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Update 1-data-exploration.R

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Kyle Belanger 2023-01-15 08:14:52 -05:00
parent 4b794c104d
commit 431e519ed1
1 changed files with 24 additions and 12 deletions
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36
ML/1-data-exploration.R
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@ -56,12 +56,14 @@ ds_high_tsh <- ds_high_tsh_raw %>%
dplyr$mutate(ft4_dia = dplyr$if_else(`50995` < 0.93, 1, 0)) %>% dplyr$mutate(ft4_dia = dplyr$if_else(`50995` < 0.93, 1, 0)) %>%
#can rename with a vector using either of these #can rename with a vector using either of these
# dplyr$rename_with(~names(test_list_names), dplyr$all_of(test_list_names)) # dplyr$rename_with(~names(test_list_names), dplyr$all_of(test_list_names))
dplyr$rename(!!!test_list_names) dplyr$rename(!!!test_list_names) %>%
dplyr$select(-FT4)
# basic visualization ----------------------------------------------------- # basic visualization -----------------------------------------------------
#graph of missing tests
g_count <- dplyr$as_tibble(colSums(is.na(ds_high_tsh)), rownames = NA ) %>% g_count <- dplyr$as_tibble(colSums(is.na(ds_high_tsh)), rownames = NA ) %>%
tibble::rownames_to_column() %>% tibble::rownames_to_column() %>%
ggplot(aes(x = rowname, y = value)) + ggplot(aes(x = rowname, y = value)) +
@ -69,9 +71,21 @@ g_count <- dplyr$as_tibble(colSums(is.na(ds_high_tsh)), rownames = NA ) %>%
gp2$theme( gp2$theme(
axis.text.x = gp2$element_text(angle = 90) axis.text.x = gp2$element_text(angle = 90)
) )
g_count g_count
#table of missing tests
dplyr$as_tibble(colSums(is.na(ds_high_tsh)), rownames = NA ) %>%
tibble::rownames_to_column() %>% knitr::kable()
# count of diagnostics ft4 and freq
t1 <- ds_high_tsh %>%
dplyr$count(ft4_dia) %>%
dplyr$mutate(freq = n/sum(n)) %>%
knitr::kable()
t1
# correlation plot
ds_corr <- cor(ds_high_tsh %>% dplyr$select(-subject_id, - charttime) ds_corr <- cor(ds_high_tsh %>% dplyr$select(-subject_id, - charttime)
%>% dplyr$mutate(dplyr$across(gender, ~dplyr$recode(.,M = 1, F = 2))) %>% dplyr$mutate(dplyr$across(gender, ~dplyr$recode(.,M = 1, F = 2)))
,use = "complete.obs") ,use = "complete.obs")
@ -79,29 +93,22 @@ ds_corr <- cor(ds_high_tsh %>% dplyr$select(-subject_id, - charttime)
#code for saving corr plot #code for saving corr plot
png(here("figures","corrplot_high.png"), type = 'cairo') png(here("figures","corrplot_high.png"), type = 'cairo')
corrplot::corrplot(ds_corr, method = "number") corrplot::corrplot(ds_corr, method = "number")
dev.off() dev.off()
#quick recode of gender, will still do recoding during feature engineering #quick recode of gender, will still do recoding during feature engineering
g1 <- ds_high_tsh %>% g1 <- ds_high_tsh %>%
dplyr$select(-subject_id, - charttime) %>% dplyr$select(-subject_id, - charttime) %>%
dplyr$mutate(dplyr$across(gender, ~dplyr$recode(.,M = 1, F = 2))) %>% dplyr$mutate(dplyr$across(gender, ~dplyr$recode(.,M = 1, F = 2))) %>%
tidyr$pivot_longer(cols = dplyr$everything()) %>% tidyr$pivot_longer(cols = dplyr$everything()) %>%
ggplot(aes(x = value)) + ggplot(aes(x = value)) +
gp2$geom_histogram() + gp2$geom_histogram(na.rm = TRUE) +
gp2$facet_wrap(~name, scales = "free") gp2$facet_wrap(~name, scales = "free")
g1 g1
# this takes a bit to load. No discernable paterns in the data
g2 <- ds_high_tsh %>% g2 <- ds_high_tsh %>%
dplyr$select(-gender, -subject_id, - charttime) %>% dplyr$select(-gender, -subject_id, - charttime) %>%
tidyr$pivot_longer(cols = !ft4_dia) %>% tidyr$pivot_longer(cols = !ft4_dia) %>%
@ -109,5 +116,10 @@ g2 <- ds_high_tsh %>%
gp2$geom_boxplot(outlier.shape = NA, na.rm = TRUE) + gp2$geom_boxplot(outlier.shape = NA, na.rm = TRUE) +
gp2$geom_jitter(size=.7, width=.1, alpha=.5, na.rm = TRUE) + gp2$geom_jitter(size=.7, width=.1, alpha=.5, na.rm = TRUE) +
gp2$facet_wrap(~name, scales = "free") gp2$facet_wrap(~name, scales = "free")
g2 g2