In [1]:

    

library(data.table)


    

In [2]:

    

library(fmsb)


    

In [3]:

    

library(ggplot2)


    

In [4]:

    

longitudinal_misalignment <- read.table("../../../results/misalignment_and_edits_10_17_post_processed.tsv", header=TRUE, sep="\t")


    

In [5]:

    

summary(longitudinal_misalignment)


    

    






     yyyymm       aligned_entities difference_in_alignment_with_previous
 Min.   :201211   Min.   :0.4443   Min.   :-0.0510158                   
 1st Qu.:201356   1st Qu.:0.4869   1st Qu.:-0.0141284                   
 Median :201502   Median :0.5656   Median :-0.0079630                   
 Mean   :201470   Mean   :0.6260   Mean   : 0.0080788                   
 3rd Qu.:201604   3rd Qu.:0.7723   3rd Qu.:-0.0003932                   
 Max.   :201705   Max.   :0.9063   Max.   : 0.7595628                   
   bot_edits        semi_automated_edits non_bot_edits       anon_edits   
 Min.   :     700   Min.   :      0      Min.   : 170872   Min.   :  507  
 1st Qu.: 3282669   1st Qu.:  55931      1st Qu.: 610830   1st Qu.:23099  
 Median : 4779455   Median : 959002      Median : 703269   Median :30159  
 Mean   : 5224430   Mean   :1226118      Mean   : 727592   Mean   :28382  
 3rd Qu.: 6549304   3rd Qu.:2047789      3rd Qu.: 915014   3rd Qu.:34510  
 Max.   :13948677   Max.   :3816710      Max.   :1121824   Max.   :47741  
 current_bot_edits_count current_semi_automated_edits_count
 Min.   :      700       Min.   :       0                  
 1st Qu.: 80837934       1st Qu.:  176970                  
 Median :151697293       Median :11054806                  
 Mean   :148843235       Mean   :18611192                  
 3rd Qu.:224092394       3rd Qu.:33153272                  
 Max.   :287343656       Max.   :67436512                  
 current_non_bot_edits_count current_anon_edits_count
 Min.   :  263997            Min.   :    507         
 1st Qu.: 7872859            1st Qu.: 364144         
 Median :15778474            Median : 797231         
 Mean   :17303029            Mean   : 744762         
 3rd Qu.:26154904            3rd Qu.:1110726         
 Max.   :40017566            Max.   :1561035         

In [6]:

    

first_month_trimmed_longitudinal_misalignment <- longitudinal_misalignment[2:55,]


    

In [7]:

    

# bot edits over all edits versus alignment


    

In [8]:

    

cor(longitudinal_misalignment$aligned_entities, longitudinal_misalignment$bot_edits/longitudinal_misalignment$all_edits, method="spearman")


    

    





0.696608946608947

In [9]:

    

summary(lm(longitudinal_misalignment$bot_edits/longitudinal_misalignment$all_edits ~ longitudinal_misalignment$aligned_entities))


    

    






Call:
lm(formula = longitudinal_misalignment$bot_edits/longitudinal_misalignment$all_edits ~ 
    longitudinal_misalignment$aligned_entities)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.78670 -0.05437  0.04127  0.09526  0.25714 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)
(Intercept)                                 0.28957    0.09149   3.165  0.00257
longitudinal_misalignment$aligned_entities  0.65796    0.14201   4.633 2.38e-05
                                              
(Intercept)                                ** 
longitudinal_misalignment$aligned_entities ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1605 on 53 degrees of freedom
Multiple R-squared:  0.2883,	Adjusted R-squared:  0.2748 
F-statistic: 21.47 on 1 and 53 DF,  p-value: 2.379e-05

In [10]:

    

# semi-automated edits over all edits versus alignment


    

In [11]:

    

cor(longitudinal_misalignment$aligned_entities, longitudinal_misalignment$semi_automated_edits/longitudinal_misalignment$all_edits, method="spearman")


    

    





-0.839119506102836

In [12]:

    

summary(lm(longitudinal_misalignment$semi_automated_edits/longitudinal_misalignment$all_edits ~ longitudinal_misalignment$aligned_entities))


    

    






Call:
lm(formula = longitudinal_misalignment$semi_automated_edits/longitudinal_misalignment$all_edits ~ 
    longitudinal_misalignment$aligned_entities)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.19674 -0.05369 -0.01504  0.06523  0.21880 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)
(Intercept)                                 0.63142    0.04774   13.23  < 2e-16
longitudinal_misalignment$aligned_entities -0.74650    0.07410  -10.07 6.49e-14
                                              
(Intercept)                                ***
longitudinal_misalignment$aligned_entities ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.08374 on 53 degrees of freedom
Multiple R-squared:  0.6569,	Adjusted R-squared:  0.6505 
F-statistic: 101.5 on 1 and 53 DF,  p-value: 6.486e-14

In [13]:

    

# non_bot edits over all edits versus alignment


    

In [14]:

    

cor(longitudinal_misalignment$aligned_entities, longitudinal_misalignment$non_bot_edits/longitudinal_misalignment$all_edits, method="spearman")


    

    





-0.242207792207792

In [15]:

    

summary(lm(longitudinal_misalignment$non_bot_edits/longitudinal_misalignment$all_edits ~ longitudinal_misalignment$aligned_entities))


    

    






Call:
lm(formula = longitudinal_misalignment$non_bot_edits/longitudinal_misalignment$all_edits ~ 
    longitudinal_misalignment$aligned_entities)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.10305 -0.04500 -0.02311  0.01128  0.85350 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)
(Intercept)                                 0.07532    0.07672   0.982    0.331
longitudinal_misalignment$aligned_entities  0.08772    0.11909   0.737    0.465

Residual standard error: 0.1346 on 53 degrees of freedom
Multiple R-squared:  0.01013,	Adjusted R-squared:  -0.008542 
F-statistic: 0.5426 on 1 and 53 DF,  p-value: 0.4646

In [16]:

    

# anon edits over all edits versus alignment


    

In [17]:

    

cor(longitudinal_misalignment$aligned_entities, longitudinal_misalignment$anon_edits/longitudinal_misalignment$all_edits, method="spearman")


    

    





0.0598124098124098

In [18]:

    

summary(lm(longitudinal_misalignment$anon_edits/longitudinal_misalignment$all_edits ~ longitudinal_misalignment$aligned_entities))


    

    






Call:
lm(formula = longitudinal_misalignment$anon_edits/longitudinal_misalignment$all_edits ~ 
    longitudinal_misalignment$aligned_entities)

Residuals:
       Min         1Q     Median         3Q        Max 
-0.0035879 -0.0012042 -0.0002774  0.0012706  0.0055999 

Coefficients:
                                            Estimate Std. Error t value
(Intercept)                                0.0036897  0.0010119   3.646
longitudinal_misalignment$aligned_entities 0.0008146  0.0015707   0.519
                                           Pr(>|t|)    
(Intercept)                                0.000607 ***
longitudinal_misalignment$aligned_entities 0.606181    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.001775 on 53 degrees of freedom
Multiple R-squared:  0.005049,	Adjusted R-squared:  -0.01372 
F-statistic: 0.269 on 1 and 53 DF,  p-value: 0.6062

In [19]:

    

#bot edits


    

In [20]:

    

plot(longitudinal_misalignment$bot_edits/longitudinal_misalignment$all_edits)


