calendR | Ready to print calendars with ggplot2 | Data Visualization library

by R-CoderDotCom R Version: v1.1 License: MIT

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kandi X-RAY | calendR Summary

calendR is a R library typically used in Analytics, Data Visualization, D3 applications. calendR has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Ready to print monthly and yearly calendars made with ggplot2. The calendars will be created by default in the system locale. Change it with Sys.setlocale(locale = "the_preferred_language"). Check the full calendR package tutorial.

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Support

calendR has a low active ecosystem.

It has 182 star(s) with 19 fork(s). There are 10 watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 10 have been closed. On average issues are closed in 10 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of calendR is v1.1

Quality

calendR has 0 bugs and 0 code smells.

Security

calendR has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

calendR code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

calendR is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

calendR releases are available to install and integrate.

Installation instructions are not available. Examples and code snippets are available.

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calendR Key Features

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calendR Examples and Code Snippets

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Community Discussions

Trending Discussions on calendR

Installing R packages on a GCE hosted google colab Jupyter notebook

Mark vector of dates using calendR (or other package?)

QUESTION

Installing R packages on a GCE hosted google colab Jupyter notebook

Asked 2021-Nov-12 at 15:51

I am trying to install the calendR package on colab.

I am using the following:

...

ANSWER

Answered 2021-Nov-12 at 15:51

Default kernel

Google colab uses Python 3 Google Compute Engine backend based on docker container with ubuntu 18.04 by default. It is designed for python and has the ipython kernel. However, There is R installed as well. To install calendR, create and run a new cell with this content:

Source https://stackoverflow.com/questions/69943647

QUESTION

Mark vector of dates using calendR (or other package?)

Asked 2021-Apr-13 at 13:15

I have a vector of dates with a certain amount of visits per date and the specific type of visit.

Data (the 'ficol' column can be ignored):

...

ANSWER

Answered 2021-Apr-13 at 13:15

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
#>           
#>  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  
#>            
#>  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
#>       
#> 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
#>               
#>  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")

Source https://stackoverflow.com/questions/67075027

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install calendR

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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