Download the data used in this tutorial.
Then, install and load libraries:
# install.packages(c('tidyverse', 'dplyr', 'furniture'))
library(tidyverse) # for tidy up the data
Open csv data:
dt <- read.csv('columbus.csv')
View(dt)
Change name of columns from capital letter to small letter
names(dt) <- tolower(names(dt))
View(dt)
Piping, grouping and summarizing
dt %>% head(5) # View fist five columns
dt %>%
group_by(ew) %>%
summarize(mean_crime = mean(crime, na.rm=T))
# A tibble: 2 x 2
# ew mean_crime
# <int> <dbl>
# 0 303.
# 1 295.
Selecting variables
selected_dt <- dt %>%
select(neig, evi, al, crime)
View(selected_dt)
Filter: Only choose observations that we need
filtered_dt<-dt %>%
filter(ew ==0) # only choose 'ew' equal to 0
View(filtered_dt)
filtered_dt1 <- dt %>%
filter(ew==0 & open<2) # only choose 'ew' equal to 0 and 'open' smaller than 2
View(filtered_dt1)
filtered_dt2<-dt %>%
filter(ew==0 | open<2) # "&" and "|" mean both condition
Select and filter
selected_filtered_dt <- dt%>%
select(neig, evi, al,ew, crime)%>%
filter(ew==1 & evi >0.3)
View(selected_filtered_dt)
Mutate
The mutate() function is used to add new variables.
library(dplyr)
df <- tibble(x = c(1,3,5), y =c(2, 4, 6))
df %>% mutate(z = y / x)
df %>% mutate(z = x * y, .after = x)
df %>%
select(x, y) %>%
mutate(
z = y * 2,
z_squared = z * z
)
Reshaping
library(furniture)
Create a wide data frame:
df_wide <- data.frame('ID'=c('a', 'b', 'c', 'd'),
'time1'=c(1:4),
'time2'=c(3,5,6,9)) # create wide data frame
df_wide
# ID time1 time2
#1 a 1 3
#2 b 2 5
#3 c 3 6
#4 d 4 9
Convert wide to long:
df_long<-long(df_wide,
c('time1', 'time2'),
v.names='value')
df_long
# ID time value
#a.1 a 1 1
#b.1 b 1 2
#c.1 c 1 3
#d.1 d 1 4
#a.2 a 2 3
#b.2 b 2 5
#c.2 c 2 6
#d.2 d 2 9
Convert long to wide:
df_wide<-wide(df_long,
v.names=c('value'),
timevar='time') # convert long to wide data frame
df_wide
# ID value.1 value.2
#a.1 a 1 3
#b.1 b 2 5
#c.1 c 3 6
#d.1 d 4 9
Joining (merging)
Create data:
df1 <- data.frame(ID=1:3,
x1=c('a1', 'a2', 'a3'),
stringsAsFactors = F)
df2<- data.frame(ID=2:4,
x2 = c('z1', 'z2', 'z3'),
stringsAsFactors = F)
df3<- data.frame(ID=3:5,
x2=c('a1', 'a2', 'a3'),
x3=c('z1', 'z2', 'z3'),
stringsAsFactors = F)
inner_join() merges the variables of both data frames, but retains only rows with a shared ID.
inner_join(df1, df2, by='ID')
left_join() retains all rows of the data table, which is inserted first into the function.
left_join(df1, df2, by='ID')
right_join() function retains all rows of the data on the right
right_join(df1, df2, by='ID')
full_join(df1, df2, by='ID') #retains the most data of all the join functions.
semi_join(df1, df2, by='ID') #return all rows from x where there are matching values in y, keeping just columns from x
anti_join(df1, df2, by='ID')#return all rows from x where there are not matching values in y, keeping just columns from x.
Join multiple data frames
full_join(df1, df2, by='ID') %>%
full_join(.,df3, by='ID')
# join by multiple columns:
full_join(df2, df3, by=c('ID', 'x2'))
If you enjoy the content, please consider subscribing to my YouTube channel for future updates.
To access video tutorials and receive a certificate, enroll in my Udemy course.