    

In [21]:

    

#semi automated edits


    

In [22]:

    

plot(longitudinal_misalignment$semi_automated_edits/longitudinal_misalignment$all_edits)


    

In [23]:

    

#anon automated edits


    

In [24]:

    

plot(longitudinal_misalignment$anon_edits/longitudinal_misalignment$all_edits)


    

In [25]:

    

#non_bot edits


    

In [26]:

    

plot(longitudinal_misalignment$non_bot_edits/longitudinal_misalignment$all_edits)


    

In [27]:

    

# Correlation between alignment difference and bot edits over all edits


    

In [28]:

    

cor(first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous, first_month_trimmed_longitudinal_misalignment$bot_edits/first_month_trimmed_longitudinal_misalignment$all_edits, method="spearman")


    

    





-0.339203354297694

In [29]:

    

summary(lm(first_month_trimmed_longitudinal_misalignment$bot_edits/first_month_trimmed_longitudinal_misalignment$all_edits ~ first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous))


    

    






Call:
lm(formula = first_month_trimmed_longitudinal_misalignment$bot_edits/first_month_trimmed_longitudinal_misalignment$all_edits ~ 
    first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.37524 -0.11909  0.01757  0.13473  0.34611 

Coefficients:
                                                                                    Estimate
(Intercept)                                                                          0.70082
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous -2.32112
                                                                                    Std. Error
(Intercept)                                                                            0.02254
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous    1.10609
                                                                                    t value
(Intercept)                                                                          31.092
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous  -2.098
                                                                                    Pr(>|t|)
(Intercept)                                                                           <2e-16
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous   0.0407
                                                                                       
(Intercept)                                                                         ***
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1587 on 52 degrees of freedom
Multiple R-squared:  0.07807,	Adjusted R-squared:  0.06034 
F-statistic: 4.404 on 1 and 52 DF,  p-value: 0.04074

In [30]:

    

# Correlation between alignment difference and current bot edits over all edits


    

In [31]:

    

cor(first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous, first_month_trimmed_longitudinal_misalignment$current_bot_edits_count/first_month_trimmed_longitudinal_misalignment$current_all_edits_count, method="spearman")


    

    





-0.438536306460835

In [32]:

    

summary(lm(first_month_trimmed_longitudinal_misalignment$current_bot_edits_count/first_month_trimmed_longitudinal_misalignment$current_all_edits_count ~ first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous))


    

    






Call:
lm(formula = first_month_trimmed_longitudinal_misalignment$current_bot_edits_count/first_month_trimmed_longitudinal_misalignment$current_all_edits_count ~ 
    first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.230429 -0.039399  0.002489  0.044869  0.159867 

Coefficients:
                                                                                     Estimate
(Intercept)                                                                          0.801569
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous -3.476327
                                                                                    Std. Error
(Intercept)                                                                           0.009844
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous   0.483054
                                                                                    t value
(Intercept)                                                                          81.429
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous  -7.197
                                                                                    Pr(>|t|)
(Intercept)                                                                          < 2e-16
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous  2.4e-09
                                                                                       
(Intercept)                                                                         ***
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.06931 on 52 degrees of freedom
Multiple R-squared:  0.499,	Adjusted R-squared:  0.4894 
F-statistic: 51.79 on 1 and 52 DF,  p-value: 2.405e-09

In [33]:

    

# Correlation between alignment difference and semi-automated edits over all edits


    

In [34]:

    

cor(first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous, first_month_trimmed_longitudinal_misalignment$semi_automated_edits/first_month_trimmed_longitudinal_misalignment$all_edits, method="spearman")


    

    





0.246511632385665

In [35]:

    

summary(lm(first_month_trimmed_longitudinal_misalignment$semi_automated_edits/first_month_trimmed_longitudinal_misalignment$all_edits ~ first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous))


    

    






Call:
lm(formula = first_month_trimmed_longitudinal_misalignment$semi_automated_edits/first_month_trimmed_longitudinal_misalignment$all_edits ~ 
    first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.18002 -0.15086 -0.01356  0.10423  0.32887 

Coefficients:
                                                                                    Estimate
(Intercept)                                                                          0.16931
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous  0.36410
                                                                                    Std. Error
(Intercept)                                                                            0.02021
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous    0.99188
                                                                                    t value
(Intercept)                                                                           8.376
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous   0.367
                                                                                    Pr(>|t|)
(Intercept)                                                                         3.25e-11
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous    0.715
                                                                                       
(Intercept)                                                                         ***
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1423 on 52 degrees of freedom
Multiple R-squared:  0.002585,	Adjusted R-squared:  -0.0166 
F-statistic: 0.1347 on 1 and 52 DF,  p-value: 0.7151

In [36]:

    

# Correlation between alignment difference and current semi-automated edits over all edits


    

In [37]:

    

cor(first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous, first_month_trimmed_longitudinal_misalignment$current_semi_automated_edits_count/first_month_trimmed_longitudinal_misalignment$current_all_edits_count, method="spearman")


    

    





0.265027634838956

In [38]:

    

summary(lm(first_month_trimmed_longitudinal_misalignment$current_bot_edits_count/first_month_trimmed_longitudinal_misalignment$current_semi_automated_edits_count ~ first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous))


    

    






Call:
lm(formula = first_month_trimmed_longitudinal_misalignment$current_bot_edits_count/first_month_trimmed_longitudinal_misalignment$current_semi_automated_edits_count ~ 
    first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous)

Residuals:
    Min      1Q  Median      3Q     Max 
-172.61 -128.86 -113.37   37.51  639.94 

Coefficients:
                                                                                    Estimate
(Intercept)                                                                            117.5
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous  -2103.7
                                                                                    Std. Error
(Intercept)                                                                               32.0
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous     1570.1
                                                                                    t value
(Intercept)                                                                           3.674
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous  -1.340
                                                                                    Pr(>|t|)
(Intercept)                                                                         0.000565
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous 0.186124
                                                                                       
(Intercept)                                                                         ***
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 225.3 on 52 degrees of freedom
Multiple R-squared:  0.03337,	Adjusted R-squared:  0.01478 
F-statistic: 1.795 on 1 and 52 DF,  p-value: 0.1861

In [39]:

    

# Correlation between alignment difference and non-bot edits over all edits


    

In [40]:

    

cor(first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous, first_month_trimmed_longitudinal_misalignment$non_bot_edits/first_month_trimmed_longitudinal_misalignment$all_edits, method="spearman")


    

    





0.308862206975414

In [41]:

    

summary(lm(first_month_trimmed_longitudinal_misalignment$non_bot_edits/first_month_trimmed_longitudinal_misalignment$all_edits ~ first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous))


    

    






Call:
lm(formula = first_month_trimmed_longitudinal_misalignment$non_bot_edits/first_month_trimmed_longitudinal_misalignment$all_edits ~ 
    first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.163327 -0.029986 -0.004632  0.022524  0.195435 

Coefficients:
                                                                                    Estimate
(Intercept)                                                                         0.125671
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous 1.963609
                                                                                    Std. Error
(Intercept)                                                                           0.007043
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous   0.345594
                                                                                    t value
(Intercept)                                                                          17.844
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous   5.682
                                                                                    Pr(>|t|)
(Intercept)                                                                          < 2e-16
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous 6.09e-07
                                                                                       
