<jupyter_start><jupyter_text>Correction post publication:While the reported results in the paper are based on original file, we later found that the file which our results are based on was not up to date. Thus, here, we also provide the correct area sizes. The main difference to the paper is that now all the two-way interaction effects are significant. All other comparisons stayed the same.<jupyter_code>df = read.csv("./out/Anova_Areas_total_v2.csv")

# In case you which to calculate the origianl resulst, use the follwoing file:
#df = read.csv("./data/Anova_Areas_total-as-reported-in-paper.csv")
# We are interested in the grip area which are caused by the fingers on the back. 
# Thus, we exclude the thumb in our ANOVAs. 
df = df[df$Finger != "Thumb",]
summary(df)
df$Finger <- factor(df$Finger)
df$Cond <- factor(df$Cond)
df$Phone <- factor(df$Phone)
df$PId <- factor(df$PId)<jupyter_output><empty_output><jupyter_text>ANOVA on all Areas<jupyter_code>aov<-ezANOVA(data = df,
       dv = TouchedArea,
       within = c(Finger, Phone, Cond),
       wid = PId,
       type = 3,
       detailed = F,
       return_aov = F)
aov 
aov$`Sphericity Corrections`$GGe * c(3, 3, 9, 3, 3, 9)
aov$`Sphericity Corrections`$GGe * c(45, 45, 135, 45, 45, 135)
lme_velocity <- lme(TouchedArea ~ Finger*Phone*Cond, data = df, random=~1|PId)
summary(glht(lme_velocity, linfct=mcp(Cond = "Tukey")), test = adjusted(type = "bonferroni"))
summary(glht(lme_velocity, linfct=mcp(Phone = "Tukey")), test = adjusted(type = "bonferroni"))<jupyter_output>Warning message in mcp2matrix(model, linfct = linfct):
“covariate interactions found -- default contrast might be inappropriate”<jupyter_text>ANOVA only on walking<jupyter_code>df2 = df[df$Cond == "walking",]
aov2<-ezANOVA(data = df2,
       dv = TouchedArea,
       within = c(Finger, Phone),
       wid = PId,
       type = 3,
       detailed = F,
       return_aov = F)
aov2
aov2$`Sphericity Corrections`$GGe * c(3, 3, 9)
aov2$`Sphericity Corrections`$GGe * c(45, 45, 135)
lme2 <- lme(TouchedArea ~ Phone, data = df2, random=~1|PId)
summary(glht(lme2, linfct=mcp(Phone = "Tukey")), test = adjusted(type = "bonferroni"))<jupyter_output><empty_output><jupyter_text>ANOVA only on seated<jupyter_code>df2 = df[df$Cond == "seated",]
aov2<-ezANOVA(data = df2,
       dv = TouchedArea,
       within = c(Finger, Phone),
       wid = PId,
       type = 3,
       detailed = F,
       return_aov = F)
aov2
aov2$`Sphericity Corrections`$GGe * c(3, 3, 9)
aov2$`Sphericity Corrections`$GGe * c(45, 45, 135)
lme2 <- lme(TouchedArea ~ Phone, data = df2, random=~1|PId)
summary(glht(lme2, linfct=mcp(Phone = "Tukey")), test = adjusted(type = "bonferroni"))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1343669250645995, 'fraction_numerical': 0.024363233665559248, 'mean_word_length': 4.48582995951417, 'pattern_counts': {'":': 0, '<': 26, '<?xml version=': 0, '>': 16, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '569699', 'license': 'MIT', 'n_tokens_mistral': 1099, 'n_tokens_neox': 1007, 'n_words': 266, 'path': '_R02-GripArea.ipynb', 'repo_name': 'interactionlab/unintended-input-dataset', 'size': '2709'}