Hello,
I'm trying to knit my document but I get this code:
Calls: ... process_file -> split_file -> lapply -> FUN -> parse_block
Execution halted
Does anyone know how to fix this?
Thank you in advance.
I've also updated the knitr package
Hi,
we need more information on what you are doing. It is highly possible this comes from your Rmd chunks content. Without it we won't be able to help.
I would really encourage you to review the following guide, FAQ: Tips for writing R-related questions. For example, the guide emphasizes asking coding questions with formatted code-chunks and a reprex, which would make it easier for people who want to help you to pick up your issue and attempt to run with a solution.
thanks
Hello,
Thank you for the response. My Rmd document is quite big but I will try to attach it:
library(tidyverse)
library(knitr)
library(here)
library(dplyr)
library(Hmisc)
library(cowplot)
library(kableExtra)
library(afex)
library(broom)
library(GGally)
library(ggfortify)
library(tidyr)
library(ggplot2)
library(ltm)
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library(readr)
ddata <- read_csv("Downloads/Research Dissertation: Inner Reach for Poetry_March 13, 2022_16.52.csv")
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filter(.data = ddata, Finished > 0) %>% nrow()
new_ddata <- ddata %>% filter ((Finished > 0))
new_ddata
dem_data <- new_ddata %>%
dplyr::select(Q1, Q2, Q3, ResponseId)
dem_data
dem_data <- new_ddata
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dem_data2 = dplyr::select(dem_data, -RecipientLastName, -RecipientFirstName, -RecipientEmail, -ExternalReference, -Q4, -Q6, -Q13_1, -Q16, -Q17, -Q9, -DistributionChannel, -StartDate, -EndDate, -Status, -IPAddress, -Finished, -LocationLatitude, -LocationLongitude)
dem_data2 <- rename(dem_data2, Age = Q1, Gender = Q2, Ethnicity = Q3)
dem_data2 <- dem_data2[-c(1), ]
dem_data2 <- dem_data2
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Condition <- c("Lyrical", "Lyrical", "Lyrical", "Non-Lyrical", "Lyrical", "Non-Lyrical", "Lyrical", "Non-Lyrical", "Lyrical", "Lyrical", "Lyrical", "Lyrical", "Non-Lyrical", "Non-Lyrical", "Lyrical", "Lyrical", "Non-Lyrical", "Lyrical", "Non-Lyrical", "Lyrical", "Non-Lyrical", "Lyrical", "Non-Lyrical", "Non-Lyrical", "Non-Lyrical", "Lyrical", "Lyrical", "Lyrical", "Non-Lyrical", "Non-Lyrical", "Non-Lyrical", "Non-Lyrical", "Non-Lyrical", "Non-Lyrical", "Non-Lyrical", "Lyrical", "Non-Lyrical", "Lyrical", "Lyrical", "Non-Lyrical", "Lyrical", "Lyrical", "Non-Lyrical", "Non-Lyrical", "Non-Lyrical", "Non-Lyrical", "Lyrical","Non-Lyrical","Lyrical", "Non-Lyrical", "Non-Lyrical", "Non-Lyrical", "Non-Lyrical", "Lyrical", "Non-Lyrical", "Non-Lyrical", "Non-Lyrical", "Non-Lyrical")
dem_data2['Condition'] <- Condition
dem_data2 <- dem_data2[-c(38, 21, 10),]
dem_data2 <- dem_data2
dem_data2 <- dem_data2 %>% as.data.frame(apply(dem_data2, 2, as.numeric))
sapply(dem_data2, class)
dem_data2 <- dem_data2 %>% type_convert(col_types = cols(ResponseId = col_character()))
age_desc <- dem_data2 %>%
