gauchmarc/quantitative-methods-hs22
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A bit improved

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Marc Gauch 2022-11-25 16:31:02 +01:00
parent 3ae01d771f
commit 1871414592
2 changed files with 94 additions and 88 deletions
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@ -34,26 +34,27 @@ if (!require(moments)) {
## Frequency Table ordered from wish.com
```{r}
freq <- function(data) {
freq <- function(data, rounded_digits = 2) {
# counts
total_count <- length(data)
na_count <- length(data[is.na(data)])
valid_count <- length(data) - na_count
valid_count <- total_count - na_count
frequency <- table(data)
p <- prop.table(frequency)
percent <- round(p * 100, digits = 2)
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)
valid_percent <- round(p * 100, digits = rounded_digits)
na_percent <- round(na_count / length(data) * 100, digits = rounded_digits)
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)
Valid_Total <- c(valid_count, valid_percent_sum, 100, NaN)
print(freq_table)
count <- c(valid_count, na_count, valid_count + na_count)
percent <- c(valid_percent, na_percent, valid_percent + na_percent)
df <- data.frame(count, percent, row.names = c("valid", "NA", "Total"))
print(df)
NAs <- c(na_count, na_percent, NaN, NaN)
Total <- c(total_count, 100, NaN, NaN)
print(rbind(freq_table, Valid_Total, NAs, Total))
}
```
*Source: https://tellmi.psy.lmu.de/tutorials/deskriptive-statistiken-und-grafiken.html#haeufigkeiten-diskret and adapted*