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library(tidyverse)
anjie_d <- read.csv("/Users/caoanjie/Desktop/Summer2020/cross-cultural/picture_description/pilot-data - anjie-coding.csv")
anjie_d %>% 
  DT::datatable()


shan_d <- read.csv("/Users/caoanjie/Desktop/Summer2020/cross-cultural/picture_description/pilot-data - shan-coding.csv")

shan_d <- shan_d %>%
  select(Coder, Participant, Language.Background, Language.Used, presentation_mode, item_number, raw_text, coding_item, position, score, note) 
anjie_d %>% 
  filter(!is.na(score)) %>% 
  mutate(Language_Background = Language.Background, 
         Language_Used = Language.Used) %>% 
  group_by(coding_item, position, Language_Background) %>% 
  summarise(
    mean = mean(score), 
    sd = sd(score),
    n = n()) %>% 
  mutate(ci_range_95 =  1.96 * (sd/sqrt(n)),
         ci_lower = mean - ci_range_95,
         ci_upper = mean + ci_range_95)  -> summary_anjie_d_lb

summary_anjie_d_lb %>% 
  ggplot(aes(x = coding_item, y = mean, fill = position)) + 
  geom_col(position = "dodge") + 
  geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper), 
               position=position_dodge(.9),
               width=.2) +
  theme(axis.text.x = element_text(angle = 90))+
  facet_wrap(~Language_Background) 



anjie_d %>% 
  filter(!is.na(score)) %>% 
  mutate(Language_Background = Language.Background, 
         Language_Used = Language.Used) %>% 
  group_by(coding_item, position, Language_Used) %>% 
  summarise(
    mean = mean(score), 
    sd = sd(score),
    n = n()) %>% 
  mutate(ci_range_95 =  1.96 * (sd/sqrt(n)),
         ci_lower = mean - ci_range_95,
         ci_upper = mean + ci_range_95)  -> summary_anjie_d_lu


summary_anjie_d_lu %>% 
  ggplot(aes(x = coding_item, y = mean, fill = position)) + 
  geom_col(position = "dodge") + 
  geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper), 
               position=position_dodge(.9),
               width=.2) +
  theme(axis.text.x = element_text(angle = 90))+
  facet_wrap(~Language_Used) 

NA
NA
shan_d %>% 
  filter(!is.na(score)) %>% 
  mutate(Language_Background = Language.Background, 
         Language_Used = Language.Used) %>% 
  group_by(coding_item, position) %>% 
  summarise(
    mean = mean(score), 
    sd = sd(score),
    n = n()) %>% 
  mutate(ci_range_95 =  1.96 * (sd/sqrt(n)),
         ci_lower = mean - ci_range_95,
         ci_upper = mean + ci_range_95)  -> summary_shan_d 

summary_shan_d
ABCDEFGHIJ0123456789
coding_item
<fctr>
position
<fctr>
mean
<dbl>
sd
<dbl>
n
<int>
ci_range_95
<dbl>
ci_lower
<dbl>
ci_upper
<dbl>
descriptive_accountbackground1.3000001.3243286400.41041370.88958631.710414
descriptive_accountfocal2.1250001.2022948400.37259501.75240502.497595
first_mention_objectbackground1.8974361.1875422390.37271151.52472442.270147
first_mention_objectfocal1.1250000.3349321400.10379651.02120351.228797
relational_accountbackground1.5128211.0481009390.32894771.18387281.841768
relational_accountfocal1.1282050.6561245390.20592540.92227971.334131

summary_shan_d %>% 
  ggplot(aes(x = coding_item, y = mean, fill = position)) + 
  geom_col(position = "dodge") + 
  geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper), 
               position=position_dodge(.9),
               width=.2) +
  theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("shan's coding, Chinese only")

NA
NA
NA

participant analysis

anjie_d_clean <- anjie_d %>%
    select(Coder, Participant, Language.Background, Language.Used, presentation_mode, item_number, raw_text, coding_item, position, score, note) 

shan_d_clean <- shan_d %>% 
      select(Coder, Participant, Language.Background, Language.Used, presentation_mode, item_number, raw_text, coding_item, position, score, note) 

  
both_coding <- bind_rows(anjie_d_clean, shan_d_clean)
Unequal factor levels: coercing to characterbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorUnequal factor levels: coercing to characterbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorUnequal factor levels: coercing to characterbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorUnequal factor levels: coercing to characterbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorUnequal factor levels: coercing to characterbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vector
both_coding
ABCDEFGHIJ0123456789
Coder
<chr>
Participant
<int>
Language.Background
<chr>
Language.Used
<chr>
presentation_mode
<fctr>
item_number
<fctr>
anjie1ChineseChinesephoneitem_1
anjie1ChineseChinesephoneitem_1
anjie1ChineseChinesephoneitem_1
anjie1ChineseChinesephoneitem_1
anjie1ChineseChinesephoneitem_1
anjie1ChineseChinesephoneitem_1
anjie1ChineseChinesephoneitem_2
anjie1ChineseChinesephoneitem_2
anjie1ChineseChinesephoneitem_2
anjie1ChineseChinesephoneitem_2
both_coding %>% 
  group_by(position) %>%
  mutate(position_num = row_number()) %>% # create unique identifier to deal with duplicates
  select(Coder, Participant, item_number, Language.Used, position, coding_item, score,position_num) %>% 
  pivot_wider(names_from = position, values_from = score) %>%  
  drop_na() %>% 
  select(-position_num) %>% 
  mutate(sensitivity_score = background-focal) %>% 
  group_by(Coder, Participant, Language.Used) %>% 
  summarize(
    sum_ss = sum(sensitivity_score)
  )  -> summary_both_coding


summary_both_coding
ABCDEFGHIJ0123456789
Coder
<chr>
Participant
<int>
Language.Used
<chr>
sum_ss
<int>
anjie1Chinese-6
anjie2Chinese-14
anjie3Chinese-38
anjie4English-16
anjie5English9
anjie6English10
shan1Chinese14
shan2Chinese22
shan3Chinese-24

summary_both_coding %>% 
  ggplot(aes(x = as.factor(Participant), y = sum_ss, fill = Language.Used)) + 
  geom_col(position = "dodge") +
  ggtitle("Calculated Sensitivity Score by participant") + 
  facet_wrap(~Coder)

NA
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