: 21 x 4
##    quarter ticket  累計 前四半期との差
##      <dbl>  <dbl> <dbl>          <dbl>
##  1   2013.    188   188             NA
##  2   2013.    207   395             19
##  3   2013.    161   556            -46
##  4   2014.    174   730             13
##  5   2014.    136   866            -38
##  6   2014.    136  1002              0
##  7   2014.    131  1133             -5
##  8   2015.    236  1369            105
##  9   2015.    143  1512            -93
## 10   2015.    146  1658              3
## # ... with 11 more rows

年次集計

“年”を求めるにはlubridate::year関数を用います。
　

x %>% 
  dplyr::filter(tracker == "Defect") %>% 
  dplyr::mutate(year = lubridate::year(open)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(year) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(year) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前年との差` = ticket - dplyr::lag(ticket))

## # A tibble: 6 x 4
##    year ticket  累計 前年との差
##   <dbl>  <dbl> <dbl>      <dbl>
## 1  2013    556   556         NA
## 2  2014    577  1133         21
## 3  2015    662  1795         85
## 4  2016    460  2255       -202
## 5  2017    440  2695        -20
## 6  2018    173  2868       -267

gg_bar <- x %>% 
  dplyr::filter(tracker == "Defect") %>% 
  dplyr::mutate(year = lubridate::year(open)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(year) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(year) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前年との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = year)) + 
    ggplot2::geom_bar(ggplot2::aes(y = ticket), stat = "identity")

gg_line <- x %>% 
  dplyr::filter(tracker == "Defect") %>% 
  dplyr::mutate(year = lubridate::year(open)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(year) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(year) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前年との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = year)) + 
    ggplot2::geom_line(ggplot2::aes(y = `累計`))

gg_line_2 <- x %>% 
  dplyr::filter(tracker == "Defect") %>% 
  dplyr::mutate(year = lubridate::year(open)) %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(year) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(year) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前年との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = year)) + 
    ggplot2::geom_line(ggplot2::aes(y = `前年との差`))

layout <- rbind(c(1, 1), c(2, 3))
gridExtra::grid.arrange(gg_bar, gg_line, gg_line_2, layout_matrix = layout)

滞留期間の可視化

2018年のチケットが優先度ごとにどれだけ期間滞留しているかを可視化してみます。分布を把握したい場合はヒストグラム、分布範囲を把握した場合は箱ひげ図が便利です。
　

x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::filter(status != "Closed") %>% 
  dplyr::mutate(days = lubridate::today() - open + 1) %>% 
  dplyr::group_by(priority) %>% 
  dplyr::summarise(min = min(days), med = median(days), max = max(days))

## # A tibble: 4 x 4
##   priority min      med        max     
##   <ord>    <time>   <time>     <time>  
## 1 Low      241 days 274.0 days 307 days
## 2 Normal   158 days 239.5 days 312 days
## 3 High     170 days 241.0 days 297 days
## 4 Urgent   249 days 259.5 days 270 days

gg_bar <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::filter(status != "Closed") %>% 
  dplyr::mutate(days = lubridate::today() - open + 1) %>% 
  ggplot2::ggplot(ggplot2::aes(x = days, fill = priority)) + 
    ggplot2::geom_histogram(alpha = 0.5, position = "identity")

gg_boxplot <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::filter(status != "Closed") %>% 
  dplyr::mutate(days = lubridate::today() - open + 1) %>% 
  ggplot2::ggplot(ggplot2::aes(x = priority, y = days, colour = priority)) + 
    ggplot2::geom_boxplot() + 
    ggplot2::geom_jitter() +
    ggplot2::coord_flip()

gridExtra::grid.arrange(gg_bar, gg_boxplot, nrow = 2)

同様にカテゴリごとの滞留期間を可視化してみます。

x %>% 
  dplyr::filter(open >= "2018-1-1") %>% 
  dplyr::filter(status != "Closed") %>% 
  dplyr::mutate(days = lubridate::today() - open + 1) %>% 
  dplyr::group_by(category) %>% 
  dplyr::summarise(min = min(days), med = median(days), max = max(days),
                   mode = which.max(table(days)))

