使用 calendR（或其他包？）标记日期向量
Mark vector of dates using calendR (or other package?)
我有一个日期向量，其中包含每个日期的特定访问量和特定的访问类型。
数据（'ficol'列可以忽略）：
structure(c("2021-06-15", "15", "60", "T0s", "2021-06-16", "15",
"60", "T0s", "2021-06-17", " 8", "32", "T0s", "2021-06-21", "15",
"60", "T0s", "2021-06-22", "15", "60", "T0s", "2021-06-23", "15",
"60", "T0s", "2021-06-24", "15", "60", "T0s", "2021-06-28", "15",
"60", "T0s", "2021-06-29", "15", "60", "T0s", "2021-06-30", "15",
"60", "T0s", "2021-07-01", "15", "60", "T0s", "2021-07-05", "15",
"60", "T0s", "2021-07-06", "15", "60", "T0s", "2021-07-07", "15",
"60", "T0s", "2021-07-08", "15", "60", "T0s", "2021-07-12", "15",
"60", "T0s", "2021-07-13", "15", "60", "T0s", "2021-07-14", "15",
"60", "T0s", "2021-07-15", "15", "60", "T0s", "2021-07-19", "15",
"60", "T0s", "2021-07-20", "15", "60", "T0s", "2021-07-21", "15",
"60", "T0s", "2021-07-22", "15", "60", "T0s", "2021-07-26", "15",
"60", "T0s", "2021-07-27", "15", "60", "T0s", "2021-07-28", "15",
"60", "T0s", "2021-07-29", "15", "60", "T0s", "2021-06-30", "30",
"60", "T1s", "2021-07-01", " 8", "16", "T1s", "2021-07-05", "26",
"52", "T1s", "2021-07-06", "30", "60", "T1s", "2021-07-07", "30",
"60", "T1s", "2021-07-08", " 4", " 8", "T1s", "2021-07-12", " 4",
" 8", "T1s", "2021-07-13", "29", "58", "T1s", "2021-07-14", "27",
"54", "T1s", "2021-07-20", "30", "60", "T1s", "2021-07-21", "30",
"60", "T1s", "2021-07-26", "30", "60", "T1s", "2021-07-27", "30",
"60", "T1s", "2021-07-28", "30", "60", "T1s", "2021-08-02", "30",
"60", "T1s", "2021-08-03", " 8", "16", "T1s", "2021-08-23", "12",
"60", "T3s", "2021-08-24", "12", "60", "T3s", "2021-08-25", "12",
"60", "T3s", "2021-08-26", " 2", "10", "T3s", "2021-08-30", "12",
"60", "T3s", "2021-08-31", "12", "60", "T3s", "2021-09-01", "12",
"60", "T3s", "2021-09-06", "12", "60", "T3s", "2021-09-07", "12",
"60", "T3s", "2021-09-08", "12", "60", "T3s", "2021-09-13", "12",
"60", "T3s", "2021-09-14", "12", "60", "T3s", "2021-09-15", "12",
"60", "T3s", "2021-09-16", "12", "60", "T3s", "2021-09-20", "12",
"60", "T3s", "2021-09-21", "12", "60", "T3s", "2021-09-22", "12",
"60", "T3s", "2021-09-23", "12", "60", "T3s", "2021-09-27", "12",
"60", "T3s", "2022-01-10", "15", "60", "T5s", "2022-01-11", "15",
"60", "T5s", "2022-01-12", " 8", "32", "T5s", "2022-01-17", "15",
"60", "T5s", "2022-01-18", "15", "60", "T5s", "2022-01-19", " 6",
"24", "T5s", "2022-01-24", "15", "60", "T5s", "2022-01-25", "15",
"60", "T5s", "2022-01-26", " 6", "24", "T5s", "2022-01-31", "15",
"60", "T5s", "2022-02-01", "15", "60", "T5s", "2022-02-02", " 6",
"24", "T5s", "2022-02-03", "12", "48", "T5s", "2022-02-07", "15",