summarise(
mean = mean(Age, na.rm = T),
sd = sd(Age, na.rm = T),
min = min(Age, na.rm = T),
max = max(Age, na.rm = T)
)
age_desc
table(dem_data2$Gender)
table(dem_data2$Ethnicity)
table(dem_data2$Q8)
coo_desc <- dem_data2 %>%
mutate(Gender = fct_explicit_na(Condition)) %>%
group_by(Condition) %>%
summarise(n = n(),
perc = n()/nrow(dem_data2) * 100,
mean_age = mean(Age, na.rm = T),
sd_age = sd(Age, na.rm = T))
coo_desc
coo_desc %>% kable() %>% kable_styling()
coo_desc %>%
kable(col.names = c("Condition", "*N*", "%", "*M*~age~", "*SD*~age~"),
caption = "Table 1 *Descriptive statistics by Condition*",
digits = 2) %>%
kable_styling()
relevance_data <- dem_data2 %>%
dplyr::select(ResponseId, Q18_1, Q18_2, Q18_3, Q18_4, Q18_5, Q18_6, Q18_7, Q18_8, Q18_9, Q18_10, Condition)
relevance_data
relevance_data <- relevance_data
relevance_comp <- relevance_data %>%
group_by(ResponseId) %>%
mutate(relevance_comp = mean(c(Q18_1, Q18_2, Q18_3, Q18_4, Q18_5, Q18_6, Q18_7, Q18_8, Q18_9, Q18_10), na.rm = T)) %>%
dplyr::select(ResponseId, Q18_1, Q18_2, Q18_3, Q18_4, Q18_5, Q18_6, Q18_7, Q18_8, Q18_9, Q18_10, relevance_comp)
relevance_comp
relevance_data <- relevance_data %>%
group_by(ResponseId) %>%
mutate(relevance_comp = mean(c(Q18_1, Q18_2, Q18_3, Q18_4, Q18_5, Q18_6, Q18_7, Q18_8, Q18_9, Q18_10), na.rm = T)) %>%
ungroup()
relevance_data <- relevance_data %>%
group_by(ResponseId) %>%
mutate(nars_total = sum(Q18_1, Q18_2, Q18_3, Q18_4, Q18_5, Q18_6, Q18_7, Q18_8, Q18_9, Q18_10),
nars_comp = nars_total/10) %>%
ungroup()
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relevance_data <- dem_data2 %>%
dplyr::select(ResponseId, Q18_1, Q18_2, Q18_3, Q18_4, Q18_5, Q18_6, Q18_7, Q18_8, Q18_9, Q18_10, Condition)
relevance_data
relevance_data <- relevance_data
relevance_comp <- relevance_data %>%
group_by(ResponseId) %>%
mutate(relevance_comp = mean(c(Q18_1, Q18_2, Q18_3, Q18_4, Q18_5, Q18_6, Q18_7, Q18_8, Q18_9, Q18_10), na.rm = T)) %>%
dplyr::select(ResponseId, Q18_1, Q18_2, Q18_3, Q18_4, Q18_5, Q18_6, Q18_7, Q18_8, Q18_9, Q18_10, relevance_comp)
relevance_comp
relevance_data <- relevance_data %>%
group_by(ResponseId) %>%
mutate(relevance_comp = mean(c(Q18_1, Q18_2, Q18_3, Q18_4, Q18_5, Q18_6, Q18_7, Q18_8, Q18_9, Q18_10), na.rm = T)) %>%
ungroup()
relevance_data <- relevance_data %>%
group_by(ResponseId) %>%
mutate(nars_total = sum(Q18_1, Q18_2, Q18_3, Q18_4, Q18_5, Q18_6, Q18_7, Q18_8, Q18_9, Q18_10),
nars_comp = nars_total/10) %>%
ungroup()
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relevance_plot <- relevance_data %>%
ggplot(aes(x = Condition, y = relevance_comp)) +
labs(x = "Experimental Condition", y = "Mean Relavance Rating") +
theme_cowplot()
relevance_plot +
geom_point(position = "jitter")
relevance_plot +
geom_point(stat = "summary",
fun.y = "mean")
relevance_plot +
geom_point(stat = "summary",
fun.y = "mean",
size = 4,
shape = 21,
fill = "black") +
ylim(0, 8) +
stat_summary(fun.data="mean_cl_boot",geom="errorbar", width = .25)
relevance_plot_final <- relevance_plot +
