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Cleaning sum 24 #590

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merged 7 commits into from
Jun 14, 2024
Merged

Cleaning sum 24 #590

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323a251
remvoing redundant jhur package
carriewright11 Jun 14, 2024
ea21a1d
making a slide fit better
carriewright11 Jun 14, 2024
b6a6aad
fitting better
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b824145
fitting better
carriewright11 Jun 14, 2024
0cd9985
add jhur back
carriewright11 Jun 14, 2024
8d6aa71
fitting better
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carriewright11 Jun 14, 2024
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36 changes: 20 additions & 16 deletions modules/Data_Cleaning/Data_Cleaning.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -106,7 +106,6 @@ Check the values for your variables, are they what you expect?
`count()` is a great option because it helps you check if rare values make sense.

```{r, message=FALSE}
library(jhur)
bike <- read_csv(file =
"http://jhudatascience.org/intro_to_r/data/Bike_Lanes.csv")
bike %>% count(subType)
Expand Down Expand Up @@ -461,10 +460,10 @@ Need quotes for conditions and new values!
```{r}
data_diet %>%
mutate(Treatment_recoded = case_when(
Treatment == "O" ~ "Other",
Treatment == "Mint" ~ "Peppermint",
Treatment == "mint" ~ "Peppermint",
Treatment == "peppermint" ~ "Peppermint")) %>%
Treatment == "O" ~ "Other",
Treatment == "Mint" ~ "Peppermint",
Treatment == "mint" ~ "Peppermint",
Treatment == "peppermint" ~ "Peppermint")) %>%
count(Treatment, Treatment_recoded)

```
Expand Down Expand Up @@ -528,7 +527,7 @@ data_diet %>%
Treatment == "Mint" ~ "Peppermint",
Treatment == "mint" ~ "Peppermint",
Treatment == "peppermint" ~ "Peppermint",
TRUE ~ Treatment)) %>%
TRUE ~ Treatment)) %>%
count(Treatment, Treatment_recoded)
```

Expand All @@ -539,10 +538,11 @@ Perhaps we want values that are O or Other to actually be NA, then `case_when` c

```{r}
data_diet %>%
mutate(Treatment_recoded = case_when(Treatment == "Ginger" ~ "Ginger",
Treatment == "Mint" ~ "Peppermint",
Treatment == "mint" ~ "Peppermint",
Treatment == "peppermint" ~ "Peppermint")) %>%
mutate(Treatment_recoded = case_when(
Treatment == "Ginger" ~ "Ginger",
Treatment == "Mint" ~ "Peppermint",
Treatment == "mint" ~ "Peppermint",
Treatment == "peppermint" ~ "Peppermint")) %>%
count(Treatment, Treatment_recoded)
```
## case_when() can also overwrite/update a variable
Expand All @@ -551,10 +551,11 @@ You need to specify what we want in the first part of `mutate`.

```{r}
data_diet %>%
mutate(Treatment = case_when(Treatment == "Ginger" ~ "Ginger",
Treatment == "Mint" ~ "Peppermint",
Treatment == "mint" ~ "Peppermint",
Treatment == "peppermint" ~ "Peppermint")) %>%
mutate(Treatment = case_when(
Treatment == "Ginger" ~ "Ginger",
Treatment == "Mint" ~ "Peppermint",
Treatment == "mint" ~ "Peppermint",
Treatment == "peppermint" ~ "Peppermint")) %>%
count(Treatment)

```
Expand All @@ -567,7 +568,8 @@ data_diet %>%
data_diet %>%
mutate(Treatment_recoded = case_when(
Treatment == "Ginger" ~ "Ginger", # keep it the same!
Treatment %in% c("Mint", "mint", "Peppermint", "peppermint") ~ "Peppermint",
Treatment %in%
c("Mint", "mint", "Peppermint", "peppermint") ~ "Peppermint",
Treatment %in% c("O", "Other") ~ "Other")) %>%

count(Treatment, Treatment_recoded)
Expand Down Expand Up @@ -697,7 +699,8 @@ But we still might miss a strange value
data_diet %>%
mutate(Treatment_recoded = case_when(
Treatment %in% c("G", "g", "Ginger", "ginger") ~ "Ginger",
Treatment %in% c("Mint", "mint", "Peppermint", "peppermint") ~ "Peppermint",
Treatment %in%
c("Mint", "mint", "Peppermint", "peppermint") ~ "Peppermint",
Treatment %in% c("O", "Other") ~ "Other",
TRUE ~ Treatment))
```
Expand Down Expand Up @@ -968,6 +971,7 @@ Base R:
## some data to work with

```{r readSal, echo = TRUE, eval = TRUE, message=FALSE, warning=FALSE}
library(jhur)
Sal = read_salaries() # or
```

Expand Down
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