"60", "T5s", "2022-02-08", "15", "60", "T5s", "2022-02-09", " 6",
"24", "T5s", "2022-02-10", "15", "60", "T5s", "2022-02-14", " 9",
"36", "T5s"), .Dim = c(4L, 80L), .Dimnames = list(c("Var1", "Freq",
"ficol", "visit"), NULL))
我想要的是有一个带有日期（数据中的 Var1）的日历，每个访问类型都用颜色标记。例如，访问T0s日期为绿色，T1s日期为紫色，T3s日期为蓝色等。
我找到了 calendR 包，但整整一年都弄不明白，只能每月使用此处找到的示例：。 https://r-coder.com/calendar-plot-r/
谁能帮帮我？非常感谢！
library(tidyverse)
library(lubridate)
library(assertr)
library(calendR)
df <- structure(c("2021-06-15", "15", "60", "T0s", "2021-06-16", "15",
"60", "T0s", "2021-06-17", " 8", "32", "T0s", "2021-06-21", "15",
"60", "T0s", "2021-06-22", "15", "60", "T0s", "2021-06-23", "15",
"60", "T0s", "2021-06-24", "15", "60", "T0s", "2021-06-28", "15",
"60", "T0s", "2021-06-29", "15", "60", "T0s", "2021-06-30", "15",
"60", "T0s", "2021-07-01", "15", "60", "T0s", "2021-07-05", "15",
"60", "T0s", "2021-07-06", "15", "60", "T0s", "2021-07-07", "15",
"60", "T0s", "2021-07-08", "15", "60", "T0s", "2021-07-12", "15",
"60", "T0s", "2021-07-13", "15", "60", "T0s", "2021-07-14", "15",
"60", "T0s", "2021-07-15", "15", "60", "T0s", "2021-07-19", "15",
"60", "T0s", "2021-07-20", "15", "60", "T0s", "2021-07-21", "15",
"60", "T0s", "2021-07-22", "15", "60", "T0s", "2021-07-26", "15",
"60", "T0s", "2021-07-27", "15", "60", "T0s", "2021-07-28", "15",
"60", "T0s", "2021-07-29", "15", "60", "T0s", "2021-06-30", "30",
"60", "T1s", "2021-07-01", " 8", "16", "T1s", "2021-07-05", "26",
"52", "T1s", "2021-07-06", "30", "60", "T1s", "2021-07-07", "30",
"60", "T1s", "2021-07-08", " 4", " 8", "T1s", "2021-07-12", " 4",
" 8", "T1s", "2021-07-13", "29", "58", "T1s", "2021-07-14", "27",
"54", "T1s", "2021-07-20", "30", "60", "T1s", "2021-07-21", "30",
"60", "T1s", "2021-07-26", "30", "60", "T1s", "2021-07-27", "30",
"60", "T1s", "2021-07-28", "30", "60", "T1s", "2021-08-02", "30",
"60", "T1s", "2021-08-03", " 8", "16", "T1s", "2021-08-23", "12",
"60", "T3s", "2021-08-24", "12", "60", "T3s", "2021-08-25", "12",
"60", "T3s", "2021-08-26", " 2", "10", "T3s", "2021-08-30", "12",
"60", "T3s", "2021-08-31", "12", "60", "T3s", "2021-09-01", "12",
"60", "T3s", "2021-09-06", "12", "60", "T3s", "2021-09-07", "12",
"60", "T3s", "2021-09-08", "12", "60", "T3s", "2021-09-13", "12",
"60", "T3s", "2021-09-14", "12", "60", "T3s", "2021-09-15", "12",
"60", "T3s", "2021-09-16", "12", "60", "T3s", "2021-09-20", "12",
"60", "T3s", "2021-09-21", "12", "60", "T3s", "2021-09-22", "12",
"60", "T3s", "2021-09-23", "12", "60", "T3s", "2021-09-27", "12",
"60", "T3s", "2022-01-10", "15", "60", "T5s", "2022-01-11", "15",
"60", "T5s", "2022-01-12", " 8", "32", "T5s", "2022-01-17", "15",