(Intercept)                                                                         ***
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.04958 on 52 degrees of freedom
Multiple R-squared:  0.383,	Adjusted R-squared:  0.3712 
F-statistic: 32.28 on 1 and 52 DF,  p-value: 6.094e-07

In [42]:

    

# Correlation between alignment difference and current non-bot edits over all edits


    

In [43]:

    

cor(first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous, first_month_trimmed_longitudinal_misalignment$current_non_bot_edits_count/first_month_trimmed_longitudinal_misalignment$current_all_edits_count, method="spearman")


    

    





0.504707451877263

In [44]:

    

summary(lm(first_month_trimmed_longitudinal_misalignment$current_non_bot_edits_count/first_month_trimmed_longitudinal_misalignment$current_all_edits_count ~ first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous))


    

    






Call:
lm(formula = first_month_trimmed_longitudinal_misalignment$current_non_bot_edits_count/first_month_trimmed_longitudinal_misalignment$current_all_edits_count ~ 
    first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.089947 -0.025947 -0.004589  0.014672  0.284520 

Coefficients:
                                                                                    Estimate
(Intercept)                                                                         0.125818
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous 3.193291
                                                                                    Std. Error
(Intercept)                                                                           0.007859
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous   0.385642
                                                                                    t value
(Intercept)                                                                           16.01
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous    8.28
                                                                                    Pr(>|t|)
(Intercept)                                                                          < 2e-16
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous  4.6e-11
                                                                                       
(Intercept)                                                                         ***
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.05533 on 52 degrees of freedom
Multiple R-squared:  0.5687,	Adjusted R-squared:  0.5604 
F-statistic: 68.57 on 1 and 52 DF,  p-value: 4.6e-11

In [45]:

    

# Correlation between alignment difference and anon edits over all edits


    

In [46]:

    

cor(first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous, first_month_trimmed_longitudinal_misalignment$anon_edits/first_month_trimmed_longitudinal_misalignment$all_edits, method="spearman")


    

    





-0.121402706308367

In [47]:

    

summary(lm(first_month_trimmed_longitudinal_misalignment$anon_edits/first_month_trimmed_longitudinal_misalignment$all_edits ~ first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous))


    

    






Call:
lm(formula = first_month_trimmed_longitudinal_misalignment$anon_edits/first_month_trimmed_longitudinal_misalignment$all_edits ~ 
    first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous)

Residuals:
       Min         1Q     Median         3Q        Max 
-0.0034224 -0.0010982 -0.0002968  0.0011452  0.0056631 

Coefficients:
                                                                                      Estimate
(Intercept)                                                                          0.0042035
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous -0.0065890
                                                                                    Std. Error
(Intercept)                                                                          0.0002504
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous  0.0122868
                                                                                    t value
(Intercept)                                                                          16.788
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous  -0.536
                                                                                    Pr(>|t|)
(Intercept)                                                                           <2e-16
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous    0.594
                                                                                       
(Intercept)                                                                         ***
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.001763 on 52 degrees of freedom
Multiple R-squared:  0.0055,	Adjusted R-squared:  -0.01362 
F-statistic: 0.2876 on 1 and 52 DF,  p-value: 0.5941

In [48]:

    

# Correlation between alignment difference and current anon edits over all edits


    

In [49]:

    

cor(first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous, first_month_trimmed_longitudinal_misalignment$current_anon_edits_count/first_month_trimmed_longitudinal_misalignment$current_all_edits_count, method="spearman")


    

    





-0.296207356584715

In [50]:

    

summary(lm(first_month_trimmed_longitudinal_misalignment$current_anon_edits_count/first_month_trimmed_longitudinal_misalignment$current_all_edits_count ~ first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous))


    

    






Call:
lm(formula = first_month_trimmed_longitudinal_misalignment$current_anon_edits_count/first_month_trimmed_longitudinal_misalignment$current_all_edits_count ~ 
    first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous)

Residuals:
       Min         1Q     Median         3Q        Max 
-2.461e-03 -3.934e-05  9.242e-05  3.323e-04  1.263e-03 

Coefficients:
                                                                                      Estimate
(Intercept)                                                                          3.866e-03
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous -3.487e-03
                                                                                    Std. Error
(Intercept)                                                                          9.068e-05
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous  4.450e-03
                                                                                    t value
(Intercept)                                                                          42.630
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous  -0.784
                                                                                    Pr(>|t|)
(Intercept)                                                                           <2e-16
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous    0.437
                                                                                       
(Intercept)                                                                         ***
first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.0006384 on 52 degrees of freedom
Multiple R-squared:  0.01167,	Adjusted R-squared:  -0.007337 
F-statistic: 0.614 on 1 and 52 DF,  p-value: 0.4369

In [51]:

    

min(first_month_trimmed_longitudinal_misalignment$difference_in_alignment_with_previous)


    

    





-0.0510157745000001

In [15]:

    

first_month_trimmed_longitudinal_misalignment$bot_edit_proportion <- first_month_trimmed_longitudinal_misalignment$bot_edits/first_month_trimmed_longitudinal_misalignment$all_edit


    

    




Error in `$<-.data.frame`(`*tmp*`, "bot_edit_proportion", value = numeric(0)): replacement has 0 rows, data has 54
Traceback:

1. `$<-`(`*tmp*`, "bot_edit_proportion", value = numeric(0))
2. `$<-.data.frame`(`*tmp*`, "bot_edit_proportion", value = numeric(0))
3. stop(sprintf(ngettext(N, "replacement has %d row, data has %d", 
 .     "replacement has %d rows, data has %d"), N, nrows), domain = NA)

In [53]:

    

first_month_trimmed_longitudinal_misalignment$semi_automated_edit_proportion <- first_month_trimmed_longitudinal_misalignment$semi_automated_edits/first_month_trimmed_longitudinal_misalignment$all_edit


    

In [54]:

    

first_month_trimmed_longitudinal_misalignment$non_bot_edit_proportion <- first_month_trimmed_longitudinal_misalignment$non_bot_edits/first_month_trimmed_longitudinal_misalignment$all_edit


    

In [55]:

    

first_month_trimmed_longitudinal_misalignment$anon_edit_proportion <- first_month_trimmed_longitudinal_misalignment$anon_edits/first_month_trimmed_longitudinal_misalignment$all_edit


    

In [7]:

    

first_month_trimmed_longitudinal_misalignment$misaligned_over_aligned <- (1 - first_month_trimmed_longitudinal_misalignment$aligned_entities)/first_month_trimmed_longitudinal_misalignment$aligned_entities


    

In [8]:

    

longitudinal_edit_frequencies <- read.table("~/Desktop/all_events_session_mean_frequencies.tsv", header=TRUE, sep="\t")


    

In [9]:

    

colnames(longitudinal_edit_frequencies) <- c('yyyymm', 'under_five_seconds','five_to_ten_seconds', 'ten_to_twenty_seconds', 'twenty_to_one_hundred_seconds', 'over_one_hundred_seconds')


    

In [10]:

    

last_month_trimmed_longitudinal_edit_frequencies <- longitudinal_edit_frequencies[1:54,]


    

In [11]:

    

first_month_trimmed_longitudinal_misalignment$under_five_seconds = last_month_trimmed_longitudinal_edit_frequencies$under_five_seconds


