Plotting issueHighest value shown at the bottom of y-axis - r

Question

I'm trying to plot my dataset, i.e., the number of internship ads, using ggplot, but for some reason, the highest value shows at the bottom of y axis.
Here's the dataset:
month internship_count_2012
January 68
February 48
March 43
April 49
May 52
June 83
July 104
August 91
September 72
October 58
November 70
December 77
The image of the plot:
I've run the following code to come up with the above dataset, which was extracted from a larger, filtered dataset found here: https://www.dropbox.com/home?preview=rwjAll_internship_2012.csv
Filter the dataset by month:
rwjAll_internship_jan2012 <- filter(rwjAll_internship_2012, month == 1)
rwjAll_internship_feb2012 <- filter(rwjAll_internship_2012, month == 2)
rwjAll_internship_mar2012 <- filter(rwjAll_internship_2012, month == 3)
rwjAll_internship_apr2012 <- filter(rwjAll_internship_2012, month == 4)
rwjAll_internship_may2012 <- filter(rwjAll_internship_2012, month == 5)
rwjAll_internship_jun2012 <- filter(rwjAll_internship_2012, month == 6)
rwjAll_internship_jul2012 <- filter(rwjAll_internship_2012, month == 7)
rwjAll_internship_aug2012 <- filter(rwjAll_internship_2012, month == 8)
rwjAll_internship_sep2012 <- filter(rwjAll_internship_2012, month == 9)
rwjAll_internship_oct2012 <- filter(rwjAll_internship_2012, month == 10)
rwjAll_internship_nov2012 <- filter(rwjAll_internship_2012, month == 11)
rwjAll_internship_dec2012 <- filter(rwjAll_internship_2012, month == 12)
Get the number of internship ads by month
jan_2012_internship <- sum(rwjAll_internship_jan2012$internship)
feb_2012_internship <- sum(rwjAll_internship_feb2012$internship)
mar_2012_internship <- sum(rwjAll_internship_mar2012$internship)
apr_2012_internship <- sum(rwjAll_internship_apr2012$internship)
may_2012_internship <- sum(rwjAll_internship_may2012$internship)
jun_2012_internship <- sum(rwjAll_internship_jun2012$internship)
jul_2012_internship <- sum(rwjAll_internship_jul2012$internship)
aug_2012_internship <- sum(rwjAll_internship_aug2012$internship)
sep_2012_internship <- sum(rwjAll_internship_sep2012$internship)
oct_2012_internship <- sum(rwjAll_internship_oct2012$internship)
nov_2012_internship <- sum(rwjAll_internship_nov2012$internship)
dec_2012_internship <- sum(rwjAll_internship_dec2012$internship)
Create dataset to plot 2012 internship ads
month <- c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")
internship_count_2012<- c(jan_2012_internship, feb_2012_internship, mar_2012_internship, apr_2012_internship, may_2012_internship, jun_2012_internship, jul_2012_internship, aug_2012_internship, sep_2012_internship, oct_2012_internship, nov_2012_internship, dec_2012_internship)
all_internship_2012 <- cbind(month, internship_count_2012)
all_internship_2012 <- as.data.frame(all_internship_2012)
plot2 <- ggplot(all_internship_2012, aes(x = month, y = internship_count_2012, group = 1)) +
geom_line(colour="purple1") +
geom_point(size=0.5, colour="purple1") +
scale_x_discrete(limits = c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
How can I order the y values (internship_count_2012) so that the highest value is at the top? Thanks!

Answer 1

The following code simplifies the creation of your new dataset greatly using
the library dplyr and produces the plot you desire in just a few lines.
library(dplyr)
library(ggplot2)
all_internship_2012 <- rwjAll_internship_2012 %>%
group_by(month) %>%
summarise(internship_count_2012 = sum(internship))
month <- c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")
ggplot(all_internship_2012, aes(x = month, y = internship_count_2012)) +
geom_line(colour = "purple1") +
geom_point(size=0.5, colour="purple1") +
scale_x_continuous(breaks=1:12, labels = month) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
For info about dplyr check out this link on CRAN.