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benjamindemayobenjamindemayo
committedNov 12, 2020
adds template for paper and screened data
1 parent 29919fb commit 8514d6c

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‎.Rhistory

+359
Original file line numberDiff line numberDiff line change
@@ -23,3 +23,362 @@ library(here)
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library(fs)
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root <- here::here()
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root
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knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
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library(tidyverse)
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library(png)
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library(knitr)
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library(markdown)
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library(lmerTest)
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library(ggthemes)
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library(psych)
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library(magrittr)
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library(langcog)
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knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
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library(tidyverse)
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library(png)
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library(knitr)
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library(markdown)
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library(lmerTest)
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library(ggthemes)
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library(psych)
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library(magrittr)
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library(tidyboot)
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library(fs)
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library(ggmap)
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library(lubridate)
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source(path(here::here(), "file_paths.R"))
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source(functions_script)
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#Variables to tinker with
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min_completion_time = 8.5
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facebook_ws_raw <-
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readInWebCDI(fb_ws_directory) %>%
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select( #drop a bunch of columns that were screwing up the merge with prolific data
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-opt_out,
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-country,
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-sibling_boolean,
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-sibling_data,
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-sibling_count,
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-caregiver_other
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)
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all_wg_raw <- readInWebCDI(fb_wg_directory)
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prolific_raw <-
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readInWebCDI(prolific_data_directory) %>%
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select(
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colnames(facebook_ws_raw), #drop columns that are prolific specific
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-opt_out, #drop a bunch of columns that were screwing up the merge with facebook data
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-country,
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-sibling_boolean,
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-sibling_data,
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-sibling_count,
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-caregiver_other
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)
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#all of the ws data collected
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all_ws_raw <-
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facebook_ws_raw %>%
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bind_rows(prolific_raw) %>%
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mutate(completed = case_when(
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stringr::str_to_lower(completed) == "true" ~ TRUE,
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stringr::str_to_lower(completed) == "false" ~ FALSE
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))
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knitr::opts_chunk$set(echo = TRUE)
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library(tidyverse)
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library(png)
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library(knitr)
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library(markdown)
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library(lmerTest)
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library(ggthemes)
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library(psych)
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library(magrittr)
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library(tidyboot)
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library(fs)
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library(ggmap)
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library(lubridate)
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source(path(here::here(), "file_paths.R"))
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source(functions_script)
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#Variables to tinker with
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min_completion_time = 8.5
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all_ws_1 <-
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readInWebCDI(all_data_ws1_path) %>%
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select( #drop a bunch of columns that were screwing up the merge with prolific data
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-opt_out,
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-country,
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-sibling_boolean,
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-sibling_data,
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-sibling_count,
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-caregiver_other
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)
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all_ws_2 <-
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readInWebCDI(all_data_ws2_path) %>%
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select( #drop a bunch of columns that were screwing up the merge with prolific data
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-opt_out,
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-country,
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-sibling_boolean,
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-sibling_data,
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-sibling_count,
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-caregiver_other
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)
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all_ws_raw <-
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all_ws_1 %>%
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bind_rows(all_ws_2) %>%
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mutate(completed = case_when(
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stringr::str_to_lower(completed) == "true" ~ TRUE,
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stringr::str_to_lower(completed) == "false" ~ FALSE
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))
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all_wg_raw <- readInWebCDI(all_data_wg_path)
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knitr::opts_chunk$set(echo = TRUE)
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library(tidyverse)
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library(png)
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library(knitr)
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library(markdown)
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library(lmerTest)
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library(ggthemes)
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library(psych)
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library(magrittr)
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library(tidyboot)
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library(fs)
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library(ggmap)
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library(lubridate)
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source(path(here::here(), "file_paths.R"))
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source(functions_script)