stat_summary(fun.data="mean_cl_boot",geom="errorbar", width = .25) +
geom_point(stat = "summary",
fun.y = "mean",
size = 4,
shape = 21,
fill = "black") +
ylim(0, 8)
relevance_plot_final
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test_t_relevance <- relevance_data %>%
t.test(relevance_comp ~ Condition, ., alternative = "two.sided", var.equal = T)
test_t_relevance
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experience_data <- dem_data2 %>%
dplyr::select(ResponseId, Q19_1, Q19_2, Q19_3, Q19_4, Q19_5, Q19_6, Q19_7, Q19_8, Q19_9, Q19_10, Q19_11, Q19_12, Q19_13, Q19_14, Condition)
experience_data
experience_data <- experience_data
experience_comp <- experience_data %>%
group_by(ResponseId) %>%
mutate(experience_comp = mean(c(Q19_1, Q19_2, Q19_3, Q19_4, Q19_5, Q19_6, Q19_7, Q19_8, Q19_9, Q19_10, Q19_11, Q19_12, Q19_13, Q19_14), na.rm = T)) %>%
dplyr::select(ResponseId, Q19_1, Q19_2, Q19_3, Q19_4, Q19_5, Q19_6, Q19_7, Q19_8, Q19_9, Q19_10, Q19_11, Q19_12, Q19_13, Q19_14, experience_comp)
experience_comp
experience_data <- experience_data %>%
group_by(ResponseId) %>%
mutate(experience_comp = mean(c(Q19_1, Q19_2, Q19_3, Q19_4, Q19_5, Q19_6, Q19_7, Q19_8, Q19_9, Q19_10, Q19_11, Q19_12, Q19_13, Q19_14), na.rm = T)) %>%
ungroup()
experience_data <- experience_data %>%
group_by(ResponseId) %>%
mutate(nars_total = sum(Q19_1, Q19_2, Q19_3, Q19_4, Q19_5, Q19_6, Q19_7, Q19_8, Q19_9, Q19_10, Q19_11, Q19_12, Q19_13, Q19_14),
nars_comp = nars_total/14) %>%
ungroup()
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experience_plot <- experience_data %>%
ggplot(aes(x = Condition, y = experience_comp)) +
labs(x = "Experimental Condition", y = "Mean Writing Experience Rating") +
theme_cowplot()
experience_plot +
geom_point(position = "jitter")
experience_plot +
geom_point(stat = "summary",
fun.y = "mean")
experience_plot +
geom_point(stat = "summary",
fun.y = "mean",
size = 4,
shape = 21,
fill = "black") +
ylim(0, 8) +
stat_summary(fun.data="mean_cl_boot",geom="errorbar", width = .25)
experience_plot_final <- experience_plot +
stat_summary(fun.data="mean_cl_boot",geom="errorbar", width = .25) +
geom_point(stat = "summary",
fun.y = "mean",
size = 4,
shape = 21,
fill = "black") +
ylim(0, 8)
experience_plot_final
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test_t_experience <- experience_data %>%
t.test(experience_comp ~ Condition, ., alternative = "two.sided", var.equal = T)
test_t_experience
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relevance_means <- dem_data2 %>%
dplyr::select(Q18_1, Q18_2, Q18_3, Q18_4, Q18_5, Q18_6, Q18_7, Q18_8, Q18_9, Q18_10, Condition)
relevance_means
relevance_means <- relevance_means
mean_test_tib <- relevance_means %>%
dplyr::select(Q18_1, Q18_2, Q18_3, Q18_4, Q18_5, Q18_6, Q18_7, Q18_8, Q18_9, Q18_10) %>%
summarise_each(funs(min = min,
q25 = quantile(., 0.25),
median = median,
q75 = quantile(., 0.75),
max = max,
mean = mean,
sd = sd))
mean_tib <- relevance_means %>%
group_by(Condition) %>%
dplyr::select(Q18_1, Q18_2, Q18_3, Q18_4, Q18_5, Q18_6, Q18_7, Q18_8, Q18_9, Q18_10) %>%