    

In [12]:

    

first_month_trimmed_longitudinal_misalignment$five_to_ten_seconds = last_month_trimmed_longitudinal_edit_frequencies$five_to_ten_seconds


    

In [13]:

    

first_month_trimmed_longitudinal_misalignment$ten_to_twenty_seconds = last_month_trimmed_longitudinal_edit_frequencies$ten_to_twenty_seconds


    

In [14]:

    

first_month_trimmed_longitudinal_misalignment$twenty_to_one_hundred_seconds = last_month_trimmed_longitudinal_edit_frequencies$twenty_to_one_hundred_seconds


    

In [15]:

    

first_month_trimmed_longitudinal_misalignment$over_one_hundred_seconds = last_month_trimmed_longitudinal_edit_frequencies$over_one_hundred_seconds


    

In [16]:

    

head(first_month_trimmed_longitudinal_misalignment, n=60)


    

    






yyyymm
aligned_entities
difference_in_alignment_with_previous
bot_edits
semi_automated_edits
non_bot_edits
anon_edits
current_bot_edits_count
current_semi_automated_edits_count
current_non_bot_edits_count
current_anon_edits_count
misaligned_over_aligned
under_five_seconds
five_to_ten_seconds
ten_to_twenty_seconds
twenty_to_one_hundred_seconds
over_one_hundred_seconds

	
2
201212       
0.8462165    
 0.0866536670
  183629     
  26104      
 205220      
 2789        
   184329    
   26104     
  469217     
   3296      
0.1817307    
    2489     
 194857      
 59547       
165594       
 22567       
	
3
201301       
0.9062631    
 0.0600465527
 1932349     
  23753      
 170872      
 2215        
  2116678    
   49857     
  640089     
   5511      
0.1034324    
 2129198     
 175164      
 37403       
105278       
 14021       
	
4
201302       
0.8983001    
-0.0079629832
 2285161     
   7616      
 183937      
 2066        
  4401839    
   57473     
  824026     
   7577      
0.1132138    
 4139140     
 382572      
261319       
220869       
117135       
	
5
201303       
0.9025090    
 0.0042089126
 2264112     
   5985      
 347720      
 8121        
  6665951    
   63458     
 1171746     
  15698      
0.1080222    
 8926914     
 522664      
427469       
251525       
128767       
	
6
201304       
0.8964184    
-0.0060905713
 8067371     
   8131      
 809218      
31627        
 14733322    
   71589     
 1980964     
  47325      
0.1155505    
 4638538     
 533869      
189782       
275344       
124446       
	
7
201305       
0.8769229    
-0.0194954899
13276100     
      0      
 689197      
42572        
 28009422    
   71589     
 2670161     
  89897      
0.1403511    
 1435333     
 474989      
140721       
108021       
 20737       
	
8
201306       
0.8388038    
-0.0381190921
13948677     
      0      
 833859      
41876        
 41958099    
   71589     
 3504020     
 131773      
0.1921738    
 1965612     
 205945      
177873       
259245       
 60946       
	
9
201307       
0.8244444    
-0.0143594412
 3789628     
      0      
 757761      
45756        
 45747727    
   71589     
 4261781     
 177529      
0.2129381    
 8336499     
 907861      
285301       
471498       
131105       
	
10
201308       
0.8256896    
 0.0012452362
 8292060     
      0      
 648256      
34629        
 54039787    
   71589     
 4910037     
 212158      
0.2111088    
13910899     
 332906      
294349       
367991       
136368       
	
11
201309       
0.8254115    
-0.0002781200
 4028693     
   5048      
 688735      
38016        
 58068480    
   76637     
 5598772     
 250174      
0.2115169    
11342817     
 189875      
230139       
396945       
137355       
	
12
201310       
0.8053949    
-0.0200166201
 5075148     
  16388      
 677488      
34543        
 63143628    
   93025     
 6276260     
 284717      
0.2416269    
 3439807     
 285067      
495693       
307387       
127338       
	
13
201311       
0.7880156    
-0.0173792434
 8359407     
  15911      
 467499      
32746        
 71503035    
  108936     
 6743759     
 317463      
0.2690103    
 7781786     
 356463      
284148       
335702       
127908       
	
14
201312       
0.7777810    
-0.0102346760
 6546802     
  25155      
 796302      
33562        
 78049837    
  134091     
 7540061     
 351025      
0.2857090    
 4158787     
 224031      
191175       
288087       
127767       
	
15
201401       
0.7743517    
-0.0034293020
 5576195     
  85758      
 665596      
26237        
 83626032    
  219849     
 8205657     
 377262      
0.2914029    
 5782958     
 218899      
 84521       
335898       
125394       
	
16
201402       
0.7701717    
-0.0041799537
 8170379     
 119851      
 672833      
25973        
 91796411    
  339700     
 8878490     
 403235      
0.2984117    
 3799390     
 715910      
300962       
364922       
132519       
	
17
201403       
0.7603903    
-0.0097813800
 4989090     
 148882      
 626931      
31259        
 96785501    
  488582     
 9505421     
 434494      
0.3151140    
 6108497     
 415124      
302940       
321324       
151159       
	
18
201404       
0.7093746    
-0.0510157745
 4747388     
 158044      
 504859      
30159        
101532889    
  646626     
10010280     
 464653      
0.4096925    
 7147952     
 290488      
317137       
373478       
157953       
	
19
201405       
0.7128619    
 0.0034873677
 4263744     
 438632      
 515110      
30564        
105796633    
 1085258     
10525390     
 495217      
0.4027962    
 8079571     
 141041      
177243       
361439       
144466       
	
20
201406       
0.6964598    
-0.0164021539
 9239076     
 979907      
 533950      
34610        
115035709    
 2065165     
11059340     
 529827      
0.4358331    
 5137782     
 147745      
190072       
347931       
132886       
	
21
201407       
0.6910405    
-0.0054192489
 4174522     
1122534      
 507952      
31916        
119210231    
 3187699     
11567292     
 561743      
0.4470931    
 3349367     
 273823      
386553       
355103       
134034       
	
22
201408       
0.6790128    
-0.0120276997
 3908321     
1542138      
 622262      
30124        
123118552    
 4729837     
12189554     
 591867      
0.4727262    
 4377000     
 311029      
250598       
361112       
132163       
	
23
201409       
0.6425853    
-0.0364275219
 3313688     
1131216      
 599397      
30592        
126432240    
 5861053     
12788951     
 622459      
0.5562136    
 9056794     
1653742      
320628       
422567       
148630       
	
24
201410       
0.6196022    
-0.0229831481
 3244385     
1145370      
 633243      
33084        
129676625    
 7006423     
13422194     
 655543      
0.6139389    
 3107493     
 359469      
444934       
473663       
143918       
	
25
201411       
0.6049853    
-0.0146168089
 6345968     
1393025      
 546683      
29452        
136022593    
 8399448     
13968877     
 684995      
0.6529326    
 4181472     
 438993      
368258       
480286       
148388       
	
26
201412       
0.5866415    
-0.0183438434
 6551805     
 895267      
 563627      
45267        
142574398    
 9294715     
14532504     
 730262      
0.7046186    
 3471387     
 808956      
338436       
410083       
148683       
	