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#Variables to tinker with
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min_completion_time = 8.5
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all_ws_1 <-
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readInWebCDI(all_data_ws1_path) %>%
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select( #drop a bunch of columns that were screwing up the merge with prolific data
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-opt_out,
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-country,
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-sibling_boolean,
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-sibling_data,
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-sibling_count,
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-caregiver_other
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)
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all_ws_2 <-
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readInWebCDI(all_data_ws2_path) %>%
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select( #drop a bunch of columns that were screwing up the merge with prolific data
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-opt_out,
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-country,
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-sibling_boolean,
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-sibling_data,
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-sibling_count,
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-caregiver_other
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)
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all_ws_raw <-
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all_ws_1 %>%
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bind_rows(all_ws_2) %>%
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mutate(completed = case_when(
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stringr::str_to_lower(completed) == "true" ~ TRUE,
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stringr::str_to_lower(completed) == "false" ~ FALSE
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))
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all_wg_raw <- readInWebCDI(all_data_wg_path)
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knitr::opts_chunk$set(echo = TRUE)
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library(tidyverse)
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library(png)
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library(knitr)
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library(markdown)
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library(lmerTest)
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library(ggthemes)
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library(psych)
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library(magrittr)
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library(tidyboot)
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library(fs)
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library(ggmap)
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library(lubridate)
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source(path(here::here(), "file_paths.R"))
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source(functions_script)
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#Variables to tinker with
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min_completion_time = 8.5
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all_ws_1 <-
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readInWebCDI(all_data_ws1_path) %>%
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select( #drop a bunch of columns that were screwing up the merge with prolific data
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-opt_out,
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-country,
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-sibling_boolean,
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-sibling_data,
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-sibling_count,
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-caregiver_other
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)
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all_ws_2 <-
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readInWebCDI(all_data_ws2_path) %>%
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select( #drop a bunch of columns that were screwing up the merge with prolific data
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-opt_out,
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-country,
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-sibling_boolean,
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-sibling_data,
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-sibling_count,
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-caregiver_other
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)
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all_ws_raw <-
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all_ws_1 %>%
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bind_rows(all_ws_2) %>%
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mutate(completed = case_when(
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stringr::str_to_lower(completed) == "true" ~ TRUE,
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stringr::str_to_lower(completed) == "false" ~ FALSE
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))
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all_wg_raw <- readInWebCDI(all_data_wg_path)
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View(all_wg_raw)
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#WG
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wg_filtered <-
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all_wg_raw %>%
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filterIllnesses() %>%
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filterBirthweight() %>%
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filterMultilingual() %>%
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filterVision() %>%
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filterHearing() %>%
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getEthnicities() %>%
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getMaternalEd() %>%
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getCompletionInterval() %>%
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filter(completedBackgroundInfo == "TRUE") %>% #get rid of kids with demos
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filter_age_wg()
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ws_filtered <-
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all_ws_raw %>%
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filterIllnesses() %>%
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filterBirthweight() %>%
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filterMultilingual() %>%
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filterVision() %>%
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filterHearing() %>%
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getEthnicities() %>%
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getMaternalEd() %>%
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getCompletionInterval() %>%
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filter(completedBackgroundInfo == "TRUE") %>%
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filter_age_ws()
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#Comprehension and production measures
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ws_filtered %>%
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filter(`Total Produced` < 688 & !is.na(maternal_ed)) %>%
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ggplot(aes(age, `Total Produced`, color = maternal_ed)) +
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ggthemes::theme_few() +
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geom_jitter(alpha = 0.3, width = 0.225) +
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coord_cartesian(ylim = c(0, 686)) +
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geom_smooth(method = "lm") +
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labs(
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x = "Age in months",
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y = "Total words produced",
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color = "Maternal education"
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)
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wg_filtered %>%
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filter(!is.na(maternal_ed)) %>%
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ggplot(aes(age, `Words Understood`, color = maternal_ed)) +
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ggthemes::theme_few() +
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geom_jitter(alpha = 0.3, width = 0.225) +
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geom_smooth(method = "lm") +
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coord_cartesian(ylim = c(0, 390)) +
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labs(
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x = "Age in months",