summarise_each(funs(mean = mean,
sd = sd))
mean_tib
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final_plot_data <- structure(list(Condition = c("Non-Lyrical", "Lyrical"), Q18_1_mean = c(5.375,
4.47826086956522), Q18_2_mean = c(5.15625, 4.43478260869565),
Q18_3_mean = c(4.59375, 3.8695652173913), Q18_4_mean = c(5.1875,
4.21739130434783), Q18_5_mean = c(5.46875, 4.65217391304348
), Q18_6_mean = c(4.78125, 3.95652173913043), Q18_7_mean = c(4.78125,
4.26086956521739), Q18_8_mean = c(5.78125, 5.43478260869565
), Q18_9_mean = c(5.46875, 5.73913043478261), Q18_10_mean = c(4.90625,
4.30434782608696), Q18_1_sd = c(0.975506485486286, 1.53355099560676
), Q18_2_sd = c(1.16700263979578, 1.53226175536575), Q18_3_sd = c(1.07341405894253,
1.57550418556574), Q18_4_sd = c(0.895778630487862, 1.59420888728064
), Q18_5_sd = c(1.10670609788542, 1.46500684615757), Q18_6_sd = c(1.15659051219661,
1.49174275352279), Q18_7_sd = c(1.15659051219661, 1.684620035507
), Q18_8_sd = c(1.00753211747415, 1.47173635721156), Q18_9_sd = c(1.50235030921982,
1.32175473258942), Q18_10_sd = c(0.856074122506811, 1.42811963493946
)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-2L))
final_plot_data %>%
dplyr::select(c(
"Condition", "Q18_1_mean", "Q18_2_mean", "Q18_3_mean", "Q18_4_mean", "Q18_5_mean", "Q18_6_mean", "Q18_7_mean", "Q18_8_mean","Q18_9_mean", "Q18_10_mean","Q18_1_sd", "Q18_2_sd", "Q18_3_sd", "Q18_4_sd", "Q18_5_sd", "Q18_6_sd", "Q18_7_sd", "Q18_8_sd", "Q18_9_sd", "Q18_10_sd"
)) %>%
tidyr::pivot_longer(
cols = -Condition,
names_to = c("variable", ".value"),
names_pattern = "(.*)_(.*)"
) %>%
ggplot(aes(x = variable, y = mean, fill = Condition)) +
geom_col(position = "dodge") +
geom_errorbar(
aes(ymin = mean - sd, ymax = mean + sd),
width = 0.2,
position = position_dodge(.9)
) +
ggplot2::scale_fill_grey() +
labs(x = "Relevance Scale Questions", y = "Mean Relevance Rating") +
theme_classic()
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experience_means <- dem_data2 %>%
dplyr::select(Q19_1, Q19_2, Q19_3, Q19_4, Q19_5, Q19_6, Q19_7, Q19_8, Q19_9, Q19_10, Q19_11, Q19_12, Q19_13, Q19_14, Condition)
experience_means
experience_means <- experience_means
mean_test_tib2 <- experience_means %>%
dplyr::select(Q19_1, Q19_2, Q19_3, Q19_4, Q19_5, Q19_6, Q19_7, Q19_8, Q19_9, Q19_10, Q19_11, Q19_12, Q19_13, Q19_14) %>%
summarise_each(funs(min = min,
q25 = quantile(., 0.25),
median = median,
q75 = quantile(., 0.75),
max = max,
mean = mean,
sd = sd))
mean_tib2 <- experience_means %>%
group_by(Condition) %>%
dplyr::select(Q19_1, Q19_2, Q19_3, Q19_4, Q19_5, Q19_6, Q19_7, Q19_8, Q19_9, Q19_10, Q19_11, Q19_12, Q19_13, Q19_14) %>%
summarise_each(funs(mean = mean,
sd = sd))
mean_tib2
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final_plot_data2 <- structure(list(Condition = c("Lyrical", "Non-Lyrical"), Q19_1_mean = c(5.34782608695652,
5.09375), Q19_2_mean = c(4.52173913043478, 4.90625), Q19_3_mean = c(4.39130434782609,
4.90625), Q19_4_mean = c(4.47826086956522, 4.90625), Q19_5_mean = c(4.82608695652174,