"60", "T5s", "2022-01-18", "15", "60", "T5s", "2022-01-19", " 6",
"24", "T5s", "2022-01-24", "15", "60", "T5s", "2022-01-25", "15",
"60", "T5s", "2022-01-26", " 6", "24", "T5s", "2022-01-31", "15",
"60", "T5s", "2022-02-01", "15", "60", "T5s", "2022-02-02", " 6",
"24", "T5s", "2022-02-03", "12", "48", "T5s", "2022-02-07", "15",
"60", "T5s", "2022-02-08", "15", "60", "T5s", "2022-02-09", " 6",
"24", "T5s", "2022-02-10", "15", "60", "T5s", "2022-02-14", " 9",
"36", "T5s"), .Dim = c(4L, 80L), .Dimnames = list(c("Var1", "Freq",
"ficol", "visit"), NULL))
df1 <- df %>%
t() %>%
as_tibble() %>%
mutate(
Var1 = ymd(Var1),
Freq = as.integer(Freq),
ficol = as.integer(ficol)) %>%
rename(date = Var1) %>%
arrange(date)
df2 <- df1 %>%
filter(year(date) == 2021) # choose only 1 year
df2
#> # A tibble: 62 x 4
#> date Freq ficol visit
#> <date> <int> <int> <chr>
#> 1 2021-06-15 15 60 T0s
#> 2 2021-06-16 15 60 T0s
#> 3 2021-06-17 8 32 T0s
#> 4 2021-06-21 15 60 T0s
#> 5 2021-06-22 15 60 T0s
#> 6 2021-06-23 15 60 T0s
#> 7 2021-06-24 15 60 T0s
#> 8 2021-06-28 15 60 T0s
#> 9 2021-06-29 15 60 T0s
#> 10 2021-06-30 15 60 T0s
#> # ... with 52 more rows
# some days have many types of visits, so it's necessary to group them
df3 <- df2 %>%
group_by(date) %>%
summarise(visits = str_c(sort(visit), collapse = ", "),
.groups = "drop")
df3
#> # A tibble: 48 x 2
#> date visits
#> <date> <chr>
#> 1 2021-06-15 T0s
#> 2 2021-06-16 T0s
#> 3 2021-06-17 T0s
#> 4 2021-06-21 T0s
#> 5 2021-06-22 T0s
#> 6 2021-06-23 T0s
#> 7 2021-06-24 T0s
#> 8 2021-06-28 T0s
#> 9 2021-06-29 T0s
#> 10 2021-06-30 T0s, T1s
#> # ... with 38 more rows
df3 %>%
count(visits)
#> # A tibble: 4 x 2
#> visits n
#> <chr> <int>
#> 1 T0s 13
#> 2 T0s, T1s 14
#> 3 T1s 2
#> 4 T3s 19
df4 <- df3 %>%
mutate(color = case_when(
visits == "T0s" ~ "red",
visits == "T0s, T1s" ~ "orange",
visits == "T1s" ~ "yellow",
visits == "T3s" ~ "green"
)) %>%
assertr::verify(!is.na(color)) %>%
full_join(
tibble(date = seq(as.Date("2021-01-01"), as.Date("2022-01-01") - 1, by = "days")),
by = "date"
) %>%
mutate(yday = lubridate::yday(date)) %>%
arrange(date)
df4 %>%
filter(!is.na(visits))
#> # A tibble: 48 x 4
#> date visits color yday
#> <date> <chr> <chr> <dbl>
#> 1 2021-06-15 T0s red 166
#> 2 2021-06-16 T0s red 167
#> 3 2021-06-17 T0s red 168
#> 4 2021-06-21 T0s red 172
#> 5 2021-06-22 T0s red 173
#> 6 2021-06-23 T0s red 174
#> 7 2021-06-24 T0s red 175
#> 8 2021-06-28 T0s red 179
#> 9 2021-06-29 T0s red 180
#> 10 2021-06-30 T0s, T1s orange 181
#> # ... with 38 more rows
calendR::calendR(year = 2021,
start = "M",
special.days = df4$visits,
special.col = unique(na.omit(df4$color)),