## # A tibble: 34 x 5
##    category                  min      med      max       mode
##    <chr>                     <time>   <time>   <time>   <int>
##  1 Accounts / authentication 170 days 211 days 270 days     1
##  2 Activity view             243 days 243 days 243 days     1
##  3 Administration            164 days 217 days 270 days     1
##  4 Attachments               229 days 229 days 229 days     1
##  5 Calendar                  201 days 201 days 201 days     1
##  6 Code cleanup/refactoring  163 days 218 days 294 days     1
##  7 Custom fields             164 days 172 days 293 days     2
##  8 Database                  232 days 232 days 232 days     1
##  9 Documentation             160 days 241 days 249 days     1
## 10 Email receiving           171 days 234 days 312 days     1
## # ... with 24 more rows

x %>% 
  dplyr::filter(open >= "2018-1-1") %>% 
  dplyr::filter(status != "Closed") %>% 
  dplyr::mutate(days = lubridate::today() - open + 1) %>% 
  ggplot2::ggplot(ggplot2::aes(x = category, y = days)) + 
    ggplot2::geom_boxplot()

対処期間の可視化

カテゴリごとのチケット対処期間（開始日から終了日までの期間）を可視化してみます。
　

x %>% 
  dplyr::filter(status == "Closed") %>% 
  dplyr::mutate(days = close - open + 1) %>% 
  dplyr::group_by(category) %>% 
  dplyr::summarise(min = min(days), med = median(days), max = max(days),
                   mode = which.max(table(days)))

## # A tibble: 56 x 5
##    category                  min    med         max          mode
##    <chr>                     <time> <time>      <time>      <int>
##  1 Accounts / authentication 1 days " 3.5 days" 1369 days       1
##  2 Activity view             1 days " 6.0 days" 1247 days       1
##  3 Administration            1 days " 2.0 days" " 167 days"     1
##  4 Attachments               1 days 10.0 days   " 814 days"     2
##  5 Calendar                  1 days 51.0 days   " 386 days"     1
##  6 Code cleanup/refactoring  1 days " 8.0 days" " 499 days"     1
##  7 Core Plugins              2 days " 2.0 days" "   6 days"     1
##  8 Custom fields             1 days 11.0 days   1211 days       1
##  9 Database                  1 days " 2.0 days" 1195 days       1
## 10 Documentation             1 days " 2.0 days" " 668 days"     1
## # ... with 46 more rows

x %>% 
  dplyr::filter(status == "Closed") %>% 
  dplyr::mutate(days = close - open + 1) %>% 
  ggplot2::ggplot(ggplot2::aes(x = category, y = days)) + 
    ggplot2::geom_boxplot()

Priority vs Category, Open Tickets

x %>% 
  dplyr::filter(status != "Closed" & tracker == "Defect") %>% 
  dplyr::mutate(year = lubridate::year(open)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = priority, fill = category)) + 
    ggplot2::geom_bar(alpha = 0.5) + 
    ggplot2::facet_wrap(~ year)

Priority vs Category, Closed Tickets

x %>% 
  dplyr::filter(status == "Closed" & tracker == "Defect") %>% 
  dplyr::mutate(year = lubridate::year(close)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = priority, fill = category)) + 
    ggplot2::geom_bar(alpha = 0.5) + 
    ggplot2::facet_wrap(~ year)

Affected version vs Status

比率の可視化に利用する棒グラフも時系列で層別に描くと推移もみえるようになります。例えば、バージョンごとのチケットの完了状況をチケット起票年ごとに分けて表示することで過去バグと最近のバグの発生状況が俯瞰できるようになります。
　

x %>% 
  dplyr::filter(tracker == "Defect") %>% 
  dplyr::mutate(flag = ifelse(status == "Closed", "Closed", "Open")) %>% 
  dplyr::mutate(year = lubridate::year(open)) %>% 
  dplyr::filter(!is.na(affected)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = affected, fill = flag)) + 
    ggplot2::geom_bar(alpha = 0.75) + 
    ggplot2::facet_wrap(~ year)

四半期ごとの完了チケット数

棒グラフを用いると時系列による比率の変化がみえるようになります。
　

x %>% 
  dplyr::filter(tracker == "Defect") %>% 
  dplyr::mutate(quarter = lubridate::quarter(close, with_year = TRUE,
                                             fiscal_start = 1)) %>% 
  dplyr::mutate(flag = ifelse(is.na(close), 0, 1)) %>% 
  dplyr::group_by(quarter, priority) %>% 
  dplyr::summarise(ticket = sum(flag)) %>% 
  dplyr::arrange(quarter) %>% 
  dplyr::filter(!is.na(quarter)) %>% 
  dplyr::mutate(`累計` = cumsum(ticket),
                `前四半期との差` = ticket - dplyr::lag(ticket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = as.character(quarter))) + 
    ggplot2::geom_bar(ggplot2::aes(y = ticket, fill = priority),
                      stat = "identity", alpha = 0.5) + 
    ggplot2::labs(x = "", y = "Number of 'Closed' Defect Ticket",
                  title = "四半期ごとの完了バグチケット数の推移")