27
201501       
0.5762690    
-0.0103724847
 4767293     
 809181      
 576405      
31123        
147341691    
10103896     
15108909     
 761385      
0.7353006    
 5183010     
 273330      
304976       
378394       
158425       
	
28
201502       
0.5655679    
-0.0107011045
 4355602     
 950910      
 669565      
35846        
151697293    
11054806     
15778474     
 797231      
0.7681342    
 4758334     
 312040      
419113       
470369       
192755       
	
29
201503       
0.5460412    
-0.0195267504
 3621741     
 856343      
 696851      
28095        
155319034    
11911149     
16475325     
 825326      
0.8313638    
 9109575     
 573273      
649094       
569517       
211450       
	
30
201504       
0.5321438    
-0.0138973476
 4906989     
 920678      
 703269      
26883        
160226023    
12831827     
17178594     
 852209      
0.8791912    
 7826677     
 568012      
511494       
596211       
203592       
	
31
201505       
0.5210429    
-0.0111008906
 2495798     
1687490      
 643752      
17425        
162721821    
14519317     
17822346     
 869634      
0.9192277    
 3798996     
 447149      
346460       
431440       
174113       
	
32
201506       
0.5164983    
-0.0045446150
 5026410     
 989433      
 658508      
21095        
167748231    
15508750     
18480854     
 890729      
0.9361147    
 4109893     
 237283      
409574       
391754       
160845       
	
33
201507       
0.5284056    
 0.0119072841
 2376972     
 828451      
 762785      
21549        
170125203    
16337201     
19243639     
 912278      
0.8924856    
 4314802     
 689950      
392280       
450559       
178642       
	
34
201508       
0.5119938    
-0.0164118156
10669860     
 633649      
 751576      
20549        
180795063    
16970850     
19995215     
 932827      
0.9531487    
 3174146     
 656051      
358223       
362589       
164300       
	
35
201509       
0.5083907    
-0.0036030632
 6045863     
 959002      
 735349      
26338        
186840926    
17929852     
20730564     
 959165      
0.9669911    
 5242616     
 320762      
271638       
381517       
171400       
	
36
201510       
0.4997726    
-0.0086181523
 4974810     
 744105      
 719165      
18280        
191815736    
18673957     
21449729     
 977445      
1.0009102    
 2463692     
 368433      
345660       
457447       
169587       
	
37
201511       
0.4885873    
-0.0111852937
 8715522     
1600533      
 812937      
19624        
200531258    
20274490     
22262666     
 997069      
1.0467173    
10689553     
 256398      
303274       
443151       
180506       
	
38
201512       
0.4832378    
-0.0053494605
 6908196     
2138604      
 907407      
22687        
207439454    
22413094     
23170073     
1019756      
1.0693745    
 6317734     
 268178      
424125       
464894       
188025       
	
39
201601       
0.4830095    
-0.0002283539
 4779455     
2865238      
 800477      
18560        
212218909    
25278332     
23970550     
1038316      
1.0703528    
 6572938     
 518080      
444578       
562291       
204710       
	
40
201602       
0.4844902    
 0.0014807863
 5065078     
3529393      
 922621      
23511        
217283987    
28807725     
24893171     
1061827      
1.0640251    
 6086537     
 620981      
305391       
565656       
224336       
	
41
201603       
0.4945400    
 0.0100497119
 5160233     
3039607      
 814083      
34476        
222444220    
31847332     
25707254     
1096303      
1.0220813    
 7850415     
 577616      
403975       
638478       
231225       
	
42
201604       
0.4929242    
-0.0016157865
 3296348     
2611880      
 895301      
28845        
225740568    
34459212     
26602555     
1125148      
1.0287096    
 4173838     
 530522      
407392       
565829       
230701       
	
43
201605       
0.4836949    
-0.0092292472
 6506231     
1767218      
1106677      
30773        
232246799    
36226430     
27709232     
1155921      
1.0674189    
 8864054     
 608069      
401881       
544384       
232972       
	
44
201606       
0.4741030    
-0.0095919492
 8976199     
1962688      
1111783      
29836        
241222998    
38189118     
28821015     
1185757      
1.1092464    
 6383387     
 508692      
316926       
551566       
217959       
	
45
201607       
0.4728511    
-0.0012519194
 3268990     
2711103      
 971037      
27532        
244491988    
40900221     
29792052     
1213289      
1.1148309    
 4671588     
 850894      
293835       
583083       
233821       
	
46
201608       
0.4896451    
 0.0167940071
 4243115     
1946163      
 981530      
21177        
248735103    
42846384     
30773582     
1234466      
1.0422957    
 8625867     
 447828      
336234       
616417       
217735       
	
47
201609       
0.4864085    
-0.0032366075
 3664996     
2230161      
 986319      
26298        
252400099    
45076545     
31759901     
1260764      
1.0558853    
 8161064     
 913589      
356830       
624617       
223533       
	
48
201610       
0.4873130    
 0.0009045834
 3088833     
2951222      
 954843      
29718        
255488932    
48027767     
32714744     
1290482      
1.0520691    
 5537533     
 652366      
314068       
506247       
210660       
	
49
201611       
0.4854145    
-0.0018985542
 6130768     
3816710      
1048604      
34821        
261619700    
51844477     
33763348     
1325303      
1.0600951    
 4973007     
 359009      
242009       
637658       
218309       
	
50
201612       
0.4846416    
-0.0007728950
 2795140     
2262942      
 923329      
32236        
264414840    
54107419     
34686677     
1357539      
1.0633805    
 4991366     
 561576      
376335       
600259       
229027       
	
51
201701       
0.4758655    
-0.0087760755
 2211807     
3164563      
 985926      
30517        
266626647    
57271982     
35672603     
1388056      
1.1014341    
 4454190     
 339478      
365108       
685328       
243010       
	
52
201702       
0.4760932    
 0.0002276582
 2826225     
2132890      
1121824      
41858        
269452872    
59404872     
36794427     
1429914      
1.1004292    
 4102568     
 350076      
340814       
736682       
275863       
	
53
201703       
0.4577122    
-0.0183809511
 7990982     
3756878      
1050995      
44485        
277443854    
63161750     
37845422     
1474399      
1.1847789    
13396463     
 395184      
312311       
711409       
256852       
	
54
201704       
0.4572040    
-0.0005082078
 6926496     
2746637      
1091828      
47741        
284370350    
65908387     
38937250     
1522140      
1.1872074    
 6654342     
 481495      
273441       
706639       
296597       
	
55
201705       
0.4443325    
-0.0128714966
 2973306     
1528125      
1080316      
38895        
287343656    
67436512     
40017566     
1561035      
1.2505668    
 3304383     
 503135      
310276       
627207       
300202       

In [17]:

    

attach(first_month_trimmed_longitudinal_misalignment)


    

In [61]:

    

edit_type_regression <- lm(difference_in_alignment_with_previous ~ scale(bot_edits) + 
                                                                   scale(semi_automated_edits) + 
                                                                   scale(non_bot_edits) + 
                                                                   scale(anon_edits) + 
                                                                   scale(under_five_seconds) +
                                                                   scale(five_to_ten_seconds) +
                                                                   scale(ten_to_twenty_seconds) +
                                                                   scale(twenty_to_one_hundred_seconds) +
                                                                   scale(over_one_hundred_seconds))


    

In [62]:

    

summary(edit_type_regression)


    

    