legend.pos = "right")
我有一个日期向量，其中包含每个日期的特定访问量和特定的访问类型。
数据（'ficol'列可以忽略）：
structure(c("2021-06-15", "15", "60", "T0s", "2021-06-16", "15",
"60", "T0s", "2021-06-17", " 8", "32", "T0s", "2021-06-21", "15",
"60", "T0s", "2021-06-22", "15", "60", "T0s", "2021-06-23", "15",
"60", "T0s", "2021-06-24", "15", "60", "T0s", "2021-06-28", "15",
"60", "T0s", "2021-06-29", "15", "60", "T0s", "2021-06-30", "15",
"60", "T0s", "2021-07-01", "15", "60", "T0s", "2021-07-05", "15",
"60", "T0s", "2021-07-06", "15", "60", "T0s", "2021-07-07", "15",
"60", "T0s", "2021-07-08", "15", "60", "T0s", "2021-07-12", "15",
"60", "T0s", "2021-07-13", "15", "60", "T0s", "2021-07-14", "15",
"60", "T0s", "2021-07-15", "15", "60", "T0s", "2021-07-19", "15",
"60", "T0s", "2021-07-20", "15", "60", "T0s", "2021-07-21", "15",
"60", "T0s", "2021-07-22", "15", "60", "T0s", "2021-07-26", "15",
"60", "T0s", "2021-07-27", "15", "60", "T0s", "2021-07-28", "15",
"60", "T0s", "2021-07-29", "15", "60", "T0s", "2021-06-30", "30",
"60", "T1s", "2021-07-01", " 8", "16", "T1s", "2021-07-05", "26",
"52", "T1s", "2021-07-06", "30", "60", "T1s", "2021-07-07", "30",
"60", "T1s", "2021-07-08", " 4", " 8", "T1s", "2021-07-12", " 4",
" 8", "T1s", "2021-07-13", "29", "58", "T1s", "2021-07-14", "27",
"54", "T1s", "2021-07-20", "30", "60", "T1s", "2021-07-21", "30",
"60", "T1s", "2021-07-26", "30", "60", "T1s", "2021-07-27", "30",
"60", "T1s", "2021-07-28", "30", "60", "T1s", "2021-08-02", "30",
"60", "T1s", "2021-08-03", " 8", "16", "T1s", "2021-08-23", "12",
"60", "T3s", "2021-08-24", "12", "60", "T3s", "2021-08-25", "12",
"60", "T3s", "2021-08-26", " 2", "10", "T3s", "2021-08-30", "12",
"60", "T3s", "2021-08-31", "12", "60", "T3s", "2021-09-01", "12",
"60", "T3s", "2021-09-06", "12", "60", "T3s", "2021-09-07", "12",
"60", "T3s", "2021-09-08", "12", "60", "T3s", "2021-09-13", "12",
"60", "T3s", "2021-09-14", "12", "60", "T3s", "2021-09-15", "12",
"60", "T3s", "2021-09-16", "12", "60", "T3s", "2021-09-20", "12",
"60", "T3s", "2021-09-21", "12", "60", "T3s", "2021-09-22", "12",
"60", "T3s", "2021-09-23", "12", "60", "T3s", "2021-09-27", "12",
"60", "T3s", "2022-01-10", "15", "60", "T5s", "2022-01-11", "15",
"60", "T5s", "2022-01-12", " 8", "32", "T5s", "2022-01-17", "15",
"60", "T5s", "2022-01-18", "15", "60", "T5s", "2022-01-19", " 6",
"24", "T5s", "2022-01-24", "15", "60", "T5s", "2022-01-25", "15",
"60", "T5s", "2022-01-26", " 6", "24", "T5s", "2022-01-31", "15",
"60", "T5s", "2022-02-01", "15", "60", "T5s", "2022-02-02", " 6",
"24", "T5s", "2022-02-03", "12", "48", "T5s", "2022-02-07", "15",
"60", "T5s", "2022-02-08", "15", "60", "T5s", "2022-02-09", " 6",