週次傾向の可視化

前出の週次集計を可視化してみます。加えて平均起票数を基準として前週との起票数の差を折れ線グラフで表示しています。
　

open <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(week = lubridate::week(open)) %>% 
  dplyr::filter(!is.na(week))

close <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::filter(close >= "2018-1-1") %>% 
  dplyr::mutate(week = lubridate::week(close)) %>% 
  dplyr::filter(!is.na(week))

df_week <- 
  seq(ifelse(min(open$week) <= min(close$week), min(open$week), min(close$week)),
      ifelse(max(open$week) >= max(close$week), max(open$week), max(close$week)),
      by = 1) %>% as.data.frame()
names(df_week) <- c("week")

open <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::mutate(week = lubridate::week(open)) %>% 
  dplyr::filter(!is.na(week))

open <- df_week %>% 
  dplyr::left_join(open, by = "week") %>% 
  dplyr::mutate(flag = ifelse(is.na(open), 0, 1)) %>% 
  dplyr::group_by(week) %>% 
  dplyr::summarise(open = sum(flag)) %>% 
  dplyr::arrange(week) %>% 
  dplyr::mutate(cumopen = cumsum(open), diff = open - dplyr::lag(open))

open %>% 
  dplyr::rename(`週` = week, `チケットオープン数` = open, `累計` = cumopen,
                `前週との差` = diff)

## # A tibble: 23 x 4
##       週 チケットオープン数  累計 前週との差
##    <dbl>              <dbl> <dbl>      <dbl>
##  1     1                 10    10         NA
##  2     2                  8    18         -2
##  3     3                  9    27          1
##  4     4                 11    38          2
##  5     5                 14    52          3
##  6     6                  5    57         -9
##  7     7                 10    67          5
##  8     8                  9    76         -1
##  9     9                  9    85          0
## 10    10                  4    89         -5
## # ... with 13 more rows

open %>% 
  dplyr::mutate(diff_offset = diff + round(mean(open, na.rm = TRUE))) %>% 
  ggplot2::ggplot(ggplot2::aes(x = week)) +
    ggplot2::geom_bar(ggplot2::aes(y = open), stat = "identity", alpha = 0.25) + 
    ggplot2::geom_hline(yintercept = round(mean(open$open, na.rm = TRUE), 1),
                        colour = "#00bfc4", linetype = "dashed") + 
    ggplot2::geom_line(ggplot2::aes(y = diff_offset), colour = "#00bfc4",
                       size = 0.75)

close <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::filter(close >= "2018-1-1") %>% 
  dplyr::mutate(week = lubridate::week(close)) %>% 
  dplyr::filter(!is.na(week))
  
close <- df_week %>% 
  dplyr::left_join(close, by = "week") %>%
  dplyr::mutate(flag = ifelse(is.na(close), 0, 1)) %>% 
  dplyr::group_by(week) %>% 
  dplyr::summarise(close = sum(flag)) %>% 
  dplyr::arrange(week) %>% 
  dplyr::mutate(cumclose = cumsum(close), diff = close -dplyr::lag(close))

close %>% 
  dplyr::rename(`週` = week, `チケットクローズ数` = close, `累計` = cumclose,
                `前週との差` = diff)

## # A tibble: 23 x 4
##       週 チケットクローズ数  累計 前週との差
##    <dbl>              <dbl> <dbl>      <dbl>
##  1     1                  5     5         NA
##  2     2                  3     8         -2
##  3     3                  4    12          1
##  4     4                  7    19          3
##  5     5                  6    25         -1
##  6     6                  2    27         -4
##  7     7                  3    30          1
##  8     8                  8    38          5
##  9     9                  4    42         -4
## 10    10                  2    44         -2
## # ... with 13 more rows

close %>% 
  dplyr::mutate(diff_offset = diff + round(mean(close, na.rm = TRUE)), 1) %>% 
  ggplot2::ggplot(ggplot2::aes(x = week)) +
    ggplot2::geom_bar(ggplot2::aes(y = close), stat = "identity", alpha = 0.25) + 
    ggplot2::geom_hline(yintercept = round(mean(close$close, na.rm = TRUE)),
                        colour = "#f8766d", linetype = "dashed") + 
    ggplot2::geom_line(ggplot2::aes(y = diff_offset), colour = "#f8766d",
                       size = 0.75)