Call:
lm(formula = difference_in_alignment_with_previous ~ scale(bot_edits) + 
    scale(semi_automated_edits) + scale(non_bot_edits) + scale(anon_edits) + 
    scale(under_five_seconds) + scale(five_to_ten_seconds) + 
    scale(ten_to_twenty_seconds) + scale(twenty_to_one_hundred_seconds) + 
    scale(over_one_hundred_seconds))

Residuals:
      Min        1Q    Median        3Q       Max 
-0.039009 -0.004284  0.001421  0.005357  0.035343 

Coefficients:
                                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          -0.0058376  0.0018733  -3.116 0.003223 ** 
scale(bot_edits)                     -0.0079516  0.0027325  -2.910 0.005650 ** 
scale(semi_automated_edits)           0.0012959  0.0037134   0.349 0.728771    
scale(non_bot_edits)                  0.0052959  0.0042766   1.238 0.222164    
scale(anon_edits)                    -0.0093636  0.0026326  -3.557 0.000913 ***
scale(under_five_seconds)             0.0001031  0.0021517   0.048 0.961982    
scale(five_to_ten_seconds)           -0.0032123  0.0020348  -1.579 0.121577    
scale(ten_to_twenty_seconds)         -0.0076011  0.0024897  -3.053 0.003836 ** 
scale(twenty_to_one_hundred_seconds)  0.0098753  0.0071137   1.388 0.172061    
scale(over_one_hundred_seconds)      -0.0122660  0.0059887  -2.048 0.046538 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.01377 on 44 degrees of freedom
Multiple R-squared:  0.5949,	Adjusted R-squared:  0.5121 
F-statistic:  7.18 on 9 and 44 DF,  p-value: 2.523e-06

In [63]:

    

summary(lm(bot_edits ~          
                       scale(semi_automated_edits) + 
                       scale(non_bot_edits) + 
                       scale(anon_edits) + 
                       scale(under_five_seconds) +
                       scale(five_to_ten_seconds) +
                       scale(ten_to_twenty_seconds) +
                       scale(twenty_to_one_hundred_seconds) +
                       scale(over_one_hundred_seconds))
)

#VIF is 1.37


    

    






Call:
lm(formula = bot_edits ~ scale(semi_automated_edits) + scale(non_bot_edits) + 
    scale(anon_edits) + scale(under_five_seconds) + scale(five_to_ten_seconds) + 
    scale(ten_to_twenty_seconds) + scale(twenty_to_one_hundred_seconds) + 
    scale(over_one_hundred_seconds))

Residuals:
     Min       1Q   Median       3Q      Max 
-3539632 -1356974  -222093  1235033  5533493 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                           5321166     280576  18.965  < 2e-16 ***
scale(semi_automated_edits)            551678     550059   1.003  0.32125    
scale(non_bot_edits)                  2176736     552254   3.942  0.00028 ***
scale(anon_edits)                     1027618     363313   2.828  0.00696 ** 
scale(under_five_seconds)              534416     312267   1.711  0.09389 .  
scale(five_to_ten_seconds)            -313994     301151  -1.043  0.30268    
scale(ten_to_twenty_seconds)           412647     367782   1.122  0.26782    
scale(twenty_to_one_hundred_seconds) -2372477    1005031  -2.361  0.02264 *  
scale(over_one_hundred_seconds)      -1070793     882633  -1.213  0.23139    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2062000 on 45 degrees of freedom
Multiple R-squared:  0.5211,	Adjusted R-squared:  0.436 
F-statistic: 6.121 on 8 and 45 DF,  p-value: 2.49e-05

In [64]:

    

summary(lm(semi_automated_edits ~ scale(bot_edits) +
                                  scale(non_bot_edits) + 
                                  scale(anon_edits) + 
                                  scale(under_five_seconds) +
                                  scale(five_to_ten_seconds) +
                                  scale(ten_to_twenty_seconds) +
                                  scale(twenty_to_one_hundred_seconds) +
                                  scale(over_one_hundred_seconds))
)

#Vif is 2.22


    

    






Call:
lm(formula = semi_automated_edits ~ scale(bot_edits) + scale(non_bot_edits) + 
    scale(anon_edits) + scale(under_five_seconds) + scale(five_to_ten_seconds) + 
    scale(ten_to_twenty_seconds) + scale(twenty_to_one_hundred_seconds) + 
    scale(over_one_hundred_seconds))

Residuals:
     Min       1Q   Median       3Q      Max 
-1044662  -356270   -47331   313631  1249851 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                           1248824      86062  14.511  < 2e-16 ***
scale(bot_edits)                       124518     124153   1.003 0.321251    
scale(non_bot_edits)                   110973     195774   0.567 0.573638    
scale(anon_edits)                     -298426     112462  -2.654 0.010967 *  
scale(under_five_seconds)             -130574      96915  -1.347 0.184628    
scale(five_to_ten_seconds)              14901      93456   0.159 0.874035    
scale(ten_to_twenty_seconds)          -199361     110450  -1.805 0.077770 .  
scale(twenty_to_one_hundred_seconds)  1148706     278352   4.127 0.000157 ***
scale(over_one_hundred_seconds)        -28842     275091  -0.105 0.916966    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 632400 on 45 degrees of freedom
Multiple R-squared:  0.7407,	Adjusted R-squared:  0.6946 
F-statistic: 16.07 on 8 and 45 DF,  p-value: 6.723e-11

In [65]:

    

summary(lm(non_bot_edits ~ scale(bot_edits) +
                           scale(semi_automated_edits) + 
                           scale(anon_edits) + 
                           scale(under_five_seconds) +
                           scale(five_to_ten_seconds) +
                           scale(ten_to_twenty_seconds) +
                           scale(twenty_to_one_hundred_seconds) +
                           scale(over_one_hundred_seconds))
)

#vif is 2.83


    

    






Call:
lm(formula = non_bot_edits ~ scale(bot_edits) + scale(semi_automated_edits) + 
    scale(anon_edits) + scale(under_five_seconds) + scale(five_to_ten_seconds) + 
    scale(ten_to_twenty_seconds) + scale(twenty_to_one_hundred_seconds) + 
    scale(over_one_hundred_seconds))

Residuals:
    Min      1Q  Median      3Q     Max 
-221218  -69679    8803   78656  195428 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            736177      15100  48.753  < 2e-16 ***
scale(bot_edits)                        74850      18990   3.942  0.00028 ***
scale(semi_automated_edits)             16907      29826   0.567  0.57364    
scale(anon_edits)                       22455      20954   1.072  0.28960    
scale(under_five_seconds)              -25282      16930  -1.493  0.14231    
scale(five_to_ten_seconds)              15020      16248   0.924  0.36021    
scale(ten_to_twenty_seconds)           -37006      19295  -1.918  0.06149 .  
scale(twenty_to_one_hundred_seconds)    83598      55969   1.494  0.14225    
scale(over_one_hundred_seconds)        120960      44778   2.701  0.00970 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 111000 on 45 degrees of freedom
Multiple R-squared:  0.8045,	Adjusted R-squared:  0.7697 
F-statistic: 23.15 on 8 and 45 DF,  p-value: 1.477e-13

In [66]:

    

summary(lm(anon_edits ~ scale(bot_edits) +
                        scale(semi_automated_edits) + 
                        scale(non_bot_edits) +  
                        scale(under_five_seconds) +
                        scale(five_to_ten_seconds) +
                        scale(ten_to_twenty_seconds) +
                        scale(twenty_to_one_hundred_seconds) +
                        scale(over_one_hundred_seconds))
)