"24", "T5s", "2022-02-10", "15", "60", "T5s", "2022-02-14", " 9",
"36", "T5s"), .Dim = c(4L, 80L), .Dimnames = list(c("Var1", "Freq",
"ficol", "visit"), NULL))
我想要的是有一个带有日期（数据中的 Var1）的日历，每个访问类型都用颜色标记。例如，访问T0s日期为绿色，T1s日期为紫色，T3s日期为蓝色等。
我找到了 calendR 包，但整整一年都弄不明白，只能每月使用此处找到的示例：。 https://r-coder.com/calendar-plot-r/
谁能帮帮我？非常感谢！
library(tidyverse)
library(lubridate)
library(assertr)
library(calendR)
df <- structure(c("2021-06-15", "15", "60", "T0s", "2021-06-16", "15",
"60", "T0s", "2021-06-17", " 8", "32", "T0s", "2021-06-21", "15",
"60", "T0s", "2021-06-22", "15", "60", "T0s", "2021-06-23", "15",
"60", "T0s", "2021-06-24", "15", "60", "T0s", "2021-06-28", "15",
"60", "T0s", "2021-06-29", "15", "60", "T0s", "2021-06-30", "15",
"60", "T0s", "2021-07-01", "15", "60", "T0s", "2021-07-05", "15",
"60", "T0s", "2021-07-06", "15", "60", "T0s", "2021-07-07", "15",
"60", "T0s", "2021-07-08", "15", "60", "T0s", "2021-07-12", "15",
"60", "T0s", "2021-07-13", "15", "60", "T0s", "2021-07-14", "15",
"60", "T0s", "2021-07-15", "15", "60", "T0s", "2021-07-19", "15",
"60", "T0s", "2021-07-20", "15", "60", "T0s", "2021-07-21", "15",
"60", "T0s", "2021-07-22", "15", "60", "T0s", "2021-07-26", "15",
"60", "T0s", "2021-07-27", "15", "60", "T0s", "2021-07-28", "15",
"60", "T0s", "2021-07-29", "15", "60", "T0s", "2021-06-30", "30",
"60", "T1s", "2021-07-01", " 8", "16", "T1s", "2021-07-05", "26",
"52", "T1s", "2021-07-06", "30", "60", "T1s", "2021-07-07", "30",
"60", "T1s", "2021-07-08", " 4", " 8", "T1s", "2021-07-12", " 4",
" 8", "T1s", "2021-07-13", "29", "58", "T1s", "2021-07-14", "27",
"54", "T1s", "2021-07-20", "30", "60", "T1s", "2021-07-21", "30",
"60", "T1s", "2021-07-26", "30", "60", "T1s", "2021-07-27", "30",
"60", "T1s", "2021-07-28", "30", "60", "T1s", "2021-08-02", "30",
"60", "T1s", "2021-08-03", " 8", "16", "T1s", "2021-08-23", "12",
"60", "T3s", "2021-08-24", "12", "60", "T3s", "2021-08-25", "12",
"60", "T3s", "2021-08-26", " 2", "10", "T3s", "2021-08-30", "12",
"60", "T3s", "2021-08-31", "12", "60", "T3s", "2021-09-01", "12",
"60", "T3s", "2021-09-06", "12", "60", "T3s", "2021-09-07", "12",
"60", "T3s", "2021-09-08", "12", "60", "T3s", "2021-09-13", "12",
"60", "T3s", "2021-09-14", "12", "60", "T3s", "2021-09-15", "12",
"60", "T3s", "2021-09-16", "12", "60", "T3s", "2021-09-20", "12",
"60", "T3s", "2021-09-21", "12", "60", "T3s", "2021-09-22", "12",
"60", "T3s", "2021-09-23", "12", "60", "T3s", "2021-09-27", "12",
"60", "T3s", "2022-01-10", "15", "60", "T5s", "2022-01-11", "15",
"60", "T5s", "2022-01-12", " 8", "32", "T5s", "2022-01-17", "15",