オープン・クローズチャート

前出の週次の集計から累計データに着目したものがオープン・クローズチャートです。オープン・クローズチャートはチケットの対応状況が一目で分かるグラフです。 　

week_ticket <- open %>% 
  dplyr::full_join(close, by = "week") %>% 
  dplyr::select(week, open, close) %>%
  tidyr::gather(key, value, -week)

open %>% 
  dplyr::full_join(close, by = "week") %>% 
  dplyr::select(week, cumopen, cumclose) %>%
  tidyr::gather(key, value, -week) %>% 
  dplyr::left_join(week_ticket, by = "week") %>% 
  ggplot2::ggplot(ggplot2::aes(x = week)) + 
    ggplot2::geom_bar(ggplot2::aes(y = value.y, fill = key.y),
                      stat = "identity", alpha = 0.5, position = "dodge") +
    ggplot2::geom_line(ggplot2::aes(y = value.x, colour = key.x),
                       stat = "identity", size = 0.75) + 
    ggplot2::scale_color_hue(name = "累計",
                             labels = c(cumclose = "Closed", cumopen = "Open")) +
    ggplot2::scale_fill_hue(name = "週次",
                            labels = c(close = "Closed", open = "Open")) +
    ggplot2::labs(y = "Number of Defect Ticket",
                  title = "週次のバグチケット数の推移")

　
個別にチャートを描いた場合に見えたようにクローズ傾向が低調なため処理が完了しないチケットが増えていることが分かります。
このグラフに試験項目数（計画、実績）のバーンダウンチャートを加えれば週次管理表になります。
　

日次のオープン・クローズチャート

日次の場合は試験稼働が発生していない日はチケット起票がゼロとは言えず、無稼働日を除かないとチャートが横に寝てしまい正しい傾向が掴めませんので注意してください。
　

open <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::filter(!is.na(open))

close <- x %>% 
  dplyr::filter(open >= "2018-1-1" & tracker == "Defect") %>% 
  dplyr::filter(close >= "2018-1-1") %>% 
  dplyr::filter(!is.na(close))

start <- ifelse(range(open$open)[1] <= range(close$close)[1],
                range(open$open)[1], range(close$close)[1]) %>%
  lubridate::as_date()
                
end <- ifelse(range(open$open)[2] <= range(close$close)[2],
              range(open$open)[2], range(close$close)[2]) %>%
  lubridate::as_date()

df_days <- 
  seq(start, end, by = 1) %>% as.data.frame()
names(df_days) <- c("days")

open_ticket <- df_days %>% 
  dplyr::left_join(open, by = c("days" = "open")) %>% 
  dplyr::mutate(flag = ifelse(is.na(no), 0, 1)) %>% 
  dplyr::group_by(days) %>% 
  dplyr::summarise(open = sum(flag)) %>% 
  dplyr::arrange(days) %>% 
  dplyr::mutate(cumopen = cumsum(open))

close_ticket <- df_days %>% 
  dplyr::left_join(close, by = c("days" = "close")) %>%
  dplyr::mutate(flag = ifelse(is.na(no), 0, 1)) %>% 
  dplyr::group_by(days) %>% 
  dplyr::summarise(close = sum(flag)) %>% 
  dplyr::arrange(days) %>% 
  dplyr::mutate(cumclose = cumsum(close))

bar_data <- open_ticket %>% 
  dplyr::left_join(close_ticket, by = "days") %>% 
  dplyr::select(days, open, close) %>% 
  tidyr::gather(key, value , -days)

line_data <- open_ticket %>% 
  dplyr::left_join(close_ticket, by = "days") %>% 
  dplyr::select(days, cumopen, cumclose) %>% 
  tidyr::gather(key, value , -days)

bar_data %>% 
  dplyr::left_join(line_data, by = "days") %>% 
  ggplot2::ggplot(ggplot2::aes(x = days)) + 
    ggplot2::geom_bar(ggplot2::aes(y = value.x, fill = key.x),
                      stat = "identity", alpha = 0.5, position = "dodge") +
    ggplot2::geom_line(ggplot2::aes(y = value.y, colour = key.y),
                       stat = "identity", size = 0.75) + 
    ggplot2::scale_color_hue(name = "累計",
                             labels = c(cumclose = "Closed", cumopen = "Open")) +
    ggplot2::scale_fill_hue(name = "日次",
                            labels = c(close = "Closed", open = "Open")) +
    ggplot2::labs(y = "Number of Defect Ticket",
                  title = "日次のバグチケット数の推移")