#Vif is 1.31


    

    






Call:
lm(formula = anon_edits ~ scale(bot_edits) + scale(semi_automated_edits) + 
    scale(non_bot_edits) + scale(under_five_seconds) + scale(five_to_ten_seconds) + 
    scale(ten_to_twenty_seconds) + scale(twenty_to_one_hundred_seconds) + 
    scale(over_one_hundred_seconds))

Residuals:
     Min       1Q   Median       3Q      Max 
-14501.6  -4888.9    452.1   6090.0  15700.9 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                           28898.7     1081.3  26.725  < 2e-16 ***
scale(bot_edits)                       4110.8     1453.4   2.828  0.00696 ** 
scale(semi_automated_edits)           -5289.1     1993.2  -2.654  0.01097 *  
scale(non_bot_edits)                   2612.3     2437.7   1.072  0.28960    
scale(under_five_seconds)              -243.3     1241.5  -0.196  0.84549    
scale(five_to_ten_seconds)              406.3     1173.0   0.346  0.73069    
scale(ten_to_twenty_seconds)          -1396.3     1422.0  -0.982  0.33137    
scale(twenty_to_one_hundred_seconds)  10289.8     3809.0   2.701  0.00970 ** 
scale(over_one_hundred_seconds)       -2965.7     3428.5  -0.865  0.39161    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 7946 on 45 degrees of freedom
Multiple R-squared:  0.4841,	Adjusted R-squared:  0.3923 
F-statistic: 5.278 on 8 and 45 DF,  p-value: 0.0001088

In [67]:

    

summary(lm(under_five_seconds ~ scale(bot_edits)
                                scale(semi_automated_edits) + 
                                scale(non_bot_edits) + 
                                scale(anon_edits) + 
                                scale(five_to_ten_seconds) +
                                scale(ten_to_twenty_seconds) +
                                scale(twenty_to_one_hundred_seconds) +
                                scale(over_one_hundred_seconds))
)

#Vif is 1.31


    

    




Error in parse(text = x, srcfile = src): <text>:2:33: unexpected symbol
1: summary(lm(under_five_seconds ~ scale(bot_edits)
2:                                 scale
                                   ^
Traceback:

In [68]:

    

summary(lm(five_to_ten_seconds ~ scale(bot_edits) +
                                 scale(semi_automated_edits) + 
                                 scale(non_bot_edits) + 
                                 scale(anon_edits) + 
                                 scale(under_five_seconds) +
                                 scale(ten_to_twenty_seconds) +
                                 scale(twenty_to_one_hundred_seconds) +
                                 scale(over_one_hundred_seconds))
)

#Vif is 1.02


    

    






Call:
lm(formula = five_to_ten_seconds ~ scale(bot_edits) + scale(semi_automated_edits) + 
    scale(non_bot_edits) + scale(anon_edits) + scale(under_five_seconds) + 
    scale(ten_to_twenty_seconds) + scale(twenty_to_one_hundred_seconds) + 
    scale(over_one_hundred_seconds))

Residuals:
    Min      1Q  Median      3Q     Max 
-328109 -141792  -36583   55204 1137115 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            458351      34943  13.117   <2e-16 ***
scale(bot_edits)                       -52513      50365  -1.043    0.303    
scale(semi_automated_edits)             11041      69247   0.159    0.874    
scale(non_bot_edits)                    73051      79026   0.924    0.360    
scale(anon_edits)                       16985      49040   0.346    0.731    
scale(under_five_seconds)               29698      39891   0.744    0.460    
scale(ten_to_twenty_seconds)            60455      45558   1.327    0.191    
scale(twenty_to_one_hundred_seconds)    25061     132640   0.189    0.851    
scale(over_one_hundred_seconds)        -82426     111030  -0.742    0.462    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 256800 on 45 degrees of freedom
Multiple R-squared:  0.1365,	Adjusted R-squared:  -0.01706 
F-statistic: 0.8889 on 8 and 45 DF,  p-value: 0.5335

In [69]:

    

summary(lm(ten_to_twenty_seconds ~ scale(bot_edits) +
                                   scale(semi_automated_edits) + 
                                   scale(non_bot_edits) + 
                                   scale(anon_edits) + 
                                   scale(under_five_seconds) +
                                   scale(five_to_ten_seconds) +
                                   scale(twenty_to_one_hundred_seconds) +
                                   scale(over_one_hundred_seconds))
)

#vif is 1.22


    

    






Call:
lm(formula = ten_to_twenty_seconds ~ scale(bot_edits) + scale(semi_automated_edits) + 
    scale(non_bot_edits) + scale(anon_edits) + scale(under_five_seconds) + 
    scale(five_to_ten_seconds) + scale(twenty_to_one_hundred_seconds) + 
    scale(over_one_hundred_seconds))

Residuals:
    Min      1Q  Median      3Q     Max 
-178237  -43598   -5894   43645  254617 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            316565      12412  25.504   <2e-16 ***
scale(bot_edits)                        20035      17857   1.122   0.2678    
scale(semi_automated_edits)            -42885      23759  -1.805   0.0778 .  
scale(non_bot_edits)                   -52251      27244  -1.918   0.0615 .  
scale(anon_edits)                      -16947      17259  -0.982   0.3314    
scale(under_five_seconds)               -5333      14234  -0.375   0.7097    
scale(five_to_ten_seconds)              17551      13226   1.327   0.1912    
scale(twenty_to_one_hundred_seconds)    80147      45594   1.758   0.0856 .  
scale(over_one_hundred_seconds)         63035      38551   1.635   0.1090    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 91210 on 45 degrees of freedom
Multiple R-squared:  0.4232,	Adjusted R-squared:  0.3206 
F-statistic: 4.126 on 8 and 45 DF,  p-value: 0.0009311

In [70]:

    

summary(lm(twenty_to_one_hundred_seconds ~ scale(bot_edits) +
                                           scale(semi_automated_edits) + 
                                           scale(non_bot_edits) + 
                                           scale(anon_edits) + 
                                           scale(under_five_seconds) +
                                           scale(five_to_ten_seconds) +
                                           scale(ten_to_twenty_seconds) +
                                           scale(over_one_hundred_seconds))
)

#vif is 7.33


    

    






Call:
lm(formula = twenty_to_one_hundred_seconds ~ scale(bot_edits) + 
    scale(semi_automated_edits) + scale(non_bot_edits) + scale(anon_edits) + 
    scale(under_five_seconds) + scale(five_to_ten_seconds) + 
    scale(ten_to_twenty_seconds) + scale(over_one_hundred_seconds))

Residuals:
   Min     1Q Median     3Q    Max 
-84860 -34319  -1786  27090  95331 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                       444868       5949  74.782  < 2e-16 ***
scale(bot_edits)                  -19322       8185  -2.361 0.022639 *  
scale(semi_automated_edits)        41449      10044   4.127 0.000157 ***
scale(non_bot_edits)               19800      13256   1.494 0.142249    
scale(anon_edits)                  20949       7755   2.701 0.009699 ** 
scale(under_five_seconds)          10349       6656   1.555 0.127016    
scale(five_to_ten_seconds)          1220       6459   0.189 0.850987    
scale(ten_to_twenty_seconds)       13444       7648   1.758 0.085576 .  
scale(over_one_hundred_seconds)    73910      15501   4.768 1.98e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 43720 on 45 degrees of freedom
Multiple R-squared:  0.9293,	Adjusted R-squared:  0.9168 
F-statistic: 73.98 on 8 and 45 DF,  p-value: < 2.2e-16

