get-me-histograms.R

#echo "id,date,hour,command1,command2,command3,command4,command5" >raw.csv
#awk '{$1=$1}1' OFS="," < input.txt > raw.csv 

library(dplyr)
library(ggplot2)

setwd('/home/gil_diy/myGitRepositories/stats-my-workflow')
input_rawdf = read.csv("raw-data-sample.csv")
input <- mutate(input_rawdf, date_command = paste(date, command1))

groups <-input %>%
  group_by(date_command) %>%
  summarise(usageCount = length(date_command)) 

list_words = as.vector(unlist(lapply(groups$date_command, function(word){strsplit(word, " ")[[1]][2]})))
numberOfBars = length(list_words)

groups <- as.data.frame(groups)
#groups$date_command <- list_words
groups <- cbind(groups, list_words)
df_groups <- as.data.frame(groups)[3:30,]

# Option #1: using barplot function
# ==================================
#barplot(groups$usageCount, main="Commands distribution", xlab="Number of Gears", xlim=c(0,133))
# ,names.arg = list_words, las=2
#
#horizontal_axis = 133
#x_vector = seq(1,horizontal_axis ,by = (horizontal_axis / numberOfBars))
#text(x_vector, groups$usageCount, list_words)


# Option #2: using ggplot function
# ==================================
ggplot(data=df_groups, aes(x=list_words, y=usageCount)) +
  ggtitle("Shell Commands distribution") +
  geom_bar(stat="identity", fill="steelblue")+
  geom_text(aes(label=usageCount), vjust=-0.3, size=3.5)+
  xlab("Command name") +
  ylab("Execution counts") +
  theme(axis.text.x = element_text(angle = -80, hjust = -0.05, face = "bold",size=14),
        axis.text=element_text(size=10),
        axis.title=element_text(size=14,face="bold"),
        axis.title.x = element_text(color="darkseagreen4", size=14, face="bold"),
        axis.title.y = element_text(color="darkseagreen4", size=14, face="bold"),
        plot.subtitle=element_text(face="italic",size=12,colour="grey40"),
        plot.title=element_text(size=18,face="bold") ) +
        labs(title="Workflow stats in shell",
        subtitle="Over the period: March - April 2018")