PARG Fall Social 2022

analysis

Author

Haky Im

Published

October 20, 2022

Code

suppressMessages(library(tidyverse))
suppressMessages(library(glue))
PRE = "/Users/haekyungim/Library/CloudStorage/Box-Box/LargeFiles/imlab-data/data-Github/web-data"
##PRE="/Users/margaretperry/Library/CloudStorage/Box-Box/imlab-data/data-Github/web-data "
##PRE="/Users/temi/Library/CloudStorage/Box-Box/imlab-data/data-Github/web-data"
## COPY THE DATE AND SLUG fields FROM THE HEADER
SLUG="parg-fall-social-2022" ## copy the slug from the header
bDATE='2022-10-20' ## copy the date from the blog's header here
DATA = glue("{PRE}/{bDATE}-{SLUG}")
if(!file.exists(DATA)) system(glue::glue("mkdir {DATA}"))
WORK=DATA

## move data to DATA
#tempodata=("~/Downloads/tempo/gwas_catalog_v1.0.2-associations_e105_r2022-04-07.tsv")
#system(glue::glue("cp {tempodata} {DATA}/"))
##system(glue("open {DATA}")) ## this will open the folder

Code

df <- readxl::read_excel(glue("{WORK}/report-2022-10-20T1314.xlsx"))

New names:
• `Please explain` -> `Please explain...26`
• `Please explain` -> `Please explain...27`

Code

names(df)

 [1] "Order #"                                                                         
 [2] "Order Date"                                                                      
 [3] "First Name"                                                                      
 [4] "Last Name"                                                                       
 [5] "Email"                                                                           
 [6] "Quantity"                                                                        
 [7] "Price Tier"                                                                      
 [8] "Ticket Type"                                                                     
 [9] "Attendee #"                                                                      
[10] "Group"                                                                           
[11] "Order Type"                                                                      
[12] "Currency"                                                                        
[13] "Total Paid"                                                                      
[14] "Fees Paid"                                                                       
[15] "Eventbrite Fees"                                                                 
[16] "Eventbrite Payment Processing"                                                   
[17] "Attendee Status"                                                                 
[18] "Home Address 1"                                                                  
[19] "Home Address 2"                                                                  
[20] "Home City"                                                                       
[21] "Home State"                                                                      
[22] "Home Zip"                                                                        
[23] "Home Country"                                                                    
[24] "Will you be attending the event?"                                                
[25] "Do you have any allergies or dietary requirements?"                              
[26] "Please explain...26"                                                             
[27] "Please explain...27"                                                             
[28] "Are you interested in participating in the talent show?"                         
[29] "What equipment would you need?"                                                  
[30] "Campus affiliation  (dept/div) and role (undergrad, grad, staff, faculty, other)"
[31] "Is this your first time attending a Pan-Asian Resource Group event?"             
[32] "How did you hear about us?"

Code

df %>% count(`Campus affiliation  (dept/div) and role (undergrad, grad, staff, faculty, other)`) %>% arrange(desc(n))

# A tibble: 59 × 2
   Campus affiliation  (dept/div) and role (undergrad, grad, staff, facu…¹     n
   <chr>                                                                   <int>
 1 Grad                                                                       19
 2 grad                                                                        7
 3 Harris                                                                      6
 4 BSD                                                                         5
 5 Grad student                                                                3
 6 Harris student                                                              3
 7 PSD, grad                                                                   3
 8 Harris MPP                                                                  2
 9 PSD grad                                                                    2
10 BSD grad student                                                            1
# ℹ 49 more rows
# ℹ abbreviated name:
#   ¹`Campus affiliation  (dept/div) and role (undergrad, grad, staff, faculty, other)`

not very helpful, better to have list to select from rather than free text next time

Code

df %>% count(`How did you hear about us?`) %>% arrange(desc(n))

# A tibble: 5 × 2
  `How did you hear about us?`                                                 n
  <chr>                                                                    <int>
1 International House newsletter                                              40
2 Through a friend                                                            30
3 Through the Pan Asian or the Pan Asian Resource Group list serves           17
4 Divisional/departmental events                                              12
5 International House newsletter | Through the Pan Asian or the Pan Asian…     1

I-house newsletter was the most effective dissemination