i have data.frame looks this:
brand year eur brand1 2015 10 brand1 2016 20 brand2 2015 100 brand2 2016 500 brand3 2015 25 brand4 2015 455 ... also, attach code below:
library(plyr) library(dplyr) library(scales) set.seed(1992) n=68 year <- sample(c("2015", "2016"), n, replace = true, prob = null) brand <- sample("brand", n, replace = true, prob = null) brand <- paste0(brand, sample(1:5, n, replace = true, prob = null)) eur <- abs(rnorm(n))*100000 df <- data.frame(year, brand, eur) i need additional data transformations (add more columns) future research.
firstly, calculate positions labels (for future chart) , call pos:
df.summary = df %>% group_by(brand, year) %>% summarise(eur = sum(eur)) %>% # mutate( pos = cumsum(eur)-0.5*eur) what want is, calculate percentage grow each brand in terms of year. add line:
df.summary = ddply(df.summary, .(brand), transform, pchange = (sum(df.summary[df.summary$year == "2016",]$eur)/ sum(df.summary[df.summary$year == "2015",]$eur) )-1 ) however, constant size - growth of data frame.
could please me calculating percentage change each brand?
thanks!
also, easier if use lag:
df.summary %>% group_by(brand) %>% mutate(pchange = (eur - lag(eur))/lag(eur) * 100) # source: local data frame [10 x 5] #groups: brand [5] # # brand year eur pos pchange # <fctr> <fctr> <dbl> <dbl> <dbl> #1 brand1 2015 637896.7 318948.3 na #2 brand1 2016 721944.2 998868.8 13.17573 #3 brand2 2015 708697.6 354348.8 na #4 brand2 2016 300541.1 858968.2 -57.59248 #5 brand3 2015 454890.1 227445.1 na #6 brand3 2016 576095.6 742937.9 26.64500 #7 brand4 2015 305712.0 152856.0 na #8 brand4 2016 174073.3 392748.6 -43.05970 #9 brand5 2015 589970.7 294985.3 na #10 brand5 2016 518510.2 849225.8 -12.11254 as suggested @r2evans, if year not arranged beforehand,
df.summary %>% group_by(brand) %>% arrange(year) %>% mutate(pchange = (eur - lag(eur))/lag(eur) * 100) 
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