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y = "Total words understood",
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color = "Maternal education"
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)
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#Demographic analyses on the entire sample
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demographics_df <-
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bind_rows(
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wg_filtered %>%
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select(
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study_name,
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subject_id,
276+
age,
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ethnicity,
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maternal_ed,
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words_produced = `Words Produced`
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),
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ws_filtered %>%
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select(
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study_name,
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subject_id,
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age,
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ethnicity,
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maternal_ed,
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words_produced = `Total Produced`
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)
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)
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ethnicity_plot_df <-
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demographics_df %>%
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getEthnicitySummary() %>%
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filter(!is.na(ethnicity)) %>%
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mutate(`Current study` = prop.table(n)) %>%
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left_join(old_ethnicity_numbers, by = "ethnicity") %>%
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select(-n) %>%
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pivot_longer(
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cols = c(`Current study`, `2007 manual`),
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names_to = "study",
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values_to = "proportion"
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)
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ethnicity_plot <-
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ethnicity_plot_df %>%
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ggplot(aes(ethnicity, proportion, fill = study)) +
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geom_col(position = "dodge") +
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labs(
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y = "Proportion of\nrespondents"
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) +
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theme_few() +
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theme(
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legend.title = element_blank(),
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axis.text = element_text(size = 14),
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axis.title = element_text(size = 13),
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legend.text = element_text(size = 13),
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axis.title.x = element_blank(),
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plot.title = element_text(size = 15),
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plot.caption = element_text(hjust = 0)
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)
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ethnicity_plot
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#Maternal ed analysis on the full sample
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maternal_ed_plot_df <-
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demographics_df %>%
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count(maternal_ed) %>%
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mutate(`Current study` = prop.table(n)) %>%
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left_join(old_momed_numbers, by = "maternal_ed") %>%
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select(-n) %>%
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pivot_longer(
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cols = c(`Current study`, `2007 manual`),
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names_to = "study",
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values_to = "proportion"
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) %>%
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mutate(
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maternal_ed = fct_relevel(
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maternal_ed,
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"Some high school or less",
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"High school diploma",
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"Some college education",
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"College diploma or more"
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)
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) %>%
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filter(!is.na(maternal_ed))
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x_axis_labs <- c(
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"Some high school\n or less",
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"High school\ndiploma",
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"Some college\neducation",
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"College diploma\nor more"
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)
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maternal_ed_plot <-
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maternal_ed_plot_df %>%
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ggplot(aes(maternal_ed, proportion, fill = study)) +
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geom_col(position = "dodge") +
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theme_few() +
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theme(
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legend.title = element_blank(),
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axis.text.x = element_text(angle = 30, vjust = 0.9, hjust = 1, size = 13.5),
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axis.title.x = element_blank(),
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legend.text = element_text(size = 13),
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plot.caption = element_text(hjust = 0)
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) +
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scale_x_discrete(labels = x_axis_labs)
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maternal_ed_plot
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View(getEthnicitySummary)
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if(!"devtools" %in% rownames(installed.packages())) install.packages("devtools")
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devtools::install_github("crsh/papaja")
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devtools::install_github("crsh/papaja")
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devtools::install_github("crsh/papaja")
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devtools::install_github("crsh/papaja")
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remove.packages("digest")
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devtools::install_github("crsh/papaja")
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devtools::install_github("crsh/papaja")
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devtools::install_github("crsh/papaja")
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install.packages("digest")
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devtools::install_github("crsh/papaja")
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remove.packages("tiblle")
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devtools::install_github("crsh/papaja")
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devtools::install_github("crsh/papaja")
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install.packages("rlang")
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install.packages("rlang")
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devtools::install_github("crsh/papaja")
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install.packages("backport")
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devtools::install_github("crsh/papaja")
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install.packages("backports")
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devtools::install_github("crsh/papaja")

‎.Rproj.user/shared/notebooks/1017BE97-all_norming_analysis/1/3F0A5F2E1DE4B8BF/chunks.json

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