"60", "T5s", "2022-01-18", "15", "60", "T5s", "2022-01-19", " 6",
"24", "T5s", "2022-01-24", "15", "60", "T5s", "2022-01-25", "15",
"60", "T5s", "2022-01-26", " 6", "24", "T5s", "2022-01-31", "15",
"60", "T5s", "2022-02-01", "15", "60", "T5s", "2022-02-02", " 6",
"24", "T5s", "2022-02-03", "12", "48", "T5s", "2022-02-07", "15",
"60", "T5s", "2022-02-08", "15", "60", "T5s", "2022-02-09", " 6",
"24", "T5s", "2022-02-10", "15", "60", "T5s", "2022-02-14", " 9",
"36", "T5s"), .Dim = c(4L, 80L), .Dimnames = list(c("Var1", "Freq",
"ficol", "visit"), NULL))
df1 <- df %>%
t() %>%
as_tibble() %>%
mutate(
Var1 = ymd(Var1),
Freq = as.integer(Freq),
ficol = as.integer(ficol)) %>%
rename(date = Var1) %>%
arrange(date)
df2 <- df1 %>%
filter(year(date) == 2021) # choose only 1 year
df2
#> # A tibble: 62 x 4
#> date Freq ficol visit
#> <date> <int> <int> <chr>
#> 1 2021-06-15 15 60 T0s
#> 2 2021-06-16 15 60 T0s
#> 3 2021-06-17 8 32 T0s
#> 4 2021-06-21 15 60 T0s
#> 5 2021-06-22 15 60 T0s
#> 6 2021-06-23 15 60 T0s
#> 7 2021-06-24 15 60 T0s
#> 8 2021-06-28 15 60 T0s
#> 9 2021-06-29 15 60 T0s
#> 10 2021-06-30 15 60 T0s
#> # ... with 52 more rows
# some days have many types of visits, so it's necessary to group them
df3 <- df2 %>%
group_by(date) %>%
summarise(visits = str_c(sort(visit), collapse = ", "),
.groups = "drop")
df3
#> # A tibble: 48 x 2
#> date visits
#> <date> <chr>
#> 1 2021-06-15 T0s
#> 2 2021-06-16 T0s
#> 3 2021-06-17 T0s
#> 4 2021-06-21 T0s
#> 5 2021-06-22 T0s
#> 6 2021-06-23 T0s
#> 7 2021-06-24 T0s
#> 8 2021-06-28 T0s
#> 9 2021-06-29 T0s
#> 10 2021-06-30 T0s, T1s
#> # ... with 38 more rows
df3 %>%
count(visits)
#> # A tibble: 4 x 2
#> visits n
#> <chr> <int>
#> 1 T0s 13
#> 2 T0s, T1s 14
#> 3 T1s 2
#> 4 T3s 19
df4 <- df3 %>%
mutate(color = case_when(
visits == "T0s" ~ "red",
visits == "T0s, T1s" ~ "orange",
visits == "T1s" ~ "yellow",
visits == "T3s" ~ "green"
)) %>%
assertr::verify(!is.na(color)) %>%
full_join(
tibble(date = seq(as.Date("2021-01-01"), as.Date("2022-01-01") - 1, by = "days")),
by = "date"
) %>%
mutate(yday = lubridate::yday(date)) %>%
arrange(date)
df4 %>%
filter(!is.na(visits))
#> # A tibble: 48 x 4
#> date visits color yday
#> <date> <chr> <chr> <dbl>
#> 1 2021-06-15 T0s red 166
#> 2 2021-06-16 T0s red 167
#> 3 2021-06-17 T0s red 168
#> 4 2021-06-21 T0s red 172
#> 5 2021-06-22 T0s red 173
#> 6 2021-06-23 T0s red 174
#> 7 2021-06-24 T0s red 175
#> 8 2021-06-28 T0s red 179
#> 9 2021-06-29 T0s red 180
#> 10 2021-06-30 T0s, T1s orange 181
#> # ... with 38 more rows
calendR::calendR(year = 2021,
start = "M",
special.days = df4$visits,
special.col = unique(na.omit(df4$color)),
legend.pos = "right")