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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()

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	Coder	Participant	Language.Background	Language.Used	presentation_mode	item_number	raw_text	coding_item	position	score	X13	X14
1	anjie	1	Chinese	Chinese	phone	item_1	这是一匹白色的马，奔驰在白色的雪原上，可以看到背景有一些比较隐约的是画的树。然后树边上还有一个被白雪覆盖的隐约的小房子呃，这个白马全身都是雪白的，鬃毛也是白的。但是它的鼻头是黑色的。	first_mention_object	focal	1	technical error	technical error
2	anjie	1	Chinese	Chinese	phone	item_1	这是一匹白色的马，奔驰在白色的雪原上，可以看到背景有一些比较隐约的是画的树。然后树边上还有一个被白雪覆盖的隐约的小房子呃，这个白马全身都是雪白的，鬃毛也是白的。但是它的鼻头是黑色的。	first_mention_object	background	0
3	anjie	1	Chinese	Chinese	phone	item_1	这是一匹白色的马，奔驰在白色的雪原上，可以看到背景有一些比较隐约的是画的树。然后树边上还有一个被白雪覆盖的隐约的小房子呃，这个白马全身都是雪白的，鬃毛也是白的。但是它的鼻头是黑色的。	descriptive_account	focal	4
4	anjie	1	Chinese	Chinese	phone	item_1	这是一匹白色的马，奔驰在白色的雪原上，可以看到背景有一些比较隐约的是画的树。然后树边上还有一个被白雪覆盖的隐约的小房子呃，这个白马全身都是雪白的，鬃毛也是白的。但是它的鼻头是黑色的。	descriptive_account	background	3
5	anjie	1	Chinese	Chinese	phone	item_1	这是一匹白色的马，奔驰在白色的雪原上，可以看到背景有一些比较隐约的是画的树。然后树边上还有一个被白雪覆盖的隐约的小房子呃，这个白马全身都是雪白的，鬃毛也是白的。但是它的鼻头是黑色的。	relational_account	focal	1
6	anjie	1	Chinese	Chinese	phone	item_1	这是一匹白色的马，奔驰在白色的雪原上，可以看到背景有一些比较隐约的是画的树。然后树边上还有一个被白雪覆盖的隐约的小房子呃，这个白马全身都是雪白的，鬃毛也是白的。但是它的鼻头是黑色的。	relational_account	background	1
7	anjie	1	Chinese	Chinese	phone	item_2	一辆蓝色的老爷车行驶在林荫大道上，这个当时这个车擦的非常干净，可以看到上面反射的斑驳的太阳光，然后树都很绿。	first_mention_object	focal	1
8	anjie	1	Chinese	Chinese	phone	item_2	一辆蓝色的老爷车行驶在林荫大道上，这个当时这个车擦的非常干净，可以看到上面反射的斑驳的太阳光，然后树都很绿。	first_mention_object	background	0
9	anjie	1	Chinese	Chinese	phone	item_2	一辆蓝色的老爷车行驶在林荫大道上，这个当时这个车擦的非常干净，可以看到上面反射的斑驳的太阳光，然后树都很绿。	descriptive_account	focal	2
10	anjie	1	Chinese	Chinese	phone	item_2	一辆蓝色的老爷车行驶在林荫大道上，这个当时这个车擦的非常干净，可以看到上面反射的斑驳的太阳光，然后树都很绿。	descriptive_account	background	1

Showing 1 to 10 of 504 entries

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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_account	background	1.300000	1.3243286	40	0.4104137	0.8895863	1.710414
descriptive_account	focal	2.125000	1.2022948	40	0.3725950	1.7524050	2.497595
first_mention_object	background	1.897436	1.1875422	39	0.3727115	1.5247244	2.270147
first_mention_object	focal	1.125000	0.3349321	40	0.1037965	1.0212035	1.228797
relational_account	background	1.512821	1.0481009	39	0.3289477	1.1838728	1.841768
relational_account	focal	1.128205	0.6561245	39	0.2059254	0.9222797	1.334131


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>
anjie	1	Chinese	Chinese	phone	item_1
anjie	1	Chinese	Chinese	phone	item_1
anjie	1	Chinese	Chinese	phone	item_1
anjie	1	Chinese	Chinese	phone	item_1
anjie	1	Chinese	Chinese	phone	item_1
anjie	1	Chinese	Chinese	phone	item_1
anjie	1	Chinese	Chinese	phone	item_2
anjie	1	Chinese	Chinese	phone	item_2
anjie	1	Chinese	Chinese	phone	item_2
anjie	1	Chinese	Chinese	phone	item_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>
anjie	1	Chinese	-6
anjie	2	Chinese	-14
anjie	3	Chinese	-38
anjie	4	English	-16
anjie	5	English	9
anjie	6	English	10
shan	1	Chinese	14
shan	2	Chinese	22
shan	3	Chinese	-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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

picture description pilot

participant analysis