First, we load all the files from folder dataset and condensed them in one dataset

Method 1: RT metric

We get the frequency table of RTs for each year, based on the column RT Count.

To do this we need to separate original tweets from RTs and replies, and calculate the average per year.

##    value.retweet_count n
## 1                  106 1
## 2                  111 1
## 3                  117 1
## 4                  118 1
## 5                  122 1
## 6                  127 1
## 7                  131 1
## 8                  145 1
## 9                  153 1
## 10                 160 1
## 11                 161 1
## 12                 166 1
## 13                 169 1
## 14                 182 1
## 15                 187 1
## 16                 312 1
## 17                 318 1
## 18                 347 1
## 19                 358 1
## 20                 429 1
## 21                 615 1
## 22                 653 1
## 23                 667 1
## 24                 961 1

Saving freq table

Saving data to use in Canada and calculate the quartile of the whole dataset in AllforTimeTrack

Method 2: track daily activity

We gathered all the data in one single data frame. We proceed to convert some columns and count the tweets per day to identify the days with most activity.

## # A tibble: 22 × 2
##    created_at count
##    <date>     <int>
##  1 2019-01-24   524
##  2 2022-05-22   491
##  3 2021-05-29   478
##  4 2021-02-02   476
##  5 2021-02-03   457
##  6 2019-06-26   447
##  7 2022-11-03   360
##  8 2019-06-27   345
##  9 2022-05-23   313
## 10 2019-11-12   284
## # ℹ 12 more rows