Data Manipulation using Tidyverse

1.1 select()

Used for picking and dropping variables.

## Picking variables.

df1 <- df %>% 
  select(Sid,Gender, Age)

## Dropping variables.
### When dopping variables, you insert a hyphen in front of the variable name.

df1 <- df %>% 
  select(-Sid, -Promotion)

## Picking or dropping variables acording to a given pattern.

df1 <- df %>% 
  select(contains("f")) ## Picking variables that contains letter e.

df1 <- df %>% 
  select(-ends_with("t"))

## Picking a sequence of variables that appear together.

df1 <- df %>% 
  select(Sid:Marital_Status)

rm(df1)

1.2 filter()

Used for picking values.

filter works with mathematical operators.

• == equality

• != non-equality

• < less than

• greater than

• & and

• or

In english grammar, filter means removing unwanted material. But in R, we mostly use filter to keep observations, depending on the mathematical operators used.

## Dropping obsevations based on the condition of a numeric variable. 

### Let us drop all those who are aged 25 and above
df2 <- df %>% 
  filter(Age < 25) ## notice the number is not enclosed in quotes


### Let us keep all those who are older than 25, but younger than 40
df2 <- df %>% 
  filter(Age >= 25 & Age <40) ## notice the number is not enclosed in quotes

df2 <- df %>% 
  filter(Age > 30 & Gender == "Female")

## Dropping obsevations based on the condition of a character/factor variable. 
### Let us drop all males
df2 <- df %>% 
  filter(Gender == "Female")

df2 <- df %>% 
  filter(Gender != "Male")

rm(df2)

1.3 mutate()

Used for generating variables

## Generating a basic variable.
df3 <- df %>% 
  mutate(prop_leavedays = Leave_Days/365)

## Generating a variable, based on the conditions of another variable.
df3 <- df %>% 
  mutate(Jinsia = if_else(Gender == "Female","Mke","Mme"))

rm(df3)

There are other variations of mutate(). mutate_all(): Used for implementing the same function on all of the variables.

mutate_at(): Used for implementing a function, for specific varibles. mutate_if(): Used for implementing a function, on specific variables, if they meet a certain condition.

df4 <- df %>% 
  mutate_all(~paste0(., "_x"))

df4 <- df %>% 
  mutate_all(tolower)

rm(df4)

df4 <- df %>% 
  mutate_at(vars(Gender, County), tolower)

df4 <- df %>% 
  mutate_at(vars(Gender, County), funs(paste(.,"x")))

rm(df4)

df4 <- df %>% 
  mutate_if(is.numeric, funs(.*100))

df4 <- df %>% 
  mutate_if(is.character, as.factor)

rm(df4)

1.4 group_by() and summarise()

Used for generating summary statistics.

tab <- df %>% 
  group_by(Gender) %>% 
  count() %>% 
  rename(Count = n)

tab <- df %>% 
  group_by(Gender) %>% 
  summarise(Count = n())

tab <- df %>% 
  group_by(Department) %>% 
  summarise(Avg_Income = mean(Income, na.rm = T))
  

tab <- df %>% 
  group_by(County) %>% 
  summarise_at(vars(Age, Leave_Days, Income), funs(mean(.,na.rm = TRUE)))
  

tab <- df %>% 
  group_by(County,Gender) %>% 
  summarise_if(is.numeric, funs(mean(.,na.rm = TRUE)))

1.5 arrange

Used for ordering the data based on certain variables

df6 <- df %>% 
  arrange(Gender, Age)

df6 <- df %>% 
  arrange(Gender, desc(Age))

2.1 Wide to long

## Method 1: Using gather()

sales1 <- sales %>% 
  gather("Quarters","Sales_Values", -Year, na.rm = TRUE)

## Method 2: Using pivot_longer()
sales2 <- sales %>% 
  pivot_longer(-Year, names_to = "Quarters", values_to = "Sales_Values",values_drop_na = TRUE)

2.2 Long to wide

## Method 1: Using spread()
sales3 <- sales1 %>% 
  spread(Quarters, Sales_Values)

## Method 2: Using pivot_wider()
sales4 <- sales2 %>% 
  pivot_wider(names_from = "Quarters", values_from = "Sales_Values")

3.1 geom_bar of one variable

## Let us generate a graph that sows us the number of individuals per department.

## First come up with the table showing frequencies and percentages.

tab <- df %>% 
  group_by(Department) %>% 
  count() %>% 
  rename(Count = n) %>% 
  ungroup() %>% 
  mutate(Percentage = round(Count/ sum(Count) *100,1))