5.09375), Q19_6_mean = c(5.17391304347826, 4.875), Q19_7_mean = c(4.82608695652174,
4.84375), Q19_8_mean = c(5.08695652173913, 4.84375), Q19_9_mean = c(4.34782608695652,
4.71875), Q19_10_mean = c(5.04347826086957, 5.21875), Q19_11_mean = c(4.43478260869565,
4.78125), Q19_12_mean = c(3.69565217391304, 4.4375), Q19_13_mean = c(4.34782608695652,
4.8125), Q19_14_mean = c(4.82608695652174, 4.6875), Q19_1_sd = c(1.33514369850296,
0.962502618345831), Q19_2_sd = c(1.3097385310675, 1.11758307766531
), Q19_3_sd = c(1.52968001510662, 1.1460837947184), Q19_4_sd = c(1.3097385310675,
1.25362377954678), Q19_5_sd = c(1.46635521905795, 1.25362377954678
), Q19_6_sd = c(1.11404969340133, 1.21150399730412), Q19_7_sd = c(1.37020814321446,
1.27277636583154), Q19_8_sd = c(0.848155403763253, 1.43929586279026
), Q19_9_sd = c(1.40158013594313, 1.39664055458435), Q19_10_sd = c(1.29608714878021,
1.15659051219661), Q19_11_sd = c(1.37596534564322, 1.33765261414503
), Q19_12_sd = c(1.25895998234819, 1.36635847560988), Q19_13_sd = c(1.79920931250303,
1.35450264649237), Q19_14_sd = c(1.49703263804354, 1.55413081194768
)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-2L))
final_plot_data2 %>%
dplyr::select(c(
"Condition", "Q19_1_mean", "Q19_2_mean", "Q19_3_mean", "Q19_4_mean", "Q19_5_mean", "Q19_6_mean", "Q19_7_mean", "Q19_8_mean","Q19_9_mean", "Q19_1_sd", "Q19_2_sd", "Q19_3_sd", "Q19_4_sd", "Q19_5_sd", "Q19_6_sd", "Q19_7_sd", "Q19_8_sd", "Q19_9_sd"
)) %>%
tidyr::pivot_longer(
cols = -Condition,
names_to = c("variable", ".value"),
names_pattern = "(.*)_(.*)"
) %>%
ggplot(aes(x = variable, y = mean, fill = Condition)) +
geom_col(position = "dodge") +
geom_errorbar(
aes(ymin = mean - sd, ymax = mean + sd),
width = 0.2,
position = position_dodge(.9)
) + ggplot2::scale_fill_grey() +
labs(x = "Writing Experience Questions (1-9)", y = "Mean Writing Experience Rating") +
theme_classic()
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ewvar <- c("Q18_1", "Q18_2", "Q18_3", "Q18_4", "Q18_5", "Q18_6", "Q18_7", "Q18_8", "Q18_9", "Q18_10")
Q18_items_tib <- dem_data2[ewvar]
Q18_items_tib %>% dplyr::select(ewvar) %>%
correlation::correlation() %>%
summary()
Q18_correlation <- Q18_items_tib %>%
dplyr::select(ewvar) %>%
psych::cor.plot(upper = FALSE)
Q18_correlation
Q18_items_tib <- dem_data2[ewvar]
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cronbach.alpha(Q18_items_tib)
cronbach.alpha(Q18_items_tib, CI=TRUE, standardized=TRUE)
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I can't reproduce your file, and it is hard to know where the error happens
Is this the only information show ?
This comes probably from some part of your chunks that does not correctly parse. You could try reducing your document content to find the culprit.
It is quite specific to your document, so it is hard for some other people to help.
Also for formatting please see
I solved the problem and actually I had used the same chunk name twice. Thank you for your help still!
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