keithmcnulty

library(tidyverse)
library(readr)
library(igraph)

friends_network <- function(season = NULL, edgelist = NULL, type = c("mds", "sphere")) {
  
  friends <- c("Phoebe", "Monica", "Rachel", "Joey", "Ross", "Chandler")

  edgelist_matrix <- as.matrix(edgelist[ ,c("from", "to")])
  

keithmcnulty

library(tidyverse)
library(jpeg)
source("network_analysis/friends_network.R")

# get friends full series edgelist

edgefile_url <- "https://github.com/keithmcnulty/friends_analysis/blob/master/data/friends_edgelist_by_season.RDS?raw=true"
download.file(edgefile_url, "edgelist.RDS")

edgelist <- readRDS("edgelist.RDS")

keithmcnulty

library(igraph)
library(tidyverse)
library(networkD3)
library(jsonlite)

graph_json <- function(season = NULL, edgelist = NULL) {
  
  edgelist_matrix <- as.matrix(edgelist[ ,c("from", "to")])
  
  # create igraph network

keithmcnulty

storms_sum <- storms %>% 
  dplyr::filter(year %in% 1975:1977) %>% 
  dplyr::group_by(year, status) %>% 
  dplyr::summarise(mean = mean(pressure, na.rm = TRUE),
                   median = median(pressure, na.rm = TRUE))

storms_sum

#> # A tibble: 9 x 4
#> # Groups:   year [3]

keithmcnulty

storms_sum %>% 
  tidyr::pivot_wider(names_from = "year", values_from = c("mean", "median"))
  
#> # A tibble: 3 x 7
#>   status       mean_1975 mean_1976 mean_1977 median_1975 median_1976 median_1977
#>   <chr>            <dbl>     <dbl>     <dbl>       <dbl>       <dbl>       <dbl>
#> 1 hurricane         977.      975.      978.        984         975          987
#> 2 tropical de…     1011.     1007.     1011.       1012.       1006.        1010
#> 3 tropical st…      992.      995.     1002.        993         994.        1001

keithmcnulty

wide_storms <- storms_sum %>% 
  tidyr::pivot_wider(names_from = "year", values_from = c("mean", "median"), 
                     names_glue = "{.value}_of_{year}")

wide_storms

#> # A tibble: 3 x 7
#>   status mean_of_1975 mean_of_1976 mean_of_1977 median_of_1975 median_of_1976
#>   <chr>         <dbl>        <dbl>        <dbl>          <dbl>          <dbl>
#> 1 hurri…         977.         975.         978.           984            975 

keithmcnulty

wide_storms %>% 
  tidyr::pivot_longer(-status, names_to = c("stat", "year"), 
                      names_pattern = "(.*)_of_(.*)", 
                      values_to = "value")
                      
#> # A tibble: 18 x 4
#>    status              stat   year  value
#>    <chr>               <chr>  <chr> <dbl>
#>  1 hurricane           mean   1975   977.
#>  2 hurricane           mean   1976   975.

keithmcnulty

mtcars %>% 
  dplyr::group_by(cyl) %>% 
  dplyr::summarise_at(vars(starts_with("d")), 
                      list(mean = mean, median = median)) 
  
#> # A tibble: 3 x 5
#>     cyl disp_mean drat_mean disp_median drat_median
#>   <dbl>     <dbl>     <dbl>       <dbl>       <dbl>
#> 1     4      105.      4.07        108         4.08
#> 2     6      183.      3.59        168.        3.9 

keithmcnulty

mtcars %>% 
  dplyr::group_by(cyl) %>% 
  dplyr::summarise(across(starts_with("d"), 
                          list(mean = mean, median = median), 
                          .names = "{fn}_of_{col}"))
                          
#> # A tibble: 3 x 5
#>     cyl mean_of_disp median_of_disp mean_of_drat median_of_drat
#>   <dbl>        <dbl>          <dbl>        <dbl>          <dbl>
#> 1     4         105.           108          4.07           4.08

keithmcnulty

mtcars %>% 
  nest_by(cyl)
  
#> # A tibble: 3 x 2
#> # Rowwise:  cyl
#>     cyl                data
#>   <dbl> <list<tbl_df[,10]>>
#> 1     4           [11 × 10]
#> 2     6            [7 × 10]
#> 3     8           [14 × 10]