## Generate the graph
bargraph1 <- ggplot(data = tab, aes(x = Department, y = Percentage))+
             geom_bar(stat = "identity", fill = rstudio_blue)+
             geom_text(aes(label = Percentage), vjust = -0.25, hjust = 0.5, size=4)+
             training_theme+
             labs(title = "Distribution of Respondents per Department", x="Department",
                  y="Percentage") 

bargraph1  

3.2 geom_bar of more than one variable

## Let us generate a graph that sows us the number of individuals per department and gender category.

## First come up with the table showing frequencies and percentages.

tab <- df %>% 
  group_by(Department, Gender) %>% 
  count() %>% 
  rename(Count = n) %>% 
  ungroup() %>% 
  group_by(Department) %>% 
  mutate(Percentage = round(Count/ sum(Count) *100,1))

## Generate the graph
bargraph2 <- ggplot(data = tab, aes(x = Department, y = Percentage, fill=Gender))+
             geom_bar(stat = "identity", position = "dodge")+
             geom_text(aes(label = Percentage), vjust = -0.25, hjust = 0.5, size=4,position = position_dodge(width = 0.9))+
             training_theme+
             scale_fill_brewer(palette = "Blues")+
             labs(title = "Distribution of Respondents per Department", x="Department",
                  y="Percentage")
bargraph2 

3.3 geom_line of one variable

## Let us generate a graph that sows us the average income per department.

## First come up with the table showing frequencies and percentages.

tab <- df %>% 
  group_by(Department) %>% 
  summarise(avg_income = round(mean(Income, na.rm = T), 2))

## Generate the graph
linegraph1 <- ggplot(data = tab, aes(x = Department, y = avg_income, group = 1))+
             geom_line(stat = "identity", color = rstudio_blue)+
             geom_text(aes(label = avg_income), vjust = -0.25, hjust = 0.5, size=4)+
             training_theme+
             labs(title = "Distribution of Average Income per Department", x="Department",
                  y="Average Income")
linegraph1  

3.4 geom_line of 2 variables

## Let us generate a graph that sows us the average income per department and gender.

## First come up with the table showing frequencies and percentages.

tab <- df %>% 
  group_by(Department, Gender) %>% 
  summarise(avg_income = round(mean(Income, na.rm = T), 2))

## Generate the graph
linegraph2 <- ggplot(data = tab, aes(x = Department, y = avg_income, group = Gender, color = Gender))+
             geom_line(stat = "identity", size = 2)+
             training_theme+
             scale_color_manual(values = c("Yellow", "Red"))+
             labs(title = "Distribution of Average Income per Department", x="Department",
                  y="Average Income")
linegraph2  

3.5 geom_boxplot of one variable

## Let us generate a graph that sows us the average income per department.

## Generate the graph
boxplot1 <- ggplot(data = df, aes(x = Department, y = Income))+
             geom_boxplot( fill = rstudio_blue)+
             training_theme+
             labs(title = "Distribution of Average Income per Department", x="Department",
                  y="Average Income")
boxplot1  

3.6 geom_boxplot of two variables

## Let us generate a graph that sows us the average income per department and gender.

## Generate the graph
boxplot2 <- ggplot(data = df, aes(x = Department, y = Income, fill = Gender))+
             geom_boxplot()+
             training_theme+
             scale_fill_brewer(palette = "Blues")+
             labs(title = "Distribution of Average Income per Department", x="Department",
                  y="Average Income")
boxplot2  

3.7 geom_area of one variable

## Let us generate a graph that sows us the average income per department and gender.

## Generate the graph
area1 <- ggplot(data = df, aes(x = Income))+
             geom_area(stat = "bin", fill = rstudio_blue)+
             training_theme+
             labs(title = "Distribution of Average Income", x="Income",
                  y="Count")
area1  

3.8 geom_area of two variables

## Let us generate a graph that sows us the average income per department and gender.

## Generate the graph
area2 <- ggplot(data = df, aes(x = Income, fill = Gender))+
             geom_area(stat = "bin")+
             training_theme+
             scale_fill_brewer(palette = "Blues")+
             labs(title = "Distribution of Average Income", x="Income",
                  y="Count")
area2  

3.9 geom_histogram of one variable

## Let us generate a graph that sows us the average income per department and gender.

## Generate the graph
hist1 <- ggplot(data = df, aes(x = Income))+
             geom_histogram(stat = "bin", fill = rstudio_blue)+
             training_theme+
             labs(title = "Distribution of Average Income", x="Income",
                  y="Count")
hist1  

3.10 geom_histogram of two variables

## Let us generate a graph that sows us the average income per department and gender.

## Generate the graph
hist2 <- ggplot(data = df, aes(x = Income, fill = Gender))+
             geom_histogram(stat = "bin")+
             training_theme+
             scale_fill_brewer(palette = "Blues")+
             labs(title = "Distribution of Average Income", x="Income",
                  y="Count")
hist2