ヒートマップ

推移を見るには前述のように折れ線グラフや棒グラフを利用することが多いですが、対象期間が長くなるとグラフが見難くなる場合があります。そんな時に便利なグラフがヒートマップです。数値の大小を色で表で表しますので、特にピークやボトムの推移を把握するのに向いています。 　

x %>% 
  dplyr::filter(tracker == "Defect") %>% 
  dplyr::select(date = open) %>% 
  dplyr::count(date) %>% 
# ヒートマップに項目に応じて年、月、週、日、曜日などを求める
  dplyr::mutate(year = lubridate::year(date), month = lubridate::month(date),
                week = lubridate::epiweek(date), day = lubridate::day(date),
                wday = lubridate::wday(date, label = TRUE, week_start = 7),
                tweek = lubridate::epiweek(lubridate::floor_date(date, "month"))) %>%
# 特有の処理
  dplyr::mutate(offset = ifelse(tweek == 53, 54, 1)) %>%
  dplyr::mutate(offset = ifelse(tweek == 52, 53, offset)) %>%
  dplyr::mutate(offset = ifelse(tweek > week, tweek - week + 1, offset)) %>%
# 例外処理
  dplyr::mutate(offset = ifelse(tweek == week, 1, offset)) %>%
# 月内の週数を計算する
  dplyr::mutate(mweek = week - tweek + offset) %>%
  # dplyr::filter(year == 2016) %>%
  # dplyr::mutate(mweek = mweek(date)) %>%      # ベクトル対応できていない
  # print()
  ggplot2::ggplot(ggplot2::aes(x = wday, y = mweek, fill = n)) + 
    ggplot2::facet_grid(year ~ month) + 
    ggplot2::geom_tile() + 
    ggplot2::scale_fill_gradient(low = "lightyellow", high = "red") + 
    ggplot2::labs(x = "", y = "") + 
    ggplot2::theme_bw()

週次ヒートマップ

各月の第何曜日にピークがあるかを見る場合には月内週数計算に注意が必要です。月内週数を計算する関数がないため、週番号から月初日の週番号を差し引いて月内週数を求める必要があります。しかし、週番号は様々な定義があるため定義を理解していないと意図した結果にならない場合があります。
　

　
特にカレンダーをイメージした月内週数を得たい場合にはlubridate::isoweek関数を使用してください。ただし、 ISO 8601 の定義を理解しておく必要があります。
　
特に以下の点に注意してください。

• 週の始まりは月曜日
• 1月の最初の週は第52週や第53週になる場合がある（例：2016年1月）
• 12月の最終週は第1週になる場合がある（例：2013年12月30日）

　
月内週数は以下の計算で求められます。

\[月内週数を求めたい日（指定日）の週番号 - 月初日の週番号 + 1\]

R で計算する場合はlubridateパッケージを用います。ISO 8601で計算しますので、上記の注意事項を考慮して以下のようなコードになります。xは月内週数を計算したい日（指定日）です。

lubridate::isoweek(x) - lubridate::isoweek(lubridate::floor_date(x), "month") + offset

offsetは以下のように条件により値が異なる点に注意してください。
　

x %>% 
  dplyr::filter(tracker == "Defect") %>% 
  dplyr::select(date = open) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
# ヒートマップに項目に応じて年、月、週、日、曜日などを求める
  dplyr::mutate(year = lubridate::year(date), month = lubridate::month(date),
                week = lubridate::isoweek(date), day = lubridate::day(date),
                wday = lubridate::wday(date, label = TRUE, week_start = 1),
                tweek = lubridate::isoweek(lubridate::floor_date(date, "month"))) %>% 
# ISO 8601に特有の処理
  dplyr::mutate(offset = ifelse(tweek == 53, 54, 1)) %>% 
  dplyr::mutate(offset = ifelse(tweek == 52, 53, offset)) %>% 
  dplyr::mutate(offset = ifelse(tweek > week, tweek - week + 1, offset)) %>%
# 例外処理
  dplyr::mutate(offset = ifelse(tweek == week, 1, offset)) %>%
# 月内の週数を計算する
  dplyr::mutate(mweek = week - tweek + offset) %>%
# 縦横軸でクロス集計する
  dplyr::count(mweek, wday) %>% 
  ggplot2::ggplot(ggplot2::aes(x = mweek, y = wday, fill = n)) + 
    ggplot2::geom_tile() + 
    ggplot2::scale_fill_gradient(low = "lightyellow", high = "red") +
    ggplot2::labs(x = "", y = "曜日") +
    ggplot2::theme_bw() + ggplot2::scale_x_reverse() + ggplot2::coord_flip() +
    ggplot2::geom_text(ggplot2::aes(label = n))