3rdworldjuander

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = valence ~ C_1, data = df)
## 
## $C_1
##            diff         lwr         upr     p adj
## 4-1 -0.02632178 -0.07745919  0.02481563 0.4489219
## 6-1 -0.04175318 -0.07138856 -0.01211779 0.0027859
## 6-4 -0.01543139 -0.06915787  0.03829508 0.7788202

3rdworldjuander

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = popularity ~ C_1, data = df)
## 
## $C_1
##           diff        lwr       upr     p adj
## 4-1 -5.2670269 -9.2783422 -1.255712 0.0059544
## 6-1 -0.4215248 -2.7459343  1.902885 0.9050936
## 6-4  4.8455021  0.6308697  9.060134 0.0193477

3rdworldjuander

##               Df Sum Sq Mean Sq F value Pr(>F)   
## C_1            2   0.67  0.3350   5.616 0.0037 **
## Residuals   1792 106.91  0.0597                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 164 observations deleted due to missingness

3rdworldjuander

##               Df Sum Sq Mean Sq F value  Pr(>F)   
## C_1            2   3497  1748.3   4.762 0.00866 **
## Residuals   1793 658275   367.1                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 163 observations deleted due to missingness

3rdworldjuander

##               Df Sum Sq Mean Sq F value   Pr(>F)    
## cprog         16   2.58 0.16097   2.726 0.000256 ***
## Residuals   1778 105.01 0.05906                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 164 observations deleted due to missingness

3rdworldjuander

##               Df Sum Sq Mean Sq F value   Pr(>F)    
## cprog         16  19484    1218   3.373 6.56e-06 ***
## Residuals   1779 642288     361                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 163 observations deleted due to missingness

3rdworldjuander

library(stringr)
library(tidyr)


edges <- stream %>% filter(is.na(`User Mentions`) == F) %>% 
       select(Nickname, `Is a RT`, `User Mentions`)

#tagging retweets and mentions

edges <- edges %>%