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