report.rmd

@@ -34,27 +34,26 @@ if (!require(moments)) {
 
 ## Frequency Table ordered from wish.com
 ```{r}
-freq <- function(data, rounded_digits = 2) {
+freq <- function(data) {
-  # counts
-  total_count <- length(data)
   na_count <- length(data[is.na(data)])
-  valid_count <- total_count - na_count
+  valid_count <- length(data) - na_count
-  
   frequency <- table(data)
   p <- prop.table(frequency)
-  valid_percent <- round(p * 100, digits = rounded_digits)
+  percent <- round(p * 100, digits = 2)
-  na_percent <- round(na_count / length(data) * 100, digits = rounded_digits)
+  frequency_sum <- cumsum(frequency)
+  hkum <- cumsum(p)
+  percent_sum <- round(hkum * 100, digits = 2)
+  freq_table <- cbind(frequency, percent, frequency_sum, percent_sum)
+  valid_percent <- round(valid_count / length(data) * 100, digits = 2)
+  na_percent <- round(na_count / length(data) * 100, digits = 2)
 
-  percent <- round(frequency/total_count*100, digits = rounded_digits)
-  cumulative_percent <- round(cumsum(p) * 100, digits = rounded_digits)
-  freq_table <- cbind(frequency, percent, valid_percent, cumulative_percent)
 
-  valid_percent_sum <- sum(as.data.frame(freq_table)$percent)
+  print(freq_table)
-  Valid_Total <- c(valid_count, valid_percent_sum, 100, NaN)
 
-  NAs <- c(na_count, na_percent, NaN, NaN)
+  count <- c(valid_count, na_count, valid_count + na_count)
-  Total <- c(total_count, 100, NaN, NaN)
+  percent <- c(valid_percent, na_percent, valid_percent + na_percent)
-  print(rbind(freq_table, Valid_Total, NAs, Total))
+  df <- data.frame(count, percent, row.names = c("valid", "NA", "Total"))
+  print(df)
 }
 ```
 *Source:  https://tellmi.psy.lmu.de/tutorials/deskriptive-statistiken-und-grafiken.html#haeufigkeiten-diskret and adapted*
@@ -434,12 +433,6 @@ qqline(age)
 
 ### Historam for age {.tabset}
 
-#### Frequency {-}
-```{r}
-hist(age, freq = F)
-lines(density(age), lwd = 2, col = "black")
-```
-
 #### Auto Breaks {-}
 ```{r}
 hist(age)