Experience with pipe

Reading data

df = read.csv("~/Dropbox/VOS Study/Diabetes Incidence/Diabetes Incidence Data.csv")
df$diab = ifelse(df$v2.diab1=="Diabetes", 1, 0)

Regression

df %>% filter(sex=="F") %>% lm(wbbmd ~ age + pcfat, data = .) %>% summary()

## 
## Call:
## lm(formula = wbbmd ~ age + pcfat, data = .)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.31164 -0.06267 -0.00546  0.05976  0.31990 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.3478426  0.0260886  51.664   <2e-16 ***
## age         -0.0065742  0.0002759 -23.827   <2e-16 ***
## pcfat       -0.0004123  0.0005818  -0.709    0.479    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08938 on 1282 degrees of freedom
## Multiple R-squared:  0.319,  Adjusted R-squared:  0.318 
## F-statistic: 300.3 on 2 and 1282 DF,  p-value: < 2.2e-16

df %>%
        filter(sex=="F") %>%
        ggplot(aes(x=age, y=wbbmc)) + geom_point() + geom_smooth()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Summary tools

# freq(iris$Species, plain.ascii = FALSE, style = "rmarkdown")

freq(df$v2.diab1, report.nas = FALSE, headings = FALSE)

## 
##                      Freq        %   % Cum.
## ------------------ ------ -------- --------
##           Diabetes     80     4.33     4.33
##             Normal   1144    61.94    66.27
##       Pre-Diabetes    623    33.73   100.00
##              Total   1847   100.00   100.00

ctable(x=df$sex, y=df$v2.diab1, prop="r")

## Cross-Tabulation, Row Proportions  
## sex * v2.diab1  
## Data Frame: df  
## 
## ------- ---------- ----------- -------------- -------------- ---------------
##           v2.diab1    Diabetes         Normal   Pre-Diabetes           Total
##     sex                                                                     
##       F              53 (4.1%)    810 (63.0%)    422 (32.8%)   1285 (100.0%)
##       M              27 (4.8%)    334 (59.4%)    201 (35.8%)    562 (100.0%)
##   Total              80 (4.3%)   1144 (61.9%)    623 (33.7%)   1847 (100.0%)
## ------- ---------- ----------- -------------- -------------- ---------------