In [71]:

    

summary(lm(over_one_hundred_seconds ~ scale(bot_edits) +
                                      scale(semi_automated_edits) + 
                                      scale(non_bot_edits) + 
                                      scale(anon_edits) + 
                                      scale(under_five_seconds) +
                                      scale(five_to_ten_seconds) +
                                      scale(ten_to_twenty_seconds) +
                                      scale(twenty_to_one_hundred_seconds))
)

# vif is 5.28


    

    






Call:
lm(formula = over_one_hundred_seconds ~ scale(bot_edits) + scale(semi_automated_edits) + 
    scale(non_bot_edits) + scale(anon_edits) + scale(under_five_seconds) + 
    scale(five_to_ten_seconds) + scale(ten_to_twenty_seconds) + 
    scale(twenty_to_one_hundred_seconds))

Residuals:
   Min     1Q Median     3Q    Max 
-42017 -14227  -1082  12005  56715 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          169959.4     2881.3  58.987  < 2e-16 ***
scale(bot_edits)                      -5017.3     4135.7  -1.213   0.2314    
scale(semi_automated_edits)            -598.7     5710.8  -0.105   0.9170    
scale(non_bot_edits)                  16482.4     6101.6   2.701   0.0097 ** 
scale(anon_edits)                     -3473.8     4015.8  -0.865   0.3916    
scale(under_five_seconds)              2475.3     3288.8   0.753   0.4556    
scale(five_to_ten_seconds)            -2309.3     3110.7  -0.742   0.4617    
scale(ten_to_twenty_seconds)           6083.3     3720.4   1.635   0.1090    
scale(twenty_to_one_hundred_seconds)  42522.2     8918.0   4.768 1.98e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 21170 on 45 degrees of freedom
Multiple R-squared:  0.9003,	Adjusted R-squared:  0.8826 
F-statistic:  50.8 on 8 and 45 DF,  p-value: < 2.2e-16

In [ ]:

    




    

In [72]:

    

independent_and_dependent_variables = data.table(bot_edits = bot_edits, semi_automated_edits = semi_automated_edits, non_bot_edits = non_bot_edits, anon_edits = anon_edits, difference_in_alignment_with_previous = difference_in_alignment_with_previous, under_five_seconds = under_five_seconds, five_to_ten_seconds = five_to_ten_seconds, ten_to_twenty_seconds = ten_to_twenty_seconds, twenty_to_one_hundred_seconds = twenty_to_one_hundred_seconds, over_one_hundred_seconds = over_one_hundred_seconds)


    

In [73]:

    

edit_type_regression_without_anon <- lm(difference_in_alignment_with_previous ~ scale(bot_edits) + scale(semi_automated_edits) + scale(non_bot_edits))


    

In [74]:

    

anon_residuals = data.frame(month=as.Date(paste(yyyymm, "01", sep=""), format="%Y%m%d"), anon_edits = anon_edits, residuals= edit_type_regression_without_anon$residuals)


    

In [75]:

    

summary(anon_residuals)


    

    






     month              anon_edits      residuals         
 Min.   :2012-12-01   Min.   : 2066   Min.   :-0.0490774  
 1st Qu.:2014-01-08   1st Qu.:24126   1st Qu.:-0.0083774  
 Median :2015-02-15   Median :30338   Median : 0.0004912  
 Mean   :2015-02-15   Mean   :28899   Mean   : 0.0000000  
 3rd Qu.:2016-03-24   3rd Qu.:34526   3rd Qu.: 0.0061725  
 Max.   :2017-05-01   Max.   :47741   Max.   : 0.0702785  

In [76]:

    

ggplot(anon_residuals, aes(x=month, y=scale(residuals))) + geom_bar(stat="identity") + geom_line(aes(y=scale(anon_edits)))


    

In [77]:

    

hist(scale(anon_residuals$residuals)- scale(anon_residuals$anon_edits))


    

In [78]:

    

plot(scale(anon_residuals$residuals), scale(anon_residuals$anon_edits))


    

In [79]:

    

cor(independent_and_dependent_variables, method="spearman")


    

    






bot_edits
semi_automated_edits
non_bot_edits
anon_edits
difference_in_alignment_with_previous
under_five_seconds
five_to_ten_seconds
ten_to_twenty_seconds
twenty_to_one_hundred_seconds
over_one_hundred_seconds

	
bot_edits
 1.00000000 
-0.12531453 
0.1382504   
 0.31846770 
-0.357651992
0.11347437  
-0.097846388
-0.2136459  
-0.1550982  
-0.12986468 
	
semi_automated_edits
-0.12531453 
 1.00000000 
0.6152116   
-0.03545559 
 0.207167369
0.16747999  
 0.288524595
 0.3687762  
 0.8113992  
 0.84193673 
	
non_bot_edits
 0.13825043 
 0.61521160 
1.0000000   
 0.22645321 
 0.163788832
0.17773966  
 0.365961502
 0.1597484  
 0.7353154  
 0.74995235 
	
anon_edits
 0.31846770 
-0.03545559 
0.2264532   
 1.00000000 
-0.355441204
0.07093577  
-0.033581094
-0.1640175  
 0.1760625  
 0.07802554 
	
difference_in_alignment_with_previous
-0.35765199 
 0.20716737 
0.1637888   
-0.35544120 
 1.000000000
0.11820088  
-0.003849819
-0.1102725  
 0.1371069  
 0.18940347 
	
under_five_seconds
 0.11347437 
 0.16747999 
0.1777397   
 0.07093577 
 0.118200877
1.00000000  
 0.170116257
 0.1071469  
 0.3286068  
 0.30550791 
	
five_to_ten_seconds
-0.09784639 
 0.28852460 
0.3659615   
-0.03358109 
-0.003849819
0.17011626  
 1.000000000
 0.3709929  
 0.4404422  
 0.40728035 
	
ten_to_twenty_seconds
-0.21364589 
 0.36877622 
0.1597484   
-0.16401753 
-0.110272537
0.10714694  
 0.370992948
 1.0000000  
 0.4574423  
 0.45637507 
	
twenty_to_one_hundred_seconds
-0.15509815 
 0.81139918 
0.7353154   
 0.17606251 
 0.137106918
0.32860682  
 0.440442157
 0.4574423  
 1.0000000  
 0.92391843 
	
over_one_hundred_seconds
-0.12986468 
 0.84193673 
0.7499524   
 0.07802554 
 0.189403469
0.30550791  
 0.407280351
 0.4563751  
 0.9239184  
 1.00000000 

In [80]:

    

VIF(edit_type_regression)


    

    





2.46868162389359

In [81]:

    

qqnorm(edit_type_regression$residuals)


    

In [ ]:

    




    

In [82]:

    

names(edit_type_regression)


    

    






	
'coefficients'
	
'residuals'
	
'effects'
	
'rank'
	
'fitted.values'
	
'assign'
	
'qr'
	
'df.residual'
	
'xlevels'
	
'call'
	
'terms'
	
'model'