Analysis of FABLAT project timelines

Abstract

Sharing economic models have been the center of much attention and criticism in recent years. Their cooptation by the so-called “death star platforms” have put the concept under fire and have raised the question of whether these so-called “collaborative” models can live up to the expectations in terms of creating sustainable models that can create impact in their contexts. The present paper explores the concepts of sharing economy and platform-based cooperation and how can these concepts applied to the Fab Lab Network can become a catalyst for social-based innovation leveraging on such concepts. By asking whether a sharing economy can still become a game-changer for society, and how sharing initiatives, such as fab labs, aid to this goal, will help us to better understand how a fab lab can create new dynamics in the community is inserted in, from a social and economic standpoint. Observational data from Fab Labs from the FABLAT (Fab Lab Latin American Network) will be used for illustration purposes to outline some of the biggest collaborative initiatives in the region in terms of funding and impact.

require(ggplot2); require(dplyr); require(lubridate)

Introduction

directory<-paste0(getwd(),("/folder1"))
tmp=list.files(path="folder1")
files<-as.vector(sapply(tmp, function(x) paste0(getwd(),("/folder1/"),x)))
temp_df <- read.table(files[1],fill=TRUE,header=TRUE,sep=",",quote="")[0,]
tnames<-colnames(temp_df)
for (i in 1:length(files)){
  currentFile<-read.table(files[i],fill=TRUE,header=TRUE,sep=",",quote="")
  # print(dim(currentFile))
  if(nrow(currentFile)>0){
    temp_df<-rbind(temp_df,currentFile)
  }
}

tbl_df(temp_df)

## Source: local data frame [4,211 x 12]
## 
##                     folderFileId
##                           (fctr)
## 1  0B6yMZ7pqUvCPTUpDZFpGaWFVWWc 
## 2  0B6yMZ7pqUvCPTUpDZFpGaWFVWWc 
## 3  0B6yMZ7pqUvCPTUpDZFpGaWFVWWc 
## 4  0B6yMZ7pqUvCPTUpDZFpGaWFVWWc 
## 5  0B6yMZ7pqUvCPTUpDZFpGaWFVWWc 
## 6  0B6yMZ7pqUvCPTUpDZFpGaWFVWWc 
## 7  0B6yMZ7pqUvCPTUpDZFpGaWFVWWc 
## 8  0B6yMZ7pqUvCPTUpDZFpGaWFVWWc 
## 9  0B6yMZ7pqUvCPTUpDZFpGaWFVWWc 
## 10 0B6yMZ7pqUvCPTUpDZFpGaWFVWWc 
## ..                           ...
## Variables not shown: fileId (fctr), fileName (fctr), mimeType (fctr),
##   createdTime (fctr), modifiedTime (fctr), owners (fctr), revisionId
##   (fctr), lastModName (fctr), lastModEmail (fctr), X (fctr), X.1 (fctr)

temp_df<-mutate(temp_df,createdTime=ymd_hms(createdTime))

# created_df<-group_by(temp_df,month(temp_df$createdTime,label=T))
created_df<-group_by(filter(temp_df,mimeType!="image/jpeg"),month(temp_df$createdTime,label=T))

daycount<-summarize(created_df<-group_by(temp_df,day=date(temp_df$createdTime)),count=n()) #por día
g<-ggplot(daycount,aes(day,count))
g+geom_line(stat="identity",aes(color=count))

usercount<-arrange(summarize(created_df<-group_by(temp_df,lastModName),count=n()),desc(count))

usercount2<-mutate(usercount,percentage=count/sum(count))
usercount2

## Source: local data frame [144 x 3]
## 
##                              lastModName count percentage
##                                   (fctr) (int)      (dbl)
## 1                           Beno Juarez    710 0.16860603
## 2                          Montse Ciges    549 0.13037283
## 3                         Angelo López    507 0.12039896
## 4           Maria Gabriela Garcia Lopez    309 0.07337924
## 5                      Norella Coronell    291 0.06910473
## 6                         Cynthia Polar    233 0.05533128
## 7          LORENA CAMILLE ARENAS LOVERA    168 0.03989551
## 8                                          154 0.03657089
## 9   DANIELA VIRGINIA QUEQUEZANA VIDALON    128 0.03039658
## 10        KARINA NICOLLE ASTUCURI SAENZ     98 0.02327238
## ..                                   ...   ...        ...

h<-ggplot(usercount2[1:30,],aes(reorder(lastModName,-count),count))
h+geom_bar(stat="identity")+theme(axis.text.x = element_text(angle = 90, hjust = 1))+aes(fill=count)

Graph only shows the 25 most engaged authors.